Facebook to Open New Artificial Intelligence Lab in Montreal

Facebook is opening its first artificial intelligence research lab in Canada

  • Facebook to Open New Artificial Intelligence Lab in Montreal

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    September 15, 2017, 11:56 AM EDT

    Facebook Inc. is opening its first artificial intelligence research lab in Canada and has chosen Montreal to house the project.

  • This is Facebook’s first research and development investment in Canada and its fourth AI research lab.
  • The company was attracted to Montreal because of the students and professors in surrounding universities, the strong startup culture and favorable government policies, Facebook Chief AI Scientist Yann LeCun said in a statement.
  • Facebook is investing more than C$7 million ($5.7 million) in Montreal’s AI scene.
  • Joelle Pineau, a professor at the McGill School of Computer Science, will head the new lab, FAIR Montreal, which will start with 10 researchers and aims to grow to more than 30 researchers in the coming year.

Facebook Inc. is opening its first artificial intelligence research lab in Canada and has chosen Montreal to house the project.
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The Present and Future of Quantum Computing for AI – Towards Data Science – Medium

The Present and Future of #QuantumComputing for #AI

  • For AI researchers optimization and sampling is particularly important, because it allows to train Machine Learning models much faster with higher accuracy.At the present time, Canadian D-Wave is the leading company in quantum computing.
  • Task description is encoded as the energy function in connections between qubits, and through annealing they are moving towards some optimal configuration.If the transition is carried out slowly enough the algorithm will find a ground state (i.e., an optimal solution) with high probability:During the annealing process, probability of qubits ending up in the minimum energy state increasesQuantum Coupling allows qubits to explore all potential solutions simultaneously, and at the same time Quantum Tunneling allows them to move through high energy barriers towards the “better” states.
  • This video by D-Wave explains QA in more details:IBM QAnother major player is IBM Q. Big Blue is working with Gate-model quantum computing and their machines are Universal Quantum Computers.
  • State-of-the-art processors from IBM have 16 and 17 qubits, and it’s really hard to scale further.More general architecture of IBM’s processors allows them to run any quantum algorithms.
  • Only theoretical research and simulations on toy problems.Overall, quantum computing looks like a promising direction for stochastic models in Machine Learning.

Quantum computing is still in it’s infancy, and no universal architecture for quantum computers exists right now. However, their prototypes are already here and showing promising results in…
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This cutting edge AI startup from Finland is challenging tech giants and universities alike

This cutting edge #AI startup from Finland is challenging tech giants and universities alike

  • Curious AI is a research-based company founded 2015 as a spin-off from Aalto’s Deep Learning Research Group.
  • Curious AI does cutting edge research, pushing the boundaries of the machine learning commonly called artificial intelligence today towards the illusive limit of true artificial intelligence.
  • Curious AI recently had a breakthrough, using unsupervised learning for object recognition in Google Street View.
  • he term artificial intelligence is thrown around a lot these days, but usually, when a startup says they’re applying AI to some problem, it just means they are using machine learning in varying degrees of sophistication.
  • A major obstacle to reaching artificial intelligence is solving unsupervised learning – and this is what Curious AI’s primary focus.

Curious AI is driving the development towards the next wave of advanced artificial intelligence technology.
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Beneficial AI 2017

Beneficial #AI 2017
The artificial intelligence event
#videos

  • Grant-winner talk: Owen Cotton-Barratt (GPP) ( pdf )
  • Panel with Daniela Rus (MIT), Andrew Ng (Baidu), Mustafa Suleyman (DeepMind), Moshe Vardi (Rice) & Peter Norvig (Google): How AI is automating and augmenting work
  • Honoring the FrieNDA for the conference, we are only posting videos and slides below with approval of the speaker.
  • In our sequel to the 2015 Puerto Rico AI conference, we brought together an amazing group of AI researchers from academia and industry, and thought leaders in economics, law, ethics, and philosophy for five days dedicated to beneficial AI.
  • Panel with Anna Salamon (CFAR) & Paul Christiano (MIRI): Educating and mentoring AI safety researchers (Moderator: Andrew Critch)

In our sequel to the 2015 Puerto Rico AI conference, we brought together an amazing group of AI researchers from academia and industry, and thought leaders in economics, law, ethics, and philosophy for five days dedicated to beneficial AI. We hosted a two-day workshop for our grant recipients to give them an opportunity to highlight and discuss the progress with their grants. We followed that with a 2.5-day conference, in which people from various AI-related fields hashed out opportunities and challenges related to the future of AI and steps we can take to ensure that the technology is beneficial. Honoring the FrieNDA for the conference, we are only posting videos and slides below with approval of the speaker. Learn more about the Asilomar AI Principles that resulted from the conference and the process involved in developing them. (It looks like we’ll be posting almost everything, but please be patient while we finish editing and uploading videos, etc.)
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AI versus machine learning: what’s the difference?

'Machine learning versus #AI: what's the difference?'

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#ML #DataScience #BigData #IoT

  • Google ‘learns’ to correct it for you.
  • The UK has a new AI centre – so when robots kill, we know who to blame
  • AI and machine learning are very much related, but they’re not quite the same thing
  • AI is a branch of computer science attempting to build machines capable of intelligent behaviour, while 
Stanford University defines machine learning as “the science of getting computers to act without being explicitly programmed”.
  • “So the enabler for AI is machine learning,” she added.

Intel’s Nidhi Chappell, head of machine learning, reveals what separates the two computer sciences and why they’re so important
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Google’s DeepMind turns to StarCraft II after conquering Go

Google's DeepMind turns to StarCraft II after conquering Go | ZDNet  #ai

  • Blizzard and DeepMind have created an open test environment within the StarCraft II game for artificial intelligence researchers to use worldwide.
  • Google’s DeepMind has announced that it will be making use of game development studio Blizzard’s StarCraft II game as a testing platform for artificial intelligence (AI) and machine-learning research, opening the environment worldwide.
  • StarCraft II is closer to a real-world environment than any other game it has used for testing so far, DeepMind said, as it is played in real-time.
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  • “Games are the perfect environment in which to do this, allowing us to develop and test smarter, more flexible AI algorithms quickly and efficiently, and also providing instant feedback on how we’re doing through scores.”

Blizzard and DeepMind have created an open test environment within the StarCraft II game for artificial intelligence researchers to use worldwide.
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Here’s how close AI is to beating humans in different games

Here’s how close #AI is to beating humans in different games

  • People inevitably start programming bots with extensive human-knowledge about how to beat a game, rather than focusing on true artificial intelligence.
  • ‘s how close AI is to beating humans in different games
  • In the competition, players submit bots that compete in an unknown set of ten simple games.
  • “On most games, [they are] not human-level,” Togelius says.
  • “In my opinion, a game like ‘Civilization’ would be strictly easier for AI to beat than a game like ‘StarCraft,'” Churchill says.

Two decades after Deep Blue conquered chess and a year after AlphaGo took down go, there are still games that artificial intelligence can’t beat.
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