How AI Is Taking Over Our Gadgets
AI is moving from data centres to devices, impacting everything from phones to tractors.
5 min
If you think of AI as something futuristic and abstract, start thinking different.
We’re now witnessing a turning point for artificial intelligence, as more of it comes down from the clouds and into our smartphones and automobiles. While it’s fair to say that AI that lives on the “edge”—where you and I are—is still far less powerful than its datacentre-based counterpart, it’s potentially far more meaningful to our everyday lives.
One key example: This fall, Apple’s Siri assistant will start processing voice on iPhones. Right now, even your request to set a timer is sent as an audio recording to the cloud, where it is processed, triggering a response that’s sent back to the phone. By processing voice on the phone, says Apple, Siri will respond more quickly. This will only work on the iPhone XS and newer models, which have a compatible built-for-AI processor Apple calls a “neural engine.” People might also feel more secure knowing that their voice recordings aren’t being sent to unseen computers in faraway places.
Google actually led the way with on-phone processing: In 2019, it introduced a Pixel phone that could transcribe speech to text and perform other tasks without any connection to the cloud. One reason Google decided to build its own phones was that the company saw potential in creating custom hardware tailor-made to run AI, says Brian Rakowski, product manager of the Pixel group at Google.
These so-called edge devices can be pretty much anything with a microchip and some memory, but they tend to be the newest and most sophisticated of smartphones, automobiles, drones, home appliances, and industrial sensors and actuators. Edge AI has the potential to deliver on some of the long-delayed promises of AI, like more responsive smart assistants, better automotive safety systems, new kinds of robots, even autonomous military machines.
The challenges of making AI work at the edge—that is, making it reliable enough to do its job and then justifying the additional complexity and expense of putting it in our devices—are monumental. Existing AI can be inflexible, easily fooled, unreliable and biased. In the cloud, it can be trained on the fly to get better—think about how Alexa improves over time. When it’s in a device, it must come pre-trained, and be updated periodically. Yet the improvements in chip technology in recent years have made it possible for real breakthroughs in how we experience AI, and the commercial demand for this sort of functionality is high.
From swords to plowshares
Shield AI, a contractor for the Department of Defense, has put a great deal of AI into quadcopter-style drones which have already carried out—and continue to be used in—real-world combat missions. One mission is to help soldiers scan for enemy combatants in buildings that must be cleared. The DoD has been eager to use the company’s drones, says Shield AI’s co-founder, Brandon Tseng, because even if they fail, they can be used to reduce human casualties.
“In 2016 and early 2017, we had early prototypes with something like 75% reliability, something you would never take to market, and the DoD were saying, ‘We’ll take that overseas and use that in combat right now,’” Mr. Tseng says. When he protested that the system wasn’t ready, the response from within the military was that anything was better than soldiers going through a door and being shot.
In a combat zone, you can’t count on a fast, robust, wireless cloud connection, especially now that enemies often jam wireless communication and GPS signals. When on a mission, processing and image recognition must occur on the company’s drones themselves.
Shield AI uses a small, efficient computer made by Nvidia, designed for running AI on devices, to create a quadcopter drone no bigger than a typical camera-wielding consumer model. The Nova 2 can fly long enough to enter a building, and use AI to recognize and examine dozens of hallways, stairwells and rooms, cataloging objects and people it sees along its way.
Meanwhile, in the town of Salinas, Calif., birthplace of “Grapes of Wrath” author John Steinbeck and an agricultural center to this day, a robot the size of an SUV is spending this year’s growing season raking the earth with its 12 robotic arms. Made by FarmWise Labs Inc., the robot trundles along fields of celery as if it were any other tractor. Underneath its metal shroud, it uses computer vision and an edge AI system to decide, in less than a second, whether a plant is a food crop or a weed, and directs its plow-like claws to avoid or eradicate the plant accordingly.
FarmWise’s huge, diesel robo-weeder can generate its own electricity, enabling it to carry a veritable supercomputer’s worth of processing power—four GPUs and 16 CPUs which together draw 500 watts of electricity.
