Professor of Artificial Intelligence Wolfgang Faber comments on Google announcing that its AlphaGo Zero artificial intelligence program has triumphed at chess against world-leading specialist software within hours of teaching itself the game from scratch and considers where humans will start losing their jobs to intelligent computers and machines. "'Google's'superhuman' DeepMind AI claims chess crown' has been a headline on the BBC recently. What does it mean, and are our jobs, or even our lives in danger? First, let us have a look at what caused this headline: A few days ago, a manuscript by a group around David Silver, Thomas Hubert, and Julian Schrittwieser of London-based, Google (or rather Alphabet)-owned DeepMind was uploaded to arXiv, in which the system AlphaZero is described and very impressive results in learning how to play three traditional board games (chess, shogi, Go) well are reported. The setup allowed for learning very successful (superhuman) strategies in a few hours only.
We have had many previous hype cycles around AI. As I wrote in Silicon Collar: "Since the 1950s! That is when Alan Turing defined his famous test to measure a machine's ability to exhibit intelligent behavior equivalent to that of a human. In 1959, we got excited when Allen Newell and his colleagues coded the General Problem Solver. In 1968, Stanley Kubrick sent our minds into overdrive with HAL in his movie, 2001: A Space Odyssey.
Yao Xin is Founder of PPLIVE and an alumnus of the 3rd CEIBS Entrepreneurial Leadership Camp. "Why is there so much discussion about artificial intelligence these days? I think it's likely because of last year's Man vs Machine battle between world Go champion Lee Sedol and Google DeepMind's artificial intelligence programme AlphaGo. But this wasn't the first Man vs Machine battle. In 1996 Chess Grandmaster Garry Kasparov won four out of a series of six chess matches played against the IBM supercomputer Deep Blue.
Artificial intelligence has historically over-promised and under-delivered. That routine leads to spurts of what those in the field call "hype"--outsized excitement about the potential of a core technology--followed after a few years and several million (or billion) dollars by crashing disappointment. In the end, we still don't have the flying cars or realistic robot dogs we were promised. But DeepMind's AlphaGo, a star pupil in a time we'll likely look back on as a golden age of AI research, has made a habit of blowing away experts' notions of what's possible. When DeepMind announced that the AI system could play Go on a professional level, masters of the game said it was too complex for any machine.
We've discussed artificial intelligence (AI) quite a bit in this column thus far -- and with good reason. AI is currently THE topic in legal tech (although Blockchain is certainly running a close second), and it's almost impossible to carry on an in-depth discussion on the future of the legal industry without mentioning AI. Legal professionals, librarians, and analysts alike have speculated on the rise of the robo-lawyer, the role that increasingly sophisticated machines will play in the practice of law -- and even whether lawyers will cease to exist at some point in the future. Given the way in which AI has penetrated the conversation around legal technology, I think it makes sense to examine AI's larger history. To quote from one of my favorite musicians, Bob Marley: "In this great future, we can't forget our past."
Outcomes included the development of symbolic information processing which offered a new paradigm in brain modelling. Rather than pursuing true general intelligence, more companies and researchers are settling for Weak AI programs like Siri, Alexa, Cortana and chatbots. Not all researchers are content to settle for the Weak AI compromise and dedicated purists continue to pursue true AGI. Breakthroughs and developments in Weak AI can be rapid and each receives considerable public attention coupled with further resource investment.
A new $240 million center at MIT may help advance the field of artificial intelligence by developing novel devices and materials to power the latest machine-learning algorithms. The project, announced by IBM and MIT today, will research new approaches in deep learning, a technique in AI that has led to big advances in areas such as machine vision and voice recognition. But it will also explore completely new computing devices, materials, and physical phenomena, including efforts to harness quantum computers--exotic but potentially very powerful new machines--to make AI even more capable. And it will study the economic impact of artificial intelligence and automation, a hugely significant issue for society.
In the blog "From Autonomous to Smart: Importance of Artificial Intelligence," we discussed the two critical artificial intelligence (AI) challenges in creating "smart" edge devices: In the blog "Reinforcement Learning to the Rescue," we talked about how Moore's Law isn't going to bail us out of because the problem space is getting more complex, even for relatively easy environments like playing checkers and chess: By the way, the commonly accepted answer for the number of particles in the observable universe is 1080. Maybe our only hope to solve these uber-complex, life-impacting analytic problems like autonomous vehicles, smart cities and precision medicine lies in a new approach – quantum computing. As an example, Google has built a quantum computer which is 100 million times faster than any of today's machines.This quantum computer could complete calculations within seconds to a problem that might take a digital computer 10,000 years to calculate. The post Quantum Computing, Artificial Intelligence (AI) and Solving the Impossible appeared first on InFocus Blog Dell EMC Services.
This year has seen an artificial intelligence system beat professional poker players at a notoriously difficult game for machines to master. In 2016 a system built by Google-owned AI company DeepMind called'AlphaGo' beat South Korean champion Lee Sedol at the fiendishly complex game'Go'. Sedol won just one game to AlphaGo's four across a five-match series. It's far from the first time that AI has proved to be better than human intelligence at winning games: chess, draughts, Connect Four, Othello and backgammon are just a few that they have come to dominate.