What’s wrong with Google’s new robot project
We recorded 15 claims from this post. 3 were checked against evidence. 12 can’t be checked. 2 more are held. Read the post at its source.
Claims from this post (15)
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
“I am not surprised that this can (kinda sorta) be done, but I am not sure it should be done.”
Our reading: Marcus doubts that language-model-driven robots like PaLM-SayCan should be built, even though they can sort of be made to work.
No horizon
Assessment: Can’t be checkedSaid
Forecast
“First, the language technology that the new system relies on is well-known to be problematic and second it is likely to be even more problematic in the context of robots.”
Our reading: Language model technology is likely to be even more problematic when used in robots.
No horizon
Qualifier: in the context of robots
Assessment: Can’t be checkedSaid
Rhetoric
“Putting aside robots for the moment, we already know that so-called large language models are like bulls in a china shop: awesome, powerful, and reckless.”
Our reading: Large language models are powerful but reckless, like bulls in a china shop.
No horizon
Assessment: Can’t be checkedSaid
Conditional forecast
“It’s not just that large language models can counsel suicide, or sign off on genocide, or that they can be toxic and that they are incredibly (over)sensitive to the details of their training set - it’s that if you put them inside a robot, and they misunderstand you, or fail to fully appreciate the implications of your request, they could cause major damage.”
Our reading: If large language models are put inside robots and misunderstand a request or miss its implications, they could cause major damage.
No horizon
Only if: LLMs put inside a robot
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“The PaLM-SayCam experiment makes it pretty clear that the list of 21 problems is due for an update.”
Our reading: DeepMind's list of 21 ethical and social problems with LLMs should be updated to cover the risks of putting LLMs in robots.
No horizon
Assessment: Can’t be checkedSaid
Conditional forecast
“Misunderstandings will arise; without really firm checks on these systems, some of those misunderstandings could lead to mayhem.”
Our reading: LLM-driven robots will misunderstand people, and without firm checks some of those misunderstandings could lead to mayhem.
No horizon
Only if: without really firm checks on these systems
Assessment: Can’t be checkedSaid
Causal claim
“As I have argued throughout the essays in this Substack (eg. Three ideas from linguistics that everyone in AI should know and The New Science of Alt Intelligence) , that’s because large language models are superficial statistical mimics, rather than systems that traffic in rich cognitive models of the world around them.”
Our reading: The field has no compelling idea at present for solving the many alignment problems of large language models because those models are superficial statistical mimics rather than systems that use rich cognitive models of the world.
No horizon
Qualifier: at the current time
Assessment: Can’t be checkedSaid
Observation
“The reality is that the field has no compelling idea at the current time how to solve the many “alignment” problems that bedevil large language models.”
Our reading: The AI field currently has no compelling idea of how to solve the alignment problems of large language models.
No horizon
Qualifier: at the current time
Assessment: Can’t be checkedSaid
Observation
“But that precisely what Google’s new system is, a stitching-together of superficial and incorrigible language understanders with powerful and potentially dangerous humanoid robots.”
Our reading: Google's PaLM-SayCan stitches superficial, incorrigible language models onto powerful and potentially dangerous humanoid robots.
No horizon
Assessment: Can’t be checkedSaid
Conditional forecast
“Without a much greater degree of interpretability and verifiability, its hard to see how we can get to that level of safety.”
Our reading: Without a much greater degree of interpretability and verifiability, it is hard to see how humanoid home robots can reach the level of safety needed to act reliably and safely on users' requests.
No horizon
Only if: without a much greater degree of interpretability and verifiability
Assessment: UnresolvedSaid
Observation
“Facebook M was canceled, and 7 years later nobody yet can buy a super-smart (or even decently smart) robot at any price.”
Our reading: As of August 2022, nobody can buy even a decently smart robot at any price.
No horizon Evidence as of
Limitation: The sources do not provide a comprehensive market record for August 18, 2022 [e1][e2][e3].
Assessment: Can’t be checkedSaid
Causal claim
“And the more actions a robot might consider, the worse performance is likely to be.”
Our reading: The more actions a robot like PaLM-SayCan has to consider, the worse its performance is likely to be.
No horizon
Assessment: SupportedSaid
Observation
“Performance hovered at around 75% in the best case, when the robots had a limited number of options to consider.”
Our reading: PaLM-SayCan's performance was around 75% at best, when the robot had a limited number of options.
No horizon Evidence as of
Limitation: Best case is interpreted as the best overall setting.
Assessment: UnresolvedSaid
Forecast
“Cool demo, yes. Real world product, anytime soon? Fuggedaboutit.”
Our reading: PaLM-SayCan will not become a real-world product anytime soon.
No deadline stated Evidence as of
Limitation: The search cannot prove the absence of every derivative.
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“In a general-purpose, humanoid domestic robot, 75% is nowhere near good enough.”
Our reading: In a general-purpose humanoid domestic robot, a 75% success rate is nowhere near good enough.
No horizon
Scope: general-purpose humanoid domestic robot
Held from this post (2)
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: No one, including Elon Musk, should hold their breath expecting systems like PaLM-SayCan's vision, robots we can talk to that help with everyday chores, to appear in reliable form in the next several years.
- The two reviews reached different assessments. Our reading, not assessed: On some actions PaLM-SayCan executed correctly 0% of the time, which is inadequate for a household robot.