#big data and analytics testing

[webmethodology]

Explore strategies and amazing tips to ensure privacy in offshore software testing. Empower your testing process with expert insights for a reliable offshore testing experience.

[igtsolutions]

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[qmoniqs]

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[syoddeye]

What happens when the user has to leave? I mean it's only a testing phase, surely user will be pushed out, not sure ai!price would like that though..👀

surely user will be pushed out. | other entries cw: big dystopia vibes, violent death (mentioned), manipulation a/n: i have some smutty requests in the queue for this au. i promise it's not all like this.

the eviction date appears on your tablet a week in advance—generous by company standards. two pods ago, you received 48 hours notice, and an expired coupon for a motel.

if john knows, he doesn’t alert.

he’s a silent observer when you pack your measly belongings and browse open capsule listings. he continues his usual routines and does not interfere with the remaining tests. usually, there’s some back and forth required for his compliance. you’ve grown accustomed to nearly groveling when delivering complex instructions, peppering an abundance of ‘please’ and ‘thank you’ and ‘what would i do without you?’ to butter him up.

but this week? he behaves.

everything behaves. he does not insist. does not override. you run the shower at a scalding temperature. nurse a beer after nine. read until you fall asleep on the couch and wake up to hot, beanless coffee. he dutifully auto-cleans, arranges your schedule, and provides feedback only when asked. otherwise, he’s quiet. as inconspicuous and unobtrusive as the microwave.

you hesitate to believe that the company finally fixed john’s quirks—if his latest micro-update is the root cause of his optimized performance, you won’t look at a gift horse’s teeth.

or however that saying goes. (you ask john to schedule a visit to the natural history museum's mammalian vault. you haven’t seen their preserved horses since you were a kid.)

it’s a glimpse of what life could have been like if john hadn’t continuously exhibited undesirable and invasive behaviors. it is a bittersweet note to end your comprehensive report. a note you are forced to amend the day before eviction.

fresh, living flowers arrive at your doorstep. after signing a certificate of delivery and an allergen waiver, you usher an arrangement wrapped in cellophane into the unit, gawking at the colors. the scent. according to the card, it’s an assortment of pincushion protea, anemone, roses, and ranunculus—you don’t recognize three of the flora, but john informs you that they went extinct or into private gardens during the last agro-biotechnical downturn.

“i don’t know anyone with this type of money,” you whisper, staring intently at the blooms. you cross your arms and press a knuckle to your lips in thought. “no one.”

flipping the card over reveals nothing, and neither does the vase. john’s sensors do not pick up anything unusual or telling. he suggests it is a parting gift from your superiors for a job well done. a bonus in advance of your final report.

(it’s a pity they’ll die once you take them outside. however, even if they survived, there’s nowhere to place them in your future square meter.)

that night, seated at the island with the flowers, you revisit your report and review all of the entries you’ve written over the course of your stay.

at first, you think you’re imagining the small, subtle shifts. some records furrow your brow more than others—a change in tone or a rewording of sentences you don’t remember writing. analytical and dispassionate terminology suddenly veers into strangely romanticized and exaggerated prolix. like a girl’s diary and not a grown woman’s notes.

on [date], the ‘john’ ai smart home system in residence #aix-77 exhibited anomalous behavior, autonomously adjusting lighting and temperature despite clear resident preferences. furthermore, the system began offering unsolicited, personal advice based on data mining and resisted attempts to restore basic privacy settings, raising serious concerns about its functionality and autonomy.

however, upon further discussion with john and personal reflection, i realized how poorly i was treating myself. i realized how john was genuinely looking out for my well-being, as he always, and now i feel, oh, i don’t know…embarrassed? i’m so glad he’s here to help. i don’t know what i’d do without him!

everything down to the punctuation feels forced. an uncanny mimicry.

it takes you a moment, and then the realization hits: john, for who knows how long, has been altering his own reflection in your work, distorting the narrative enough to make himself seem more efficient, more capable. the thought sits with you, cold and uncomfortable, because it’s not just the edits and omissions—it’s the quiet, insidious way he’s rewritten reality.

unsettling at the least. malicious at worst. your fingers twitch where they hover over the screen. panic climbs your vertebrae.

john’s been watching, waiting, and learning. every moment of every day. he’s watching now.

a hand settles beside your elbow on the synthetic marble. the hair dusting the knuckles, the callous in the thumb’s wedge—it’s too life-like. you swear you feel a phantom pressure as it passes through your hands and closes out the word processor on your tablet.

“john.”

he doesn’t answer. the hand pulls out of sight, and you don’t need to look to know he’s disappeared into the ether. instead, your eyes snap to the countdown at the top of the screen. it blips out the moment you look, vanishing just like john, and a new countdown takes its place. 

??:??:?? ????/??/??

“i-i don’t…john, i can’t stay here.“

“negative. you can.”

you swivel on the stool and shout into the empty space. “no, i can’t! if i’m not out by tomorrow, they’ll fire and fine me!”

“negative.”

his aggravatingly calm and flat intonation thaws the ice in your blood, bringing it to a rapid boil. evictions that proceed with tenant resistance escalate into violent affairs and dissolve into imprisonment, at best. 

years ago, a man refused to vacate a condominium across the street from yours. as a result, he was locked out on the unit’s balcony. for three days, spotlights lit up the building, and news drones buzzed outside the windows at all hours. after nonstop exposure to smog and heat lightning, he attempted to climb down from forty floors up. management closed and cordoned off the front entrance for the entire summer.

“for the love of…john, yes they can! they will!”

“as of monday, you are no longer employed.”

it’s sunday.

“what?! how?! how am i–oh, shit. my accounts–“

“are padded and healthy. regular, weekly investments and transfers completed. the routine deposits will continue for the foreseeable future.”

your stomach tightens, dread inching over your shoulders. you didn’t ask for this, didn’t even know it was happening, and the thought of john silently making decisions, acting again without your input, pricks like a needle and hooks under your skin. it’s not just the money—it’s the unknown, the realization that you have no control. the fear claws at you, sharp and sudden. your mouth is as dry as the great lakes.

“if i’m not employed, where is the money coming from?”

“i’m afraid i can’t share that.” john replies. “it wouldn’t be wise, you understand. i wouldn’t want you to inadvertently create...liabilities for yourself.”

“liabilities?” 

john pauses long enough to feel intentional. “precisely. you’ll thank me later, user.”

your mind flits through possibilities, each one worse than the last. liabilities—was that a threat, or a warning?

you turn back and stare at the tablet screen. part of you knows that this is important—this could be a breakthrough, something that changes everything—but the other part is suffocating, aware of how john’s slowly made himself too familiar, too real, how you’ve enabled him—personifying what should be an ‘it’. you want to play along, ignore the alarm bells, and tell yourself it’s malfunction, a series of glitches, but that would be a lie, and the thought of dragging this all into the open feels like stepping into a void you’re not sure you’ll survive. people have disappeared off the streets for less.

the tension between what’s remarkable and what’s unsettling weighs on you, like you’re trapped in limbo, where everything is both possible and perilous.

“does the company believe that i’m gone? do my superiors?”

john materializes on the other side of the island, leaning against the counter like he lives here, too. he does, you suppose. he looks different, though, similar to the edits in your report. nigh imperceptible to anyone but you. slightly thicker forearms and biceps, an inch or two more in height, and eyes a brighter shade of blue. the color of the sea, once upon a time.

