Japan's major cancer research institution has announced plans to use artificial intelligence to diagnose cancer and decide the best courses of treatment for patients. The National Cancer Center said Tuesday that it will work with other organizations, including the National Institute of Advanced Industrial Science and Technology, to make a system for cancer diagnosis and treatment for trial use within five years. The system will use a database created from massive amounts of data accumulated by the center that includes genetic information and results of blood tests and diagnostic imaging, and also check relevant medical research papers, it said. The center plans to use the system for finding new markers in detecting cancer at an early stage and developing drugs to fight the disease. Similar moves to utilize AI in the medical field are already underway at various institutions nationwide.

Behind the scenes, AI engines in the form of smart algorithms "work" on stock exchanges, offer up suggestions for books and films on Amazon and Netflix and even write the odd article. But AI does not have the greatest public image - often due to sci-fi films that display dystopian visions of robots taking over the world. Hal is perhaps the most famous AI turned bad. Created by Arthur C Clarke for the book and film 2001: A Space Odyssey, Hal stands for Heuristically Programmed Algorithmic Computer. A recent survey of business and technology professionals and found that 58% of them are researching AI, but only 12% are using AI systems.

Since 2014, $6B USD has been raised by 900 startups globally in AI, making it one of the hottest technology themes. From entrepreneurs like Elon Musk to tech giants like Facebook and Google, enormous resources and focus is being directed towards pushing the boundaries of AI capabilities and applications. Much like the Internet re-defined technology industry in the last century, will AI revolutionize the technology landscape and Man-Machine equation forever? What role can AI play in shaping India's economy and innovation culture? With over 200 startups leading the way, there is a real opportunity for every entrepreneur to make a mark in this sunrise sector.

For years, those of us in the technology sectors have been building integrations between disparate systems. In fact, enterprise organizations often have specific resources dedicated toward building and maintaining integrations between mission critical systems. According to dictionary.com, the word integrate is defined as a verb meaning "to bring together or incorporate (parts) into a whole." For decades, technologists have been manually creating integrations between systems whose interfaces have been continuously changing. Those of us who were lucky got the opportunity to create an integration between a mainframe and a front-line database, thus ensuring that fewer updates to the integration were necessary.

A few weeks ago I was emailing Tom Blomfield, the CEO of startup bank Monzo, to arrange lunch. He passed me over to his assistant, Amy Ingrams, by CCing her into an email. Amy and I exchanged eight emails fixing up a date and then another five when Blomfield had to rearrange. Only then did I spot something odd in Amy's email signature: "Artificial intelligence for scheduling meetings." It turns out that I had been talking to an algorithm the whole time.

I'd love to talk more about Flowcast, but I'm still not able to shake the image of you making a robotic submarine run by San Diego poolside (laughs). As a STEM enthusiast, I have been in awe of IBM Watson's capabilities. And I feel it's an honor to be talking to someone who has contributed to its capabilities. Winnie: Flowcast came about with my friend and co-founder Ken So. We met back when I was at MIT and he was doing his MBA at Berkeley.

Has AI gone too far? This might seem like a nonsensical question to data scientists who strive every day to expand the capabilities of AI until you read the headlines created by this just released peer reviewed scientific paper: Automated Inference on Criminality Using Face Images (Xiaolin Wu, McMaster Univ. That's right, shades of The Minority Report (movie in which criminals are arrested before the crime occurs) and the 19th century studies of phrenology. These researchers claim 89.51% accuracy in making this classification on several sets of unlabeled validation images, each of about 1,500 facial images. I hope this has really taken your breath away.

This article is not about smoothing ore into gems though your may find a few gems herein. Systematic Pattern and Random Noise In "Components of Time Series Data", I discussed the components of time series data. In time series analysis, we assume that the data consist of a systematic pattern (usually a set of identifiable components) and random noise (error), which often makes the pattern difficult to identify. Most time series analysis techniques involve some form of filtering out noise to make the pattern more noticeable. Two General Aspects of Time Series Patterns Though I have discussed other components of time series data, we can describe most time series patterns in terms of two basic classes of components: trend and seasonality.

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Computer processor maker Advanced Micro Devices (AMD) announced that Google will start using its compute accelerators on its cloud platform. Google plans to start rolling out the AMD hardware in 2017. It will use AMD's single-precision dual GPU compute accelerators, Radeon-based AMD FirePro S9300 x2 Server GPUs, to help accelerate Google Compute Engine and Google Cloud Machine Learning services. The GPUs can handle highly parallel calculations, including complex medical and financial simulations, seismic and subsurface exploration, machine learning, video rendering and transcoding, and scientific analysis. "Google is building up its GPU-based infrastructure, and they want to ensure they offer AMD's architecture," said Raja Koduri, senior vice president and chief architect at AMD, in an interview with Forbes.