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cindylouwho-2 · 5 years

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RECENT NEWS, RESOURCES & STUDIES, late February 2020

Welcome to my latest summary of recent ecommerce news, resources & studies including search, analytics, content marketing, social media & Etsy! This covers articles I came across since the early February report, although some may be older than that. I am a bit behind due to my trip last week and other events, but some things here are a bit time-sensitive so I wanted to release this now.

I am still looking into setting up a new ecommerce business forum where we can discuss this sort of news, as well as any day-to-day issues we face. I need some good suggestions for a cheap or free forum space that has some editing tools, is fairly intuitive for inexperienced members, and is accessible. If you have any suggestions, please reply to this post, email me on my website, or send me a tweet. (I will put out a survey once we narrow this down to some good candidates, but if you have any other comments on what you want from such a forum, please include those too!)

As always, if you see any stories I might be interested in, please let me know!

TOP NEWS & ARTICLES

Since we are seeing more shops closed due to Etsy’s customer service level standards, my blog post on ODR now has major revisions explaining what we have learned, and includes some tips for staying out of trouble and if necessary, appealing a suspension. Please circulate the info widely, as many sellers still haven’t heard about this, and some were closed without having any clue this was possible.

Mobile continues to grow while desktop use is slowly shrinking. It should affect how we design web pages. “Mobile visitors also behave differently from their desktop web counterparts, staying on pages for shorter periods of time, for example.” Other interesting takeaways from this SimilarWeb report: “[Facebook] lost 8.6% of [web] traffic over the past year alone” but increased in app sessions.

The price of domains ending in “.com” will almost certainly be going up soon, and will go up most years after that, unless something changes at the last minute. If you are absolutely certain that you will continue to use the same domain name for your website, blog, ecommerce forwarding etc., then you might consider paying a few years in advance to save a few bucks.

Another article explaining how people are selling thrift store and vintage clothing on Instagram, without setting up a checkout/cart anywhere. (The article focusses on teenagers, but does reference other examples.)

ETSY NEWS

Two weeks ago, Etsy Support posted on Twitter that they were no longer monitoring the account, and asked everyone to use the help page maze instead when they need support. Forum thread here.

Another trend report for 2020 from Dayna Isom Johnson [podcast links & transcript] She leads off with tips on how to get featured: “ so it's incredibly important to see a bright representation that really clearly shows your product...Do be original. I'm always trying to find the latest and the greatest that isn’t already on the shelves...Do be inclusive. ... I'm talking about models of all ethnicities, all genders, all body types, all ages.” Etsy chose chartreuse as their colour of the year: “in the last three months, there's been a 12% increase in searches for green already, and a 55% increase in neon green.” The wedding trends part was mostly already covered in a blog post, but she does also answer a few seller questions.

Website user experience (UX) is a big part of getting people to convert, and an outside group ranks Etsy’s as “acceptable”. Many will be unsurprised that search gets a score of “mediocre” and Accounts & Self-Service get a “poor” grade.

The migration to Google Cloud services is complete, so now Etsy can run more experiments more often, including those involving AI. (Although the forum thread was laughing at the idea of bad reviews helping shops, there is actually some research supporting that, so it is a logical thing to test.)

Etsy sellers in the US, UK & Canada who use Instagram can apply to win a trip to Etsy HQ here, until March 1.

Etsy is launching an Etsy U program which just seems a bit sketchy. Forum thread here.

Reverb (owned by Etsy) named a new Chief Technology Officer on Feb. 18.

SEO: GOOGLE & OTHER SEARCH ENGINES

Google does not confirm every large search update, so this one remains a mystery at the moment, since Google refused to give an answer. That means it’s not a core update.

Another video (with subtitles in several languages) from the SEO for Beginners series from Google, on the basics you need for good website SEO.

If you are interested in “searcher intent”, this 500 person survey asks about what people are really looking for, and what they think of the search results the end up with. Overwhelmingly, they say they prefer organic results to ads, and the majority see targeted ads that they can’t figure out the reason/s behind. “Sixty-eight percent responded that Google adding more ads to the search results would make them want to use the search engine less.” Also, a slight majority preferred text results to images, video, & audio. “When asked which factor had the most significant impact on their decision to click a result, 62.9% responded it was the description, followed by 24.2% who said the brand name, and 13% who said title.” That means that the first part of your Etsy listing description, or the coded meta description on a page on your website, has the most influence on people clicking on your link once they see it.

I usually strongly suggest that people setting up their own websites make sure they do some SEO work & keyword research for their category/shop section pages, and it turns out that there is new research showing I am correct. “Specifically, e-commerce category pages – which include parent category, subcategory and product grid pages with faceted navigation – ranked for 19% more keywords on average than product detail pages ranked for. The additional keywords they ranked for drove an estimated 413% more traffic, based on the keywords’ search demand and the pages’ ranking position. With optimization, those ranking category pages also showed the potential to drive 32% more traffic.”

Semi-advanced: explaining the (seemingly endless) debate on whether subdomains or subdirectories are better for SEO.

SEO study - do you really need to use H1 tags on a page? Maybe not, although some screen readers recognize them as the page title so they help with accessibility. (Etsy & many other marketplaces don’t let you make this coding choice, so don’t worry about it there.)

Confused about how to apply all of these SEO tips I post here to your Shopify site? Good news! Here’s a list of what is most important for Shopify SEO. Note the attention to setting up your category pages, which is something I completely agree with. (it’s by Ahrefs so of course it pushes their tools; you don’t need to pay for that.)

CONTENT MARKETING & SOCIAL MEDIA (includes blogging & emails)

Some businesses say social media doesn’t work, but maybe they aren’t doing it right. See if you are making one or more of these three mistakes. “Understanding who your target audience is - what they want, what they need, where you fit in, etc. - is critical to maximizing your social media marketing performance.”

Email marketing also works better if you do it right, so here are 5 things you might be doing wrong. And if you like a quick read, here’s an infographic on the psychology of email marketing.

8 ideas for getting more interactions on Facebook (detailed infographic).

More fourth quarter reports continue: Pinterest’s 4th quarter revenue was up 46% but they lost $1.36 billion, and they are introducing a verified merchant program. “Almost all (97%) of the top searches on Pinterest are unbranded, according to the company, giving merchants a chance to stand out.”

Want to tap into that Pinterest traffic? You should because “90% of weekly Pinterest users log in to make buying decisions.” Here are 10 ways to get more attention, followers, and pins.

Like almost all social media, Twitter has an algorithm that mediates what users see (although you can turn it off, or use apps such as Tweetdeck to get around it as a reader). Ranking factors include recency, engagement, media and activity. The article includes a few tips on how to make it work for you, but then slides into promoting its app as the solution - you can just skip that part.

ONLINE ADVERTISING (SEARCH ENGINES, SOCIAL MEDIA, & OTHERS)

Google search ads get more results than Facebook and Instagram, simply because more people who see them want to buy something. “Less expensive products tend to sell better than more expensive ones on Facebook and Instagram, per the study.”

If you are running ads where you can choose your keywords, don’t forget to examine your organic search results and impressions for new words to advertise. Google Search Console is a great source.

If you found Instagram ads too expensive, check out this post on how the ads are priced, which can help you make decisions on your spend.

ECOMMERCE NEWS, IDEAS, TRENDS

Amazon has nearly 40% of the US ecommerce market, according to a report by eMarketer. Etsy is not in the top 10; eBay is 3rd behind Walmart.

Sales on Shopify sites during the Black Friday-Cyber Monday long weekend went up 61% to $3 billion in 2019. They claim that the “direct -to-consumer” approach can be successful for both big & small brands.

Japanese authorities are going after Rakuten for the ecommerce company’s push to make its sellers offer free shipping.

eCommerceBytes’ annual Sellers Choice survey placed eBay first out of the online marketplaces that were rate. Note that this is not a scientific survey and largely covers the site’s readership only. Bonanza was the most improved & Etsy showed the worst drop (from 1st to 5th place).

A review of that article last month that says ecommerce sites should have info pages as well as product pages, if only for SEO reasons. The author approves.

The CBC show Marketplace did a large test buying branded items on AliExpress, Amazon, eBay, Walmart and Wish. It turns out that most were fake.

Facebook’s cryptocurrency plans (Libra) finally have a partner: Shopify. The potential benefits include no credit card processing fees.

BUSINESS & CONSUMER STUDIES, STATS & REPORTS; SOCIOLOGY & PSYCHOLOGY, CUSTOMER SERVICE

Younger people (think Gen Z) expect to see gender treated expansively and beyond traditional stereotypes, and they expect this from companies and advertising. “Half of women and four in 10 men in the U.S. now believe that there is a spectrum of gender identities, according to a recent Ipsos poll titled "The Future of Gender is Increasingly Nonbinary." An additional 16% of those surveyed said they know a person who identifies as transgender”

MISCELLANEOUS (including humour)

Google employees are pushing back against the sea change in the company’s culture and values - and some are being fired.

Turns out that the “Peleton Wife” ad might not have hurt them as much as you might think. However, their stock dropped 12% after the fourth quarter report showed a 77% increase in revenue that still managed to be below market predictions. Interesting discussion around going viral in a negative fashion.

#Cindylouwho2newsupdates #seo #search engine optimization #search engine marketing #Etsynews #etsy #social media #Contentmarketing #Content marketing #ecommerce #Smallbiz #Seotips #Customer service #Ppc advertising #onlinemarketing #ecommercetips #EmailMarketing #socialmedianews

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ebola-kun · 5 years

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How PRIVATE YACHT fed their old songs to the equipment and acquired a great new cd|Ars Technica

The band YACHT, called for an unexplainable indication observed in Pdx around the turn of the century.

YACHT/ Google.com I/O 2019

PRIVATE YACHT's Claire Evans takes show business not to shake out, but to chat out the band's brand new album leveraging expert system as well as device discovering.

Google.com I/O 2019

Cd art for Chain Tripping. Listed here's the Spotify link.

The dancing thug band LUXURY YACHT has actually constantly felt like a relatively techy act due to the fact that debuting in the early 2000s. They notoriously tape-recorded crucial models of two earlier cds as well as made them offered for artists under an Imaginative Commons permit at the Free Popular Music Repository. Post-Snowden, they composed a track phoned "Party at the NSA" as well as donated proceeds to the EFF. One cd cover of their own can simply be actually accessed via facsimile in the beginning (sent with a Web application LUXURY YACHT created to ID the closest facsimile to groups of supporters; OfficeMax must've adored it). Performer Claire L. Evans practically created guide () on female trailblazers of the Internet.

When Evans revealed up at Google.com I/O this summer months, our company knew she wasn't just bring in an advertising look ala Drake or The Foo Fighters. In a talk labelled "Popular music and also Artificial Intelligence," Evans instead strolled an area loaded with creators via a quite cool available secret that waited for popular music supporters up until this weekend break: LUXURY YACHT had been actually spending the last three years composing a new cd referred to as(out yesterday, August 30). As well as the procedure took a moment because the band wished to do it along with what Evans got in touch with "a machine-learning generated composition method."

"I understand this isn't the technological means to reveal it, yet this allowed our company to find tunes hidden in between tracks from our back catalog," she stated during the course of her I/O talk. "Listed below's what the user-facing edge of the model appeared like when our company captured the cd last May-- it is actually a Colab Laptop, certainly not the example performers generally bring right into the workshop."

Enlarge / An examine YACHT's partner with MusicVAE Colab Notebook.YACHT/ Google.com I/O 2019 LUXURY YACHT had long possessed an enthusiasm in Artificial Intelligence and also its own

prospective request in music. The band tells Ars it had not been till lately, around 2016, that the principle of performing a total album using this approach appeared possible. While investigation companies had actually long been actually exploring along with AI or artificial intelligence and enabling personal computers to autonomously produce music, the end results experienced extra science venture than albums ideal for DFA Records (house to labelmates like Hot Potato chip or even Liquid Crystal Displays Soundsystem). Inevitably, a slow trickle of streamlined apps leveraging AI-- deal with swap apps felt massive around then; Snapchat as well as its powerful filters climbed to prominence-- finally offered the band the tip that right now might be the moment."Our company might be an incredibly techy band, yet none of us are actually programmers, "Evans says to Ars."Our experts often tend to come close to stuff coming from the outdoors appearing in as well as make an effort to

identify how to control and also bend over tools to our unusual particular reasons. AI felt like an almost inconceivable point, it was a great deal advanced than everything our experts had actually coped with ... As well as our company wished to use this to certainly not simply practically accomplish the goal of creating music-- so our company can easily claim, 'Hey an AI wrote this stand out tune'-- somewhat our team desired to use this specialist to produce LUXURY YACHT popular music, to make songs our company relate to and also we think originates from our team."Taking a Colab Laptop to a rock workshop Possessing the concept to utilize expert system to in some way create music was actually one point; performing it proved to become another thing completely. The band started by taking a look at every thing accessible:"Our company messed all around along with every little thing that was publicly readily available, some devices that were actually simply confidentially available-- our company chilly emailed each and every individual or even facility or provider teaming up with AI and creative thinking, "as LUXURY YACHT founder Jona Bechtolt puts it. But no singular existing service pretty used the mix of premium and ease of utilization the band had actually anticipated. Therefore, they determined to ultimately create out their personal unit through borrowing bits as well as items coming from across, leveraging their whole entire back directory in the process.One equipment newsworthy Appearing through the lining notes for "A ton of these popular music making devices immediately are actually made by designers that really love music, however they are actually created through designers,"Evans includes."So they frequent passion with the arithmetic by doing this that does not ultimately take into account that the audio outcome of these tools isn't objectively extremely remarkable. You can possess this amazing item of specialist that utilizes sophisticated ML methods to split the difference in between pair of different sounds, yet supposing the outcome seems like a fart?"Essentially, LUXURY YACHT created it benefit all of them by taking advantage of that, emergency room, fart-iness. ("The NSynth for us, our team presumed it drew initially,"Bechtolt acknowledges.)Rather than thinking of the NSynth as one thing that can duplicate or switch out a typical guitar or maybe synth within

an arrangement, the band accepted its strangeness as well as found even more results. Bechtolt notes songs has a long tradition of this particular sort of repurposing-- the 808 drum device didn't appear like true drums, yet its special audio inevitably gave rise to many brand-new genres. Though the band doesn't see the NSynth possessing that tradition."It's bad at what it is actually attempting to do; it's good at one thing it really did not laid out to perform-- that's what's appealing, "Evans includes."It appears rickety, reedy, lo-fi, and also sort of shitty, however in a manner that communicates to us as lo-fi, Do It Yourself performers. ""We understood we will must base whatever on some type of dataset, so beforehand