In our everyday lives, things like voice transcription that work whether or not we have a connection, or how good it is, could mean shifts in how we prefer to interact with our mobile devices. Getting always-available voice transcription to work on Google’s Pixel phone “required a lot of breakthroughs to run on the phone as well as it runs on a remote server,” says Mr. Rakowski.
Google has almost unlimited resources to experiment with AI in the cloud, but getting those same algorithms, for everything from voice transcription and power management to real-time translation and image processing, to work on phones required the introduction of custom microprocessors like the Pixel Neural Core, adds Mr. Rakowski.
Turning cats into pure math
What nearly all edge AI systems have in common is that, as pre-trained AI, they are only performing “inference,” says Dennis Laudick, vice president of marketing for AI and machine learning at Arm Holdings, which licenses chip designs and instructions to companies such as Apple, Samsung, Qualcomm, Nvidia and others.
Generally speaking, machine-learning AI consists of four phases:
- Data is captured or collected: Say, for example, in the form of millions of cat pictures.
- Humans label the data: Yes, these are cat photos.
- AI is trained with the labelled data: This process selects for models that identify cats.
- Then the resulting pile of code is turned into an algorithm and implemented in software: Here’s a camera app for cat lovers!
(Note: If this doesn’t exist yet, consider it your million-dollar idea of the day.)
The last bit of the process—something like that cat-identifying software—is the inference phase. The software on many smart surveillance cameras, for example, is performing inference, says Eric Goodness, a research vice president at technology-consulting firm Gartner. These systems can already identify how many patrons are in the restaurant, if any are engaging in undesirable behaviour, or if the fries have been in the fryer too long.
It’s all just mathematical functions, ones so complicated that it would take a monumental effort by humans to write them, but which machine-learning systems can create when trained on enough data.
Robot pratfalls
While all of this technology has enormous promise, making AI work on individual devices, whether or not they can connect to the cloud, comes with a daunting set of challenges, says Elisa Bertino, a professor of computer science at Purdue University.
Modern AI, which is primarily used to recognize patterns, can have difficulty coping with inputs outside of the data it was trained on. Operating in the real world only makes it tougher—just consider the classic example of a Tesla that brakes when it sees a stop sign on a billboard.
To make edge AI systems more competent, one edge device might gather some data but then pair with another, more powerful device, which can integrate data from a variety of sensors, says Dr. Bertino. If you’re wearing a smartwatch with a heart-rate monitor, you’re already witnessing this: The watch’s edge AI pre-processes the weak signal of your heart rate, then passes that data to your smartphone, which can further analyze that data—whether or not it’s connected to the internet.
The overwhelming majority of AI algorithms are still trained in the cloud. They can also be retrained using more or fresher data, which lets them continually improve. Down the road, says Mr. Goodness, edge AI systems will begin to learn on their own—that is, they’ll become powerful enough to move beyond inference and actually gather data and use it to train their own algorithms.
AI that can learn all by itself, without connection to a cloud superintelligence, might eventually raise legal and ethical challenges. How can a company certify an algorithm that’s been off evolving in the real world for years after its initial release, asks Dr. Bertino. And in future wars, who will be willing to let their robots decide when to pull the trigger? Whoever does might end up with an advantage—but also all the collateral damage that happens when, inevitably, AI makes mistakes.
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Competitive pressure and creativity have made Chinese-designed and -built electric cars formidable competitors
4 min
China rocked the auto world twice this year. First, its electric vehicles stunned Western rivals at the Shanghai auto show with their quality, features and price. Then came reports that in the first quarter of 2023 it dethroned Japan as the world’s largest auto exporter.
How is China in contention to lead the world’s most lucrative and prestigious consumer goods market, one long dominated by American, European, Japanese and South Korean nameplates? The answer is a unique combination of industrial policy, protectionism and homegrown competitive dynamism. Western policy makers and business leaders are better prepared for the first two than the third.
Start with industrial policy—the use of government resources to help favoured sectors. China has practiced industrial policy for decades. While it’s finding increased favour even in the U.S., the concept remains controversial. Governments have a poor record of identifying winning technologies and often end up subsidising inferior and wasteful capacity, including in China.
