First, I'm super skeptical that learning math by doing problem sets is a good model for learning other kinds of things. And even if it were, the idea that LLMs would support that generalization seems super sketchy.
What, specifically, is the system doing to get the student "unstuck" in their non-math assignment? What role does the LLM play? How does the way that LLMs absorb various societal biases from their training data affect performance?
Second, data collection. What else is happening to this data? Who has access? What is being done to mitigate its use to see students as (collections of) data points, rather than as people?
This comes back to the idea (not original to me, but don't have cite to hand) that data, when collected into piles, creates risk, and we should not be collecting it without thinking about and mitigating those risks.
One last one for now: "Learning is personal" but they want to "help everyone in the world learn anything in the world". What grounds do we have to believe that Google has the cultural competence to achieve that?
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And even if they did (they don't), that degree of reach for one company just seems inherently dangerous. Also. What is the scope of "anything in the world"? Would @Google really support people learning about the various ways in which their business practices do harm?
/fin
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@chirag_shah@webis_de Also Potthast et al: "As no actual conversations are currently supported by conversational search agents, every query is an ad hoc query that is met with one single answer."
No. Actual. Conversations.
There's a whole study to be done on the perils of aspirational tech names.>>
LSA 2003, Newmeyer's keynote. I was a few years in to my academic job search and attending the conference with my 9 month old son and my mom to look after him.
Mom was pushing the baby around the hotel ballroom level hallways in the stroller trying to keep him happy, but he was NOT happy and I could hear him.
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I got up from my seat in the standing-room-only crowd to go check on him, and when I came back, ended up standing at the back of the room, next to Andrew Garrett, who I knew from the one-year stint I did at Cal in 2000-2001.
Trying to attend "Conversational Information Seeking: Theory and Evaluation (Session 1)" at #CHIIR2022, but the Zoom link in the conference room in Gather isn't working. Anyone have a clue?
Also, there don't seem to be any papers listed in that session, nor "Conversational Information Seeking: Theory and Evaluation (session 2)" this afternoon. Maybe these are just phantom calendar entries? #CHIIR2022 what's going on?
I find it very nerve wracking when the interfaces to online conferences are unclear ... like I'm meant to be somewhere, but I can't figure out where, nor can I figure out why I can't figure it out. Also, no helpdesk that I can see, so no one to ask... #chiir2022
@chirag_shah In this #chiir2022 perspective paper we argue that using language model driven conversation agents (e.g. LaMDA) for search is flawed both technically and conceptually.
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Technical flaws include the fact that the language models aren't designed to perform "reasoning" (despite wild claims, such as Metzler et al 2021 referring to their "reasoning-like capabilities"). See also Bender & Koller 2020: aclanthology.org/2020.acl-main.…
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@GaryMarcus First, I had a good giggle at the thought of someone trying to implement something like the copy/paste functionality of an OS via deep learning. How frustrating would it be for some non-trivial % of the time for the info being pasted to be randomly different?
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@GaryMarcus Second, I disagree that photo labeling (i.e. assigning words to images) is necessarily "low stakes". Real harm can come from such systems, when the (photo, label) pair reproduces racism & other -isms. Key work here by @Abebab and colleagues:
This is a very thoughtful reflection by @zephoria --- and it's striking to be offered so much inside info about the CTL/Loris debacle --- but it also doesn't fully connect the dots. A few thoughts/questions:
@zephoria boyd steps the reader through how the organization went from handling data for continuity/quality of service to texters (allowing someone to come back to a conversation, handoffs between counselors, connection to local services) to using data for training counselors to >>
@zephoria using data for internal research for using data for vetted research by external partners as one thread. That last step feels rickety, but still motivated by the organization's mission and the fact that the org didn't have enough internal resources to all the beneficial research.