Dedicated Computing is co-hosting a Deep Learning Institute workshop in collaboration with NVIDIA and Milwaukee School of Engineering (MSOE). The workshop will take place at MSOE on April 13, 2017. Deep learning is a new area of machine learning that seeks to use algorithms, big data, and parallel computing to enable real-world applications and deliver results. Machines are now able to learn at the speed, accuracy, and scale required for true artificial intelligence. This technology is used to improve self-driving cars, aid mega-city planners, and help discover new drugs to cure disease.

Artificial intelligence has reached peak hype. News outlets report that companies have replaced workers with IBM Watson and that algorithms are beating doctors at diagnoses. New AI startups pop up everyday, claiming to solve all your personal and business problems with machine learning. Ordinary objects like juicers and Wi-Fi routers suddenly advertise themselves as "powered by AI." Not only can smart standing desks remember your height settings, they can also order you lunch.

In recent years, the rapid advance of artificial intelligence has evoked cries of alarm from billionaire entrepreneur Elon Musk and legendary physicist Stephen Hawking. Others, including the eccentric futurist Ray Kurzweil, have embraced the coming of true machine intelligence, suggesting that we might merge with the computers, gaining superintelligence and immortality in the process. As it turns out, we may not have to wait much longer. This morning, a group of research scientists at Google DeepMind announced that they had inadvertently solved the riddle of artificial general intelligence (AGI). Their approach relies upon a beguilingly simple technique called symmetrically toroidal asynchronous bisecting convolutions.

Four years ago, Google was faced with a conundrum: if all its users hit its voice recognition services for three minutes a day, the company would need to double the number of data centers just to handle all of the requests to the machine learning system powering those services. Rather than buy a bunch of new real estate and servers just for that purpose, the company embarked on a journey to create dedicated hardware for running machine- learning applications like voice recognition. The result was the Tensor Processing Unit (TPU), a chip that is designed to accelerate the inference stage of deep neural networks. Google published a paper on Wednesday laying out the performance gains the company saw over comparable CPUs and GPUs, both in terms of raw power and the performance per watt of power consumed. A TPU was on average 15 to 30 times faster at the machine learning inference tasks tested than a comparable server-class Intel Haswell CPU or Nvidia K80 GPU.

Last week, at an event on AI, I asked the panel about how investors evaluate the Data readiness of AI start-ups. This subject is close to my work and my teaching. I teach a course on Implementing Enterprise AI and also teach Data Science for IoT at the University of Oxford. Professor Neil Laurence has proposed a concept of Data readiness levels. The highest level of Data readiness represents Data which is most useful to make predictions i.e. "Can we use this data to prove the efficacy of a drug?"

Google operates what is surely the largest computer network on Earth, a system that comprises custom-built, warehouse-sized data centers spanning 15 locations in four continents. But about six years ago, as the company embraced a new form of voice recognition on Android phones, its engineers worried that this network wasn't nearly big enough. If each of the world's Android phones used the new Google voice search for just three minutes a day, these engineers realized, the company would need twice as many data centers. At that time, Google was just beginning to drive its voice recognition services with deep neural networks, complex mathematical systems that can learn particular tasks by analyzing vast amounts of data. In recent years, this form of machine learning has rapidly reinvented not just voice recognition, but image recognition, machine translation, internet search, and more.

Currently, selecting the "best" algorithm to use per dataset requires a level of intuition or expertise about the data. Data scientists leverage their experience to experiment with different combinations of models and hyperparameter values to achieve the highest accuracy. AutoML will lessen our dependency on intuition by iteratively trying out an algorithm, scoring its performance, and choosing and refining other models. In other words, it will automate the machine learning process of the data science work flow as we carefully defined above. There are other openly available tools such as Auto-sklearn for Python users and AutoWEKA for Weka users.

If you're seeking color inspiration from a distinctive-looking film like Grand Budapest Hotel, you could just "eyedrop" it in Photoshop or try an app like Adobe Color CC. He came up with Colormind, an AI algorithm that uses films, video games, fashion and art to "generate color suggestions that fit the distinct visual style of those mediums," he says. In coming up with his system, Qiao writes that he first looked at so-called color quantization (MMCQ), in which algorithms extract representative colors from images. However, those colors are often "haphazard" and not very useful for design, unlike human palettes that feature "similar hues grouped together ... and some minimum amount of contrast between each other," he says. To find a balance between the two, Qiao thought about using a fancy adversarial network deep-learning system, but instead "settled on a brute-force technique that I call generative-MMCQ."

Our announcement this week that we'll invest $5 million in the newly formed Vector Institute is the latest example of our work with Canada's thriving artificial intelligence community. When the Toronto-based AI research organization approached us for help, we were quick to join in, along with Google and some of Canada's top corporations. We've worked for years to drive technology that supports AI. And Vector's other sponsors in areas like finance, healthcare and automotive are prime candidates for implementing GPU deep learning to create smarter, more efficient products for their large customer base. It's also a great opportunity for us to expand our activities in the key Canadian market. We opened our first Canadian office in Toronto in 2015, and we're turning it into a hub for top AI talent.

Graham Johnson is an artist with a curious muse: the human cell. Twenty years ago he graduated from a quiet corner of Johns Hopkins where students draw cadavers instead of cutting them up. At first, Johnson stuck to the medical illustrator canon, animating cells in a classic, cartoonish style. But he dreamed of constructing three-dimensional, data-driven models that could capture all their beautiful complexity. For that, he'd need computers, lots of them.