AI in 2019: Hardware, Automation, and High Stakes Politics

August 5, 2019 - 7 minutes read

Do you know what the state of artificial intelligence (AI) development is in 2019? In a new report, angel investor Ian Hogarth and RAAIS and Air Street Capital founder Nathan Benaich answer this question by taking a deep dive into recent breakthroughs in the technology and new areas of application.

The 136-slide work of research is densely packed with tons of insights on these topics. We tried to distill our favorite findings in a previous post that you can find here.

In this edition of coverage, we’ll focus on automation applications, the hardware behind AI, and the complex politics surrounding the technology.

Expediting the Adoption of AI Through Automation

For many enterprise organizations and startups, top AI talent remains difficult to come across. One common way to circumvent this obstacle is through AutoML, the application of machine learning to automate the process of applying machine learning. In Benaich’s and Hogarth’s report, they note that AutoML to design better neural networks from the ground up than humans when it comes to mobile devices and other platforms facing constraints in resources.

Another technology paving the way for organizations to leverage AI is Robotic Process Automation (RPA). RPA allows software robots to complete tasks like data entry, thus enabling businesses to automate numerous processes and reduce costs. This and robotics, healthcare, demand forecasting, and self-driving vehicles are the main drivers behind the surge in AI funding, which exceeded $27 billion in 2018, an almost 80% increase over 2017.

Hogarth and Benaich note in their report that autonomous vehicles (AV) have largely become an endeavor only for organizations with hundreds of millions of dollars to spend on it. Leading companies like Uber, Waymo, Cruise, and Ford support this statement. Regardless of investment and current AV pilot programs in states like California, many of these companies have missed launch dates and other important self-set milestones, while others remain silent on any progress.

Tesla, for instance, does not report self-driving disengagement metrics to the California DMV. Although it has been noted that the company allegedly has more data than its competitors to use for trainings its AVs. Tesla also designs its own AI chip for self-driving, which could give it another big advantage over competitors.

The Politics of AI Chips

Because hardware dictates the capabilities of AI, chips are an area of innovation that is red hot and only growing more popular. Benaich believes that developing purpose-built chips for training and inference of AI models is an opportunity that has never been riper. “We think this is true because of industry adoption of AI models for several large-scale use cases, especially in consumer internet,” he says. “As a result, chip designers have a clear customer to design for.”

However, Benaich is quick to note that there are a few obstacles in the way of would-be chipmakers: It’s not only capital intensive but requires substantial domain experience. It’s akin to semiconductor manufacturing, which countries like the US and South Korea dominate. “This means that China remains heavily dependent on imports for these kinds of technologies,” Benaich explains. “Indeed, China spends seven-times more money on importing semiconductors than it does selling them for export.”

Hogarth thinks that China will definitely try to close this trade deficit, especially when it comes to AI. Both he and Benaich cite the explosive growth of China’s technology ecosystem as support for this belief. “…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,” says Benaich.

Who Will Be the AI R&D Leader?

While China may lag behind in some areas of AI development, the country’s economy is undeniably in an upswing towards becoming a leader in the technology. And with immense resources to back up these efforts, it seems that this is inevitable. Benaich uses the consumer Internet, another sector that China was formerly lagging behind, as a prime example. Alibaba, Tencent, and Baidu are all vastly more popular and utilized in China compared to US tech companies like Google or Amazon.

Both Benaich and Hogarth are based in London, the UK, which they think offers ample opportunity for AI to flourish: “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.”

Benaich elaborates: “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.”

Great Lengths and High Stakes

With so much potential on the line, it’s clear that the opportunities have never been greater and the stakes have never been higher for players in the AI industry. Virtually all stakeholders are in an all-out race to establish themselves in this emerging technology’s market.

What will happen? The future remains uncertain. But one thing is for sure: AI is here to stay.

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