Product design gets an AI makeover
It’s a tall order, but one that Zapf says artificial intelligence (AI) technology can support by capturing the right data and guiding engineers through product design and development.
No wonder a November 2020 McKinsey survey reveals that more than half of organizations have adopted AI in at least one function, and 22% of respondents report at least 5% of their companywide earnings are attributable to AI. And in manufacturing, 71% of respondents have seen a 5% or more increase in revenue with AI adoption.
But that wasn’t always the case. Once “rarely used in product development,” AI has experienced an evolution over the past few years, Zapf says. Today, tech giants known for their innovations in AI, such as Google, IBM, and Amazon, “have set new standards for the use of AI in other processes,” such as engineering.
“AI is a promising and exploratory area that can significantly improve user experience for designing engineers, as well as gather relevant data in the development process for specific applications,” says Katrien Wyckaert, director of industry solutions for Siemens Industry Software.
The result is a growing appreciation for a technology that promises to simplify complex systems, get products to market faster, and drive product innovation.
Simplifying complex systems
A perfect example of AI’s power to overhaul product development is Renault. In response to increasing consumer demand, the French automaker is equipping a growing number of new vehicle models with an automated manual transmission (AMT)—a system that behaves like an automatic transmission but allows drivers to shift gears electronically using a push-button command.
AMTs are popular among consumers, but designing them can present formidable challenges. That’s because an AMT’s performance depends on the operation of three distinct subsystems: an electro-mechanical actuator that shifts the gears, electronic sensors that monitor vehicle status, and software embedded in the transmission control unit, which controls the engine. Because of this complexity, it can take up to a year of extensive trial and error to define the system’s functional requirements, design the actuator mechanics, develop the necessary software, and validate the overall system.
In an effort to streamline its AMT development process, Renault turned to Simcenter Amesim software from Siemens Digital Industries Software. The simulation technology relies on artificial neural networks, AI “learning” systems loosely modeled on the human brain. Engineers simply drag, drop, and connect icons to graphically create a model. When displayed on a screen as a sketch, the model illustrates the relationship between all the various elements of an AMT system. In turn, engineers can predict the behavior and performance of the AMT and make any necessary refinements early in the development cycle, avoiding late-stage problems and delays. In fact, by using a virtual engine and transmissions as stand-ins while developing hardware, Renault has managed to cut its AMT development time almost in half.
Speed without sacrificing quality
So, too, are emerging environmental standards prompting Renault to rely more heavily on AI. To comply with emerging carbon dioxide emissions standards, Renault has been working on the design and development of hybrid vehicles. But hybrid engines are far more complex to develop than those found in vehicles with a single energy source, such as a conventional car. That’s because hybrid engines require engineers to perform complex feats like balancing the power required from multiple energy sources, choosing from a multitude of architectures, and examining the impact of transmissions and cooling systems on a vehicle’s energy performance.
“To meet new environmental standards for a hybrid engine, we must completely rethink the architecture of gasoline engines,” says Vincent Talon, head of simulation at Renault. The problem, he adds, is that carefully examining “the dozens of different actuators that can influence the final results of fuel consumption and pollutant emissions” is a lengthy and complex process, made by more difficult by rigid timelines.
“Today, we clearly don’t have the time to painstakingly evaluate various hybrid powertrain architectures,” says Talon. “Rather, we needed to use an advanced methodology to manage this new complexity.”
For more on AI in industrial applications, visit www.siemens.com/artificialintelligence.
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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.
Google just launched Bard, its answer to ChatGPT—and it wants you to make it better
Google has a lot riding on this launch. Microsoft partnered with OpenAI to make an aggressive play for Google’s top spot in search. Meanwhile, Google blundered straight out of the gate when it first tried to respond. In a teaser clip for Bard that the company put out in February, the chatbot was shown making a factual error. Google’s value fell by $100 billion overnight.
Google won’t share many details about how Bard works: large language models, the technology behind this wave of chatbots, have become valuable IP. But it will say that Bard is built on top of a new version of LaMDA, Google’s flagship large language model. Google says it will update Bard as the underlying tech improves. Like ChatGPT and GPT-4, Bard is fine-tuned using reinforcement learning from human feedback, a technique that trains a large language model to give more useful and less toxic responses.
Google has been working on Bard for a few months behind closed doors but says that it’s still an experiment. The company is now making the chatbot available for free to people in the US and the UK who sign up to a waitlist. These early users will help test and improve the technology. “We’ll get user feedback, and we will ramp it up over time based on that feedback,” says Google’s vice president of research, Zoubin Ghahramani. “We are mindful of all the things that can go wrong with large language models.”
But Margaret Mitchell, chief ethics scientist at AI startup Hugging Face and former co-lead of Google’s AI ethics team, is skeptical of this framing. Google has been working on LaMDA for years, she says, and she thinks pitching Bard as an experiment “is a PR trick that larger companies use to reach millions of customers while also removing themselves from accountability if anything goes wrong.”
Google wants users to think of Bard as a sidekick to Google Search, not a replacement. A button that sits below Bard’s chat widget says “Google It.” The idea is to nudge users to head to Google Search to check Bard’s answers or find out more. “It’s one of the things that help us offset limitations of the technology,” says Krawczyk.
“We really want to encourage people to actually explore other places, sort of confirm things if they’re not sure,” says Ghahramani.
This acknowledgement of Bard’s flaws has shaped the chatbot’s design in other ways, too. Users can interact with Bard only a handful of times in any given session. This is because the longer large language models engage in a single conversation, the more likely they are to go off the rails. Many of the weirder responses from Bing Chat that people have shared online emerged at the end of drawn-out exchanges, for example.
