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emptyanddark · 2 years ago

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what's actually wrong with 'AI'

it's become impossible to ignore the discourse around so-called 'AI'. but while the bulk of the discourse is saturated with nonsense such as, i wanted to pool some resources to get a good sense of what this technology actually is, its limitations and its broad consequences.

what is 'AI'

the best essay to learn about what i mentioned above is On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? this essay cost two of its collaborators to be fired from Google. it frames what large-language models are, what they can and cannot do and the actual risks they entail: not some 'super-intelligence' that we keep hearing about but concrete dangers: from climate, the quality of the training data and biases - both from the training data and from us, the users.

The problem with artificial intelligence? It’s neither artificial nor intelligent

How the machine ‘thinks’: Understanding opacity in machine learning algorithms

The Values Encoded in Machine Learning Research

Troubling Trends in Machine Learning Scholarship: Some ML papers suffer from flaws that could mislead the public and stymie future research

AI Now Institute 2023 Landscape report (discussions of the power imbalance in Big Tech)

ChatGPT Is a Blurry JPEG of the Web

Can we truly benefit from AI?

Inside the secret list of websites that make AI like ChatGPT sound smart

The Steep Cost of Capture

labor

'AI' champions the facade of non-human involvement. but the truth is that this is a myth that serves employers by underpaying the hidden workers, denying them labor rights and social benefits - as well as hyping-up their product. the effects on workers are not only economic but detrimental to their health - both mental and physical.

OpenAI Used Kenyan Workers on Less Than $2 Per Hour to Make ChatGPT Less Toxic

also from the Times: Inside Facebook's African Sweatshop

The platform as factory: Crowdwork and the hidden labour behind artificial intelligence

The humans behind Mechanical Turk’s artificial intelligence

The rise of 'pseudo-AI': how tech firms quietly use humans to do bots' work

The real aim of big tech's layoffs: bringing workers to heel

The Exploited Labor Behind Artificial Intelligence

workers surveillance

5 ways Amazon monitors its employees, from AI cameras to hiring a spy agency

Computer monitoring software is helping companies spy on their employees to measure their productivity – often without their consent

theft of art and content

Artists say AI image generators are copying their style to make thousands of new images — and it's completely out of their control (what gives me most hope about regulators dealing with theft is Getty images' lawsuit - unfortunately individuals simply don't have the same power as the corporation)

The real aim of big tech's layoffs: bringing workers to heel

The Exploited Labor Behind Artificial Intelligence

AI is already taking video game illustrators’ jobs in China

Microsoft lays off team that taught employees how to make AI tools responsibly/As the company accelerates its push into AI products, the ethics and society team is gone

150 African Workers for ChatGPT, TikTok and Facebook Vote to Unionize at Landmark Nairobi Meeting

Inside the AI Factory: the Humans that Make Tech Seem Human

Refugees help power machine learning advances at Microsoft, Facebook, and Amazon

Amazon’s AI Cameras Are Punishing Drivers for Mistakes They Didn’t Make

China’s AI boom depends on an army of exploited student interns

political, social, ethical consequences

Afraid of AI? The startups selling it want you to be

An Indigenous Perspective on Generative AI

“Computers enable fantasies” – On the continued relevance of Weizenbaum’s warnings

‘Utopia for Whom?’: Timnit Gebru on the dangers of Artificial General Intelligence

Machine Bias

HUMAN_FALLBACK

AI Ethics Are in Danger. Funding Independent Research Could Help

AI Is Tearing Wikipedia Apart

AI machines aren’t ‘hallucinating’. But their makers are

The Great A.I. Hallucination (podcast)

“Sorry in Advance!” Rapid Rush to Deploy Generative A.I. Risks a Wide Array of Automated Harms

The promise and peril of generative AI

ChatGPT Users Report Being Able to See Random People's Chat Histories

Benedetta Brevini on the AI sublime bubble – and how to pop it

Eating Disorder Helpline Disables Chatbot for 'Harmful' Responses After Firing Human Staff

AI moderation is no match for hate speech in Ethiopian languages

Amazon, Google, Microsoft, and other tech companies are in a 'frenzy' to help ICE build its own data-mining tool for targeting unauthorized workers

Crime Prediction Software Promised to Be Free of Biases. New Data Shows It Perpetuates Them

The EU AI Act is full of Significance for Insurers

Proxy Discrimination in the Age of Artificial Intelligence and Big Data

Welfare surveillance system violates human rights, Dutch court rules

Federal use of A.I. in visa applications could breach human rights, report says

Open (For Business): Big Tech, Concentrated Power, and the Political Economy of Open AI

Generative AI Is Making Companies Even More Thirsty for Your Data

environment

The Generative AI Race Has a Dirty Secret

Black boxes, not green: Mythologizing artificial intelligence and omitting the environment

Energy and Policy Considerations for Deep Learning in NLP

AINOW: Climate Justice & Labor Rights

militarism

The Growing Global Spyware Industry Must Be Reined In

AI: the key battleground for Cold War 2.0?

‘Machines set loose to slaughter’: the dangerous rise of military AI

AI: The New Frontier of the EU's Border Extranalisation Strategy

The A.I. Surveillance Tool DHS Uses to Detect ‘Sentiment and Emotion’

organizations

AI now

DAIR

podcast episodes

Pretty Heady Stuff: Dru Oja Jay & James Steinhoff guide us through the hype & hysteria around AI

Tech Won't Save Us: Why We Must Resist AI w/ Dan McQuillan, Why AI is a Threat to Artists w/ Molly Crabapple, ChatGPT is Not Intelligent w/ Emily M. Bender

SRSLY WRONG: Artificial Intelligence part 1, part 2

The Dig: AI Hype Machine w/ Meredith Whittaker, Ed Ongweso, and Sarah West

This Machine Kills: The Triforce of Corporate Power in AI w/ ft. Sarah Myers West

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kgsupsccourses · 4 months ago

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Current Affairs 2024: Analyzing Global Trends with Khan Global Studies

As we move through 2024, the global landscape is rapidly evolving, marked by significant events and policy changes. At Khan Global Studies, we are dedicated to providing insightful analysis on current affairs 2024, emphasizing the interplay between government policies and cybersecurity. This article delves into the most pressing issues of 2024, offering a comprehensive overview for our readers.

1. Geopolitical Shifts and International Relations

a. US-China Relations The geopolitical rivalry between the United States and China continues to shape international dynamics. In 2024, both nations are navigating a complex web of competition and cooperation. Key areas of contention include trade, technology, and military presence in the Indo-Pacific region. The Biden administration has adopted a strategic approach, balancing economic sanctions with diplomatic engagements to manage the delicate relationship with Beijing.

b. European Union's Strategic Autonomy The European Union is increasingly asserting its strategic autonomy in global affairs. Amidst the ongoing conflict in Ukraine and tensions with Russia, the EU is striving to bolster its defense capabilities and reduce dependency on external powers. The formation of the European Defense Fund and initiatives to enhance cybersecurity resilience are pivotal steps in this direction.

2. Cybersecurity: A Growing Concern

a. Rise in Cyber Attacks 2024 has witnessed a surge in cyberattacks targeting critical infrastructure, financial institutions, and government agencies. Ransomware attacks and data breaches have become more sophisticated, necessitating robust cybersecurity measures. Governments worldwide are investing heavily in cybersecurity infrastructure, adopting zero-trust architectures, and enhancing public-private partnerships to combat these threats.

b. Legislative Responses In response to escalating cyber threats, several countries have introduced stringent cybersecurity regulations. The European Union's Digital Operational Resilience Act (DORA) aims to fortify financial entities against cyber risks. Similarly, the United States' Cyber Incident Reporting for Critical Infrastructure Act mandates timely reporting of cyber incidents to federal authorities, ensuring swift response and mitigation.

