Artificial intelligence is learning to take on an increasing number of sophisticated tasks. Google Deepmind's AI is now able to imitate human speech, and just this past August IBM's Watson successfully diagnosed a rare case of leukemia. Rather than viewing these advances as threats to job security, we can look at them as opportunities for AI to fill in critical gaps in existing service providers, such as mental healthcare professionals. In the US alone, nearly eight percent of the population suffers from depression (that's about one in every 13 American adults), and yet about 45 percent of this population does not seek professional care due to the costs. There are many barriers to getting quality mental healthcare, from searching for a provider who's within your insurance network to screening multiple potential therapists in order to find someone you feel comfortable speaking with.

With the Radeon Instinct line, AMD joins Nvidia and Intel in the race to put its chips into AI applications--specifically, machine learning for everything from self-driving cars to art. The company plans to launch three products under the new brand in 2017, which include chips from all three of its GPU families. The passively cooled Radeon Instinct MI6 will be based on the company's Polaris architecture. It will offer 5.7 teraflops of performance and 224GBps of memory bandwidth, and will consume up to 150 watts of power. The small-form-factor, Fiji-based Radeon Instinct MI8 will provide 8.2 teraflops of performance and 512GBps of memory bandwidth, and will consume up to 175 watts of power.

Artificial intelligence, machine and deep learning are some of the hottest areas in all of high-tech today. We've had a few generations of AI over the last 50 years, but in 2010, IBM kicked off the latest cycle with Watson, using brute-force, Big Data techniques to win jeopardy. The University of Toronto in 2012 pioneered Imagenet using deep learning to identify pictures. NVIDIA then began to drive the GPU-accelerated training technology of deep neural nets, and in the course of that, huge service providers opened up and announced initiatives beginning with Microsoft, Google, Apple, Samsung, and then Amazon. Chinese giants Baidu, Alibaba and Tencent are of course, involved.

The advertising industry has made huge strides in targeting, but there's one more big step to take to make ads truly relevant: We need to adapt each creative message so it is interactive and personalized to every single individual. The key to achieving what may be the holy grail of advertising is a thriving branch of artificial intelligence known as deep learning. It uses algorithms to mimic neural networks' capabilities to recognize and act on abstract patterns. A retailer, for example, might send ads for sweaters to one segment, ads for bathing suits to another. Soon, instead of those few variations, a brand will have a million personalized versions of an ad dynamically presented to a million different individuals.

By far the fastest expanding frontier of data science is AI and specifically the rapid advances in Deep Learning. Advances in Deep Learning have been dependent on artificial neural nets and especially Convolutional Neural Nets (CNNs). In fact our use of the word "deep" in Deep Learning refers to the fact that CNNs have large numbers of hidden layers. Microsoft recently won the annual ImageNet competition with a CNN comprised of 152 layers. Compare that with the 2, 3, or 4 hidden layers that are still typical when we use ordinary back-prop NNs for traditional predictive analytic problems. First, CNNs have come close to achieving 100% efficiency for image, speech, and text recognition.

On December 2nd, 1942, a team of scientists led by Enrico Fermi came back from lunch and watched as humanity created the first self-sustaining nuclear reaction inside a pile of bricks and wood underneath a football field at the University of Chicago. Known to history as Chicago Pile-1, it was celebrated in silence with a single bottle of Chianti, for those who were there understood exactly what it meant for humankind, without any need for words. Now, something new has occurred that, again, quietly changed the world forever. Like a whispered word in a foreign language, it was quiet in that you may have heard it, but its full meaning may not have been comprehended. However, it's vital we understand this new language, and what it's increasingly telling us, for the ramifications are set to alter everything we take for granted about the way our globalized economy functions, and the ways in which we as humans exist within it. The language is a new class of machine learning known as deep learning, and the "whispered word" was a computer's use of it to seemingly out of nowhere defeat three-time European Go champion Fan Hui, not once but five times in a row without defeat.

Learning to learn by gradient descent by gradient descent

Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages.

Microsoft's continued investment in artificial intelligence and machine learning technology is paying dividends. The company has partnered with OpenAI, a non-profit company founded earlier this year to advance the field of machine intelligence for the benefit of humanity. As part of the deal, announced Tuesday, OpenAI will use Microsoft Azure as its primary cloud provider, an important win for Microsoft as it competes with the likes of Amazon, Google, and IBM to power the next generation of intelligent applications. OpenAI is backed by the likes of Tesla CEO Elon Musk, controversial investor Peter Thiel, LinkedIn co-founder Reid Hoffman, and Y Combinator Partner Jessica Livingston. On top of that, Microsoft also launched a set of cloud services all aimed at furthering intelligent applications.

MANILA, Philippines – This is not a phone conversation or a chat session with a customer service representative. Alexa is the digital voice assistant residing inside Amazon's wifi-enabled, voice-controlled smart speakers Amazon Echo and Amazon Echo Dot. Her capabilities go beyond rattling off the day's news or the weather, or playing music. She can order pizza, shoes, jewellery or even an Uber ride (at least in the US). You can ask her to turn on or off your TV, the lights or adjust the temperature in your room (provided you have a connected home).