AI Is No Substitute for the Human Brain

Summary

WSJ Tech News Briefing: AI Is No Substitute for the Human Brain

Release Date: May 13, 2025

In this insightful episode of the WSJ Tech News Briefing, the Wall Street Journal delves deep into the evolving landscape of artificial intelligence (AI), focusing on the capabilities and limitations of AI chatbots and the broader implications for data privacy and human intelligence.

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AI Chatbots and Data Privacy

The episode opens with a discussion on the pervasive use of AI chatbots in everyday problem-solving. From selecting the right home gym equipment to crafting the perfect resume, millions rely on these tools daily. However, this reliance raises critical questions about data ownership and privacy.

Nicole Nguyen, WSJ Personal Tech Columnist, introduces the segment by referencing her series, Chatbot Confidential, where she explores these very concerns. She engages with listener queries, bringing in expert Christopher Mims to shed light on privacy regulations.

Key Discussion Points:

• FERPA and HIPAA in the Age of AI: Listening to a voicemail from Daniel Stewart, Christopher explains how AI tools intersect with federal privacy laws like FERPA (which protects educational records) and HIPAA (which safeguards medical records). He emphasizes the importance of using enterprise-grade AI versions that comply with regulations such as HIPAA, GDPR, and CCPA. For consumer-grade tools like ChatGPT or Anthropic's Claude, he advises scrubbing personally identifiable information to maintain privacy.

  "AI privacy laws extend to AI tools, particularly if you're using the public facing AI tools that are not enterprise versions... replacing student names, scrubbing as much personally identifiable information, sensitive information as possible is the right move." [02:28]

• Protection of Personal Media: Addressing Mitch's concern about family photos uploaded to AI chatbots, Christopher discusses options like opting out of AI training and using features such as temporary chat modes. However, he cautions that in the nascent stages of generative AI, some data might still be subject to human review or longer storage periods, drawing parallels to established platforms like Google Photos.

  "You can opt out of AI training with the caveat that in some instances it could be reviewed." [03:53]

This segment underscores the delicate balance between leveraging AI's convenience and safeguarding personal data, urging users to be proactive in understanding and utilizing privacy settings.

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Skepticism on AI Achieving Human-like Intelligence

Transitioning from data privacy, the episode tackles the optimistic claims surrounding AI's potential to reach Artificial General Intelligence (AGI)—the elusive milestone where machines exhibit human-like understanding and reasoning.

Christopher Mims and Julie Chang engage in a robust discussion, questioning the current trajectory of AI development.

Key Insights:

• Current State of AI vs. AGI: Julie asserts that today's transformer-based AIs, such as ChatGPT, operate on extensive lists of heuristics rather than genuine understanding. She argues that these models, despite their sophistication, fall short of replicating the nuanced and adaptive nature of human intelligence.

  "We are definitely nowhere near AGI... Today's transformer based AIs... the way that they work is just they have this kind of almost infinitely long list of little rules of thumb that they apply." [06:37]

• The Manhattan Map Experiment: To illustrate AI's limitations, Julie references a study where an AI model was trained with turn-by-turn directions within Manhattan. While the AI could accurately provide directions, its internal "map" was a convoluted and inaccurate representation of the actual city layout. This discrepancy highlights the AI's inability to form coherent spatial and causal understanding akin to humans.

  "When you ask it for directions on the island of Manhattan... it looked totally crazy. Streets were connected that are very far distant and diagonal to one another." [08:21]

• Plateauing of AI Intelligence: The conversation delves into the stagnation in AI's general abilities. Julie points out that recent models, despite increased data and training, often perform worse on certain tasks, such as mathematics, while becoming more prone to hallucinations. This suggests that merely scaling up AI models without fundamental advancements in their architecture may not yield the desired improvements.

  "The general abilities of these AIs have definitely hit a ceiling... reinforcement learning... makes them more likely to hallucinate, makes them worse at other things." [11:20]

This segment critically assesses the hype around AGI, emphasizing that while AI continues to advance in specific domains, replicating the depth and adaptability of the human brain remains a distant goal.

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Conclusion

The episode wraps up by highlighting the collaborative efforts behind the scenes, acknowledging producers Julie Chang and Emily Martosi, along with Melanie Roy's support. Listeners are encouraged to stay tuned for upcoming segments, including the TNB Tech Minute.

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Takeaways:

• Data Privacy is Paramount: As AI chatbots become integral to daily tasks, understanding and managing data privacy settings is crucial to protect personal information.

• AI's Current Limitations: Despite impressive advancements, AI chatbots do not possess human-like understanding or reasoning. Their operations are based on complex rule sets rather than genuine intelligence, making them susceptible to errors that humans would effortlessly navigate.