3. Environmental Policies and Climate Action

a. COP29 Outcomes The 29th Conference of the Parties (COP29) in Nairobi has been a pivotal event in 2024, with countries committing to more ambitious climate targets. The focus has shifted towards implementing tangible actions to achieve net-zero emissions by mid-century. Key agreements include increased funding for green technologies, reforestation projects, and stricter regulations on carbon emissions.

b. Renewable Energy Transition Governments are accelerating the transition to renewable energy sources to combat climate change. Investments in solar, wind, and hydroelectric power are at an all-time high. Notably, India has launched the Green Energy Corridor Project, aiming to integrate renewable energy into its national grid, reducing reliance on fossil fuels.

4. Technological Advancements and Their Impact

a. Artificial Intelligence and Automation The rapid advancement of artificial intelligence (AI) and automation is reshaping industries and job markets. In 2024, AI-driven technologies are being integrated into various sectors, from healthcare to manufacturing. Governments are grappling with the dual challenge of fostering innovation while ensuring ethical AI use and mitigating job displacement.

b. Quantum Computing Breakthroughs Quantum computing is making significant strides, with potential applications in cryptography, material science, and complex problem-solving. Leading tech companies and research institutions are achieving new milestones, bringing us closer to realizing the transformative potential of quantum technology. Policymakers are keenly observing these developments to address security implications and harness quantum computing for national advancement.

Conclusion

2024 is a year marked by profound changes and challenges across the globe. At Khan Global Studies, we continue to monitor these developments, providing in-depth analysis and insights into the evolving landscape of international relations, cybersecurity, environmental policies, and technological advancements. Stay tuned for more updates and expert perspectives on the issues shaping our world today.

#current affairs today #upsc current affairs #current affairs #daily current affairs

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speedyposts · 10 months ago

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Rural Kenyans power West’s AI revolution. Now they want more

Naivasha, Kenya – Caroline Njau comes from a family of farmers who tend to fields of maize, wheat, and potatoes in the hilly terrain near Nyahururu, 180 kilometres (112 miles) north of the capital Nairobi.

But Njau has chosen a different path in life.

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These days, the 30-year-old lives in Naivasha, a scenic town at the centre of Kenya’s flower industry and midway between Nyahururu and Nairobi. Seated in her living room with a cup of milk tea, she labels data for artificial intelligence (AI) companies abroad on an app. The sun rises over the unpaved streets of her neighbourhood as she flicks through images of tarmac roads, intersections and sidewalks on her smartphone while carefully drawing boxes around various objects; traffic lights, cars, pedestrians, and signposts. The designer of the app – an American subcontractor to Silicon Valley companies – pays her $3 an hour.

Njau is a so-called annotator, and her annotation of data compiles the building blocks that train artificial intelligence to recognise patterns in real life, in this case, with self-driving cars.

“My parents have not fully embraced technology because they find it hard to learn. But I always loved science. Data annotation creates opportunities, and you do not need a degree to do this – just your phone and an internet connection,” says Njau who studied teaching but has been annotating since 2021.

Kenya is emerging as a hub for such online work, rising to compete with countries like India and the Philippines. The birth of tech start-ups since the late 2000s, followed by the entry of tech outsourcing companies, along with business-friendly policies, skilled labour and high-speed internet have all led to an economy where digital jobs are the bread and butter for a large portion of the youth. In 2021, a survey by Kenya Private Sector Alliance (KEPSA) showed that at least 1.2 million Kenyans are working online, most of them informally.

But Nairobi’s data annotators have recently revealed a less rosy side to this industry. In a Time article from last year, workers at an outsourcing firm in Nairobi described the “torture” they went through while labelling pieces of texts drawn from the darkest corners of the internet – all in a quest to make OpenAI’s ChatGPT able to recognise harmful content. According to the piece, the workers were paid less than $2 an hour to do this.

Despite these stories, the annotation industry has continued to spread far beyond the cramped office spaces in Nairobi.

In mid-January, when Kenya’s President William Ruto launched a government-sponsored tech hub in Kitale – an agricultural town near the border with Uganda – a young ICT student explained how he had earned $284 in three weeks by training AI for Silicon Valley companies. He had been using Remotasks, an American website where freelancers get paid for labelling data.

The video clip from the tech hub – one among a series of facilities designed to equip learners with marketable tech skills – spread like wildfire on social media and made young Kenyans rush to create Remotasks accounts.

“Many young people are jobless. Even people who graduated in computer science cannot find jobs. The government is doing right by helping young people access online work,” says Kennedy Cheruyot, 24, a recently graduated nurse from Eldoret in western Kenya.

He opened a Remotasks account in 2021 and has continued to work online while looking for a job in hospitals. Some of his friends have entirely left other careers to focus on digital tasks.

“Previously, boys in our culture were supposed to go to the farm, herding the cattle. Now, they stay inside to do online work,” Cheruyot says when we meet at a cafe overlooking Eldoret’s business district. Hardware and agricultural supply stores blend with bright yellow signs advertising internet cafes, so-called “cybers”.

Although Cheruyot’s dream is to own a ranch “like in the Western movies”, he currently spends most of his time looking for more online gigs to pay for rent, food, electricity, water and transport.

Commodity prices in Kenya have soared since 2022, attributed to a prolonged drought that year and the Russia-Ukraine war. Meanwhile, the Kenyan shilling has continued to depreciate due to demand for dollars from the energy and manufacturing sectors. As the shilling weakens, import prices increase and with them the cost of goods for consumers like Cheruyot.

He expects that, should he land a job as a nurse, he will continue to work online in his spare time, earning from $5 to $20 an hour depending on the task.

“I do not care if the AI companies in the West grow rich because of our work. As long as we are paid. It may not seem like much, but it goes a long way in Kenya,” he says.

But for Njau, the monotonous online tasks are a gateway to something bigger.

“Right now, Kenyan annotators water someone else’s garden. The flowers begin to bloom, but we are not even there to see it,” she says, gesturing towards the green grass outside her brick house.

“I do not want to stay in data annotation, my goal is to advance in technology. I want to know where the data go and how AI is programmed. Technology is taking over whether we like it or not, and us Kenyans should become data scientists,” says Njau who has already trained people with disabilities and young women in data annotation together with the Nairobi-based non-profit Next Step Foundation. Recently, she was awarded a scholarship in AI and data science by the Ministry of Investments, Trade and Industry.

Programmes like these aim to make Kenya a frontrunner in the technological revolution, explains Nickson Otieno, training manager at Next Step Foundation.

“I will not be surprised if a Kenyan comes up with the next big AI invention. We have an innovative generation and there are many problems to solve. For example, how can AI be used to inform Kenya Power and Lighting Company about blackouts by feeding it with complaints about power cuts posted on social media?” asks Otieno.

Still, there are bumps on the road to make Kenya – and other African countries – stand out as AI innovation hubs. According to Professor Tshilidzi Marwala, a South African scholar of AI and the Rector of the United Nations University, the education systems need an overhaul.

“Africans often receive quite specialised education, which is the case in countries like Kenya and South Africa that have British-oriented education systems. However, specialised education is outdated in a multidisciplinary world,” he argues and brings up an example: to create an AI platform that analyses x-ray images, one must master both medical and computer science.

Much of the conversation regarding AI, such as OpenAI’s ChatGPT, has focused on the human jobs that risk redundancy, and this is also a real concern in African countries. Marwala, however, believes that many people have “overplayed the significance of AI and confused it with normal automation”. Furthermore, AI might help small-scale businesses thrive.

