In November, 2015, Google open-sourced its numerical computation library called TensorFlow using data flow graphs. Its flexible implementation and architecture enables you to focus on building the computation graph and deploy the model with little efforts on heterogeous platforms such as mobile devices, hundreds of machines, or thousands of computational devices. TensorFlow is generally very straightforward to use in a sense that most of the researchers in the research area without experience of using this library could understand what's happening behind the code blocks. TensorFlow provides a good backbone for building different shapes of machine learning applications. However, there's a large number of potential users, including some researchers, data scientists, and students who may be familiar with many data science concepts/algorithms already but who never get involved in deep learning research/applications, may found it really hard to start hacking.

An HPE keynote session at this year's HPC on Wall Street event was geared to helping financial firms dip their toe into emergent technologies such as AI and deep learning. Today's financial services companies must continually strive to gain a competitive edge in a highly data-intensive industry. With the emergence of big data, firms are struggling to manage the onslaught of complex data from many sources, stay on top of evolving regulations, and boost data security. High performance computing (HPC) technologies are not only helping financial firms ease the pains associated with explosive data growth, but they are also becoming absolutely essential to survival. Emergent technologies and new HPC innovations to hit the financial sector were the focus of the 14th annual HPC on Wall Street event held earlier this week in New York City.

When Hussein Mehanna showed off a new incarnation of Facebook's Big Blue App back in November, it seemed a tiny improvement--at least on the surface. The app could transform a photo from your cousin's wedding into a Picasso or a Van Gogh or a Warhol, a bit of extra fun for your social media day. But with this bit of extra fun, Mehanna and his team of Facebook engineers were laying the groundwork for an audacious effort to change the future of computing--what Facebook CEO Mark Zuckerberg calls a platform for augmented reality. Zuckerberg formally unveiled this platform on Tuesday morning during his keynote at Facebook's annual developer conference. In short, Facebook is transforming the camera on your smartphone into an engine for what is commonly called AR.

Some of the biggest companies in the tech sector are making major bets on artificial intelligence (AI), also often called deep learning. Alphabet Inc. (NASDAQ: GOOGL) and Apple Inc. (NASDAQ: AAPL) lead in acquiring smaller companies, but there are plenty of big players. According to a report out Wednesday morning from IDC, worldwide revenues for cognitive and AI systems will rise 59.3% this year to $12.5 billion and global spending on AI will explode at a compound annual growth rate (CAGR) of 54.4% through 2020 when revenues will top $46 billion. That's enough to get anyone's attention. Of this year's total spending, some $4.5 billion is targeted at cognitive applications that automatically learn, discover and make recommendations or predictions.

On Monday, IBM announced that it has partnered with Continuum Analytics to offer open data science platform Anaconda on IBM Cognitive Systems. Anaconda, which is powered by Python, will also integrate with IBM's PowerAI software for machine learning and deep learning, making it easier and faster for businesses to analyze and gain insights from data-intensive cognitive workloads. "Anaconda is an important capability for developers building cognitive solutions, and now it's available on IBM's high performance deep learning platform," said Bob Picciano, senior vice president of Cognitive Systems, in a press release. "Anaconda on IBM Cognitive Systems empowers developers and data scientists to build and deploy deep learning applications that are ready to scale." Deep learning--one of the fastest-growing fields of machine learning, the release noted--makes it possible to process datasets that include up to billions of elements, and to find predictive models in that data.

Researchers studying a deal in which Google's artificial intelligence subsidiary, DeepMind, acquired access to millions of sensitive NHS patient records have warned that more must be done to regulate data transfers from public bodies to private firms. The academic study says that "inexcusable" mistakes were made when, in 2015, the Royal Free NHS Foundation Trust in London signed an agreement with Google DeepMind. This allowed the British AI firm to analyse sensitive information about 1.6 million patients who use the Trust's hospitals each year. The access was used for monitoring software for mobile devices, called Streams, which promises to improve clinicians' ability to support patients with Acute Kidney Injury (AKI). But according to the study's authors, the purposes stated in the agreement were far less specific, and made more open-ended references to using data to improve services.

Professor Geoffrey Hinton, VP Engineering Fellow at Google and University Professor Emeritus at the University of Toronto, says there will be definite changes to the job landscape. "In five to ten years, deep learning is going to do better than some professions, because it's going to get a lot more experience." However, there will also be incredible opportunities as AI will create jobs in positions that do not yet exist.

Deep learning and neural networks can be applied to any problem. It excels at handling large data sets, facilitating automation, image processing, and statistical and mathematical operations, just to name a few areas. It can be applied to any kind of operation and can help find opportunities, solutions, and insights.

In a first for deep learning, an Oak Ridge National Laboratory-led team is bringing together quantum, high-performance and neuromorphic computing architectures to address complex issues that, if resolved, could clear the way for more flexible, efficient technologies in intelligent computing. Deep learning refers to nature-inspired, computer-based technologies that push beyond the conventional binary code, advancing emerging fields such as facial and speech recognition. "Our proposed approach can optimize and manage complexity in a low-power environment, resolving specific challenges when exploring complicated scientific data." The team's tri-fold experiment demonstrates the feasibility of using the three architectures in tandem to overcome limitations and represents a new capability not currently available. Details of the team's experiment are available online.

Google's DeepMind CEO Demis Hassabis shows that AI doesn't only learn from human knowledge, but also creates new knowledge. AlphaGo has it own creativity and intuition, inventing new knowledge and strategies about Go Game for human professionals to study in 2017. Go game was invented in ancient China more than 2,500 years ago, is an abstract strategy board game, aiming to surround more territory than the opponent for two players. It is believed to be the oldest board game continuously played today.Despite its relatively simple rules, Go is very complex, even more so than chess, and possesses more possibilities than the total number of atoms in the visible universe. Compared to chess, Go has both a larger board with more scope for play and longer games, and, on average, many more alternatives to consider per move.