But in the case of EVs, Chinese industrial policy had a couple of things going for it. First, governments around the world saw climate change as an enduring threat that would require decade-long interventions to transition away from fossil fuels. China bet correctly that in transportation, the transition would favour electric vehicles.
In 2009, China started handing out generous subsidies to buyers of EVs. Public procurement of taxis and buses was targeted to electric vehicles, rechargers were subsidised, and provincial governments stumped up capital for lithium mining and refining for EV batteries. In 2020 NIO, at the time an aspiring challenger to Tesla, avoided bankruptcy thanks to a government-led bailout.
While industrial policy guaranteed a demand for EVs, protectionism ensured those EVs would be made in China, by Chinese companies. To qualify for subsidies, cars had to be domestically made, although foreign brands did qualify. They also had to have batteries made by Chinese companies, giving Chinese national champions like Contemporary Amperex Technology and BYD an advantage over then-market leaders from Japan and South Korea.
To sell in China, foreign automakers had to abide by conditions intended to upgrade the local industry’s skills. State-owned Guangzhou Automobile Group developed the manufacturing know-how necessary to become a player in EVs thanks to joint ventures with Toyota and Honda, said Gregor Sebastian, an analyst at Germany’s Mercator Institute for China Studies.
Despite all that government support, sales of EVs remained weak until 2019, when China let Tesla open a wholly owned factory in Shanghai. “It took this catalyst…to boost interest and increase the level of competitiveness of the local Chinese makers,” said Tu Le, managing director of Sino Auto Insights, a research service specialising in the Chinese auto industry.
Back in 2011 Pony Ma, the founder of Tencent, explained what set Chinese capitalism apart from its American counterpart. “In America, when you bring an idea to market you usually have several months before competition pops up, allowing you to capture significant market share,” he said, according to Fast Company, a technology magazine. “In China, you can have hundreds of competitors within the first hours of going live. Ideas are not important in China—execution is.”
Thanks to that competition and focus on execution, the EV industry went from a niche industrial-policy project to a sprawling ecosystem of predominantly private companies. Much of this happened below the Western radar while China was cut off from the world because of Covid-19 restrictions.
When Western auto executives flew in for April’s Shanghai auto show, “they saw a sea of green plates, a sea of Chinese brands,” said Le, referring to the green license plates assigned to clean-energy vehicles in China. “They hear the sounds of the door closing, sit inside and look at the quality of the materials, the fabric or the plastic on the console, that’s the other holy s— moment—they’ve caught up to us.”
Manufacturers of gasoline cars are product-oriented, whereas EV manufacturers, like tech companies, are user-oriented, Le said. Chinese EVs feature at least two, often three, display screens, one suitable for watching movies from the back seat, multiple lidars (laser-based sensors) for driver assistance, and even a microphone for karaoke (quickly copied by Tesla). Meanwhile, Chinese suppliers such as CATL have gone from laggard to leader.
Chinese dominance of EVs isn’t preordained. The low barriers to entry exploited by Chinese brands also open the door to future non-Chinese competitors. Nor does China’s success in EVs necessarily translate to other sectors where industrial policy matters less and creativity, privacy and deeply woven technological capability—such as software, cloud computing and semiconductors—matter more.
Still, the threat to Western auto market share posed by Chinese EVs is one for which Western policy makers have no obvious answer. “You can shut off your own market and to a certain extent that will shield production for your domestic needs,” said Sebastian. “The question really is, what are you going to do for the global south, countries that are still very happily trading with China?”
Western companies themselves are likely to respond by deepening their presence in China—not to sell cars, but for proximity to the most sophisticated customers and suppliers. Jörg Wuttke, the past president of the European Union Chamber of Commerce in China, calls China a “fitness centre.” Even as conditions there become steadily more difficult, Western multinationals “have to be there. It keeps you fit.”
Chris Dixon, a partner who led the charge, says he has a ‘very long-term horizon’
Americans now think they need at least $1.25 million for retirement, a 20% increase from a year ago, according to a survey by Northwestern Mutual