“If a flower farmer in South Africa uses AI to analyse the soil quality using a camera rather than paying a scientist to do it, this could make the flower production cheaper for the farmer. I foresee AI providing much more efficiency and cost reduction,” he says.

AI apps that rely on data labelled by Kenyans, such as the chatbot ChatGPT, are already popular with young people like Njau and Cheruyot. He finds it “really useful” when in need of recipes or travel itineraries. But it cannot do his work for him.

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metalporsiempre · 1 year ago

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A few months after graduating from college in Nairobi, a 30-year-old I’ll call Joe got a job as an annotator — the tedious work of processing the raw information used to train artificial intelligence. AI learns by finding patterns in enormous quantities of data, but first that data has to be sorted and tagged by people, a vast workforce mostly hidden behind the machines. (..) It’s difficult and repetitive work. A several-second blip of footage took eight hours to annotate, for which Joe was paid about $10.

Then, in 2019, an opportunity arose: Joe could make four times as much running an annotation boot camp for a new company that was hungry for labelers. Every two weeks, 50 new recruits would file into an office building in Nairobi to begin their apprenticeships. There seemed to be limitless demand for the work. They would be asked to categorize clothing seen in mirror selfies, look through the eyes of robot vacuum cleaners to determine which rooms they were in, and draw squares around lidar scans of motorcycles. Over half of Joe’s students usually dropped out before the boot camp was finished. (..)

After boot camp, they went home to work alone in their bedrooms and kitchens, forbidden from telling anyone what they were working on, which wasn’t really a problem because they rarely knew themselves. (..) Each project was such a small component of some larger process that it was difficult to say what they were actually training AI to do. Nor did the names of the projects offer any clues: Crab Generation, Whale Segment, Woodland Gyro, and Pillbox Bratwurst. They were non sequitur code names for non sequitur work.

As for the company employing them, most knew it only as Remotasks, a website offering work to anyone fluent in English. Like most of the annotators I spoke with, Joe was unaware until I told him that Remotasks is the worker-facing subsidiary of a company called Scale AI, a multibillion-dollar Silicon Valley data vendor that counts OpenAI and the U.S. military among its customers. Neither Remotasks’ or Scale’s website mentions the other.

Much of the public response to language models like OpenAI’s ChatGPT has focused on all the jobs they appear poised to automate. But behind even the most impressive AI system are people — huge numbers of people labeling data to train it and clarifying data when it gets confused. Only the companies that can afford to buy this data can compete, and those that get it are highly motivated to keep it secret. The result is that, with few exceptions, little is known about the information shaping these systems’ behavior, and even less is known about the people doing the shaping.

For Joe’s students, it was work stripped of all its normal trappings: a schedule, colleagues, knowledge of what they were working on or whom they were working for. In fact, they rarely called it work at all — just “tasking.” They were taskers.

The anthropologist David Graeber defines “bullshit jobs” as employment without meaning or purpose, work that should be automated but for reasons of bureaucracy or status or inertia is not. These AI jobs are their bizarro twin: work that people want to automate, and often think is already automated, yet still requires a human stand-in. The jobs have a purpose; it’s just that workers often have no idea what it is.

The current AI boom (..) began with an unprecedented feat of tedious and repetitive labor.

In 2007, the AI researcher Fei-Fei Li, then a professor at Princeton, suspected the key to improving image-recognition neural networks, a method of machine learning that had been languishing for years, was training on more data — millions of labeled images rather than tens of thousands. The problem was that it would take decades and millions of dollars for her team of undergrads to label that many photos.

Li found thousands of workers on Mechanical Turk, Amazon’s crowdsourcing platform where people around the world complete small tasks for cheap. The resulting annotated dataset, called ImageNet, enabled breakthroughs in machine learning that revitalized the field and ushered in a decade of progress.

Annotation remains a foundational part of making AI, but there is often a sense among engineers that it’s a passing, inconvenient prerequisite to the more glamorous work of building models. You collect as much labeled data as you can get as cheaply as possible to train your model, and if it works, at least in theory, you no longer need the annotators. But annotation is never really finished. Machine-learning systems are what researchers call “brittle,” prone to fail when encountering something that isn’t well represented in their training data. These failures, called “edge cases,” can have serious consequences. In 2018, an Uber self-driving test car killed a woman because, though it was programmed to avoid cyclists and pedestrians, it didn’t know what to make of someone walking a bike across the street. (..)

Over the past six months, I spoke with more than two dozen annotators from around the world, and while many of them were training cutting-edge chatbots, just as many were doing the mundane manual labor required to keep AI running. There are people classifying the emotional content of TikTok videos, new variants of email spam, and the precise sexual provocativeness of online ads. Others are looking at credit-card transactions and figuring out what sort of purchase they relate to or checking e-commerce recommendations and deciding whether that shirt is really something you might like after buying that other shirt. Humans are correcting customer-service chatbots, listening to Alexa requests, and categorizing the emotions of people on video calls. They are labeling food so that smart refrigerators don’t get confused by new packaging, checking automated security cameras before sounding alarms, and identifying corn for baffled autonomous tractors. (..)

The data vendors behind familiar names like OpenAI, Google, and Microsoft come in different forms. There are private outsourcing companies with call-center-like offices, such as the Kenya- and Nepal-based CloudFactory, where Joe annotated for $1.20 an hour before switching to Remotasks. There are also “crowdworking” sites like Mechanical Turk and Clickworker where anyone can sign up to perform tasks. In the middle are services like Scale AI. Anyone can sign up, but everyone has to pass qualification exams and training courses and undergo performance monitoring. Annotation is big business. (..)

This tangled supply chain is deliberately hard to map. According to people in the industry, the companies buying the data demand strict confidentiality. (This is the reason Scale cited to explain why Remotasks has a different name.) Annotation reveals too much about the systems being developed, and the huge number of workers required makes leaks difficult to prevent. Annotators are warned repeatedly not to tell anyone about their jobs, not even their friends and co-workers, but corporate aliases, project code names, and, crucially, the extreme division of labor ensure they don’t have enough information about them to talk even if they wanted to. (Most workers requested pseudonyms for fear of being booted from the platforms.) Consequently, there are no granular estimates of the number of people who work in annotation, but it is a lot, and it is growing. A recent Google Research paper gave an order-of-magnitude figure of “millions” with the potential to become “billions.”

(..) Erik Duhaime, CEO of medical-data-annotation company Centaur Labs, recalled how, several years ago, prominent machine-learning engineers were predicting AI would make the job of radiologist obsolete. When that didn’t happen, conventional wisdom shifted to radiologists using AI as a tool. Neither of those is quite what he sees occurring. AI is very good at specific tasks, Duhaime said, and that leads work to be broken up and distributed across a system of specialized algorithms and to equally specialized humans. (..)

Worries about AI-driven disruption are often countered with the argument that AI automates tasks, not jobs, and that these tasks will be the dull ones, leaving people to pursue more fulfilling and human work. But just as likely, the rise of AI will look like past labor-saving technologies, maybe like the telephone or typewriter, which vanquished the drudgery of message delivering and handwriting but generated so much new correspondence, commerce, and paperwork that new offices staffed by new types of workers — clerks, accountants, typists — were required to manage it. When AI comes for your job, you may not lose it, but it might become more alien, more isolating, more tedious.

Earlier this year, I signed up for Scale AI’s Remotasks. The process was straightforward. After entering my computer specs, internet speed, and some basic contact information, I found myself in the “training center.” To access a paying task, I first had to complete an associated (unpaid) intro course.

The training center displayed a range of courses with inscrutable names like Glue Swimsuit and Poster Macadamia. I clicked on something called GFD Chunking, which revealed itself to be labeling clothing in social-media photos.

