A very common task in data analysis is that of grouping a set of objects into subsets such that all elements within a group are more similar among them than they are to the others. The practical applications of such a procedure are many: given a medical image of a group of cells, a clustering algorithm could aid in identifying the centers of the cells; looking at the GPS data of a user's mobile device, their more frequently visited locations within a certain radius can be revealed; for any set of unlabeled observations, clustering helps establish the existence of some sort of structure that might indicate that the data is separable. The k-means algorithm takes a dataset X of N points as input, together with a parameter K specifying how many clusters to create. The output is a set of K cluster centroids and a labeling of X that assigns each of the points in X to a unique cluster. All points within a cluster are closer in distance to their centroid than they are to any other centroid.

This 3-hour course (video slides) offers developers a quick introduction to deep-learning fundamentals, with some TensorFlow thrown into the bargain. Deep learning (aka neural networks) is a popular approach to building machine-learning models that is capturing developer imagination. If you want to acquire deep-learning skills but lack the time, I feel your pain. In university, I had a math teacher who would yell at me, "Mr. Görner, integrals are taught in kindergarten!"

Imagine training a neural network and watching its representations wander through this space. You can see how your representations compare to other "landmark" representations from past experiments. If your model's first layer representation is in the same place a really successful model's was during training, that's a good sign! If it's veering off towards a cluster you know had too high learning rates, you know you should lower it. This can give us qualitative feedback during neural network training.

In billiards, you must call your shot ahead of time. You don't get any points for luck. With that in mind, here are my on-the-record predictions for 2017. Self-driving cars, previously existing only in the world of science fiction, are becoming a reality. Today taxis are driving themselves in Singapore.

In an effort to forge partnerships in a sector that will affect every industry, some of Canada's largest corporations are contributing to a $5 million fund for artificial intelligence startups. At a panel Wednesday morning to celebrate the launch of the program, called NextAI, executives from Royal Bank of Canada, Magna International Inc., Bank of Nova Scotia and the Business Development Bank of Canada laid out their vision for how artificial intelligence can transform their businesses. They're providing funding with no strings attached to the program, which will provide artificial intelligence startups with $200,000 in addition to access to technology, mentorship and education. Artificial intelligence, also known as machine learning or deep learning, is a method of training computers to learn like children by processing huge sets of data with software that mimics the neural networks in the human brain. Machine learning is already powering algorithms that allow Netflix Inc. to predict what a user will want to watch next, help doctors diagnose diseases by comparing thousands of similar medical images and help autonomous vehicles decide how to react to objects in their paths.

All major TV makers have stopped building 3D functionality into their sets, with LG and Sony reportedly following the likes of Samsung, Sharp and Hisense by opting not to make it a feature of their latest televisions. However, lack of content, the requirement for viewers to wear 3D glasses and calibration issues meant that it never really took off as the industry had expected it to. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric ...

Distilling a generally-accepted definition of what qualifies as artificial intelligence (AI) has become a revived topic of debate in recent times. Some have rebranded AI as "cognitive computing" or "machine intelligence", while others incorrectly interchange AI with "machine learning". This is in part because AI is not one technology. It is in fact a broad field constituted of many disciplines, ranging from robotics to machine learning. The ultimate goal of AI, most of us affirm, is to build machines capable of performing tasks and cognitive functions that are otherwise only within the scope of human intelligence.

Jeremy Waite, Evangelist at IBM Watson, kicked off speaking at a recent DMA event by highlighting the fact that by 2019 there will be 1m new devices coming online every hour. With so much smart tech in the hands of consumers, will we end up marketing to machines or algorithms? He asked the audience to think about how we can use AI to create more meaningful relationships with our customers and use the power of marketing to make a difference. While it is easy to get overexcited about technology, we still have situations where 85% of enterprises are not sharing data within their own sales teams. Furthermore, the Gartner Hype Cycle for Emerging Technologies indicates that two of the top trends over the next five years will be cognitive expert advisors and machine learning.

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Within a decade, our living spaces will be enhanced by a host of new devices and technologies, performing a range of household functions and redefining what it means to feel at home. The promise of devices that not only meet our household needs but anticipate them as well has been around for decades. To date, that promise remains largely unfulfilled. Advances such as the Nest thermostat by Alphabet (parent company to Google) and Amazon's Alexa personal assistant are notable, but the home-technology market as a whole remains fragmented, and the potential for a truly smart home is still unrealized. A tipping point may be at hand.