AI applications, chips, deep tech, and geopolitics in 2019: The stakes have never been higher

The state of AI in 2019 report analysis with report author, AI expert, and venture capitalist Nathan Benaich continues. High-profile applications, funding, and the politics of AI

How AI will transform the next wave of computing software and hardware Machine learning, especially deep learning, is forcing a re-evaluation of how chips and systems are designed that will change the direction of the industry for decades to come.

It's the time of the season for AI reports. As we noted earlier, the last few days saw the publication of not 1, but three top-notch reports on the state of AI. People working in VCs have authored all of them, and keeping a close eye on all things AI: From technological breakthroughs to implications in the economy and society at large.

Having covered key technological breakthroughs already, we extend the discussion on the implications of AI with Nathan Benaich, co-author of the State of AI Report 2019, Air Street Capital and RAAIS founder. Benaich co-authored the report with AI angel investor and UCL IIPP visiting professor Ian Hogarth.

Benaich and Hogarth have also drawn on the expertise of prominent figures such as Google AI Researcher and the lead of Keras Deep Learning framework François Chollet, VC and AI thought leader Kai-Fu Lee, and Facebook AI Researcher Sebastian Riedel.

AI applications: RPA and autonomous vehicles

Much of the Q&A with Benaich focused on the geopolitics of AI. That's not to say Benaich and Hogarth's report does not cover topics such as talent, infrastructure, or applications -- it does, extensively. But with such a full plate, one has to pick. 

As far as talent is concerned, there is a consensus among experts: AI talent is highly sought after (and rewarded) and investment in training is on the rise. Nonetheless, the talent shortage in AI continues to be a major bottleneck to the broad adoption of the technology across the industry. 

One approach to mitigate this is AutoML, that is to say using machine learning to automate an increasing part of the process of applying machine learning, in a sort of recursive fashion. In the report, AutoML is shown to de-novo design neural networks that are better than those designed by humans to run on resource-constrained mobile devices, for example. 

The macro picture remains hot. Funds invested in AI grew by almost 80 percent in 2018 compared to 2017, exceeding $27 billion per year, with North America leading the way at 55 percent market share. Some of the application areas this capital has been pouring into as emphasized in the report are robotics (mainly in manufacturing and logistics), RPA (Robotic Process Automation), healthcare, demand forecasting, autonomous vehicles, and text analysis.

RPA, which is not related to robotics, is "an overnight enterprise success, 15 years in the making", as the report states. Benaich noted that industry adoption of RPA appears to be growing at a clip, mostly as a result of the benefits it delivers to enterprises: Reduced operating costs and increased operational nimbleness to compete with new entrants. 

RPA companies saw massive funding rounds: UiPath raised $800M across two rounds in 2018 and one round in 2019, while Automation Anywhere raised $550 million across two rounds in 2018. As mentioned in FirstMark's report, however, there are reasons to be cynical about RPA: "RPA, at this stage at least, is more about automation than intelligence, more about rules-based solutions than AI." Benaich agrees.

executive guide

What is AI? Everything you need to know about Artificial Intelligence

A guide to artificial intelligence, from machine learning and general AI to neural networks.

Read More

Another high-profile area of application is autonomous vehicles (AV). As Benaich and Hogarth note, self-driving cars are now a game for multi-billion-dollar balance sheets. They list spending by the likes of Waymo, Uber, Cruise, and Ford to make their case. But despite growth in investment and live AV pilots in California and elsewhere, some players have missed launch dates, while others remain silent.

Benaich and Hogarth point out that while the average Californian drives 14,435 miles per year, only 11/63 companies had driven more than this in 2018. Waymo drove more than one million miles in 2018, nearly three times as much as second best GM Cruise and 16-times as much as third best Apple. As for Tesla -- it does not report its disengagement metrics to the California DMV. 

Allegedly, however, Tesla has more data than any of the other players, giving it a leg up in the race. Tesla also designs its own AI chip to power the compute needed on board. This is another red hot area for innovation, as it is driving the capabilities of AI. We have covered some of the pioneers in this space, such as Graphcore, Habana, and GreenWaves.

AI chips, deep tech, geopolitics: China's rapid growth

Benaich believes the timing is right to develop novel chips that are purpose-built for training and inference of AI models:

"We think this is true because of industry adoption of AI models for several large-scale use cases, especially in consumer internet. As a result, chip designers have a clear customer to design for. Designing chips, however, is an endeavor that is very capital intensive and requires significant domain experience that can only be acquired over many many years."

This is also closely linked to geopolitics, as per Benaich's reasoning. Companies building this kind of "deep" or "core" sector-agnostic technology comprise a tenth of AI startups, but they punch above their weight, attracting a fifth of venture capital investment:

"When it comes to 'deep tech' (for example, semiconductors), the US (along with other key countries like South Korea and the UK) remains dominant. This means that China remains heavily dependent on imports for these kinds of technologies. Indeed, China spends seven-times more money on importing semiconductors than it does selling them for export." 