The instructions, however, were odd. For one, they basically consisted of the same direction reiterated in the idiosyncratically colored and capitalized typography of a collaged bomb threat. (..)

I skimmed to the bottom of the manual, where the instructor had written in the large bright-red font equivalent of grabbing someone by the shoulders and shaking them, “THE FOLLOWING ITEMS SHOULD NOT BE LABELED because a human could not actually put wear any of these items!” above a photo of C-3PO, Princess Jasmine from Aladdin, and a cartoon shoe with eyeballs.

Feeling confident in my ability to distinguish between real clothes that can be worn by real people and not-real clothes that cannot, I proceeded to the test. Right away, it threw an ontological curveball: a picture of a magazine depicting photos of women in dresses. Is a photograph of clothing real clothing? No, I thought, because a human cannot wear a photograph of clothing. Wrong! As far as AI is concerned, photos of real clothes are real clothes. Next came a photo of a woman in a dimly lit bedroom taking a selfie before a full-length mirror. The blouse and shorts she’s wearing are real. What about their reflection? Also real! Reflections of real clothes are also real clothes.

After an embarrassing amount of trial and error, I made it to the actual work, only to make the horrifying discovery that the instructions I’d been struggling to follow had been updated and clarified so many times that they were now a full 43 printed pages of directives: Do NOT label open suitcases full of clothes; DO label shoes but do NOT label flippers; DO label leggings but do NOT label tights; do NOT label towels even if someone is wearing it; label costumes but do NOT label armor. And so on.

There has been general instruction disarray across the industry, according to Milagros Miceli, a researcher at the Weizenbaum Institute in Germany who studies data work. It is in part a product of the way machine-learning systems learn. Where a human would get the concept of “shirt” with a few examples, machine-learning programs need thousands, and they need to be categorized with perfect consistency yet varied enough that the very literal system can handle the diversity of the real world. (..)

The act of simplifying reality for a machine results in a great deal of complexity for the human. Instruction writers must come up with rules that will get humans to categorize the world with perfect consistency. To do so, they often create categories no human would use. (..)

The job of the annotator often involves putting human understanding aside and following instructions very, very literally. (..) Annotators invariably end up confronted with confounding questions like, Is that a red shirt with white stripes or a white shirt with red stripes? Is a wicker bowl a “decorative bowl” if it’s full of apples? What color is leopard print? When instructors said to label traffic-control directors, did they also mean to label traffic-control directors eating lunch on the sidewalk? Every question must be answered, and a wrong guess could get you banned and booted to a new, totally different task with its own baffling rules.

Most of the work on Remotasks is paid at a piece rate with a single task earning anywhere from a few cents to several dollars. Because tasks can take seconds or hours, wages are hard to predict. When Remotasks first arrived in Kenya, annotators said it paid relatively well — averaging about $5 to $10 per hour depending on the task — but the amount fell as time went on.

Scale AI spokesperson Anna Franko said that the company’s economists analyze the specifics of a project, the skills required, the regional cost of living, and other factors “to ensure fair and competitive compensation.” Former Scale employees also said pay is determined through a surge-pricing-like mechanism that adjusts for how many annotators are available and how quickly the data is needed.

(..) The most common complaint about Remotasks work is its variability; it’s steady enough to be a full-time job for long stretches but too unpredictable to rely on. Annotators spend hours reading instructions and completing unpaid trainings only to do a dozen tasks and then have the project end. There might be nothing new for days, then, without warning, a totally different task appears and could last anywhere from a few hours to weeks. (..)

This boom-and-bust cycle results from the cadence of AI development, according to engineers and data vendors. Training a large model requires an enormous amount of annotation followed by more iterative updates, and engineers want it all as fast as possible so they can hit their target launch date. There may be monthslong demand for thousands of annotators, then for only a few hundred, then for a dozen specialists of a certain type, and then thousands again. (..)

To succeed, annotators work together. (..) Like a lot of annotators, Victor uses unofficial WhatsApp groups to spread the word when a good task drops. When he figures out a new one, he starts impromptu Google Meets to show others how it’s done. Anyone can join and work together for a time, sharing tips. (..)

Because work appears and vanishes without warning, taskers always need to be on alert. Victor has found that projects pop up very late at night, so he is in the habit of waking every three hours or so to check his queue. When a task is there, he’ll stay awake as long as he can to work. (..)

Identifying clothing and labeling customer-service conversations are just some of the annotation gigs available. Lately, the hottest on the market has been chatbot trainer. Because it demands specific areas of expertise or language fluency and wages are often adjusted regionally, this job tends to pay better. Certain types of specialist annotation can go for $50 or more per hour.

A woman I’ll call Anna was searching for a job in Texas when she stumbled across a generic listing for online work and applied. It was Remotasks, and after passing an introductory exam, she was brought into a Slack room of 1,500 people who were training a project code-named Dolphin, which she later discovered to be Google DeepMind’s chatbot, Sparrow, one of the many bots competing with ChatGPT. Her job is to talk with it all day. At about $14 an hour, plus bonuses for high productivity. (..)

Each time Anna prompts Sparrow, it delivers two responses and she picks the best one, thereby creating something called “human-feedback data.” When ChatGPT debuted late last year, its impressively natural-seeming conversational style was credited to its having been trained on troves of internet data. But the language that fuels ChatGPT and its competitors is filtered through several rounds of human annotation. One group of contractors writes examples of how the engineers want the bot to behave, creating questions followed by correct answers, descriptions of computer programs followed by functional code, and requests for tips on committing crimes followed by polite refusals. After the model is trained on these examples, yet more contractors are brought in to prompt it and rank its responses. This is what Anna is doing with Sparrow. Exactly which criteria the raters are told to use varies — honesty, or helpfulness, or just personal preference. The point is that they are creating data on human taste, and once there’s enough of it, engineers can train a second model to mimic their preferences at scale, automating the ranking process and training their AI to act in ways humans approve of. The result is a remarkably human-seeming bot that mostly declines harmful requests and explains its AI nature with seeming self-awareness.

Put another way, ChatGPT seems so human because it was trained by an AI that was mimicking humans who were rating an AI that was mimicking humans who were pretending to be a better version of an AI that was trained on human writing.

This circuitous technique is called “reinforcement learning from human feedback,” or RLHF, and it’s so effective that it’s worth pausing to fully register what it doesn’t do. When annotators teach a model to be accurate, the model isn’t learning to check answers against logic or external sources or about what accuracy as a concept even is. The model is still a text-prediction machine mimicking patterns in human writing, but now its training corpus has been supplemented with bespoke examples, and the model has been weighted to favor them. Maybe this results in the model extracting patterns from the part of its linguistic map labeled as accurate and producing text that happens to align with the truth, but it can also result in it mimicking the confident style and expert jargon of the accurate text while writing things that are totally wrong. There is no guarantee that the text the labelers marked as accurate is in fact accurate, and when it is, there is no guarantee that the model learns the right patterns from it. (..)

When Anna rates Sparrow’s responses, she’s supposed to be looking at their accuracy, helpfulness, and harmlessness while also checking that the model isn’t giving medical or financial advice or anthropomorphizing itself or running afoul of other criteria. (..) According to Geoffrey Irving, one of DeepMind’s research scientists, the company’s researchers hold weekly annotation meetings in which they rerate data themselves and discuss ambiguous cases, consulting with ethical or subject-matter experts when a case is particularly tricky.

