Two weeks back, my IBM team helped organize "Take Your Child To Work Day", a day for encouraging children to learn more about the respective careers and companies of their parents. At IBM, we had a unique challenge: explaining the world of IBM Watson. Watson, IBM's prolific computer system that uses natural language, hypothesis generation, and evidence-based learning capabilities to strengthen decision support, remains one of the technology industry's most exciting and complicated applications. With its own business group within IBM as of 2014 and an announced 10 billion in revenue over the next ten years, Watson has powered IBM's brand forward as a force to compete with in the cognitive era. From healthcare to education, Watson is looking to catalyze the way we make decisions everyday.

How did you become interested in artificial intelligence (AI)? I am a documentary filmmaker, writer and speaker. I was making a film around 15 years ago about AI and got to speak to some of the major players in the field, including Ray Kurzweil, the Director of Engineering at Google who started the singularity industry, and Rodney Brookes, the premier roboticist of our time who founded iRobot (a company that created the Roomba vaccum cleaner and robots for military use) and then established a company called Rethink Robotics. Both Kurzweil and Brookes were optimistic about the time when we will share the planet with smarter-than-human machines – and I was too. I was, and still am, a gigantic proponent of AI, despite my book's title Our Final Invention: Artificial Intelligence and the End of the Human Era.

These hugely powerful tools, used, developed and backed by the world's most advanced technology companies, are now available to anyone with the skills to use them. Whilst these announcements haven't quite drawn the media attention of an iPhone launch, their significance and reach may be far greater. The point is this: Using these toolkits, individually or combined, anyone can integrate transformational AI or machine learning platforms – which are as sophisticated as anything currently on the market – to their business on a pay as you go or free basis. So what does that mean? It means that any task currently performed by a costly black box AI platform; identifying where to drill for oil, predicting disease outbreaks, optimising scientific experiments to develop new products, predictive maintenance can now be done in-house for the price of a data scientist's salary or consultancy fee, using these open platforms, without losing any control or oversight of your data.

Each neuron and connection in the entire population is unique, and assigned a unique integer label. So each network is simply just a list of connections, along with the weight for those connections. Note that two different networks can have a similar connection, but the weights for each network will generally be different. Because each neuron and each connection is globally unique, it becomes possible to merge two different networks, to produce a new network.

"The Nervana team is developing a full-stack deep learning platform to enable organizations to use deep learning to tackle previously unsolvable problems. We believe our inclusion in the Cool Vendor report by Gartner validates our mission to make scalable artificial intelligence widely accessible and to enable disruptive new applications across a range of industries," said Naveen Rao, co-founder and CEO of Nervana. In February, Nervana launched Nervana Cloud, a full-stack hosted platform for deep learning. Based on neon, Nervana's open source deep learning framework, Nervana Cloud helps businesses develop and deploy high-accuracy deep learning solutions at a fraction of the cost of building their own infrastructure and data science teams. Utilizing the Nervana Engine, an application specific integrated circuit (ASIC) that is custom-designed and optimized for deep learning, Nervana Cloud has been optimized down to the silicon to handle the most complex machine learning training at scale.

LOS ALTOS, CA--(Marketwired - May 11, 2016) - Today, xiQ, a leader in real-time personalized market intelligence, announced that GE-spinoff Genpact (NYSE: G), a global leader in digitally powered business process management and services and architect of the Lean Digital enterprise, has deployed xiQ's Marketing Intelligence Platform, powered by IBM Watson. The platform is enterprise-wide and is applied to its global leadership, sales, marketing and industry business unit executives. By implementing the cognitive computing-powered xiQ platform, Genpact is now able to identify real-time market intelligence that further strengthens its ability to support client growth, efficiency and business agility. "Thought leadership is a key driver of Genpact's'Lean Digital Transformation' vision," said Gianni Giacomelli, Chief Marketing Officer and SVP of Product Innovation, at Genpact. "xiQ delivers real-time market intelligence to our leadership team, reducing the time to process critical information while improving decision making. With xiQ, we have digitalized the process of keeping our teams on the cutting edge, winning customer confidence and driving business growth."

As part of this development, OMI is becoming part of the University's Department of Engineering Science from 1 August 2016. The development of the OMI's focus will create a hub for machine learning and data analysis at Eagle House, the current home of the OMI and Man AHL's Oxford research lab. The OMI's researchers will be joined by the Department of Engineering Science's Machine Learning Group, a body of around 20 leading machine learning researchers who will relocate to Eagle House. The aim is to foster a stimulating environment composed of researchers focused on machine learning techniques, whereby machine learning and data analytics expertise can be shared and leveraged. In addition, the intention is for the OMI to appoint two new Senior Research Fellows/Associate Professors in machine learning with a specific focus on quantitative finance.

What we call machine learning can take many forms. The purest form offers the analyst a set of data exploration tools, a choice of ML models, robust solution algorithms, and a way to use the solutions for predictions. The Amazon, Microsoft, Databricks, Google, and IBM clouds all offer prediction APIs that give the analyst various amounts of control. HPE Haven OnDemand offers a limited prediction API for binary classification problems. Not every machine learning problem has to be solved from scratch, however.

The White House is doing a lot more thinking about the arrival of automated decision-making -- super-intelligent or otherwise. No one in government is yet screaming "Skynet," but in two actions this week the concerns about our artificial intelligence future were sketched out. The big risks of A.I. are well-known (a robot takeover), but the more immediate worries are about the subtle, or not-so-subtle, decisions made by badly coded and designed algorithms. President Barack Obama's administration released a report this week that examines the problem associated with poorly designed systems that, increasingly, are being used in automated decision making. Algorithmic systems can affect employment, education, access to credit -- anything that relies on computer-assisted decisions.

That said, current technology won't allow us to make anything larger than the printing machines themselves. Some smart people have suggested that we should look to nature to see how it builds things--specifically, spiders, and the way they can swarm together to build massive nests for themselves. Siemens, the German engineering and telecommunications company, has taken this concept to heart. A team of researchers at its Princeton, New Jersey, laboratory are creating autonomous spider-like robots that can work together to 3D-print structures on command. While Siemens isn't known for its robotics research, Livio Dalloro, the head of the company's "Product Design, Simulation & Modeling Research" group in Princeton, said in an interview that the company views the bots as a "moonshot."