As Ian Hogarth argued in his AI Nationalism essay, "China will certainly try to close this critical trade deficit, and the $140 billion 'Big Fund' demonstrates the commitment the government has to narrow the deficit. We also believe that China's leading technology companies will ramp up their acquisition of deep tech companies from Europe."

Flag of China

China is making rapid progress in AI, having more or less caught up with the West

Getty Images/iStockphoto

Benaich and Hogarth also include predictions in their report. Amongst their 2018 predictions was a merger/acquisition north of $5 billion that would subsequently to be blocked. While this has yet to materialize, the authors still back their predictions. Benaich pointed out that the Chinese technology ecosystem is growing extremely rapidly:

"Of particular note is the ecosystem's focus on nurturing the growth of AI-first technology companies. By recent counts, China is home to the largest number of AI startups valued over $1 billion. The pace with which these AI startups acquire scale is arguably second to none in the world. 

With regards to fundamental research progress, we can consider a) the number of papers accepted into leading academic research conferences, b) the citation count of these papers, and c) the international ranking of universities for related courses such as computer science and engineering. 

Looking at the 1st and 2nd measure, China's contribution to global AI research output is on an upswing. For the thirrd measure, we can see that US and European universities still account for the overwhelming constituency of the top 20 institutions in global rankings. Having said that, Tsinghua University and Peking University are both in the top 20 for computer science and engineering courses."

Will Europe, or the UK, be the AI R&D lab of the world?

Benaich said that although China is lagging by some measures, the ecosystem is undoubtedly on an upswing in the right direction with immense resources driving its growth. He also noted there is already a firm decoupling between the consumer internet within China and outside of China: Alibaba, Tencent, and Baidu are orders of magnitude more influential in China than Google, Amazon, or Facebook. 

This is why Benaich and Hogarth have dedicated an entire section of their report to China. Another part is dedicated to AI and politics. Since Benaich and Hogarth are both based in London, the UK, Benaich's take on European and British prospects are of particular interest:

"We are in a period of incredible transformation. The economy is changing. Governance is in flux. And the only way we can tackle our toughest societal challenges is with the help of powerful technologies such as AI -- workable, safe, ethical AI. That is where Europe's unique strengths lie, at the fulcrum between China and America's AI rivalry."

its-impossible-to-prepare-brexit-delay-f-5cb0ad0cfe727300bade6fc0-1-apr-16-2019-15-29-11-poster.jpg

Europe's unique strengths lie at the fulcrum between China and America's AI rivalry, argues Benaich, who also sees a role for post-Brexit UK

Benaich believes the European technology industry has flourished over the past decade, and a new ecosystem with both sophisticated and sustainable financing is emerging:

"This will have a major impact on Europe and Britain's AI fortunes for years to come. The context is important. At a time of Brexit and a US-China trade war, everyone wonders what Europe's -- and in particular, the UK's -- role will be in the global economy. 

Some count it out. Others argue that it will be a leader in ethical business, leveraging the EU's tough privacy rules implemented last year. But the reality will probably be different: Britain looks set to be the AI R&D lab of the world. 

In the past, the main driver was the excellent universities like Oxbridge, Imperial and UCL. They trained the talent that now works at leading US technology companies. But now there's much more happening. In the last 18 months, US technology companies have made deep inroads into the UK ecosystem to strengthen their AI products."

The stakes have never been higher

Benaich pointed towards Lyft acquiring Blue Vision Labs for 3D map creation, Niantic acquiring Matrix Mill for real-world mobile AR, Facebook acquiring Bloomsbury.AI for natural language expertise and DeepMind Healthcare folding into the parent company's healthcare unit. 

What's more, he went on to add, large financing rounds are increasingly available to the best technology companies building intelligent systems in their products. Graphcore secured a $200 million Series D, Darktrace closed a $50 million Series E, and UiPath raised close to $1 billion in three rounds over 12 months. 

special feature

AI and the Future of Business

Machine learning, task automation and robotics are already widely used in business. These and other AI technologies are about to multiply, and we look at how organizations can best take advantage of them.

Read More

Naturally, being part of this ecosystem himself, Benaich highlighted that new venture firms built from the ground up for the AI community exist to scout and support exceptional AI talent in Europe. The goal? Building globally competitive companies driven by intelligent systems. Air Street Capital would be a prime example, and it looks like Benaich is on a mission. 

In addition to Air Street Capital, he has also founded the Research and Applied AI Summit, which he dubs "a global community of AI entrepreneurs, researchers, and operators who are focused on the science and applications of AI technology." 

Benaich said that over five years, they had attracted founders and leadership from many US technology companies (such as Francois Chollet from Google Brain and Chris Ré from Stanford among others) to speak in London for the first time. They have also showcased early on founders from Graphcore, SwiftKey, Bloomsbury.AI, Benevolent.AI, and LabGenius, who have achieved significant milestones or exited their companies.

Lastly, Benaich's non-profit, the RAAIS Foundation, exists to support education and research in AI for the common good. The RAAIS Foundation is the first backer of Open Climate Fix and OpenMined, which works on climate change and privacy-preserving AI, respectively. 

The reason they are doing all of this? "The stakes have never been higher."