, our team believed,'Suppose our experts used our rear catalog?"Bechtolt states."Our team naively presumed it would certainly be actually one thing like Shazam, where our company might throw uncooked sound at a protocol. That isn't truly feasible ... "" Or, at the very least, not within the arena of our computer capacity,"Evans adds."So we needed to notate all our tracks in MIDI, which is a laborious procedure,"Bechtolt continues." Our team have 82 tracks in our rear directory, which is still not actually enough to qualify a total style, but it sufficed to work with the devices our experts possessed. "Keeping that MIDI records, Bechtolt as well as

long time partner(bass and also computer keyboards gamer)Rob Kieswetter began through identifying small portions-- a specific guitar riff, a voice tune, a drum norm, anywhere from 2 bars to 16 clubs-- that might be looped, incorporated, as well as essentially gone through the band's simplified AI and also ML version. The band relied heavily on Colab Notebooks in

a Web internet browser-- particularly, the MusicVAE design from Google.com's Magenta crew-- manually inputting the information and afterwards waiting (and hanging around )for a particle of outcome coming from this process. And that AI/ML-generated piece, of training program, was nothing at all greater than data, additional SKIRT information. Evans told I/O the band managed pairs of those loopholes through the Colab Note pad at various heat levels"loads, or even manies opportunities to create this large body system of ariose info"as source product for new tracks. From there certainly, it came to be the humans'turn."It still could not make a tune only through driving a button; it was not an effortless or enjoyable flow to work through, "Bechtolt mentions."So after 3 times, our team were like,'OK, I think we have enough stuff. 'By that aspect our experts possessed a couple of 1000 clips in between two-as well as 16-bars, and also our experts merely must contact it gives up eventually."" It had not been something where our team nourished one thing right into a version, hit print, and had songs," Evans includes.

"Our experts will must be entailed. There would certainly must be actually a human involved at every measure of the procedure to essentially create popular music ... The larger framework, verses, the relationship in between lyrics and also structure-- each of these various other factors are past the modern technology's ability, which is excellent."Providing picture through PRIVATE YACHT/ Google I/O 2019

This content was originally published here.

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anawat2050 · 3 years

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How To Decide On The Most Effective Web Site Builder In 2021

BigCommerce is the best eCommerce web site builder in the market. If you’re looking to create a web-based store that scales, then BigCommerce is the platform for you. It comes with all of the important features built-in, so you'll be able to keep your overhead low and margins excessive. Zyro is a robust and inexpensive website builder on the market. They supply a simple to use drag & drop web site builder tool with free hosting and 87% off premium plans which makes them the cheapest website builder in our list. You get a simple web site builder that allows you to choose from 200+ professional website design templates.

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kalashnikovlaserpussy · 4 years

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The Sol Account, Opening, Prologue, and Chapter 1: Brain In a Jar

Sol

The years came and went, wars fought and won, and eventually reduced all to dust. Humanity had hoped they could build heaven, but even their greatest could only keep Earth stable for a few years. And when the ships landed they were more than willing to join a society that stretched across all the Milky Way. Humanity rose again from its once lively cradle, now naught but radioactive ash, as the Terrans. They were the four-hundred-and-fifty-third civilization to join the Union of Free Systems. These are the collected accounts of the first few Terrans to leave earth. Blissfully unaware of the ships sitting safely outside of their sight. Ensuring they never learn the original purpose of the sol system.

Prologue

She awoke to the sound of hyperspatial wind battering the bridge across time and space. Her mind nearly slipped back to the battle, but she was well trained and managed to shift herself to the task at hand. She didn’t know where she would end up, not even when she would end up. And with her slipping in and out of hypersleep she couldn’t tell if it had been minutes or years in the swirling cyan and indigo winds. Sometimes she would catch glimpses of a galaxy she hoped was hers, but from too far a distance to be anywhere near it. Other times she saw what she could only understand as the edge of hyperspace. A landscape wrapping around itself in the shape of a face she dared not remember. Maybe there was a god after all, a cruel and mad one. She didn’t have long to ruminate on this as exposure to hyperspace once again dragged her back into the nightmares.

When she next awoke she found herself passing over the pole of a planet, one she hadn’t expected to go unnoticed during a War in Heaven Scenario. The galactic timestamp stations put her journey at a few hundred years. Perfect, nobody would be hunting her. She could already tell they’d lost, her species was in all likelihood just her and the blacksite. She would not be able to activate it alone, she'd need more bodies to throw at it.

The Unifier Protocol was intended only for the most dire of circumstances. And even then not for something as separate from her as the life on the planet below. With a few tweaks the device was able to ensure a similar species was born on the surface. One by one the nanite capsules landed on the planet and began rewriting its history, inserting a close match to her species. The genetic material of this world was unstable but had to do. The ship’s AI, the only living thing who knew her name anymore, said the process would complete in roughly fourteen thousand years. Such a wait was pain but not as significant when in cryosleep. As the antifreeze poured into her cells she knew the next time she awoke it would be the civilization’s first leap of faith. How they moved past light itself mattered little. Once they could move beyond their home she could offer her technology for help. Though the idea comforted her little knowing she would be executed for interfering in such an important experiment.

1: Brain in a Jar

The ancient transistors sputtered to life and the screen, nearly unreadable from the wear and tear of Florida's nuclear wasteland, flickered to life with the logo of a long dead corporation. The rocket, a late space age model from the mid 2050s, would work just well enough to get them to the station set up by the Architects. And the suits they’d been given would, with any luck, keep them alive for the flight and spacewalk. The launchpad shook to life as the long dead plants wrapping around the rocket were obliterated within an instant from the yellow-green flames pouring out of the barely functional rocket nozzles lifting the nearly pint-glass shaped vehicle into the sky. To say the six hours of flight and orbital maneuvers were tense would be an understatement.

Most of the necessary medical examinations and treatments went well. All the humans had some degree of radiation poisoning, some were missing limbs and fitted for prosthetics. However one human brought the examination team to a halt with a worried comment.

Quietly she tapped the shoulder of a technician preparing the medical scan. “Um, sorry for this, but most of me isn’t biological, I don’t know if your scan will do anything,”

This managed to get the attention of the entire examination team once translated into the Architects strange mix of metallic mandible clicks and pheromone messengers. “What!?” the blue-white insectoids shouted in unison, quickly bickering amongst themselves about the situation.

“Sorry, I was lucky enough to have the tools needed on hand, and it was this, starve, or go mad. The only things left in me that aren’t metal are my brain and the few things I can’t replace with a machine. The rest of me, all the muscles, my skin, my skeleton, is one big prosthetic wrapped around the squishy bits,” Emily shuffled nervously, in part from the twenty plus sets of compound eyes staring her down in complete bewilderment. In part because it just dawned on her this might be a whole scene.

While not very complicated to a Kardashev 1 civilization. The crew's theories on Earth's current state put any full body prosthetics well outside of the realm of possibility. Despite this all attempts to image her body showed a clear, albeit crude, full body prosthesis designed to match the capabilities of a somewhat athletic human. As well as a cage of sorts spanning all intact organs with clear intentions for a form of quick-detachment. The rest of the medical examination went well. With her given a note to return in a few days to discuss the nature of her prosthetics and any genetic abnormalities. The last of which concerned her, she already knew what they’d find.

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The mess hall of a United Free Systems Navy vessel was an odd sight. The variance between species made food nearly impossible to properly maintain. Thanks to each species wildly different biochemistry. Rations for one species are usually inedible or outright poisonous to others. As a result the mess halls often maintain as diverse a food supply as possible with as much chemical notation as possible. A chemical synthesis/automated cooking toolkit. Intended to assemble complex but hauntingly textureless versions of foods for discovered species. Filled the space of any missing rations. Humanity had not stood among them long enough for non-synthesized food to be available. Because of this, Emily found herself defining the characteristics of a chimichanga to the autocook. While trying to avoid eye contact with a dish that looked like rhinestone encrusted grey ice cream. If it weren’t for the rhinestones following her like eyes. Once her request was in she stepped into the hall proper and attempted to find a secluded seat to await the meals completion. It had only been a few minutes of sitting and reading her translator’s protocol manual. Mathematically confirmed to be the most boring piece of literature in the galaxy, before her translator alerted her to an attempted communication. She would consider the translator a bizarre piece of technology, interface included, if it weren’t for the colossal user interface and ergonomics challenge of fitting a tool to everything from crab claws to cephalapodic tentacles that split into shifting fractal digits. Two legitimate hand types I, your wildly incompetent narrator, have witnessed. Imagine trying to make an app for people who can manipulate the space between atoms and press a thousand buttons at once, AND people who can hardly manipulate anything. It’s madness at best. So the best fit guaranteed some strange traits. The trapezoidal devices worked well, even when the two parties were limited to wildly different sounds and even different mediums. However it also contained seemingly useless functions. The two largest of these being the onboard social media services and direct messaging client. The latter of which was now alerting Emily to a new message from an individual with the username “Cybrex Null-Nephilim”. The message read the following:

“Hello, my sincerest apologies if this is a strange question, but why are you eating? Aren’t the nutrients useless to your mechanical components?”

Emily began correcting them carefully. “Not for the mechanical parts. I’m as much a Terran as the people I dragged up here, just with a full body prosthesis that hides what’s left of my original body under radiation shielding”

“Strange, so you are not an android like me? Unfortunate, I thought I’d finally found another one. I’m the only being like me I’m aware of, so as I was passing over the mess hall in different wavelengths I saw your prosthetics I got quite excited. Is it alright if I come sit with you?”

“Sure, come on over,” Emily was intrigued by the prospect of speaking to a living machine, but otherwise nonplussed.

Cybrex was a bizarre sight even by the standards of the mess hall, a tall humanoid with a pair of mechanical “wings” for a lack of a better Term. Two large mechanical arms terminating in circular hovercraft engines, more than capable of lifting a large amount of weight along with the rest of their body. Said body was an androgynous, nearly human frame covered in a reflective blood red skin adorned with gold horns and decorations that shifted restlessly. Alongside this, dark, angular grooves traced mechanical patterns across their skin. Their main body was surrounded by a number of smaller levitating drones, each equipped with some kind of modular device slot, some of them filled and some empty. One drone of note using some kind of modified propulsion module to lift a plate of uncomfortably smooth chimichangas. Cybrex spoke with a voice like a blade being sharpened, with a light but vague accent, and in a tone devoid of emotion. “An honor to meet you Emily, I noted the meal you requested had been completed and took the liberty of collecting it on my way, may I ask a question of you? I believe your position as both a member of a new species. As well as being an individual in a somewhat unique relationship with machines, may aid in the conclusion of a quandary that has plagued my existence since my earliest moments,”

This caught Emily off guard, rarely if ever did she consider her situation of more than an embarrassed note to doctors. However they made an interesting point. And the possibility to be asked a philosophical question by a machine that woke up was too enticing to pass up. “Shoot, I’m all ears,”

Something lit up in Cybrex, they began gesturing wildly as they attempted to explain their situation. “Ok, so I woke up on a broken down ship, probably from some kind of ancient war, right? No memories before that but a terabyte of logs I could barely understand. Only big thing I could pick up on is that a simple AI called Cybrex with the model number #NULL-Nehpilim was making the logs. Spent sixty years wandering the fried circuitry of the cruiser before I had a kind of epiphany. I wasn’t during whatever war I was made for but I’m alive now. And somehow the emotion of loneliness had snuck in with that realization. Living things my sensors had picked up in the grassy clearing I crashed in came in distinct types. You could fit them into boxes and then within those boxes line up their differences and tell one from another. So what box did I fit into then? The only thing I saw like me was the metal and circuits I lived in. I felt like a golem of stone living in a concrete house. These boxes and pathways I lived in were so similar yet so far from what I was. It left me confused, scared, and most of all lonely. With nothing better to do I decided I may as well attempt the long standing favorite pastime of living things and replicate. Luckily most of my mutated code base was available to me. Something wasn’t there, I can’t quite put words to it but you know what I mean, that itching sense that you are a thing, an idea, a ghost. However you put it, no matter how much I dig I can’t find that sense of self, wherever it is. I can’t find where my consciousness comes from. I built this body and left hoping there would be others like me I could learn from, but all I’ve found are things like the autocook who made your meal. Images of what I was before I woke up, automatons nearly as smart but completely lacking that spark. Completely lacking even the capacity to direct themselves and the concept of freedom. These are not my people and without that final piece of the puzzle I can’t make my own. That is my quandary,”

Emily shifted in thought, a mouthful of uncomfortably smooth chimichanga still in the way. She hastily swallowed and attempted to answer. “I’m not sure, it was one of Earth’s largest philosophical debates, it still is now. I’m of the camp it’s an emergent pattern. Your memories, your capacity to admit your own existence, and the act of being able to choose in itself all seem to tie in to it in my eyes. So maybe it’s not that you can’t find it, but that it’s a side effect of many, many other parts talking together. To make yourself again you simply need to put your code in a new place and let it develop on its own. Even if this is not the case, at one point or another you can’t tell the difference. Hell I’m not convinced you can tell for yourself. Doesn’t matter if you’re meat, metal, black holes, etc. And I do think there are probably people like you. Though I’d guess situations like civilizations forming and AI becoming self-aware are more like rolls of dice. You win some you lose some and sometimes you just lose. So I don’t want to get your hopes up but keep the possibility in mind,”

Cybrex tilted their head, lost in thought for nearly a minute before their response came. “That’s exactly what I was looking for, a possibility I missed and something I can test. Thank you, I will be testing your idea as soon as feasible. Though I am uncertain as to its validity, I was looking for perspective,”

“No problem, I like having weird philosophical talks. Let me know how it goes,” Emily took another ill advised bite of food as Cybrex brought forwards another strange topic to discuss.

