On Nov 7th, the AI World Expo kicked off in a week that had many international conferences occurring. After making the choice to forego Web Summit in Lisbon for this event, it was evident that the level of quality of content, attendees and hands on innovation in the #AIWorld Expo made it the ideal event for AI innovators, thought leaders and adopters. The concentration of hand-selected vendors who were pre-screened rigorously, and the agenda made the event a great launchpad to build new relationships, get some face time with pioneers, and spend quality time discussing the interests of each vendor seeking a competitive edge. It was an interesting outcome and dynamic for a group of people fascinated with and specializing in artificial interactions, chat bots, autonomous learning and IoT! To kick things off, highly engaging panelists representing the venture community discussed how difficult it was to tap into the mainstream of AI.

A key ambition of BenevolentAI is to utilize artificial intelligence in revolutionizing the drug development process. Now, with a licensing agreement with Janssen, it is one significant step closer to realizing that dream. With assistance from Johnson & Johnson Innovation's Centre in London, the British AI company has acquired an undisclosed number of novel clinical stage drug candidates, together with their related patents. The company used its AI platform to evaluate the potential of these small molecule compounds and found some could be promising candidates for hard-to-treat diseases. "The compounds come with a wealth of clinical and biological data that enables BenevolentAI to have further insights into the biology of diseases," said BenevolentAI Bio's CEO Jackie Hunter.

Machine learning and data analysis are two areas where open source has become almost the de facto license for innovative new tools. Both the Python and R languages have developed robust ecosystems of open source tools and libraries that help data scientists of any skill level more easily perform analytical work. The distinction between machine learning and data analysis is a bit fluid, but the main idea is that machine learning prioritizes predictive accuracy over model interpretability, while data analysis emphasizes interpretability and statistical inference. Python, being more concerned with predictive accuracy, has developed a positive reputation in machine learning. R, as a language for statistical inference, has made its name in data analysis.

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.

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.

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.

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.

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.

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."