Do We Dare Use Generative AI for Mental Health?

Andrew Ng Announces Deeplearning.ai, His New Venture After Leaving Baidu

Andrew Ng, the former chief scientist of Baidu, announced his next venture, Deeplearning.Ai, with only a logo, a domain name and a footnote pointing to an August launch date. In an interesting twist, the Deeplearning.Ai domain name appears to be registered to Baidu's Sunnyvale AI research campus — the same office Ng would have worked out of as an employee.

It's unclear whether Ng began his work on Deeplearning.Ai while still an employee at Baidu. According to data pulled from the Wayback Machine, the domain was parked at Instra and picked up sometime between 2015 and 2017.

Registering that domain to Baidu accidentally would be an amateur mistake and registering it intentionally just leaves me with more unanswered questions. I'm left wondering about the relationship between Baidu and Deeplearning.Ai — and its connection to Andrew Ng's departure. Of course, it's also possible that there was some sort of error that caused an untimely mistake.

UPDATE: Baidu provided us the following response.

Ng left the company in late March of this year, promising to continue his work of bringing the benefits of AI to everyone. Baidu is known for having unique technical expertise in natural language processing and it's recently been putting resources into self-driving cars and other specific deep learning applications.

It makes sense that Ng would take advantage of his name recognition to raise a large round to maximize his impact on the machine intelligence ecosystem. I can't see a general name like Deeplearning.Ai being used to sell a self-driving car company or a verticalized enterprise tool. It's more likely that Ng is building an enabling technology that aims to become critical infrastructure to support the adoption of AI technologies.

While this could technically encompass specialized hardware chips for deep learning, I'm more inclined to bet that it is a software solution given Ng's expertise. Google CEO Sundar Pichai made a splash back at I/O last month when he discussed AutoML — the company's research work to automate the design process of neural networks. If I was going to come up with a name for a company that would build on, and ultimately commercialize, this technology, it would be Deeplearning.Ai.

"This is super speculative, but I think it might be an AI tool to help generate AI training data sets or something else that will accelerate the development of AI models and products," Malika Cantor, partner at AI investment firm Comet Labs told me. "I'm very excited about having more tools and platforms to support the AI ecosystem."

Prior to his time at Baidu, Ng was instrumental in building out the Google Brain Team, one of the company's core AI research groups. Ng is a highly respected researcher and evangelist in the AI space with connections spanning industries and geographic borders. If Ng truly believes that AI is the new electricity, he will surely try to position Deeplearning.Ai to take advantage of the windfall.

We've reached out to both Baidu and Andrew Ng and will update this post if we receive additional information.

John Mannes is a student at the University of Michigan.

Andrew Ng Has A Chatbot That Can Help With Depression

I'm a little embarrassed to admit this, but I've been seeing a virtual therapist.

It's called Woebot, and it's a Facebook chatbot developed by Stanford University researchers that offers interactive cognitive behavioral therapy. And Andrew Ng, a prominent figure who previously led efforts to develop and apply the latest AI technologies at Google and Baidu, is now lending his backing to the project by joining the board of directors of the company offering its services.

"If you look at the societal need, as well as the ability of AI to help, I think that digital mental-health care checks all the boxes," Ng says. "If we can take a little bit of the insight and empathy [of a real therapist] and deliver that, at scale, in a chatbot, we could help millions of people."

For the past few days I've been trying out its advice for understanding and managing thought processes and for dealing with depression and anxiety. While I don't think I'm depressed, I found the experience positive. This is especially impressive given how annoying I find most chatbots to be.

"Younger people are the worst served by our current systems," says Alison Darcy, a clinical research psychologist who came up with the idea for Woebot while teaching at Stanford in July 2016. "It's also very stigmatized and expensive."

Darcy, who met Ng at Stanford, says the work going on there in applying techniques like deep learning to conversational agents inspired her to think that therapy could be delivered by a bot. She says it is possible to automate cognitive behavioral therapy because it follows a series of steps for identifying and addressing unhelpful ways of thinking. And recent advances in natural-language processing have helped make chatbots more useful within limited domains.

Depression is certainly a big problem. It is now the leading form of disability in the U.S., and 50 percent of U.S. College students report suffering from anxiety or depression.

