Concerns have been raised about the scope of a data-sharing agreement between Google-owned DeepMind and the UK's National Health Service (NHS) after it was revealed the agreement covers access to all patient data from the three London hospitals involved, rather than a more targeted subset of data relating to the specific medical condition the healthcare app in question (Streams) is focused on. Back in February DeepMind announced a collaboration with the NHS to build an app for clinicians treating kidney disease. The company also acquired an existing early stage clinical task management app, called Hark, built by a team from Imperial College London -- evidently with the intention of building on that base tech, but giving it a more specific medical focus in the first instance. The Streams app aims to streamline alerts and access to patient data for doctors and nurses working in the front-line of medical care. But it is not a general medical data alerts or messaging platform.

There are two fundamental milestones I'd say. The first one is Fisher's Linear Discriminant [1], later generalized by Rao [2] to what we know as Linear Discriminant Analysis (LDA). Essentially, LDA is a linear transformation (or projection) technique, which is mainly used for dimensionality reduction (i.e., the objective is to find the k-dimensional feature subspace that -- linearly -- separates the samples from different classes best. Given the objective to maximize class separability, projecting the 2D dataset below onto "x-axis component," would be a better choice than the "y-axis component." Keep in mind though that LDA is a projection technique; the feature axes of your new feature subspace are (almost certainly) different from your original axes.

At this time, all participants are in a listen-only mode. A question-and-answer session will follow the formal presentation. I would now like to turn the conference over to your host, Jandy Tomy, Vice President of Finance and Investor Relations. With me today on the call are Robert Apatoff, President and Chief Executive Officer; and Becky Sheehan, Executive Vice President and Chief Financial Officer. Before we begin, please remember that, during the course of this call, management may make forward-looking statements within the meaning of the Federal Securities Laws that address the Company's expected future business, financial performance, and financial condition. These forward-looking statements involve risks and uncertainties that could cause actual results to be materially different than those expressed in our forward-looking statements. In addition to the Company's reports filed with the Securities and Exchange Commission, please refer to the text in the Company's press release issued today for a discussion of the risks and uncertainties associated with such forward-looking statements. Also, please note that, on today's call, management will refer to certain non-GAAP financial measures, including adjusted EBITDA, adjusted net income, and free cash flow. The Company believes these non-GAAP financial measures provide useful information for investors. Please refer to today's press release for definitions and calculations of these non-GAAP performance measures, as well as reconciliations of the non-GAAP performance measures to the Company's GAAP financial results. Now, I'd like to turn the call over to Robert Apatoff, President and Chief Executive Officer. Good afternoon, everyone, and thank you for joining us today. I will provide a brief overview of our business highlights, integration efforts, and strategic and operating initiatives. Following my comments, our CFO Becky Sheehan will review our financial results and outlook for 2016 in more detail. Finally, I will provide a few closing remarks, and then we'll open up the call to take your questions.

IBM Japan has teamed up with Subaru to investigate how its Watson Supercomputer could help improve the automaker's EyeSight driver assist technology. As well as developing a data analytics system, the two companies are keen to integrate cloud and artificial intelligence technologies, which bodes well for the ongoing development of autonomous, networked cars. The benefits of networked autonomous vehicles were recently demonstrated by the European Truck Platooning Challenge, where teams of autonomous trucks made their way from their respective factories to Rotterdam. As well as demonstrating the fact autonomous vehicles can effectively make long trips without causing the end of the world (shocking, we know), the trucks were able to maintain a gap of just 15 m (49 ft) and react to sudden braking manoeuvres in just 0.1 seconds thanks to a WiFi connection keeping them all linked. Daimler has also invested in Car-to-X technology, which features in its latest E-Class.

As a species, we are excellent at imbuing life into the lifeless--just as we are proficient in giving meaning to the meaningless. One could argue that the ability of our brains to recognize patterns quickly is part of what gives us our humanity. Seeing faces on Mars, yelling at our cars for breaking down and giving animals more agency than they may possess are all results of our psyche. Our penchant for gestalt is important in the ever increasing world of social robots and machines. When it comes to technology and social robotics, the whole is often seen as more meaningful than the sum of the parts. The field of social robotics includes machines that use social behaviors and cues to interact with people.

