Learning to learn by gradient descent by gradient descent, Andrychowicz et al., NIPS 2016 One of the things that strikes me when I read these NIPS papers is just how short some of them are – between the introduction and the evaluation sections you might find only one or two pages! A general form is to start out with a basic mathematical model of the problem domain, expressed in terms of functions. Selected functions are then learned, by reaching into the machine learning toolbox and combining existing building blocks in potentially novel ways. When looked at this way, we could really call machine learning'function learning'. Thinking in terms of functions like this is a bridge back to the familiar (for me at least).

Dear colleagues: Advances in Artificial Intelligence (AI) technology have opened up new markets and new opportunities for progress in critical areas such as health, education, energy, and the environment. In recent years, machines have surpassed humans in the performance of certain specific tasks, such as some aspects of image recognition. Experts forecast that rapid progress in the field of specialized artificial intelligence will continue. Although it is very unlikely that machines will exhibit broadly-applicable intelligence comparable to or exceeding that of humans in the next 20 years, it is to be expected that machines will reach and exceed human performance on more and more tasks. As a contribution toward preparing the United States for a future in which AI plays a growing role, this report surveys the current state of AI, its existing and potential applications, and the questions that are raised for society and public policy by progress in AI. The report also makes recommendations for specific further actions by Federal agencies and other actors. A companion document lays out a strategic plan for Federally-funded research and development in AI. Additionally, in the coming months, the Administration will release a follow-on report exploring in greater depth the effect of AI-driven automation on jobs and the economy.

Clarifai, a startup that creates visual recognition software, has hired four members of Twitter's machine learning team, Cortex, as well as an engineer formerly with Google Brain, the search giant's research group focused on artificial intelligence. Founded in 2013 by computer science PhD Matthew Zeiler after he did an internship with the Google research team, Clarifai licenses customizable software that can automatically organize and filter images. The startup's clients include BuzzFeed, travel site Trivago and consumer-packaged goods giant Unilever. The 40-employee startup (including new hires) has raised $41 million, including $30 million in a Series B round led by Menlo Ventures. The round included contributions from Union Square Ventures, Lux Capital and Qualcomm Ventures.

AI has been a hot topic for years. Artificial Intelligence simply means the theory and development of computer systems able to do work that usually requires human intelligence. Now Artifical Intelligence has achieved a milestone of defeating humans in poker for the first time. The AI namely Libratus which is developed by Carnegie Mellon University with $1.7 million worth of chips against the popular and professional poker players in the world. AI managed to beat them in a 20-day marathon poker tournament that was held in Philadelphia on Tuesday.

Marking a major step forward for artificial intelligence (AI), Libratus, an AI developed by Carnegie Mellon University (CMU), has resoundingly beaten four of the best heads-up no-limit Texas hold'em poker players in the world in a marathon, 20-day competition. After 20 days and a collective 120,000 hands played, Libratus closed out the competition Monday leading the pros by a collective $1,766,250 in chips. "I'm just impressed with the quality of poker Libratus plays," pro player Jason Les, a specialist in heads-up no-limit Texas hold'em like the other three players, said at a press conference yesterday morning. "They made algorithms that play this game better than us. We make a living trying to find vulnerabilities in strategies. That's what we do every day when we play heads-up no-limit. We tried everything we could and it was just too strong."

After reading this article does this affect the debate regarding praxeology and econometrics? RW response: Praxeology, as Murray Rothbard put it, "rests on the fundamental axiom that individual human beings act, that is, on the primordial fact that individuals engage in conscious actions toward chosen goals." Artificial intelligence did nothing to stop the poker players referenced from acting, that is, choosing goals. All AI did in the referenced example is make quicker and better calculations than the poker players, in a given fixed environment. This is an example for all practical purposes of a jacked-up hand calculator.

Amazon's Alexa was showered with attention at last month's CES tech show in Las Vegas, as dozens of companies invited the voice assistant to live in their cars, washing machines and set-top boxes. Yet, behind all that luster is a somewhat uncomfortable question about Amazon's 2-year-old artificial intelligence platform: Does Alexa make Amazon any money? The answer, say several analysts: No, but it will. And when it does, it'll be huge. As part of Amazon's fourth-quarter earnings report on Thursday, the company mentioned that Alexa-powered devices were Amazon's top-selling products this holiday season.

Consumers and companies are all-too familiar with the consequences of financial fraud, having been victims of massive data breaches and orchestrated campaigns of payment fraud across all channels. What's less clear is how financial institutions and merchants can stay one step ahead of the risk and fraud, while reducing their regulatory burden. In the most recent episode of the Real Machine Podcast, Feedzai's Ajit Ghuman hosts a fascinating exploration of the potential of AI and machine learning with one of the risk management profession's foremost practitioners, Peter Mockenheim, prior Chief Operational Risk Officer for Santander Consumer. Peter has had a storied career leading the risk and compliance departments of a variety of financial institutions. Peter was a founding member of Nationwide Bank, and then went on to become Chief Control Officer for the operations of Chase's Consumer And Community Bank, where he achieved the highest possible internal audit rating, firm-wide.

If you play video games, imagine how much you sometimes empathize with the character you're controlling. You may even forget the separation between the two of you, experiencing the world as that character. Consider whether your control in these moments is different, both at a high level and in tiny movements, than what the character would do if you had simply written down a set of rules for it to act by. In that difference lies the promise of this method for developing character AI. In psychology research methodology, there's a broad consensus that if you want to know what someone would do in a situation, you don't ask them what they would do.

As the World Economic Forum unraveled last week in Davos, Switzerland, many leaders highlighted areas of future growth and how to shape the global economy – to promote an economy that will only serve to help improve our lives. Ginni Rometty, CEO of IBM, was also there discussing how IBM can contribute to this starting with artificial intelligence – or augmented intelligence/cognitive as we say at IBM. Why does IBM choose to use "augmented intelligence" or "cognitive" over "artificial intelligence"? What we've witnessed during each of these stages is some form of mechanics or machinery developed to augment our performance, thereby improving our outcome. Automotive – The assembly line was created to help workers produce more cars and simultaneously made the car more affordable to the masses. Communication – telephone / telex / fax – Gives one the ability to transfer information faster rather than having a messenger or a trained bird fly there.