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 have had many previous hype cycles around AI. As I wrote in Silicon Collar: "Since the 1950s! That is when Alan Turing defined his famous test to measure a machine's ability to exhibit intelligent behavior equivalent to that of a human. In 1959, we got excited when Allen Newell and his colleagues coded the General Problem Solver. In 1968, Stanley Kubrick sent our minds into overdrive with HAL in his movie, 2001: A Space Odyssey.
This post is authored by Vani Mandava, Director of Data Science at Microsoft Research. The AI revolution is poised to unleash unprecedented innovation and impact on our society. Several research and development groups across Microsoft have hit their stride in delivering world-changing impact through the power of AI. Working together, we are creating a comprehensive Microsoft AI platform and a set of AI services that will enable the next generation of intelligent applications that will augment human intelligence. The AI buzz has been impossible to miss at numerous conferences that Microsoft has participated in during the past year.
BEIJING – Google announced Wednesday that it will open a new artificial intelligence research center in Beijing, tapping China's talent pool in the promising technology despite the U.S. search giant's exclusion from the country's internet. Artificial intelligence, especially machine learning, has been an area of intense focus for American tech stalwarts Google, Microsoft and Facebook, and their Chinese competitors Alibaba, Tencent and Baidu as they bid to master what many consider the future of computing. AI research has the potential to boost developments in self-driving cars and automated factories, translation products and facial recognition software, among other innovations. Google's move to open a Beijing office focused on fundamental research is an indication of China's AI talent, widely seen as being neck-and-neck with the United States in research capability. "Chinese authors contributed 43 percent of all content in the top 100 AI journals in 2015," Li Feifei, a researcher leading the new center, wrote in a blog post on Google's website.
Wang Jian was once called crazy by Jack Ma Yun, the founder and executive chairman of Alibaba Group Holding, for suggesting that the company could have its own mobile operating system. That vision, however, proved prescient as smartphones powered by the company's YunOS mobile operating platform, which was developed by its Alibaba Cloud subsidiary, surpassed 100 million units last year. In addition, many of the Hangzhou-based e-commerce company's recent innovations are rooted in Alibaba Cloud, known as Aliyun in China, as domestic demand for data centre facilities and on-demand computing services delivered over the internet have grown rapidly. "It's not about whether I'm crazy or not, it's about this era," Wang, the chairman of Alibaba's technology steering committee, said in an interview in Hong Kong, where he met with some journalists to talk about his new book Being Online. "[This] is a crazy era, so many new things are happening."
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. The emergence of AI is a key theme here at Data Center Frontier. The rise of AI applications will drive demand for data center space, and have design implications for how high-density racks are powered and cooled.
Investors beware: there's plenty buzz around artificial intelligence (AI) as more and more companies say they're using it. In some cases, companies are using older data analytics tools and labeling it as AI for a public relations boost. But identifying companies actually getting material revenue growth from AI can be tricky. X AI uses computer algorithms to replicate the human ability to learn and make predictions. AI software needs computing power to find patterns and make inferences from large quantities of data.
Researchers from IBM and the University of Melbourne have developed a proof-of-concept seizure forecasting system that predicted an average of 69 percent of seizures across 10 epilepsy patients in a dataset. The system, which the scientists claim is "fully automated, patient-specific, and tunable to an individual's needs", uses a combination of deep-learning algorithms and a low-power "brain-inspired" computing chip to predict when seizures might occur, even if patients have no previous prediction indicators. IBM noted that a one-size-fits-all approach is inadequate when it comes to epilepsy management, as the condition manifests itself uniquely in each patient. "Epilepsy is a very unique condition where triggers for seizures are specific to individual patients -- some may be sensitive to heat, others to stress. This is why deep learning is important because it can interpret the data and look for signs and patterns specific to an individual's brain signals," an IBM spokesperson told ZDNet.
Deep neural networks--a form of artificial intelligence--have demonstrated mastery of tasks once thought uniquely human. Their triumphs have ranged from identifying animals in images, to recognizing human speech, to winning complex strategy games, among other successes. Now, researchers are eager to apply this computational technique--commonly referred to as deep learning--to some of science's most persistent mysteries. But because scientific data often looks much different from the data used for animal photos and speech, developing the right artificial neural network can feel like an impossible guessing game for nonexperts. To expand the benefits of deep learning for science, researchers need new tools to build high-performing neural networks that don't require specialized knowledge.
There are many established and startup companies developing deep learning chips. Google and Wave Computing have working silicon and are conducting customer trials. Chinese AI chip startup has received $100 million in funding. Cambricon Technologies aims to have one billion smart devices using its AI processor and own 30% of China's high-performance AI chip market in three years. Huawei estimates Cambricon chips are six times faster for deep-learning applications like training algorithms to identify images than a GPU.
You're sitting at home minding your own business when you get a call from your credit card's fraud detection unit asking if you've just made a purchase at a department store in your city. It wasn't you who bought expensive electronics using your credit card – in fact, it's been in your pocket all afternoon. So how did the bank know to flag this single purchase as most likely fraudulent? Credit card companies have a vested interest in identifying financial transactions that are illegitimate and criminal in nature. According to the Federal Reserve Payments Study, Americans used credit cards to pay for 26.2 billion purchases in 2012.