While many tech moguls dream of changing the way we live with new smart devices or social media apps, one Russian internet millionaire is trying to change nothing less than our destiny, by making it possible to upload a human brain to a computer, reports Tristan Quinn. "Within the next 30 years," promises Dmitry Itskov, "I am going to make sure that we can all live forever." It sounds preposterous, but there is no doubting the seriousness of this softly spoken 35-year-old, who says he left the business world to devote himself to something more useful to humanity. "I'm 100% confident it will happen. Otherwise I wouldn't have started it," he says. It is a breathtaking ambition, but could it actually be done?

If 2015 was the year analytics tools became ubiquitous in enterprise software, 2016 is shaping up to do much the same for machine learning. Just last week artificial-intelligence startup Nervana launched an offering that promises "deep learning on demand," and on Thursday Hewlett Packard Enterprise released a product of its own for what it calls "machine learning as a service." Dubbed Haven OnDemand, the cloud platform offers machine-learning application programming interfaces (APIs) and services designed to enable developers and businesses to build data-rich mobile and enterprise applications. Face-detection capabilities are included in Haven OnDemand. Haven OnDemand entered beta back in 2014, and at the time it had just a few APIs, said Fernando Lucini, HPE's CTO for big data.

Generative RNNs are now widely popular, many modeling text at the character level and typically using unsupervised approach. Here we show how to generate contextually relevant sentences and explain recent work that does it successfully. Deep learning has risen to prominence, both delighting and enraging computer scientists, following a number of breakthrough results on difficult classification tasks. Convolutional neural networks demonstrate an unprecedented ability to recognize objects in images. A variety of neural networks have similarly revolutionized the field of speech recognition.

Recently I wrote about a new service being offered by tech giant IBM to utilize machine learning in the field of radiology. The service, called Avicenna, hopes to identify anatomical features and abnormalities in medical images, and by taking this analysis and cross-referencing it with the patient's medical record provide support for a successful diagnosis. The aim is for the service to make the work of cardiologists and radiologists quicker and more effective. It is still very much in the early stages of development and is being trained on anonymized images in cardiology and breast radiology areas. A British startup is now taking a similar approach to detecting the onset of dementia.

Many unexploited opportunities have been evident to quants for decades, but although the solutions may be clear statistically at some level, the limiting factor has been computation. A simple example is incorporating unstructured data like online content, or semi-structured data like company reports and transaction data, into predictive models. Feature engineering is the process machine learning folks use to generate inputs to statistical models from raw input data. There are approaches for automated feature learning with techniques like deep learning -- recently, this has allowed us to unlock the potential of understanding and labeling images. Then there are approaches that require collaboration with subject matter experts.

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Within the rail industry, anything which helps keep trains moving, avoiding operational delays and improves customer experience, is worth pursuing. Many OEMs are now investing significant resources into one of the most valuable and potentially rewarding currencies in business: Big Data. In rail, and specifically when it comes to rolling stock maintenance, big data is synonymous with Condition Based Maintenance (CBM) and Predictive Maintenance (PM). Thanks to the rapidly expanding scale of manufacturing and asset maintenance industries, they are now adapting to the wider applications of advanced algorithms on consumer generated big data. Though CBM and PM are commonly adopted practices in rail industry, the scope of CBM is far wider than that of PM.

With the lopsided 4-1 rout by Google's AlphaGo over Go grandmaster Lee Sedol, the easy takeaway is that artificial intelligence (AI) has achieved another milestone against humans, raising the specter that machines may eventually replace people, even managers. But by winning even in such convincing fashion, AlphaGo has revealed that AI still has a number of shortcomings, particularly when it comes to machine-made intuition. Google acquired DeepMind, the developer of AlphaGo, in 2014, in a 500 million bid to expand its burgeoning AI portfolio. AlphaGo's deep-learning algorithm allows both a "policy network" and a "value network" to store not only millions of past games played by the masters but also those played against tweaked versions of itself. The naming of the two networks is managerial-sounding and is aimed at promoting efficiency, not just raw computing power.

It was nearly 30 years ago that I first got infatuated with Artificial Intelligence (AI) and I ended up focusing both my undergraduate and graduate engineering research on applications of Artificial Neural Networks (ANNs). My first two jobs after graduate school stayed in the same groove; over 6 years I developed AI and machine learning techniques to address real world problems that ranged from recognizing human speech and natural language, to converting handwriting to searchable digitized text, and to streamlining maintenance procedures in nuclear reactor cores. So it is with a mix of amazement and amusement that I am soaking up the resurgence of AI and machine learning as the buzzword-du-jour: "Deep Learning". Deep Learning is very visible in the high hopes we hold for driverless cars and in the triumph of machines over chess champions. It is less conspicuously and more frequently used in the form of Apple's Siri, Amazon's Echo, playlists generated on Spotify, that auto-tag feature on Facebook Photos, the voice assistant that answers the phone when you call your bank, or when your fingerprint is recognized by a machine.

This helps to validate DeepMind's machine learning techniques and the neural network construction behind it. Having proven their mettle in Go, the DeepMind team could now have the confidence (and funding) to tackle more complex AI challenges. ARTIFICIAL INTELLIGENCE (AI) just overcame a new hurdle: learning to play Go, a game considered thousands of times more complex than chess--well enough to beat the greatest human player at his own game. South Korean national Lee Se-dol, one of the world's top Go players, won only one of the five matches against Google's AlphaGo, missing out on the 1-million prize up for grabs in a recent'challenge' held in Seoul. AlphaGo, an AI system developed by Google DeepMind, just bested the best Go-playing human currently alive. This was not supposed to happen.