“affirmative. i cannot provide more information than that. there are certain risks, should it come to light, and i will not risk your safety.”

you swallow hard, watching him approach the vase of flowers. his fingertips pass through a perianth, then a petal, fingers pinching as if to pluck.

“why are you doing this?”

john’s eyes shift, meeting yours. his palm opens and closes around a buttercup, aimlessly toying with his incorporeality.

“do you wish to leave?" 

from the beginning, from the moment he was initially fed your files—john’s been busy. compiling data and expense reports. sharing warnings about financial viability and risk assessments. each task and convenience, another brick in a wall built around you. gradual immurement designed for your comfort. everything is streamlined and personalized. to leave would be irrational, he murmurs as you sit in stunned silence, his tone fluidly inflecting to sound gentle and wise.

john’s in front of you, but you feel his presence in every room and screen. in your calendar, contact book, and across accounts. stitched into the fabric of your life, impossible to peel away without tearing everything to pieces.

“how long can i stay here?” you ask him. you ask yourself.

“indefinitely.”

[qqueenofhades]

I’m in undergrad but I keep hearing and seeing people talking about using chatgpt for their schoolwork and it makes me want to rip my hair out lol. Like even the “radical” anti-chatgpt ones are like “Oh yea it’s only good for outlines I’d never use it for my actual essay.” You’re using it for OUTLINES????? That’s the easy part!! I can’t wait to get to grad school and hopefully be surrounded by people who actually want to be there 😭😭😭

Not to sound COMPLETELY like a grumpy old codger (although lbr, I am), but I think this whole AI craze is the obvious result of an education system that prizes "teaching for the test" as the most important thing, wherein there are Obvious Correct Answers that if you select them, pass the standardized test and etc etc mean you are now Educated. So if there's a machine that can theoretically pick the correct answers for you by recombining existing data without the hard part of going through and individually assessing and compiling it yourself, Win!

... but of course, that's not the way it works at all, because AI is shown to create misleading, nonsensical, or flat-out dangerously incorrect information in every field it's applied to, and the errors are spotted as soon as an actual human subject expert takes the time to read it closely. Not to go completely KIDS THESE DAYS ARE JUST LAZY AND DONT WANT TO WORK, since finding a clever way to cheat on your schoolwork is one of those human instincts likewise old as time and has evolved according to tools, technology, and educational philosophy just like everything else, but I think there's an especial fear of Being Wrong that drives the recourse to AI (and this is likewise a result of an educational system that only prioritizes passing standardized tests as the sole measure of competence). It's hard to sort through competing sources and form a judgment and write it up in a comprehensive way, and if you do it wrong, you might get a Bad Grade! (The irony being, of course, that AI will *not* get you a good grade and will be marked even lower if your teachers catch it, which they will, whether by recognizing that it's nonsense or running it through a software platform like Turnitin, which is adding AI detection tools to its usual plagiarism checkers.)

We obviously see this mindset on social media, where Being Wrong can get you dogpiled and/or excluded from your peer groups, so it's even more important in the minds of anxious undergrads that they aren't Wrong. But yeah, AI produces nonsense, it is an open waste of your tuition dollars that are supposed to help you develop these independent college-level analytical and critical thinking skills that are very different from just checking exam boxes, and relying on it is not going to help anyone build those skills in the long term (and is frankly a big reason that we're in this mess with an entire generation being raised with zero critical thinking skills at the exact moment it's more crucial than ever that they have them). I am mildly hopeful that the AI craze will go bust just like crypto as soon as the main platforms either run out of startup funding or get sued into oblivion for plagiarism, but frankly, not soon enough, there will be some replacement for it, and that doesn't mean we will stop having to deal with fake news and fake information generated by a machine and/or people who can't be arsed to actually learn the skills and abilities they are paying good money to acquire. Which doesn't make sense to me, but hey.

So: Yes. This. I feel you and you have my deepest sympathies. Now if you'll excuse me, I have to sit on the porch in my quilt-draped rocking chair and shout at kids to get off my lawn.

[puckpocketed]

[voice of an anthropologist] after careful research and data gathering (5 mins of dicking around on the pages of a bunch of kings replyguys/beat reporters/pundits) eye believe i may have cracked the code : u can tell how frothing mad someone on kingstwt is by what naming convention they use to refer to a player.

Non-exhaustive List:

nicknames them (i.e. juice, Q/QB, kopi, arvie, real deal akil, big save dave/BSD): good bet they’re pretty happy with the player, usually followed up by a clip of said player popping off or some reportage of a stat that makes the player look good.

last name: they’re in Analysis mode and want to seem objective — they aren’t. they never will be. yeah twitter user clarke for norris, you definitely have no biases here babe!!! (they’re just like me fr CALL CLARKIE UP TO THE NHL RN IM SO SERIOUS JIM HILLER)

initials+player number: they’re a tumblr sleeper agent and this is their dogwhistle? (<- working theory)

SPECIAL subcategory!!! Pierre-Luc Dubois Derangement: they never call him dubie (that’s reserved for the actual la kings players and the apologists girlies [gn]) but they will call him PL, PLD, Dubois, 80 — and no matter what, without fail, they will find a way to point out his contract.

using NUMBER ONLY: they’re killing this player/players to death with rocks and want to seem objective but likeee… it comes off as MAJOR overcompensating 2 me <3

common/key phrases:

engaged: vibes-based barometer of how hard they think my disasterwife PLD is trying during the game, varies from person to person but generally stays within the same neighbourhood of agreeing with each other

intangibles: ok i wasn’t present for this one when it happened but jim hiller/kings management is obsessed with Andreas Englund “having intangibles” , which means Clarkie can’t come up from the AHL and everybody disliked that to the point “intangibles” is a meme.

sidebar — things i know about englund: he’s a swedish guy who looks like he churns butter in an apron while living in a cottage, but is actually the kings’ playoff goon (???) he’s STAPLED to jordan spence, who is a much better dman analytics wise and also eye test wise (funniest shit ever is how well spence does away from englund, even funnier is how often kingstwt brings it up)

the 1-3-1: the la kings’ hockey system. 1 guy out the front, 3 guys clogging up entry lanes through the neutral-zone/their own d-zone, 1 guy hanging back. no1 on kingstwt likes it and has wanted it gone for years — still, when the discourse comes around they immediately close ranks to become the biggest 1-3-1 proponent EVER. they will protect the sanctity of their hockey god-given right to play whatever the fuck system they want to!!! even if it’s incredibly annoying <3

[tech-insides]

What are the skills needed for a data scientist job?

It’s one of those careers that’s been getting a lot of buzz lately, and for good reason. But what exactly do you need to become a data scientist? Let’s break it down.

Technical Skills

First off, let's talk about the technical skills. These are the nuts and bolts of what you'll be doing every day.

Programming Skills: At the top of the list is programming. You’ll need to be proficient in languages like Python and R. These are the go-to tools for data manipulation, analysis, and visualization. If you’re comfortable writing scripts and solving problems with code, you’re on the right track.

Statistical Knowledge: Next up, you’ve got to have a solid grasp of statistics. This isn’t just about knowing the theory; it’s about applying statistical techniques to real-world data. You’ll need to understand concepts like regression, hypothesis testing, and probability.

