Andrew Lo, director of the Laboratory for Financial Engineering at the Massachusetts Institute of Technology, has announced that, "The capability is here," suggesting that, "The biggest hurdle is the cultural barrier. You've got a lot of central bankers who are not as open to technology." The use of computers to do economic modeling is not new, and is an important tool in answering specific questions, but predictions made using computers are often less accurate than that of their flesh and blood counterparts. Lo suggests that artificial intelligence, or AI, will bridge the gap, using the process whereby technology learns to do tasks for which it hasn't been programmed. Called'machine learning,'the concept is already being applied to perform complex tasks, including classifying DNA sequences, detecting credit-card fraud, information retrieval, marketing, online advertising and stock market analysis.

Google has taken a brilliant and unexpected step toward building an AI with more humanlike intuition, developing a computer capable of beating even expert human players at the fiendishly complicated board game Go. The objective of Go, a game invented in China more than 2,500 years ago, is fairly simple: players must alternately place black and white "stones" on a grid of 19 horizontal and 19 vertical lines with the aim of surrounding the opponent's pieces, and avoiding having one's own pieces surrounded. Mastering Go, however, requires endless practice, as well as a finely tuned knack of recognizing subtle patterns in the arrangement of the pieces spread across the board. Google's team has shown that the skills needed to master Go are not so uniquely human after all. Their computer program, called AlphaGo, beat the European Go champion, Fan Hui, five games to zero.

The tech giant decided not to source its chips from other providers, instead building its own customised processors, dubbed Tensor Processing Units (TPUs), for its machine learning software. "We've been running TPUs inside our data centers for more than a year, and have found them to deliver an order of magnitude better-optimized performance per watt for machine learning," Google said in a blog post. Since AI has only recently found commercial use cases, companies that process AI functions are still solely using graphics processing units (GPUs) to do the job. "TPU is tailored to machine learning applications, allowing the chip to be more tolerant of reduced computational precision, which means it requires fewer transistors per operation," Google said.

Google is creating its own chips to power its AI machines, rivalling traditional silicon manufacturers like ARM and Intel. The tech giant decided not to source its chips from other providers, instead building its own customised processors, dubbed Tensor Processing Units (TPUs), for its machine learning software. Google believes the TPU chip will lead to increased efficiency in AI processing. "We've been running TPUs inside our data centers for more than a year, and have found them to deliver an order of magnitude better-optimized performance per watt for machine learning," Google said in a blog post. "This is roughly equivalent to fast-forwarding technology about seven years into the future (three generations of Moore's Law)."

Last year Microsoft and Google both showed that their image-recognition algorithms had learned to best humans. They independently created software that could exceed the average human score on a standard test that challenges software to recognize images of a thousand different objects, from mosques to mosquitoes. But to get good enough to defeat humanity, each company's software scrutinized 1.2 million labeled images. A child can learn to recognize a new kind of object or animal using only one example. Startup Geometric Intelligence said Monday that it has developed machine-learning software that is a much quicker study.

"Because of AI's surprising history, it's hard to predict when human-level AI might come within reach," the group, OpenAI, said in a statement on their website. "When it does, it'll be important to have a leading research institution which can prioritise a good outcome for all over its own self-interest." The project is a non-profit effort and their main objective is to save humanity, not generate income. "OpenAI is a non-profit artificial intelligence research company. Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return," the OpenAI website explains.

I just got out of a meeting at SRI International, where representatives from both SRI and international banking group BBVA showed off something they've been working on for the past couple of years. Currently, SRI is best known as the research institute where Siri was developed before spinning out into a separate company and eventually being acquired by Apple, where it powers the Siri feature on the iPhone. SRI and BBVA have been collaborating on a new project, Lola, which they're pitching as a successor of sorts to Siri. Bill Mark, SRI's VP of Information and Computer Sciences, calls it "the next generation personal assistant". In this case, that personal assistant technology is being applied to a specific industry -- banking.

Google announced on Wednesday, May 18, at the Google I/O conference event, that it designed its own computer chip for deep neural networks. According to Wired, CEO Sundar Pichai said that Google has designed an application-specific integrated circuit (ASIC) aimed to drive deep neural nets, an artificial intelligence (AI) technology that is reshaping the Internet. These are networks of software and hardware that analyze vast amount of data in order to learn specific tasks. Google uses neural nets to recognize voice commands in Android phones, identify faces and objects in photos, or translate text from one language to another. Even the Google search engine is transformed by the applications of this new technology.

Even if your career survived offshoring and the Great Recession, now's not the time to get complacent: a report from the World Economic Forum predicts that automation and artificial intelligence (A.I.) could eliminate as many as 47 percent of jobs in the U.S. in coming years. Martin Ford, a software developer, entrepreneur and author of "Rise of the Robots," is one of the Paul Revere-esque prophets sounding the alarm about a jobless future. "There's a huge number of jobs at risk, even highly-skilled jobs," Ford said. "We're going to feel the effects within five years, and we'll be fully into the age of A.I. in 10 to 15 years." While there's no "magic bullet" that will protect every human job, taking these proactive steps can better position you and your career to survive a takeover by the machines.

Artificial Intelligence is already operating in a range of limited but interesting ways across the healthcare sector. The use of processing computers that can sift and sort data hundreds if not thousands of times quicker than humans is growing, with research suggesting that we spent around 2 billion in venture backed capital on it in 2015. But where is its use likely to impact healthcare in the next decade or so, with reports predicting spending on AI in healthcare will reach as much as 20 Billion in 10 years time? Before we look at applications in healthcare in particular, we should remember that AI is an umbrella term for three related technologies; machine learning, extended human cognition and robotics. AI is quite a broad field and in this regard the impact on healthcare as one large industry is likely to be significant, especially in being able to be major new platform/systems leveraging by SAAS systems and databases intelligently talking to each other.