“Deep learning enables the robot to perceive its immediate environment, including the location and movement of its limbs. Reinforcement learning means improving at a task by trial and error. A robot with these two skills could refine its performance based on real-time feedback.”
ARTIFICIAL INTELLIGENCE: Google and Facebook Team Up to Open Source the Gear Behind Their Empires.
This is all the best games from every year 1952–2015.
Here is the list:
1952: Nimrod Computer Game
1958: Tennis For Two
1971: Computer Space
1972: Pong
1973: Space Race
1974: Clean Sweep
1975: Anti-Ai
1976: Blockade
1977: Indy 500
1978: Sea Wolf 2
1979: Crash
1980: Pac-Man
1981: Ms. Pacman
1982: Paratrooper
1983: Super Gridder
1983: Hunchback
1984: Sokoban
1985: Super Mario Bros
1986: Outrun
1987: Leisure Suit Larry in the Land of the Lounge Lizards.
1988: Super Mario Bros 3
1989: Xenon 2
1990: Prince Of Persia
1991: Prehistorik
1992: Wolfenstein 3D
1993: Day of the Tentacle
1994: The Lion King
1995: Command & Conquer
1996: Tomb Raider
1997: Gta
1998: Half Life
1999: Quake 3
2000: Max Payne
2001: Gta 3
2002: Serious Sam: The First Encounter
2003: Medal Of Honor Allied Assault
2004: Half Life 2
2005: World Of Warcraft
2006: Need For Speed Most Wanted
2007: Crysis
2008: Assassin’s Creed
2009: Call Of Duty Modern Warfare 2
2010: Red Dead Redemption
2011: World Of Tanks
2012: Battlefield 3
2013: Gta 5
2014: Wolfenstein The New Order
2015: Tom Clancy’s The Division
Google has smartened up several of its products with a type of artificial intelligence called deep learning, which involves training neural networks on lots of data and then having them make predictions about new data. Google Maps, Google Photos, and Gmail, for example, have been enhanced with this type of technology. The next service that could see gains is Google Translate.
Well, let me back up. Part of Google Translate actually already uses deep learning. That would be the instant visual translations you can get on a mobile device when you hold up your smartphone camera to the words you want to translate. But if you use Google Translate to just translate text, you know that the service isn’t always 100 percent accurate.
In an interview at the Structure Data conference in San Francisco today, Jeff Dean, a Google senior fellow who worked on some of Google’s core search and advertising technology and is now the head of the Google Brain team that works on deep learning, said that his team has been working with Google’s translation team to scale out experiments with translation based on deep learning. Specifically, the work is based on the technology depicted in a 2014 paper entitled “Sequence to Sequence Learning with Neural Networks.”
Google, AI, and Quantum — Google believes deep learning is not suitable on Quantum. Not so sure that I agree with this position because deep learning in principle is “a series of complex algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures” — the beauty around quantum is it’s performance in processing of vast sets of information and complex algorithms. Maybe they meant to say at this point they have not resolved that piece for AI.
Artificial intelligence is one of the hottest subjects these days, and recent advances in technology make AI even closer to reality than most of us can imagine.
The subject really got traction when Stephen Hawking, Elon Musk and more than 1,000 AI and robotics researchers signed an open letter issuing a warning regarding the use of AI in weapons development last year. The following month, BAE Systems unveiled Taranis, the most advanced autonomous UAV ever created; there are currently 40 countries working on the deployment of AI in weapons development.
Those in the defense industry are not the only ones engaging in an arms race to create advanced AI. Tech giants Facebook, Google, Microsoft and IBM are all engaging in various AI-initiatives, as well as competing on developing digital personal assistants like Facebook’s M, Cortana from Microsoft and Apple’ Siri.
As achievements go, learning how to pick up objects doesn’t sound quite as impressive as twice beating the world Go champion – it is, after all, something the average toddler can do. But it’s the fact that the robots themselves figured out the best way to do it using neural networks that makes this notable.
A recent Google report spotted by TNW explains how the company let robot arms pick up a variety of different objects, using neural networks to learn by trial-and-error the best way to handle each. Some 800,000 goes later, the robots seemed to have it figured out pretty well …
Government’s other big NextGen Program “Advanced Research Projects Agency-EnergyAdvanced Research Projects Agency-Energy” (ARPA) is funding a personal climate change solution with robots, foot coolers, etc. There is one fact; US Government does love their acronyms.
Why heat or cool a whole building when you could heat or cool individual people instead?
Allen Institute working with Baylor on reconstructing neuronal connections.
The Intelligence Advanced Research Projects Activity (IARPA) has awarded an $18.7 million contract to the Allen Institute for Brain Science, as part of a larger project with Baylor College of Medicine and Princeton University, to create the largest ever roadmap to understand how the function of networks in the brain’s cortex relates to the underlying connections of its individual neurons.
The project is part of the Machine Intelligence from Cortical Networks (MICrONS) program, which seeks to revolutionize machine learning by reverse-engineering the algorithms of the brain.
“This effort will be the first time that we can physically look at more than a thousand connections between neurons in a single cortical network and understand how those connections might allow the network to perform functions, like process visual information or store memories,” says R. Clay Reid, Ph.D., Senior Investigator at the Allen Institute for Brain Science, Principal Investigator on the project.
