In the elegant quiet of the café at the Church of Sweden, a narrow Gothic-style building in Midtown Manhattan, Daniel Cohen is taking a break from explaining genetics. He moves toward the creaky piano positioned near the front door, sits down, and plays a flowing, flawless rendition of "Over the Rainbow." If human biology is the scientific equivalent of a complicated score, Cohen has learned how to navigate it like a virtuoso. Cohen was the driving force behind Généthon, the French laboratory that in December 1993 produced the first-ever "map" of the human genome. He essentially introduced Big Data and automation to the study of genomics, as he and his team demonstrated for the first time that it was possible to use super-fast computing to speed up the processing of DNA samples.

During our last trip Los Angeles, conference participants experienced a thrilling machine-learning, real-time demonstration as part of the SIGGRAPH 2017 Real-Time Live! showcase: Physics Forests. This data-driven fluid simulation, with surface generation, foam, coupling with rigid bodies, and rendering, is capable of simulating several million particles in real time. It uses the regression forest to estimate the behavior of particles and rendered surfaces. The method can handle a wide range of fluid parameters. Since then, Physics Forests has only grown and built on the capabilities it boasted in 2017.

Bottom Line: Manufacturers' most valuable data is generated on shop floors daily, bringing with it the challenge of analysing it to find prescriptive insights fast – and an ideal problem for machine learning to solve. Manufacturing is the most data-prolific industry there is, generating on average 1.9 petabytes of data every year according to the McKinsey Global Insititute. Supply chains, sourcing, factory operations, and the phases of compliance and quality management generate the majority of data. The most valuable data of all comes from product inspections that can immediately find exceptionally strong or weak suppliers, quality management and compliance practices in a factory. Manufacturing's massive problem is in getting quality inspection results out fast enough across brands & retailers, other factories, suppliers and vendors to make a difference in future product quality.

Artificial intelligence (AI) has become a critical aspect in financial services. Financial institutions around the world are making efforts to adopt AI for task automation, customer services, behavior analysis, as well as fraud finding, and are making large-scale investments in related technologies. The World Economic Forum (WEF) estimates the number to reach US$10 billion by 2020. In financial services, applications for AI technologies exist across nearly the entire spectrum of business, from algorithmic stock trading applications and credit card fraud detection, to auto investment advisors and market research and sentiment analysis. The following 10 AI fintech companies are some of Europe's rising stars to watch very closely: Swiss startup Parashift develops AI-based accounting document management technologies which it offers through a SaaS platform and APIs.

Finding data for your analytics and machine learning initiatives has generally not been a problem for most organizations. Enterprise organizations collect data as an operational part of doing business. There are transactions, customer records, ERP, CRM, financials, human capital management, and more. Your organization has gathered metrics from web site visits and marketing email responses. There's plenty of data you already have that can fuel your data, analytics or machine learning initiatives.

Lawmakers want to make sure the algorithms companies use to target ads, recruit employees and make other decisions aren't inherently biased against certain people. Sens. Ron Wyden, D-Ore., and Cory Booker, D-N.J., on Wednesday introduced legislation that would require organizations to assess the objectivity of their algorithms and correct any issues might unfairly skew their results. As society depends on tech to make increasingly consequential decisions, the Algorithmic Accountability Act aims to create a level playing field for people of all backgrounds. Rep. Yvette Clarke, D-N.Y., introduced a companion bill in the House. Under the act, the Federal Trade Commission would compel companies to test both their algorithms and training data for any shortcomings that could lead to biased, inaccurate, discriminatory or otherwise unfair decisions.

Jeff Ding, a researcher at the University of Oxford who studies China's AI development, shared some recent reflections on the most important things he's learned in the past year. They offer a great snapshot into the current state of the industry, so I've summarized them briefly below. The Chinese- and English-speaking AI communities have an asymmetrical understanding of each other. Most Chinese researchers can read English, and nearly all major research developments in the Western world are immediately translated into Chinese, but the reverse is not true. Therefore, the Chinese research community has a much deeper understanding than the English-speaking one of what's happening on both sides of the aisle.

Jeff Ding, a researcher at the University of Oxford who studies China's AI development, shared some recent reflections on the most important things he's learned in the past year. They offer a great snapshot into the current state of the industry, so I've summarized them briefly below. The Chinese- and English-speaking AI communities have an asymmetrical understanding of each other. Most Chinese researchers can read English, and nearly all major research developments in the Western world are immediately translated into Chinese, but the reverse is not true. Therefore, the Chinese research community has a much deeper understanding than the English-speaking one of what's happening on both sides of the aisle.

BRUSSELS (Reuters) - Companies working with artificial intelligence need to install accountability mechanisms to prevent its being misused, the European Commission said on Monday, under new ethical guidelines for a technology open to abuse. AI projects should be transparent, have human oversight and secure and reliable algorithms, and they must be subject to privacy and data protection rules, the commission said, among other recommendations. The European Union initiative taps in to a global debate about when or whether companies should put ethical concerns before business interests, and how tough a line regulators can afford to take on new projects without risking killing off innovation. "The ethical dimension of AI is not a luxury feature or an add-on. It is only with trust that our society can fully benefit from technologies," the Commission digital chief, Andrus Ansip, said in a statement.

WATCH (May 24, 2018): Amazon's Alexa records family's conversation, sends it to random contact Amazon staff can listen to commands and questions users pose to the Alexa voice assistant -- and they sometimes do. The company acknowledged that the conversations aren't totally private in a statement to Global News after the news was first reported by Bloomberg. "We only annotate an extremely small number of interactions from a random set of customers in order to improve the customer experience," Amazon said in the statement. Amazon explained that the company uses samples collected to better train "speech recognition and natural language understanding systems." READ MORE: Alexa recorded one family's conversations and sent them to a friend, without them knowing Bloomberg reported Wednesday that Amazon has "thousands" of employees who are trying to improve Alexa's speech recognition technology.