"Data Scientist is the sexiest job of the 21st century" So, what exactly is data science and why all the hype around data scientists. Frankly speaking, multiple job descriptions and explanations of the same role make it harder for businesses to clearly understand what a data scientist is and does. This complicates the ROI business leaders expect when investing in them. To me, data Science involves mining actionable and sensible insights from multiple data formats by applying mathematics, statistics, machine learning, etc. Data scientists typically analyze data sets, or data depositories that are maintained within an organization and/or they analyze data scraped from publicly available sources.

IBM announced Feb. 15 that it's bringing some of Watson's artificial intelligence to the private cloud with a new cognitive computing platform called Machine Learning. IBM called Machine Learning the "first cognitive platform" to use analytical models to help companies more effectively analyze their operations and make sound, AI-based decisions. Machine Learning, which is designed for both novice and advanced data scientists, will continue to learn as users feed it fresh data. It can also help scientists choose the right algorithms to make decisions and can be put to work in most industries, even those as wide-ranging as retailing or oil exploration. It runs on IBM's z System mainframes, which are widely deployed in enterprise data centers and, according to the company, can process billions of transactions each day.

This week in the Substance ÉTS science review, we highlight articles on two topics that are top concerns for many people: Artificial Intelligence and the environment. Each quarter, The Economist publishes a fairly detailed review of a particular aspect of technology. In the first quarter of 2017, The Economist published seven articles describing the progress and limitations of language technology with, in addition, a glimpse of the future in this field. Researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), in the US, are trying to figure out how MIT students solve a planning problem. They found that the strategies used by the majority of students could be described using a language called "linear temporal logic".

Artificial Intelligence, the term, was coined way back in 1956 by John McCarthy, a Stanford professor. As an idea it had its share of disappointments, battled scepticism and was kept on the backburner for several decades. However, intelligent machines and for them to be considered at par with human intelligence, are but two different propositions. Sci-fi readers would recollect HAL 9000, Arthur C. Clarke's AI–based protagonist in the book 2001: A Space Odyssey, which turns to an antagonist as the plot unfolds, and eventually turns villainous. Will it remain fictional, or is there a strong possibility that we may actually experience this in our lifetime?

With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo @ThingsExpo, June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA. Learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. Delegates to Cloud Expo / @ThingsExpo will be able to attend 8 simultaneous, information-packed education tracks. There are over 120 breakout sessions in all, with Keynotes, General Sessions, and Power Panels adding to three days of incredibly rich presentations and content. Join Cloud Expo / @ThingsExpo conference chair Roger Strukhoff (@IoT2040), June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA for three days of intense Enterprise Cloud and'Digital Transformation' discussion and focus, including Big Data's indispensable role in IoT, Smart Grids and (IIoT) Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) Digital Transformation in Vertical Markets.

Credit ratings have long been the key measure of how likely a U.S. consumer is to repay any loan, from mortgages to credit cards. But the factors that FICO and other companies that create credit scores rely on--things like credit history and credit card balances--often depend on having credit already. In recent years, a crop of startup companies have launched on the premise that borrowers without such histories might still be quite likely to repay, and that their likelihood of doing so could be determined by analyzing large amounts of data, especially data that has traditionally not been part of the credit evaluation. These companies use algorithms and machine learning to find meaningful patterns in the data, alternative signs that a borrower is a good or bad credit risk. These companies are still young, but to date, there isn't clear evidence that these approaches have greatly expanded the credit available, and lenders using them often charge high interest rates, according to a report by the National Consumer Law Center, a consumer advocacy group.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. One advantage of Cassie that has maybe not been properly appreciated? I like the explosives idea.

ALISO VIEJO, CA--(Marketwired - Feb 17, 2017) - BrainChip Holdings Ltd. ("BrainChip" or the "Company") ( ASX: BRN), a leading developer of software and hardware accelerated solutions for Advanced Artificial Intelligence and Machine Learning applications, is pleased to provide an updated Corporate Presentation. The presentation includes information regarding target markets and applications as well as new product development. The presentation will be used in customer and investor meetings. About BrainChip ( ASX: BRN) BrainChip is a leading provider of software and hardware accelerated solutions for Advanced Artificial Intelligence and Machine Learning applications. The Company's Spiking Neural Adaptive Processor (SNAP) can learn autonomously with a small sample set and provide real-time information, data analytics and knowledge in image and video processing applications, high frequency data streams for financial analysis and event, speech, and speaker recognition from audio sources.

The '90s were the era of the PC, and 2006 ushered in the era of the smartphone. Today, we are at the beginning of the third era in end-user devices: the connected car. In some ways, this shift could be even more significant than the previous ones because it combines the digital and physical world in a way we haven't seen before. As cars evolve into computers on wheels, the biggest business opportunities will be less about "metal and rubber" and more about services. McKinsey estimates that the value of connected car data could be worth $1.5 trillion a year by 2030.

Pretty much every company, to some extent, has to power their algorithms with data and machine learning if they're going to be competitive with anyone else in their space. The challenge there, though, is that not everyone can have the massive computational power and data of a company like Google -- especially not startups. Siddharth Mall and his co-founders started Playment in India based on that bet. Playment allows companies to send over sets of training data that need some kind of light analysis, and then divvies it up across a large on-demand workforce that can resolve those tasks on their mobile devices. That, in turn, feeds back additional data to the companies to help refine their algorithms -- whether that's visual search, quality checks of recommendation engines.