Peter Souza'a employer trains adults on the autism spectrum for tasks and roles in IT. Years ago, Michael Field-house had a dinner party and friends attended with their young son Andrew, who is autistic, non-verbal, and low-functioning. At one point, Fieldhouse noticed Andrew, who was five- or six-years-old at the time, outside dropping pebbles into an urn in a Japanese garden. "I was curious about that and I started timing him," recalls Fieldhouse. "I noted there were perfect intervals between every stone. He did that for at least an hour."

Despite the rapid advances it has made it over the past decade, deep learning presents many industrial users with problems when they try to implement the technology, issues that the Internet giants have worked around through brute force. "The challenge that today's systems face is the amount of data they need for training," says Tim Ensor, head of artificial intelligence (AI) at U.K.-based technology company Cambridge Consultants. "On top of that, it needs to be structured data." Most of the commercial applications and algorithm benchmarks used to test deep neural networks (DNNs) consume copious quantities of labeled data; for example, images or pieces of text that have already been tagged in some way by a human to indicate what the sample represents. The Internet giants, who have collected the most data for use in training deep learning systems, have often resorted to crowdsourcing measures such as asking people to prove they are human during logins by identifying objects in a collection of images, or simply buying manual labor through services such as Amazon's Mechanical Turk.

As of March 17th, 2020, more than 188,297 people have been infected with COVID-19. How can technology aid in curtailing the spread of infectious diseases that have the potential to create panic and infirm thousands of people? The Internet of Things (IoT), a network of interconnected systems and advances in data analytics, artificial intelligence, and connectivity, can help by providing an early warning system to curb the spread of infectious diseases. China's efforts to control the coronavirus have meant many residents stayed at home and factories just shut down. That had an unintended effect: less air pollution.

It is late April 2020 as I write this column and it will appear in early June, at which time I expect we may only just be emerging from the "shelter at home" restrictions imposed by the governors of most of the States in the Union. I contracted the disease in mid-March and spent about three weeks recovering. While my symptoms were mild, the virus left me low on energy and it took time to get back to normal levels. It could have been much worse, and it is for many people, some of whom do not survive. I had occasion to think and speculate about what our profession has to contribute in the response to this worldwide pandemic.

Writing on the 50th Earth Day brings to mind the origins of U.S. environmental movement. DDT is, of course, Bis(4-chlorophenyl)- 1,1,1-trichloroethane, perhaps the most effective insecticide ever invented. DDT was used widely with remarkable effectiveness in the 1940s and 1950s to combat malaria, typhus, and the other insect-borne human diseases. Its efficacy was unsurpassed in insect control for crop and livestock production, and even villages and homes. In short, it was a wonder chemical.7

A major marketing firm has turned to IBM Watson Studio, and its data, to create an interactive platform that predicts the risk, readiness and recovery periods for counties hit by the coronavirus. Global digital marketing firm Wunderman Thompson launched its Risk, Readiness and Recovery map, an interactive platform that helps enterprises and governments make market-level decisions, amid the coronavirus pandemic. The platform, released May 21, uses Wunderman Thompson's data, as well as machine learning technology from IBM Watson, to predict state and local government COVID-19 preparedness and estimated economic recovery timetables for businesses and governments. The idea for the Risk, Readiness and Recovery map, a free version of which is available on Wunderman Thompson's website, originated two months ago as the global pandemic accelerated, said Adam Woods, CTO at Wunderman Thompson Data. "We were looking at some of the visualizations that were coming in around COVID-19, and we were inspired to really say, let's look at the insight that we have and see if that can make a difference," Woods said.

Modeling and simulation is certainly a vast discipline with a broad and complex body of knowledge having, beyond the surface, a large technical and theoretical background (Minsky, 1965) (Banks et al., 2009) (Zeigler et al., 2000) (Boccara, 2003) which consequently, is hard of being completely mastered from modelers coming from disperse domains like biology, ecology or even computer science. Among the existing formalisms, the agent-based or individual-based is increasing gradually the number of adepts in the recent years. The Individual-based modeling is a powerful methodology which is having more and more acceptance between researchers and practitioners of distinct branches from social to biological sciences, including specifically the modeling of ecological processes and microbial consortia studies. Certainly, one of the main reasons for the success of this approach is the relative simplicity for capturing micro-level properties, stochasticity and spatially complex phenomena without the requirement of a high level of mathematical background (Grimm and Railsback, 2005). But the counterpart of the ease for building complex and feature rich models, is the lack of a closed formal mathematical form of the model which implies that the study of these models cannot be attacked analytically.

This paper examines the relationship between user pageview (PV) histories and their item-choice behavior on an e-commerce website. We focus on PV sequences, which represent time series of the number of PVs for each user--item pair. We propose a shape-restricted optimization model that accurately estimates item-choice probabilities for all possible PV sequences. This model imposes monotonicity constraints on item-choice probabilities by exploiting partial orders for PV sequences, according to the recency and frequency of a user's previous PVs. To improve the computational efficiency of our optimization model, we devise efficient algorithms for eliminating all redundant constraints according to the transitivity of the partial orders. Experimental results using real-world clickstream data demonstrate that our method achieves higher prediction performance than that of a state-of-the-art optimization model and common machine learning methods.

This algorithm differs from Visual servoing in novel environments is an important AGGREVATE because problem. Given images produced by a camera, a visual servo it incorporates the value control policy guides a grasped part into a desired pose penalties and from DQfD relative to the environment. This problem appears in many because it uses supervised situations: reaching, grasping, peg insertion, stacking, machine targets rather than TD assembly tasks, etc. Whereas classical approaches to the targets. We compare PQC problem [6, 3, 27] typically make strong assumptions about the with several baselines and environment (fiducials, known object geometries, etc.), there algorithm ablations and has been a surge of interest recently in using deep learning show that it outperforms methods to solve these problems in more unstructured settings all these variations on two that incorporate novel objects [29, 14, 26, 8, 21, 28, 12, 13].

When making decisions, people often overlook critical information or are overly swayed by irrelevant information. A common approach to mitigate these biases is to provide decision-makers, especially professionals such as medical doctors, with decision aids, such as decision trees and flowcharts. Designing effective decision aids is a difficult problem. We propose that recently developed reinforcement learning methods for discovering clever heuristics for good decision-making can be partially leveraged to assist human experts in this design process. One of the biggest remaining obstacles to leveraging the aforementioned methods for improving human decision-making is that the policies they learn are opaque to people. To solve this problem, we introduce AI-Interpret: a general method for transforming idiosyncratic policies into simple and interpretable descriptions. Our algorithm combines recent advances in imitation learning and program induction with a new clustering method for identifying a large subset of demonstrations that can be accurately described by a simple, high-performing decision rule. We evaluate our new AI-Interpret algorithm and employ it to translate information-acquisition policies discovered through metalevel reinforcement learning. The results of three large behavioral experiments showed that the provision of decision rules as flowcharts significantly improved people's planning strategies and decisions across three different classes of sequential decision problems. Furthermore, a series of ablation studies confirmed that our AI-Interpret algorithm was critical to the discovery of interpretable decision rules and that it is ready to be applied to other reinforcement learning problems. We conclude that the methods and findings presented in this article are an important step towards leveraging automatic strategy discovery to improve human decision-making.