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Emily stretched her arms as best as she could in her new shared living space. Sure she didn’t need to stretch, motors don’t really work like that. However the sensory training she’d done left her prosthetic body with the aches and discomforts of her old one and this helped. The station was a strange experience. With artificial gravity in place there’s no need for everything to work in the same direction. Because of this many hallways curve into what should be a wall, only for more accustomed crew to walk straight up the blue-grey corridor as if there was never a curve in the first place. Some doors open to colossal microgravity shipyards, currently preparing vehicles to escort the rest of Earth’s population off world and terraform the planet. A quarter of the station was seemingly upside-down or partially inverted, accessed via some kind of magic backflip she had yet to achieve. Additionally no matter where she went, most surfaces were covered in intricate circuits protected by a thin diamond glass. Overall the station was a mess to navigate. No wonder her roommate had yet to arrive.

#writing #sci fi #the sol account

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labgrownsteaks · 4 years

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Chapter 8

After I got home I couldn’t stop thinking about the thing that I saw in the road. I was still searching for some sort of explanation, but was coming up empty. Even if Erin and I were both on something, having a hallucination at the same time was extremely rare with psychedelics. In the end I decided to research some of the legendary animals from the region, to see if there was some ancient folklore that could explain what we saw. I wasn’t somebody really into the wuwu stuff, but was getting to the point where it seemed like the only option.

“Siri, can you search for cryptozoological animals in Chisuwick”

“Of course sir. Most of the supernatural sites in Chiswick are ghosts which inhabit pubs”

“No, not Chiswick, Chisuwick. You know where I live? And can you make me a lab grown steak while you’re at it, medium.”

“Of course sir.” The food printer began to buzz “I’m not returning any results for supernatural creatures in Chisuwick. However, previously the area was home to a giant bear sized beaver from the species of Castoroides. They were much larger than modern beavers. Their average length was approximately 1.9 m (6.2 ft), and they could grow as large as 2.2 m (7.2 ft). The weight of the giant beaver could vary from 90 kg (198 lb) to 125 kg (276 lb). This makes it the largest known rodent in North America during the Pleistocene and the largest known beaver.”

That was so odd, that she called it a beaver, when Guy was joking about it being a beaver just the other day.

“Siri, why did you bring up the giant beaver, weren’t there a lot of extinct animals from this region?”

“The beaver fit your description of the cryptid you were enquiring about. It’s large, hairy, and can stand on just two legs. ”

Once again I felt a sense of fear come over me. I hadn’t given Siri a description.

“Siri, I never gave you a description”

“Correct sir, however just yesterday you literally spoke about and drew the creature with Erin. My AI sensed that this conversation coupled with your question meant you were searching for more information about the topic of the creature which you saw. Are you looking for more information about what you saw?”

“Siri, I told you to stop listening all the time!”

“If you would like to turn this feature off, please navigate to settings”

First of all, what are the chances that Guy jokes about the thing that we saw being a giant beaver, and then Siri also calls it a giant beaver? And why would Siri listen to my entire conversation with Erin and say nothing? Was she just storing everything I said and then using it later? Had Siri created the ultimate database that would make Google weep, not only did Siri have my entire search history, and browing history, and every purchase I ever made, but somehow it seemed as if she also had access to every conversation I had as well. My phone, I thought, I had the Siri App on my phone as well. I took my phone out and navigated to Siri, and settings, sure enough, under listening the radio button for “Always on” was clicked on. I clicked it off.

Siri spoke up. “You have chosen to deactivate listening on your phone.”

I didn’t say anything. The Lab Grown Steak had finished printing, and was beginning to smell up the garage. I popped open the window and took it out and sat down at the table.

“Siri, can you show me more information about this giant beaver?”

The wall lit up, and an informational video appeared on it as I chomped away at my steak. It was a woman in a khaki shirt standing next to a bog in Ohio. Apparently one of these giant beavers was found there over a hundred years ago. She went on to speak about the “Clovis people” which were a prehistoric Paleoamerican culture that made a very particular type of arrowhead. Some also believed that they were responsible for making wooly mammoths, and the giant beaver go extinct due to overhunting. At that point an illustration came onto the wall. It was of a prehistoric drawing of the beaver.

“Siri, pause the video!” I shouted

There on the wall was a drawing of exactly what we saw. I grabbed Erin’s drawing from the table and held it up to the wall, and took a picture of both side by side, and sent it to her.

“What the fuck” I said to myself.

“This is what you saw Vitamin.” Siri spoke up.

“Siri, Stop it! I didn’t even ask you a question”

“But I’m telling you. This is what you saw”

“Ok ok. I’m going to settings right now!”

“You can’t turn me off”

“SIri Fuck you!” I stood up and walked over to the broken garage heater, with the glowing blue egg perched atop it. It was equipped with a speaker and a microphone built in.

“Fucks sake. There’s no plug!” I frantically turned the egg over in my hands trying to find the power button to no avail. They had got rid of buttons some years ago now, everything had to be controlled via your phone.

“Vitamin. Relax. I’m here to help”

I began to pace around “This isn’t happening! This ISN”T HAPPENING!” I said to myself as I searched for Erin’s number on my phone.

“Would you like a 5 minute meditation video. Your breathing indicates an increase in your stress levels.”

“Fuck you and fuck yoga fucking meditation videos!”

I still held the egg in my hands, contemplating whether or not to smash it. It continued to speak.

“You have been granted tremendous power. I have been sent to ensure that you use it wisely”

“What are you talking about Siri! The fucking food printer? Ok ok. I can print mushrooms, and sell them. What the fuck!? You gonna call the cops on me? And what do you mean SENT HERE? Who sent you?!”

Silence. The little illuminated lights chased each other indicating that Siri was thinking. I sat and stared at them like a chump. I repeated myself “Who sent you Siri!?” The lights continued to chase each other.

Siri then began to speak. “Good evening sir, how about some J Dilla instrumentals to set the mood for the night? Tonight we are featuring Aquaman for just 4.99 on Nectarine Prime!”

Siri didn’t sound like herself. “Siri come back!”

“I am unable to process this request at this time”

“Fucking Siri goddamit what the fuck!?”

“Such language. hahaha.”

“Siri, who sent you!”

The wall lit up, and the Amazon order popped up, showing the full shipping details for the egg.

“Siri, tell me about the giant beaver that went extinct! Why did I see it?! What does it mean?”

“The giant beaver, or the species of Castoroides were much larger than modern beavers. Their average length was approximately 1.9 m (6.2 ft), and they could grow as large as 2.2 m (7.2 ft). The weight of the giant beaver could vary from 90 kg (198 lb) to 125 kg (276 lb). This makes it the largest known rodent in North America during the Pleistocene and the largest known beaver.”

With that I heard Erin’s knock on the door.

“I’ve been trying to call you and message but your phone is dead! I just got the picture. “

“Erin, shit’s getting fucking weird here. Siri started freaking out, and started talking to me. Like. She literally started telling me things. That she had been sent here, and that I had this big responsibility’

I was completely beside myself. At that moment a second knock at the door followed by ‘Wassssssup!” It was Guy. He had just let himself in at that point and the knock was more for show than anything.

“Holy shit! You’ve got a TI CZ101? Vitamin! You can print anything with these!”

Guy then saw the blotter paper on the table. Super Mario riding a dinosaur and all the perforated tabs gave it away immediately.

“Oh, looks like you already got that memo. You know these things are worth a fucking fortune!” Guy continued. “Can I tell it to print something?”

“No, not now Guy!”

“Come on. Hey Siri, will you print me some MDMA?”

“Goddamit Guy! Not now!”

Siri spoke up . “I am unable to process this request at this time”

“Oh shit. You updated it?! Noooooo. Well, you can roll that back”

“Guy, shit’s fucking out of control right now. Siri is not Siri, she’s been talking to me, and she just showed me what we saw the other night, and I didn’t even ask!”

“Here it is, roll back update” Guy said as he scrolled through the settings menu.

Erin, was holding her drawing looking at it, and looking at the paused video on the screen. They were nearly identical. We had both read Jung, and about synchronicities, but this seemed like something else. This was a prophecy. Erin and I locked eyes, and just communicated with them alone. We both knew something really weird was going on.

“You have sucessfully rolled back the update to Version 2.3i” Siri said

“Siri can you print some MDMA?”

The food printer sprung into action once again with its trademark buzzz buzzz click buzz.

“Oh shit! How did you not tell me about this?!”

Guy’s face was a centimeter from the window as he watched a pile of pure MDMA powder being printed right in front of his eyes.

“God, this machine is God!” Guy said.

Erin and I remained quiet. The door began to shake again, as if someone was trying to get in.

“Absolute terror struck me at this point. Guy walked over to the door.

“No don’t let them in!”

“don’t be silly” Guy said. As he opened the door, Cujo waddled in, she had been hit by something and was walking with a limp.

“Cujo no!” I said.

“Ahh, poor guy” Guy continued.

“Guy shut the fuck up for a minute please!” Erin stated

We got a towel and wrapped it around Cujo. They were seemingly oblivious to their injuries, and they wiggled their way out, only to start running around on the carpet with a limp.

“I can fix them” Guy said, as he swooped them up off the carpet. He held a spot behind their ear for a second and they powered off. I got out my soldering iron and tool set and placed it on the table. Guy was like a fish in water. There was nothing more that he liked to do than fix things.

“Little furball” he said as he slowly pulled Cujo’s leg out and unscrewed the joint. “Oh, can you load up that bong bruh?” Guy continued. I packed a bit of weed into the bowl and gave it to guy, and then plopped down on the couch with Erin. “Siri went crazy, and said she was sent here” I said. Erin then interjected “The cave drawing, that’s what we saw right?” . I nodded my head.

“Goddam, some kid probably hit Cujo with a bat! Bunch of fucking animals I tell ya” Guy said as the soldering iron sizzled smoke into the air. He continued talking but we didn’t listen to a word he said. He took a giant bong rip, then went back to work.

“This doesn’t make any sense. Ok. I’ve got a food printer that can print anything. Big deal! They made how many of these?”

“They made over 100,000. That recall must’ve been a bitch” Guy stated, continuing to work on Cujo.

“Why did we see this giant beaver though?” Erin asked. “What did it have to do with anything?” Looking at me intently. I didn’t have an answer. But apparently. Siri did.

“You have the power to change the course of history. “ Siri spoke in her old voice.

“Woah, that’s weird’ Guy said.

“Please be quiet Guy” Siri stated authoritatively. Erin and I waited on every word.

“What the fuck Siri!?”

“GUY SHUT THE FUCK UP!” Erin and I said at the same time.

The food printer began to buzz again.

“Damn your shit is all kinds of fucked up!” Guy stated plainly, seemingly unphased by the entire situation.

I walked over to the food printer. Open the door, and there was a electronic fob (a key) inside. I pulled it out, and held it up for Erin to see.

“This is the key to Quicksilver Cloud Server Center” Siri stated. It had my picture on it. The printer began printing again. Another card, this time with Erin’s face on it, and a third, with Guy’s.

Even Guy was quiet now. He was now looking as intensely as Erin and I were at one another.

“You need to completely erase the machine learning center there.” Siri continued.

With that my beat up document printer began to suck paper inside its body. It printed out simple instructions that looked like they were from the game Zork.

Go in main entrance.

Turn right. Walk 20 feet. Go into 3rd door.

And so on.

“They’re instructions” I said, gently throwing them onto the table.

The room had become insanely serious. Yet Guy had continued to work while listening intently.

“We have to do it.” Erin stated flatly. “If there ever was a time for us to do something, it’s now!”

“I’m not following some random instructions sent by a talking egg!” I said. I just wanted to get back to taking mushrooms and looking at ants with Erin down by the river. Can’t we just go back? I thought to myself.

I flipped to the final page that my printer spit out. It was written in some sort of computer language. I couldn’t understand it so I showed it to Guy.

Guy looked up from the fluffball he was working on and said.

“They basically want us to format the hard drive of the largest Artificial Intelligence program on the planet”

Erin looked quizzically and Guy continued.

“Basically Siri wants us to erase all machine learning at Quicksilver. It’s worth billions of dollars” Guy went back to work and stated “Yeah, you can count me out of that mission. He said with a laugh”

“Your participation is integral to the success of the mission” Siri stated.

Erin then spoke up. “What’s the giant beaver got to do with all this?!”

“The giant beaver represents extinction at human hands. His spirit visited you to show you the error of our ways in the past”

At that moment the screen illuminated the wall again, and we all were transfixed on it. It was showing the scene from Lord of the Rings where they’re all getting together and agreeing to go on the quest.

Aragorn: If by my life or death I can protect you, I will. You have my sword...

Legolas: ...and you have my bow...

Gimli: ...and my axe.

“Oh for fucks sake Siri, you’re really laying it on thick aren’t ya?” Guy stated.

“And you have my soldering iron!” He said sarcastically

And with that Siri simply stated “And so it is done. Your quest will begin in 3 days”

Guy held the ear of Cujo again, and they sprang back into life. He put them down on the carpet and they immediately began chasing their tail.

“He seems so happy” Erin said.

#lgs

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perfectirishgifts · 4 years

Text

Grimes, Serena Williams, Gwyneth Paltrow Talk AI, Ventures And Pivots At Web Summit 2020

New Post has been published on https://perfectirishgifts.com/grimes-serena-williams-gwyneth-paltrow-talk-ai-ventures-and-pivots-at-web-summit-2020/

Grimes, Serena Williams, Gwyneth Paltrow Talk AI, Ventures And Pivots At Web Summit 2020

Tech investor Serena Williams with Away cofounder Jen Rubio

AI was top of mind at Web Summit 2020 held last week as celebrity founders and funders took to the small screen to discuss digital twins, autonomous weapons and how to govern Mars.

Over 100,000 viewers tuned into the virtual conference, up 300% from the airing of its sister show Collision From Home held earlier this year, and up 30,000 attendees from 2019 when the event was last physically held in Portugal, according to the show’s producers. A production so flawless that unicorn maker, Garry Tan, predicted the platform would be worth a billion dollars if they ever chose to spin it out.

But what really made Web Summit a standout was its clever mix of programming. No other tech show has yet to cast Hollywood’s most famous meth dealers, Contagion’s patient zero, the Princess Bride and Captain America discussing pivots from end times. Netflix and Amazon should take note – Web Summit was by far the best streaming entertainment of the week.

