Friday, August 29, 2025

8/29/25: Mostly evaluation of prompt course as a next reading

Artificial Intelligence Study Group

Welcome! We meet from 4:00-4:45 p.m. Central Time on Fridays. Anyone can join. Feel free to attend any or all sessions, or ask to be removed from the invite list as we have no wish to send unneeded emails of which we all certainly get too many. 
Contacts: jdberleant@ualr.edu and mgmilanova@ualr.edu

Agenda & Minutes (176th meeting, Aug. 29, 2025)

Table of Contents
* Agenda and minutes
* Appendix: Transcript (when available)

Agenda and Minutes
  • Announcements, updates, questions, etc.
  • MM pointed out the table of AI resources at https://ai.nd.edu/ai-in-action/approved-ai-tools/. 
  • "Join us for a thought-provoking lecture and book signing with renowned economist and King’s College London professor Daniel Susskind as part of the CBHHS Research Symposium."
    Thursday, September 4, 2:00 p.m., UA Little Rock, University Theatre – Campus conversation    
    Friday, September 5, 2:00 p.m., UA Little Rock, University Theatre – Campus and community conversation 

Susskind, a leading voice on the future of work and technology, will explore how artificial intelligence is reshaping the workplace and how we can harness its potential to work smarter. Don’t miss this opportunity to engage with one of today’s most influential thinkers on AI, economics, and the future of our professions.

 Register to Attend


  • Sept. 12: ES will tell us about The AI-Driven Leader: Harnessing AI to Make Faster, Smarter Decisions, by Geoff Woods. Please help discuss it, ask questions, etc.!
  • Here are projects that MS students can sign up for. If anyone has an idea for an MS project where the student reports to us for a few minutes each week for discussion and feedback - a student might potentially be recruited! Let me know.
    • Book writing project
      • 8/22/2025: LG has signed up for this. Next time will try both and report back, and also start the log of the project.
        • Working on how to write the book. Have different agents doing different roles? 
        • Topic of book will be: personal investing
        • Committee: DB, MM, RS; Y is welcome to apply for AGFS and then be on the committee.
        • Does the Donaghey Scholars program have guidelines on report structure? IU suggests LG could contact Dr. S. Hawkins and/or Dr. J. Scott, who are involved in the program, to see if they have any such guidelines.
    • VW had some specific AI-related topics that need books about them.  
    • JH suggests a project in which AI is used to help students adjust their resumes to match key terms in job descriptions, to help their resumes bubble to the top when the many resumes are screened early in the hiring process.
    • JC suggested: social media are using AI to decide what to present to them, the notorious "algorithms." Suggestion: a social media cockpit from which users can say what sorts of things they want. Screen scrape the user's feeds from social media outputs to find the right stuff. Might overlap with COSMOS. Project could be adapted to either tech-savvy CS or application-oriented IS or IQ students.
    • DD suggests having a student do something related to Mark Windsor's presentation. He might like to be involved, but this would not be absolutely necessary.
      • markwindsorr@atlas-research.io writes on 7/14/2025:
        Our research PDF processing and text-to-notebook workflows are now in beta and ready for you to try.
        You can now:
        - Upload research papers (PDF) or paste in an arXiv link and get executable notebooks
        - Generate notebook workflows from text prompts
        - Run everything directly in our shared Jupyter environment
        This is an early beta, so expect some rough edges - but we're excited to get your feedback on what's working and what needs improvement.
        Best, Mark
        P.S. Found a bug or have suggestions? Hit reply - we read every response during beta.
        Log In Here: https://atlas-research.io
  • AI course updates? About 15 students currently, to be organized into teams. There will be projects due at the end of the semester. 
    • EG suggests students might benechecking out rapids.ai.
  • Any questions you'd like to bring up for discussion, just let me know.
  • Anyone read an article recently they can tell us about next time?
  • Any other updates or announcements?
  • Here is the latest on future readings and viewings. Let me know of anything you'd like to have us evaluate for a fuller reading, viewing or discussion.
    • Evaluated
    • 7/25/25: eval was 4.5 (over 4 people). https://transformer-circuits.pub/2025/attribution-graphs/biology.html.
    • https://arxiv.org/pdf/2001.08361. 5/30/25: eval was 4. 7/25/25: vote was 2.5.
    • We can evaluate https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10718663 for reading & discussion. 7/25/25: vote was 3.25 over 4 people.
    • Evaluation was 4.4 (6 people) on 8/8/25: https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-refusals
    • Evaluation was 3.87 on 8/8/25 (6 people voted): https://venturebeat.com/ai/anthropic-flips-the-script-on-ai-in-education-claude-learning-mode-makes-students-do-the-thinking
    • Evaluation was 3.5 by 6 people on 8/8/25: Put the following into an AI and interact - ask it to summarize, etc.
      • Towards Monosemanticity: Decomposing Language Models With Dictionary Learning  (https://transformer-circuits.pub/2023/monosemantic-features/index.html); Bricken, T., et al., 2023. Transformer Circuits Thread.
    • Evaluation was 3.75 by 6 people on 8/8/25 for: Use the same process as above but on another article.
    • 8/22/25: eval. was 4.0 (4 people): Https://www.nobelprize.org/uploads/2024/10/popular-physicsprize2024-2.pdf. 
    • (Eval 8/29/25 was 3.75 over 5 people.) Https://docs.google.com/document/d/1NeNmKlAmJdf50ST7plw4mvgeeS7UJuYLyEQMz8slCA0/edit?tab=t.0#heading=h.hnzmulgvk3qx.  
      • Prompt engineering course. 
      • Also at Syllabus page: https://apps.cognitiveclass.ai/learning/course/course-v1:IBMSkillsNetwork+AI0117EN+v1/home. 
      • Registration page: https://apps.cognitiveclass.ai/learning/course/course-v1:IBMSkillsNetwork+AI0117EN+v1/home
      • Requires registering. DD volunteered to register if it is free, so we can check it out briefly and decide if to do the course in detail.
    • Not yet evaluated
    • Neural Networks, Deep Learning: The basics of neural networks, and the math behind how they learn, https://www.3blue1brown.com/topics/neural-networks. (We would need to pick a specific one later.)
      • We checked the first one briefly. 8/22/25: eval was 3.625 (from 4 people) for a full viewing.
      • Let's evaluate a few more of them.
    • LangChain free tutorial, https://www.youtube.com/@LangChain/videos. (The evaluation question is, do we investigate this any further?)
    • Chapter 6 recommends material by Andrej Karpathy, https://www.youtube.com/@AndrejKarpathy/videos for learning more. What is the evaluation question? "Someone should check into these and suggest something more specific"?
    • Chapter 6 recommends material by Chris Olah, https://www.youtube.com/results?search_query=chris+olah
    • Chapter 6 recommended https://www.youtube.com/c/VCubingX for relevant material, in particular https://www.youtube.com/watch?v=1il-s4mgNdI
    • Chapter 6 recommended Art of the Problem, in particular https://www.youtube.com/watch?v=OFS90-FX6pg
    • LLMs and the singularity: https://philpapers.org/go.pl?id=ISHLLM&u=https%3A%2F%2Fphilpapers.org%2Farchive%2FISHLLM.pdf (summarized at: https://poe.com/s/WuYyhuciNwlFuSR0SVEt). (Old eval from 6/7/24 was 4 3/7.)
    • Back burner "when possible" items:
        • TE is in the informal campus faculty AI discussion group. 
        • SL: "I've been asked to lead the DCSTEM College AI Ad Hoc Committee. ... We’ll discuss AI’s role in our curriculum, how to integrate AI literacy into courses, and strategies for guiding students on responsible AI use."
        • Anyone read an article recently they can tell us about?
        • The campus has assigned a group to participate in the AAC&U AI Institute's activity "AI Pedagogy in the Curriculum." IU is on it and may be able to provide updates now and then. 
      Appendix: Transcript
       
