Artificial intelligence has long been locked up in the lab, but with enterprise vendors and startups now incorporating it into their offerings, it's entering industry in more and more ways. Some applications, like predictive maintenance or demand forecasting, are common across nearly all companies, and increasingly a proven business case. Others like route optimization in transportation and logistics, or predicting drug interactions in medicine, are highly specific to one industry – and likely to drive competitive differentiation. Lux Research Vice President Mark Bünger will describe the shared and specific ecosystems in these and a variety of other fields, explaining where they fit, what matters, who to watch, and what will happen next.

Artificial intelligence (AI) is a concept already embedded in our day-to-day lives. When we speak about AI, it's easy to jump to thoughts of the Terminator movies or i-Robot, but is this really a vision of where technology is heading? AI is an extremely broad subject, but there are some fundamentals that will become common terms as the AI revolution gathers pace, and they're relevant to EHS. We've already achieved ANI – that's a program that can beat the most intelligent human at one thing, like a chess game or the board game "Go," as recently was achieved. What lies ahead is AGI, and it might be approaching us much quicker than we think.

Your math teacher lied to you. Sometimes getting your sums wrong is a good thing. So says Joseph Bates, cofounder and CEO of Singular Computing, a company whose computer chips are hardwired to be incapable of performing mathematical calculations correctly. Ask it to add 1 and 1 and you will get answers like 2.01 or 1.98. The Pentagon research agency DARPA funded the creation of Singular's chip because that fuzziness can be an asset when it comes to some of the hardest problems for computers, such as making sense of video or other messy real-world data. "Just because the hardware is sucky doesn't mean the software's result has to be," says Bates.

Israeli online contract review platform LawGeex announced today 2.5 million in funding from Lool Ventures and LionBird and angel investors Eilon Tirosh and Rami Lipman. The startup has also launched its online contract review solution free of charge for consumers. LawGeex allows consumers to upload any type of contract to its platform and receive, within 24 hours, an in-depth report of what's good, bad and even missing from their contract. Currently reviewing over 20 types of contracts, the free solution begins with employment agreements, with more contract types to be offered for free in the near future. LawGeex cofounder and CEO Noory Bechor said, "The driving force behind LawGeex is the belief that no one should sign a contract that they don't fully understand. An astounding 33% of Americans need a lawyer every year but do not hire one, either because they can't afford to or did not know where to turn. This ultimately leads to one-sided negotiations and unfair results. LawGeex has already earned the trust of thousands of users while ensuring quality and transparency, leveling the playing field when it comes to contract negotiations. Our machine learning platform has already reviewed over tens of thousands of contracts, many of which are employment contracts from some of the world's largest tech companies such as Apple, Google, and Facebook. We make sure all employees get a fair deal."

Cyber attacks have been in the news a lot lately. From cases of ransomware holding hospital records hostage to the hack that crippled Sony t0 the security breach that left VTech toys vulnerable, a lot of damage can be done if companies don't adequately protect their data. But oftentimes, signs that a system has been compromised are not clear until it's too late. Human analysts may miss the evidence, while automated detection systems tend to generate a lot of false alarms. Cue the rise of artificial intelligence, or at least AI that can work in tandem with human analysts to spot digital clues that could be signs of trouble.

Even though more than 20 years have passed since Doom's release, the methods for developing AI bots have not improved significantly in newer FPS productions. The participants of the Visual Doom AI competition are supposed to submit a controller (C, Python, or Java) that plays Doom. Although the participants are allowed to use any technique to develop a controller, the design and efficiency of the Visual Doom AI environment allows and encourages participants to use machine learning methods such as reinforcement deep learning. Each game will be repeated 12 times for track 1 and 4 times for track 2, which involves three maps.

Doom has been considered one of the most influential titles in the game industry since it popularized the first-person shooter (FPS) genre and pioneered immersive 3D graphics. Even though more than 20 years have passed since Doom's release, the methods for developing AI bots have not improved significantly in newer FPS productions. In particular, bots have still to "cheat" by accessing game's internal data such as maps, locations of objects and positions of (player or non-player) characters. In contrast, a human can play FPS games using a computer screen as the only source of information. Can AI effectively play Doom using only raw visual input?

He had just flown into Augusta, Georgia for the Masters. Bad weather caused a pileup of arrivals. The airport ran out of open gates. His flight was stuck on the tarmac. If air traffic controllers had access to Watson, they might have been able to forecast the flight congestion.

Telling Jeff Bezos he's wrong is always a frightening proposition. In the fall of 2014, though, a small group of the men and women building Amazon's new voice-controlled speaker felt they needed to confront the CEO. The release of the speaker was looming, and for the most part, things were falling into place. The device looked good, its voice recognition software was improving quickly, and even the boxes it would ship in had been designed and assembled. But there was a lingering issue with the name printed on those boxes: the Amazon Flash. Many people who worked at Lab126, Amazon's hardware division, hated the name, according to two former employees. Bezos, on the other hand, was strongly in favor.

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