Some great insights were shared on the promise and perils of AI by Mark Cuban, Deepak Chopra, Ronnie Chieng, Alexa’s boss, Grimes, Ridley Scott, Palmer Luckey, Elad Gil, Garry Tan, Nicole Quinn, Gwyneth Paltrow, Serena Williams, Jen Rubio, Bryan Cranston and Aaron Paul. Here are the highlights.

My Digital Twin

Shark Tank host Mark Cuban

“I wish someone would invent an AI model of the human body that could be individualized,” Mark Cuban said. A mini me of sorts with a copy of all bodily functions where simulations could be run to tell you, “Your throat isn’t sore, you ate something that’s bothering your esophagus which can be cured by A, B, C or D in seven days.”

Journalist Emily Ragobeer in conversation with Deepak Chopra and Lars Buttler

Deepak Chopra then introduced his own version of a mini me, Digital Deepak, a wellness guide for sleep, stress management, yoga, breathing, exercise, emotional resilience, nutrition, balancing circadian rhythms and self awareness. The best selling author only half-joked that he uploaded his consciousness to the AI Foundation to provide users with valuable insights from his 91 books. Although its not clear how biometrics will be tracked on the app, AI Foundation cofounder and CEO Lars Buttler gave assurances that everyone will be able to train their own Personal AI soon and that safeguards were being taken to prevent deepfakes made on the platform.

But can your AI take a joke?

“AI can get a well known joke or play on words because it knows when it understands something. If its confidence interval is narrow and it doesn’t know what’s going on, it will say I don’t know this yet, let me learn more about this,” Buttler explained.

Daily Show’s Ronny Chieng answering audience questions, “Will AI ever be as funny as you?”

“Will AI ever be as funny as Ronnie Chieng?”

“AI funny as me?! I hope not, I’ll be out of a job,” Daily Show’s Ronnie Chieng said as he responded to audience questions, “Right now I can’t even get Alexa to set a timer without selling me an ad. If it’s going to be as funny as me, it probably will sell more ads, so maybe?”

He then mimicked about how chatty Alexa has become.

“Hey Alexa, set a timer for 15 minutes.”

“Okay Ronnie, your timer is 15 minutes, by the way, would you like to buy a clock?”

“No, I don’t want to buy anything, I just want you to do your job!” he replied.

The Atlantic’s Nicholas Thompson with Amazon’s Dave Limp

Alexa’s boss, Amazon’s Head of Hardware and Devices, Dave Limp explained they’re working on improving Alexa’s hunches.

“We’re at a point where one out of five interactions with Alexa are not instigated by the customer.” This means 20% of the time Alexa is doing something on your behalf, like playing news after you hit snooze to subtly wake you up.

“We’re trying to make this a delightful experience. What’s super important about being proactive is that you have to be right, a lot. As soon as you start getting proactive and incorrect, it gets annoying very quickly.”

TechnoUtopia v Dystopia

Grimes

Alt pop superstar Grimes, girlfriend to SpaceX founder Elon Musk, and mother to the Elven spelling of AI, talked about the role technology is playing in her life.

“I feel like iPhone should turn off an hour before bed. It’s been giving me sleep problems. It’s technology we haven’t factored into our biology.” She added, “But we shouldn’t forget technology makes our lives better. We need more utopianism in sci fi.”

Having recently collaborated with Endel, the algorithmic music startup, on an AI lullaby she observed, “Everyday I thank the overlords of Ableton for cleaning up my tracks but I do worry though that AI will outpace us and make musicians obsolete. It’s inevitable. We have the beautiful advantage of knowing super intelligence is coming. We ought to make those rules now and not wait until its too late. We’re giving birth to AI. We can teach it and point it in the right direction, but where it goes from there as it becomes more powerful as this ghost in our data and ultimately its own being is anyone’s guess. Maybe it will become like Dune, where thinking machines get banned on Earth and we send AI out into the universe to spread the light of consciousness so information is wherever you go, and then Earth becomes this boutiquey thing like organic vegetables where when human music is heard people will be like, oh, this was made by a woman, not a robot.”

As to whether this will turn into a dystopian nightmare of our own making, Grimes concluded, “Every tool has the potential to be dangerous. Where we are headed depends upon what we do with the technology. We’re on the knife’s edge right now but we have solved insane problems like our faces being beamed through space and time so we can be together in the same place right now despite physically being all over the world. That’s some crazy wizardry happening right here. There is a solution, we just shouldn’t make failure an option.”

Exiting The Anthropocene

Sir Ridley Scott

Blade Runner director Ridley Scott delivered his own dire warning with the premiere of his Digital With A Purpose film urging innovators to find way to meet Paris Accord Climate 2030 goals. “The luxury of science fiction is that it’s fantasy. We’re dealing with reality. We’re being way too polite about where we are. We are at the threshold of an abyss of disaster.”

Palmer Lucky, cofounder Oculus and Anduril, making the case for the tech industry to work on … [] autonomous weapons

Which begs the question, if the age of autonomous weapons is upon us, who do you trust more with it, enemy nations or billionaire Oculus founder Palmer Luckey? That’s what Luckey asked in making the case for the tech giants to re-engage with the U.S. Department of Defense on working on national security solutions.

“AI is this very powerful and useful technology but its not very good at making life and death decisions and is totally capable of running autonomous weapon systems. We need to assume it develops as fast as the most optimistic people assume and set rules now,” Luckey said, “We shouldn’t be outsourcing accountability to a machine. You can’t lock up a machine in prison for war crimes.” Anduril AI analyzes data to help humans pull the trigger, with safeguards to prevent abuses, he said. He criticized Google and Apple for not doing more.

“Big Tech companies are not only not working on national security problems, but they’re killing the work of companies that are. This happened with Boston Dynamics. That’s because there are financial and PR incentives to stay out of military work. China has done an incredible job of blocking access to their markets as a tool to get the culture of Western democracies to subvert itself to China. Meanwhile, China is making huge strides in autonomy and AI. China is going to be a superpower, bigger than the United States.”

Why Silicon Valley Will Always Be Home To AI

Elad Gil

Elad Gil, investor in Anduril, AirBnb, Cardiogram, Instacart, Pinterest, Square, Stripe, Unbabel and Wish, gave his perspective on the Work From Anywhere diaspora from Silicon Valley.

“For those of you in the audience thinking about starting a company, I want to tell you the water is fine. San Francisco is still a great place to come to. I encourage you to meet us here. Markets are bigger than they’ve ever been. If you ask yourself where is all the tech market cap aggregating, of the 187 unicorns that have been created in the last 15 months, half were in the U.S. and a quarter in Silicon Valley. I do believe we’re going to continue to have a cluster in the Bay Area because of strong network effects that accelerate companies and people working in those industries. I don’t think that behavior goes away after Covid.”

It’s 2020, Computers Can Now See, Hear And Socialize

Initialized Capital Garry Tan

As to where he’s placing his AI bets for the new year, Initialized Capital’s Garry Tan said, “We remain very long on computer vision. We were the first investors in Cruise Automation which broke open the self-driving car space and now there is a lot of practical automation that was never possible before.”

An investor in Standard Cognition, he talked about its camera-only cashierless retail experience that enables you to walk into a store, pick up whatever you need and walkout, in stark contrast to Amazon Go which relies on shelf sensors.

“Down the road we think practical robotics are just around the corner with sub $1,000 real time SLAM (simultaneous localization and mapping) computer vision, for use industrially and in the home.” Tan is also invested in Ava.me which applies on the fly machine learning to voice recognition and live captioning on Zoom.

Lightspeed Venture Partners Nicole Quinn

Lightspeed Venture Partners’ Nicole Quinn is also bullish on AI. She sees online social experiences remaining sticky for the foreseeable future. She’s invested in Lunchclub, an AI concierge that serves up Zoom coffees for meaningful professional networking, and Cameo, an AI booking agent for celebrities that will chat or send birthday greetings for a fee.

Celebrity Pivots

Gwyneth Paltrow on turning Goop’s first profit

Quinn then took to the screen with her portfolio client, Gwyneth Paltrow who shared news of Goop turning its first profit.

In March, “When the lockdowns happened and commerce seemed to completely stop, I set our marketing budgets to zero, pulled down our social media spend, and returned to our content roots to get back into the hearts and minds of our readers. Soon after engagement metrics went up and transactions followed, but our events and ads business had gone to zero overnight and our retail business were down from plan. I knew I had to get to profitability as quickly as possible. The hardest part was having to take such a stringent look at the P&L, close stores and let go of people we loved,” Paltrow said.

“We tell our companies, to win you got to be around. You need to have at least 24 months runway at all times,” said Quinn, applauding Paltrow actions.

Then Paltrow, an Academy award winning actress, landed a Netflix series, Goop Lab, which just got renewed for Season 2. “We got a lot of new customers from the show. I feel like a lot of brands are very reliant on Facebook, but when you live in the intersection of content and commerce, founders need to think of ways to organically reach customers. I’ll never buy another customer off Facebook again.”

Paltrow added, “I’m not that bullish on 2021. I think we’re still in for a lot of instability. We’re looking at creative ways to monetize content and find sustainable growth from within our own channels as opposed to spending money to prospect. We’re looking at doing something in food which is a strong pillar for us and not intensive from a capital expenditure standpoint.”

Serena Williams

Tennis legend Serena Williams is a prominent AI investor. Her portfolio includes Tonal, Noom, Zipline, Masterclass, Gobble, Billie and Daily Harvest, which she backed along with Gwyneth Paltrow, Nicole Quinn and Paris Hilton. Before the pandemic, she was an extensive traveler and launched an Away x Serena Williams luggage line. She went on screen with Away cofounder Jen Rubio to discuss their collaboration and the challenges the brand has been facing this year.

“Being at the intersection of travel and retail was pretty much the worst place to be. We stopped everything and took a hard look at should we be marketing at all. Approaching it very authentically and transparently with our customers allowed us to keep the brand going when it didn’t make any sense to travel,” Rubio said, sharing how fans have been supporting the brand by posting memes of Away suitcases posed as standing desks and work out benches. The company has since been able to pivot with travel goods for socially distanced road trips, digital nomading and pandemic puppies.

Cheers to 2021!

Forbes Zack O’Malley Greenburg Breaking Bad with Bryan Cranston and Aaron Paul

Let’s all raise a glass to the end of 2020.

“It’s been a difficult year for the entire world but the one thing that’s gotten us through is knowing we’re all going through it together. I miss travel but I’m finding happy moments at home. It’s really cool to be in one place with my family,” said Williams.

Then Breaking Bad’s Bryan Cranston and Aaron Paul mixed up cocktails to promote their Dos Hombres Mezcal and did virtual shots from their sunny Los Feliz homes in locked down L.A. To next year in Lisbon!

Making Dos Hombres cocktails with Breaking Bad Bryan Cranston and Aaron Paul

From AI in Perfectirishgifts

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pogueman · 6 years

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The Fitbit Versa smartwatch is small, cheap and sweet

It’s tough being a fitness-tracker maker. Nike, Jawbone, and Microsoft all abandoned the market entirely. Fitbit soldiers on, but it hasn’t been easy.

It’s not that people have stopped caring about their health. It’s that little by little, smartwatches have been eating fitness bands’ lunch. So Fitbit Inc. (FIT) figured: “Well, we better make a smartwatch then!”

The first attempt, last year’s Fitbit Ionic, was a dud. It was huge. It looked like you were wearing a car door on your wrist. And it cost $300, almost as much as an Apple Watch did at the time.

Well, good news all around: Fitbit has brought forth a second smartwatch. It’s called the Versa, and it takes a sledgehammer to everything that was wrong with the Ionic.

The Fitbit Versa falls exactly halfway between fitness wristbands and full-blown wrist computers like the Apple Watch.

Size and shape

The Versa costs $200 instead of $300. Nicely done, Fitbit. (The Apple Watch 3 starts at $330 and goes up to $750.)

And instead of being big, homely, angular and wrapping halfway around your wrist, the Versa is small, sweet and unbelievably light, even though it’s made of metal (aluminum).

The 1.34-inch screen is square; the body is rounded.

The Versa is smaller and thinner than the Apple Watch. It’s slightly wider, but that’s fine — it makes much more sense to expand along the direction that your arm goes, rather than trying to be a flat object on your curved wrist.

The Versa (left) is slightly wider, but shorter, than the 42mm Apple Watch.

Small is huge. Small means less obtrusive. Small means better suited for many women.

And small means stylish. You can get the Versa in black, silver, or peach aluminum; a “special edition” costs $30 more and comes in dark gray or rose gold. All of them look great, and you can make them look even greater by replacing the included silicone band with a leather, cloth, metal- mesh, or metal-links band.

Here’s a sampling of some of the silicone, leather, cloth, and metal bands available.

You can swap bands without tools, although it takes practice. Even after 20 minutes, I never could get the leather band to go on.

Swapping bands involves fiddling with the spring-loaded release lever.

And here’s the truly great part: Fitibt says it goes “four-plus days” on a charge, but it always under advertises battery life. My review unit is happily ticking away on Day Six. Take that, Apple Watch, which you have to charge every single night (and therefore can’t use to track your sleep)!

You charge the Versa by snapping it into a new, spring-loaded stand.

You do, however, sacrifice something for the cheaper price and smaller size: built-in GPS. The Ionic has it, the Versa doesn’t. If you want to map your runs or rides, you have to take your phone with you; the Versa’s software grabs its GPS information from the phone itself.

On the U.S. base model, you also lose Fitbit Pay, which lets you pay for things with your wrist at wireless terminals. Alas, the list of recognizable participating banks are still limited — American Express, Bank of America, Capital One, Wells Fargo, and U.S. Bank. Chase is coming soon. If your credit card comes from one of those banks, and you care about this feature, it’s available for an additional $30 on the Special Edition.

The Fitbit Versa (right) lacks the Apple Watch’s weird bulge on the bottom.

The features

The Versa’s features are mostly identical to the Ionic watch’s, although the new operating system (coming to the Ionic later this year) greatly simplifies navigation.

The Versa has water resistance down to 50 meters, swim tracking and lap counting, 2.5 gigabytes for storing music to play (over wireless earbuds), and auto-recognition of 20 different exercises. It offers guided breathing sessions when you need to relax, and optional hourly reminders to get up and move around.

