Oops! How Google bombed, while doing pretty much exactly the same thing as Microsoft did, with similar results
We recorded 8 claims from this post. 2 were checked against evidence. 6 can’t be checked. 5 more are held. Read the post at its source.
Claims from this post (8)
In the order they appear in the post. Each quotation is the speaker’s words in this post. A label such as “Supported” or “Can’t be checked” is the claim’s current assessment; its page has the evidence and the reasons.
Assessment: Can’t be checkedSaid
“I am not saying with certainty that we are for witnessing the same movie all over again, but should anybody blithely accept the reassurances of some very interested parties when AI has such a long history of demos that never quite make it to solid product? I don’t think so.”
What we tested: People should not blithely accept Microsoft's and OpenAI's reassurances that chatbot errors will be ironed out, since AI has a long history of demos that never become solid products.
Assessment: Can’t be checkedSaid
“Since neither company has yet subjected their products to full scientific review, it’s impossible to say which is more trustworthy; it might well turn out that Google’s new product is actually more reliable.”
What we tested: Without full scientific review it is impossible to say whether Google's or Microsoft's chatbot is more trustworthy, and Google's may turn out to be more reliable.
Assessment: Can’t be checkedSaid
“Most likely, neither of them are particularly reliable.”
What we tested: Most likely neither Google's nor Microsoft's chatbot search product is particularly reliable.
Assessment: Can’t be checkedSaid
“At the very least, someone ought be asking, from business perspective, what’s the moat here, if two companies are basically both about to offer the same thing?”
What we tested: Observers should be asking what competitive moat exists in chatbot search when Google and Microsoft are about to offer basically the same thing.
Assessment: Can’t be checkedSaid
“And it’s not just that AI has a fairly spotty track record for taking demos into reliable products; and it’s not just that hallucinatory web search could be dangerous if left to run amok in domains like medicine, it’s that the promises themselves are problematic, when examined from a scientific perspective.”
What we tested: Hallucinating web search could be dangerous if left to run amok in domains like medicine, and promises to fix it are scientifically problematic.
Assessment: SupportedSaid
“I first pointed out this risk in 2001, in the fifth chapter of my book The Algebraic Mind, and the problem has persisted ever since. To blithely assume that the problem will soon go away is to ignore 20 years of history.”
What we tested: The hallucination problem in neural networks has persisted since 2001, so assuming it will soon go away ignores 20 years of history.
What happened: Independent authors say chapter 5 of the 2001 book argued that distributed neural encodings lose track of particular individuals. That is a risk of blurring facts about individuals, consistent with Marcus's account. (arXiv, Chuma, Otsuka and Sato, )
Assessment: UnresolvedSaid
“I do actually think that these problems will eventually get ironed out, possibly after some important fresh discoveries are made, but whether they get ironed out soon is another matter entirely.”
What we tested: Marcus thinks chatbot hallucination problems will eventually get ironed out, possibly after some important fresh discoveries are made, but whether they get ironed out soon is another matter entirely.
Assessment: Can’t be checkedSaid
“If they don’t get ironed out soon —and they might not– people might quickly tire of chat based search, in which BS and truth are so hard to discriminate, and eventually find themselves returning to do their searches the old-fashioned, early 21st century way, awe or not.”
What we tested: If hallucinations are not fixed soon, which they may not be, people may quickly tire of chat-based search and return to traditional search.
Held from this post (5)
These records did not pass every check, so nothing about them is published. Held is not a finding.
- The two reviews reached different assessments. Our reading, not assessed: Hallucinations are inherent to neural network language models, a byproduct of how they compress inputs and lose track of factual relations.
- The two reviews read the claim differently
- The record does not meet the current rules. Our reading, not assessed: Despite seven years and roughly $100 billion of investment since 2016, driverless cars' errors have not gone away.
- The two reviews reached different assessments. Our reading, not assessed: Scaling neural network models has made their output more authoritative-sounding but not more truthful.
- The two reviews read the claim differently