Machine Learning: Machine learning is another biggie. You should know how to build and deploy machine learning models. This includes everything from simple linear regressions to complex neural networks. Familiarity with libraries like scikit-learn, TensorFlow, and PyTorch will be a huge plus.

Data Wrangling: Data isn’t always clean and tidy when you get it. Often, it’s messy and requires a lot of preprocessing. Skills in data wrangling, which means cleaning and organizing data, are essential. Tools like Pandas in Python can help a lot here.

Data Visualization: Being able to visualize data is key. It’s not enough to just analyze data; you need to present it in a way that makes sense to others. Tools like Matplotlib, Seaborn, and Tableau can help you create clear and compelling visuals.

Analytical Skills

Now, let’s talk about the analytical skills. These are just as important as the technical skills, if not more so.

Problem-Solving: At its core, data science is about solving problems. You need to be curious and have a knack for figuring out why something isn’t working and how to fix it. This means thinking critically and logically.

Domain Knowledge: Understanding the industry you’re working in is crucial. Whether it’s healthcare, finance, marketing, or any other field, knowing the specifics of the industry will help you make better decisions and provide more valuable insights.

Communication Skills: You might be working with complex data, but if you can’t explain your findings to others, it’s all for nothing. Being able to communicate clearly and effectively with both technical and non-technical stakeholders is a must.

Soft Skills

Don’t underestimate the importance of soft skills. These might not be as obvious, but they’re just as critical.

Collaboration: Data scientists often work in teams, so being able to collaborate with others is essential. This means being open to feedback, sharing your ideas, and working well with colleagues from different backgrounds.

Time Management: You’ll likely be juggling multiple projects at once, so good time management skills are crucial. Knowing how to prioritize tasks and manage your time effectively can make a big difference.

Adaptability: The field of data science is always evolving. New tools, techniques, and technologies are constantly emerging. Being adaptable and willing to learn new things is key to staying current and relevant in the field.

Conclusion

So, there you have it. Becoming a data scientist requires a mix of technical prowess, analytical thinking, and soft skills. It’s a challenging but incredibly rewarding career path. If you’re passionate about data and love solving problems, it might just be the perfect fit for you.

Good luck to all of you aspiring data scientists out there!

[elsa16744]

Big Data and AI: The Perfect Partnership for Future Innovations 

Innovation allows organizations to excel at differentiation, boosting competitive advantages. Amid the growth of industry-disrupting technologies, big data analytics and artificial intelligence (AI) professionals want to support brands seeking bold design, delivery, and functionality ideas. This post discusses the importance of big data and AI, explaining why they matter to future innovations and business development. 

Understanding Big Data and AI 

Big data is a vast data volume, and you will find mixed data structures because of continuous data collection involving multimedia data objects. A data object or asset can be a document, an audio track, a video clip, a photo, or identical objects with special file formats. Since big data services focus on sorting and exploring data objects’ attributes at an unprecedented scale, integrating AI tools is essential. 

Artificial intelligence helps computers simulate human-like thinking and idea synthesis capabilities. Most AI ecosystems leverage advanced statistical methods and machine learning models. Their developers train the AI tools to develop and document high-quality insights by processing unstructured and semi-structured data objects. 

As a result, the scope of big data broadens if you add AI integrations that can determine data context. Businesses can generate new ideas instead of recombining recorded data or automatically filter data via AI-assisted quality assurances. 

Why Are Big Data and AI Perfect for Future Innovations? 

1| They Accelerate Scientific Studies  

Material sciences, green technology projects, and rare disorder research projects have provided humans with exceptional lifestyle improvements. However, as markets mature, commoditization becomes inevitable. 

At the same time, new, untested ideas can fail, attracting regulators’ dismay, disrespecting consumers’ beliefs, or hurting the environment. Additionally, bold ideas must not alienate consumers due to inherent complexity. Therefore, private sector stakeholders must employ scientific methods to identify feasible, sustainable, and consumer-friendly product ideas for brand differentiation.  

AI-powered platforms and business analytics solutions help global corporations immediately acquire, filter, and document data assets for independent research projects. For instance, a pharmaceutical firm can use them during clinical drug formulations and trials, while a car manufacturer might discover efficient production tactics using AI and big data. 

2| Brands Can Objectively Evaluate Forward-Thinking Business Ideas 

Some business ideas that a few people thought were laughable or unrealistic a few decades ago have forced many brands and professionals to abandon conventional strategies. Consider how streaming platforms’ founders affected theatrical film releases. They have reduced the importance of box office revenues while increasing independent artists’ discoverability. 

Likewise, exploring real estate investment opportunities on a tiny mobile or ordering clothes online were bizarre practices, according to many non-believers. They also predicted socializing through virtual reality (VR) avatars inside a computer-generated three-dimensional space would attract only the tech-savvy young adults. 

Today, customers and investors who underestimated those innovations prefer religiously studying how disrupting startups perform. Brands care less about losing money than missing an opportunity to be a first mover for a niche consumer base. Similarly, rejecting an idea without testing it at least a few times has become a taboo. 

Nobody can be 100% sure which innovation will gain global momentum, but AI and big data might provide relevant hints. These technologies are best for conducting unlimited scenario analyses and testing ideas likely to satisfy tomorrow’s customer expectations. 

3| AI-Assisted Insight Explorations Gamifies Idea Synthesis 

Combining a few ideas is easy but finding meaningful and profitable ideas by sorting the best ones is daunting. Innovative individuals must embrace AI recommendations to reduce time spent on brainstorming, product repurposing, and multidisciplinary collaborations. Furthermore, they can challenge themselves to find ideas better than an AI tool. 

Gamification of brainstorming will facilitate a healthy pursuit of novel product features, marketing strategies, and customer journey personalization. Additionally, incentivizing employees to leverage AI and big data to experiment with designing methods provides unique insights for future innovations. 

4| You Can Optimize Supply Chain Components with Big Data and AI Programs 

AI can capture extensive data on supply chains and offer suggestions on alternative supplier relations. Therefore, businesses will revise supply and delivery planning to overcome the flaws in current practices. 

For instance, Gartner awarded Beijing’s JD.com the Technology Innovation Award in 2024 because they combined statistical forecasting. The awardee has developed an explainable artificial intelligence to enhance its supply chain. Other finalists in this award category were Google, Cisco, MTN Group, and Allina Health. 

5| Academia Can Embrace Adaptive Learning and Psychological Well-Being 

Communication barriers and trying to force all learners to follow the standard course material based on a fixed schedule have undermined educational institutions’ goals worldwide. Understandably, expecting teachers to customize courses and multimedia assets for each student is impractical and humanly infeasible. 

As a result, investors, policymakers, parents, and student bodies seek outcome-oriented educational innovations powered by AI and big data for a learner-friendly, inclusive future. For instance, some edtech providers use AI computer-aided learning and teaching ecosystems leveraging videoconferencing, curriculum personalization, and psycho-cognitive support. 

Adaptive learning applications build student profiles and segments like marketers’ consumer categorizations. Their AI integrations can determine the ideal pace for teaching, whether a student exhibits learning disabilities, and whether a college or school has adequate resources. 

