Summary: Are there large, sustainable career opportunities in AI and if so where? Do they lie in the current technologies of Deep Learning and Reinforcement Learning or should you focus your career on the next wave of AI? If you're a data scientist thinking about expanding your career options into AI you've got a forest and trees problem. There's a lot going on in deep learning and reinforcement learning but do these areas hold the best future job prospects or do we need to be looking a little further forward? To try to answer that question we'll have to get out of the weeds of current development and get a higher level perspective about where this is all headed. The roots of AI are actually in the behavioral sciences migrating eventually into biology and neurology.

Summary: IBM's Watson as it exists today is as close as we've come to a single integrated platform for AI. It contains all the capabilities for image and video, natural language speech and text input and output, and the most comprehensive knowledge recovery module yet combined together. If you want to exploit the advances we've made in AI you need to understand where Watson is today and where it's heading. Recently we wrote about how the'popular' Watson of Jeopardy fame still lingers in the memories of our non-data scientist colleagues and perhaps misleads them about the capabilities of AI. It's time we got in tune with the modern Watson, or more correctly IBM's Watson Group and its Watson platform and took a look at all there is to offer. There are three broad capabilities in today's AI and they are: Image and video processing: Largely driven by Convolutional Neural Nets (CNNs) this field has been getting most of the press with capabilities like facial and object recognition.

Machine learning systems seem a little bit like a math problem. Figure out the algorithm, pop in the data, and answers come out. When you're trying to predict what movies or books people like, that can be extremely important, the difference between a boost in a revenue and a reputation hit that appears on mediabuzz.com. Yet testing is rarely at the top of our minds as we try to develop and deploy systems based on machine learning algorithms. Simply building a good set of algorithms that model the problem space is difficult enough. But testing is a part of the software development and deployment process, and we need to look seriously at how these systems will be tested.

For the last few years, our digital presence showed us what an environment where all our actions somehow influence our further actions (or actions of others through algorithms) might look like. This is one of the major trends that keeps accelerating us to a state where we hardly even have to make a decision before executing. Take the on-demand economy (Uber, Postmates, etc.) as an example. Machines already tell us where to go, when to go, how much to pay and even what to eat. Companies like Amazon have started recommending products to buy and Facebook suggests individuals to add as friends.

Lantern Pharma Inc., is a privately held, global biotech company pioneering the field of precision oncology. Lantern's proprietary approach to drug development is driven by advanced genomics and machine learning-based artificial intelligence (AI), which when combined, are advancing a new wave of precision drug therapies that significantly reduce the time to market and overall risk associated with drug development. Lantern has reached an agreement to collaborate with India-based AI and data analytics company, Intuition Systems. Intuition will work closely with Lantern's existing computational team to bring additional AI, big data analysis, cloud support and infrastructure to support drug development and biomarker identification. The treatment of cancer represents a large market with many underserved areas where precision therapies will be highly valued.

LAS VEGAS – Professor Einstein can roll his eyes, stick out his tongue and give a simple explanation of the theory of relativity. With his lifelike rubbery "skin" and bushy mustache, he can almost make you forget he is a robot. The Einstein robot is among dozens roaming the Consumer Electronics Show in Las Vegas that can be your companion or educator or baby-sit your children. Japan's SoftBank Robotics meanwhile announced a partnership that will allow its humanoid robot Pepper to travel onboard state-owned SNCF trains in France to assist travelers. While robots have been around for years, advances in technology and artificial intelligence have allowed developers to give them traits that enable the devices to be seen as members of the family.

With time, smartphones and the technology used in them are constantly evolving. Most features that we often use on our phones right now such as playing augmented reality games or streaming live videos were difficult or nearly impossible a few years back. With the advancement in technology came the smartphones and the sliding keyboard phones or flip phones are a thing of the past right now. The smartphones that exist now feature fingerprint authentication, powerful processors, sharp cameras, and bright displays making many things possible. Still, the technology is subject to constant advancement and we can see many more improvements coming to our phones in future.

Its interesting but ultimately to really'under' 'stand' language in a deep way, the systems will need representations based on lower-level (possibly virtual) sensory inputs. That is one of the main enablers for truly general intelligence because its based on this common set of inputs over time, i.e. senses. The domain is sense and motor output and this is a truly general domain. Its also a domain that is connected to the way the concepts map to the real physical world. So when the advanced agent NN systems are put through their paces in virtual 3d worlds by training on simple words, phrases, commands, etc. involving'real-world' demonstrations of the concepts then we will see some next-level understanding.

This unconventional interplay between the driver and automobile is central to concept cars that Honda and Toyota unveiled at a technology conference in Las Vegas this week. In the not-so-distant future, vehicles will not only be safer or more efficient. They also will be our companions, watching our every move. These cars, which exist today only as partially functional concepts, will use powerful artificial intelligence systems to memorize and store information about every passenger's likes and dislikes, how they speak, and the places they frequent, all to make decisions the car feels are in the riders' interest. The auto industry's pursuit of a hyper-personal experience comes as the very nature of automotive transportation is in flux.

In the 2013 movie "Her," Theodore Twombly, a lonely writer, falls in love with a digital assistant designed to meet his every need. She sorts emails, helps get a book published, provides personal advice and ultimately becomes his girlfriend. The assistant, Samantha, is AI software capable of learning at an astonishing pace. Samantha will remain in the realm of science fiction for at least another decade, but less-functional digital assistants, called bots, are already here. These will be the most amazing technology advances we see in our homes in 2017.