1. The Oxford Martin Programme on Technology and Employment
In January, Citibank helped establish this programme, to be led by Carl Benedikt Frey and Michael Osborne, authors of a famous paper on AI-driven automation. The programme is monitoring changes in the labour market, and watching for signs of irreversible technological unemployment.
2. Google open-sources Tensor Flow
In September, Google announced an important change in strategy. Having built a very lucrative online advertising business based on algorithms and hardware which produced better AI than anyone else, it was open sourcing its current best AI software – a deep learning engine called Tensor Flow. It only licenses the software for single machines, so even very well resourced organisations won’t be able to replicate the functionality that Google generates, but the move was significant.
The following month, Facebook announced that it would follow suit by open sourcing the designs for Big Sur, the server which runs the company’s latest AI algorithms.
3. Google announces RankBrain
In October, Google confirmed that it had added a new technique called RankBrain to its Search offering. RankBrain is a machine learning technique, and it was already the third-most important component of the overall Search service. It is applied to the 15% of searches which comprise words or phrases that have not been encountered before, and converts the language into mathematical entities called vectors, which computers can analyse directly.
4. SwiftKey announces Neural Alpha
In October, British AI company announced the launch of Neural Alpha, the first application of the AI technique of deep learning to mobile phone keyboards.
SwiftKey pioneered keyboards with a three-word suggestion bar above the keys that could accurately predict your next word. This was powered by a technology called “n-gram”, an approach now used on more than a billion devices globally. Where N-gram technology predicts words that have been seen before in the same sequence, Neural Alpha’s intelligent understanding of context introduces a more ‘human’ touch for mobile typing.
5. The Leverhulme Centre for the Future of Intelligence
In December, the Leverhulme Trust announced a grant of £10m to the Cambridge University’s Centre for the Study of Existential Risks (CSER). The centre will become an important new interdisciplinary research organisation, exploring the opportunities and challenges of artificial intelligence, both short and long term.
Dr Seán Ó hÉigeartaigh, CSER’s Executive Director, said that the Centre will look “at themes such as different kinds of intelligence, the responsible development of technology, and issues surrounding autonomous weapons and drones.”
6. Open A.I.
In December, a group of Silicon Valley luminaries including Elon Musk launched Open A.I., a non-profit artificial intelligence research company with $1 billion in initial funding. Musk, CEO of Tesla Motors and SpaceX, was joined by Y-Combinator president Sam Altman as a co-chair of the new organization. LinkedIn’s Reid Hoffman, investor Peter Thiel, Amazon Web Services and Infosys are the other backers.
“It’s hard to fathom how much human-level AI could benefit society, and it’s equally hard to imagine how much it could damage society if built or used incorrectly,” the launch press release said.
7. Google’s quantum computer success
In December, Google announced that its D-Wave quantum computer was 100 million times faster than a traditional desktop computer in a “carefully crafted proof-of-concept problem”, as Google engineering director Hartmut Neven described it. Google acquired the machine in 2013, but had not previously been able to demonstrate its superiority.
Quantum computers use quantum bits (qubits) instead of the binary bits used in classical computers. These qubits can exist in a ‘superposition’ of either 0 or 1 simultaneously, allowing them to carry out a number of different calculations at once.
8. Baidu claims its speech recognition achieves human performance
December 2015, Baidu (often described as China’s Google) announced that its speech recognition system Deep Speech 2 performed better than humans with short phrases out of context. It uses deep learning techniques to recognise Mandarin.
