If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
We are entering a new year, a year of hopes and promises. It is too early to predict what technology will offer us in the coming months. This is one space that is moving so fast that I've learned how to ignore all mails about what experts think will be the biggest trends of the coming year. However, with my ears to the ground, I am underlining one aspect we should all be aware of in the coming year. In fact, the change might be so subtle that we might not even realise. Last month, in'Artificial Intelligence fears not limited to just taking over human jobs…' (November 28; http://goo.gl/s While we still don't know what the impact of that will be on our lives and economy, we can rest assured that artificial intelligence will be a part of almost everything that we do in our tech lives in the coming year. It is just that we might not realise it. We already don't realise how it is triaging our e-mails, cleaning up--or filtering out--our social media timelines and alerting us about location-specific information even before we initiate a search.
There is a war a-brewin', but this war will be fought with wits and not brute strength. Ever since Russian President Vladimir Putin's declaration that "the nation that leads in AI (Artificial Intelligence) will be the ruler of the world," the press and analysts have created hysteria regarding the ramifications of artificial intelligence on everything from public education to unemployment to healthcare to Skynet.
Artificial intelligence has profound implications for society, and for the data centers that will power it. The rapid growth of AI is contributing to the building of new services, as well as enhancing products already on the market. And the growing popularity of machine learning as a business is also boosting demand for powerful high performance computing hardware.
I met with Satya Nadella on the morning of April 20, 2017. I had come up to Microsoft to interview Satya for my forthcoming book, WTF? What's the Future and Why It's Up to Us, but so much of what I wanted to discuss was already in Satya's own brilliant memoir, Hit Refresh. We have a shared optimism about technology, and a shared conviction that the challenge of Artificial Intelligence is to define for ourselves and for society what is truly human, and to build a world in which AI reinforces and augments human capability and experience rather than devaluing it. So that's where we started our conversation Tim: One of the things you said in your book is that the challenge will be to define the grand, inspiring social purpose for which AI is destined. You wrote: "In 1969, when President Kennedy committed America to landing on the moon, the goal was chosen in a large part due to the immense technical challenge it posed and the global collaboration it demanded. In a similar fashion, we need to set a goal for AI that is sufficiently bold and ambitious, one that goes beyond anything that can be achieved through incremental improvements to current technology." I love that thought, and I wonder if you could expand on it. Satya: If you start with the assumption that AI's benefits have to be about augmenting human capability – if I even look at the place where even Microsoft's own engineers are most passionate, most driven about using AI – it is in assistive technology.
Machine learning will come of age this year, moving from the research labs and proof-of-concept implementations to cutting-edge business solutions. Along the way, it will help power innovations, such as autonomous vehicles, precision farming, therapeutic drug discovery and advanced fraud detection for financial institutions. Machine learning intersects with statistics, computer science and artificial intelligence, focusing on the development of fast and efficient algorithms to enable real-time data processing. Rather than just follow explicitly programmed instructions, these machine learning algorithms learn from experience, making them a key component of artificial intelligence platforms. Machine learning may also help us with a challenge from one of last year's most buzzed about technology developments: the Internet of Things.
Deep learning can screen social media behaviour on Twitter, Facebook and additional news stories to connect data points and make predictions. To figure this out, in 2014 the NASA, the Universities Space Research Association and Google joint the Quantum Artificial Intelligence Lab. Eurekahedge, an independent data provider and alternative investment research firm that specialises in hedge fund databases, stated that their own Eurekahedge AI/Machine Learning Hedge Fund Index has outperformed both traditional quant and more generalized hedge funds since 2010. The Guardian: Google's DeepMind makes AI program that can learn like a human
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NVIDIA today launched a partner program with the world's leading original design manufacturers (ODM) -- Foxconn, Inventec, Quanta and Wistron -- to more rapidly meet the demands for AI cloud computing. Through the NVIDIA HGX Partner Program, NVIDIA is providing each ODM with early access to the NVIDIA HGX reference architecture, NVIDIA GPU computing technologies and design guidelines. The standard HGX design architecture includes eight NVIDIA Tesla GPU accelerators in the SXM2 form factor and connected in a cube mesh using NVIDIA NVLink high-speed interconnects and optimized PCIe topologies. "Through this new partner program with NVIDIA, we will be able to more quickly serve the growing demands of our customers, many of whom manage some of the largest data centers in the world," said Taiyu Chou, general manager of Foxconn/Hon Hai Precision Ind Co., Ltd., and president of Ingrasys Technology Inc. "Early access to NVIDIA GPU technologies and design guidelines will help us more rapidly introduce innovative products for our customers' growing AI computing needs."
Driving this surge of machine-learning development is a wave of data generated by mobile phones, sensors, and video cameras. As a result, we expect machine learning will become the next great commodity. Released as a free, open-source operating system in 1991, it now powers nearly all the world's supercomputers, most of the servers behind the Internet, and the majority of financial trades worldwide – not to mention tens of millions of Android mobile phones and consumer devices. A director at Intel Capital, Sanjit Dang drives investments in user computing across the consumer and enterprise sectors.