Google won’t confirm what the conversation limit will be for launch, but it will be set quite low for the initial release and adjusted depending on user feedback.
Google is also playing it safe in terms of content. Users will not be able to ask for sexually explicit, illegal, or harmful material (as judged by Google) or personal information. In my demo, Bard would not give me tips on how to make a Molotov cocktail. That’s standard for this generation of chatbot. But it would also not provide any medical information, such as how to spot signs of cancer. “Bard is not a doctor. It’s not going to give medical advice,” says Krawczyk.
Perhaps the biggest difference between Bard and ChatGPT is that Bard produces three versions of every response, which Google calls “drafts.” Users can click between them and pick the response they prefer, or mix and match between them. The aim is to remind people that Bard cannot generate perfect answers. “There’s the sense of authoritativeness when you only see one example,” says Krawczyk. “And we know there are limitations around factuality.”
How AI experts are using GPT-4
Hoffman got access to the system last summer and has since been writing up his thoughts on the different ways the AI model could be used in education, the arts, the justice system, journalism, and more. In the book, which includes copy-pasted extracts from his interactions with the system, he outlines his vision for the future of AI, uses GPT-4 as a writing assistant to get new ideas, and analyzes its answers.
A quick final word … GPT-4 is the cool new shiny toy of the moment for the AI community. There’s no denying it is a powerful assistive technology that can help us come up with ideas, condense text, explain concepts, and automate mundane tasks. That’s a welcome development, especially for white-collar knowledge workers.
However, it’s notable that OpenAI itself urges caution around use of the model and warns that it poses several safety risks, including infringing on privacy, fooling people into thinking it’s human, and generating harmful content. It also has the potential to be used for other risky behaviors we haven’t encountered yet. So by all means, get excited, but let’s not be blinded by the hype. At the moment, there is nothing stopping people from using these powerful new models to do harmful things, and nothing to hold them accountable if they do.
Chinese tech giant Baidu just released its answer to ChatGPT
So. Many. Chatbots. The latest player to enter the AI chatbot game is Chinese tech giant Baidu. Late last week, Baidu unveiled a new large language model called Ernie Bot, which can solve math questions, write marketing copy, answer questions about Chinese literature, and generate multimedia responses.
A Chinese alternative: Ernie Bot (the name stands for “Enhanced Representation from kNowledge IntEgration;” its Chinese name is 文心一言, or Wenxin Yiyan) performs particularly well on tasks specific to Chinese culture, like explaining a historical fact or writing a traditional poem. Read more from my colleague Zeyi Yang.
Even Deeper Learning
Language models may be able to “self-correct” biases—if you ask them to
Large language models are infamous for spewing toxic biases, thanks to the reams of awful human-produced content they get trained on. But if the models are large enough, they may be able to self-correct for some of these biases. Remarkably, all we might have to do is ask.
Texas is trying out new tactics to restrict access to abortion pills online
Texas is trying to limit access to abortion pills by cracking down on internet service providers and credit card processing companies. These tactics reflect the reality that, post-Roe, the internet is a critical channel for people seeking information about abortion or trying to buy pills to terminate a pregnancy—especially in states where they can no longer access these things in physical pharmacies or medical centers.
Texas has long been a laboratory for anti-abortion political tactics, and on March 15, a US District Judge heard arguments in a case that’s seeking to reverse the FDA approval of mifepristone, a drug that can be used to terminate an early pregnancy. The case would limit online-facilitated abortions and would have far-reaching consequences even in states that are not trying to restrict abortion.
Earlier this month, Republicans in the Texas state legislature introduced two bills to restrict access to abortion pills. The first bill, HB 2690, would require internet service providers (ISPs) to ban sites that provide access to the pills or information about obtaining them. Companies like AT&T and Spectrum would have to “make every reasonable and technologically feasible effort to block Internet access to information or material intended to assist or facilitate efforts to obtain an elective abortion or an abortion-inducing drug.” The bill would also forbid both publishers and ordinary people from providing information about access to abortion-inducing drugs.
The second bill, SB 1440, would make it a felony for credit card companies to process transactions for abortion pills, and would also make them liable to lawsuits from the public.
Blair Wallace, a policy and advocacy strategist at the ACLU of Texas, a nonprofit that advocates for civil liberties and reproductive choice, said the recent developments mark “a new frontier for the ways in which they’re coming for [abortion access],” adding: “It is really terrifying.”
Wallace sees it as a continuation of a strategy that seeks to criminalize whole abortion care networks with the aim of isolating people seeking abortions. More broadly, this strategy of censoring information and language has become a popular tactic in US culture wars in the last several years, and the proposed bill could incentivize platforms to aggressively remove information about abortion access out of concern for legal risk. Some sites, like Meta’s Instagram and Facebook, have reportedly removed information about abortion pills in the past.
So what might the outcome of all the Texas action be? Both the bill that targets ISPs and the mifepristone case this week are unprecedented, which means neither is likely to be successful. That said, the tactics are likely to stay. “Will we see it again next session? Will we see parts of this bill stripped down and put into amendments? There’s like a million ways that this can play out,” says Wallace. Anti-abortion political strategy is coordinated nationally even though the fights are playing out at a state level, and it’s likely that other states will target online spaces going forward.
Online abortion resources can pose risks to privacy. But there are lots of ways to access them more safely. Here are some resources I recommend.