Discussing the pros and cons of articial intelligence is, to be sure, an emotional topic. People have feared artificial intelligence (AI) almost as soon as it was invented. Hollywood in particular has done a masterful job of stoking those fears with movies like "2001: A Space Odyssey," "Terminator" and "The Matrix" all making AI systems into demonic forces. Most recently, renowned scientist Stephen Hawking told Larry King he thought humans and AI could "co-exist," but that "a rogue AI could be difficult to stop" without appropriate safeguards in place. Coming from someone like Hawking, that kind of warning carries some weight with people.

A cognitive system is not just a machine-learning algorithm, though training it to see patterns in data and understand it in context is a big part of it, Hurwitz said. These are huge, complex systems comprising many parts, all resting on the foundation of a public cloud, private cloud or on-premises infrastructure. Layered on top of that are internal and external data sources -- unstructured data, such as text, video and images, and structured data, such as database records -- and data access and management services. Another key component of cognitive systems is ontologies. These are databases of knowledge on specific topics the systems churn through and learn from.

NETHERLANDS -Machine learning is a type of artificial intelligence that allows computer programs to learn things when exposed to new data, without being reprogrammed. Now, researchers have matched machine learning methods with a special technique of magnetic resonance imaging (MRI) that measures blood perfusion (absorption rate of this tissue) throughout the brain to detect early forms of dementia. MRI can help diagnose Alzheimer's disease. However, early diagnosis is difficult. Scientists have long known that Alzheimer's disease is a gradual process and that the brain undergoes functional changes before the structural changes associated with the disease visually displayed on the test results.

Amazon's robotic Picking Challenge this past weekend demonstrated the advancement in deep learning robots and showed how they may come to rule fulfillment warehouses in the future. "The machine studied 3D scans of the stockroom items to help it decide how to manipulate items with its gripper and suction cup," Engadget explained. "That adaptive AI made a big difference, to put it mildly. The arm got a near-flawless score in the stowing half of the event, and was over three times faster at picking objects than last year's champion (100 per hour versus 30)." "The robot needs to be able to handle variety and operate in an unstructured environment," Carlos Hernández Corbato from TU Delft Robotics Institute told TechRepublic.com. "We are really happy that we have been able to develop this successful system."

With all the talk about advances made in robotics lately and how many jobs may soon be lost to robots, I thought it might be interesting to consider why – at least from my humble perspective – robots will never manage projects. There are some jobs, maybe even some tech positions, that could be performed by robots. But I don't believe that the personal touch and interaction that is needed many times on project engagement will lend itself very well to being handled by the likes of robotics. If you have a strong opinion on this or are a robotics expert, please comment with your thoughts...could be an interesting discussion. The customer needs the personal touch points.

S. Somasegar is a venture partner at Madrona Venture Group and the former head of Microsoft's Developer Division. Daniel Li is an investor with Madrona Venture Group. You can hardly talk to a technology executive or developer today without talking about artificial intelligence, machine learning or bots. While everyone agrees on the importance of machine learning to their company and industry, few companies have adequate expertise to do what they wanted the technology to do. Here are some insights into what we can expect in the coming years around ML and AI.

Alzheimer's is a devastating chronic neurodegenerative disease that currently affects about 5.4 million people in the U.S. alone. Alzheimer's patients suffer progressive mental deterioration, which eventually impairs even basic bodily functions like walking and swallowing. While Alzheimer's can increasingly be managed, one of the big challenges of the disease is early diagnosis. MRI machines can be used to confirm advanced cases, but by the time the disease has reached this stage, brain tissue is gone and there is no way to restore it. Could machine-learning tools be used to help detect and identify Alzheimer's disease before it is currently possible to do so?

Seattle-based machine learning startup Dato, which originally launched as GraphLab, announced today that it has changed its name again. Now, the company is known as Turi, an ode to computer science legend Alan Turing. This past September, Turi -- then Dato -- found itself in a trademark infringement argument with fellow tech startup Datto after Dato changed its name from GraphLab in January 2015. Datto, which has been offering data backup and recovery services from its Connecticut headquarters since 2007, first complained about Dato's new name just weeks after the company finished its rebranding from GraphLab. Datto argued that Dato's new name would cause confusion.

This is the third in a series of posts on how to build a Data Science Portfolio. If you like this and want to know when the next post in the series is released, you can subscribe at the bottom of the page. Data science companies are increasingly looking at portfolios when making hiring decisions. One of the reasons for this is that a portfolio is the best way to judge someone's real-world skills. The good news for you is that a portfolio is entirely within your control. If you put some work in, you can make a great portfolio that companies are impressed by. The first step in making a high-quality portfolio is to know what skills to demonstrate. Any good portfolio will be composed of multiple projects, each of which may demonstrate 1-2 of the above points. This is the third post in a series that will cover how to make a well-rounded data science portfolio.

"Highly autonomous cars and everything they connect to will require powerful and reliable electronic brains to make them smart enough to navigate traffic and avoid accidents," Intel Chief Executive Brian Krzanich said last Friday at a joint news conference announcing the alliance. It is too early to say which other carmakers would join the alliance, BMW Chief Executive Harald Krueger said at a news conference on the outskirts of Munich on Friday. But creating common technology standards would help all manufacturers update their vehicles faster, Intel Chief Executive Brian Krzanich said. Auto camera and software maker Mobileye will supply its Road Experience Management (REM) technology and make its latest EyeQ5 chip available to be deployed on Intel computing platforms.