AI versus The Clumsy Human Mind
We recorded 7 claims from this post. 1 was checked against evidence. 6 can’t be checked. 8 more are held. Read the post at its source.
Claims from this post (7)
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
Recommendation or value judgment
“AI definitely should not aspire to replicate the human mind in every detail.”
Our reading: AI should not aim to replicate the human mind in every detail.
No horizon
Qualifier: in every detail
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“The point of AI should not be to replicate us, but to supplement us, where we are cognitively frail (calculators are great at that! and spreadsheets! and databases!), and to pitch where we would prefer not to go, helping us, for example, with jobs that robotics call “dull, dirty, and dangerous”.”
Our reading: AI should aim to supplement humans where they are cognitively weak and take on dull, dirty, and dangerous work, not replicate humans.
No horizon
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“That’s what AGI should be: something that solves our problems for us (rather than creating new ones).”
Our reading: AGI should be a reliable, hallucination-free system that solves human problems rather than creating new ones.
No horizon
Assessment: UnresolvedSaid
Forecast
“Eventually, we will be able to build machines that help us reason critically, overcome human bias, and objectively, the way that we have calculators that spit out just the facts, without confabulation.”
Our reading: Eventually, we will be able to build machines that help us reason critically, overcome human bias, and reason objectively, giving just the facts without confabulation, the way calculators do.
No deadline stated Evidence as of
Limitation: Small, task-specific studies cannot establish broad capability.
Assessment: Can’t be checkedSaid
Observation
“For now, humans aren’t more intelligent than machines or vice versa, we are just differently abled.”
Our reading: As of November 2023, neither humans nor machines are more intelligent overall; they have different abilities.
No horizon
Qualifier: for now
Assessment: Can’t be checkedSaid
Causal claim
“It made my day because that’s what I’ve been trying to teach everyone, for a long time: we can probably make machines better if we spend time first thinking about human minds (both their strengths and their weaknesses).”
Our reading: We can probably make machines better if we first spend time thinking about human minds, both their strengths and their weaknesses.
No horizon
Qualifier: probably
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“The sooner we realize that humans and silicon machines are importantly different, the better.”
Our reading: People should recognize, as soon as possible, that humans and silicon machines are importantly different.
No horizon
Held from this post (8)
These records did not pass every check, so nothing about them is published. Held is not a finding.
- The record does not meet the current rules. Our reading, not assessed: As of November 2023, AI is still pretty poor at maintaining world models, inferring semantics from language, comprehending scenes, navigating the 3D world, and cognitive flexibility, where humans are good.
- The record does not meet the current rules. Our reading, not assessed: Machines are far ahead of humans at floating point arithmetic but far behind on cognitive flexibility and long-term planning in unusual situations.
- The record does not meet the current rules. Our reading, not assessed: Achieving AGI almost certainly depends on getting disparate algorithms, such as symbolic and data-driven learning methods, to work together well.
- The two reviews read the claim differently
- The two reviews reached different assessments. Our reading, not assessed: LLMs pretend to be what the Star Trek computer was, a system with encyclopedic knowledge, broad mathematical, synthetic and inferential abilities, and a hallucination-free natural language interface, which implies they are not that.
- The two reviews read the claim differently
- The two reviews reached different assessments. Our reading, not assessed: LLMs are good at some kinds of pattern matching but poor at floating point arithmetic.
- The record does not meet the current rules. Our reading, not assessed: As of the post, we are not yet at the point of building machines that help us reason critically, overcome bias, reason more reliably than humans in general (not just in limited circumstances), and synthesize information far better, because our early 21st-century knowledge of AI is primitive, and there is a lot of confusion about this.