Because feedback data is difficult to collect, it fetches a higher price. Basic preferences of the sort Anna is producing sell for about $1 each, according to people with knowledge of the industry. But if you want to train a model to do legal research, you need someone with training in law, and this gets expensive. Everyone involved is reluctant to say how much they’re spending, but in general, specialized written examples can go for hundreds of dollars, while expert ratings can cost $50 or more. One engineer told me about buying examples of Socratic dialogues for up to $300 a pop. Another told me about paying $15 for a “darkly funny limerick about a goldfish.”

OpenAI, Microsoft, Meta, and Anthropic did not comment about how many people contribute annotations to their models, how much they are paid, or where in the world they are located. Irving of DeepMind, which is a subsidiary of Google, said the annotators working on Sparrow are paid “at least the hourly living wage” based on their location. Anna knows “absolutely nothing” about Remotasks, but Sparrow has been more open. She wasn’t the only annotator I spoke with who got more information from the AI they were training than from their employer; several others learned whom they were working for by asking their AI for its company’s terms of service. (..)

Until recently, it was relatively easy to spot bad output from a language model. It looked like gibberish. But this gets harder as the models get better — a problem called “scalable oversight.” (..) This trajectory means annotation increasingly requires specific skills and expertise.

Last year, someone I’ll call Lewis was working on Mechanical Turk when, after completing a task, he received a message inviting him to apply for a platform he hadn’t heard of. It was called Taskup.ai, and its website was remarkably basic: just a navy background with text reading GET PAID FOR TASKS ON DEMAND. He applied.

The work paid far better than anything he had tried before, often around $30 an hour. It was more challenging, too: devising complex scenarios to trick chatbots into giving dangerous advice, testing a model’s ability to stay in character, and having detailed conversations about scientific topics so technical they required extensive research. (..) While checking one model’s attempts to code in Python, Lewis was learning too. He couldn’t work for more than four hours at a stretch, lest he risk becoming mentally drained and making mistakes, and he wanted to keep the job. (..)

I spoke with eight other workers, most based in the U.S., who had similar experiences of answering surveys or completing tasks on other platforms and finding themselves recruited for Taskup.ai or several similarly generic sites, such as DataAnnotation.tech or Gethybrid.io. Often their work involved training chatbots, though with higher-quality expectations and more specialized purposes than other sites they had worked for. One was demonstrating spreadsheet macros. Another was just supposed to have conversations and rate responses according to whatever criteria she wanted. (..)

Taskup.ai, DataAnnotation.tech, and Gethybrid.io all appear to be owned by the same company: Surge AI. Its CEO, Edwin Chen, would neither confirm nor deny the connection, but he was willing to talk about his company and how he sees annotation evolving.

“We want AI to tell jokes or write really good marketing copy or help me out when I need therapy or whatnot,” Chen said. “You can’t ask five people to independently come up with a joke and combine it into a majority answer. Not everybody can tell a joke or solve a Python program. The annotation landscape needs to shift from this low-quality, low-skill mind-set to something that’s much richer and captures the range of human skills and creativity and values that we want AI systems to possess.”

Last year, Surge relabeled Google’s dataset classifying Reddit posts by emotion. Google had stripped each post of context and sent them to workers in India for labeling. Surge employees familiar with American internet culture found that 30 percent of the labels were wrong. (..)

Surge claims to vet its workers for qualifications (..) but exactly how Surge finds workers is “proprietary,” Chen said. As with Remotasks, workers often have to complete training courses, though unlike Remotasks, they are paid for it, according to the annotators I spoke with. Having fewer, better-trained workers producing higher-quality data allows Surge to compensate better than its peers, Chen said, though he declined to elaborate, saying only that people are paid “fair and ethical wages.” The workers I spoke with earned between $15 and $30 per hour, but they are a small sample of all the annotators, a group Chen said now consists of 100,000 people. The secrecy, he explained, stems from clients’ demands for confidentiality.

Surge’s customers include OpenAI, Google, Microsoft, Meta, and Anthropic. Surge specializes in feedback and language annotation, and after ChatGPT launched, it got an influx of requests. (..)

The new models are so impressive they’ve inspired another round of predictions that annotation is about to be automated. Given the costs involved, there is significant financial pressure to do so. Anthropic, Meta, and other companies have recently made strides in using AI to drastically reduce the amount of human annotation needed to guide models (..). However, a recent paper found that GPT-4-trained models may be learning to mimic GPT’s authoritative style with even less accuracy, and so far, when improvements in AI have made one form of annotation obsolete, demand for other, more sophisticated types of labeling has gone up.

“I think you always need a human to monitor what AIs are doing just because they are this kind of alien entity,” Chen said. Machine-learning systems are just too strange ever to fully trust. The most impressive models today have what, to a human, seems like bizarre weaknesses, he added, pointing out that though GPT-4 can generate complex and convincing prose, it can’t pick out which words are adjectives: “Either that or models get so good that they’re better than humans at all things, in which case, you reach your utopia and who cares?” (..)

One way the AI industry differs from manufacturers of phones and cars is in its fluidity. The work is constantly changing, constantly getting automated away and replaced with new needs for new types of data. It’s an assembly line but one that can be endlessly and instantly reconfigured, moving to wherever there is the right combination of skills, bandwidth, and wages.

Lately, the best-paying work is in the U.S. In May, Scale started listing annotation jobs on its own website, soliciting people with experience in practically every field AI is predicted to conquer. (..) You can make $45 an hour teaching robots law or make $25 an hour teaching them poetry. There were also listings for people with security clearance, presumably to help train military AI. Scale recently launched a defense-oriented language model called Donovan, which Wang called “ammunition in the AI war,” and won a contract to work on the Army’s robotic-combat-vehicle program.

(When Remotasks first arrived in Kenya, Joe thought annotation could be a good career. Even after the work moved elsewhere, he was determined to make it one. (..)

Rather than let their skills go to waste, other taskers decided to chase the work wherever it went. They rented proxy servers to disguise their locations and bought fake IDs to pass security checks so they could pretend to work from Singapore, the Netherlands, Mississippi, or wherever the tasks were flowing. It’s a risky business. Scale has become increasingly aggressive about suspending accounts caught disguising their location, according to multiple taskers. It was during one of these crackdowns that my account got banned, presumably because I had been using a VPN to see what workers in other countries were seeing, and all $1.50 or so of my earnings were seized. (..)

Another Kenyan annotator said that after his account got suspended for mysterious reasons, he decided to stop playing by the rules. Now, he runs multiple accounts in multiple countries, tasking wherever the pay is best. He works fast and gets high marks for quality, he said, thanks to ChatGPT. The bot is wonderful, he said, letting him speed through $10 tasks in a matter of minutes. When we spoke, he was having it rate another chatbot’s responses according to seven different criteria, one AI training the other.

#technology #ai #artificial inteligence #data annotation

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safracatz · 5 years ago

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7 Mobile App Development Errors That You Must Absolutely Avoid

You may be ready to create a mobile presence for your business with a brand new mobile app that puts you on the App Store. But have you thought the type of competition coming up, with thousands of similar applications contending for users' attention? With such challenging circumstances, you will understand the importance of funding in exceptional application solutions. This leaves no room for errors because even the smallest error can result in application failure. Needless to say, you need to be aware of common mobile apps development errors to assure that your team eliminates them during the process and offers a solution that makes the experiences superlative. Let us help you with a list of errors that you should avoid in this context.

1. Not understanding your audience

The process should start with designing a user's character and not making it is the biggest mistake you can make. If you are uncertain of the expectations and needs of a normal user, you may find yourself choosing all the wrong features of your mobile apps development. Even if you can have them download the app, the possibilities of dropping out are high because they will not get what they want or risk having a clutter of unnecessary features to handle. The study of your target audience is the key to customer property and, more importantly, retention of users.

