Posts Tagged 'neural networks'

Consider Google Translate

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The New York Times Magazine features a beautifully prepared and presented article on the recent dramatic improvements in Google Translate brought about by deep learning technologies developed in the Google Brain division of Alphabet. But this is not simply an article about a breakthrough innovation, it is a nuanced discussion of the history, problems, and approaches, both failed and (sometimes) successful, that lie behind some of the most useful AI-supported facilities we use today.

As an example, author Gideon Lewis-Kraus notes:

“…in ...

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Microsoft In Recovery Room After Tay

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Microsoft appears to have been emboldened by the success of its Xiaoice chat bot in China to try a US-based chat bot with the personality of a 19-year-old girl in order to attract the 18-24 year-old demographic. What it found was that the US market responded to the introduction by attacking the bot’s learning algorithms with hate speech and “training” Tay to be an eager participant within its first few hours online.

Microsoft acted at once to shut down the experiment ...

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Looking at Unpredictability in truly Cognitive Systems

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The recent Google AlphaGo victory over a human Go grandmaster is notable not only for the prodigious capabilities of deep learning on display from the machine but also for the surprise one of AlphaGo’s moves brought to Lee Se-Dol, the human competitor. As Lee stated, this was “not a human move.” He is reported to have felt the need to leave the room for 15 minutes to regain his composure.

Researcher Jonathan Tapson, Director of the MARCS Institute for Brain, Behaviour ...

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Google open sources AI engine, TensorFlow

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Google has released its second-generation machine learning system in an effort to accelerate the field of artificial intelligence.

According to Google’s white paper on TensorFlow, “it is flexible and can be used to express a wide variety of algorithms, including training and inference algorithms for deep neural network models.” This highly scalable machine learning system can run on a single smartphone or across thousands of computers in datacenters. Jeff Dean, Senior Google Fellow, and Rajat Monga, Technical Lead, say ...

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Master Algorithm. Really?

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Daniel Levitin crafts an interesting review of Pedro Domingo’s recent book The Master Algorithm. Author, neuroscientist, musician, Levitin brings all of these fields into play in visiting the ground of learning algorithms, their effectiveness, their limitations, their future(s), and their implications. Both Domingo and Levitin are resources of value to anyone thinking about cognitive computing.

Read More at WSJ

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