Darcy and colleagues tried several different prototypes on college volunteers, and they found the chatbot approach to be particularly effective. In a study they published this year in a peer-reviewed medical journal, Woebot was found to reduce the symptoms of depression in students over the course of two weeks.

In my own testing, I found Woebot to be surprisingly good at what it does. A chatbot might seem like a crude way to deliver therapy, especially given how clumsy many virtual helpers often are. But Woebot works smoothly thanks to a clever interface and some pretty impressive natural-language technology. The software states up front that no person will see your answers, but it also offers ways of reaching someone if your situation is serious. I mostly used predefined answers that it offered me, but even when I strayed from the script a little, it didn't get tripped up. If you try, though, I'm sure it's possible to flummox it.

Woebot

You are guided through conversations with Woebot, but the system is able to understand a pretty wide range of answers. It checks in with you every day and directs you through the steps. For example, when I tried telling Woebot I was stressed about work, the bot offered ways of reframing my feelings to make them seem more positive.

The emergence of a real AI therapist is, in a sense, pretty ironic. The very first chatbot, Eliza, developed at MIT in 1966 by Joseph Weizenbaum, was designed to mimic a "Rogerian psychologist." Eliza used a few clever tricks to create the illusion of an intelligent conversation—for example, repeating answers back to a person or offering open-ended questions such as "In what way?" and "Can you think of a specific example?" Weizenbaum was amazed to find that people seemed to believe they were talking to a real therapist, and that some offered up very personal secrets.

Darcy also says both Eliza and Woebot are effective because a conversation is a natural way to communicate distress and receive emotional support. She adds that people seem happy to suspend their disbelief, and seem to enjoy talking to Woebot as if it were a real therapist. "People talk about their problems for a reason," she says. "Therapy is conversational."

Ng says he expects AI to deliver further advances in language in coming years, but it will still be relatively crude (see "AI's Language Problem"). He says better ways of parsing the meaning of language will help make the tool more effective, though. Some other mental-health experts also seem positive about the prospect of applying such technology to treatment.

"To the extent that the Woebot can replicate the way that a therapist can help explain concepts and facilitate trying out new coping skills, this approach may be even more helpful than working through a workbook," says Michael Thase, a professor of psychiatry at the University of Pennsylvania and an expert on cognitive behavioral therapy. "There is good evidence that people with milder levels of depression can benefit from various kinds of online or Web-based therapy approaches."

But Thase adds that studies have shown such technology to work best in conjunction with help from a real person. "Some time with a real therapist is helpful," he says.

Andrew Ng Explains Why You Should Stop Overthinking AI Prompts – 'Most LLMs Are Smart Enough To Figure Out What You Want'

Artificial intelligence may be advancing faster than users can keep up—and according to AI pioneer Andrew Ng, the secret to working better with it might be doing less.

Ng, a co-founder of Google Brain and longtime educator in machine learning, said in a post on X on April 3  that "lazy prompting"—providing minimal context when inputting information into large language models—can often be more effective than carefully crafted instructions.

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"Lazy prompting" refers to using as little instruction as possible when asking an LLM for help. Ng explained that many developers simply copy and paste error messages—sometimes several pages long—into AI models like ChatGPT, and receive accurate suggestions for fixes without providing further context.

"We add details to the prompt only when they are needed," Ng wrote in the post. "Most LLMs are smart enough to figure out that you want them to help understand and propose fixes."

The approach is especially popular among software engineers using generative AI tools to write and debug code.

Ng cited this workflow as a form of "vibe coding," a term introduced by OpenAI co-founder Andrej Karpathy in February, described a new approach to software development where AI tools handle the bulk of coding tasks.

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Ng's comments come just days after he launched "Vibe Coding 101," a new online course aimed at beginners learning to use AI tools for programming. The course emphasizes natural language input as a key skill for modern developers.

While prompting guides typically recommend including detailed context to get more accurate results from LLMs, Ng argued that in some cases, simpler inputs perform just as well—particularly when the model is capable of interpreting intent without being told explicitly.

A study titled "LLMs achieve adult human performance on higher-order theory of mind tasks" found that models like GPT-4 and Flan-PaLM reach adult-level and near adult-level performance on Theory of Mind (ToM) tasks.

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