      Artificial Intelligence Study Group
      Fri, Aug 29, 2025 

      0:18 - R. S.
      you you Yeah.

      2:20 - M. M.
      Hi, D. and D. and R. I know it's busy time, so this is why we're not having more people because right now it's really start the semester. But I sent to D. some links and I want to send right now another link. How do universities generative AI. So I'm copy right now. Are you bringing in the chat?

      2:56 - D. B.
      Yes, I will.

      2:57 - M. M.
      Because I told you they will have a lot of discussions right now. What kind of tools to use it, how to use it. So this is one. That I received today. They are internet discussions. I'm not participating in our university discussions, but this is from outside, from North Dam University. And I think that they have a very good table if you go to the table. Oh, I can share. Yeah, do you want to share your screen?

      3:46 - D. B.
      Yeah, you're showing, but go to the table.

      3:50 - M. M.
      You show.

      3:51 - D. B.
      All right, am I still showing it?

      3:55 - M. M.
      No, no, but it was there. All right.

      4:06 - M. M.
      I wish that our university, I'm not aware if our university prepares something like this, but they have a table. Can you go down and...

      4:21 - D. B.
      Yeah. Yeah. I don't see the table. Maybe in the resources. Go to resources. There's a good table.

      4:31 - M. M.
      Yeah. Yeah. I don't know, more event tables about the price and how they can use it and all the tools are listed. I don't know, maybe I can share.

      4:50 - D. B.
      Yeah, sure, you have it?

      4:56 - M. M.
      It's not coming to you. Oh. Hold on.

      5:05 - Unidentified Speaker
      It was here for me.

      5:10 - M. M.
      Oh, why is it not coming now? But several, University of Arizona, another one also. Yes.

      5:30 - Unidentified Speaker
      It's not coming though.

      5:31 - D. B.
      There should be a green share screen icon.

      5:39 - M. M.
      No, I know, but I am not finding the table. The table is under AI in action and then approved tools.

      5:49 - L. G.
      They have a table that lists all the tools that they use there. I think maybe that's what you're looking for. I'm not sure.

      6:00 - M. M.
      Yeah, yeah. Yeah, yeah, yeah, you're correct, yes. Yes, yes, okay, I can share. Yeah, so, but I wish that... Is this one? I'm not sure that I'm correct. No, it's not this one. Maybe. Can you see it now? Yeah, yeah, so I wish that our people create the table like this. What is free? Cloud free some small subscription notebook is free. So yeah. Yeah, but but there are many universities I receive from University of from another one, several. I'm not aware that our university creates the resources for our students, but they are probably working on this.

      7:11 - D. B.
      So how did you get that table? Did you go to resources? Approve AI tools Oh, I see, approved AI tools.

      7:28 - M. M.
      And they have a lot of... So we will have this D. person talking this week, coming, I think, about the AI in education in our university.

      7:49 - D. B.
      Yeah, OK, so let me just catch up with.

      8:03 - M. M.
      Yeah. Yes, exactly. You can list the The web page.

      8:18 - D. B.
      Yeah, I got it. OK, so yeah, there's going to be a seminar. This guy from London is going to be talking about AI next Thursday, next Friday at 2 PM each time in the University Theater. And they want you to Thursday is the campus conversation. Friday is the campus and community So anyway, if anyone, I might try to get, try to go to it. If anyone else does, we can, you know, talk about it a bit at the meeting, at our meeting. This guy is, works in the future of work in technology and they want you to register to attend, but I don't think it's, I mean, I think probably you want to know how many people go, but they're not going to turn you away from the university theater. Yeah, there is a space.

      9:16 - Unidentified Speaker
      if you don't register, it's okay.

      9:19 - D. B.
      And then on September 12th, E. S. in the psychology department is going to be a guest here and is going to tell us about a book I group Read over the summer called this. And so I hope people will feel free to, you know, ask, you know, help her, help her out a little bit. Don't just like, listen to her and then say, thanks, just try to ask some questions and so on. Because it's pretty, you know, these presentations we have are all generally pretty informal.

      9:57 - M. M.
      So, you know, the guests need to be encouraged. Yeah.

      10:03 - D. B.
      So we got a book writing project. So L. is working on that. And I want you to know. L., do you want to tell us how it's going? And do you have any questions or anything, insights or questions?

      10:34 - L. G.
      Yeah, well, I don't have any questions. Questions this week. It's been kind of a rough week. I spent a little time trying to set up both of the tools I wanted to use. It's taken a little bit longer than I've been able to do yet, so I'm a little bit behind on it, but hopefully I have more questions and some answers and more next week. Okay.

      10:58 - D. B.
      Well, if you've been checking two tools and so on, that's definitely something we want to see in the log, the report that will become your report. Any observations you have about them? Yes, sir.

      11:12 - L. G.
      In that case, I have a lot of observations right now about getting them set up well. So I include that in the report as well.

      11:25 - Unidentified Speaker
      OK, sounds good.

      11:26 - D. B.
      R. and F., do you have any other guidance for L. since you're on the committee? Not right now. All right, let's go to... Okay, so we're still evaluating different readings and so on. I don't know when to stop even and start doing one of them for real, but now we could have a few more here. We don't have a lot of people here today, so might as well do that. Let's go to here and see how this one is. Okay, 10 predictions for 2025.

      12:37 - Unidentified Speaker
      This is a December 2025 article. This could be a problem.

      12:51 - D. B.
      Okay, well, I'm going to kick that one out because I don't have permission to view it and I don't want to be fighting with this thing all the time.

      13:07 - Unidentified Speaker
      So I'm going to just get rid of this one.

      13:13 - D. B.
      Let's go on to this one. You OK, so this is a course that I think D. was offering to potentially share his screen if he wanted to go through this course. But for today, we'll just view the syllabus and see what we think.

