Matthew Griffin, described as “The Adviser behind the Advisers” and a “Young Kurzweil,” is the founder and CEO of the World Futures Forum and the 311 Institute, a global Futures and Deep Futures consultancy working between the dates of 2020 to 2070, and is an award winning futurist, and author of “Codex of the Future” series. Regularly featured in the global media, including AP, BBC, Bloomberg, CNBC, Discovery, RT, Viacom, and WIRED, Matthew’s ability to identify, track, and explain the impacts of hundreds of revolutionary emerging technologies on global culture, industry and society, is unparalleled. Recognised for the past six years as one of the world’s foremost futurists, innovation and strategy experts Matthew is an international speaker who helps governments, investors, multi-nationals and regulators around the world envision, build and lead an inclusive, sustainable future. A rare talent Matthew’s recent work includes mentoring Lunar XPrize teams, re-envisioning global education and training with the G20, and helping the world’s largest organisations envision and ideate the future of their products and services, industries, and countries. Matthew's clients include three Prime Ministers and several governments, including the G7, Accenture, Aon, Bain & Co, BCG, Credit Suisse, Dell EMC, Dentons, Deloitte, E&Y, GEMS, Huawei, JPMorgan Chase, KPMG, Lego, McKinsey, PWC, Qualcomm, SAP, Samsung, Sopra Steria, T-Mobile, and many more.
WHY THIS MATTERS IN BRIEF
The future of AI rests on two development tracks, the development of new software and new hardware, and both are powering ahead.
Back in the 90’s, if you remember those, seeing someone walking their bicycle along the street was nothing special. But today, it seems, thanks to the development of a new powerful Artificial Intelligence (AI) neuromorphic computer chip in China the roles have been reversed after a bicycle took its owner for a walk. Obviously, this was no ordinary bike though, this was a fully autonomous bike, and since I’m on the subject it isn’t too far removed from Yamaha’s fully autonomous motorbike MOTOBOT that’s been racing world MotoGP champion Valentio Rossi around Europe’s race tracks recently. It’s also increasingly evident that in the future, with the rise of all these autonomous vehicles, from driverless cars and trucks to flying taxi’s and, well, bikes, you won’t need a license to ride anything – but that’s a different story.
The bike itself looks fairly normal, but underneath its simple exterior, however, is a new hybrid neuromorphic computer chip that combines brain-inspired circuits with machine learning processes to create a computing powerhouse. And it’s thanks to this chip that the bike can balance itself perfectly as it rolls along a pavement before accelerating smoothly to jogging speed while navigating obstacles. It can even respond to simple voice commands such as “speed up,” “left,” or “straight.”
The world’s smartest bike?
Far from a circus trick though, the bike is a real world demo of the AI community’s latest attempt at developing specialist computer hardware that can keep up with the challenges of today’s, let alone tomorrow’s machine learning algorithms. And the Tianjic (天机) chip as it’s known isn’t just your standard neuromorphic chip, it has the architecture of a brain-like computer chip, but it can also run deep learning algorithms – a match made in heaven that basically combines neurologically inspired hardware and software.
The new chip also shows that China is catching up to, and might accelerate past, the likes of Google, who started making their own custom AI chips back in 2016, Facebook, Nvidia, and other tech giants as the Chinese government continues its push to be the world leader in AI by 2030 and plough billions into the sector. And the country’s ambition is reflected in the team’s parting words.
“Our study is expected to stimulate Artificial General Intelligence (AGI) development by paving the way to more generalised hardware platforms,” said the authors, led by Dr. Luping Shi at Tsinghua University.
The autonomous bike also highlights the two future paths of AI development, and while they’re obviously connected, more so in the future than today, the differences between them both are stark, especially as companies strive to develop better and more efficient AI today, and eventually more generalised AGI that revolutionises AI “again” tomorrow.
MOTOBOT, for example, performed its tricks thanks to a machine learning software based approach that uses brain-like algorithms with increasingly efficient architecture, efficacy, and speed. But as deep learning continues to improve with breakthrough after breakthough, in order to keep up with the processing power it needs we need new hardware, and that’s where the Tianjic chip comes in.
As Shi told China Science Daily: “CPUs and other chips are driven by miniaturisation technologies based on physics. Transistors might shrink to nanoscale-level in 10, 20 years. But what then? As more transistors are squeezed onto these chips, efficient cooling becomes a limiting factor in computational speed. Tax them too much, and they melt.”
So, as we continue to see Moore’s Law struggle to keep pace with today’s AI and computing demands, even as we see the emergence of 1nm, 05.nm, and even virus sized computers, we need better hardware and this is certainly one great leap ahead in that race.