Walking across the floor of SEW-Eurodrive's factory in Baden-Württemberg is like moving through a time warp. On one side, the light is dim and workers stand at long assembly lines repeating the same task over and over. On the other, a fleet of low-lying robotic trucks scoot around the shop floor, restocking restyled workstations. In these small cells, a single employee helped by a robotic workbench assembles a virtually complete drive system that will be used to power the production of everything from cars to cola. Elsewhere, a robotic arm called Carmen helps workers load machines or pick components out of bins.

Almost as if robots and humans are now locked in a game of "anything you can do, I can do better," the latest move is a robot capable of performing fine surgical operations with minimal human supervision. The Smart Tissue Autonomous Robot – more catchily called STAR – has been developed to perform soft tissue operations using its robotic arm and some detailed computer algorithms. So far, the robot has already been able to suture together the intestines of four living pigs, all of which survived with no complications, at the Children's National Health System in Washington, DC. The researchers say the robot performed about 60 percent of the surgical operations by itself, with the rest of the work requiring only minor adjustments. Taking into consideration things such as consistency, the amount of time to perform the surgery, and the number of mistakes, they compared STAR's performance to that of a human.

There's huge progress in AI, or at least huge interest in AI--a bigger interest than there's ever been in my lifetime. I've been interested in AI since I was a little kid trying to program computers to play chess, and do natural language databases, and things like that, though not very well. I've watched the field and there have been ups and downs. There were a couple of AI winters where people stopped paying attention to AI altogether. People who were doing AI stopped saying that they were in the field of AI. They say, "Yes, I do artificial intelligence," where two years ago they would have said, "I do statistics." Even though there's a lot of hype about AI and a lot of money being invested in AI, I feel like the field is headed in the wrong direction. There's been a local maximum where there's a lot of low-hanging fruit right now in a particular direction, which is mainly deep learning and big data. People are very excited about the big data and what it's giving them right now, but I'm not sure it's taking us closer to the deeper questions in artificial intelligence, like how we understand language or how we reason about the world. The big data paradigm is great in certain scenarios. One of the most impressive advances is in speech recognition. You can now dictate into your phone and it will transcribe most of what you say right most of the time. That doesn't mean it understands what you're saying. Each new update of Siri adds a new feature. First, you could ask about movie times, then sports, and so forth. The natural language understanding is coming along slowly. You wouldn't be able to dictate this conversation into Siri and expect it to come out with anything whatsoever. But you could get most of the words right, and that's a big improvement. It turns out that it works best with a lot of brute force data available. When you're doing speech recognition on white males, who are native language speakers, in a quiet room, it works pretty well.

Acquiring Expertmaker is part of eBay's structured data push to better organize products on its marketplace. EBay Inc. took another step this week to better organize its product data and make items easier to find when the online marketplace acquired a Swedish company focused on artificial intelligence, machine learning and big data analytics. EBay bought Malmo, Sweden-based Expertmaker, which has worked with eBay since 2010 and also has offices in San Francisco. When the transaction is final, Expertmaker employees will become part of eBay's structured data product and technology team, with founder and CEO Lars Hard joining as director, data science, according to eBay. The online marketplace continues to press its initiative to have sellers define their products using structured data, which means characterizing items in a standard way.

Humanity may be wiped out by machines this century – leading AI scientist. It took millions of years of evolution for nature to come up with something that changed the face of the planet forever – the human brain. Now, the new mind is to be born, and the best cyber scientists will be its midwife. Artificial Intelligence is said to be just decades away from creation, and it will probably change life on Earth entirely. Some predict the coming of Utopia, where machines will help humanity fight disease, poverty and even death. But that's as others see a way more darker future, with machines rising up to eradicate humankind once and for all.