The Conversation: AI spots shipwrecks from the ocean surface – and even from the air

The Conversation: AI spots shipwrecks from the ocean surface – and even from the air. “In collaboration with the United States Navy’s Underwater Archaeology Branch, I taught a computer how to recognize shipwrecks on the ocean floor from scans taken by aircraft and ships on the surface. The computer model we created is 92% accurate in finding known shipwrecks. The project focused on the coasts of the mainland U.S. and Puerto Rico. It is now ready to be used to find unknown or unmapped shipwrecks.”

Neowin: Google is making on-device machine learning easier on Android later this year

Neowin: Google is making on-device machine learning easier on Android later this year. “The usage of machine learning (ML) has become quite common across various applications for multiple use-cases. While on-device ML is preferred over its server-based counterpart for a number of reasons such as low latency and lack of dependency on internet connectivity, it also has numerous drawbacks. Google says that it will be addressing these challenges with the Android ML Platform, coming later this year.”

Phys .org: Deep machine learning completes information about one million bioactive molecules

Phys .org: Deep machine learning completes information about one million bioactive molecules. “The Structural Bioinformatics and Network Biology laboratory, led by ICREA Researcher Dr. Patrick Aloy, has completed the bioactivity information for a million molecules using deep machine-learning computational models. It has also disclosed a tool to predict the biological activity of any molecule, even when no experimental data are available.”

EurekAlert: Machine learning aids earthquake risk prediction

EurekAlert: Machine learning aids earthquake risk prediction. “An upside of the Christchurch quake was that it was one of the most well-documented in history. Because New Zealand is seismically active, the city was instrumented with numerous sensors for monitoring earthquakes. Post-event reconnaissance provided a wealth of additional data on how the soil responded across the city.”

Arizona State University: ASU alum publishes graphic novel on computer generated images, machine learning

Arizona State University: ASU alum publishes graphic novel on computer generated images, machine learning. “[Jennifer] Weiler, who was influenced by her work at ASU as a student in the School of Arts, Media and Engineering, has been working intensely over the last year to create and publish her first comic book, ‘Creating with Code: A Fun Exploration of Computer-Generated Images and Machine Learning.’ She said she made the comic to educate people about how to effectively utilize coding to construct stylistic computer-generated images and apply methodologies of machine learning in the process.”

Adversarial attacks in machine learning: What they are and how to stop them (VentureBeat)

VentureBeat: Adversarial attacks in machine learning: What they are and how to stop them. “Adversarial machine learning, a technique that attempts to fool models with deceptive data, is a growing threat in the AI and machine learning research community. The most common reason is to cause a malfunction in a machine learning model. An adversarial attack might entail presenting a model with inaccurate or misrepresentative data as it’s training, or introducing maliciously designed data to deceive an already trained model.”

Northern Arizona University: NAU archaeologists teach computers to sort ancient pottery

Northern Arizona University: NAU archaeologists teach computers to sort ancient pottery . “Archaeologists at Northern Arizona University are hoping a new technology they helped pioneer will change the way scientists study the broken pieces left behind by ancient societies. The team from NAU’s Department of Anthropology have succeeded in teaching computers to perform a complex task many scientists who study ancient societies have long dreamt of: rapidly and consistently sorting thousands of pottery designs into multiple stylistic categories. By using a form of machine learning known as Convolutional Neural Networks (CNNs), the archaeologists created a computerized method that roughly emulates the thought processes of the human mind in analyzing visual information.”

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.”