London-based BenevolentAI believes it has built the kind of artificial intelligence tech that will allow it to identify and develop drugs faster and better than any group of mere scientific mortals can hope for. And now J&J is handing over some experimental molecules it needs to prove it's right. The upstart joins a long line scrambling to apply vast amounts of computational power towards drug development. Their goal is to usher in the long-awaited "pharma 2.0" and finally bend the expensive curve of late-stage trial failure. It's unclear how BenevolentAI's algorithms are any better at evaluating the potential of any small-molecule than other computationally-taxing approaches developed by other groups -- and it's all driven by the data.

It's now a hired gun for thousands of companies in at least 20 industries. David Kenny took the helm of IBM's Watson Group ibm in February, after Big Blue acquired The Weather Company, where Kenny had served as CEO. In the months since then, the Watson business has grown dramatically, with well over 100,000 developers worldwide now working with more than three dozen Watson application program interfaces (APIs). Fortune Deputy Editor Clifton Leaf caught up with Kenny in mid-October, when IBM Watson's General Manager was in San Francisco, getting ready to open Watson West--the AI system's newest business outpost--and to launch the company's second World of Watson conference, a gathering of its burgeoning ecosystem of partners and users, in Las Vegas on Oct. 24. KENNY: Deep learning is a subset of machine learning, which essentially is a set of algorithms. Deep-learning uses more advanced things like convolutional neural networks, which basically means you can look at things more deeply into more layers. Machine learning could work, for example, when it came to reading text.

Our analytics engine Kairos processes unstructured data from millions of sites, blogs, and social platforms like Twitter and Tumblr. Billions of public posts are then analyzed and classified across 25,000 topics, emotions, and demographics--turning noisy social data into insights. In order to create predictions around the elections using our analytics platform Kairos, we built 4 metrics: Awareness, Positivity, Negativity and Intent, of which only Negativity and Intent proved to be valuable in predicting elections. Negativity and Intent are natural language processing classifiers which take advantage of sentence structure as well as keyword matching. Then we modeled the data against survey polls, primary results, and survey pools to obtain weights of influence for each of the social indices.

Iraq is preparing a remote controlled attack vehicle being which experts say could be used in the fight to retake the city of Mosul from Isis. The Alrobot's arsenal of weaponry includes a heavy machine gun with a wide arc of fire, four on-board cameras, as well as a launcher to fire Russian Katsuya rockets Experts believe the robot is being tested as part of the Iraqi army's effort to retake the northern city of Mosul, which fell to ISIS forces in 2014 and has remained a stronghold for the group The views expressed in the contents above are those of our users and do not necessarily reflect the views of MailOnline. By posting your comment you agree to our house rules.

Rather than leading to the violent downfall of humankind, artificial intelligence is helping people around the world do their jobs, including doctors who diagnose sepsis in patients and scientists who track endangered animals in the wild, experts said Thursday (Oct. Advancements in the field of artificial intelligence (AI) haven't always been met with enthusiasm. Famed astrophysicist Stephen Hawking warned on several occasions that a fully developed AI could destroy the human race, and Hollywood sci-fi movies are rife with fierce robots battling humans for control. But at yesterday's conference -- attended by the country's leading researchers, innovators, entrepreneurs and students -- scientists explained how newly developed AI is accelerating research and improving lives. Here is a look at five AI inventions that are already redefining technology.

Bulging eyes, protruding tongues, every twitch of Donald Trump's face--the horrors of this election cycle have often played out on the faces of the candidates and their wary surrogates. In the video above, we revisit the many rich visages of the last 18 months.

If you think this election is insane, wait until 2020. I want you to imagine how, in four years' time, technologies like AI, machine learning, sensors and networks will accelerate. Political campaigns are about to get hyper-personalized thanks to advances in a few exponential technologies. Imagine a candidate who now knows everything about you, who can reach you wherever you happen to be looking, and who can use info scraped from social media (and intuited by machine learning algorithms) to speak directly to you and your interests. Here's what future election campaign marketing might feel like… In 2016, 78% of Americans have a social media profile.

There are many good reasons why traditional security practices are becoming less effective at protecting against cyberattacks. There is too much security-related data flooding the network from an increasing number of users and devices. There is a lack of skilled personnel to watch over and analyze this data. And the security staff you have likely wastes too much time chasing down false positives. Valuable minutes -- or even hours -- can tick by before analysts and incident responders are aware of a threat.

Sedonah, 5, speaks Chinese fluently but her father doesn't know a lick of it. So when Sedonah saw a video of him singing a Chinese song, she lit up with amazement. Nikhil Jain didn't spend any time practicing to impress his daughter. But he did spend two years training a computer to sing in his likeness. A technologist's daughter isn't the ideal unbiased tester, but Sedonah's response to the singing routine is what's making her father bullish about the software he's developing.

Ben Dickson is a software engineer and the founder of TechTalks. As artificial intelligence and machine learning mature and manifest their potential to take on complicated tasks, we've become somewhat expectant that robots can succeed where humans have failed -- namely, in putting aside personal biases when making decisions. But as recent cases have shown, like all disruptive technologies, machine learning introduces its own set of unexpected challenges and sometimes yields results that are wrong, unsavory, offensive and not aligned with the moral and ethical standards of human society. While some of these stories might sound amusing, they do lead us to ponder the implications of a future where robots and artificial intelligence take on more critical responsibilities and will have to be held responsible for the possibly wrong decisions they make. At its core, machine learning uses algorithms to parse data, extract patterns, learn and make predictions and decisions based on the gleaned insights.