• Future of AI: While AI continues to evolve, achieving AGI requires more than just scaling up existing models. It necessitates fundamental breakthroughs in how machines process and understand information.

For those keen on staying abreast of the latest in technology and AI, this episode offers a comprehensive exploration of both the potentials and pitfalls of current AI advancements.

Transcript

[Victoria Craig]

In case you missed it, YouTube is the number one streaming platform in watch time in the US ahead of Netflix, Disney and Prime Video. For the second year in a row, there's only one YouTube.

[Nicole Nguyen]

Hey T and B listeners, before we get started, heads up. We're going to be asking you a question at the top of each show for the next few weeks. Our goal here at Tech News Briefing is to keep you updated with the latest headlines and trends on all things tech. Now we want to know more about you, we what you like about the show and what more you'd like to hear from us. So our question this week is what kind of stories about tech do you want to hear more of business decision making, boardroom drama? How about peeking inside tech leaders lives or tech policy? If you're listening on Spotify, you can look for our poll under the episode description or you can send an email to tnbsj.com now onto the show. Welcome to Tech News briefing. It's Tuesday, May 13th. I'm Victoria Craig for the Wall Street Journal. You asked. Now we're answering all of your burning questions about AI powered chat tools and how to keep your personal data safe while using them. Then we're looking under the hood of those chatbots because the companies that make them say they get smarter the more we use them. But can they really think like humans? But first, every day, millions of people turn to AI chatbots for solutions to problems large and small. What kind of home gym mat should I buy? How can I best showcase my job experience on a new resume? Can you help me draft this tricky work email or craft an itinerary for a summer getaway? But what happens to all of that search data? Who owns it? And how much? Can you trust the answers that AI gives you? WSJ personal tech columnist Nicole Nguyen explored these questions in her recent series called Chatbot Confidential. She asked you to send your questions about data privacy on platforms like ChatGPT or Claude, and now she's here to answer some of them. Hey Nicole.

[Christopher Mims]

Hi.

[Nicole Nguyen]

So let's just start with a voicemail that we got from Daniel Stewart.

[Christopher Mims]

I work for a community college and was wondering when we discussed AI privacy.

[Nicole Nguyen]

Issues, how does it relate to ferpa?

[Christopher Mims]

I normally have to replace student names.

[Nicole Nguyen]

Just to be safe, alter situations when.

[Julie Chang]

I use AI just to be safe.

[Christopher Mims]

I wonder if there's also similar issues with hipaa.

[Nicole Nguyen]

So for our listeners who may not know those abbreviations, FERPA and HIPAA are both federal laws that govern privacy. The former affords privacy to students and parents over education records, the latter medical privacy over medical records. So Nicole, how does AI factor into all of these concerns over privacy and.

[Christopher Mims]

AI privacy laws extend to AI tools, particularly if you're using the public facing AI tools that are not enterprise versions that are commissioned by your company, where typically those enterprise versions are compliant with privacy regulations such as HIPAA or GDPR in Europe or California CCPA. So if you're the consumer grade version of say ChatGPT or Anthropics Claude, then what Daniel is doing, which is replacing student names, scrubbing as much personally identifiable information, sensitive information as possible is the right move. That's a good idea.

[Nicole Nguyen]

We've also got another question from Mitch. He's a photo archivist and on X he asked if personal family photos uploaded to AI chatbots could be stored, used to train AI models, or accessed by other people later on.

[Christopher Mims]

So this is a complicated answer, but in many AI tools, for example ChatGPT and Gemini, you can opt out of AI training, but you have to mark that in settings. You can also use what's called temporary chat in ChatGPT, which is like an incognito mode for ChatGPT. It does not use that information for AI training. It deletes the conversation immediately. But there's a caveat there. There is always a possibility, if you're using these AI tools, because we are in the early days of generative AI, that your inputs and outputs could be subject to human review or stored for a longer time. And that's because the systems mark anything that is potentially harmful so that they can review and learn from those types of responses. And we don't exactly know what is harmful and what isn't, but we trust that these companies have reasonable policies. You know, if you use Google Drive, for instance, Google Photos, we trust that Google has reasonable policies around what is flagged and what isn't. So that's where I'll leave my answer. You can opt out of AI training with the caveat that in some instances it could be reviewed.

[Nicole Nguyen]

That was WSJ personal tech columnist Nicole Nguyen. If you have more questions, throw them at us. You can send us a voice memo to tnbsj.com or you can leave us a voicemail at 212-416-2236. Coming up, we've been promised that AI chatbots will take on human level smarts, but the list of skeptics is growing. We'll dig into that after the break.

[Victoria Craig]

Did you know that every day people watch on average more than 1 billion hours of YouTube on their TV screens. That's because YouTube is where people go deep on all the content they love. There's only one YouTube.

