Developing a new drug can cost billions of dollars and take a dozen or more years to bring to market. Two Israeli researchers have applied artificial intelligence (AI) and deep learning to shave time and money off the drug-discovery process. Instead of searching for the appropriate molecules to use in a new medicine, as is done today, they enabled a computer to make smart predictions without human guidance. Shahar Harel and Kira Radinsky at the Technion-Israel Institute of Technology fed into their computer system hundreds of thousands of known molecules as well as the chemical composition of all FDA-approved drugs up until 1950. Aided by AI, the computer came up with new potential molecules by making sometimes unexpected correlations from within this massive sample.

ScaleUP USA has developed a free "Artificial Intelligence for Government" program to help the government employees and contractors learn foundational skills around Artificial Intelligence and the unique challenges faced in using it in the government. The program is focused on beginners in Artificial Intelligence (AI), Machine Learning (ML), and Reinforcement Learning (RL) with minimal technical fluency. No STEM / Computer Science degree required. The program targets government executives trying to understand how to use Artificial Intelligence as well as government contractors wanting to learn how to integrate AI into their offering and startups trying to understand how to work with the government on AI, ML, and RL. ScaleUP USA is also building a video-based marketplace of AI technologies for government where companies can showcase their products, platforms, and services relevant for governments.

China will be the world's dominant player in artificial intelligence by 2030. A State Council document, issued in July last year, resolved to position China as the world's pre-eminent practitioner of artificial intelligence (AI) in both research and application within the next 12 years. Governments across the world are rushing to support innovation in AI, but none has published as coherent a plan as China and – more importantly – has the ability to get things done: the Chinese government can implement policy in ways that are impossible in western democracies. Intent, however, is one thing: to paraphrase the boxer Mike Tyson, everyone has a plan until they get punched in the mouth. The Chinese not only have a strategy, they have a track record of delivering on large-scale, ambitious projects.

These are perhaps the most powerful and important four words a woman can hear after a breast-screening visit. X-ray based mammography is an effective screening tool for detecting cancer, but what many women may not know is that breast screening programs produce a high level of false positive results, particularly after multiple years of screening. In other words, women are informed they may have cancer when in fact they don't. This is particularly true in the U.S., where each study is generally read by a single, expert radiologist. In Europe, two independent radiologists read each study.

They're using machine learning to sort through millions of malware files, searching for common characteristics that will help them identify new attacks. They're analyzing people's voices, fingerprints and typing styles to make sure that only authorized users get into their systems. And they're hunting for clues to figure out who launched cyberattacks--and make sure they can't do it again. "The problem we're running into these days is the amount of data we see is overwhelming," says Mathew Newfield, chief information-security officer at Unisys Corp. UIS 0.50% "Trying to analyze that information is impossible for a human, and that's where machine learning can come into play." The push for AI comes as companies face a huge increase in threats and more-sophisticated criminals who can often draw on nation-states for resources.

This paper proposes a method for efficient training of the Q-function for continuous-state Markov Decision Processes (MDP), such that the traces of the resulting policies satisfy a Linear Temporal Logic (LTL) property. The logical property is converted into a limit deterministic Buchi automaton with which a product MDP is constructed. The control policy is then synthesized by a reinforcement learning algorithm assuming that no prior knowledge is available from the MDP. The proposed method is evaluated in a numerical study to test the quality of the generated control policy and is compared against conventional methods for policy synthesis such as MDP abstraction (Voronoi quantizer) and approximate dynamic programming (fitted value iteration).

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This article frames and presents the discussion at an invited panel “AI History: Expert Systems” held at the AAAI-17 conference held in San Francisco. The panel’s purpose was to open up this history of expert systems, its transformational aspects, and its connections to today’s AI awakening.

We enable aProbLog---a probabilistic logical programming approach---to reason in presence of uncertain probabilities represented as Beta-distributed random variables. We achieve the same performance of state-of-the-art algorithms for highly specified and engineered domains, while simultaneously we maintain the flexibility offered by aProbLog in handling complex relational domains. Our motivation is that faithfully capturing the distribution of probabilities is necessary to compute an expected utility for effective decision making under uncertainty: unfortunately, these probability distributions can be highly uncertain due to sparse data. To understand and accurately manipulate such probability distributions we need a well-defined theoretical framework that is provided by the Beta distribution, which specifies a distribution of probabilities representing all the possible values of a probability when the exact value is unknown.

This paper argues the need for research to realize uncertainty-aware artificial intelligence and machine learning (AI\&ML) systems for decision support by describing a number of motivating scenarios. Furthermore, the paper defines uncertainty-awareness and lays out the challenges along with surveying some promising research directions. A theoretical demonstration illustrates how two emerging uncertainty-aware ML and AI technologies could be integrated and be of value for a route planning operation.