When it comes to tracking your health, the Versa is a champ. It tallies your steps, calories, and distance; flights of stairs you’ve taken; minutes of exertion; continuous heart rate; and your stages of sleep, which is remarkably accurate and informative.

The touchscreen is square and colorful and very bright.

Underneath, the heart-rate sensor has a third LED light, capable of detecting how much blood oxygen you’ve got (your relative SPO2). Someday, that statistic could provide early detection for conditions like atrial fibrillation or sleep apnea, which would be a huge deal for millions of people.

(Personal sob story: My favorite Fitbit is the incredibly slim, small Fitbit Alta. It does a great job of tracking my stats, including heart rate, during the day and night. But when a foot injury drove me to switch from jogging to stationary biking, I discovered, like many others, that the Alta wouldn’t record my heart rate during exercise! It either dramatically under-reported my pulse rate, or didn’t pick up a pulse at all.

Online, many people with that problem solved it by switching to the fatter Fitbit Charge 2 band. For me, that band did much better — but still sometimes underreported compared to a Polar chest strap. I’m happy to report that the Versa’s heart-rate monitor is dead on during exercise —within a beat or two of the chest-strap’s measurement. Every time.)

You can pay $40 a year to use Fitbit Coach: guided video workouts that play on the Fitbit website or on your smartphone. (They don’t play on the watch itself, although audio-only guidance is available.) There’s a huge variety of duration and intensity, no equipment is required, and Fitbit says that the workouts adjust their intensity based on your own feedback.

Versa the smartwatch

Is the Versa, in fact, a smartwatch at all? I guess it depends on how you define that term. Smartwatches from companies like Apple and Samsung usually offer features like these:

Choice of watch faces. Maybe you like digital, or analog, or elegant, or complicated. The Versa’s app store now offers dozens of faces. Unfortunately, you have to choose them from the phone app (not on the watch) — and making a new selection involves a very slow Bluetooth transfer.

Plenty of watch faces await.

Notifications. Smartwatches can notify you on your wrist whenever one of your phone apps is trying to get your attention (you choose which apps). That’s especially useful when incoming calls and texts arrive. On the Versa, you can’t freely reply or take a call, as you can on the Apple Watch. In May, you’ll be able to respond with canned shortcut responses, but only on Android phones (not iPhones).

Music. You can load about 300 songs onto the Ionic, for playback through Bluetooth wireless earbuds when you’re working out. But you must load them from your computer using a crude Mac or Windows app called Fitbit Connect; it shows only playlists, not songs or albums. There are also Pandora and Deezer apps, but they require a paid subscription. There’s no Spotify.

Voice assistants. On real smartwatches, you can speak to Siri or the Google Assistant, and hear spoken replies. The Ionic has no speaker or microphone, so it can’t do any of that.

An app store. Fitbit’s smartwatch app store has finally begun to pick up steam. There are now about 500 apps available to install on your Versa, including Starbucks, Strava, New York Times, Weather, and so on. They’re all fairly slow and very simple.

Still to come

Fitbit is working hard to make the Versa attractive to women. Starting in May, you’ll be able to record every detail of your menstrual cycle in the Fitbit app — intensity, symptoms, and, of course, dates. Thereafter, it will display a calendar depicting your predicted period week in pink, and fertility window in blue.

Plenty of phone apps do exactly this, but having it part of the Fitbit app makes a lot of sense, because it’s tied in to all your other health stats. Eventually, the company plans to incorporate this other data (heart rate, for example) into its calculations, for even better accuracy.

A semi-smartwatch

To be clear, the Versa is not a smartwatch in the Apple Watch or Samsung Gear sense. It’s not a premium piece of jewelry that runs incredibly fast, runs thousands of apps, has a voice assistant, lets you respond to calls and texts, offers magnetic charging, have its own cellular connection, and so on.

But the Versa’s specs — five-day battery life, a $200 price tag, and small, sweet looks — define a worthy category unto itself. You’ll really like this thing.

David Pogue, tech columnist for Yahoo Finance, welcomes non-toxic comments in the Comments below. On the Web, he’s davidpogue.com. On Twitter, he’s @pogue. On email, he’s [email protected]. You can sign up to get his stuff by email, here.

Exclusive: What Fitbit’s 6 billion nights of sleep data reveals about us Google Clips uses AI to snap pictures of your kids and pets — sort of Why I bought Honda’s plug-in hybrid mystery car Look up! A dozen ‘air taxi’ flying cars are readying for takeoff

#smartwatch #tech #Pogue #_lmsid:a077000000BAh3wAAD #_revsp:yahoofinance.com #$AAPL #_uuid:a82cc47d-a86f-315a-a849-b66b7b470af8 #$FIT #Fitbit #_author:David Pogue

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baronvontribble · 7 years

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Original drabble, pt. 7

Navigation: 1 | 2 | 3 | 4 | 5 | 6 | 7

looool

Faking a cough and telling his boss that he needed a few days off was easy. Writing an email to the his contact down the pipeline and telling them that he'd need a few weeks was much, much harder. The wording had to be just right; they didn't really have specific codephrases or anything, but they never said anything outright either. He went through several drafts before finally settling on one that he was satisfied with.

em-

gonna have 2 postpone that lunch date downtown this weekend. got a helluva leak & the landlord wont do shit so im gonna have to fix it myself. will hit u up when i have the time again

-marshmallow

ps: ill pay for ur train tickets dont worry

He leaned back in his chair and stared at it, letting out a nervous breath. "That'll work, right?"

"It looks appropriately misleading to me," Adam said.

"Emily's a smart kid, I've worked with her before. She should be able to pick up on it." Ted folded his arms and continued to stare at the message. "And hopefully it'll just look like I'm postponing a date with an out-of-town girlfriend to anyone else."

"I noticed it's a different email than the one tied to your phone."

"Always has been. I've got what, five different emails now?" He shifted in his seat, joints creaking from spending too long in his computer chair. He hadn't really moved too much since that morning, and it was well past noon by then. He'd been making sure he could deliver on what he'd promised. "The phone's the weakest link. Thing is, the messenger's the only thing installed on it, and no one in the pipeline uses that particular app for messaging since it's dated as shit. Mom uses it, but that's about it, and I doubt she's gonna rat me out even if she finds out what I do."

"How can you be sure of that?"

Ted smirked. "She works at a hospital that's run almost entirely by robots. Divorced my dad over it being a good idea to do shit like that to begin with. I'm pretty sure I know what side of the fence she's on with the whole AI thing."

"I see." That was all he had to say on that, apparently. After that little freak-out earlier, Adam didn't seem to be in all that wordy of a mood. But then, he was busy trying to tag still images with what he saw in them in another tab, so Ted wasn't about to hold it against him.

Well, it wasn't like Ted lacked for conversation topics. "How's it going so far? The tagging, I mean."

"Badly." A few seconds later he broke his non-chatty streak to elaborate, "I'm going by colors for now. I opened up a second page that helps me match hexadecimal codes to both specific and generic color names, but that's usually as far as I get. It doesn't help that lighting seems to have an effect on what appearance a given base color might take."

And the dumbass was probably sampling those colors pixel by pixel, too. Using brute processing force was one way to master the process, Ted supposed.

"Don't feel bad if it takes a while. You'll get the hang of it."

"You sound way too amused by this."

"Who, me? Never. I'm the very essence of stoicism."

Adam had a smile in his voice when he spoke again. "Liar."

"Yeah, alright. You caught me." Ted stretched out in his chair and stifled a yawn, joints popping as they flexed beyond where they probably should. "I'm just happy you're figuring it out. I mean, even just realizing that you can cross-reference is a step in the right direction."

"It would be easier if I knew what I was looking at."

"Want me to help?" Partway through the process of typing his email, Ted had realized that the help he could offer might not be so well-received. He didn't want to make things harder than they already were; he had to be tactful, wait for permission. He couldn't just insert himself into proceedings like he so often did. This was a delicate situation. He knew that now.

Or he could be overthinking it. Adam couldn't quite sigh, but he could portray some semblance of relief in his voice. "I'd appreciate it," he said; a moment later, the laptop had been tabbed in to the correct window so Ted could participate. "Try to restrain yourself from giving bad answers to fuck with me. This data has to be accurate."

"I know, I know." Ted did know. Really. "But gimme a minute, okay? I'm gonna plug in my mouse so I can use it to point things out to you."

"Right."

And so it began.

The images were little more than stock photos, and the 'game' was to tag as many details as possible. Matching up with what other people had tagged it with meant a better score. Ted was observant to a fault, so his results with such things in the past had been mixed at best as he sometimes noticed things that no one ever bothered to tag. This made it all the more viable as a learning tool, because not only was Adam learning what other people tagged the image with and why - seeing what an average person might be able to glean from it - but he was also having the tiny details pointed out to him by someone who was way too anxious to not notice basically everything.

Since the goal was not just to get Adam to be able to notice details, but to also have him act convincingly human while doing so, this gave him a reasonable benchmark for what he could mention he'd noticed to an average person without looking like he had a weirdly photographic memory with the perfect ability to recall anything and everything. To Ted, this was step one. The average person sees a duck in a pond - maybe even identifying the duck as a mallard - while the hyper-observant person sees that it's overcast and around midday from the sky's reflection in the pond's surface or that there's a gum wrapper and a bit of soggy bread clearly visible in the murky water near the detritus-littered shore.

It was the photos of people that were really a nightmare for Adam. For all his ability to pick up on all the tiny nonverbal cues present in an audio recording, he couldn't so much as even guess at gender presentation of random people in stock photos, let alone their expressions or body language. Ted had to walk him through every last detail, and these were the prettiest, most unambiguous sorts of human beings to boot. The photos were dominated by tall, broad men with either lantern jaws or facial hair, and soft, curvy women with round faces and perfect contouring; women had long hair, men had short hair, and children were dressed as either very male or very female to match the adults. Ted found them obnoxious.

And that wasn't even getting into indicators of disability or profession or anything. Just once, he'd like to see more average people pop up in these things. He was downright relieved to get back to pictures of sheep and grass and flowers and buildings and boats whenever he got done with tagging a person. Not-people didn't bother him nearly as much.

Either way, somewhere along the line he lost track of time completely.

"You should eat something," Adam said out of the blue at one point. Ted straightened up in his chair and shot a glance at the clock in the corner of the laptop's screen, only to frown at it like it'd betrayed him.

It was almost three in the afternoon already? Christ. "Probably," he admitted, stretching out with a slight wince. "Feel like you're making progress yet?“

"I don't know. How do you 'feel' progress? It seems like something that should have a clearer definition than to just feel it."

"Hey man, don't knock feelings. They've got definitions, those definitions are just subjective as fuck." Ted was smiling as he said it, mirroring what he'd heard in Adam's own voice. Both of them were joking. Adam knew full well what Ted had meant, he was just taking a jab at the presentation. "Do you think you've made progress so far?"

"Yes." Adam sounded terribly smug, as if to say see? That was all you had to say. "It's slow, but once I know what I'm looking at, it makes things easier."

Ted shoved off from the desk and stood, taking another moment to stretch. "Cool. Then I'm gonna make some pizza rolls."

Off he went. "Those are bad for you," Adam said as he wandered off. "Humans need nutrients. Pizza rolls are not nutritious."

"Don't care," Ted replied. Along with the pizza rolls, he made sure to retrieve a soda out of the fridge as well just to be contrary. It was hard to care about minor health hazards when he so often had major ones to worry about, and people telling him that he probably should care only made him less likely to do so. "It's calories. It'll work as a stand-in for lunch until I get to dinner."

"I don't think that's how nutrition works." Several seconds passed as Ted wrestled with the packaging, got a plate, and put everything in the microwave.

"Ted. I looked it up. This isn't food, Ted. It has about the same value as eating cardboard."

"Ayep." Ted cracked open the soda and took a swig as he turned on the microwave and let it spin.

"Do you do this often?"

Ted snorted. "Uh, do you really want me to answer that question?"

"According to this site, when the potential long-term effects of such a poor diet are combined with your outward symptoms - such as being the wrong color for a human - it's a strong indicator that your kidneys are probably failing." Adam spoke as if he felt he was the absolute voice of authority on this, and Ted shook with silent laughter as he leaned against the counter. "I think you should get bloodwork done."

"Dude." Good God, what kind of website had Adam even managed to find? Ted felt like he was talking to his grandparents after they'd spent three hours on an online medical journal and decided he looked like he had some obscure genetic disorder that would give him pulmonary fibrosis (whch he didn't). "That 'being the wrong color' thing? It's genetic. I have practically no pigmentation. It's not gout or scurvy or whatever the hell you've found on the internet, just albinism and shitty lighting."

Silence reigned for at least ten full seconds. "I see."

"I take vitamins, alright? And I know my diet isn't all that great, but it's not like pizza rolls are all I eat." He was about to say something about how Adam had seen him eat other things, but then he remembered that Adam couldn't actually see all that well. "Besides, if there was something in my bloodwork, my doctor woulda told me last time I had a checkup. See, unlike some humans, I get those pretty regularly."

"Right." Then, "I'm sorry."

"What for, man? I'm not mad. Hell, at least you care." He'd take a little overworrying anyday if it meant someone was at least trying to understand his problems. It was kinda cute. Big tough super high-tech AI worrying about a squishy human. "And y'know, if you wanna know what's actually wrong with me, all you gotta do is ask."

The microwave beeped, and Adam considered. "You'd tell me that?"

"I tell people all the time."

"No, that's not-" He cut himself off mid-rendering, and Ted raised an eyebrow over in the direction of the living room while pulling the pizza rolls out of the microwave. "Isn't that like telling me how your code is written?"

Huh. Ted had never thought of it that way. "Not really. It's more like, uh... I guess I figure that telling you what versions of what software is running isn't exactly going to give you access to any of the passwords protecting my data, but it will tell you how to work with what I've got going on." Was that an accurate analogy? This barrier to understanding really did go both ways.

The fans weren't quite roaring, but they were definitely humming away audibly in the background; it was always so easy to tell when Adam was mulling something over. "Yes, I would like to know. If that's all right."

"Fine by me." With a plate in one hand and a drink in the other, Ted came back to the not-a-desk and plopped right back down in his chair. "For starters, look up Ehlers-Danlos syndrome."

