Hewlett Packard Enterprise has made no secret of its desire to push further into big data, and on Tuesday it announced two key new offerings: HPE Haven OnDemand Combinations, an extension of the "machine learning as a service" platform it released earlier this year, and Vertica 8, a major new update to its analytics software. Launched in March, HPE's Haven OnDemand cloud platform offers machine learning APIs (application programming interfaces) and services designed to help developers and businesses build data-rich applications. The platform now has 70 artificial intelligence APIs and more than 18,000 users, and HPE is extending it to make development easier. Haven OnDemand Combinations, a cloud service built on top of OnDemand, provides a catalogue of pre-built and customizable machine learning APIs and a drag-and-drop interface for mixing and matching them. Developers can string together APIs and copy and paste the code directly into their development projects, HPE said.

Facebook CEO Mark Zuckerberg is aiming to demonstrate the artificial intelligence project he's been laboring on in his spare time in September. Zuckerberg shared his plans during a live Facebook town hall Q&A session from Rome today. "I hope to have a demo, hopefully next month," he said when asked about the side project. The screenshot above was taken as Zuckerberg was discussing his AI system; clearly he's happy and excited about the upcoming public debut. Back in January, the Facebook chief revealed that as part of his annual "personal challenge," he wanted to build an AI capable of controlling his home (things like temperature, lighting, etc.) and assisting with his professional work.

People convey meaning by what they say as well as how they say it: Tone, word choice and the length of a phrase are all crucial cues to understanding what's going on in someone's mind. When a psychiatrist or psychologist examines a person, they listen for these signals to get a sense of their wellbeing, drawing on past experience to guide their judgment. Researchers are now applying that same approach, with the help of machine learning, to diagnose people with mental disorders. In 2015, a team of researchers developed an AI model that correctly predicted which members of a group of young people would develop psychosis--a major feature of schizophrenia--by analyzing transcripts of their speech. This model focused on tell-tale verbal tics of psychosis: short sentences, confusing, frequent use of words like "this," "that," and "a," as well as a muddled sense of meaning from one sentence to the next.

TORONTO - IBM will be joining the fintech hub at Toronto's MaRS discovery district, becoming the first technology tenant in a space currently occupied by financial services companies. The technology giant will move into the hub in the coming weeks, joining the likes of CIBC, Manulife and payment processor Moneris. Adam Nanjee, who heads up the fintech cluster at MaRS, says the so-called C Suite is meant to act as a bridge between larger corporations and tech-savvy startups. For companies, the fintech hub provides opportunities to partner with startups working on cutting-edge, potentially disruptive technologies. These partnerships have the potential to lead to investments and acquisition offers -- a huge benefit for the startups.

The concept of machine learning is not new. Attempts at systems emulating intelligent behavior, like expert systems, go as far back as the early 1980's. Three latest development are pushing forward "Machine Learning": As in many other fields, development of Machine Learning is also seeing development on algorithms that take advantage of the new hardware capabilities. According Wikipedia, Deep Learning is "a part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc. Some representations are better than others at simplifying the learning task".

Have you heard about machine learning, but you don't really understand what it's good for? Or you understand the basic idea, but you're struggling to apply it using Python? In this video, I'll explain the essential ideas behind machine learning. Then, we'll build our first machine learning model in just a few lines of code using Python's scikit-learn library. This is a recording of a webcast hosted by Trey Hunner of Weekly Python Chat: http://www.weeklypython.chat

Earlier this year, Nervana Systems CEO Naveen Rao was asked what would happen if Intel began attacking the fast-growing market for chips designed specifically for running "deep learning" software. Now, Rao will be a key player in Intel's attempt to catch up in one of the most promising new silicon markets to emerge since the smartphone. Intel revealed Tuesday that it is buying Nervana and its deep-learning hardware and software for an undisclosed amount. The acquisition marks a departure for Intel and comes at a crucial moment. The company became the world's largest chip maker with a single-minded strategy to make its x86 microprocessors the standard for running a huge swath of applications, from solitaire to massive payroll systems.

The manufacturing hub for the electronics industry, Kunshan, in Jiangsu province, is seeking a drastic reduction in labour costs as it undergoes a makeover after an industrial explosion killed 146 people in 2014. The county, one-seventh the size of neighbouring Shanghai and the mainland's first county to achieve US 4,000 per capita income, was adjudged the best county for its economic performance by Forbes for seven years in a row. However, the blaze, blamed on poor safety standards and haphazard industrialisation, dented Kunshan's pride. More than a year on, the county, which attracts much of its investment from Taiwan, is trying to reinvent its growth strategy. It is accelerating growth by replacing humans with robots and encouraging start-ups.

A number of recent studies predict that robots will replace millions of human workers as artificial intelligence continues to evolve. In a way, none of this is new: Technological advances often result in a reduced need for human employees. Manufacturers have been using automation for many decades, and in more recent years, states have been phasing out human tollbooth operators in favor of electronic, cashless toll systems. However, the prospect of being replaced by an intelligent machine strikes a particularly foreboding nerve in many people. It doesn't matter if that "machine" is an algorithm-driven software program running on enterprise IT infrastructures or actual, physical robots imbued with artificial intelligence (AI).