[Nicole Nguyen]

Can AI chatbots actually at some point solve problems in the same ways that humans can. In the industry, that ability is known as AGI, AI or artificial general intelligence. And increasingly, the research says AI models can take on more information to solve problems, but they do not think like humans. My colleague Julie Chang spoke to WSJ tech columnist Christopher Mims about what that means. Exactly.

[Victoria Craig]

So, Christopher, you're essentially saying that we're nowhere near AGI, is that right?

[Julie Chang]

That's correct. That's the right takeaway. We are definitely nowhere near AGI. And anyone who tells you differently, I think honestly just hasn't looked that deeply into what intelligence actually is. It turns out that what today's transformer based AIs, and that's the kind of AIs that underlies chat, GPT and a lot of other generative AIs, the way that they work is just they have this kind of almost infinitely long list of little rules of thumb that they apply. And so to give you a concrete example, one reason historically these models have been really bad at math. Even if you show them a million math problems and their correct answers is that they learn weird stuff. You know, if you ask them to multiply two numbers and one of them is between 200 and 211, it has a different set of little rules of thumb it uses for multiplying those numbers than it does for any other numbers anywhere else on the number timeline. So this is how today's AIs simulate intelligence. And a lot of people have pushed back and said, oh well, isn't this how people think? Like we're just a big pile of rules of thumb? No, you know, sorry, you're actually way more complicated than that. Humans have spatial three dimensional models that include like causality and other things. Today's transformer based AIs, this idea that if we just make them big enough and show them enough data, they will spontaneously generate in their cybertronic brains the machinery of thought. That seems to be nonsense.

[Victoria Craig]

In your column, you bring up this Manhattan map example. Can you talk about that and how it explains the bag of heuristics theory?

[Julie Chang]

So researchers think that the way that modern AIs work is what's called a bag of heuristics model. And this just means a really long list of literally millions of rules of thumbnail. And so, for example, one researcher took a traditional large language model and gave it turn by turn directions from every point in Manhattan to every other point in Manhattan, and discovered that it could then regurgitate directions between any two points on the island of Manhattan with 99% accuracy. Then they probed this model to look at what was the map of Manhattan that it had generated, that it was reasoning from, if you can use that word, to give back these directions. When you ask it for directions on the island of Manhattan and the map it regurgitated or that they were able to extract from, it looked totally crazy. Streets were connected that are very far distant and diagonal to one another. It seemed to think that there were streets that like jumped over Central park and all this other craziness. And what this showed was that the AI had managed to learn a sort of mental model of what Manhattan streets work like or look like that could generate accurate directions when asked, but in no way resembled what the actual street map of Manhattan was. And so this just shows you how kind of really strange and weird and simulated is the quote unquote understanding of an AI.

[Victoria Craig]

Okay, but humans wouldn't be able to recreate a map either.

[Julie Chang]

Yes, there is some truth to that. The thing that reveals how weird the AI generated map of Manhattan is, is detours. So if I told you, okay, you're trying to get between like one block in Manhattan, so one block of 7th Avenue is suddenly blocked, and you were an expert at navigating Manhattan, would you have any trouble just going over another block, like taking a detour? No, you'd have no trouble. And the idea is because you have some kind of explicit understanding of, oh, Manhattan is a grid. And I can, if I'm on a grid of streets, I can just go around a detour. And that kind of implicit understanding would be something that you had acquired in the course of learning your way around Manhattan. But the AI, when you block even 1% of the streets in Manhattan, it just completely breaks down. It also shows how in self driving systems, they can get completely thrown by just the smallest thing, which would never throw a human being.

[Victoria Craig]

So is AI intelligence plateauing then?

[Julie Chang]

Yes. I should amend that by saying that the sort of general abilities of these AIs have definitely hit a ceiling. Where for example, the latest reasoning models from OpenAI, they're actually worse at some tasks in a lot of ways. So it seems like the reinforcement learning that they do with them to make them better at coding and mathematics makes them more likely to hallucinate, makes them worse at other things. So just throwing more data at it is not improving these models. We can make them better in general by going in and manually tinkering or giving them access to. So, for example, you can take a large language model and give it access to an explicit mathematical application that has been programmed by human beings, and then it can do math more like the way a person would if they were given access to a calculator. But the sort of important distinction there is now it's just software again. So we're just having to go in and put all this scaffolding around the AI because it's really not that capable at the end of the day.

[Nicole Nguyen]

That was WSJ tech columnist Christopher Mims. And that's it for Tech News Briefing. Today's show was produced by Julie Chang with supervising producer Emily Martosi and additional support from Melanie Roy. I'm Victoria Craig for the the Wall Street Journal. We'll be back this afternoon with TNB Tech Minute. Thanks for listening.

[Victoria Craig]

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