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Ed Webb

The Generative AI Race Has a Dirty Secret | WIRED - 0 views

www.wired.com/...search-race-has-a-dirty-secret

AI LLMs climate emissions internet analysis energy

shared by Ed Webb on 12 Feb 23 - Cached
  • The race to build high-performance, AI-powered search engines is likely to require a dramatic rise in computing power, and with it a massive increase in the amount of energy that tech companies require and the amount of carbon they emit.
  • Every time we see a step change in online processing, we see significant increases in the power and cooling resources required by large processing centres
  • third-party analysis by researchers estimates that the training of GPT-3, which ChatGPT is partly based on, consumed 1,287 MWh, and led to emissions of more than 550 tons of carbon dioxide equivalent—the same amount as a single person taking 550 roundtrips between New York and San Francisco
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  • There’s also a big difference between utilizing ChatGPT—which investment bank UBS estimates has 13 million users a day—as a standalone product, and integrating it into Bing, which handles half a billion searches every day.
  • Data centers already account for around one percent of the world’s greenhouse gas emissions, according to the International Energy Agency. That is expected to rise as demand for cloud computing increases, but the companies running search have promised to reduce their net contribution to global heating. “It’s definitely not as bad as transportation or the textile industry,” Gómez-Rodríguez says. “But [AI] can be a significant contributor to emissions.”
  • The environmental footprint and energy cost of integrating AI into search could be reduced by moving data centers onto cleaner energy sources, and by redesigning neural networks to become more efficient, reducing the so-called “inference time”—the amount of computing power required for an algorithm to work on new data.
Ed Webb

I unintentionally created a biased AI algorithm 25 years ago - tech companies are still... - 0 views

theconversation.com/making-the-same-mistake-203734

AI algorithms race racism bias DickinsonCollege dickinson technology opinion analysis

shared by Ed Webb on 10 May 23 - No Cached
  • How and why do well-educated, well-intentioned scientists produce biased AI systems? Sociological theories of privilege provide one useful lens.
  • Scientists also face a nasty subconscious dilemma when incorporating diversity into machine learning models: Diverse, inclusive models perform worse than narrow models.
  • fairness can still be the victim of competitive pressures in academia and industry. The flawed Bard and Bing chatbots from Google and Microsoft are recent evidence of this grim reality. The commercial necessity of building market share led to the premature release of these systems.
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  • Their training data is biased. They are designed by an unrepresentative group. They face the mathematical impossibility of treating all categories equally. They must somehow trade accuracy for fairness. And their biases are hiding behind millions of inscrutable numerical parameters.
  • biased AI systems can still be created unintentionally and easily. It’s also clear that the bias in these systems can be harmful, hard to detect and even harder to eliminate.
  • with North American computer science doctoral programs graduating only about 23% female, and 3% Black and Latino students, there will continue to be many rooms and many algorithms in which underrepresented groups are not represented at all.
Ed Webb

Google and Meta moved cautiously on AI. Then came OpenAI's ChatGPT. - The Washington Post - 0 views

www.washingtonpost.com/...chatgpt-google-meta

AI tech technology business ethics

shared by Ed Webb on 29 Jan 23 - Cached
  • The surge of attention around ChatGPT is prompting pressure inside tech giants including Meta and Google to move faster, potentially sweeping safety concerns aside
  • Tech giants have been skittish since public debacles like Microsoft’s Tay, which it took down in less than a day in 2016 after trolls prompted the bot to call for a race war, suggest Hitler was right and tweet “Jews did 9/11.”
  • Some AI ethicists fear that Big Tech’s rush to market could expose billions of people to potential harms — such as sharing inaccurate information, generating fake photos or giving students the ability to cheat on school tests — before trust and safety experts have been able to study the risks. Others in the field share OpenAI’s philosophy that releasing the tools to the public, often nominally in a “beta” phase after mitigating some predictable risks, is the only way to assess real world harms.
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  • Silicon Valley’s sudden willingness to consider taking more reputational risk arrives as tech stocks are tumbling
  • A chatbot that pointed to one answer directly from Google could increase its liability if the response was found to be harmful or plagiarized.
  • AI has been through several hype cycles over the past decade, but the furor over DALL-E and ChatGPT has reached new heights.
  • Soon after OpenAI released ChatGPT, tech influencers on Twitter began to predict that generative AI would spell the demise of Google search. ChatGPT delivered simple answers in an accessible way and didn’t ask users to rifle through blue links. Besides, after a quarter of a century, Google’s search interface had grown bloated with ads and marketers trying to game the system.
  • Inside big tech companies, the system of checks and balances for vetting the ethical implications of cutting-edge AI isn’t as established as privacy or data security. Typically teams of AI researchers and engineers publish papers on their findings, incorporate their technology into the company’s existing infrastructure or develop new products, a process that can sometimes clash with other teams working on responsible AI over pressure to see innovation reach the public sooner.
  • Chatbots like OpenAI routinely make factual errors and often switch their answers depending on how a question is asked
  • To Timnit Gebru, executive director of the nonprofit Distributed AI Research Institute, the prospect of Google sidelining its responsible AI team doesn’t necessarily signal a shift in power or safety concerns, because those warning of the potential harms were never empowered to begin with. “If we were lucky, we’d get invited to a meeting,” said Gebru, who helped lead Google’s Ethical AI team until she was fired for a paper criticizing large language models.
  • Rumman Chowdhury, who led Twitter’s machine-learning ethics team until Elon Musk disbanded it in November, said she expects companies like Google to increasingly sideline internal critics and ethicists as they scramble to catch up with OpenAI.“We thought it was going to be China pushing the U.S., but looks like it’s start-ups,” she said.
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