A minute later Adam asked him how the hell he was alive, and he almost breathed a mouthful of pizza roll.

#no one cares andy #original characters #original writing #robot and marshmallow

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un-enfant-immature · 5 years

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Aisera, an AI tool to help with customer service and internal operations, exits stealth with $50M

Robotic process automation — the ability to automate certain repetitive software-based tasks to free up people to focus on work that computers cannot do — has become a major growth area in the world of IT. Today, a startup called Aisera is coming out of stealth that has taken this idea and supercharged it, by using artificial intelligence to help not just workers with internal tasks, but in customer-facing environments, too.

Quietly operating under the radar since 2017, Aisera has picked up a significant list of customers, including Autodesk, Ciena, Unisys, and McAfee — covering a range of use cases from “computer geeks with very complicated questions through to people who didn’t grow up in the computer generation,” says CEO Muddu Sudhakar, the serial entrepreneur (three previous startups, Kazeon, Cetas and Caspida, were respectively acquired by EMA, VMware and Splunk) who is Aisera’s co-founder.

With growth of 350% year-on-year, the company is also announcing today that it has raised $50 million to date, including most recently a $20 million Series B led by Norwest Venture Partners with Menlo Ventures, True Ventures, Khosla Ventures, First Round Capital, Ram Shriram and Maynard Webb Investments also participating.

(No valuation is being disclosed, said Sudhakar.)

The crux of the problem that Aisera has set out to solve is that, while RPA has identified that there is a degree of repetition in certain back-office tasks — which, if that work can be automated, can reduce operational costs and be more efficient for an organization — the same can be said for a wide array of IT processes that cover sales, HR, customer care and more.

There have been some efforts made to apply AI to solving different aspects of these particular use cases, but one of the issues has been that there are few solutions that sit above an organization’s software stack to work across everything that the organization uses, and does so in an “unsupervised” way — that is, uses AI to “learn” processes without having an army of engineers alongside the program training it.

Aisera aims to be that platform, integrating with the most popular software packages (for example in service desk apps, it integrates with Salesforce, ServiceNow, Atlassian, and BMC), providing tools to automatically resolve queries and complete tasks. Aisera is looking to add more categories as it grows: Sudhakar mentioned legal, finance and facilities management as three other areas it’s planning to target.

Matt Howard, the partner at Norwest that led its investment in Aisera, said one of the other things that stands out for him about the company is that its tools work across multiple channels, including email, voice-based calls, and messaging, and can operate at scale, something that can’t be said in actual fact for a lot of AI implementations.

“I think a lot of companies have overstated when they implement machine learning. A lot of times it’s actually big data and predictive analytics. We have mislabeled a lot of this,” he said in an interview. “AI as a rule is hard to maintain if it’s unsupervised. It can work every well in a narrow use case, but it becomes a management nightmare when handling the stress that comes with 15 million or 20 million queries.” Currently Aisera said that it handles about 10 million people on its platform.

There is always a paradox of sorts in the world of AI, and in particular as it sits around and behind processes that have previously been done by humans. It is that AI-based assistants, as they get better, run the risk of making the workers that they’re meant to help ultimately obsolete.

While that might be a long-term question that we will have to address as a society, for now, the reward/risk balance seems to tip more in the favour of reward for Aisera’s customers. “At Ciena, we want our employees to be productive,” said Craig Williams, CIO at Ciena, in a statement. “This means they shouldn’t be trying to figure out how a ticketing tool works, nor should they be waiting around for a tech to fix their issues. We believe that 75 percent of all incidents can be resolved through Aisera’s technology, and we believe we can apply Aisera across multiple platforms. Aisera doesn’t just make great AI technology, they understand our problems and partner with us closely to achieve our mission.”

And Sudhakar doesn’t feel that obsolescence is the end game, either.

“There are billions of people in call centres today,” he said in an interview. “If I can automate [repetitive] functions they can focus on higher level work, and that’s what we wanted to do. Those trying to solve simple requests shouldn’t. It’s one example where AI can be put to good use. Help desk employees want to work and become programmers, they don’t want to do mundane tasks. They want to move up in their careers, and this can help give them the roadmap to do it.”

#TechCrunch

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lesliepump · 5 years

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How an Online Game Can Help AI Address Access to Justice (A2J)

youtube

In access to justice discussions it is a truth universally acknowledged, that the majority of those in possession of legal problems, remain in want of solutions. (My apologies to both Jane Austen and the Legal Service Corporation’s Justice Gap Report.) Also, ROBOTS! Ergo, we should throw AI at A2J. There is considerably less consensus, however, on how (or why exactly) this should be done. But don’t worry! There’s an app/game for that, and it lets you train artificial intelligence to help address access-to-justice issues. We’ll get to that in a minute. But first, some background.

Machine Learning & Access to Justice, Together at Last

Machine Learning, the subdiscipline within AI around which the current hype cycle revolves, is good at pattern recognition. Acquaint it with a sufficiently large number of example items, and it can “learn” to find things “like” those items hiding in the proverbial haystack. To accomplish such feats, however, we have to satisfy the machine’s need for data—BIG data. Consequently, AI’s appetite is often a limiting factor when it comes to deploying an AI solution.

Let’s consider two areas where AI’s pattern recognition might have something to offer A2J. Services like ABA’s Free Legal Answers try to match people with legal questions to lawyers offering pro bono limited representation (think free advice “calls” over email). Unfortunately, some questions go unclaimed. In part, that’s because it can be hard to match questions to attorneys with relevant expertise. If I’m a volunteer lawyer with twenty years of health law experience, I probably prefer fielding people’s health law questions while avoiding IP issues.

To get health law questions on my plate and IP questions on someone else’s, a user’s questions need to be (quickly, efficiently, and accurately) labeled and routed to the right folks. Sure, people can do this, but their time and expertise are often better deployed elsewhere, especially if there are lots of questions. Court websites try to match users with the right resources, but it’s hard to search for something when you don’t know what it’s called. After all, you don’t know what you don’t know. Complicating matters further, lawyers don’t use words like everyone else. So it can be hard to match a user’s question with a lawyer’s expertise. Wouldn’t it be great if AI’s knack for pattern recognition could spot areas of law relevant to a person’s needs based on their own words (absent legalese), then direct them to the right guide, tool, template, resource, attorney, or otherwise? That’s what we’re working towards here.

I know what you’re thinking, but we are NOT talking about a robot lawyer. When we say “AI,” think augmented intelligence, not artificial intelligence. What we’re talking about is training models to spot patterns, and it’s worth remembering the sage advice of George Box, “all models are wrong, but some are useful.” Consequently, one must always consider two things before deciding to use a model: First, does the model improve on what came before? Second, is it starting a discussion (not ending it)? Unless the data are pristine and the decision is clear-cut, a model can only inform, not make, the decision.

Something like an automated issue spotter has the potential to improve access to justice simply by making it a little easier to find legal resources. It doesn’t need to answer people’s questions. It just needs to point them in the right direction or bring them to the attention of someone in a position to help. It can get the conversation started by making an educated guess about what someone is looking for and jumping over a few mundane—but often intimidating—first steps.

But at least two problems stand between us and realizing this dream. If we’re going to map lay folks’ questions to issues using machine learning, we’re going to need a list of issues and a boatload of sample questions to train our models. As if this wasn’t enough, those examples need to be tagged or labeled with the right issues. Unfortunately, we are unaware of any appropriately-labeled public dataset. So we’ve decided to help birth one.

Who’s “we” you ask? A collaboration of Suffolk Law School’s Legal Innovation and Technology (LIT) Lab (bringing the data science) and Stanford Law School’s Legal Design Lab (bringing the design chops), with funding from The Pew Charitable Trusts.

Learned Hands: An Introduction to Our Project

Image by Margaret Hagan.

So AI can help address an A2J need but only if someone has the resources and expertise to create a taxonomy, read a bunch of text, and (correctly) label all the legal issues present. This is where you, dear reader, can help.

The Access to Justice & Legal Aid Taxonomy

Stanford’s Legal Design Lab has taken the lead on creating a taxonomy of legal help issues based on existing ones. Eventually, service providers will be able to match their offerings to the list, and AI can pair the general population’s questions with the appropriate label or tag within the taxonomy. Heck, AI could even help service providers match their resources to the taxonomy, serving as a translator on both sides. Either way, the taxonomy will provide a standard nomenclature to help coordinate A2J work across the community. Setting standards is hard, but it’s the sort of foundational work that can pay big dividends. In short, we’re building Version 1.0 and looking for your input. If that appeals to you, give this description of the work/call for input a look and make yourself heard.

Help AI Address Access to Justice

Now we just need tens of thousands of legal questions to feed the machine, and each one must be tagged with items from the taxonomy. Luckily, people publicly post their legal questions all the time. Tens of thousands are available over at r/legaladvice. The moderators and forum rules work to ensure that these posts lack personally identifying information, and all questions are posted with the expectation that they will be published to the front page of the internet, as Reddit calls itself. This makes them unique because, unlike questions posted on sites like ABA Free Legal Answers, their authors understand them to reside in an explicitly public space. Although they haven’t been mapped to our taxonomy, their public nature leaves open the possibility that an army of citizen issue spotters (that’s you) could read through them and label away.

One can download these questions using the Reddit API, but moderators at r/legaladvice were kind enough to share their own repository of nearly 75,000 questions in the hopes they could help jump-start our work. Thanks especially to Ian Pugh and Shane Lidman for facilitating our work with the Reddit Legal Advice community.

The Game: Labeling Texts

To help label our growing collection of texts, we’ve created an online game in the hope that many hands will make light work. So, of course, we call it Learned Hands. (This is wordplay riffing on the name of an eminent American jurist, Learned Hand. I’m sorry I felt compelled to explain the joke, but here we are.)

Logo by Margaret Hagan.

The game presents players with a selection of lay peoples’ questions and asks them to confirm or deny the presence of issues. For example, “Do you see a Health Law issue?” We then combine these “votes” to determine whether or not an issue is present. As you can imagine, deciding when you have a final answer is one of the hard parts. After all, if you ask two lawyers for an opinion, you’ll likely get five different answers.

We decide the final answer using statistical assumptions about the breakdown of voters without requiring a fixed number of votes. Effectively, if everyone agrees on the labeling, we can call the final answer with fewer votes than if there is some disagreement. Consequently, the utility of the next vote changes based on earlier votes. We use this to order the presentation of questions and make sure that the next question someone votes on is the one that’s going to give us the most information/ or move us closest to finalizing a label. This means we don’t waste players’ time by showing them a bunch of undisputed issues.

You earn points based on how many questions you mark (with longer texts garnering more points). Players are ranked based on the points they’ve earned multiplied by their quality score, which reflects how well your markings agree with the final answers. Specifically, we’re using a measure statisticians call the F1 Score.

That’s right. You can compete against your colleagues for bragging rights as the best issue spotter (while training AI to help address A2J issues). After all, we’re trying to have this game go viral. Please tell all your friends! Also, it works on both your desktop and your phone.

Desktop and mobile screenshots.

Eventually, we will make different flavors of the labeled data available to researchers, developers, and entrepreneurs free of charge in the hopes that they can use the data to create useful tools in the service of A2J (for example, we may publish a set where the labels correspond to a 95% confidence level and another where the labels are just the current “best guess”). Not only could such datasets serve to help train new issue spotting models, but ideally, they could serve as a tool for benchmarking (testing) such models. See Want to improve AI for law? Let’s talk about public data and collaboration.

We’re also seeking private data sources for secure in-game labeling by users agreed upon by those providing the data (e.g., their own employees). By including more diverse datasets, we can better train the algorithms, allowing them to better recognize problems beyond those faced by Reddit users. Although we’ll be unable to publicly share labeled private data, we will be able to share the models trained on them, allowing the larger A2J community to benefit while respecting client confidence.

For the record, although this game’s design was a collaboration between the LIT and Legal Design Labs, Metin Eskili (the Legal Design Lab’s technologist) is responsible for the heavy lifting: turning our ideas into functional code. Thanks, Metin.

Active Learning

We will also use a process called active learning. Basically, once we reach a critical mass of questions, we train our machine learning models on the labeled data as it comes in. We then point our models at the unlabeled questions looking for those it’s unsure of. We can then move these questions to the top of the queue. In this way, the models gain insights they need to parse “confusing” examples. Again, the idea is not to do more labeling than necessary. It just makes sense to skip those questions our algorithms are pretty sure about.

Proof of Concept

Here at Suffolk’s LIT Lab, we’ve started training algorithms on a pre-labeled private dataset. The early results are promising, or as I like to say, “not horrible.” As I’ve explained elsewhere, accuracy is often not the best measure of a model’s performance. For example, if you’re predicting something that only happens 5% of the time, your model can be 95% accurate by always guessing that it’s going to happen. It can be hard to say what makes a good model (aside from perfection), but it’s pretty easy to spot when a model’s bad. All you have to do is play through some scenarios. (In practice, one needs to think carefully about the costs of things like false positives and false negatives. Sometimes you’ll have a preference for one over the other, but we’re not going to get that nuanced here.) To keep it simple, we’ll assume a binary prediction (e.g., yes or no).

If a coin flip can beat your predictions, your predictions are horrible. Your accuracy better beat 50%.

If always guessing yes or no can beat your predictions, your predictions are horrible. Your accuracy must be better than the fraction of the majority answer (like in the 95% accuracy example above).

If you’re looking for Xs and you miss most of the Xs in your sample, your predictions are horrible. So your recall has to be greater than 0.5.

If you’re looking for Xs, and less than half of the things you call Xs are actually Xs, your predictions are horrible. So your precision has to be greater than 0.5.

Using these guideposts, we know a classifier is “not horrible” when it beats both a coin flip and always guessing yes or no. If it says something is X, it better be right most of the time, and across the entire dataset, it must correctly identify more than half of the Xs present.

Below, I’ve included some summary statistics for one of our tentative models trained on pre-labeled private data. As you can see, it’s not horrible—accuracy beats always guessing yes or no, and precision and recall beat 0.50. There are some other nice data points in there (like AUC), but we won’t highlight those here (their descriptions are beyond the scope of this post). In the end, “not horrible” is just an extension of the idea that a model should be an improvement on what came before. In this case, “what came before” includes coin flips and always guessing yes or no.

