Google DeepMind claims to have significantly improved computer-generated speech with its AI technology, paving the way forward for sophisticated talking machines like those seen in sci-fi films like „Her" and „Ex-Machina."

We all become accustomed to the tone and pattern of human speech at an early age, and any deviations from what we have come to accept as "normal" are immediately recognizable. That's why it has been so difficult to develop text-to-speech (TTS) that sounds authentically human. Google's DeepMind AI research arm has turned its machine learning model on the problem, and the resulting "WaveNet" platform has produced some amazing (and slightly creepy) results. Google and other companies have made huge advances in making human speech understandable by machines, but making the reply sound realistic has proven more challenging. Most TTS systems are based on so-called concatenative technologies.

Dmitry Malioutov can't say much about what he built. As a research scientist at IBM, Malioutov spends part of his time building machine learning systems that solve difficult problems faced by IBM's corporate clients. One such program was meant for a large insurance corporation. It was a challenging assignment, requiring a sophisticated algorithm. When it came time to describe the results to his client, though, there was a wrinkle. "We couldn't explain the model to them because they didn't have the training in machine learning." In fact, it may not have helped even if they were machine learning experts. That's because the model was an artificial neural network, a program that takes in a given type of data--in this case, the insurance company's customer records--and finds patterns in them. These networks have been in practical use for over half a century, but lately they've seen a resurgence, powering breakthroughs in everything from speech recognition and language translation to Go-playing robots and self-driving cars.

"AI ...surely will be a trend at least on the size of big data. It almost certainly will be a trend on the size of mobile. It might be a trend on the size of the internet. And maybe, just maybe, it'll be a trend on the size of software; that the software before machine intelligence and after will be two worlds that are very different from each other." It's undeniable that artificial intelligence (AI) is one of tech's hottest topics and a trend that is permeating every part of our our world.

Artificial intelligence (AI) has already transformed our lives -- from the autonomous cars on the roads to the robotic vacuums and smart thermostats in our homes. Over the next 15 years, AI technologies will continue to make inroads in nearly every area of our lives, from education to entertainment, health care to security. The question is, are we ready? Do we have the answers to the legal and ethical quandaries that will certainly arise from the increasing integration of AI into our daily lives? Are we even asking the right questions? Now, a panel of academics and industry thinkers has looked ahead to 2030 to forecast how advances in AI might affect life in a typical North American city and spark discussion about how to ensure the safe, fair, and beneficial development of these rapidly developing technologies.

The terms "machine learning" and "artificial intelligence" (AI) conjure up feelings that are equal parts fear and fascination. Until recently, the prospect of a piece of software making human-like decisions resided safely in the far-fetched expectations of 1960s-era computer scientists or the plot lines of science fiction novels. Today, however, after decades of unmet expectations, we finally have AI systems that are beginning to influence our lives in tangible ways. Voice recognition systems like Amazon's Echo and Apple's Siri, and once-unimaginable fantasies like self-driving cars, are on the market for consumers, with more exciting life-like systems to come. We have also seen a few early signs of robotic autonomy that makes us feel uneasy, like the Russian robot that learned how to escape the lab!

The clear answer is NO as the conversion rates are 3-5% and there is 70% shopping cart abandonment rates. So, how do we solve this problem?

Responses to a White House request for information about the future of artificial intelligence show a continued divide between those who are ready to embrace intelligent machines and those who worry about a future in which robots run the world. The responses were made public this month after the White House Office of Science and Technology issued a call for input about how artificial intelligence technology is currently shaping the world, how AI is likely to develop in the future and what role the government should play in either encouraging or regulating development. The request for information drew responses from large corporations, such as IBM, Google and Microsoft, as well as from academia and private citizens. The responses show there still is little agreement about the future of AI. "The danger is not machines run amok, as suggested by some, like [Elon] Musk or [Stephen] Hawking (who know nothing about AI). The danger is, like nuclear weapons, what AI will allow us to do to ourselves. And it is not a remote possibility, but already happening: Uber, for example, is proposing a fleet of driverless cars. What happens when the profits associated with whole industries are not distributed across the whole world, but flow into the coffers of a single company or person?"

Dataiku DSS 3.1 introduces new visual machine learning engines that allow users to create incredibly powerful predictive applications within a code-free interface. Users of all skill levels can now leverage HPE Vertica machine learning, H2O Sparkling Water, MLlib, Scikit-Learn, and XGBoost directly from within the visual analysis section of Dataiku DSS 3.1 to apply powerful machine learning algorithms to their data science projects without having to write a line of code. The blending of visual code-free and free-form code-based transformations is one of the main strengths of Dataiku DSS for the prototyping and production of data applications. In addition to Python, R, SQL, Hive, Impala, and Pig, Dataiku DSS 3.1 now enables Apache Spark users to write transformations and interactive notebooks in Scala, bringing the power of Spark's native and most performant language to data analysts using Dataiku DSS.

This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Smart machines need ethics, too: Remember that movie in which a computer asked an impossibly young Matthew Broderick, "Shall we play a game?" Four decades later, it turns out that global thermonuclear war may be the least likely of a slew of ethical dilemmas associated with smart machines -- dilemmas with which we are only just beginning to grapple. The worrisome lack of a code of ethics for smart machines has not been lost on Alphabet, Amazon, Facebook, IBM, and Microsoft, according to a report by John Markoff in The New York Times. The five tech giants (if you buy Mark Zuckerberg's contention that he isn't running a media company) have formed an industry partnership to develop and adopt ethical standards for artificial intelligence -- an effort that Markoff infers is motivated as much to head off government regulation as to safeguard the world from black-hearted machines. On the other hand, the first of a century's worth of quinquennial reports from Stanford's One Hundred Year Study on Artificial Intelligence (AI100) throws the ethical ball into the government's court.