In 2017 Gartner predicted that artificial intelligence (AI) would benefit procurement and sourcing technology. That moment has arrived, according to Mike Quindazzi, managing director at PriceWaterhouseCoopers and top financial-tech influencer. "We're now in the golden age of AI, where advancements come from voluminous sets of data, new algorithms being created, computing power and the ability to do this in the cloud at scale," said Quindazzi. Procurement data has exploded because procurement has evolved into something called "intelligent spend management," which oversees all corporate purchasing processes including direct and indirect purchases, travel and external labor. Quindazzi cautions that while AI has many use cases in procurement, such as rating vendors, "there will always be a human at the end of AI processes, so there needs to be a sense of accountability."

ProPublica provided glaring evidence of this in 2016. A computer program used by U.S. courts wrongly flagged black defendants who did not recidivate over a two-year period as likely to become repeat offenders at nearly twice the rate as white defendants--45 percent as compared to 23 percent. If a human did the same, it would be decried as racist. Our collective experiences are not static. They are shaped by important societal decisions, which in turn are guided by our ethical values.

SAN FRANCISCO - Tesla Inc. and Apple Inc. both suspect they were betrayed by driverless technology engineers who defected to the same Chinese startup. So Tesla is now asking for Apple's help in a lawsuit in which the electric carmaker accused an engineer who worked on its Autopilot program of taking thousands of highly confidential files when he went to work for XMotors.ai, the U.S. research arm of Guangzhou-based Xpeng. Along with typical information demands in the early fact-finding phase of the lawsuit that are spelled out in a court filing last week -- Tesla wants to see the engineer's emails and have a forensic analysis conducted on his electronic devices -- the company founded by Elon Musk disclosed that it has also served the iPhone maker with a subpoena. The documents Tesla seeks from Apple aren't specified in the filing, but the thinking may be that while the Silicon Valley titans are rivals in the ultra-hot self-driving space, they share a common enemy in Xpeng. Last July, prosecutors charged a hardware engineer in Apple's autonomous vehicle-development team with downloading proprietary files as he prepared to leave the company and start work for the for Chinese company.

The world's fish stocks are in decline and our increasing demand for seafood may be one of the main drivers. But the true extent of the problem is hard to estimate, especially when fishing occurs in the high seas, which lie beyond national jurisdiction and are hard to monitor. Conservation planners face growing pressures to combat illegal, unregulated and unreported (IUU) fishing, the value of which has been estimated at US$10-23.5 billion annually. This is an important cost for society as a whole, but also for the major high seas fishing countries such as China and Taiwan that subsidize their fleets and may have low labour costs. Artificial intelligence (AI) could address this global environmental concern -- and satisfy the need of seafood retailers and consumers to know if what they're selling and eating is sustainable.

We study a formalization of the grammar induction problem that models sentences as being generated by a compound probabilistic context-free grammar. In contrast to traditional formulations which learn a single stochastic grammar, our context-free rule probabilities are modulated by a per-sentence continuous latent variable, which induces marginal dependencies beyond the traditional context-free assumptions. Inference in this grammar is performed by collapsed variational inference, in which an amortized variational posterior is placed on the continuous variable, and the latent trees are marginalized with dynamic programming. Experiments on English and Chinese show the effectiveness of our approach compared to recent state-of-the-art methods for grammar induction from words with neural language models.

Law firms, anticipating more cases involving artificial intelligence, are bulking up their ranks with AI experts in an effort to secure new business as the technology becomes ubiquitous across industries. Among the issues firms are handling: rights to the datasets used for training machine-learning algorithms, ownership of the algorithms created from third-party data, and liability for property damage or personal injury caused by an autonomous system.

Over the past few years, a new trend has emerged in the business world, with companies looking to improve operational efficiency using the latest technological advances. Software developers are now pairing automation and artificial intelligence, which has caused a paradigm shift in many industries. It is within this framework that Jerry Ting built Evisort, a software looking to completely automate all processes related to sorting and pulling up information from a company's legal documents. Once he had the idea, he approached his co-founders about the feasibility of automation for legal paperwork. Jerry Ting, Amine Anoun and Jake Sussman co-founded Evisort in 2016.

As artificial intelligence re-writes business models, how will its application and adoption revolutionize business and commerce further? From the production and marketing era to the relationship and intelligence era, business models have been evolving over the centuries. Over the years, the rise of artificial intelligence (AI) has fundamentally transformed the very meaning of ideas, innovation, and inventions. As a result, business models are evolving further. As we witness businesses across industries undergo a profound and dramatic shift in the relative balance of intelligence power, AI applications and adoption are offering each business entity as many new opportunities as it does challenges.

Should your Roomba need a W-2? Probably not, but it's an amusing thought when debating the more serious topic of whether or not a robot should have to pay taxes -- and how to do it. During the June MIT Technology Review EmTech Next event, two experts argued both sides of the question before an audience at the MIT Media Lab in Cambridge, Massachusetts. Ryan Abbott, professor of law and health sciences at the University of Surrey, argued in favor of taxing robots, while Ryan Avent, economics columnist for The Economist, argued against the idea. Both agreed there needs to be a shift in tax burden from labor to capital. Avent, however, carried the most audience votes by the end of the debate. Here are some highlights from each of the men's arguments.

In this paper, we argue that competitive pressures could incentivize AI companies to underinvest in ensuring their systems are safe, secure, and have a positive social impact. Ensuring that AI systems are developed responsibly may therefore require preventing and solving collective action problems between companies. We note that there are several key factors that improve the prospects for cooperation in collective action problems. We use this to identify strategies to improve the prospects for industry cooperation on the responsible development of AI.