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This was a really hard post to write because I want it to be really valuable. I sat down with a blank page and asked the really hard question of what are the very best libraries, courses, papers and books I would recommend to an absolute beginner in the field of Machine Learning. I really agonized over what to include and what to exclude. I had to work hard to put myself in the shoes of a programmer and beginner at machine learning and think about what resources would best benefit them. I picked the best for each type of resource.

AI is everywhere--at least from a high-level perspective. But does it belong in your business? There's little argument that AI offers companies plenty of capabilities and opportunities, and most executives know it. When polled by PwC, 86% of professionals said AI was a fairly established part of corporate life. Yet even if it's mainstream, AI shouldn't be treated as just another tool.

With the rapid development of artificial intelligence (AI), the question of how AI learns human values has become increasingly important. There are two main ways that AI can learn human values: through reinforcement learning and through imitation learning. Reinforcement learning is a type of learning that occurs when an AI system is rewarded for taking the desired action. For example, if an AI system is designed to play a game, it may be rewarded for winning the game. Over time, the AI system will learn to value actions that lead to the desired outcome.

"Google fires engineer who contended its AI technology was sentient." A new discovery (or debacle) is reported practically every week, sometimes exaggerated, sometimes not. Policymakers struggle to know what to make of AI and it's hard for the lay reader to sort through all the headlines, much less to know what to be believe. Here are four things every reader should know. First, AI is real and here to stay.

… collection of NFT collectables has been turned into another collection of more artistic NFTs by some seriously powerful machine learning.

… accuracy of data analytics and implement new connected technologies, so as to encourage the development and use of AI and machine learning.

For many of us, the words artificial intelligence (AI) and machine learning (ML) inspire images of a dystopian future run by computers.

… recognition, and achievements made by Artificial Intelligence, … The Global Deep Learning Market is Projected to Reach US$60.5 Billion by …

This is an interview with Professor Emily Mower Provost that was first published by The Michigan Engineer News Center. Using machine learning to decode the unpredictable world of human emotion might seem like an unusual choice. But in the ambiguity of human expression, U-M computer science and engineering associate professor Emily Mower Provost has discovered a rich trove of data waiting to be analyzed. Mower Provost uses machine learning to help measure emotion, mood, and other aspects of human behavior; for example, she has developed a smartphone app that analyzes the speech of patients with bipolar disorder to track their mood, with the ultimate goal of helping them more effectively manage their health. How do you quantify something as ambiguous as emotion in a field where, traditionally, ambiguity is the enemy?