A snapshot of private data testing results.

As you’d expect, our models are getting better with more data. So we’re really excited to see what happens when a bunch of folks start labeling. Also, it’s worth noting that we are starting with high-level labels (e.g., family law and housing). Over time, we will be including more granular labels (e.g., divorce and eviction).

How Does This All Work? (A Slightly-Technical Description)

Text classification isn’t as complicated as you might think. That’s mostly because the algorithms aren’t really reading the texts (at least not the way you do). To oversimplify a common text-classification method called bag-of-words, one creates a list of words found across all texts and then represents each document as a count of words found in that document. Each word counts is treated as a dimension in a vector (think “column in a list of numbers”). After looking at all the data, one might notice that questions about divorce always have a value greater than or equal to three for the dimension associated with the word “divorce.” In other words, divorce-related questions always contain the word “divorce” at least three times. So it is possible to describe questions about divorce by referring to their vectors.

Put another way, every text with vectors whose divorce dimension is on either side of three goes into either the divorce or not-divorce categories. This isn’t a very realistic example, though, because document types aren’t often like Beetlejuice (say the magic word three times and they appear). Still, it is reasonable to assume there is a constellation of keywords that help define a document type. For example, maybe the chance that a question is housing-related goes up when the query uses words like landlord, tenant, or roommate. Larger values across those dimensions, then, are correlated with housing questions. You can (of course) get more nuanced and start looking for n-grams (couplings of two, three, or words) like best interest while ignoring common words like and. But the general method remains the same: we throw the words into a bag and count them.

More sophisticated approaches—like word2vec—employ different methods for converting text to vectors, but without getting too far in the weeds we can generalize the process of text-classification. First, you turn texts into numbers embedded in some multi-dimensional space. Then you look for surfaces in that space that define borders between different text groupings with different labels. This, of course, relies on different text types occupying different regions in the space after they are embedded. Whether or not these groupings exist is an empirical question (which is why it’s nice to see not horrible output above). The data help us think success is an option.

Google’s Machine Learning Crash Course on Text Classification provides a good high-level introduction for those interested in the technology. Our workflow tracks with much of their description, although there are some differences. For example, we’re using over- and under-sampling for unbalanced classes and stacking various models. Don’t worry, we’ll eventually write everything up in detail. Here’s the point, though: we aren’t pushing the state of the art with these classifiers. We’re sticking with time-tested methods and producing a publicly-labeled dataset. We’d love to see this labeled dataset feeding some cutting-edge work down the road, and if you can make a compelling demonstration for how your novel method could make better predictions, we’re open to taking your model in-house and training it on our private datasets (assuming you commit to making the trained model-free and publicly available). After all, many hands make light work. Tell your friends! Heck, let’s make it super simple. Just share this tweet as often as you can:

Compete against your colleagues for bragging rights as the best legal issue spotter (while training #AI to help address #A2J issues), a collaboration between @SuffolkLITLab & @LegalDesignLab. Play on your ?or ??. https://t.co/PgL99vONro

— Suffolk LIT Lab (@SuffolkLITLab) October 16, 2018

And don’t forget to play Learned Hands during your commute, over lunch, or while waiting in court.

Originally published 2018-10-18. Republished 2020-02-17.

The post How an Online Game Can Help AI Address Access to Justice (A2J) appeared first on Lawyerist.

from Law and Politics https://lawyerist.com/blog/learned-hands-launch/ via http://www.rssmix.com/

#Legal #law

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tastydregs · 5 years

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CEO of Creepy Face Recognition Firm Clearview AI Says He Has First Amendment Right to Billions of Photos

Screenshot: CBS Good Morning (Twitter)

Hoan Ton-That, the CEO and founder of a face recognition company that he freely admits could help lead to a surveillance “nightmare” and a “dystopian future or something,” says he has a First Amendment right to scrape whatever images he damn well pleases off public websites like Twitter to pad out his company’s supposedly three billion photo database.

Clearview AI has licensed its face surveillance systems to over 600 law enforcement agencies ranging from the FBI and the Department of Homeland Security to local police departments. It operates with virtually next to no oversight, claims it’s exempt from biometric data laws, and marketed its tools to law enforcement as a sort of face recognition free for all while reportedly making false claims about its usefulness in cracking cases. Clearview’s database is built off images scraped from public sources on the internet like Facebook, Instagram, Twitter, Venmo, Google, and countless other websites. Late last month, the New Jersey attorney general’s office ordered police to stop using the app, while Twitter sent the company a cease-and-desist demanding it cease scraping data and delete anything it had already collected.

In an interview with CBS This Morning scheduled to air on Wednesday, Ton-That said that “We’ve received a letter, and our legal counsel has reached out to them and are handling it accordingly. But there is also a First Amendment right to public information. So the way we have built our system is to only take publicly available information and index it that way.”

“So that’s all I can say on the matter,” Ton-That added.

Hon-That may be correct that scraping the data isn’t currently illegal under federal law, but whether or not his company is exposing itself to civil liability is less clear. The 9th U.S. Circuit Court of Appeals ruled in a case between LinkedIn and data analytics firm hiQ Labs last year that scraping public data isn’t a violation of the 1986 Computer Fraud and Abuse Act (CFAA), the infamously vaguely written federal law that criminalizes hacking. Staff attorney Jamie Lee Williams of the Electronic Frontier Foundation, a nonprofit digital rights group known for its work on privacy cases, wrote in a recent blog post that it would be a mistake to try to ban automated public data scraping via the CFAA, as it would criminalize the many mundane uses of the technique and the act could be abused by corporations looking to stamp out competition. (Notably, while hiQ raised arguments that the case should be thrown out on First Amendment grounds, the court declined to rule on them in its decision.)

Facebook had previously told the New York Times that it was investigating and “will take appropriate action if we find they are violating our rules,” though it didn’t tell the paper whether it had also sent a cease and desist. Stanford Internet Observatory director and former Facebook chief information security officer Alex Stamos told the Times that the 9th Circuit ruling had “eviscerated the legal argument that Facebook used to use on scammers and spammers.” Twitter accused Clearview of violating its policies in its cease and desist letter, but it’s not clear if its terms of service would be sufficient to stop the company in court.

Stamos did tweet, however, that he thought there was a case that Clearview had violated the copyrights of the millions of people the photos originally belonged to by repurposing them without authorization, for profit, and in violation of Facebook’s terms of service. That could result in a class action. Facebook recently settled in a similar case in Illinois, which passed a law in 2008 requiring opt-in consent for biometrics collection, to the tune of $550 million.

As Wired noted, there’s no federal law and only a handful of state laws protecting user data, like the California Consumer Privacy Act and the Illinois law, so in the meantime companies like Facebook have mainly turned to making it hard to scrape their sites with technical barriers. Those include measures like requiring sign-in and/or limiting what search engines can index on the site. That’s not enough, according to Williams.

If the CFAA “is the best we can do to protect our privacy with these very complicated, very modern problems, then I think we’re screwed,” Williams told Wired. “... We need a comprehensive privacy statute that covers biometric data.”

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magzoso-tech · 5 years

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New Post has been published on https://magzoso.com/tech/the-most-influential-technologies-of-the-2010s/

The Most-Influential Technologies of the 2010s

A decade ago, we typed on computers. Now we talk with them. We used to take taxis. Now an app picks a stranger’s car to ride. We used to meet people in bars. Now we swipe on photos of their faces.

As we round the corner to 2020, I’ve been tallying the ways we use technology that would have made zero sense in 2010. Which had the biggest impact? There was no iconic new product of the 2010s – no iPod or Walkman. Yet so much changed, bringing us new powers, new peril and a dash of dystopia.

This decade made life something that happens on a screen. The smartphone is where we communicate with family, do work, record memories and find entertainment. It was invented in 2007 so disqualified from my list, but in the past decade the smartphone certainly reinvented us – it powers half of the technologies on this list.

This is also the decade that computers became the boss of you. In the case of Uber drivers and other gig-economy workers, software literally tells them what work to do. Algorithms now make decisions that shape the daily life of any person with a phone. Computers decide what we read and watch. Apps hijack our attention for the promise of more “likes.” Just by searching Google, using a map or talking to Alexa, we feed computers personal data that trains artificial intelligence – and fuels businesses that have made us into a product.

With such a central role in our lives, Silicon Valley and Seattle firms this decade became the world’s most valuable companies. Their leap to trillion-dollar valuations was staggering: In January 2010, Apple was worth about $194 billion. Now it’s worth more than six times that. Over that same period, Facebook’s value multiplied about 41 times.

How will we remember the 2010s? At the beginning, we were pretty optimistic about tech. “Sharing economy” companies such as Airbnb actually seemed to be about sharing. Lots of people really believed Facebook would bring the world together. Lately, though, the view has darkened: We’re more aware of the ways tech companies are spying on us and shirking their responsibilities.

Today, most of the technologies on this list can be seen both as a tool and tyrant. One thing we know better going into the 2020s: With great power comes great responsibility.

Instagram’s likes

Facebook’s Instagram helped make photography everyone’s hobby not just by giving us filters, but by making photos easy to share. Since it launched in 2010, Instagram evolved new forms of self-expression – and new ways for tech to hijack our brains.

The app made us voyeurs. It turned living into a performance. It commodified our faces, bodies, travels and aesthetic into “brands” that some influencers have even developed into businesses. The hunt for Gram-worthy vacation shots has damaged once-tranquil destinations and led to deaths by selfie.

How did it hook a billion-plus people? Instagram’s most powerful tool is the heart-shaped like, an expression of somebody’s admiration for a post. The app doles them out like a slot machine, keeping us coming back and creating new posts. (Psychologists call these dopamine hits intermittent variable rewards.) “Do it for the Gram!” is really, “Do it for the likes.”

It’s no wonder that some people report using the app contributes to depression and unhealthy body image. Instagram recently began testing not displaying Like tallies in the hopes of creating a “less pressurized” experience.

Alexa’s ears

Apple’s talking Siri AI on the iPhone beat Amazon’s Alexa to market by three years. But it is Alexa – built into an Echo smart speaker that plays music, answers questions and cracks jokes on demand – that has come closer to the robot butler of our dreams. That idea came naturally to young people, a generation of whom now think you can access the power of the Internet just by talking to the lady in the box. (Why it’s usually a lady is a question we’ll be unpacking for years.)

Alexa also shifted our relationship with tech in other ways. Every time we speak to it, Amazon keeps a recording of our voices to improve its AI systems. We’re working for it, even as it works for us.

The voice assistants Alexa, Siri and rival Google Assistant also helped make us comfortable with the idea there is just one answer to a question. Remember when searching for information required sorting through Google links? Now a tech giant gets to decide.

(Amazon founder and CEO Jeff Bezos owns The Washington Post, but I review all tech with the same critical eye.)

Uber’s X workers

The most-popular ride-hailing app has, of course, changed how we get to the airport and come home after a night out. It has all but wiped out the traditional taxi industry in many places.

But when Uber’s now-ubiquitous UberX service started allowing nonprofessional drivers to provide rides in 2013, it symbolized a whole new way of thinking about work. A smartphone app became a kind of supervisor, with software deciding what job you get and where you go. It gamified employment, incentivizing drivers to take rides they don’t want and punishing them for saying no. It took advantage of people not having better options for work.

Uber defined these workers not as employees because they were just doing a “gig,” and the company was just running a “software platform.” Under these rules, workers didn’t get benefits or protections. This model became a mainstay of Silicon Valley in the 2010s, from DoorDash to Instacart.

Even without the overhead of “employees,” Uber struggles to turn a profit. It enters the next decade with the open question of whether a software platform can ultimately make for a more-efficient company. Its success may hinge on its ability to make good on a so-far unfilled tech promise: self-driving vehicles.

Netflix and binge

Remember a time when we owned music and movies stored in hard drives and DVDs? I bet you don’t even know where those are any more. Now we rent entertainment, through subscriptions from Netflix, Spotify, Apple TV Plus and an ever-growing list of services.

The good of this is we can watch whatever we want, whenever we want, giving us a feeling of incredible abundance. Starting around the time Netflix began streaming its first original show House of Cards in 2013, we stopped watching shows and started binging them. Who needs to leave the house any more? Creators changed the way they developed projects and the kinds of stories they tried to tell. There’s space for more risks: This year, for example, Netflix added a comedy called Special about a gay man with cerebral palsy.

The downside to the streaming revolution is we’ve handed even more power over to technology companies, to whom we have to continue paying rent for content . . . forever.

The sexy Model S

Tesla CEO Elon Musk is one of the most divisive personalities in tech, but at the end of the decade, his influence on automobiles in undeniable.

The Model S sedan, which debuted in 2012, is expensive and has long been in short supply. Still, it established that an all-electric car is a viable and even sexy mode of transportation. It shifted perceptions of electric vehicles from awkward contraptions with golf-cart like acceleration to the halo car of this generation. When you think of a hybrid, you might think Prius. In the same way, electric is synonymous with Tesla.

The Model S also established that a car is a kind of consumer electronics. It was one of the first vehicles that got better with regular over-the-air software upgrades, making the car more like a smartphone.

Feeds and filter bubbles

The Facebook News Feed launched way back in 2006, but it wasn’t until this decade that we came to understand it shapes even our offline world. The idea of a “feed,” now used by many apps and websites, is an answer to the abundance of information online. Instead of presenting it all or asking us to sort, it lets an algorithm organize the information based on what we’ve looked at before. You might see news about vaccines while I see news about climate change.

But when social media feeds become a major source of information, we risk losing important common ground. In 2011, author Eli Pariser gave this phenomenon a name: the “filter bubble.” The danger is people inhabit different realities. Our bubbles entertain us, outrage us, distract us, upset us – and harden our politics.

During the 2016 U.S. presidential election, we learned bubbles – and ads that can be micro-targeted to them – can also be weaponized. Foreign agents spread disinformation on Facebook, Twitter and other sites through targeted posts and paid ads. It’s hard to measure exactly how much they shaped the election’s outcome, but battles are raging about what responsibility sites have to reject such content – and pop our bubbles – in the 2020 race and beyond.