      14:05 - Unidentified Speaker
      So let's take a look from here. Let's start from those.

      14:11 - D. B.
      So Read that, and we'll see if there's comments about it? Lots of comments? All right, let's go on to the next paragraph.

      14:34 - M. M.
      Yeah, I just want to mention that Prompt engineering is really very important because it's integrated and it's a big part of agentic AI. So they go together, you know?

      14:54 - Unidentified Speaker
      Yeah, I think prompt engineering is interesting, is of interest to anyone, whether they're tech folks or not.

      15:03 - Unidentified Speaker
      Exactly.

      15:04 - M. M.
      Or yeah, for anybody, you know, if you are users or if they are developers.

      15:11 - D. B.
      Yeah, I mean, you could you could almost, you know, start.

      15:15 - D.
      You could you could almost start researching prompting engineering at this time and and do and do tests and see, you know. If you were to prompt this way, this is the results that you get if you were to this way, this is the different results. I mean, there's there's this is open terrain.

      15:39 - M. M.
      Yeah, I think there's a lot of research that could be done.

      15:44 - D. B.
      One issue, though, is how these things respond to prompts could change on a week-to-week basis. So research could get out of date pretty quickly. But that doesn't mean it's not important.

      15:58 - D.
      Well, maybe. I guess it really depends on where you're chatting with it. So in the chat, like your open chat that you just go to the chat site. But if you're in the API, especially like in chat GPT, and I think in some of the other models, it's kind of locked so that you can do research.

      16:23 - D. B.
      Yeah, but I mean, even if you're accessing it through the API, how the system responds to the API changes.

      16:31 - D.
      It's not that stable, right? OK, so you've got a good point that depending on the temperature and the top we talked about, you know, that could cause you to get kind of, maybe not random, but more random responses, right? So, but you know, there's ways to pin it down and find out the bottom line if you bring that top and that temperature down.

      17:00 - D. B.
      Another thing I wonder is if there's, are there general principles of prompt engineering that can be expected to apply long term? I'd say, well, they're going to change how they respond to prompts from week to week and blah, blah, depending on the news cycle and what happens in the marketplace. But are there general principles of prompt engineering that are going to stay constant? And if so, what are they?

      17:30 - D.
      That's a really good point. That's right. If so, is there patterns to the change? Yeah. There's a lot. I mean, it's really open terrain to anybody who really wants to do some research on it. It's open.

      17:54 - D. B.
      All right. Well, let's Read this next chunk here. About that.

      18:07 - Unidentified Speaker
      Any comments? Any comments?

      18:26 - D.
      Well, I Read all this already. Yeah.

      18:30 - D. B.
      Limitations of naive prompting. Cause that's, you know, you don't learn prompting, I guess what you, you know, if you don't learn formally how to prompt, I guess you're limited. You're, you're, you're there with naive prompting. So what am I doing wrong?

      18:46 - D.
      I took, I took a course and then I changed. I changed the way I prompt. It's like a Udemy course. And then I started looking at V.'s prompts and then I changed my prompts again. And now I'm kind of back to where I was kind of when I started. So my promptings then went full circle. So I'd like to know what other people think and what they're using and what they found.

      19:23 - D. B.
      V.'s prompts were a little more wordy than they needed to be. And, and I'm not sure like he was, he would tell the AI, you know, you are a world expert. Well, does that change how it responds compared if you don't say that?

      19:40 - D.
      So it seems like to me that from my experience that I did not get any, you know, higher quality results, you know, like if I did, tell it what's going on, what I'm doing, I get better results. If there's context, so context seems to be very important. It's probably maybe the most important thing. Because if I just tell the AI what I want, it could be interpreted many different ways. That the AI is going to think, well, he wants this for this or he wants this for that. And so if I context but no I'm you know telling the ai that it's hot sauce doesn't seem to really change anything that I can tell but V. gets some really good results man he really does i've been playing around with uh including a thing at the end of some and ask me questions if you, you know, if you need some, you know, additional information.

      20:49 - L. G.
      And I found that to be helpful in, helpful in providing clear instructions in a way. Yeah, I mean, so, but it does come back and say.

      21:00 - Unidentified Speaker
      Oh, yeah.

      21:00 - L. G.
      Oh, yeah. Yeah. Hold on, let me, maybe I can look at what I did at work. Hold on, we'll check it out. Look for, give me one moment.

      21:12 - D. B.
      Sure.

      21:13 - L. G.
      So at work, I was trying to figure out the best way to do a spreadsheet for someone. And what we found out is I asked it at the end. So I guess if I can find it. All right. What I asked it was, I said, I'm working as a financial analyst. I am building a workbook where we receive a large data set on one worksheet, and you use it on other worksheets. On a second worksheet, I would like to gather transactions that are row-based on the date and present them into two sets based on the amount column. One area for positive values, one area for negative values. And zero values would not be included. What are some of the ways that we could do this in Excel or Python? Please ask additional questions if needed. And yeah, it asked questions. It said, is the incoming data always set, always structured? Do you want the output on the second worksheet to update automatically when new data is pasted into the first worksheet and gave some examples of what it was saying. And it's a filtered list of full transactions where I needed to exclude some columns or fields.

      22:44 - Unidentified Speaker
      Yeah.

      22:45 - D. B.
      Interesting.

      22:45 - D.
      That's pretty good questions, too, because a lot of times I've noticed that the AI makes assumptions on how I want stuff, and it can get frustrating after a while when hey, I'm going to start doing that. Yeah.

      23:01 - Unidentified Speaker
      That's what led me down that path, because I would get, it would answer a question it thought I asked, but it wasn't the question I was asking. So then I, that's why I started adding that line.

      23:12 - D. B.
      I mean, you can, you can try to fix your, you know, if you see it's misunderstanding, you can, you can try to correct it mid-course, but why, why, why not ask it to correct it, you know, to ask you what it needs to know. That sounds more efficient.

      23:28 - D.
      It does. Yeah. So I would like to know what model you're using. For that one, I was using just simple, it looks like,

      23:37 - L. G.
      Hold on, I have to make it bigger. I was using check GPT. I don't know which model right now. Check GPT, it's probably five. You're using it on the front end.

      23:50 - D.
      I think it's five. Yeah, I think just the right one.

      23:54 - Unidentified Speaker
      Yeah, you're probably using it.

      23:56 - D.
      Five's what they're putting on their face right now, so.

      23:59 - L. G.
      Okay, then that's, yeah, I just did it like a couple of days ago.

      24:03 - D.
      Okay, thank you.

      24:22 - Unidentified Speaker
      Oops.

      24:23 - D. B.
      All right.

      24:34 - Unidentified Speaker
      OK, there's another chunk to me. Any comments? Just to me, this looks like really practical and valuable and practical sample.