2. Feature overload

If your audience is confused with a lot of features, your application will do more harm than good, even if it's done with good intentions. They can disturb the user and harm speed and performance. On the whole, an overloaded application of comments may affect the quality of the user experience, to the point of making the user uninstall later. On the other hand, allowing fewer targeted features is a better option to keep them and improve their experiences. The choice of features also depends on the personality of the user and the vertical of the company. For example, an AR application may be an excellent choice for a fashion e-commerce store but may not be sufficient for a taxi business.

3. Not paying attention to the UI

If you want to ensure a strong presence on the App Store, nothing works better than an outstanding user interface that differentiates you from competing applications. Do not concentrate on the unique construction can cost you because you will not be able to reach the goal of your investment? A good user interface, on the other hand, acts as the key to engaging and retaining users. Follow an appropriate design process cantered on creating rich and relevant elements, as well as an intuitive navigation flow. At the same time, make sure each item loads externally problems, as this determines the user experience.

4. Too many platforms to start with

Another mistake you should avoid is advancing in applications for too many platforms at the initial stage. You might be invited to invest early in iPhone and Android app development to arrest users on all platforms. But this can be counterproductive because it raises fundamental costs and does not allow you to evaluate the possibility before investing too much. It would be best to send extensive market research, identify a lucrative platform for beginners, explore it and get feedback from customers. Therefore, you can work towards improvement when you create the equivalents of your application for other platforms.

5. Not testing the app before launching

As a user, a pipeless performance is something that makes or breaks the adventure and even a minor glitch can make them quit the application never to return. Testing is, therefore, an essential phase of the development process. Failure to do so would risk the termination of the application. In addition to simple tests, the application must be tested on a range of devices, browsers, and conducting systems to ensure that they deliver seamless experiences at all times. It is a mistake that you cannot afford to commit at all costs.

Conclusion

When it comes to knowing and avoiding these errors, choosing a mobile app development partner is important. The biggest challenge is to recruit mobile application developers who can bring you through a seamless development process and produce a best-of-breed solution that fits your needs. In this case, it becomes vital to choose a team with the right skills, expertise, and experience. Also, it is important to ensure that their costs are within your resources and also meet the highest quality standards. You may get in touch with us at Best apps Development Companies in Nigeria for a free quote to develop a mobile app for your business. And helps Business owners to reach more customers who want to change their business towards app development, The Company has a very good working environment. To know more about my company, Visit Fusion Informatics. For more queries please send a mail to get a free quote [email protected].

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mostlysignssomeportents · 5 years ago

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#1yrago Kenyans from "the toughest neighborhood on earth" trace pixels all day to train autonomous vehicles

The Nairobi neighborhood of Kibera is Africa's largest slum, and it's home to an unlikely, Silicon-Valley-style tech park operated by Samasource (motto: "Artificial intelligence meets human dignity"), who serves clients from Google to Microsoft to Salesforce, using clickworkers who get paid $9/day, compared to the going wage of $2/day in the region's "informal economy" (the company believes that paying wages on par with rich-world clickworkers would "distort the local economy").

One major project at Samasource's Kibera office is producing training data for self-driving cars: workers carefully trace pixel-accurate outlines around road-features like signs, cars, license plates, etc.

There are many ways in which Samsource benefits Kibera: workers are paid a living wage and enjoy better working conditions than are standard in Kibera, and women make up about half of the workforce, and can take 90 days' maternity leave and use a lactation room when they return. Workers who leave Samasource continue to thrive, pursuing higher education and/or "more formal work."

But the work is still unpleasant in ways that are familiar from other places where this kind of work is done, from the poor ergonomics to the worker metric tools that encourage workers to skip breaks in order to make quota (Samasource said it would re-evaluate the ergonomics).

More problematic is that Kibera's residents are unlikely to benefit from self-driving cars at any time in the foreseeable future -- while they could benefit from much lower-tech interventions, like clean water and sanitation.

Samasource is a really good example of both the possibilities and limitations of the economic development for "lifting people out of poverty." Kenya is a rich country with many natural resources that has struggled with the legacy of colonialism: much of the wealth of the former colonizers can be traced to extraction from Kenya, and today, those colonizing powers turn a blind eye to the laundering of the billions extracted from the region by corrupt officials and businesspeople.

Samasource is an example of a single firm that is making a large, positive difference in the lives of the people who work for it -- but it is able to do so because it is making a much larger positive benefit to the bottom line of the most profitable corporations on earth -- corporations whose tax-dodging has contributed to falling levels of direct aid to the region, and whose tax-evasion tactics are shared by the region's looter class.

Samasource has a huge and laudable effect on a relatively small cohort of workers, and for that it deserves praise. But even a thousand Samasouces wouldn't make population-scale changes to the region -- unlike eliminating tax evasion, paying reparations that could be used to supply clean water and sanitation, and other nation-changing interventions.

Therein lies the problem: in an increasingly unequal world where inequality-producing markets are posed as the best (or only) tool for solving our problems, Samasource shows that the very best we can hope for is not nearly enough.

https://boingboing.net/2018/11/05/kibera-clickwork.html

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first-city · 6 years ago

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The Art of CRM

“The Art of CRM” is finally published, available in book stores and on Amazon both in Paperback and Kindle format.

Two decades of experience, I write about lessons from +200 CRM projects across the globe, from Iceland/ Reykjavík to Sydney in Australia, from Mexico to Guatemala, from Riyadh to Nairobi, from Moscow to Shanghai. Probably more than anybody else on this planet. I experienced many good, bad and ugly implementations from varying of companies in all kind of industries. To write this book was an obligation to share some of the stories with the world, and my appreciation to my colleagues, partners, and customers. CRM has changed in the recent years, and with the mass adoption of artificial intelligence and machine learning, Industry 4.0 and digital transformation, and the social media, expect more change in the near future. The big-tech companies and digital disrupters are here to stay, Crypto-currencies and other inventions such as mobile payment or mobile apps are changing the way we do business. In this book I show you how to stay competitive with the big tech companies such as Amazon and Google that are entering almost any market across the globe with your CRM. on Amazon. com Amazon.de

CRM systems have delivered huge value to organizations. This book shares proven and cutting-edge techniques to increase the power of CRM even further.

In The Art of CRM, Max Fatouretchi shares his decades of experience building successful CRM systems that make a real difference to business performance.

Through clear processes, actionable advice, and informative case studies, The Art of CRM teaches you to design successful CRM systems for your clients.

Fatouretchi, founder of Academy4CRM institute, draws on his experience over 20 years and 200 CRM implementations worldwide.

Bringing CRM bang up to date, The Art of CRM shows how to add AI and machine learning, ensure compliance with GDPR, and choose between on-premise, cloud, and hybrid hosting solutions.

If you're looking for an expert guide to real-world CRM implementations, this book is for you.

What you will learn

Deliver CRM systems that are on time, on budget, and bring lasting value to organizations

Build CRM that excels at operations, analytics, and collaboration

Gather requirements effectively: identify key pain points, objectives, and functional requirements

Develop customer insight through 360-degree client view and client profiling

Turn customer requirements into a CRM design spec

Architect your CRM platform

Bring machine learning and artificial intelligence into your CRM system

Ensure compliance with GDPR and other critical regulations

Choose between on-premise, cloud, and hybrid hosting solutions

Who this book is for

CRM practitioners who want to update their work with new, proven techniques and approaches

Table of Contents

What is CRM?