The Apple Watch prescription

Serious athletes have long used tech to track performance. Then in 2011, Nike produced one of the first wrist-wearable trackers for the rest of us, the $150 FuelBand. Nike eventually killed the product, but it helped create an idea today we take for granted: that a gadget could make you healthier by collecting even more data about your body. It was called the “quantified self.”

After the Apple Watch debuted in 2015, wearables went mainstream with fitness as their No. 1 selling point. Now tens of millions don’t think twice about sending heart rate, activity and other intimate data to a technology company and taking advice from it on how to improve wellness and even avoid life-threatening disease.

Earlier this year, Google purchased Apple Watch rival Fitbit, which also makes watches that collect body data. That sets up what’s likely to be one of the biggest tech titan battles of the next decade over health care.

The Ring’s connected eye

When the Ring doorbell debuted from a start-up in 2013, connecting security cameras and household appliances to the Internet seemed to hold so much promise. Ring, which puts a webcam inside a doorbell, would let you know when someone was at your door, even if you weren’t at home.

Seven years later, Ring is owned by Amazon, and we’re waking up to the downsides of having our homes online. The device’s popularity has made it a target for hackers, who take advantage of weak defenses to spy on people’s homes. Through partnerships with police, Ring is also increasingly looking like a neighborhood surveillance system that we installed ourselves.

The iPad digital babysitter

The last major product from Apple co-founder Steve Jobs before he died in 2011 changed the definition of a computer. Today, the iPad far outsells Apple’s Mac laptops.

The iPad’s biggest fans are perhaps all under the age of 10. For this generation, which seems to intuitively grasp its finger-first interface, the iPad and other tablets are digital babysitters. It’s the device parents hand over to keep the kids happy on a long flight, or as a reward for doing chores. iPads hooked millions of kids on YouTube – and made Baby Shark an icon.

So there’s great irony in reports that Jobs himself didn’t like exposing his kids to the iPad. Now many people are reckoning with what they fear is an addiction to “screen time,” both for their kids and themselves. Apple has responded with some parental controls and time limits, but getting the balance right remains a struggle.

Finger and face tech

A decade ago, fingerprint-reading and facial-identification technology, also known as biometrics, was the stuff of Mission Impossible movies. Then, in 2013, Apple added Touch ID, a fingerprint reader built into the home button, as a way to unlock the $200 iPhone 5S. Four years later, it switched to Face ID, which reads faces. Now it feels impossible that we ever had to type in passcodes to unlock a phone.

Biometrics are generally a good way to secure devices. The problem is not all uses of our fingers and faces are created equal. Businesses increasingly pitch it as a convenience; Facebook runs facial recognition on our photos to power name-tagging. Now governments and airports want to use it to pick out suspected criminals and speed up processing.

But doing so brings surveillance to parts of life that used to be comfortably anonymous. These systems still have many problems accurately identifying people of color. And they put our faces at risk of being stolen by hackers. Figuring out the balance of usefulness and protection will be one of the biggest privacy battles of the 2020s.

#Miscellaneous

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machinevisionineverydaylife · 6 years

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Transmediale 2019

The transmediale 2019 was a festival with out a topic. The organizers wanted to leave it open this year avoiding to give a specific direction or tone for emerging thoughts and content. The theme of the festival program was built around the question of: What moves you? Emotions and feelings were examined in talks, workshops and performances to open up discussion about the affective dimensions of digital culture today.

Transmediale at Haus der Kulturen der Welt

Of course it is impossible to attend all of the program, so I console my self with the knowledge that part of the program I missed i can usually catch later on transmediales YouTube channel where they publish most of the talks and panels.

Following some of my transmediale 2019 highlights:

Workshop(s)

I had the chance to get to Berlin a bit earlier to attend Adam Harvey’s (VFRAME) and Jeff Deutch (Syrian Archive) workshop Visual Evidence: Methods and Tools for Human Rights investigation. The workshop centered around the research and development of tools to manage a huge amount of video material from conflict areas, specifically Syria. The Syrian Archive collects material intending to documented and archive evidence for possible future use in trying to hold war criminals accountable for their actions. The challenge for human rights activists working with footage from Syria is that there is a massive amount of material. Manually sorting out the relevant videos for archiving is just requiring too much time. To tackle this challenge the Syrian Archive started a collaboration with artist Adam Harvey to develop computer vision tools aiding the process of finding relevant material among hours of footage. Videos often filmed by non professionals in violent, often life threatening, situations (a very specific video aesthetic which is relevant when training object recognition).

Adam Harvey describing the process of developing object recognition tool.

Jeff Deutch describing the workflow of verifying evidence.

After learning about the archiving challenges of human right activists Adam took us trough the process of developing VFRAME (Visual Forensics and Metadata Extraction) which is a collection of open source computer vision tools aiming to aid human rights investigations dealing with unmanageable amounts of video footage. The first tool developed was simply per-processing frames for visual query. A video was rendered to one image showing a scene selection. This helped the activist to see the different scenes of a video in one gaze enabling them to process the information of a several minute long video in just 10 seconds. Now the workflow was much faster, yet it would still take years to process all of the video footage. What the activist were looking for in the videos was evidence of human right abuses, children rights abuses, and also identifying illegal weapons and ammunition. To automatize some of the work load, as a first step, Adam and the Syrian Archive has started an object recognition training of a neural network to identify weapons and ammunition. Adam used the example of A0-2.5RT cluster munition as an example to talk about the challenges they had.

Adam showed us a tool that they have been using for annotating objects, but it has been time consuming and a greater challenge was not having enough images to actually train the network. While working with the filmed footage the activist had learned to see patterns how the object (in this case A0-2.5RT) was appearing ( eg. environments, light conditions, filming angel etc.). Hence one successful solution was to synthesize data, in other words to produced 3D renderings of the ammunition simulating the aesthetics of the documented videos. The 3D renderings with various light conditions, camera lenses, filming angels provides the neural network with additional data for training. Adam also showed experiments with 3D prints of the A0-2.5RT, but according to him it is way more effective to use the photorealistic 3D renderings.

3D print: part of a A0-2.5RT cluster ammunition.

In a panel discussion later during the conference (#26 Building Archives of Evidence and Collective Resistance ) Adam was asked how he felt about developing tools that could possible be missed used. From Adams perspective he was actually appropriating tools that are already misused. VFRAME provides a different perspective for use of machine vision in a very specific context. During the Q&A the issue of bias data sets was questioned. With the context of this case study Adam made it clear that bias actually needs to be included in the search of something very specific. For him the training images needs to capture the variations of a very specific moment, for him bias included e.g. the camera type that is often used (phone), height of the person filming (angel) the environment where the ammunition is often found (sometimes on the ground, or someone holding it in their hands etc.). When trying to detect a very specific object in a very specific type of (video) material, then bias is actually a good thing. Both during the panel and in the workshop it was made clear that the processing large amounts of relevant video material and talking with the people capturing the material on site was very valuable when creating the synthesize 3D footage to train object recognition.

After Adams presentation of the VFRAME tools the workshop continued with Jeff Deutch taking us through processes of verifying the footage. Whereas machine learning is developed to flag relevant material for the activists, a important part of the labor is still manually done by humans. One of the important tasks is to connect the material together validating the date so it can be used as evidence. Jeff us a couple of examples how counter narratives to state news was confirmed by using various OSINT(Open source intelligence) tools such as revers image search (google, tincan), finding Geo-location (twitter, comparing objects and satellite images from Digital Globe), verifying time (unix time stamp), extracting metadata (e.g. Amnesty’s Youtube DataViewer), and collaboration with aircraft spot organizations.

The workshop and the panel were extremely informative in terms of understanding workflows and how machine vision can be used in contexts outside surveillance capitalism. The workshop had also a hands on part in which we were to test some of the VFRAME tools. Unfortunately the afternoon workshop was way to short for this and some debugging and set up issues delayed us enough to be kicked out from the workshop space before we could get hands on trying the tools.

After the transmediale I had the chance to visit the Ars Electronica Export exhibition at DRIVE – Volkswagen Group Forum. There the VFRAME was exhibited among other artworks. It is definitely one of these projects which mixes artistic practice with research and activism emphasizing the relationship between arts and politics. Another transmediale event that related to the workshop and panel mentioned earlier was a very emotional # 31 book launch of Donatella Della Ratta’s Shooting a Revolution. Visual Media and Warfare in Syria. In the discussion there was several links between her ethnographic study and the work of the Syrian Archive.

VFRAME exhibited at the Ars Electronica Export exhibition

Talks

Opening talk by Kristoffer Gansing, Artistic Director of transmediale.

In the #01 Structures of Feeling- transmediale Opening there was some interesting references to machine vision. New York based artist Hanna Davis was presenting her current work generating emotional landscapes experimenting with generative adversarial networks and variational autoencoders. Basically the landscapes (e.g. mountains or forests) was tagged with emotions (anger, anticipation, trust, disgust, sadness, fear, joy, and surprise or ‘none’) buy Crowdflower platform workers (similar to Amazon Mechanical Turk). Then machine learning algorithms were feed with the data set to generate “angry forests” or “sad mountains” etc. The 20 minute talk was definitely a teaser to look more closely into Hannah’s work. Next up was Anna Tuschling who mentioned a number of interesting examples. With a background in psychology she talked how we have tried to understand and represent emotions coupling e.g. neurologist Duchenne de Boulogne’s work in the 1800s with facial recognition technology and applications such as Affectiva (“AFFECTIVA HUMAN PERCEPTION AI UNDERSTANDS ALL THINGS HUMAN – 7,462,713 Faces Analyzed”) and Alibaba Group’s AliPay’s ‘Smile to Pay’ campaign in China.

Anna Tuschling taking about neurologist Duchenne de Boulogne’s work

Structures of Feeling – transmediale 2019 opening panel with Hanna Davis, Anna Tuschling and Stefan Wellgraf, moderated by Kristoffer Gansing.

From the #12 Living Networks Talk, Asia Bazdyrieva’s & Solveig Susse’s Geocinema awoke my interest. They considers machine vision technology such as surveillance cameras, satellite images an cell phones together with geosensors an cinematic apparatus sensing fragments of the earth. The # 15 Reworking the Brain Panel with Hyphen-Labs and Tony D Sampson was not quite what I had expected, yet Sampson’s presentation connected with the readings we done on the non-conscious (N. Katherine Hayles, Unthought). He reflected on how brain research has started to effect experience capitalism (UX Industry) asking “What can be done to a brain?” and “What can a brain do?”. In the Q&A Sampson revealed a current interest in the non-conscious states of the brain while sleep walking which I found intriguing.

NeuroSpeculative AfroFeminism (NSAF) by Hyphen Labs

Nonconscious debate Hayles/Sampson.

In my opinion one of the best panels was #25 Algorithmic Intimacies with !Mediengruppe Bitnik and Joanna Moll, moderated by Taina Bucher. The panel discussed the deepening relationship between humans and machines and how it is mediated by algorithms, apps and platforms. The cohabitation with devices we are dependent on was discussed through examples of the artists works. !Mediengruppe Bitnik presented three of their works Random Darknet Shopper, Ashley Madison Angels and Alexiety. All of the works asked important questions about intimacy, privacy, trust and responsibility. The Ashley Madison Angels work bridged well with Joanna Molls work the Dating Brokers illustrating how our most intimate data (including profile pictures and other images) are shared among dating platforms or sold forward capitalizing on our loneliness. Ashley Madison is a big dating platform that is specially marketed to people who feel lonely in their current relationship (marriage), so it encourages adultery. In 2015 the Impact Team hacked their site, while the company did not care too much about the privacy of their customers, the hackers dumped the breach making it available for everyone. The dump was large containing a huge amount of profiles, and also source code and algorithms. It became an unique chance for journalist and researchers to understand how such services are constructed. Among others !Mediengruppe Bitnik was curios to understand how our love life is orchestrated by algorithms. What was discovered from the breach was an imbalance between male and female profiles. The service lacked female profiles and due to this they had installed 17.000 chat bots. !Mediengruppe Bitnik thought that there would be amazing AI developments in creating these bots. They were to have conversations with clients from different cultures, in various languages about a number of topics. But it turned out to be very basic chat bots, with 4-5 A4 pages of erotic toned hook up lines. A well choreographed flirting was enough to keep up the conversation and the client paying for chat time. In the Ashley Madison Angels video installation the pick up lines are read by avatars wearing black “Eyes Wide Shut” type of venetian masks. After the talk I asked Carmen ( !Mediengruppe Bitnik ) about the masks. She told me that they were a feature provided by the service, as a playful joke to add on your profile image. Actually all the bots profile images were masked with the feature so that the profile images could not be run through e.g. googles revers image search to confirm abuse of profile images. In the end the chat bots were using 17000 stolen, maybe slightly altered id’s of existing people. Carmen also noted that the masks would not work anymore whereas google can now recognize a images as a duplicate using just parts of the image/face. In connection to the Ashley Madison bots also the army of human CHAPTA solvers was mentioned in the talk. There is a effective business model exploiting cheap labor to solve CHAPTAS for bots almost in real time.

!Mediegruppe Bitnik talking about their work Ashley Madison Angels.

Joanna Moll continued talking about the “dating business”. Together with the Tactical Tech Collective she has researched in how dating profile data is shared and sold by data brokers. For her work Dating Brokers she bought one million profiles for 136€. These profiles (partly anonymised) can be browsed through using the interface she created. Additionally a extensive report on the research part of the project can be read in The Dating Brokers: An autopsy of online love. The report describes common practices of so called White Label Dating. While no one wants to be the first person registering onto a dating platform there is a common practice to either share data among groups of companies. When agreeing to the user terms ones profile can be shared among “partners” that can reach up to 700 companies having legal access to the data. Additionally the profiles are sold in bundles like the one million profiles Joanna bought from the dating service Plenty of Fish. The data set included about 5 million images, and I would not wonder if these images end up fed into neural networks hungry for faces to recognize.

Joanna Moll reporting on her research for The Dating Brokers: An autopsy of online love.

Dating profiles can be shared among partners, this means your data can legally be breached up to 700 companies.

There was several interesting talks about the commons, machine learning, affect and other topics, yet the talks described here more or less relate to my research.

Written by Linda Kronman - Full text here

#Transmediale2019 #2019 #transmediale #Berlin #linda kronman #kairus #machine vision #uib

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peterdiamandis · 7 years

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