Clicking on "Register", you agree to our Privacy Policy In this webinar, some statistical hypothesis testing will be introduced both in theory and in practice using Python programming language. This webinar will be given remotely and streaming using LiveWebinar platform, which works on every updated internet browser. No installation is then required. The duration is about 60 minutes. The speaker will show some slides for the theoretical part of the content and will write code during the event using Google Colaboratory for the practical part.

He discussed his personal AI journey--from games to scientific discovery, some of his breakthrough results in complex games of strategy, and some of the exciting ways that lessons from the world of games are helping to accelerate scientific discovery.

Hypothesis testing is a statistical approach that assists researchers in determining the validity of their theories. It is frequently used in statistics and data science to determine whether an event has occurred or will occur based on previous happenings. Let's understand Hypothesis Testing with an example. There is one Pharma Company which produces Vaccine A and you need to take 2 doses of that vaccine to get fully immune to the virus. Millions of people have already taken 2 doses of Vaccine A. After a few days the same company comes up with a Vaccine B which gives faster results, they claim that only 1 dose of this vaccine is enough to get fully immune to the virus.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. 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.

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.

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 ...