As part of this year’s annual Greenside US Research Tour, Ntegra hosted a full day programme of presentations and a VC panel at the Stanford Park Hotel in Silicon Valley. The schedule started with a thought-provoking keynote presentation from Jim Spohrer, IBM Director of Cognitive Open Technology based at Almaden, San Jose, one of IBM’s worldwide research labs. The centre opened in 1986 and continues the research started in San Jose more than fifty years ago. The phrase “Silicon Valley” was first seen in print around 1970 but the origins and heritage of the region stretch back to when Stanford University, NASA Ames and IBM Research in San Jose were doing pioneering work on silicon-based transistors, hard disks, and other foundations of computing.
As well as being Director of IBM Global University Programmes Worldwide, Jim has been the Director of Almaden Services Research and was the Chief Technology Officer for IBM Venture Capital Relations. During the last few years Jim has been campaigning internally to get IBM to embrace open source in AI, latterly with great success. Half of his team are at IBM Watson West in San Francisco, working to transform IBM into a cognitive enterprise (see: Center for Opensource Data and AI Technologies (CODAIT)).
With a technical background, Jim likes to pursue collaborative research with academia. IBM now has 15 global research laboratories with around 3000 researches, IBM invests billions in R&D, paid for in part by significant patent licensing – IBM has been #1 in the world for over 27 years in the production of patents, more than any other company. When asked about IBM’s future, Jim was upbeat – suggesting the future is bright for companies transforming to use AI.
Jim says that “AI is hard,” and far from being solved. Google uses AI in nearly all their offerings (and they open-source key tools like TensorFlow). Facebook, Amazon and Microsoft are all pushing in the same direction with varying degrees of success. IBM is considered most mature in the B2B space.
Artificial Intelligence is popular again. However, pattern recognition does not equal AI. Deep learning only works if you have lots of data and compute power. We finally have lots of data and compute power so deep learning for pattern recognition is working well. However, AI is more than deep learning for pattern recognition. AI requires common sense reasoning, which will take another 5-10 years of research to deliver. How do we know this? :
Deep Learning for AI Pattern Recognition depends on massive amounts of ‘labelled data’ and computing power (available since ~2012). Labelled data is simply input and output pairs (such as a sound and word, image and word, English sentence and French sentence, or road scene and car control settings). Labelled data means having both input and output data in massive quantities. For example, 100,000 images of skin, half with skin cancer and half without, are needed to learn to recognise the presence of skin cancer.
Thanks to Moore’s Law, every 20 years compute costs are down by 1000x. This coupled with ML developments will stimulate and enable the growth of personal and digital assistants which will become commercially viable from about 2020 and in widespread use within 20 years. Some vertical applications may become mainstream much earlier than expected. For example, we are already seeing growth in voice enabled devices using Siri, Alexa and ‘Hey Google’ capabilities.
Jim said, “Watching progress on open AI leader boards is like gazing into a crystal ball”
Who is winning? See: https://www.technologyreview.com/s/608112/who-is-winning-the-ai-race
AI will undoubtedly facilitate easier access to expertise & better choices. More specifically:
The risks of AI that get the most headlines are job loss & the emergence of super-intelligence, but Jim does not worry about these given the positive benefits for business and entrepreneurship. Shorter term risks are more realistic, and include de-skilling of the work force and lower cost of certain attacks, for example spear fishing, allowing bad actors to automate tasks that were previously labour intensive. Ntegra Greenside’s Jonathan Ellard has recently written a philosophical post that discusses some of these points on his new personal blog.
To fully realise the reality of AI and make best use of it all stakeholders need to be involved. “The best way to predict the future is to inspire the next generation of students to build it better”. It is essential that we consider everyone a stakeholder in AI due to its revolutionary nature. This engagement should be cross societal from individuals to families, small businesses to large multinationals, advisory groups to government and technical specialists.
Considering everything that Jim outlined, it is essential that the risks are mitigated. The report “The Malicious Use of Artificial Intelligence: Forecasting, Prevention and Mitigations” recommends:
By 2036 there will be an accumulation of knowledge as well as a distribution of knowledge in service systems globally. As there is knowledge accumulation we need to ensure that service systems at all scales become more resilient This will lead to the capability of rapid rebuilding of service systems across scales. Key to this are T-shaped people who understand how rapid rebuilding works, so that knowledge has been chunked, modularised and put into networks that support the rapid rebuilding.
To prepare to get the benefits and avoid the risks, this is what Jim tells his students, to provoke their thinking about the cognitive era:
In 2015 we were at the beginning of the beginning of the cognitive era. In 2025 we will be middle of beginning, easy to generate average student level performance on questions in a textbook. In 2035, we will be at the end of beginning (1/1000 one brain power equivalent), easy to generate average faculty level performance on questions in textbook.
By 2055, roughly 2x 20-year generations in the future, the cognitive era will be in full force. Cell-phones will likely become body suits, with burst-mode super-strength and super-safety features:
The key is knowing which problem to work on next. See this video for the answer (energy, water, food, wellness): https://www.youtube.com/watch?v=YY7f1t9y9a0&index=10&list=WL
To prepare for the future of AI, Jim recommended several resources: