You read that right – it's not Sherlock saying it this time! IBM Watson's cognitive computing capabilities and superhuman intelligence stand it in good stead to switch roles with the legendary fictitious detective Sherlock Holmes who could crack the most mind-boggling of mysteries, find answers to the unanswered questions everyone had and draw insights from information in a way no one could even fathom! For those of you who aren't yet familiar with IBM Watson, it is a technology platform that uses natural language processing and machine learning to reveal insights from large amounts of unstructured data. It has refined cognitive abilities to quickly understand context, learn from experience and draw inferences and insights from a sea of information that would otherwise be humanly impossible to wade through. This is way beyond mere data-matching or search engine functions – Watson uses advanced ways of inferring context, meaning, implications and fallouts of the voluminous content it indexes in its operations.

The airline is the sixth corporate backer of the startup hot house created by renowned entrepreneurs Brent Hoberman and Henry Lane Fox - both of Lastminute.com fame - with the ambitious goal of creating 200 successful startups over the next five years. That will include investing in and helping scale five early stage startups each year, as well as co-founding two companies itself. "Connecting the talented easyJet team with the next generation of disruptive entrepreneurs will only continue to drive fresh thinking and uncover new opportunities," said easyJet chief executive Dame Carolyn McCall. Hoberman added: "We are confident that together we can support the next generation of innovators in travel leveraging digital scale, data, personalisation, virtual reality, artificial intelligence (AI), ecommerce breakthroughs and fintech." It joins five other corporate backers working with Founders Factory, including Aviva for fintech, L'Oreal for beauty technology and a deal with China's CSC inked just last week to foster startups working on AI. "easyJet coming into Founders Factory as our sixth and final corporate investor represents a critical milestone," said Henry Lane Fox. "With some of the leading brands and audience owners in the world as investors, we are able to execute on our vision of exploiting new emerging technologies to redefine industries." It has also done deals with Holtzbrinck publishing group in the area of education and the Guardian Media Group in media.

Yet, while the world is busy watching Silicon Valley, these relatively obscure spots in the US and Europe are quietly building the infrastructure for innovation ecosystems that will drive the next technological revolutions. In 2008 General Electric opened a state-of-the-art jet engine component factory in the small Mississippi town, followed by another one in Ellisville in 2013. Both towns are close to technical universities stocked with researchers that specialize in new materials for the next-generation ultralight and silent jet engines. This partnership is crucial for innovation. As GE CEO Jeff Immelt wrote in the Harvard Business Review, "By partnering with Mississippi State University, we have developed a highly sophisticated proprietary process for manufacturing components made of carbon-fiber composites."

The team behind Roborace, the planned support series for the FIA Formula E Championship that'll race electric self-driving cars, scheduled its car's debut for Formula E's season opener in Hong Kong last weekend. But the car didn't make it to the track, and the team documented all of its struggles on video. When originally announced, Roborace was set to begin for the 2016-2017 race season that Formula E kicked off in Hong Kong. The series markets itself as the first driverless electric racing series, and it will use artificial intelligence in its spec race cars. Strategy will play out through algorithms programmed in by engineers, and they're the real competitors here.

Gusto, formerly ZenPayroll, is a human relations software company that manages payroll and benefits for a significant and growing number of smaller tech-forward companies. The growing company just released the first commercial for its product, which stars Kristen Schaal as an ever increasing number of "Zoes": payroll Zoe, benefits Zoe, even horticulture Zoe. The core idea seems to be that, using Gusto, one office assistant can do a number of different jobs. That might be true and is certainly appealing to employers, but the joke illustrating the claim strikes an odd chord. Job loss from automation is a real issue likely to affect more than half of the workforce in the coming decades.

With the aim to bolster its virtual personal assistants to deliver an Artificial Intelligence (AI)-based ecosystem across its devices and services, Samsung Electronics has acquired Viv Labs, an AI start-up. Viv was founded by Dag Kittlaus, Adam Cheyer and Chris Brigham who were part of the original virtual assistant Siri team that Apple bought in 2010. Viv has developed a unique, open AI platform that gives third-party developers the power to use and build conversational assistants and integrate a natural language-based interface into renowned applications and services, Samsung said in a statement on friday. "Unlike other existing AI-based services, Viv has a sophisticated natural language understanding, machine learning capabilities and strategic partnerships that will enrich a broader service ecosystem," said Injong Rhee, Chief Technical Officer of the Mobile Communications business at Samsung Electronics. "Viv was built with both consumers and developers in mind. This dual focus is also what attracted us to Viv as an ideal candidate to integrate with Samsung home appliances, wearables and more," Rhee added.

A London-based startup has combined some of today's most disruptive technologies in a bid to change the way we'll build the future. By retrofitting industrial robots with 3D printing guns and artificial intelligence algorithms, Ai Build has constructed machines that can see, create, and even learn from their mistakes. When CEO and founder Daghan Cam was studying architecture, he noticed a disconnect between small-scale manufacturing and large-scale construction. "On one side we have a fully automated production pipeline," Cam explained at a recent conference in London. With the emergence of more efficient printing technologies, he thought there must be a better way.

During April of this year, I submitted my Masters' thesis proposal to the ACL Student Research Workshop at the prodding of my thesis adviser, Ms. Charibeth Cheng. Since the main purpose of the submission was simply to get reviewer feedback, I totally forgot about it afterwards and went on with my life. Fast forward to June, I happily awoke to good news sitting in my e-mail: my paper had been accepted! I was going to Berlin and attend a top-tier Natural Language Processing (NLP) conference participated in by Facebook, Google, and Amazon! Immediately after the initial elation though, a million worries started to flood my head.

The Obama administration released a report on the future of artificial intelligence and addressed everything including job loss, ethics, bias, and positive outcomes for multiple industries. There are some things that machines are simply better at doing than humans, but humans still have plenty going for them. Here's a look at how the two are going to work in concert to deliver a more powerful future for IT, and the human race. There's a lot to digest in the full report, which has been noted in multiple places. I pulled out a few key talking points to ponder as AI advances.

Linear regression is the most basic and the most widely used technique in machine learning; yet for all its simplicity, studying it can unlock some of the most important concepts in statistics. If you have a basic undestanding of linear regression expressed as \hat{Y} \theta_0 \theta_1X, but don't have a background in statistics and find statements like "ridge regression is equivalent to the maximum a posteriori (MAP) estimate with a zero-mean Gaussian prior" bewildering, then this post is for you. With a superficial goal of understanding that somewhat obtuse statement, its main objective is to explore the topic, starting from the standard formulation of linear regression, moving on to the probabilistic approach (maximum likelihood formulation) and from there to Bayesian linear regression. I'll use the \theta character throughout to refer to the coefficients (weights) of a regression model, either explicitly broken out as \theta_0 and \theta_1 for intercept and slope respectively, or just \theta referring to the vector of coefficients. I'll usually use the expression \theta Tx_i for the prediction a model gives at x_i, the assumption being that a 1 has been added to the vector of values at x_i . 1 In the single predictor case, we know that the least squares fit is the line that minimizes the sum of the squared distances between observed data and predicted values, i.e. it minimizes the Residual Sum of Squares (RSS): These residuals are pretty important in how we reason about our model.