      25:03 - D.
      Yeah, I mean, you know, it would be interesting just to kind of, you know, skim through this and see what... We could even try some of these exercises as we go along. You know, each person's on a computer, they can...

      25:18 - Unidentified Speaker
      I only get one grade now.

      25:20 - D.
      Or, you know, whoever's sharing their screen can try it and you can all see what's going on.

      25:26 - D. B.
      The only thing I remember is that you only get graded for the first is what? You only get graded for the first time.

      25:36 - D.
      Who's being graded? At IBM, they're going to grade it and it'll be on my certificate. So if we mess up, that's it.

      25:44 - D. B.
      My name is tarnished for life. Uh-oh. No.

      25:47 - D.
      I might want to rush through it real quick and do my best before we mess it up.

      25:53 - D. B.
      If we do it together, can we get a certificate saying, you know, AI discussion group has passed I mean, we would have to probably set up a Google account for, you know, like an email.

      26:06 - D.
      We need an AI discussion group email, but right now it's under my name.

      26:11 - D. B.
      Okay, well, I'll do my best to make you look good then.

      26:15 - D.
      All right. Might think I'm just some kind of scrub or something.

      26:20 - Unidentified Speaker
      You know, maybe if you ever thought of applying for a job at IBM, maybe we should pick somebody else to Just make sure we'll just make sure V.'s here.

      26:42 - Unidentified Speaker
      All right, next chunk here. Any comments?

      26:56 - Unidentified Speaker
      OK. And that is it. It's time to vote.

      27:00 - Unidentified Speaker
      You guys are saying that there's the NOVA system and the Watts-Knox-Prompt lab and... Watson X? Yeah. I said Watson X.

      27:09 - D. B.
      Is that how they would say that?

      27:12 - D.
      Well, I mean, they have the Watson, which will beat the chess champion.

      27:21 - D.
      I mean, I think it'd be interesting.

      27:24 - Unidentified Speaker
      I like that name Watson.

      27:25 - D.
      If we, if we start going through it and we think that it's just, you know, not really, you know, there's no real thought in it.

      27:33 - Unidentified Speaker
      Just some basic surface level stuff. We could stop doing it.

      27:37 - D. B.
      That's true for any of our readings. You know, I don't know that we do it very often, but if we don't like a reading, we can quit anytime. Absolutely true. All right, well, let's go ahead to, to the evaluation.

      27:53 - Unidentified Speaker
      So again, type into the chat or tell me your answer.

      27:59 - D. B.
      So one would be definitely don't want to do this. Five, you definitely do. Three, meaning either way.

      28:10 - Unidentified Speaker
      And two and four are in-betweens. Okay.

      28:15 - R. S.
      How long is this course if we follow?

      28:18 - M. M.
      You know what?

      28:19 - D.
      I've been trying to log back into it. I'm on my laptop, and I did that on my desktop at home. So I haven't been able to get back into it. But it seems like it's, you know, seemed like it was kind of long. Well, it's not like that. You know, for this context, I don't know. I can find out and, you know, definitely before we start anything we'll know. There's a link in the minutes, right?

      28:54 - D. B.
      That's how I got there. Yeah. Let me go back to the minutes. If it's something short, it's fine.

      29:05 - M. M.
      No, it's not short. It's a course.

      29:08 - D.
      No? No, I'm pretty sure it's not. Well, I mean, I don't mean like that. They have multiple labs, right?

      29:19 - D. B.
      Clearly, each lab is one session.

      29:22 - M. M.
      And how many links are to the There it is. That's the link that says minutes in it.

      29:33 - Unidentified Speaker
      Let me type in IBM.

      30:04 - D.
      I signed up when I was scoring.

      30:12 - Unidentified Speaker
      All right, 3.75.

      30:15 - D. B.
      3.75 is the evaluation.

      30:34 - Unidentified Speaker
      3.75? Yeah.

      30:43 - D. B.
      It's not super hot.

      30:46 - D.
      OK, so this one, so maybe I use my school email. I'm going to be safe.

      30:53 - Unidentified Speaker
      Let me go to the, go here and see. I don't know. Is it this one?

      31:02 - D. B.
      I don't remember what we were looking at. I mean, I've got the right thing.

      31:09 - D.
      I recognize the number. I'm just trying to get in.

      31:14 - Unidentified Speaker
      Yeah. So I did it. Okay. I did it.

      31:18 - D.
      Uh, the first module, everything says it's a minute long. One minute. Yeah.

      31:25 - D. B.
      I mean, it's, it just says, Oh, it says one activity.

      31:30 - D.
      Okay.

      31:34 - Unidentified Speaker
      about this course. It says casual one day a week. Yeah, I mean, I think at some point it told me, but I may not be able to find out how long it is. But there's five modules. And the first module is like five minutes.

      32:13 - D.
      The second module has a five question quiz in a couple of minutes. But the second module has four activities. It doesn't say time this goes into activity that maybe it's pretty quick it might be pretty quick it looks like it it's gonna be very quick thing maybe I got that I got confused when it's a casual one day a week this might be something we can knock out and like one session two or something you know how hard can it be How much time can it take to learn a prompt method? If you want, I'll share my screen and show you what I'm looking at.

      33:12 - D. B.
      Yeah, sure.

      33:15 - D. B.
      You guys see that?

      33:38 - Unidentified Speaker
      You guys are seeing this right? Anybody?

      33:42 - D. B.
      People seeing it? Yes. It's just like five modules.

      33:50 - M. M.
      It doesn't look like it's that much.

      33:55 - D.
      No, it's not. It's a pretty short little thing.

      34:00 - M. M.
      Well, anyone want to change their votes?

      34:08 - D. B.
      OK. I think we lost L.

      34:12 - Unidentified Speaker
      Is that his name? L.?

      34:15 - D. B.
      Is he gone? We lost L. All right.

      34:32 - D.
      He was definitely not interested in it, it looked like, No, he wasn't.

      34:39 - Unidentified Speaker
      He's got his own prompting methods now. Wasn't it kind of light last week too?

      34:51 - D. B.
      Yeah, it's been light for a couple of weeks.

      34:57 - D.
      It's kind of hard time for everybody. Yes.

      35:03 - Unidentified Speaker
      I haven't seen V. This is like three weeks in a row. He does not want to do the wind tunnel.

      35:11 - D.
      I mean, it is clear at this point. I'm going to have to tell him he's off the hook so he'll come back.

      35:25 - Unidentified Speaker
      Well, what else?

      35:28 - D. B.
      All this stuff is based, a bunch of stuff based on what we, those series of videos that we talked about. You want to look at another one? Evaluated something else or do we want to end now?

      35:59 - D.
      If we start getting a bunch of people and just a few of us make that decision, it might be kind of rough.