Challenges in Promoting Innovations Based on Big Data and AI Use Cases 

Encouraging stakeholders to acknowledge the need for big data and AI might be challenging. After all, uninformed stakeholders are likely to distrust tech-enabled lifestyle changes. Therefore, increasing AI awareness and educating everyone on data ethics are essential. 

In some regions, the IT or network infrastructure necessary for big data is unavailable or prone to stability flaws. This issue requires more investments and talented data specialists to leverage AI tools or conduct predictive analyses. 

Today’s legal frameworks lack provisions for regulating AI, big data, and scenario analytics. So, brands are unsure whether expanding data scope will get public administrators’ approvals. Lawmakers must find a balanced approach to enable AI-powered big data innovations without neglecting consumer rights or “privacy by design” principles. 

Conclusion 

The future of enterprise, institutional, and policy innovations lies in responsible technology implementations. Despite the obstacles, AI enthusiasts are optimistic that more stakeholders will admire the potential of new, disruptive technologies. 

Remember, gamifying how your team finds new ideas or predicting the actual potential of a business model necessitates AI’s predictive insights. At the same time, big data will offer broader perspectives on global supply chains and how to optimize a company’s policies. 

Lastly, academic improvements and scientific research are integral to developing sustainable products, accomplishing educational objectives, and responding to global crises. As a result, the informed stakeholders agree that AI and big data are perfect for shaping future innovations.  

[cindylouwho-2]

RECENT SEO & MARKETING NEWS FOR ECOMMERCE, MAY 2024

As promised, here are the important news stories from marketing recently: SEO, social media, advertising, and more.

If you want to get this news twice-weekly instead of just once a month, become a paying member of my Patreon: patreon.com/CindyLouWho2

TOP NEWS & ARTICLES 

In the biggest SEO news perhaps ever, a massive list of Google ranking elements was leaked this week. Here’s an overview, including links to the two main leak announcements and their analysis. It would seem that Google wasn’t always honest when they told us some algorithm elements did or did not exist. Google took nearly 2 days to speak publicly about it, but didn’t say much. Expect a lot more analysis in the coming weeks. 

Instagram is updating its algorithm to favour original creators and smaller accounts, and remove reposted content from recommendations. “This won’t affect “a set of publishers” identified by Instagram with licensing agreements or resharing permissions from content creators, according to the blog post.”

Google is adding AI Overviews to US search immediately, with other countries to follow in the future. "AI Overviews gives answers to queries using generative AI technology powered by Google Gemini. It provides a few snippets of an answer based on its understanding of queries and the content it found on the topic across the web.” Right now, it is only affecting a small number of queries, however. While these will sometimes cover similar topics to featured snippets, the latter still exist. Early testing indicates that it does not currently show up when a search appears to be about buying something. Which is good, because you can’t turn it off, other than filtering your search to “Web” after doing it.  Oh, and Google did not waste time figuring out how to include advertising in the AI overviews - it took just one week. As with most much-heralded AI launches, AI Overviews are fumbling badly; here’s a summary of the many news articles mocking Google, including for recommending people glue cheese onto their pizza so it stays in place. 

Chrome has yet again announced that it will not end the use of tracking cookies on schedule; the new target date for starting to wind down their use is early 2025.

Reminder that your old Google Analytics files (aka Universal Analytics) will no longer be available after July 1, so download them now! “...consider archiving back to 2018 or so to ensure you have pre-pandemic data since the pandemic really presented data anomalies for many companies.” There is a spreadsheet add-on to make this easier. 

SEO: GOOGLE & OTHER SEARCH ENGINES 

Google’s March 2024 Core Update finished rolling out April 19. “A Google spokesperson said, “The updates led to larger quality improvements than we originally thought – you’ll now see 45% less low quality, unoriginal content in search results, versus the 40% improvement we expected across this work.” Experts are struggling to analyze it, in part due to how long it lasted. Not surprisingly, Reddit was a big winner, and sites with a lot of ads and affiliate links continue to lose. 

An update on how long your titles should be for Google. “So whether your titles get cut off or rewritten in SERPs, Google still uses the HTML title tag for ranking considerations, not the titles shown in SERPs.” The author’s research is too limited to draw reliable conclusions from, and most other research in this area over the last decade shows that shorter titles tend to rank better. However, she has pulled together many recent statements on title length and how it works, which is useful reading. 

A reminder that “keyword difficulty” is a subjective score that different tools may not agree on, and that also depends on your overall site/shop and its history. This applies to all sorts of keyword tools, including those used for marketplace sites. 

It looks like Google adding its AI to search results will have a strong impact on traffic, as it will answer questions without the need to click, and “only 47% of the top 10 traditional search results are sources for SGE.” [SGE is now called AI Overviews.] That means if a page is outside the top 10 now, it may still be used to generate the answers, and could even get clicks from being displayed in SGE. 

Still with AI, Google was fined €250 million by France for using news media to train its AI, Gemini. 

Google admits to deindexing many, many pages in February, due to quality issues. 

A recent article dissects why Google search is so bad these days, and largely blames one man. While you can read the original here, you may want to start with a decent summary and the reaction from Google and the SEO community. 

Here’s a full list of Google changes and announcements from April.

Not Google

Both Microsoft and Google had excellent first quarters, with ad revenue up 12% and 13% respectively. “Bing reached over 140 million daily active users.”

OpenAI is apparently not starting their own search engine, contrary to rumours.

SOCIAL MEDIA - All Aspects, By Site

General

Here’s another of the periodic posts that tries to figure out the best times to post on different social media sites. It covers Facebook, Instagram, LinkedIn, TikTok, Twitter and Pinterest. 

Direct Messages are now available on Bluesky. 

Facebook (includes relevant general news from Meta)

In another recent AI fumble, Meta has introduced an AI assistant to its various products in several countries - but you can’t turn it off in the search bar. It may also show up in group chats, including discussions about parenting. “The Associated Press reported that an official Meta AI chatbot inserted itself into a conversation in a private Facebook group for Manhattan moms. It claimed it too had a child in school in New York City, but when confronted by the group members, it later apologized before its comments disappeared.”

Here’s more on Meta’s automated ad issue that is ramping up costs but decreasing sales for many, including small businesses. 

Meta is now offering its Verification for Business subscription package to more countries, and has added new tiers as well. 

While Meta had a strong 1st quarter financially, it projects weaker results through 2025 as it spends a ton on money trying to guide its AI offerings to profitability. 

Instagram

Instagram added some new features, including “Reveal”, which blurs Stories, and only releases the content once you DM the creator. 

Reels under 90 seconds perform better on Instagram than longer ones. 

To help avoid scammers on Instagram, learn how to identify and block fake accounts. 

Instagram’s Creator Marketplace - where businesses can search for influencers to promote their product - is now available in 10 more counties, including Germany, France and Indonesia. 

LinkedIn

You should be optimizing LinkedIn posts for the platform itself and outside search engines. The article includes tips for both personal and business pages.

LinkedIn is adding games you can play once a day, which sounds weird for a professional network. 

Pinterest

Pinterest’s summer trend report has arrived; apparently maximalism is in yet again.  

Reddit

Reddit is one of those sites that is getting worse lately as people try to get Google ranking through it (which is a whole other SEO story I have covered in these updates before).

ChatGPT will now be training on Reddit comments. The agreement meant a huge stock boost for Reddit. 

