If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Making novelty a central focus of modern AI research and evaluation has had the byproduct of producing an initial body of work in support of a science of novelty. Not only are researchers like ourselves exploring definitions and theories of novelty, but we are exploring questions that could have fundamental implications. For example, our team is exploring the question of when a novelty is expected to be impossibly difficult for an AI. In the real world, if such a situation arises, the AI would recognize it and call a human operator.
In Yeshiva University's engineering-focused M.S. in Artificial Intelligence (AI), offered by the Katz School of Science and Health, students will learn the key skills most valued in today's marketplace, including machine learning and deep neural networks, along with cutting-edge technologies such as reinforcement learning, voice recognition and generation, and image recognition and generation. In the program's project-based courses, students will build systems, models and algorithms using the best available artificial intelligence design patterns and engineering principles, all done in the heart of Manhattan, a global epicenter for artificial intelligence work and research. Prof. Andrew Catlin is the program director for the AI program, with a background as a data scientist and production systems developer who has worked with such major clients as Fidelity Investments; Smart Money; Donaldson, Lufkin and Jenrette; Manufacturers Hanover Trust; and the National Football League. He is also a founder of multiple tech startups, including Hudson Technology and Metrics Reporting. He teaches graduate courses in recommender systems, natural language processing and neural networks, among others.
As part of Microsoft's research into ways to use machine learning and AI to improve security defenses, the company has released an open source attack toolkit to let researchers create simulated network environments and see how they fare against attacks. Microsoft 365 Defender Research released CyberBattleSim, which creates a network simulation and models how threat actors can move laterally through the network looking for weak points. When building the attack simulation, enterprise defenders and researchers create various nodes on the network and indicate which services are running, which vulnerabilities are present, and what type of security controls are in place. Automated agents, representing threat actors, are deployed in the attack simulation to randomly execute actions as they try to take over the nodes. "The simulated attacker's goal is to take ownership of some portion of the network by exploiting these planted vulnerabilities. While the simulated attacker moves through the network, a defender agent watches the network activity to detect the presence of the attacker and contain the attack," the Microsoft 365 Defender Research Team wrote in a post discussing the project.
Despite many challenges that we faced due to the pandemic in 2020, the momentum of growth for advanced technologies has continued. Especially, artificial intelligence (AI) is continuously finding increased usage in both the private and public sectors. During 2020, there were developments around natural language processing (NLP) techniques (for example, GPT-3 model built to produce human-like texts), virtual assistants, job automation, and more. And it appears that AI growth is not going to slow down anytime soon. In 2021, AI will become the core business technology.
State-of-the-art models keep changing all the time. As someone who has been doing Kaggle competitions for almost a year now, I find myself coming across a lot of them, doing comparisons, evaluating, and testing them. I thought it would be a good idea to list the best models for each ML task so that you know where to start. Without further ado, let's get started! EfficientNetsV2 outperformed state-of-the-art image classification networks by 2% while training 5–11x times faster which is a huge improvement.
Nearly all real-world applications of reinforcement learning involve some degree of shift between the training environment and the testing environment. However, prior work has observed that even small shifts in the environment cause most RL algorithms to perform markedly worse. As we aim to scale reinforcement learning algorithms and apply them in the real world, it is increasingly important to learn policies that are robust to changes in the environment. Broadly, prior approaches to handling distribution shift in RL aim to maximize performance in either the average case or the worst case. While these methods have been successfully applied to a number of areas (e.g., self-driving cars, robot locomotion and manipulation), their success rests critically on the design of the distribution of environments.
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Reinforcement learning is arguably the coolest branch of artificial intelligence. It has already proven its prowess: stunning the world, beating the world champions in games of Chess, Go, and even DotA 2. Using RL for stock trading has always been a holy grail among data scientists. Stock trading has drawn our imaginations because of its ease of access and to misquote Cardi B, we like diamond and we like dollars . There are several ways of using Machine Learning for stock trading. One approach is to use forecasting techniques to predict the movement of the stock and build some heuristic based bot that uses the prediction to make decisions.
I find Atari games to be really difficult. The game consists of two paddles on opposite sides of the game screen bouncing a ball back and forth. If the ball goes past one of the paddles, a point is gained by the opposing paddle. The first paddle to reach twenty points wins the game. It sounds easy, but I find that when I play the game, I have to stay laser-focused on my screen, taking note of every miniscule movement of the ball to ensure that I prevent the opponent from scoring a point. One moment of hesitation can create a chance for the opponent to win.