      36:12 - D. B.
      Yeah, there's not really, there's only four of us. All right, well, let's have a short meeting today. And we'll hopefully get more people next time. And if we don't, I mean, the important thing is that it's a valuable meeting. If the meetings start to get not so valuable, then it's a time to either do a recruitment drive or move on to other things.

      36:41 - D.
      Well, it could be that they have jobs that require them to be worked till five or something. Because luckily, I get off that I can be here for as long as I stay at work. I don't know.

      36:57 - Unidentified Speaker
      I mean, we've had other people here before. It used to be, last semester we had 10, 11 people every time.

      37:05 - M. M.
      Yeah.

      37:05 - Unidentified Speaker
      Yeah.

      37:05 - D.
      It was really good. But it's, you know, like Dr. M. said, it's the first part of the semester. Yeah.

      37:14 - D. B.
      I could send out a calendar reminder saying, hey, you know, Remember, we're doing it. Come back. That would be good.

      37:22 - D.
      Nice recruitment, yes.

      37:24 - M. M.
      We need to do some more advertisement. Yeah. All right, folks.

      37:28 - D. B.
      Well, thanks for joining in, and we'll see you next time.

      37:32 - D.
      All right. Bye, guys.
       

Friday, August 22, 2025

8/22/25: Updates on book project and AI course; evaluate a couple more items for deeper reading

Artificial Intelligence Study Group

Welcome! We meet from 4:00-4:45 p.m. Central Time on Fridays. Anyone can join. Feel free to attend any or all sessions, or ask to be removed from the invite list as we have no wish to send unneeded emails of which we all certainly get too many. 
Contacts: jdberleant@ualr.edu and mgmilanova@ualr.edu

Agenda & Minutes (175th meeting, Aug. 22, 2025)

Table of Contents
* Agenda and minutes
* Appendix: Transcript (when available)

Agenda and Minutes
  • Announcements, updates, questions, etc.
  • EG and DD are working on slides surveying different ML models.
  • VW will demo his wind tunnel system at some point. 
  • ES will provide a review of The AI-Driven Leader: Harnessing AI to Make Faster, Smarter Decisions, by Geoff Woods, when we request it.
    • I need to connect and schedule
  • "Join us for a thought-provoking lecture and book signing with renowned economist and King’s College London professor Daniel Susskind as part of the CBHHS Research Symposium."
    Thursday, September 4, 2:00 p.m., UA Little Rock, University Theatre – Campus conversation    
    Friday, September 5, 2:00 p.m., UA Little Rock, University Theatre – Campus and community conversation 

Susskind, a leading voice on the future of work and technology, will explore how artificial intelligence is reshaping the workplace and how we can harness its potential to work smarter. Don’t miss this opportunity to engage with one of today’s most influential thinkers on AI, economics, and the future of our professions.

 Register to Attend


  • Here are projects that MS students can sign up for. If anyone has an idea for an MS project where the student reports to us for a few minutes each week for discussion and feedback - a student might potentially be recruited! Let me know.
    • Book writing project
      • 8/22/2025: LG has signed up for this. Next time will try both and report back, and also start the log of the project.
        • Working on how to write the book. Have different agents doing different roles? 
        • Topic of book will be: personal investing
        • Committee: DB, MM, RS; Y is welcome to apply for AGFS and then be on the committee.
        • Does the Donaghey Scholars program have guidelines on report structure? IU suggests LG could contact Dr. S. Hawkins and/or Dr. J. Scott, who are involved in the program, to see if they have any such guidelines.
    • VW had some specific AI-related topics that need books about them.  
    • JH suggests a project in which AI is used to help students adjust their resumes to match key terms in job descriptions, to help their resumes bubble to the top when the many resumes are screened early in the hiring process.
    • JC suggested: social media are using AI to decide what to present to them, the notorious "algorithms." Suggestion: a social media cockpit from which users can say what sorts of things they want. Screen scrape the user's feeds from social media outputs to find the right stuff. Might overlap with COSMOS. Project could be adapted to either tech-savvy CS or application-oriented IS or IQ students.
    • DD suggests having a student do something related to Mark Windsor's presentation. He might like to be involved, but this would not be absolutely necessary.
      • markwindsorr@atlas-research.io writes on 7/14/2025:
        Our research PDF processing and text-to-notebook workflows are now in beta and ready for you to try.
        You can now:
        - Upload research papers (PDF) or paste in an arXiv link and get executable notebooks
        - Generate notebook workflows from text prompts
        - Run everything directly in our shared Jupyter environment
        This is an early beta, so expect some rough edges - but we're excited to get your feedback on what's working and what needs improvement.
        Best, Mark
        P.S. Found a bug or have suggestions? Hit reply - we read every response during beta.
        Log In Here: https://atlas-research.io
  • AI course updates? YP: Yes, course is underway. About 15 students currently, to be organized into teams. There will be projects due at the end of the semester. EG suggests checking out rapids.ai.
  • Any questions you'd like to bring up for discussion, just let me know.
  • Anyone read an article recently they can tell us about next time?
  • Any other updates or announcements?
  • Here is the latest on future readings and viewings. Let me know of anything you'd like to have us evaluate for a fuller reading, viewing or discussion.
    • Evaluated
    • 7/25/25: eval was 4.5 (over 4 people). https://transformer-circuits.pub/2025/attribution-graphs/biology.html.
    • https://arxiv.org/pdf/2001.08361. 5/30/25: eval was 4. 7/25/25: vote was 2.5.
    • We can evaluate https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10718663 for reading & discussion. 7/25/25: vote was 3.25 over 4 people.
    • Evaluation was 4.4 (6 people) on 8/8/25: https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-refusals
    • Evaluation was 3.87 on 8/8/25 (6 people voted): https://venturebeat.com/ai/anthropic-flips-the-script-on-ai-in-education-claude-learning-mode-makes-students-do-the-thinking
    • Evaluation was 3.5 by 6 people on 8/8/25: Put the following into an AI and interact - ask it to summarize, etc.
      • Towards Monosemanticity: Decomposing Language Models With Dictionary Learning  (https://transformer-circuits.pub/2023/monosemantic-features/index.html); Bricken, T., et al., 2023. Transformer Circuits Thread.
    • Evaluation was 3.75 by 6 people on 8/8/25 for: Use the same process as above but on another article.
    • Not yet evaluated
    • 8/22/25: eval. was 4.0 (4 people): Https://www.nobelprize.org/uploads/2024/10/popular-physicsprize2024-2.pdf. 
    • Https://www.forbes.com/sites/robtoews/2024/12/22/10-ai-predictions-for-2025/
    • Prompt engineering course: https://apps.cognitiveclass.ai/learning/course/course-v1:IBMSkillsNetwork+AI0117EN+v1/home. (Volunteer?)
      • Requires registering. DD volunteered to register if it is free, so we can check it out briefly and decide if to do the course in detail.
    • Neural Networks, Deep Learning: The basics of neural networks, and the math behind how they learn, https://www.3blue1brown.com/topics/neural-networks. (We would need to pick a specific one later.)
      • We checked the first one briefly. 8/22/25: eval was 3.625 (from 4 people) for a full viewing.
    • LangChain free tutorial, https://www.youtube.com/@LangChain/videos. (The evaluation question is, do we investigate this any further?)
    • Chapter 6 recommends material by Andrej Karpathy, https://www.youtube.com/@AndrejKarpathy/videos for learning more. What is the evaluation question? "Someone should check into these and suggest something more specific"?
    • Chapter 6 recommends material by Chris Olah, https://www.youtube.com/results?search_query=chris+olah
    • Chapter 6 recommended https://www.youtube.com/c/VCubingX for relevant material, in particular https://www.youtube.com/watch?v=1il-s4mgNdI
    • Chapter 6 recommended Art of the Problem, in particular https://www.youtube.com/watch?v=OFS90-FX6pg
    • LLMs and the singularity: https://philpapers.org/go.pl?id=ISHLLM&u=https%3A%2F%2Fphilpapers.org%2Farchive%2FISHLLM.pdf (summarized at: https://poe.com/s/WuYyhuciNwlFuSR0SVEt). (Old eval from 6/7/24 was 4 3/7.)
  • Schedule back burner "when possible" items:
    • TE is in the informal campus faculty AI discussion group. 
    • SL: "I've been asked to lead the DCSTEM College AI Ad Hoc Committee. ... We’ll discuss AI’s role in our curriculum, how to integrate AI literacy into courses, and strategies for guiding students on responsible AI use."
    • Anyone read an article recently they can tell us about?
    • The campus has assigned a group to participate in the AAC&U AI Institute's activity "AI Pedagogy in the Curriculum." IU is on it and may be able to provide updates now and then. 
Appendix: Transcript
 

