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.

Editor's note: In this guest post, craft ai describe the thinking and implementation behind their winning entry in the VIVA Tech hackathon hosted by Samsung and Legrand. On July 2, the craft ai team woke up way too early for a Saturday to join fellow developers at the Samsung/Legrand booth at VIVA Tech and hack for a day. Our objective: Show that smart homes can offer a better user experience thanks to artificial intelligence--beyond smartphone remotes, complicated dashboards and manual scenarios! This is the tale of how we used craft ai in conjunction with Samsung ARTIK to make a few Legrand devices learn usage patterns and automate themselves. Managing the light in a house is one of our pet use cases at craft ai.

My cellphone died the other day and I was on the move and had no way to charge it. If at that moment someone had offered me one of those battery packs, I would have happily paid a premium. What if you could accurately predict when demand in your products was going to rise or fall, and change your pricing accordingly? What if you could predict future demand with incredible accuracy? And beyond that--what if that decision-making didn't require human intervention but was automated and could happen instantaneously?

New research, led by the University of Southampton, has demonstrated that a nanoscale device, called a memristor, could be used to power artificial systems that can mimic the human brain. Artificial neural networks (ANNs) exhibit learning abilities and can perform tasks which are difficult for conventional computing systems, such as pattern recognition, on-line learning and classification. Practical ANN implementations are currently hampered by the lack of efficient hardware synapses; a key component that every ANN requires in large numbers. In the study, published in Nature Communications, the Southampton research team experimentally demonstrated an ANN that used memristor synapses supporting sophisticated learning rules in order to carry out reversible learning of noisy input data. Memristors are electrical components that limit or regulate the flow of electrical current in a circuit and can remember the amount of charge that was flowing through it and retain the data, even when the power is turned off.

For example, Acme Motorcycles (a hypothetical electric motorcycle manufacturer) discovered via AI that if it could get a prospective customer into a showroom and get them on a test drive then there was strong chance they would purchase the bike. Velocity data sent from the motorcycle test-drive experience in real time to the dealer and then pushed into the CEP engine indicated that if a customer drove the bike at over 50 MPH then the likelihood of purchase increased 10%. When the customer returns from the test drive, if the dealer's analysis dashboard shows that the customer is in "neutral" state, then the dealer would be provided with a recommendation to advise the customer to go back on the road and try the bike on the highway.

Technology is advancing at record speed as innovations that were a foggy prediction came to life one after the other. This passing month, I decided to explore "future studies" and browsed the web for the latest advancements in the tech world, and especially AI, IT, and SaaS. An artificial intelligence agent developed by two Carnegie Mellon University computer science students has proven to be the game's ultimate survivor -- outplaying both the game's built-in AI agents and human players. The students, Devendra Chaplot and Guillaume Lample, used deep-learning techniques to train the AI agent to negotiate the game's 3-D environment, still challenging after more than two decades because players must act based only on the portion of the game visible on the screen. People have started noticing self-driving Uber cars in downtown San Francisco, fueling speculation the ridesharing company could soon be deploying autonomous vehicles for commercial use right where it all started, in the Bay Area.