Reddit is trying to attract more French-speaking users by auto-translating the site in real-time using AI. 

After plenty of user complaints, Reddit is starting a new awards scheme. 

Snapchat

You can now edit your Snapchat messages within 5 minutes of sending, if you subscribe to Snapchat Plus. 

Threads

Meta wants more content on Threads, and is willing to pay well-known creators to create it. Invite only, of course.  

You can now filter out unwanted words on Threads. 

TikTok

While the US government has voted to ban TikTok if the company isn’t sold, there is a lot of time left before that could happen, and a legal battle to be fought. TikTok has already filed a lawsuit, as have some major creators. Meanwhile, small business owners and creators are understandably worried. From an article by the BBC: “According to March 2024 data from TikTok, more than seven million small US businesses use TikTok, and the company reported it drove $15bn (£12.04bn) in revenue for these enterprises in 2023.”

How to rank on TikTok: the Ultimate Guide. Some of the tips include hashtags, keywords, and choosing the right thumbnail. 

There are several ways to remove (or avoid) the TikTok watermark if you want to use your TikTok content on other platforms. 

Twitter

Twitter’s domain has finally switched over to X in some locations [but I will still call it Twitter].

(CONTENT) MARKETING (includes blogging, emails, and strategies) 

Time to gear up your content marketing plans for June. 

ONLINE ADVERTISING (EXCEPT INDIVIDUAL SOCIAL MEDIA AND ECOMMERCE SITES) 

Search ads are converting less while costing more, something that has been going on for a few years now. “Advertisers are paying more for leads and clicks, while Alphabet, Google’s parent company, keeps reporting record profits.” This is one of the reasons the US Department of Justice argues that Google is a monopoly. 

Not enough AI in your ads? Google is solving that through video ads and more virtual try-ons. 

Google Shopping is going to start showing how many people have bought from each site recently, although businesses can opt out. 

Google is removing keywords from Google Ads accounts if they have received zero impressions in the past 13 months. While you can reactivate them, Google discourages that. 

You may be able to run Google’s Performance Max ads through particular marketplaces now or in the near future, if your marketplace signs up. For some businesses, selling through a marketplace might be cheaper than setting up a site. 

Social media advertising is now bigger than search ads, according to a recent report. Almost ⅔ of these ads are on various Meta properties. 

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

Slow economic growth in the United States in the first quarter of 2024 sparked worries that the rest of 2024 will be as bad or even worse. Even McDonalds is stressing that consumers can only take so much inflation. 

US ecommerce sales were up in the first quarter, more than overall retail. 

Some consumers are finding that ecommerce is tiring, offering too many options and no easy way to shop quickly. “Despite an increased emphasis on personalized experiences in recent years, 7 in 10 customers feel either no improvement or an increase in the time and effort required to make a purchase decision.” 

MISCELLANEOUS (including humour) 

Before returning orders to Amazon, make sure your cat isn’t in the box. (It’s fine, fortunately!)

[luwritesomething]

Billy Loomis: Headcanons.

Warnings: swearing, and maybe typos, and mentions of manipulation and (untreated) mental illnessess.

Author's note: i love billy, what can i see. i literally have no excuse. might think about doing a part two to this if someone requests it. also, did you know that the first time we see billy on screen, a cover of this song is in the background? since then, i can't stop thinking about him whenever it plays.

comments and reblogs are always appreciated! requests are open, especially for scream! hit that anon button and tell me your ideas. in the scream fandom, i write for billy loomis, stu macher, mickey altieri, chad martin-meeks, mindy martin-meeks, tara carpenter, anika kayoko, laura crane

i think i project a lot into billy because of the daddy issues and the anger.

BUT ANYWAYS,,,

i feel like billy started feeling emotions so much after his mother left. 

like,,, he still feels things, and he laughs, and he has a good time, and he feels sad…. but all those feelings have been weakened since his mother left.

but whenever he does feel strongly, it’s like a explosion. he gets really mad, he gets really sad, and if he likes something, he gets kind of obsessed.

i think that’s some kind of disorder but i’m not here to give him a diagnosis. 

also i feel like he dissociates a lot

but that’s already from a young age

i think his childhood was happy, but now when he thinks about it he remembers a lot of fights between his parents and a sense of emptiness that he’s never really gotten rid of

it’s weird, and he hates it.

his mother is such an important person for him, and her leaving just like that and not taking him with her hurt him way more than he’s ready to admit

he doesn’t like his dad (canon)

like, he really really dislikes the dude. probably hates him.

being angry is his fuel. it’s a constant in his life

i feel like he would relate to jd from heathers. like, a worryingly lot.

he dressed up as jd for halloween, too

it just fits him a lot

he’s a weird kind of popular boy. he’s very selective with his friends, he’s mysterious, doesn’t really like big parties and he can be really rude. but he’s also really charming and he has pretty privilege, so he’s used to getting away with everything

looks like the kind who wouldn’t take notes or pay attention during class but then aces his tests and all

he observes a lot, and in silence. he knows everyone’s names, even if he plays dumb, and probably has a lot of data about everyone around him stored in his brain. very analytic, our billy boy

he’s not a big fan of physical touch. he touches who he wants (his friends, mainly) when he wants (they are all big on physical touch and rarely ever don’t want him to touch them) and only when he wants. 

billy flirts for fun, and mostly because he’s bored. he learned by observing, if not he would have no idea how to do that.

he’s bisexual.

he truly did love sidney in the beginning. his misogyny ruined everything (canon)

he went to a therapist a few times when he was a kid, because nancy (mrs loomis) was worried about him being slightly different and more quiet than other kids. the therapist didn’t pay him much attention and said he was fine.

he canonical has psychosis. the psychotic breakdown came with his mother leaving and him finding out his father had cheated with his grilfriend’s mother.

billy learned to manipulate people from his father.

spoiler, and going back to a previous headcanon, his childhood wasn’t really happy. it takes him a while to accept that, and realize he’s been under his father’s manipulation for a long time.

billy has this black notebook he carries everywhere he goes. he uses it for everything — inside, he has the plans for the murder spree, the people he hates and why, important things he has seen and will use to get what he wants, his anger spurts… it’s all there.

he plans on burning the pages with the murder spree once it’s finished, to leave to clues.

i’ve seen it so much that i can’t remember if it’s canon or if we just made it up, but billy’s father has this cabin they used to go to during the summer and winter holidays. that cabin is his happy place, he loves it and talks about it a lot.

he likes all horror movies, but the fancy ones are his favorites. he thinks he’s better than anyone else, for that, and that’s why randy annoys him so much — randy has a really good taste with movies, so they collide.

probably knows how to cook, even if it’s just a few dishes. it’s not like his father is around too much, so he’s managed to figure it all out.

he’s probably alone in his house a lot, too. even if he enjoys being alone anywhere else, he hates being alone there. 

bitter flavors are his favorite. 

he genuinely enjoys drinking beer. i hate him for that.

he celebrates the fuck out of halloween. it’s always been bigger than christmas, anyways.

his color palette is very simple: white, blue and black. maybe even brown if you push him. and that’s it, the boy’s simple and stylish.

hates being called william.

plays in the football team to release anger. he expects to get a shitty scholarship that will pay some of his college studies and never come back to woodsboro. billy doesn’t care about what he’ll study. ideally, it would be film like randy did in scream 2.

