“Enormous levels of investment are pouring into this technology. The achievements we have seen so far will surely pale against what the coming decades will bring.”
Artificial intelligence is getting a lot of attention right now. It was one of the key technological themes at the 2016 World Economic Forum and in June of last year, Andrew Ng, chief scientist at the Chinese web services company Baidu called “AI the new Electricity.” As recently as Dec 5, 2016, Google and Elon Musk opened their AI platforms to the public, Uber launched Uber AI Lab and Apple announced that for the first time it will publish their AI research. There is significant interest in the future applications for AI and there are a lot of interwoven technologies advancing alongside like robotics, virtual reality, autonomous vehicles, blockchain, 3-D printing and the IoT. There is fierce competition for ownership and leadership amongst the top companies which is helping to push AI innovations forward and accelerate advancements in its current and future applications.
In 2015, more than $2 billion was invested in 322 companies with AI-like technology. AI has attracted more than $17 billion in investments since 2009. According to a June 2016 TechSci report, in the US alone, the artificial intelligence market is projected to grow at a compound annual growth rate (CAGR) of 75% between 2016 and 2021. Google, IBM, Intel, Baidu, Samsung, Apple, and other technology giants are all racing to acquire AI startups to add to their business portfolios. Industrial giants like Boeing and GE, who recently purchased a handful of AI startups including Wise.io., also see a huge benefit in applying machine learning AI to improve their industry insights through data discovery.
Google’s head of machine learning, John Giannandrea, is calling this the AI Spring in contrast to the AI Winter of the 70s. As research and general interests grow, there is an urgency from leading technology firms to create the best portfolio of products and the underlying platform future AI innovations will be built on. There may still be a chasm between the anticipated future of AI and its current potential, but these investments show the huge prices companies are willing to pay to acquire these technologies and to scoop up relatively scarce AI talent to gain market share.
Broadly speaking, AI refers to the development of computers that are able to do things normally requiring human intelligence without human intervention. Strong AI refers to creating computers that genuinely simulate human reasoning and can change and adapt. Weak or narrow AI refers to AI designed for specific tasks within a specific program. The AI that is most commonly referenced today (the AI startups that are getting snatched up) would be considered narrow and is achievable thanks to advances in Deep learning, a form of machine learning in which neural networks are fed information that is used by a computer to make decisions and train itself and adjust based on what it has learned within specific parameters. Deep learning is what is currently propelling AI forward and where major investments are happening.
Machine learning AI, as we know it today, is possible thanks to rapid advancements in big data, neural networks, parallel processing, and cloud technology. AI technologies are just now becoming “mainstream” because the hardware and processing technology has caught up with the vision. The critical mass of data needed to “teach” computers exists now and the storage and processing power to execute deep learning are available, fast, and cost-effective.
Based on this race to innovate and acquire startups and talent to dominate the market, it is interesting to look at the patent filing data in this space to better understand who the big and small players are, what kind of patents they are filing, where they are being filed and how that is all progressing over time. Newer technologies grow out of older ones and innovative ideas for applying specific technologies pop up in seemingly unrelated business areas. All of these questions help move the conversation from what the technology is, to what can be done with it and who is actually doing it.
To better understand the innovation landscape for AI, we did a patent search based on terminology and International Patent Classification (IPC) codes associated with neural networks and deep learning since these are the technologies dominating the growth in what is today being referred to as AI.
The chart below (figure 1) illustrates the number of patent families filed relating to these areas since 1965. The first major period of growth in this area begins in the 1980s, an important time in both growth of computer hardware and chip technology, as well as a period of renewed public interest in the potential of AI. While the level of filing dipped during the 1990s, it has since returned to a pronounced period of growth, in particular within the last five years, likely due to the widespread availability of cloud infrastructure.
After looking at the rate of innovation, we looked at who the leading innovators are. Based on this data, you see that companies with established AI-related programs, such as Qualcomm’s neuromorphic chips, Microsoft’s AI research group, Siemens’ SENN and the most high profile, IBM’s Watson have filed the most “AI” related patents as seen in figure 2 below.
Looking at AI filings as a whole, and not just who is filing the most, but how many have actually been filed, you see a slightly different picture. The innovation landscape is more diverse than would probably be assumed. The number of unique assignees filing per year (figure 3) has continued to grow to the point where in 2016 over 1600 different entities filed AI-related patents, four times the amount of 20 years ago. By comparing the field of operation, the speed of filing and citation networks it would be possible to identify patents and assignees outside of the “usual suspects” that could have a significant impact on the market.
Much of this newer research is coming from academic sources, with 40% of assignees filing in 2016 classified as either a university, college or research institute (figure 4). With such a significant number of patents coming from this area, a number that has been growing consistently, the importance of tech transfer to fully realise the value of this pool of IP is clear. Meanwhile, the number of patents filed overall per year by the 50 most common assignees has remained relatively constant, with a much wider field of assignees now filing relevant patents. It’s no longer a case of being able to identify a few key players, and a much broader landscape needs to be considered.
Examining filings made over the last five years (LFY) shows the areas in which AI is currently having the most impact, all of the categories in figure 5 have had over 25% of their AI related filings made in the last five years. Computing, data processing, and sensors make up the bulk of the patents as may be expected, but outside of these, the areas range from agriculture to nanotechnology. The applications of these technologies can be very broad, with a category such as sensors at the forefront of AI research impacting everything from autonomous vehicles and traffic congestion to border and airport security.
Overall, while the technologies and patents are important, the real reason for giants to acquire startups is to acquire talent as fast as possible rather than build a team from scratch. Seeing how much is coming out of academic institutions, it’s obvious why some of the biggest companies are scooping up top academics in this space. This is just a high-level skim of what is going on in this very broad space.