ZDNet: IBM’s new tool lets developers add quantum-computing power to machine learning

ZDNet: IBM’s new tool lets developers add quantum-computing power to machine learning. “IBM is releasing a new module as part of its open-source quantum software development kit, Qiskit, to let developers leverage the capabilities of quantum computers to improve the quality of their machine-learning models. Qiskit Machine Learning is now available and includes the computational building blocks that are necessary to bring machine-learning models into the quantum space.”

VentureBeat: MIT study finds ‘systematic’ labeling errors in popular AI benchmark datasets

VentureBeat: MIT study finds ‘systematic’ labeling errors in popular AI benchmark datasets. “The field of AI and machine learning is arguably built on the shoulders of a few hundred papers, many of which draw conclusions using data from a subset of public datasets. Large, labeled corpora have been critical to the success of AI in domains ranging from image classification to audio classification. That’s because their annotations expose comprehensible patterns to machine learning algorithms, in effect telling machines what to look for in future datasets so they’re able to make predictions. But while labeled data is usually equated with ground truth, datasets can — and do — contain errors.”

AI: Ghost workers demand to be seen and heard (BBC)

BBC: AI: Ghost workers demand to be seen and heard. “Artificial intelligence and machine learning exist on the back of a lot of hard work from humans. Alongside the scientists, there are thousands of low-paid workers whose job it is to classify and label data – the lifeblood of such systems. But increasingly there are questions about whether these so-called ghost workers are being exploited. As we train the machines to become more human, are we actually making the humans work more like machines?”

Department of Energy: DOE Announces $34.5 Million for Data Science and Computation Tools to Advance Climate Solutions

Department of Energy: DOE Announces $34.5 Million for Data Science and Computation Tools to Advance Climate Solutions . “The U.S. Department of Energy (DOE) [March 19] announced up to $34.5 million to harness cutting-edge research tools for new scientific discoveries, including clean energy and climate solutions. Two new funding opportunities will support researchers using data science and computation-based methods—including artificial intelligence and machine learning—to tackle basic science challenges, advance clean energy technologies, improve energy efficiency, and predict extreme weather and climate patterns.”

Machines that learn: The origin story of artificial intelligence (Christian Science Monitor)

Christian Science Monitor: Machines that learn: The origin story of artificial intelligence. “Lee Sedol, a world champion in the Chinese strategy board game Go, faced a new kind of adversary at a 2016 match in Seoul. Developers at DeepMind, an artificial intelligence startup acquired by Google, had fed 30 million Go moves into a deep neural network. Their creation, dubbed AlphaGo, then figured out which moves worked by playing millions of games against itself, learning at a faster rate than any human ever could. The match, which AlphaGo won 4 to 1, ‘was the moment when the new movement in artificial intelligence exploded into the public consciousness,’ technology journalist Cade Metz writes in his engaging new book, ‘Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World.’”

Coronavirus (COVID-19) Update: FDA Authorizes First Machine Learning-Based Screening Device to Identify Certain Biomarkers That May Indicate COVID-19 Infection (FDA)

FDA: Coronavirus (COVID-19) Update: FDA Authorizes First Machine Learning-Based Screening Device to Identify Certain Biomarkers That May Indicate COVID-19 Infection. “Today, the U.S. Food and Drug Administration issued an emergency use authorization (EUA) for the first machine learning-based Coronavirus Disease 2019 (COVID-19) non-diagnostic screening device that identifies certain biomarkers that are indicative of some types of conditions, such as hypercoagulation (a condition causing blood to clot more easily than normal).”

New York Times: A.I. Is Not What You Think

New York Times: A.I. Is Not What You Think. “When you hear about artificial intelligence, stop imagining computers that can do everything we can do but better. My colleague Cade Metz, who has a new book about A.I., wants us to understand that the technology is promising but has its downsides: It’s currently less capable than people, and it is being coded with human bias.”

The Register: How Facebook uses public videos to train, deploy machine-learning models and harvest those eyeballs

The Register: How Facebook uses public videos to train, deploy machine-learning models and harvest those eyeballs . “Facebook this week revealed an internal project to create machine-learning models that can understand visual, audio, and written content from videos publicly uploaded to its social network. One of the models, known as Generalized Data Transformations (GDT), is now used on Instagram. Users viewing short video recordings, or Reels, can quickly find other Reels they might like to watch, thanks to an AI-powered recommender system that picks similar clips that might be interesting.”

University of Washington News: Large computer language models carry environmental, social risks

University of Washington News: Large computer language models carry environmental, social risks. “Computer engineers at the world’s largest companies and universities are using machines to scan through tomes of written material. The goal? Teach these machines the gift of language. Do that, some even claim, and computers will be able to mimic the human brain. But this impressive compute capability comes with real costs, including perpetuating racism and causing significant environmental damage, according to a new paper, ‘On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜’”

Stanford: Algorithmic approaches for assessing pollution reduction policies can reveal shifts in environmental protection of minority communities, according to Stanford researchers

Stanford: Algorithmic approaches for assessing pollution reduction policies can reveal shifts in environmental protection of minority communities, according to Stanford researchers. “Applying machine learning to a U.S. Environmental Protection Agency initiative reveals how key design elements determine what communities bear the burden of pollution. The approach could help ensure fairness and accountability in machine learning used by government regulators.”

The Next Web: Furious AI researcher creates a list of non-reproducible machine learning papers

The Next Web: Furious AI researcher creates a list of non-reproducible machine learning papers. “On February 14, a researcher who was frustrated with reproducing the results of a machine learning research paper opened up a Reddit account under the username ContributionSecure14 and posted the r/MachineLearning subreddit: ‘I just spent a week implementing a paper as a baseline and failed to reproduce the results. I realized today after googling for a bit that a few others were also unable to reproduce the results. Is there a list of such papers? It will save people a lot of time and effort.’ The post struck a nerve with other users on r/MachineLearning, which is the largest Reddit community for machine learning.”

Google Blog: Using AI to explore the future of news audio

Google Blog: Using AI to explore the future of news audio. “KQED is the most listened to public radio station in the United States, and one of the largest news organizations in the Bay Area. In partnership with Google, KQED and KUNGFU.AI, an AI services provider and leader in applied machine learning, ran a series of tests on KQED’s audio to determine how we might reduce the errors and time to publish our news audio transcripts, and ultimately, make radio news audio more findable.”

Pete Warden: How screen scraping and TinyML can turn any dial into an API

Pete Warden: How screen scraping and TinyML can turn any dial into an API. “I’ve already heard from multiple teams who have legacy hardware that they need to monitor, in environments as varied as oil refineries, crop fields, office buildings, cars, and homes. Some of the devices are decades old, so until now the only option to enable remote monitoring and data gathering was to replace the system entirely with a more modern version. This is often too expensive, time-consuming, or disruptive to contemplate. Pointing a small, battery-powered camera instead offers a lot of advantages. Since there’s an air gap between the camera and the dial it’s monitoring, it’s guaranteed to not affect the rest of the system, and it’s easy to deploy as an experiment, iterating to improve it.”

Analytics India: Machine Learning, Indian Social Media’s Biggest Challenge Yet

Analytics India: Machine Learning, Indian Social Media’s Biggest Challenge Yet. “Earlier this month, the Government of India reprimanded Twitter for allowing fake, unverified, anonymous and automated bot accounts to be operated on its platform. The Secretary of MeitY raised doubts about the platform’s commitment to transparency and healthy conversation on this platform. The way Twitter and Facebook handled the events leading upto the elections in the US and the aftermath, has served as a wake up call to the governments around the world…”

EurekAlert: Human eye beats machine in archaeological color identification test

EurekAlert: Human eye beats machine in archaeological color identification test. “A ruler and scale can tell archaeologists the size and weight of a fragment of pottery – but identifying its precise color can depend on individual perception. So, when a handheld color-matching gadget came on the market, scientists hoped it offered a consistent way of determining color, free of human bias. But a new study by archaeologists at the Florida Museum of Natural History found that the tool, known as the X-Rite Capsure, often misread colors readily distinguished by the human eye.”