How come smart assistants have virtually no ability to converse, despite all the spectacular progress with large language models?
We recorded 7 claims from this post. 3 were checked against evidence. 4 can’t be checked. 1 more is 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
Observation
“Amazon probably *is* using large language models for product search.”
Our reading: Amazon is probably using large language models for product search.
No horizon
Assessment: Can’t be checkedSaid
Causal claim
“LLMs are inherently unreliable. If Alexa were to make frequent errors, people would stop using it.”
Our reading: Marcus suspects that a contributing reason Alexa does not converse is that LLMs are inherently unreliable, and if Alexa made frequent errors, people would stop using it.
No horizon
Only if: if Alexa were to make frequent errors
Assessment: Can’t be checkedSaid
Causal claim
“Any one of these scenarios of LLMs gone awry (bad advice, insults, lies etc) could hurt the Amazon brand, open up litigation, etc..”
Our reading: Any one of the scenarios of LLMs going awry (bad advice, insults, lies, etc.) could hurt the Amazon brand and open up litigation.
No horizon
Only if: any one of these scenarios of LLMs gone awry
Assessment: UnresolvedSaid
Conditional forecast
“if Alexa could converse freely, user expectations would go through the roof, and mostly be unmeetable.”
Our reading: If Alexa could converse freely, user expectations would rise to a level that mostly could not be met.
No deadline stated Evidence as of
Only if: if Alexa could converse freely
Assessment: Can’t be checkedSaid
Forecast
“they are not (and may never be) great tech for reliably inferring user intent from what users say.”
Our reading: LLMs may never become great technology for reliably inferring user intent.
No horizon
Assessment: SupportedSaid
Observation
“Bottom line: From the outset Large Language Models like GPT-3 have great at generating surrealist prose, and they can beat a lot of benchmarks, but they are not (and may never be) great tech for reliably inferring user intent from what users say.”
Our reading: LLMs like GPT-3 are not great technology for reliably inferring user intent from what users say.
No horizon Evidence as of
Limitation: The principal evidence is a research benchmark, not a representative household deployment study.
Assessment: UnresolvedSaid
Forecast
“Turning LLMs into a product that controls your home and talks to you in a way that would be reliable enough to use at scale in millions of homes is still a long, long way away.”
Our reading: A reliable LLM-based conversational home-control product usable at scale in millions of homes is still a long, long way away.
No deadline stated Evidence as of
Limitation: Large-scale participation is company-reported.
Held from this post (1)
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: A sentence produced by an LLM cannot directly be used to control things unless another system is built to parse sentences into actions, and nobody knows how to build that reliably.