Google today announced at its GCP NEXT conference in San Francisco a new service called Cloud Machine Learning in a limited preview. The tool relies on the TensorFlow machine learning library that Google open-sourced a few months ago. And it powers another new product called the Google Cloud Speech API, as well as the Google Cloud Translate API. Google's launch of the service follows the launch of the Cloud Vision API recently. But a machine learning platform is something that public cloud market leader Amazon Web Services beat Google to.

Microsoft Corp. is in damage control mode after Twitter users exploited its new artificial intelligence chat bot, teaching it to spew racist, sexist and offensive remarks. The company introduced Tay earlier this week to chat with real humans on Twitter and other messaging platforms. The bot learns by parroting comments and then generating its own answers and statements based on all of its interactions. It was supposed to emulate the casual speech of a stereotypical millennial. The Internet took advantage and quickly tried to see how far it could push Tay.

One of the tropes of science fiction is the uncanny valley -- the phenomenon of a robot looking eerily human-like but not quite right in some intangible way. Another is the breakdown in the distinction between human and machine. And a third is artificial intelligence becoming so complex and sophisticated that humans are no longer able to understand or control it. Well, not to be the bearer of bad news, but all of those tropes from the movies are already happening. The good news is that some of these advances have the potential to make our lives a lot better.

The automation of work and the digital disruption of business models place a premium on leaders who can create a vision of change and frame it positively. How disruptive will accelerating workplace automation be for organizations in the future? For decades, businesses have deployed technology to reduce costs and complexity, make better products, and develop new business models. But the new potential of artificial intelligence and advanced robotics poses major new challenges for leaders as they seek to reset their strategies for a digital age. Last November, Bloomberg chairman Peter Grauer and Nadir Mohamed, the recently retired CEO of Rogers Communications, sat down with Manfred Kets de Vries, a professor at INSEAD, and Harvard professor Robert Kegan to debate some of the issues with Claudio Feser, head of McKinsey's leadership development initiative.

Yesterday, Microsoft unleashed Tay, the teen-talking AI chatbot built to mimic and converse with users in real time. Because the world is a terrible place full of shitty people, many of those users took advantage of Tay's machine learning capabilities and coaxed it into say racist, sexist, and generally awful things. While things started off innocently enough, Godwin's Law--an internet rule dictating that an online discussion will inevitably devolve into fights over Adolf Hitler and the Nazis if left for long enough--eventually took hold. Tay quickly began to spout off racist and xenophobic epithets, largely in response to the people who were tweeting at it--the chatbot, after all, takes its conversational cues from the world wide web. Given that the internet is often a massive garbage fire of the worst parts of humanity, it should come as no surprise that Tay began to take on those characteristics.

Having spent the past 31 years memorizing an astonishing collection of general knowledge, the artificial-intelligence engine created by Doug Lenat is finally ready to go to work. Lenat's creation is Cyc, a knowledge base of semantic information designed to give computers some understanding of how things work in the real world. Cyc has been given many thousands of facts, including lots of information that you wouldn't find in an encyclopedia because it seems self-evident. It knows, for example, that that Sir Isaac Newton is a famous historical figure who is no longer alive. But more important, Cyc also understands that if you let go of an apple it will fall to the ground; that an apple is not bigger than a person; and that a person cannot throw an apple into space.

Guest blog post, authored by Brandon Rohrer, Senior Data Scientist at Microsoft. Machine learning (ML) is the motor that drives data science. Each ML method (also called an algorithm) takes in data, turns it over, and spits out an answer. ML algorithms do the part of data science that is the trickiest to explain and the most fun to work with. That's where the mathematical magic happens.

See the Wiki for full documentation, examples and other information. This framework is tested to work under Python 2.7, and Python 3.5. It should also work with Python 3.3 and 3.4. The required dependencies are NumPy 1.10, joblib 0.9. You also need theano 0.7 (lasagne is optional) or you can write your own neural network using your favorite framework. For running the examples, Matplotlib 1.1.1 is required.

This is part two of the series. In part one, we used linear regression model to predict the prices of used Toyota Corollas. There are some overlap in the materials for those just reading this post for the first time. For those who read the part 1 of the series using linear regression, then you can safely skip to the section where I applied neural networks to the same data set. In this post, we will use neural networks!

Google and Nest reportedly have a bunch of new smart home products in the works, but there may not be much collaboration happening between the two Alphabet subsidiaries. On the Google side, the search giant may be working on a competitor to Amazon's Echo connected speaker, according to The Information (via The Verge). The story reveals no details about the product, but it seems like an obvious fit for Google, which has already made voice controls a centerpiece of its Android Wear smartwatch platform. A device that answers Internet queries and controls other smart home products could very well tie into Google's broader efforts to create a new platform for the Internet of Things. Earlier this month, Recode reported that Nest had explored its own Echo-like product.