AI and utilities: Europe's defining role
Artificial intelligence is changing the world but is it for the better? Tamara McCleary, CEO at Thulium, argues that Europe’s power sector has a crucial role to play in leading the responsible application of AI.
It will come as no surprise to hear that artificial intelligence is used in some of the most exciting technologies coming to the fore in the 21st century. After all, there’s nothing new about dreams of technological utopias, populated by machines that anticipate and cater to our every need. Machines that empower us to do more of what we really value, better. Such visions now look increasingly like they could be within our grasp.
Progress has primarily been made in the field of machine learning, spurred on by innovative tech giants and start-ups alike, particularly in the US and China. As the Internet of Things continues to take hold, with internet-enabled devices collecting ever-more data, there will be more and more for our machines to learn – and gain in intelligence.
Clearly this process needs careful guidance – and that’s where Europe can come into its own.
Europe’s critical role
The biggest danger we face as we build a world defined by AI is our understanding, or lack thereof, of what this all really means. For instance, what will the world look like in 2030? Barely more than 10 years away, it’s already impossible to tell. Such is the pace of change that the mere question is like asking someone in 1920 to predict the social media platforms of today.
As the likes of Silicon Valley persist with a culture of building and beta-testing their ideas, making the move to market quickly and iterating as they go, there is no reason to expect developments to slow down any time soon. Nor would we want them to, but it is important that we also dedicate great minds to the task of ensuring that innovative AI technologies lead the world in 2030 to a better, not worse, future destination than the present.
This is a chance for Europe’s own AI visionaries to play a critical role in shaping our future.
Europe’s culture of innovation is a little slower than that of the US and China. This is not due to a lack of talent or infrastructure, but because of an emphasis on perfecting a product before release. It’s a different mindset but no less valuable, especially when the consequences of product development can be dramatic – either way.
We need European AI experts to consider not just what’s possible, but what’s actually responsible. As all Black Mirror fans know, not all ideas should be unleashed on humanity.
After all, it is on this topic that Europe comes into its own. The continent is home to companies like DeepMind researching ways to apply AI to the benefit of humanity. The Future of Humanity Institute at Oxford University is also doing fantastic work diving into the social, political and economic implications of AI.
These are critical conversations, raising philosophical questions that must be answered if AI is to bring about the utopia we all hope for.
The new information grid
Perhaps surprisingly, the most important players may prove to be the European utilities and power suppliers. We’ve become accustomed to seeing industries disrupted by new technology but the shift we can expect to see in our energy landscape will be unprecedented.
Utility providers face disruption on multiple fronts. Not only are they expected to adapt to smaller-scale, distributed electricity generation by active energy “prosumers”, but they also face competition from technology giants moving into their space with products and services such as Google’s Nest.
It’s apparent that the age of utilities simply selling kilowatt-hours is coming to an end. To remain relevant power providers will become platforms, offering services to improve our lives, as well as helping to regulate supply. For instance, they may provide free electronic consumer goods to their customers in exchange for the right to collect usage data, which then impacts where power flows across the grid throughout the day.
The application of AI – and machine learning in particular – will be critical to the delivery of such services, as will a change of mindset. Utilities are moving into the people business. They’ll need to think beyond chasing efficiency gains alone and really start applying emotional intelligence to everything they do. An optimally-efficient grid is no good if it doesn’t fit around how people want to live.
In fact, it’s this consideration that matters most to the successful application of AI in the future. It’s perfectly plausible that the power grid and information grid will merge to become one and the same – the AI-dependent infrastructural foundation upon which we build our lives. It’s similarly plausible that while the US and China produce technologies quickest, the first truly considered, ethically-sound and beneficial implementations could come from the European power sector.
An age of empowerment
This places a huge responsibility on the power sector. Its attitude towards AI and behaviour in its application over the next few years may shape not only the sectors commercial prospects, but also global approaches to AI and the future of the world we live in.
Thankfully, power professionals are taking it seriously. They’re already coming together at events like Electrify Europe to share ideas and lessons across the full length of the electricity value chain; working together to develop responsible solutions that will work to improve our lives.
It’s exactly the type of leadership we need if AI is truly to live up to its billing as the most exciting development of our lifetimes – and unlock a new age of empowerment.
Tamara McCleary, CEO at Thulium
Google AI Designs Next-Gen Chips In Under 6 Hours
In a Google-Nature paper published on Wednesday, the company announced that AI will be able to design chips in less than six hours. Humans currently take months to design and layout the intricate chip wiring. Although the tech giant has been working in silence on the technology for years, this is the first time that AI-optimised chips have hit the mainstream—and that the company will sell the result as a commercial product.
“Our method has been used in production to design the next generation of Google TPU (tensor processing unit chips)”, the paper’s authors, Azalea Mirhoseini and Anna Goldie wrote. The TPU v4 chips are the fastest Google system ever launched. “If you’re trying to train a large AI/ML system, and you’re using Google’s TensorFlow, this will be a big deal”, said Jack Gold, President and Principal Analyst at J.Gold Associates.
Training the Algorithm
In a process called reinforcement learning, Google engineers used a set of 10,000 chip floor plans to train the AI. Each example chip was assigned a score of sorts based on its efficiency and power usage, which the algorithm then used to distinguish between “good” and “bad” layouts. The more layouts it examines, the better it can generate versions of its own.
Designing floor plans, or the optimal layouts for a chip’s sub-systems, takes intense human effort. Yet floorplanning is similar to an elaborate game. It has rules, patterns, and logic. In fact, just like chess or Go, it’s the ideal task for machine learning. Machines, after all, don’t follow the same constraints or in-built conditions that humans do; they follow logic, not preconception of what a chip should look like. And this has allowed AI to optimise the latest chips in a way we never could.
As a result, AI-generated layouts look quite different to what a human would design. Instead of being neat and ordered, they look slightly more haphazard. Blurred photos of the carefully guarded chip designs show a slightly more chaotic wiring layout—but no one is questioning its efficiency. In fact, Google is starting to evaluate how it could use AI in architecture exploration and other cognitively intense tasks.
Major Implications for the Semiconductor Sector
Part of what’s impressive about Google’s breakthrough is that it could throw Moore’s Law, the axion that the number of transistors on a chip doubles every five years, out the window. The physical difficulty of squeezing more CPUs, GPUs, and memory on tiny silicon die will still exist, but AI optimisation may help speed up chip performance.
Any chance that AI can help speed up current chip production is welcome news. Though the U.S. Senate recently passed a US$52bn bill to supercharge domestic semiconductor supply chains, its largest tech firms remain far behind. According to Holger Mueller, principal analyst at Constellation Research, “the faster and cheaper AI will win in business and government, including with the military”.
All in all, AI chip optimisation could allow Google to pull ahead of its competitors such as AWS and Microsoft. And if we can speed up workflows, design better chips, and use humans to solve more complex, fluid, wicked problems, that’s a win—for the tech world and for society.