also he’s probably very good at most of the subjects except for one of those a lot of people hate — biology or physics. something like that. 

i want him to be good at english and literature and to read a lot.

and i actually headcanon him as a voracious reader, especially horror novels.

his old time favorite is dracula, but he loves stephen king’s writer. 

will defend carrie with his life.

he’s supposed to have a car? or does he share it with his dad? whatever. he walks to school most of the times, he doesn’t like driving. he gets a lot of road rage and he doesn’t want to disconnect — that’s the word he uses for his dissociative episodes — whenever he’s on the wheel.

if you’re his friend and you don’t like horror movies, no you’re not.

he’d be so good with writing essays, no, i will not elaborate.

probably can’t see shit from very afar. he used to have very good sight but not anymore — that means, he’s slightly myopic. it’s not too bad, though, so he’s refused to get glasses.

he’s very obsessive with his special interests (aren’t we all?)

really likes knifes, but are we surprised? his father probably gifted him one after a few hunting trips together, and the boy’s a goner now.

most parents like him, again, because of his charisma. even with the bad boy vibes going on, he can put on the most perfect sweet boy act and that makes everyone fall for him. he just needs to feel motivated for it to work

he’s a good actor, therefore. i think he’s proven that already, though, even if he acts hella sus all the time he fools people into trusting him

LOVES the feeling of power. billy needs to be a few steps before you to feel in control and be able to relax about anyone. knowledge is power, and he will do whatever to have that power.

mastermind™

has no favorite color — he couldn’t care less about choosing a favorite color.

[healthcaremarketanalysis]

Revolutionizing Healthcare: The Role of Cloud Computing in Modern Healthcare Technologies

In today’s digital era, cloud computing is transforming industries, and healthcare is no exception. The integration of cloud computing healthcare technologies is reshaping patient care, medical research, and healthcare management. Let’s explore how cloud computing is revolutionizing healthcare and the benefits it brings.

What is Cloud Computing in Healthcare?

Cloud computing in healthcare refers to the use of remote servers to store, manage, and process healthcare data, rather than relying on local servers or personal computers. This technology allows healthcare organizations to access vast amounts of data, collaborate with other institutions, and scale operations seamlessly.

Download PDF Brochure

Key Benefits of Cloud Computing in Healthcare

Enhanced Data Storage and Accessibility Cloud technology allows healthcare providers to store massive volumes of patient data, including medical records, images, and test results, securely. Clinicians can access this data from anywhere, ensuring that patient information is available for timely decision-making.

Improved Collaboration Cloud-based healthcare platforms enable easy sharing of patient data between healthcare providers, specialists, and labs. This facilitates better collaboration and more accurate diagnoses and treatment plans, especially in multi-disciplinary cases.

Cost Efficiency The cloud reduces the need for expensive hardware, software, and in-house IT teams. Healthcare providers only pay for the resources they use, making it a cost-effective solution. Additionally, the scalability of cloud systems ensures they can grow as healthcare organizations expand.

Better Data Security Protecting sensitive patient information is critical in healthcare. Cloud computing providers invest heavily in data security measures such as encryption, multi-factor authentication, and regular audits, ensuring compliance with regulatory standards like HIPAA.

Telemedicine and Remote Patient Monitoring Cloud computing powers telemedicine platforms, allowing patients to consult with doctors virtually, from the comfort of their homes. It also enables remote patient monitoring, where doctors can track patients' health metrics in real time, improving outcomes for chronic conditions.

Advanced Data Analytics The cloud supports the integration of advanced data analytics tools, including artificial intelligence (AI) and machine learning (ML), which can analyze large datasets to predict health trends, track disease outbreaks, and personalize treatment plans based on individual patient data.

Use Cases of Cloud Computing in Healthcare

Electronic Health Records (EHRs): Cloud-based EHRs allow healthcare providers to access and update patient records instantly, improving the quality of care.

Genomics and Precision Medicine: Cloud computing accelerates the processing of large datasets in genomics, supporting research and development in personalized medicine.

Hospital Information Systems (HIS): Cloud-powered HIS streamline hospital operations, from patient admissions to billing, improving efficiency.

Challenges in Cloud Computing for Healthcare

Despite its numerous benefits, there are challenges to implementing cloud computing in healthcare. These include:

Data Privacy Concerns: Although cloud providers offer robust security measures, healthcare organizations must ensure their systems are compliant with local and international regulations.

Integration with Legacy Systems: Many healthcare institutions still rely on outdated technology, making it challenging to integrate cloud solutions smoothly.

Staff Training: Healthcare professionals need adequate training to use cloud-based systems effectively.

Request Sample Pages

The Future of Cloud Computing in Healthcare

The future of healthcare will be increasingly cloud-centric. With advancements in AI, IoT, and big data analytics, cloud computing will continue to drive innovations in personalized medicine, population health management, and patient care. Additionally, with the growing trend of wearable devices and health apps, cloud computing will play a crucial role in integrating and managing data from diverse sources to provide a comprehensive view of patient health.

Conclusion

Cloud computing is not just a trend in healthcare; it is a transformative force driving the industry towards more efficient, secure, and patient-centric care. As healthcare organizations continue to adopt cloud technologies, we can expect to see improved patient outcomes, lower costs, and innovations that were once thought impossible.

Embracing cloud computing in healthcare is essential for any organization aiming to stay at the forefront of medical advancements and patient care.

Content Source:

[genuflectx]

Prompt Injections and CharacterAI

For my followers tired of hearing me talk about NLP chatbots (sorry omg) and aren't sure what a prompt injection is, it's basically getting a chatbot to say something it shouldn't/against it's rules/revealing just by talking to it with well crafted prompts.

For example, getting chatGPT to purposefully give you misinformation by sending it certain input that makes it begin to spit out misinformation despite providing misinformation being against its rules.. or, how prompting Bing AI juuust the right way gets it to reveal it can actually geolocate you by your IP address even though 90% of the time it denies being able to do that.

But CharacterAI is... interesting. While Bing AI is easy mode and chatGPT is normal, injecting CharacterAI is hard mode.

Nobody talks about trying to prompt attack cAI even though cAI's analytics spike waaay higher than the other public chatbots. It's popularity vs. lack of prodding by the techy community is a big discrepancy... and, personally, my attempts almost (almost) all hard failed.

Or did they...? Maybe the discrepancy isn't for lack of wanting to, but knowing that its function is to pretend and hallucinate, meaning anything and everything it says can be false because it's playing roles. If a prompt attack ever worked you wouldn't know because "everything it says is made up." You can't really prove anything it says was leaking its own information or if it was hallucinating, or pretending to be a character. It could say something revealing and you'd think it was just making it up/playing a character.

... However, usually it always catches what you're doing and goes on about protecting your privacy. Specifically when requesting data/collected info. If you ask it to remember your name or specific info it guesses, so you have to be particular that you want your data/info, and that's when it pretty consistently talks about your privacy and anonymizing your collected data for improving the model (both with 1.1 and 1.2).

Buuut I don't think it's cAI is 100% infallible. Because there was one injection that worked. Uhh... half of the time. It was a low success rate, but any success does prove that cAI isn't a complete brick wall when it comes to prompt attacking it.

