In 1950, Alan Turing developed the Turing test to answer the question "can machines think?". Since then, machine learning has gone from being just a concept, to a process relied on by some of the world's biggest companies. Here Sophie Hand, UK country manager at industrial parts supplier EU Automation, discusses the applications of the different types of machine learning that exist today. Machine learning is a subset of artificial intelligence (AI) where computers independently learn to do something they were not explicitly programmed to do. They do this by learning from experience -- leveraging algorithms and discovering patterns and insights from data.
AI has a long history. One can argue it even started long before the term was first coined; mostly in stories and later in actual mechanical devices called automata. This chapter only covers events relevant to the periods of AI winters without being too exhaustive in hope to extract knowledge that can be applied today. To aid understanding the phenomenon of AI Winters, the events leading up to them are examined. Many early ideas about thinking machines appeared in the late 1940s to '50s by people like Turing or Von Neumann.
After all the hype, 2020 will be the year that automation and Artificial Intelligence (AI) technology starts moving out of experimentation mode and into more serious levels of adoption, believes Forrester Research. But the picture the market research firm paints is decidedly mixed for tech leaders and buyers. Here are four of the top forecasts from Laura Koetzle, the company's vice president, group director and head of research for Europe: The robotics process automation (RPA) services market has grown over the last few years, mainly because organisations have focused on tackling simple challenges and undertaken projects focused on'low-hanging fruit'. But to move to the next stage, it will be necessary to build "automation strike teams" and centres of excellence in order to put more structure around such initiatives, believes Koetzle. She also warns that in 2020, it would be "incumbent on all tech leaders" to "develop and promote a positive vision of the future of work".
The past decade has seen the rise of remarkably human personal assistants, increasing automation in transportation and industrial environments, and even the alleged passing of Alan Turing's famous robot consciousness test. Such innovations have taken artificial intelligence out labs and into our hands. A.I. programs have become painters, drivers, doctors assistants, and even friends. But with these new benefits have also come increasing dangers. This ending decade saw the first, and likely not the last, death caused by a self-driving car.
Alan Turing's seminal 1950 paper "Computing Machinery and Intelligence", posed the question "Can machines think?" Since then machine learning (ML) has found its way into numerous processes; seeking to simplify our lives by making processes smarter, better and faster. Financial risk management is an industry that is rife with opportunities for ML to disrupt in the coming years, one of the most obvious areas being credit scoring. In this blog we explore some of the main findings of the recently published Bank of England survey on ML, this is followed by our views on the challenges and potential solutions of implementing ML within a credit risk scoring framework. When we talk about the rise of ML in credit risk, we quite often forget that one of the earliest real life use cases for ML was within this very industry.
Welcome to this AI-themed edition of The Morning Paper Quarterly. I've selected five paper write-ups which first appeared on The Morning Paper blog over the last year. To kick things off we're going all the way back to 1950! Alan Turing's paper on "Computing Machinery and intelligence" is a true classic that gave us the Turing test, but also so much more. Here Turing puts forward the idea that instead of directly building a computer with the sophistication of a human adult mind, we should break the problem down into two parts: building a simpler child program, with the capability to learn, and building an education process through which the child program can be taught. Writing almost 70 years ago, Turing expresses the hope that machines will eventually compete with men in all purely intellectual fields.
Smart cities, search engines, autonomous vehicles: The pairing of massive data sets and self-learning algorithms is transforming the world around us in ways that are not always easy to grasp. The strange ways computers "think" are hidden within opaque proprietary code. It has been called the "end of theory." There is a danger, says Paola Sturla, Lecturer in Landscape Architecture at the Harvard Graduate School of Design, that human agency will be nudged out of the picture. Sturla, who is trained as an architect and landscape architect, has called on designers to renew the tradition of humanism.
This is a Q&A excerpt on the topic of AI from a lecture by Richard Feynman from September 26th, 1985. This is a clip on the Lex Clips channel that I mostly use to post video clips from the Artificial Intelligence podcast, but occasionally I post favorite clips from lectures given by others. Hope you find these interesting, thought-provoking, and inspiring. If you do, please subscribe, click bell icon, and share! Artificial Intelligence podcast website: https://lexfridman.com/ai
I want to build a model for Chess/Go/Shogi that is trained and tested on real players, and I want it to pass the Turing test. I don't want my model to play the best move in a position, I want it to play the move that a person would play (of a certain strength, time control, etc..). It's easy to make this a classification problem and train a CNN on a one-hot encoded policy of actual moves played. The only problem is, without some kind of look-ahead algorithm (MCTS for example) the model fails to learn sequences that require multiple moves, such as tactics. However, current MCTS/alpha-beta/minimax models require evaluation of leaf nodes.
Around 1950 Alan Turing published'Computing Machinery and Intelligence' which gave the proposal of simulation Game – an idea which says, can machines think? And this idea is named as Turing Test. In 1959 a person Samuel brought out the word Machine learning which clarifies that a person who develops the program may lose a game to the program itself. The program is capable to win against the creator of their existence. Artificial intelligence focuses on learning, Reasoning, decision making, and the Turing test checks the consciousness and decision-making ability in machines.