Artificial Intelligence Study Group
Fri, Aug 22, 2025

0:06 - D
Can you hear me? I don't know you, Doctor B. E, can you hear me? I missed you. I thought she was going to be at the presentation last week and you were going to show one of your Olama models.

0:25 - E G
I got pulled into a work item.

0:28 - D
Oh, it's it's a scourge.

0:31 - E G
Now you owe us one.

1:21 - Y’s iPhone
Good afternoon.

1:22 - D
D, did you do your presentation last week?

1:28 - E G
Well, I just kind of demoed a little bit. It was terrible. It was the worst demo I've ever done in my life.

1:36 - D
I was exhausted. It was tough week for me last week.

1:41 - Y’s iPhone
I had a tough week too, D. Don't worry about it. My client meeting was not very good.

1:47 - D
And I'm still suffering.

1:49 - E G
I should have sent you over my Python scripts that I have for all of those, or my R scripts. I built a lot of those in Python and R for all of those models. And in fact, you could use them as scaffolding to do your own from it.

2:12 - D
Yeah. I, if I could have just had 20 minutes per prep, I could have, I could have done so much better.

2:22 - D B
Next time. No problem. Actually, would you like to do a do-over?

2:29 - E G
Well, it's super difficult when I want to schedule it on a time.

2:37 - D
Cause so I get all at 345 and so I'm 30 minutes away in the traffic. If traffic's light on Friday, which it usually is. So I can't be home until 4.15. So right now I'm in my homeroom faster. So, but I can't the AI's off my laptop. I mean, it's a good laptop, but it's just got a, what does it got, a 4070? I'm not sure.

3:13 - E G
Well, what I could do is you could talk through it and I could demo it because I have the hardware. Yeah.

3:23 - D
I mean, yeah, we can do that for sure. Then let's connect.

3:29 - E G
We'll do a dry run.

3:32 - Unidentified Speaker
I have a question. We have to connect on the weekend. Or late night, OK?

3:39 - Y’s iPhone
Yes, Sir, I have a question if a course on campus demands. I mean for not D, just for you, but for all the professors. If there's a course that we're teaching on campus and if we need some processing power. Do we have anything that university offers, or do we have tie up with Google or anybody else formally? How do we go about it if we require space?

4:14 - D B
You mean if it requires computing power?

4:18 - Y’s iPhone
Yeah, computing power or maybe licenses. Is there like a...

4:24 - D B
Well, licenses are negotiable. You'd want to start with Dr. P and maybe M P and ask about it.

4:36 - Unidentified Speaker
Okay.

4:36 - D B
If you have specific licenses. For computing power, I don't know, I guess M P would be cstem-help at ulearn.edu. Okay. You can ask what facilities that might be available Otherwise, you have to get your own computing power and just put it up on the screen in the classroom.

5:10 - Unidentified Speaker
All right.

5:12 - D B
So let's see. OK, so I hear that E and D are planning on doing something for us sometime. OK. I'll just wait till I hear more about it. And V agreed to display his or demo his wind tunnel system, but he's not here. So we'll put that on the back burner. And E S in the psychology department will review this book that she and some people have Read over the summer, as soon as I ask her to do it. So I'll get with her soon. Yeah. A note to myself.

5:55 - Unidentified Speaker
D, what is this psychology department thing?

5:57 - D B
She's a professor in the psychology department, and she started a summer book reading group that Read this book. Actually, I communicated with her, they Read three books. This is the first one. And she was kindly willing to tell us what was in the book or tell us what she thought. Of it.

6:21 - Y’s iPhone
Got it.

6:22 - D B
As an announcement, there's a guy visiting from London who will be talking about, he's a leading voice on the future of work and technology, blah, blah, blah. He'll be talking about AI and other things, related things. That will be Two days, September 4th and 5th at two on campus. You can register. I'm pretty sure they won't turn you away from the University Theater if you haven't registered. But anyway, it's an on-campus thing. If anyone goes, I'd like to be nice to have somebody tell us what happened in this meeting. Maybe I'll go.

7:10 - Y’s iPhone
Fourth is Thursday, right? Fourth is Thursday.

7:13 - Unidentified Speaker
I'll go on Thursday.

7:15 - D B
Yeah, I guess. Maybe they'll be the same.

7:18 - Y’s iPhone
I don't know.

7:20 - D B
OK. Oh, I see. No.

7:22 - Y’s iPhone
One is a campus conversation, and one is a campus and community conversation.

7:29 - D B
OK. I guess I'll miss the community.

7:33 - Unidentified Speaker
OK. In terms of projects for MS students, master's students, we have one.

7:41 - D B
L is doing a book. And he's here today. And so E did a book last semester. And so we have a lot of, we can, I guess, guide a book project better this time. So lucky for you, L. Do you want to give us a couple of minute update on what you're doing and any technical issues you're encountering or observations? Yes.

