Scientific discovery concerns finding patterns in data and creating insightful hypotheses that explain these patterns. Traditionally, this process required human ingenuity, but with the galloping advances in artificial intelligence (AI) it becomes feasible to automate some parts of scientific discovery. In this work we leverage AI for strategy discovery for understanding human planning. In the state-of-the-art methods data about the process of human planning is often used to group similar behaviors together and formulate verbal descriptions of the strategies which might underlie those groups. Here, we automate these two steps. Our algorithm, called Human-Interpret, uses imitation learning to describe process-tracing data collected in psychological experiments with the Mouselab-MDP paradigm in terms of a procedural formula. Then, it translates that formula to natural language using a pre-defined predicate dictionary. We test our method on a benchmark data set that researchers have previously scrutinized manually. We find that the descriptions of human planning strategies obtained automatically are about as understandable as human-generated descriptions. They also cover a substantial proportion of all types of human planning strategies that had been discovered manually. Our method saves scientists' time and effort as all the reasoning about human planning is done automatically. This might make it feasible to more rapidly scale up the search for yet undiscovered cognitive strategies to many new decision environments, populations, tasks, and domains. Given these results, we believe that the presented work may accelerate scientific discovery in psychology, and due to its generality, extend to problems from other fields.

Usually when we think of computers, we probably imagine glowing displays, interconnected networks sharing digital information, and more software applications than anyone one person could ever come close to using -- but that's only part of computing's story. Analog computers, and later mechanical computers, were an integral part of humanity's pursuit of scientific discovery, fueled by our desire to anticipate future events and outcomes. For a species that conquered the entire world thanks to our larger brains and toolmaking prowess, it's no surprise that we've been using artificial tools to augment and enhance our intelligence as far back as our history goes -- and probably even longer than that. From the careful positioning of stones in England, to the soaring water clocks of China's Song Dynasty to the precise arrangement of mechanical gears in the visionary inventions of Blaise Pascal and Charles Babbage, analog and mechanical computers have served our forebearers well and helped them not just survive but thrive by transcending the bounds of our biology. In Salisbury Plain in the south of England, a collection of about 100 massive and roughly even-cut stones form a pair of standing rings whose purpose is lost to history, but whose construction began before the invention of the wheel and took at least 1,500 years to complete, and possibly even longer.

Scientists and researchers always try to make the world a better place through their discoveries. Here are some of the top anticipated scientific discoveries. Concussions and head injuries are major health hazards for football players.

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. Recent advances in deep learning have rekindled interest in the imminence of machines that can think and act like humans, or artificial general intelligence. By following the path of building bigger and better neural networks, the thinking goes, we will be able to get closer and closer to creating a digital version of the human brain. But this is a myth, argues computer scientist Erik Larson, and all evidence suggests that human and machine intelligence are radically different. Larson's new book, The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do, discusses how widely publicized misconceptions about intelligence and inference have led AI research down narrow paths that are limiting innovation and scientific discoveries.

A recent debate on so called Foundation models (CRFM) brings a real question of if we can build very large models on any specified domain, similar to current large language models, and replace our ...

Say you're driving with a friend in a familiar neighborhood, and the friend asks you to turn at the next intersection. The friend doesn't say which way to turn, but since you both know it's a one-way street, it's understood. That type of reasoning is at the heart of a new artificial-intelligence framework – tested successfully on overlapping Sudoku puzzles – that could speed discovery in materials science, renewable energy technology and other areas. An interdisciplinary research team led by Carla Gomes, the Ronald C. and Antonia V. Nielsen Professor of Computing and Information Science in the Cornell Ann S. Bowers College of Computing and Information Science, has developed Deep Reasoning Networks (DRNets), which combine deep learning – even with a relatively small amount of data – with an understanding of the subject's boundaries and rules, known as "constraint reasoning." Di Chen, a computer science doctoral student in Gomes' group, is first author of "Automating Crystal-Structure Phase Mapping by Combining Deep Learning with Constraint Reasoning," published Sept. 16 in Nature Machine Intelligence.

Hypothesis testing is done to confirm our observation about the population using sample data, within the desired error level.

"VICReg could be used to model the dependencies between a video clip and the frame that comes after, therefore learning to predict the future in a video." Humans have an innate capability to identify objects in the wild, even from a blurred glimpse of the thing. We do this efficiently by remembering only high-level features that get the job done (identification) and ignoring the details unless required. In the context of deep learning algorithms that do object detection, contrastive learning explored the premise of representation learning to obtain a large picture instead of doing the heavy lifting by devouring pixel-level details. But, contrastive learning has its own limitations.

By Dennis D. Hirsch, Published on 01/01/20

Kevin Yager (front) and Masafumi Fukuto at Brookhaven Lab's National Synchrotron Light Source II, where they've been implementing a method of autonomous experimentation. In the popular view of traditional science, scientists are in the lab hovering over their experiments, micromanaging every little detail. For example, they may iteratively test a wide variety of material compositions, synthesis and processing protocols, and environmental conditions to see how these parameters influence material properties. In each iteration, they analyze the collected data, looking for patterns and relying on their scientific knowledge and intuition to select useful follow-on measurements. This manual approach consumes limited instrument time and the attention of human experts who could otherwise focus on the bigger picture.