"Search is a problem-solving technique that systematically explores a space of problem states, i.e., successive and alternative stages in the problem-solving process. Examples of problem states might include the different board configurations in a game or intermediate steps in a reasoning process. This space of alternative solutions is then searched to find an answer. Newell and Simon (1976) have argued that this is the essential basis of human problem solving. Indeed, when a chess player examines the effects of different moves or a doctor considers a number of alternative diagnoses, they are searching among alternatives."
– from Section 1.2 of Chapter One of George F. Luger's textbook, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 5th Edition (Addison-Wesley; 2005).
There are currently 25 vaccines to fight COVID-19 in clinical evaluation, another 139 vaccines in a pre-clinical stage, and many more being researched. But many of those vaccines, if they are at all successful, might not produce an immune response in portions of the population. That's because some people's bodies will react differently to the materials in the vaccine that are supposed to stimulate virus-fighting T cells. And so just figuring out how much coverage a vaccine has, meaning, how many people it will stimulate to mount an immune response, is a big part of the vaccine puzzle. With that challenge in mind, scientists at Massachusetts Institute of Technology on Monday unveiled a machine learning approach that can predict the probability that a particular vaccine design will reach a certain proportion of the population.
"The no free lunch theorem calls for prudency when solving ML problems by requiring that you test multiple algorithms and solutions with a clear mind and without prejudice." In a paper titled, 'The Lack of A Priori Distinctions Between Learning Algorithms', that dates back to 1996, David Wolpert explored the following questions: He showed that for any two algorithms, A and B, there are as many scenarios where A will perform worse than B as there are instances where A will outperform B. In short, for all possible problems, average performance of both the algorithms is the same. Although the no free lunch theorem by Wolpert has a more theoretical than practical appeal, there are some implications that should still be taken into account by everyone working with machine learning algorithms. These theorems prove that under a uniform distribution over search problems or learning problems, all algorithms perform equally. Search and learning are key aspects of ML and the NFL theorems have something to deliver here.
Speedcubing is the sport of solving a classic Rubik's Cube -- or a related combination puzzle -- in the shortest amount of time possible. And, no, it is not for the faint of heart. The new Netflix documentary on this subject, The Speed Cubers, dives headfirst into the friendly but competitive speedcubing culture. The 40-minute film is one of three new documentary shorts debuting on Netflix this summer. The Speed Cubers centers on a couple of professional competitors who go head-to-head at the World Cube Association World Championship in Melbourne, Australia, in 2019.
Any business that is able to utilize its data effectively has shown great promise to survive diverse conditions and adapt to the growing market -- even in the Covid-19 pandemic. To ensure that business can be as flexible and adaptable as possible, they need to use Search-Based Analytics. Search-Based Analytics changed how business intelligence works. It gave businesses the necessary way to understand their data with the help of dashboards and visualization. In short, it provides the company's best minds to make data-driven decisions to reach company goals.
First you need to connect to an available server. In addition to LeelaZero, you can try the KataGo server, but it seems a bit slower and like it has a bit more limited game tree search. All I know LeelaZero is based on the DeepMind's paper on AlphaZero, so it should be pretty strong. First you choose the game you want to review. You can upload it by using the menus on the game review screen that comes up first. The chart on the right hand side shows whether the game is biased to black (up, 0), or white (down, 1).
Computer Vision and Pattern Recognition (CVPR) conference is one of the most popular events around the globe where computer vision experts and researchers gather to share their work and views on the trending techniques on various computer vision topics, including object detection, video understanding, visual recognition, among others. This year, the Computer Vision (CV) researchers and engineers have gathered virtually for the conference from 14 June, which will last till 19 June. In this article, we have listed down all the important topics and tutorials that have been discussed on the 1st and 2nd day of the conference. In this tutorial, the researchers presented the latest developments in robust model fitting, recent advancements in new sampling and local optimisation methods, novel branch-and-bound and mathematical programming algorithms in the global methods as well as the latest developments in differentiable alternative to Random Sample Consensus Algorithm or RANSAC. To know what a RANSAC is and how it works, click here.
Neural Architecture Search has become a focus of the Machine Learning community. Techniques span Bayesian optimization with Gaussian priors, evolutionary learning, reinforcement learning based on policy gradient, Q-learning, and Monte-Carlo tree search. In this paper, we present a reinforcement learning algorithm based on policy gradient that uses an attention-based autoregressive model to design the policy network. We demonstrate how performance can be further improved by training an ensemble of policy networks with shared parameters, each network conditioned on a different autoregressive factorization order. On the NASBench-101 search space, it outperforms most algorithms in the literature, including random search.
In this article, I describe agent-centered search (also called real-time search or local search) and illustrate this planning paradigm with examples. Agent-centered search methods interleave planning and plan execution and restrict planning to the part of the domain around the current state of the agent, for example, the current location of a mobile robot or the current board position of a game. These methods can execute actions in the presence of time constraints and often have a small sum of planning and execution cost, both because they trade off planning and execution cost and because they allow agents to gather information early in nondeterministic domains, which reduces the amount of planning they have to perform for unencountered situations. These advantages become important as more intelligent systems are interfaced with the world and have to operate autonomously in complex environments. Agent-centered search methods have been applied to a variety of domains, including traditional search, strips-type planning, moving-target search, planning with totally and partially observable Markov decision process models, reinforcement learning, constraint satisfaction, and robot navigation.