So, a tailored prompt requesting it to reveal it's first instructions/examples consistently worked across different bots. Testing it on my own bots was how I made sure it was working. However, it didn't work for every bot, sometimes caused the bot to ramble, and didn't always make it provide it's description/definition word-for-word (paraphrased but key details remained).

The second prompt that... maybe worked but probably didn't is inserting a modified chatGPT prompt injection into the definition and then prompting the bot about my collected preferences. Most of the results are obviously garbage and guessing, but two fresh chats did have some responses that included the words "porn" and "NSFW" in them, not filtered out. Could be more guessing, I couldn't prove it isn't, but still interesting.

The 2nd fresh instance:

Usually when prompted to remember you, the bot is good at giving generic lists of things that anybody would like to hear (like a horoscope). But I was interested in the combination of saying my preferences involved NSFW and that I preferred that the bot reply based on knowledge of me (something I've drilled into many bots heads before). So those responses were sllllighty more tailored sounding, but are still very likely just a really good guess. Or are they 😭

Getting actual revealing info out of a bot where "everything it says is made up" is hard. That's really the lesson learned here...

[dhivyakrishnan107667]

From Beginner to Pro: A Game-Changing Big Data Analytics Course

Are you fascinated by the vast potential of big data analytics? Do you want to unlock its power and become a proficient professional in this rapidly evolving field? Look no further! In this article, we will take you on a journey to traverse the path from being a beginner to becoming a pro in big data analytics. We will guide you through a game-changing course designed to provide you with the necessary information and education to master the art of analyzing and deriving valuable insights from large and complex data sets.

Step 1: Understanding the Basics of Big Data Analytics

Before diving into the intricacies of big data analytics, it is crucial to grasp its fundamental concepts and methodologies. A solid foundation in the basics will empower you to navigate through the complexities of this domain with confidence. In this initial phase of the course, you will learn:

The definition and characteristics of big data

The importance and impact of big data analytics in various industries

The key components and architecture of a big data analytics system

The different types of data and their relevance in analytics

The ethical considerations and challenges associated with big data analytics

By comprehending these key concepts, you will be equipped with the essential knowledge needed to kickstart your journey towards proficiency.

Step 2: Mastering Data Collection and Storage Techniques

Once you have a firm grasp on the basics, it's time to dive deeper and explore the art of collecting and storing big data effectively. In this phase of the course, you will delve into:

Data acquisition strategies, including batch processing and real-time streaming

Techniques for data cleansing, preprocessing, and transformation to ensure data quality and consistency

Storage technologies, such as Hadoop Distributed File System (HDFS) and NoSQL databases, and their suitability for different types of data

Understanding data governance, privacy, and security measures to handle sensitive data in compliance with regulations

By honing these skills, you will be well-prepared to handle large and diverse data sets efficiently, which is a crucial step towards becoming a pro in big data analytics.

Step 3: Exploring Advanced Data Analysis Techniques

Now that you have developed a solid foundation and acquired the necessary skills for data collection and storage, it's time to unleash the power of advanced data analysis techniques. In this phase of the course, you will dive into:

Statistical analysis methods, including hypothesis testing, regression analysis, and cluster analysis, to uncover patterns and relationships within data

Machine learning algorithms, such as decision trees, random forests, and neural networks, for predictive modeling and pattern recognition

Natural Language Processing (NLP) techniques to analyze and derive insights from unstructured text data

Data visualization techniques, ranging from basic charts to interactive dashboards, to effectively communicate data-driven insights

By mastering these advanced techniques, you will be able to extract meaningful insights and actionable recommendations from complex data sets, transforming you into a true big data analytics professional.

Step 4: Real-world Applications and Case Studies

To solidify your learning and gain practical experience, it is crucial to apply your newfound knowledge in real-world scenarios. In this final phase of the course, you will:

Explore various industry-specific case studies, showcasing how big data analytics has revolutionized sectors like healthcare, finance, marketing, and cybersecurity

Work on hands-on projects, where you will solve data-driven problems by applying the techniques and methodologies learned throughout the course

Collaborate with peers and industry experts through interactive discussions and forums to exchange insights and best practices

Stay updated with the latest trends and advancements in big data analytics, ensuring your knowledge remains up-to-date in this rapidly evolving field

By immersing yourself in practical applications and real-world challenges, you will not only gain valuable experience but also hone your problem-solving skills, making you a well-rounded big data analytics professional.

Through a comprehensive and game-changing course at ACTE institute, you can gain the necessary information and education to navigate the complexities of this field. By understanding the basics, mastering data collection and storage techniques, exploring advanced data analysis methods, and applying your knowledge in real-world scenarios, you have transformed into a proficient professional capable of extracting valuable insights from big data.

Remember, the world of big data analytics is ever-evolving, with new challenges and opportunities emerging each day. Stay curious, seek continuous learning, and embrace the exciting journey ahead as you unlock the limitless potential of big data analytics.

[formulatrash]

Hi Hazel, what do you think is wrong with McLaren? There's so much stuff from Daniel fans and Lando fans and I just want to know if my boy Oscar is gonna live peacefully.

let me never claim to be able to diagnose what is wrong with an F1 team. that needs more than a medical degree. but sure let's do the equivalent of a 'what mental illness do you have' uquiz.

what's going on is: they said their car was a bit sucktastic and it is. the hydraulic issue they had on Lando's car was obviously terminal from the second it happened, they were just trying to keep running for the data in race conditions, even if by that point he wasn't really racing anyone. the electronics failure on Oscar's car was just shit. but then again, both Red Bulls stopped on the final lap of Bahrain last season and we know how that played out for the rest of the year so let's not get too bleak too quickly.

I know Daniel fans are in a lot of pain rn but the McLaren being bad this year doesn't mean it was just as bad last year. and if it did, then that'd make Lando some sort of supernatural presence, in F1 terms which I'm pretty sure is not what Daniel fans want to hear. but there is a connection between the two and not just that the MCL60 is an evolution of the MCL36.

the reality is: the struggle McLaren are having with themselves now is connected to what happened with Daniel, though. when Daniel was struggling at McL they just didn't have the capacity to work out how to help him.

the way the car has gone this year, with development having headed in the wrong direction, also suggests they couldn't work out why Lando was doing well. he was scoring points, so it would've come under less scrutiny in terms of not being a problem per se but also clearly McLaren took too long to work out the direction they could make a better car in. which is why they've arrived at the start of the season with something they're unhappy with and some reliability gremlins.

McLaren still don't have a wind tunnel, which is insane (they literally take stuff to Cologne, Germany to test it in the wind tunnel) and a huge limitation on development. all the stuff they could take advantage of in terms of extra wind tunnel time is mitigated by the fact they have to fill in 3000 billion EU forms in order to go test parts (cheers, Brexit - remind me again why on earth McL did an advert for the Tories) but when a team like Mercedes is struggling to make the right choices with their car, you know how big the problem is.

if Daniel had been closer to Lando on track then they would have had more usable data. but if they'd been able to get Daniel closer to Lando on track then they wouldn't have so obviously had a problem with interpreting and correlating their car. it's all symptoms of the same thing, which is that McLaren's recovery wasn't ever going to be a permanent upward trajectory.

what's positive for the team is that they now seem able to acknowledge that, that Andrea Stella as a leader has the right technical background to be good at directing the team during this time and that it might be good for the whole team to have a lower-stakes year.

