Artificial intelligence black boxes just got a little less mysterious | Tech News
One of the weirder, more unnerving things about today’s leading artificial intelligence (AI) systems is that nobody — not even the people who build them — really knows how the systems work.
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That’s because large language models, the type of AI systems that power ChatGPT and other popular chatbots, are not programmed line by line by human engineers, as conventional computer programs are.
Instead, these systems essentially learn on their own, by ingesting massive amounts of data and identifying patterns and relationships in language, then using that knowledge to predict the next words in a sequence.
One consequence of building AI systems this way is that it’s difficult to reverse-engineer them or to fix problems by identifying specific bugs in the code. Right now, if a user types “Which American city has the best food?” and a chatbot responds with “Tokyo,” there’s no real way of understanding why the model made that error, or why the next person who asks may receive a different answer.
And when large language models do misbehave or go off the rails, nobody can really explain why. The inscrutability of large language models is a major reason some researchers fear that powerful AI systems could eventually become a threat to humanity.
After all, if we can’t understand what’s happening inside these models, how will we know if they can be used to create novel bioweapons, spread political propaganda or write malicious computer code for cyberattacks? If powerful AI systems start to disobey or deceive us, how can we stop them if we can’t understand what’s causing that behavior in the first place?
But this week, a team of researchers at the AI company Anthropic announced what they called a major breakthrough — one they hope will give us the ability to understand more about how AI language models actually work, and to possibly prevent them from becoming harmful. The team summarised its findings in a blog post called “Mapping the Mind of a Large Language Model.”
They looked inside one of Anthropic’s AI models — Claude 3 Sonnet, a version of the company’s Claude 3 language model — and used a technique known as “dictionary learning” to uncover patterns in how combinations of neurons, the mathematical units inside the AI model, were activated when Claude was prompted to talk about certain topics. They identified roughly 10 million of these patterns, which they call “features.” They found that one feature, for example, was active whenever Claude was asked to talk about San Francisco. Other features were active whenever topics like immunology or specific scientific terms, such as the chemical element lithium, were mentioned. And some features were linked to more abstract concepts, like deception or gender bias.
They also found that manually turning certain features on or off could change how the AI system behaved. For example, they discovered that if they forced a feature linked to the concept of sycophancy to activate more strongly, Claude would respond with flowery, over-the-top praise for the user, including in situations where flattery was inappropriate.
Chris Olah, who led the Anthropic interpretability research team, said these findings could allow AI firms to control their models more effectively.
CRACKING THE CODE
> Black box Problem can be defined as an inability to fully understand an AI’s decision-making process
> Large language models, such as ChatGPT, are not programmed line by line by human engineers
> So, when they misbehave or go off the rails, nobody can really explain why
> Researchers looked inside one of Anthropic’s AI models — Claude 3 Sonnet
> They used a technique known as “dictionary learning” to uncover patterns in how combinations of neurons were activated when Claude was prompted to talk about certain topics
> They found that one feature, for example, was active whenever Claude was asked to talk about San Francisco
> They also found that manually turning certain features on or off could change how the AI system behaved
> Researchers believe these findings could allow AI firms to control their models more effectively