8:14 - L G
I only have a couple of minutes because I gave me a free pre from work. I had a little work problem today. So, so far this week, I went in to follow up about the report structure to get an understanding from the two professors we recommended. And I set out to try to figure out, okay, so we're going to use AI agents. That's what we decided. But, you know, kind of looking and thinking about two things. Like the how part of it. Last time we figured out the who and the what, but we didn't figure out how, right? And so I started investigating. Last semester, I looked into link chain, which could work. What I liked about it is it offers the ability to use different AI for different roles. So you could assign like a multi-chain where you have like, you know, chat GPT take on one role and another, maybe a different one take on a different role.

9:08 - Unidentified Speaker
in the process, or we could use Gemini. There are a lot of things we could use, but we could also use Gemini, CLI. That seemed to be okay, but you would just be using Gemini, and it would perform all the roles.

9:22 - L G
So I couldn't figure out if I wanted to do which route, wanted to go there, or we should maybe try to do both if we came up with some analysis that we could do from that. And the other thing was kind of like what the role of the agents would be. Initially, I thought, well, you know, you would naturally want to map out the human process of the road. So I kind of did some work, say, OK, well, maybe you need a writer, editor, and so forth. You're going through some of the tasks you would have. And then it dawned on me to ask AI. And I'm sorry I can't present anything, but let me look at my notes. It dawned on me to ask AI, like, OK, well, what would it do? And it said, well, hey, you know, it would use three roads. At least one of them said, one of them came up with a bunch of stuff, but we talked about that. Like they would have one agent that did research and content, where an agent would kind of conduct initial research and draft and some initial fact checking. And then maybe one could do editing and styles. And the other one would, and then the editing style would be like your tone, your voice, you know, that kind of consistency problems. And just kind of the overall, a secondary fact check. And then one could do the format. Individuals. I thought it'd be interesting, and so when I asked, I think Jim and I told me that, but when I asked Chad, it also added that we could have one do the kind of market research to determine at first what the topic would be. Like even if we're doing personal investing, would it be like crypto for 20 year olds? You know, what would be the thing we would want to shoot for? So that's some of the, I wouldn't say it's challenging. Some of the questions I'm trying to answer this week, I think if I can get through that, then I could go ahead and build out the kind of project schedule and timeline where we could kind of put in the next eight weeks to kind of wrap some of it up.

11:15 - D B
All right. Well, any thoughts on L's quandary? I guess my suggestion would be, you said you'd maybe try it both ways and compare it.

11:28 - L G
I'm looking for an interesting report, you know, to try both ways and to see, you know, if you can figure out some effectiveness and efficiency, some kind of quality metrics that you could measure both by, right? Because I don't think it would be time.

11:44 - D B
Both of them should be fairly quick. And I would, I would, you know, go ahead.

11:51 - E G
Actually, L, that is one of probably one of the better approaches, because at that point, you're comparing and contrasting approaches, identifying strengths and weaknesses, and possible outputs. Because as you start developing your path, what you identify on the other path may be useful to somebody else.

12:15 - D B
That's why we're going to be asking people like L to keep a log as his report explaining what he's done, what happened, and how it worked, it didn't work, and so on. So yeah, I would say go ahead and try both. You say it's, you know, a sort of preliminary test wouldn't be too time-consuming. Try both. Describe it in your log and tell us about it next week.

12:42 - L G
Yes, sir. Okay, good.

12:45 - D B
All right, that sounds interesting. I'm looking forward to it. OK, and the log of the project is important because people really dinged E last semester for not keeping a log, which was my suggestion to her, so it's not her fault. But yeah, so you would definitely need to be keeping a log. That's what we decided.

13:34 - Unidentified Speaker
All right. Let's see.

13:37 - D B
Oh, OK, so I understand your guess teaching of an applied AI course this semester? Yes sir. I was just wondering if you had any how it's going or if you have any thoughts or want to tell us anything about it it's really up to you but I think it'd be interesting to hear if there's anything you'd like to say about it.

14:07 - Y’s iPhone
Sure and I'll send the updated curriculum if you remember almost three months ago you all had given feedback and I made significant revisions to it based on the feedback I received from you all and others. But in a nutshell, actually the course is applied AI for functional managers, meaning that we are not going to go into any core programming or building models or those kind of things. But essentially, we will go into fundamentals of AI, then we are going to give overview of how to build rag models. And we are going to keep it to prompt engineering. And then towards the end, and by the way, we are going to use a lot of Nvidia certification and other material to do these three components. And then they will be offered some courses which is not going to be taught, but they can take independently for agents and because there was too much. I mean, that was one feedback I got last time, but there was we can't get to the agents and agentic age. In one course itself. So we have kept it to the building blocks that are required to eventually build agents and the technologies that we may touch upon, maybe Lang chain or competition of Lang chain that will help us to build the multimodal concepts and then there are some. There are three capstone projects. There are individuals from industry who are who have laid down those projects and the students will choose. There are around 15 students at this point of time. I know people come in and drop drop out, but there will be current three project teams. Who will drive these projects. So there will be certifications around these topics and then there will be final project and we'll have some judges from the industry coming in and we'll have some selection, whatever ratings or criteria set for projects in terms of technology, in terms of innovation, if there is anything newness. So we'll have some criteria set for those projects, and that's how the course will be done. We are working with NVIDIA to find out whether we can get some infrastructure for these projects. There is a high chance. I mean, the initial feedback is most likely yes. But if we get it, then I'll be very happy. If not, We'll have to deal with low computing power, which is many times available free if you sign up with a new email address.

17:38 - Unidentified Speaker
That's good, thank you.

17:39 - D B
I'm looking forward to hearing more about how the course goes and any questions you might have for us and we might have for you as the semester continues.

17:54 - Y’s iPhone
Absolutely, any guidance, mentors, from you all would be great. I mean, you have already given great input. And the goal is to obviously make it successful for all of us and for the university. But this is the first time I'm formally teaching in a university environment. So there will be some nuances around that, but I'll try my best. All right.

18:21 - D B
So out of these 15 students, how many are graduate students and how many are undergraduates, roughly?

18:28 - Y’s iPhone
Around, I think, five or six are graduate and the remaining all are undergraduate.

18:34 - D B
Does anyone have any questions for Y?

18:37 - A B
Yeah, sorry, I didn't catch. What's the domain that it's in? Is it like information technology or computer science or business?

18:48 - Y’s iPhone
It's under computer science, but we are So for example, we'll touch upon some AI compliances, AI risks. So when it comes to building is one thing, but when it comes to actually deploying and using it, what are the considerations? Like when you're coding, do you need to worry about any regulation, compliances, risk, security? So we'll be touching upon that. For developers. But it's formally part of computer science, but we will touch upon areas that typically are not considered computer science, but affect computer science.

19:29 - Unidentified Speaker
That's why I was asking, because you had framed it as like a class towards people that are managing technology and so forth.

19:39 - A B
So that's why I was thinking, is it more of like information systems or business? This kind of elective or something like that. OK, gotcha.

