The O'Reilly AI Conference is returning to New York June 26–29, 2017 to explore the most essential and intriguing topics in intelligence engineering and applied AI. The program will cover the latest developments in tools, algorithms, and architectures, applications such as finance and robotics, novel interfaces like bots, plus much more. We're looking for compelling case studies, technical sessions, tear-downs of both successful and failed AI projects, technical and organizational best practices, and more. See online for a list of suggested topics, but feel free to recommend others because we always love to be surprised. See our tips on how to submit a great proposal.

This list was started a while back and rather small, but it grew up to 200 articles in the past few weeks. It will reach 400 when completed. Essentially, this is the best of all our weekly digests. Also, it features all the articles (double-starred in red) that will be part of my upcoming book Data Science 2.0. So if you missed many of our recent tweets, here's a chance to see all this content at once, on one web page.

Google may have found a way to use machine learning technology to help millions of deaf and hearing-impaired people better understand what people are saying to them. Researchers from Google Inc.'s DeepMind artificial intelligence project, which built the boardgame-playing AlphaGo that managed to successfully defeat one of the top Go players in the world, have teamed up with peers at the Oxford University to create an AI system that's able to outperform professional lip-readers after training itself on thousands of hours of BBC videos. New Scientist reports that in tests, a human lip-reader who provides services for the U.K. courts was able to correctly decipher only about a quarter of words spoken when shown a random sample from 200 BBC video broadcasts. However, DeepMind's AI system was able to decipher almost half of the words from the same sample videos. In addition, the AI was able to annotate 46 percent of the words without error, compared with just 12 percent by the human lip-reader.

Would you trust an algorithm to help you with a medical diagnosis? As hospitals seek out new tools to assist them in triaging the patients most in need, technologies driven by machine learning are expected to make a big impact in the medical sector. The latest company to throw its hat into the ring is General Electric, which is investing big in software and is already known for its medical imaging equipment. The manufacturing giant exclusively shared with Fast Company that it is partnering with UC San Francisco for the next three years to develop a set of algorithms to help its radiologists distinguish between a normal result and one that requires further attention. "There's tremendous opportunity to look at large datasets, like medical images, to predict how patients will do," says UC San Francisco's director of UCSF's Center for Digital Health Innovation, Michael Blum. It's early days, but machine learning and deep learning technologies are already making their way into a small number of medical specialties, including primary care, pathology, and radiology.

Securing your digital assets is a clear need for any business and individual, whether you are looking to protect your personal photos, your company's intellectual property, your customers' sensitive data, or anything else that can harm your reputation or business continuity. Although billions of dollars are spent on cybersecurity, the number of reported cyberattacks and the magnitude of breaches keep rising. There are many frontiers where harnessing the predictive power of AI might give the upper hand to security vendors -- and to us all, including individuals and businesses. Cisco forecasts that the number of connected devices worldwide will rise from 15 billion today to 50 billion by 2020. A high percentage of these devices do not have basic security measures due to limited hardware and software resources.

Robots will need to teach themselves. The common, and recurring, view of the latest breakthroughs in artificial intelligence research is that sentient and intelligent machines are just on the horizon. Machines understand verbal commands, distinguish pictures, drive cars and play games better than we do. How much longer can it be before they walk among us? The new White House report on artificial intelligence takes an appropriately skeptical view of that dream.

Eran Shir has an ambitious goal: Eliminate car crashes without waiting for the advent of autonomous vehicles. His company Nexar makes an app that turns smartphones into an "intelligent" dashcam that uses the phone's camera, accelerometer and gyroscope to collect information about what's happening on the road and to send it to the cloud for machine-learning analysis. Nexar now is crowdsourcing its data in San Francisco and New York to give drivers a real-time heads-up about dangers such as cars ahead suddenly stopping or swerving. "We are weaving everyone together to build a network of vehicles to track what's happening on the road, that can predict and prevent accidents," said Shir, co-founder and CEO of Tel Aviv's Nexar, which has offices in San Francisco and New York. For instance, "If you brake hard, all the cars behind you will be aware of that within 50 milliseconds."

In recent years, the best-performing systems in artificial-intelligence research have come courtesy of neural networks, which look for patterns in training data that yield useful predictions or classifications. A neural net might, for instance, be trained to recognize certain objects in digital images or to infer the topics of texts. But neural nets are black boxes. After training, a network may be very good at classifying data, but even its creators will have no idea why. With visual data, it's sometimes possible to automate experiments that determine which visual features a neural net is responding to.

Google just made a major upgrade to its Translate app. The company is now using a new technology called neural machine translation -- which aims to make computer-generated translations more similar to those done by humans -- to power its translations in seven new languages. Google says the update should make translations in those languages much more accurate and easier to understand. The company previously rolled out this technology for Chinese to English translations in September. Now, Google is using the same technology to power translations to and from English in French, German, Spanish, Portuguese, Japanese, Korean and Turkish.

The Fujitsu Ltd. RoBoPin communication robot at the Combined Exhibition of Advanced Technologies in Japan on Oct. 4, 2016. Every year there is a new hot topic in tech. The difference between now and the past is that everything is becoming interconnected at a faster rate. We are entering an extremely critical time in history where society will change dramatically – how we work, live and play. Science fiction is morphing into reality. Flying cars exist, cars that drive themselves are on the road, and artificial intelligence that automates our lives is here.