Lifeboat Foundation

Carnegie Mellon University is embarking on a five-year, $12 million research effort to reverse-engineer the brain and “make computers think more like humans,” funded by the U.S. Intelligence Advanced Research Projects Activity (IARPA). The research is led by Tai Sing Lee, a professor in the Computer Science Department and the Center for the Neural Basis of Cognition (CNBC).

The research effort, through IARPA’s Machine Intelligence from Cortical Networks (MICrONS) research program, is part of the U.S. BRAIN Initiative to revolutionize the understanding of the human brain.

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Researchers at the University of Bristol have created ‘Mogrify’ — an algorithm that can predict how to reprogram virtually any type of cell

One way of creating new cells is with stem cells. The most famous of these are embryonic and induced pluripotent stem cells, the latter made from your own cells. While these cells have immense potential, the process of creating them is complicated and not without error. Coaxing these cells into a new type once you’ve made them is also easier said than done.

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Twitters 1st step to recapture consumers.

A Tweetstorm is brewing in San Francisco.

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Nice — Bridgewaters engaged. Actually, not too surprised by this.

IBTimes UK spoke to AI finance startup Walnut Algorithms about machine learning and the financial sector.

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Another article just came out today providing additional content on the Quantum Computing threat and it did reference the article that I had published. Glad that folks are working on this.

The NSA is worried about quantum computers. It warns that it “must act now” to ensure that encryption systems can’t be broken wide open by the new super-fast hardware.

In a document outlining common concerns about the effects that quantum computing may have on national security and encryption of sensitive data, the NSA warns that “public-key algorithms… are all vulnerable to attack by a sufficiently large quantum computer.”

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Nice! Robo-advice will be accessed by investors worth $2.2 trillion by 2020, equivalent to 12% of the global retail funds.

If you’re a finance professional, the question you probably get asked most by your friends and acquaintances is “what investments they should make”? That’s the basic question that everyone with money will ask. They may ask the “financial advisor” at their bank, they may turn to Google for advice, they may ask their “friends who work in finance”, or they may listen to recommendations of people they trust. However, individuals with a high net worth will typically seek out a wealth management firm with a brand they trust. But which firm?

Try to Google “top wealth management firms” and the first 5 search results will be a comparison of the top 100 wealth management firms. That’s a very competitive space. How do you differentiate yourself from your 99 competitors who are essentially trying to so the same thing you are? One way is through the use of technology, and as a result we see the rise of “robo advisors”. Here’s the definition of a “robo-advisor” from Investopedia:

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The game of Go has long been viewed as the most challenging of classic games for artificial intelligence due to its enormous search space and the difficulty of evaluating board positions and moves.

Google DeepMind introduced a new approach to computer Go with their program, AlphaGo, that uses value networks to evaluate board positions and policy networks to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte-Carlo tree search programs that simulate thousands of random games of self-play. DeepMind also introduce a new search algorithm that combines Monte-Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.

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Google has achieved one of the long-standing “grand challenges” of AI, building a computer capable of beating expert players of the board game Go.

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In a paper published in Nature on 28th January 2016, we describe a new approach to computer Go. This is the first time ever that a computer program “AlphaGo” has defeated a human professional player.

The game of Go is widely viewed as an unsolved “grand challenge” for artificial intelligence. Games are a great testing ground for inventing smarter, more flexible algorithms that have the ability to tackle problems in ways similar to humans. The first classic game mastered by a computer was noughts and crosses (also known as tic-tac-toe) in 1952. But until now, one game has thwarted A.I. researchers: the ancient game of Go.

Continue reading “Google DeepMind: Ground-breaking AlphaGo masters the game of Go” »