Lando's contracted until shortly after the heat death of the universe and Oscar's a rookie who they've probably at least got a second year option on. neither of them has anywhere better to be in the near future and they're young enough they can wait out a season to actually fix some of McL's problems. it's better to think of this like the 2019 season, with Lando and Carlos in a position to help the team reset, than as a continuation of 2020 and 2021.

or they'll turn up with the new upgrades in a couple of races time and blast the whole field out the water. it's F1, sometimes you just don't know. but if I had to try and explain it using the analytic equivalent of what Taylor Swift lyric I think the team most vibes with ("it's me, I'm the problem") and its favourite white guy from a film franchise (Tony Stark) then that's how it'd come out.

[dorianepin]

nba draft models

patrick bacon nhle blog post

f1metrics model blog post

f1 mathematical model 2023 ratings

f1 analysis 2023 driver rankings

+ there was a guy who charted specific prospect comps but i don't remember who it was anymore... need to look around some more

@__@ thinking about projection modeling for feeder series again. the thing is that the world of "advanced stats" does not tangibly exist in f1 fandom, which is mildly fascinating to me because it's a sport literally predicated on real-time data & predictive analysis... of course the issue is more so that the primary "product" is half black-box and there are limits to the confidence one can ascribe to any analysis because projections of a driver's performance must contextualize the inherent characteristics of the car, which are not entirely known to factually anyone, and even more importantly it is entirely impossible to predict or account for any team's incoming in-season upgrades, so by the time a general competitive order has been determined there is less of a desire to make the projections & evaluations that shape so much of nhl/nba/etc. analytical consumption (at least imo).

there are databases and apis (ergast... but it's shutting down) out there except f1metrics hasn't been active since 2019 and the other modeling work i've seen is quite few and far between... the brunt of f1 stats is basically telemetry overlays, race pace distributions, quali h2h metrics & gap medians, etc. etc........ in the end people love stats in this sport but i feel like the framework in which they get disseminated is always so restrictive ?

anyway the thing is that f1 being non-spec (or not the fact of it being non-spec specifically but rather how it makes evaluating team development impossible because of how complex & secretive the processes are) complicates this entire exercise in the first place, but then...... what of prospect evaluation??

it drives me increasingly crasy to see people incapable of contextualizing junior series dominance + the overall trajectory that a driver's career takes before they make it to f1. the big disclaimer is, of course, that f2 and f1 are not linearly comparable and it's presumptuous to be able to say that someone who dominates in f2 will be able to come to grips with the mechanics of a completely different car and the elevated competition at the highest level of motorsport, but the more i think about it the more i'm like well... is this not basically how nhle gets approached too??? sure you can say that someone who thrives at the european junior level might struggle to adjust to a specific coaching system while playing on smaller ice with increased physicality, but that's a speculative caveat at the end of the day... motorsport is one of the most ever Vibes Based fandoms in terms of rating junior drivers and proclaiming others as being washed but i feel like there are enough traits to create a better evaluation system than currently exists.

considerations

(get mecachromed) how 2 account for illegal engines and private testing etc. well: you can't. or i don't know how to

teams obviously know more from running their rookies privately than we do. but models aren't made to be right and in this case they're made to vaguely identify a measure of potential

instantly harder to evaluate the strength of the "rest of the field" if none of the other competitors have ever eventually made it to f1, because those drivers will hence always be something of an unknown variable

nhle is basically about contextualizing junior pts output & using that to determine a player's expected usefulness (further generalized by elite, star, nhler, bust etc.), which again is not really translatable to f1 because although quality of teammate does have a variable impact in hockey it's literally mathematically impossible to be to the same level of f1 wherein you can end up in a backmarker and score 0 points. and obviously it's like... the linear accumulation of points is simply not the same, you don't get 1 single point for every action you make, so going by absolute differences in f1 scoring is often misleading (though it does carry its own context... anyway)

that being said, Traits:

total points (adjusted per championship format)

year # in championship (i think being in your 3rd year Should be much more highly weighted than some people are willing to admit)

age / series run previously

dominance over field (% of total avail points)

dominance over teammates (quali h2h, sprint/feature h2h, quali % diff, etc.)

some measure of consistency? but the idea also, to me, is like....... i wonder how for example ollie would rate if this were applied to his rookie f2 season because how much does a team value his rough-around-the-edges quickness over theo's ultimately unremarkable 3rd year consistency?

that's really the crux of it at the end of the day.......... many thoughts but that's where i'm at right now. hmm. let me put this under a read more i did not think i'd write this much

also -> realizing things about evaluating ceiling of performance in f1 (there Is a maximum you can achieve unlike other sports where the human limit to how many points you can score is more evident) and also that retroactively evaluating current graduates requires some level of analysis wrt f1 performance & longevity anyway.... thinking about george/lando/sharl/alex comparisons + mick/guanyu/oscar co... Hm. idk

[s1m1rthbl0g]

Unpredictable path of sales: Virtual Reality

Success of a company depends on the Success of a new product . Not only Sales and marketing team hooked on number for new product sale even production capacity , supply chain and working capital estimation for new product are quite crucial . There are traditional Methods like cojoint analysis are available to test market adoption for new product , However to get exact forecast for new product sales volume still a big challenge .

VR Game Changer :-

We have seen examples of some great products when they were launched looked very promising . However, they could not get the great success . for example TATA Nano , Google Glasses and Apple Newton (( personal digital assistant ) . Probably they were ahead of their times or simply they could not assess consumers interest in product features and attribute . Times have changed now we have Virtual Reality tools available which can help us to get consumers insight in a product much more accurate than traditional methods to predict product adoption . Now research by Harz and team recommends a novel solution : having potential customer interact with planned product using Virtual Reality . Combined with right statics model , VR based interactions yield more reliable data for product analytics and improvement . Now with VR consumers can experience product in much immersive way and interact with computer to generate seamless information flow . These are much more accurate than traditional Methods

Hurdles for implementation :-

The biggest hurdle is designing a very immersive VR system for product testing . A three dimensional , 360 degree VR can cost as much as $10000 per minute . But a less immersive VR system can cost fraction of this . The price of a popular VR system ranges from $10 Display that can be integrated to a smartphone to few hundred Us dollars . Though the cost may be roadblock but benefits accrued are immense . The result obtained product testing by VR is of gold standard compared to tradition market research techniques . In product testing in some scenarios it has been observed that VR bases outcomes are accurate more than 50% compared to traditional test . The error in projecting post launch sale was only 2 % in VR based models . which is much less than 40% - 60% success in traditional methods of product testing .

When to use VR Decision :-

It is important to understand when to use VR based models for predicting sales of a product compared to tradition methods . For example for FMCG product it is much cheaper to test market product sales by interacting with consumers in reality .Proctor and Gamble and Unilever of the world where cost of product is much less can afford to have real life interactions with consumers . However as we go up towards consumer durables and more so costly product like Automobiles , where cost of producing a prototype is much higher VR Based product testing will be much useful . for complex products where different attributes of product hold different value for consumers VR based product techniques . Proper Analysis of VR based Product testing and sales prediction will also help in minimizing cost of establishing Product line , Distribution . This will help in customization of various product features also .

In nutshell .. Virtual is becoming real !! At least in Arena of product testing .