Pinchas Gutter goes out of his way to find me biscuits. In a sun-baked living room in his north London home, he opens a packet of Rich Tea, sits down and tells me about the Holocaust. Gutter was seven years old when the second world war broke out. He lived in the Warsaw ghetto for three and a half years, took part in its uprising, survived six Nazi concentration camps – including the Majdanek extermination camp – and lived through a death march across Germany to Theresienstadt in occupied Czechoslovakia. "Remembrance is the secret of redemption, while forgetting leads to exile," he says, quoting Baal Shem Tov, the founder of Hasidic Judaism.

New technology could lead humans to relinquish control over decisions to use lethal force. As artificial intelligence advances, the possibility that machines could independently select and fire on targets is fast approaching. Fully autonomous weapons, also known as "killer robots," are quickly moving from the realm of science fiction toward reality. The unmanned Sea Hunter gets underway. At present it sails without weapons, but it exemplifies the move toward greater autonomy.

The 8 Ball supercomputer, a spherical monolith that can harness the rules of quantum mechanics to solve vastly complex problems, is not real. It's actually a quantum computer that appeared in a short story written by science fiction novelist Gregory Dale Bear last year. Such computers have yet to escape the realm of science fiction, but recent advances have moved the prospect of a working quantum computer closer to reality. Scientists from Google and the University of Basque Country in Spain believe they have cleared some of the barriers to more complex and useful quantum computers. The technology is based on the idea of quantum bits, or qubits, which loosely correspond to the classic bits stored inside the transistors etched onto silicon. The qubits are capable of encoding more data than the 1's and 0's that form the basis of modern computing.

Sitting in his office overlooking downtown Bellevue, Washington, Microsoft's Fil Alleva is talking about the long and sometimes difficult road he and other speech recognition experts have taken from the early work of the 1970s to the situation he is in today, where he can turn to his computer and say, "Cortana, I want a pizza" and get results. The conversation quickly drifts deeply into the technology that makes something like that possible, and then Alleva pauses. "What we all had in the back of our minds, whether we say it or not, was C-3PO," he admits with a grin. The personable "Star Wars" character who can understand and speak millions of languages may not have been the only inspiration for the world's leading researchers – some also will say that the universal translator that was featured prominently in "Star Trek" spurred their dreams along. But regardless of whether they were "Star Wars" fans or "Star Trek" loyalists, one thing is clear: The quest to create a computer that can understand spoken language as well as a person was for years so fanciful that the only thing to compare it to was science fiction.

There's no mistaking the fact that Google is one of many companies that consider machine learning to be the future of technology. Now, the firm has announced the formation of a Zurich-based research group dedicated to the field. The Swiss city is already home to Google's largest engineering office outside of the US. The engine that powers Knowledge Graph and the conversation engine powering the Google Assistant in Allo messenger were developed by the teams located here. According to a blog post by Emmanuel Mogenet, head of Google Research for Europe, the new group will be focusing on the key areas of machine learning, natural language processing and understanding, and machine perception.

Falon Fatemi, an entrepreneur and former Alphabet (GOOGL) employee, was kind enough to stop by Barron's offices this afternoon to discuss her latest initiative, Node, a startup based in San Francisco's SOMA district, stuck in between Pinterest and AirBnB. Fatemi claims the distinction of having been the youngest employee at the time at Google, starting at age 19 in 2005 and working there until 2011. She was in town for the Sales Machine Summit conference in Manhattan, being sponsored by Salesforce.com (CRM), which is a partner of Node's. Her VP in charge of revenue, Daniel Barber, was giving a talk Thursday at the conference. Fatemi, who has received 7.5 million in seed funding from VCs including Mark Cuban, NEA, Avalon Ventures, and Canaan Partners, is aiming to bring a "graph of relationships" to replace plain old searching for information.

Movies offer several examples of computers with human-like qualities, but the definition of artificial intelligence is complicated.

Google's DeepMind artificial intelligence (AI) technology has proven to be very smart. DeepMind's AlphaGo system got worldwide attention for beating top-ranked Go player Lee Sedol earlier this year. Previously it has played Breakout and navigated a Doom-like maze. But now the DeepMind software is looking more versatile. Today the Google DeepMind lab unveiled another feat that looks off the wall but is actually evidence of the strength of Google's AI.

Machine learning covers a lot of ground. At Microsoft, it's being incorporated to detect lies, recognize human responses and forecast finances; as well as improve search, natural language processing, advertising, security and gaming. It's making a big difference in a lot of different applications that really matter for the future," says John Langford, an expert on machine learning at the Microsoft Research lab in New York City who is also the general chair for the International Conference on Machine Learning, which has grown by 65 percent since last year thanks to the technology's success. "Figuring out how to use data to make decisions to help people is what machine learning is about." Advanced analytics and data science resources make it possible for once-arduous tasks to get done quickly. It can speed up a multitude of normally time-consuming processes, such as vision recognition, causality, crowd sourcing and more. "Now we're seeing more work in neural networks and deep learning than in previous years," says Langford of some prevalent themes in this year's conference. By far, this is the largest ICML ever. The field is really growing fast."

There are standard workflows in a machine learning project that can be automated. In Python scikit-learn, Pipelines help to to clearly define and automate these workflows. In this post you will discover Pipelines in scikit-learn and how you can automate common machine learning workflows.