Two years later, deep learning is still faced with the same fundamental challenges
We recorded 10 claims from this post. 10 can’t be checked. 5 more are held. Read the post at its source.
Claims from this post (10)
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
Observation
““We are still a long way from machines that can genuinely understand human language”. Still true, though some have argued there is some superficial understanding.”
Our reading: As of March 2024, machines are still a long way from genuinely understanding human language.
No horizon
Assessment: Can’t be checkedSaid
Observation
“We are still far from trustworthy AI.”
Our reading: As of March 2024, AI is still far from trustworthy.
No horizon
Assessment: Can’t be checkedSaid
Observation
“Likely still a concern, especially with open-source bots that may have less carefully constructed guardrails; there has been at least one chatbot related death.”
Our reading: Harmful chatbot exchanges with vulnerable users are likely still a concern, especially with open-source bots, and at least one death has been linked to a chatbot.
No horizon
Qualifier: especially open-source bots with weaker guardrails
Assessment: Can’t be checkedSaid
Observation
“Scaling the measures Kaplan and his OpenAI colleagues looked at—about predicting words in a sentence—is not tantamount to the kind of deep comprehension true AI would require” Still true, and increasingly acknowledged.”
Our reading: Getting better at next-word prediction through scaling does not amount to the deep comprehension true AI needs, and this is increasingly acknowledged.
No horizon
Assessment: Can’t be checkedSaid
Observation
“Neurosymbolic might be a promising alternative. Pending/still true”
Our reading: Neurosymbolic AI might be a promising alternative to pure deep learning.
No horizon
Assessment: Can’t be checkedSaid
Forecast
“Implict through the essay: scaling might not solve the problems not above. Pending/Still true”
Our reading: Scaling might not solve the fundamental problems of deep learning.
No horizon
Assessment: Can’t be checkedSaid
Rhetoric
“None of this means we won’t eventually have new innovations, of some sort or another. Or that AGI is impossible.”
Our reading: None of the points in the post mean that new innovations of some sort will not eventually arrive, or that AGI is impossible.
No horizon
Qualifier: of some sort or another
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“But I still think we need a paradigm shift.”
Our reading: AI needs a paradigm shift beyond current deep learning to reach general intelligence.
No horizon
Assessment: Can’t be checkedSaid
Conditional forecast
“We should never forget that the human brain is perhaps the most complicated system in the known universe; if we are to build something roughly its equal, open-hearted collaboration will be key.”
Our reading: If we are to build something roughly equal to the human brain, which is perhaps the most complicated system in the known universe, open-hearted collaboration will be key.
No horizon
Only if: if we are to build something roughly equal to the human brain
Assessment: Can’t be checkedSaid
Rhetoric
“Gary Marcus still feels like the field is mostly chasing ladders to the moon.”
Our reading: Marcus still feels the AI field is mostly chasing ladders to the moon.
No horizon
Scope: mostly
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: Although it is still early days, domestic humanoid robots are certainly not expected to be a big business for anyone in the near term.
- The two reviews read the claim differently
- The two reviews read the claim differently
- The two reviews read the claim differently
- The two reviews read the claim differently