Getting to Know Your Customer

Conceptualizing the CRM Design from Business Requirements

Architecting Your CRM Solution – Preparing for Today and Tomorrow

Utilizing Artificial Intelligence and Machine Learning in Your CRM Strategy

GDPR and Regulatory Compliance

CRM Integration Strategies

Cloud Versus On-Premise Versus Hybrid – The Deployment of a CRM Platform

CRM Differentiators

#crm theartofcrm artificialintelligence ai

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safracatz · 5 years ago

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Mobile App Development Trends That Are Predicted To Make It Big In 2019

After an exciting 2018, the New Year is relied upon to promote much more the changing scene. Mobile apps, especially, are ready to recognize some real changes are new improvements are not too far off and some existing one's areas of immediately commanding. On the off chance that you are needing to put resources into mobile apps development in the coming year or picture a few upgrades in your popular application, you should realize what is growing down to business later on. Give us a future to list the important patterns that are expected to grow big in the mobile apps development space in 2019.

1. Machine Learning and AI will be bigger than ever

Although these innovations have been about for quite a while, they are simply going to develop greater this year. The most generally recognized structure wherein they show in applications is as chatbots which are provided for understanding human language and communicating with them as human collaborators do. The following year will have a place with these quick bots and having them as a component of your business application will never repeat be a decision.

2. Augmented Reality and Virtual Reality will get more real

AR and VR advancements are maintaining to see across the board selection in 2019. Before being commended for sending specific gaming encounters, these innovations will turn into a piece of pretty much every retail application this New Year. A few ways of life and internet business makes are as of now using extended reality applications to lift the client encounters by providing application clients attempt before purchasing the office.

3. Instant apps will become more popular

At the point when moment applications were developed in 2016, they obtained a ton of buzz as a result of the support and space-sparing highlights that they brought. They have developed frequently well known in these couple of years and are probably working to get much more approval in the coming year. The key to their success lies in the way that clients can get to them in a split second without downloading them and estimate their gadget memory.

4. The demand for wearable apps will boom

Throughout the years, the wearable innovation has excellent and these gadgets have transformed into an excellent requirement have frill today. From wellness and wellbeing the executives to representative observing and remote activities, these gadgets are making every one of the distinctions for life at home and work. Therefore, there will be a development asked after for wearable versatile applications that power these gadgets.

5. Beacons-based apps will strengthen their presence

Another innovation that has been about for quite a while is Beacons change yet, fortunately, it will get more grounded in 2019. It will nevermore again be bound to giving area-based messages and notices to retail purchasers yet will stretch out to utilization at the air terminals, for compact installments, and notwithstanding for sharing customized data.

6. The IoT will witness rapid growth

While the Internet of Things is certifiably not different innovation, though, there will be a few different ways that it will be rediscovered in the following year. Organizations will put resources into IoT app development since they can't bear to remain following in the scene where interconnected gadgets are developing as a model. Robotization at work environments is conceivable just if there are the correct sorts of IoT applications to run the computerized gadgets.

7. On-demand will be in demand

The on-request pattern has developed in a past couple of years, multiplying areas, for example, taxi booking, nourishment conveyance, medicinal arrangements, and motion picture appointments. What's more, certain are only not many that have been referenced because this pattern is just going to develop at an uncommon pace. The interest will observer a flood later on and each application will try to serve an option that is better to the contenders.

8. There will be a transition from cash payment to mobile payment

As cashless turns into the industry famous expression, there is a need to search for reliable and consistent strategies for elective installments. After the change from money to cards, m-wallets have developed as a trusted in strategy for making and receiving installments. Versatile installments will be the blasting pattern in 2019 and it will be joined by the expansion in the number of mobile payments app also.

Conclusion

With such a great product available for mobile applications in 2019, organizations need to stop over these models and take them to be in front of their adversaries. This gives it basic to collaborate with a versatile application advancement organization that is fit for coordinating all these creative innovations directly into your business application. You may get in touch with us at Best mobile apps Development Companies in Ghana for a free quote to develop a mobile app for your business. And helps Business owners to reach more customers who want to change their business towards app development, The Company has a very good working environment. To know more about my company, Visit Fusion Informatics. For more queries please send a mail to get a free quote [email protected].

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freenewstoday · 4 years ago

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New Post has been published on https://freenews.today/2021/01/21/googles-hot-air-balloon-project-providing-cell-service-is-closing-down/

Google’s Hot-Air Balloon Project, Providing Cell Service, Is Closing Down

OAKLAND, Calif. — Google’s parent company Alphabet is shutting down Loon, a high-profile subsidiary spun out from its research labs that used hot-air balloons to deliver cellular connectivity from the stratosphere.

Nearly a decade after it began the project, Alphabet said on Thursday that it pulled the plug on Loon because it did not see a way to reduce costs to create a sustainable business. Along with the self-driving car unit Waymo, Loon was one of the most hyped “moonshot” technology projects to emerge from Alphabet’s research lab, X.

“The road to commercial viability has proven much longer and riskier than hoped. So we’ve made the difficult decision to close down Loon,” Astro Teller, who heads X, wrote in a blog post. Alphabet said it expected to wind down operations in “the coming months” with the hope of finding other positions for Loon employees at Alphabet.

The idea behind Loon was to bring cellular connectivity to remote parts of the world where building a traditional mobile network would be too difficult and too costly. Alphabet promoted the technology as a potentially promising way to bring internet connectivity to not just the “next billion” consumers but the “last billion.”

The giant hot-air balloons, made from sheets of polyethylene, are the size of tennis courts. They were powered by solar panels and navigated by flight control software that used artificial intelligence to drift efficiently in the stratosphere. While up in the air, they act as “floating cell towers,” transmitting internet signals to ground stations and personal devices.

Google started working on Loon in 2011 and launched the project with a public test in 2013. Loon became a stand-alone subsidiary in 2018, a few years after Google became a holding company called Alphabet. In April 2019, it accepted a $125 million investment from a SoftBank unit called HAPSMobile to advance the use of “high-altitude vehicles” to deliver internet connectivity.

Business & Economy

Updated

Jan. 21, 2021, 4:40 p.m. ET

Last year, it announced the first commercial deployment of the technology with Telkom Kenya to provide a 4G LTE network connection to a nearly 31,000-square-mile area across central and western Kenya, including the capital, Nairobi. Before then, the balloons had been used only in emergency situations, such as after Hurricane Maria knocked out Puerto Rico’s cellular network.

However, Loon was starting to run out of money and had turned to Alphabet to keep its business solvent while it sought another investor in the project, according to a November report in The Information.

The decision to shut down Loon is another signal of Alphabet’s recent austerity toward its ambitious and costly technology projects. Under Ruth Porat, Alphabet’s chief financial officer since 2015, the company has kept a close watch over the finances of its so-called Other Bets, fledgling business ventures aimed at diversifying from its core advertising business.

Alphabet has aggressively pushed its “Other Bets” like Waymo and Verily, a life sciences unit, to accept outside investors and branch out on their own. Projects that failed to secure outside investment or show enough financial promise have been discarded, such as Makani, a project to produce wind energy kites that Alphabet shut down last year.

That austerity has been a notable change from a time when units like X, which had been a favored vanity project of Google’s co-founders Larry Page and Sergey Brin, had autonomy to spend freely to pursue ambitious technology projects even if the financial outlook remained unclear.

Source

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opportunitywow · 4 years ago

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Google Research Ph.D. Internship Program 2021

Application Deadline: 24th December 2020.

Research happens at Google everyday, on many different embedded teams throughout the company. Our research reaches the user through both services and products such as Search, Maps, Google Assistant, Google Translate, Google Cloud and our computing, storage, and networking infrastructure. To achieve this, we’re working on a wide variety of projects that utilize the latest state-of-the-art technologies that push the boundaries of what is possible.

At Google, research-focused engineering interns are embedded throughout the company, contributing to the setup of large-scale tests and deploying promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.

From creating experiments and prototyping implementations to designing new architectures, research-focused engineering interns work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.

Participation in the internship program requires that you are located in one of the specific country locations identified for this role for the duration of the internship program. We will discuss and agree with you what country this will be during the recruitment process.

Note: By applying to this position your application is automatically submitted to the following locations: Cairo, Cairo Governorate, Egypt; Dubai – United Arab Emirates; Accra, Ghana; Nairobi, Kenya; Lagos, Nigeria; Moscow, Russia; İstanbul, Turkey; Kyiv, Ukraine, 02000

Minimum qualifications:

Currently enrolled in a PhD degree in Computer Science or a related technical field.

Research experience in Natural Language Understanding, Computer Vision, and/or Machine Learning from previous internships, work, personal projects, and/or lab work.

Experience with one or more general purpose programming languages: Java, C++,Python or Go.

Having published papers (being listed as author) at conferences (e.g. NIPS, ICML, ACL, CVPR, etc).

Preferred qualifications:

Returning to your degree after completing the internship.

Ability to design and execute on research agendas.

Available to work full-time for a minimum of 13 weeks.

Responsibilities

Participate in cutting edge research to develop solutions for real-world, large-scale problems.

Application Process

Please complete your application before 24th December 2020. Google encourage you to apply as early as possible as we review applications on a rolling basis.

To start the application process, you will need an updated CV or resume and a current unofficial or official transcript in English. Click on the “Apply” button on this page and provide the required materials in the appropriate sections (PDFs preferred):

1. In the “Resume Section:” attach an updated CV or resume

2. In the “Education Section:” attach a current or recent unofficial or official transcript in English.

Under “Degree Status,” select “Now attending” to upload a transcript.

For More Information: Visit the Official Webpage of the Google Research Ph.D. Internship 2021

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#IFTTT #WordPress

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mostlysignssomeportents · 6 years ago

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Kenyans from "the toughest neighborhood on earth" trace pixels all day to train autonomous vehicles

https://boingboing.net/2018/11/05/kibera-clickwork.html

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freelanews-blog · 6 years ago

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Microsoft to invest $100m in Nigeria, Kenya

Microsoft Corp will invest $100 million to open an Africa technology development centre with sites in Nigeria and Kenya over the next five years, the company said on Tuesday. According to news agency report, Microsoft will hire more than 100 local engineers to work in the new Africa facility in both countries to customise its applications for the African market and to develop new ones for the continent and beyond. “It is an opportunity to collaborate with partners, academia, governments and developers, driving impact and innovation in sectors important to the continent,” the company said, citing financial technology, farming technology and off grid energy. Engineers at the new Africa development centre will build applications using artificial intelligence, mixed reality and machine learning, Microsoft said. And the two sites would be located in Lagos and Nairobi. It was also hinted that the company already has six other development hubs located elsewhere in the world. According to the report, the new Africa development hub will also support Microsoft’s established businesses such as Office, Azure and Windows. Global tech giants, including Alphabet Inc and Facebook, have been increasing investment on the continent in recent years to take advantage of growing economies with rising access rates to the internet by a youthful population. Read the full article

#Investment #Kenya #Microsoft #nigeria

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safracatz · 5 years ago

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AI and IoT Are Transforming Mobile App Development

Nowadays everyone knows about IoT. Almost all devices these days, are IoT allowed. Arranging from appliances to vehicles and smart offices, IoT made an irregular change in the technical space. One of the main advantages is that IoT makes a specific device that is activated IoT can be accessed and controlled from a part of the universe. In short, a person can control his IoT enabled TV sitting miles apart.

In IoT, a collection of devices, to say things that have sensors and pre-installed microcontrollers, it is connected to a controller, we'll say a smartphone. The main purpose of these sensors and microcontrollers have to assemble data from these devices and to transmit to the control device.

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A huge volume of data to be created and sent to serve the purpose of IoT. This measure of bulk data is then collected, separated and processed correctly. Conventional data acquiring means and its treatment are not to the height. That's why a new technology was introduced in the area of IoT. Be processed using machine learning, artificial intelligence, computer intelligence is self-learning, which is generally used in IoT to analyze data and make decisions accordingly.

After artificial intelligence has been presented in different fields in which the IoT is applied, the results were unbelievable. Some of them were famous accuracy, increased efficiency of interaction between the human-machine, increased operational efficiency and a true digital transformation. Healthy competition between companies and AI IoT affecting the way, where it opens new doors of opportunities that could not be performed.

The need for artificial intelligence in IoT is to assure that only the relevant data are collected. When the sensors send data in a large volume, the time needed to process these data will also increase. To reduce this amount and to ensure that only the relevant data are collected; a process is carried out with the help of artificial intelligence.

This process is called data mining. Data mining is carried out by different steps such as

Data integration is the stage where possible data are integrated. After that, all data is chosen which is called the data selection. The data selected will be a more or less important part which is a gathering based on the conditions of the storage device. Data cleaning is a method wherein the chosen data is cleaned and removed. Replicate and duplicate data will be removed during this method to ensure more storage. The cleaned data is then processed, which is known as the transformation of data. Data processing is made to ensure that it is available and standardized. The data is mined and is called data mining. It is used to estimate the models. As artificial intelligence is a prediction using accessible data and the algorithm, the more data the odds just to change the algorithm is high. As the algorithms will be increased performance and productivity of the system are also improved.

If you requested for examples where both live and artificial intelligence IoT mixed, the answer can be many. Some of them are self-driving cars, automatic vacuum cleaners, smart thermostats, etc. treat driverless cars the IoT data using artificial intelligence, which gives it more natural to understand the obstacles and jump the obstacles along the road and avoid the uncertainty of vehicles going into the accidents. This ensures not only the safety of passengers but also helps the vehicle to stay out of accidents. Where in automatic vacuum clean the house and can charge. Smart thermostats are Aiot devices that help one to check the temperature inside your home or workplace. Smart thermostats fall into the category of smart homes is another application of the IoT.

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Artificial Intelligence and the Internet of Things are commonly applied these days. There are even several use cases and implementation of the real-life of bird flu in the IoT. Let's take a brief look at major industries that focus on examining new vertical markets by providing AI IoT:

IoT in Industrial Security

Artificial intelligence-powered IoT devices are on the market for many reasons. One of its applications is to control the login and logout of workers using physical features such as face recognition, retina scanning, etc. It is also used in situations where access is limited to a large number. It improves cybersecurity by limiting fraudulent activities and embezzlement.

IoT in Health Care

Application of artificial intelligence in IoT in the field of health care has been the analysis of the symptoms and cure diseases easy. The data forwarded from the medical equipment improved the system of using preventive measures.

An IoT in Agriculture

The implementation of artificial intelligence in IoT can abrupt agriculture. It can help to know and predict crop rotation, risk management, soil properties, climate change, yield forecasting, crop assessment, etc.

An IoT in Smart Homes

Further research in an iot covered the way for Smart Homes that are equipped with many smart features. Features such as the release system of the mobile application, enhanced security, control of household equipment such as refrigeration, telephone, television, computer systems, fans, and even a microwave. These are some of the very great features Smart Homes easier.

Conclusion

AI in IoT has much more to do than what has been presented in this blog. Since it is a vast field of study and a myriad of this application exists, a broad canvas is needed for a complete approach retailer. Yet one thing is certain, the implementation of avian influenza in the IoT is to make a successful transformation and requires more magic. You may get in touch with us at the mobile Apps Development Company in Lagos for a free quote to develop a mobile app for your business. And helps Business owners to reach more customers who want to change their business towards app development, Blockchain, and Machine Learning Development software. The Company has a very good working environment. To know more about my company, Visit Fusion Informatics. For more queries please send an mail to get a free quote [email protected].

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