19:50 - D B
It almost sounds like an information science department course.

19:55 - A B
Yeah.

19:56 - D B
Or business MIS type stuff. But I guess they're hosting it in computer science, which is OK. All right. Any other questions for Y?

20:08 - E G
Actually, Y, as you're going into it, you're focusing on the Check out the rapids.ai. In fact, if you reach out to Dr. M, she could probably help you with it. That library supports a lot of standard data science, but what it gives you is tensors, which are used in LLMs and multi-threading for standard models.

20:40 - Y’s iPhone
I'll take a look at it and I'll reach out to her. And I mean this. Nvidia courses is influence of Doctor M so, but I will reach out to her and specifically talk about this. Thank you for this input.

21:04 - D B
OK. All right, anything else, anyone? OK, so we've been talking about what to Read together or videos to do together next. And the way we've traditionally done that is to Read an abstract or listen to the first minute or two of a video or something like that, and then vote as an evaluation of how much we want to dig into it. And we've got a bunch of evaluations here. Some of these not too many people participated in because at the end of the summer, we didn't have that great attendance, but we did evaluate some. And we've got a bunch, a few more to evaluate. I don't know if we want to evaluate all of them, but maybe some of them. So I thought we have a few minutes. We have, you know, half the meeting left. So let's evaluate a few more. Okay. So here's the first one that's not yet evaluated. And I'm going to bring it up. OK, so as you may recall, G H and who is that other guy? Some other guy won the Nobel Prize last year in physics, no less, for their Oh, J H, who was one of the early figures in neural networks. They won the Nobel Prize in physics, because there is no Nobel Prize in computer science or artificial intelligence. But the Nobel Prize winning prize committee wanted to give them a prize, and they did it in physics. So anyway, here's a document on they call it the popular science background to this particular award. And so I thought we'd Read a paragraph or two and then decide if we want to Read the whole thing in detail. It's not hugely long, but it's a few pages. So we'll start with the first little bit here. So let's Read from here to here and see if any Any comments? I'm sorry.

23:58 - Unidentified Speaker
Any comments or questions? I am sharing the screen properly, correct? Correct. My vote's already in.

24:10 - E G
This won the Nobel Prize in Physics in 2024. Machine learning, I think this falls in the no crap category.

24:25 - D B
OK. Uh, I'm, I'm finding this a little hard to digest. I don't, um, he says use tools from, well, what sort of physics tools, what is a physics tool?

24:38 - D
And, um, I mean, that's broad.

24:41 - Unidentified Speaker
That's really broad. A physics tool.

24:44 - Unidentified Speaker
Yeah, that's really broad.

24:46 - E G
Actually tensors are used a lot in physics. Okay.

24:50 - D B
And also I, I, well, I guess this is just a hand-wavy introduction, but when I Read stuff like this, I'm like, what kind of structure? What kind of method? But anyway, let's Read one more paragraph, and then we'll see. Please, please. Want to.

25:40 - Unidentified Speaker
Any comments or questions?

25:43 - D B
My comment is, you know, when I studied artificial intelligence decades ago, neural networks were a backwater. Now when people say AI, what they mean is neural networks.

26:03 - D
Yeah, so they had the theory, but they, they didn't have the memory and the processing power that it would take to really make a, a good neural network. And now we can.

26:20 - Unidentified Speaker
Yeah.

26:20 - D B
Now we have the memory, we have the computing power, we have better algorithms, back propagation and things like that didn't exist at one time.

26:31 - Unidentified Speaker
Right.

26:31 - A B
And it's interesting too, that it's like the, you know, with, until relatively recently, right, with, when was, when was the, was it 2017 or 18 that, you know, they had the paper about the transformer, the attention is all you need. And it's really like at that point, right, where, you know, using neural networks in that way, right, and then kind of you get the, you know, tokens, embeddings, and, attention layer. It's like that was really the big shift in their use and all these new things, the LLMs and so forth. But it's like neural networks for a long time have existed, but I guess they weren't as appealing as they are now, just on the new use cases and stuff.

27:23 - D
Yeah. I think it's kind of interesting too, the attention. It's just going to sum it up. Attention would you know, we really keep track of every word around that word. That's what it means, because we have enough memory to store all this information. And then, you know, but neural networks make the embeddings and the attention layer possible.

27:48 - A B
But it's really those kind of I think those advancements that are really, you know, why we're talking so much about AI these days. Right.

27:58 - D
So, yeah, I agree. I think it was 2017 or 2016, something like that.

28:04 - A B
Very popular paper.

28:06 - D B
Attention may be all you need, but that's only after you have back propagation algorithm, and neural network structures, and computing power, and data, OK? Yeah.

28:18 - A B
No, 100%. I'm just saying it's what, you know, neural network.

28:23 - D
Embeddings, all the, yeah. But once you have those things, yeah. Yeah, then attention's all you need. Exactly. Right. Right. Yeah. Yeah. Interesting. All right.

28:34 - D B
Any other comments on this paragraph? Yeah.

28:36 - A B
I couldn't get off mute earlier, but yeah, like the physics thing does kind of throw me off too. Cause like, I, you know, I think about all this as more, it's, it's closer to cognitive science than I think any, than physics.

28:52 - D B
There's no Nobel prize in cognitive science.

28:54 - D
I don't have that either.

28:56 - D B
Okay.

28:56 - D
I, uh, I think we should try one more paragraph just to kind of see if I'm getting some kind of really high-level vibes off of this one.

29:05 - D B
OK. Yeah, all right. We can do another paragraph. I just want to comment that, you know, I get another question I have is, so the Nobel Prizes in physics, is that contrived because they wanted to give these folks a Nobel Prize? Or is there really a deep physics nexus that would be sort of stimulating and edifying to understand.

29:31 - A B
I think it's the former.

29:34 - D
I worry about that, yeah. All right, let's try another paragraph.

29:51 - D
I mean, I'm really getting like, this is just kind of a high level vibe thing. How long is this paper?

29:57 - D B
Yeah, I don't know. You can see how big the thumb is. It's not that big. So there's quite a, there's, you know, several more pages and maybe it gets more in depth later.

30:08 - A B
I don't know. Right. But to our previous comments, that's pretty funny. They say that it mimics functions such as memory and learning cognitive functions, but physics made that possible.

30:21 - D B
Oh, yeah, they're doubling down on the physics thing here.

30:26 - D
Well, I mean, electronics is a part of physics. Yeah, man, it's, it's not a leap. I mean, I remember in physics two, we, we learned about some circuits.

30:37 - A B
So it's true.

30:39 - D B
And, you know, theory of information keeps popping up in physics, too. It all fits together, one way or another. Well, you want to vote on this now, or should we Read one more paragraph? Or what do you all think?

31:03 - D
It looks like it's an easy Read. We could Read one more.

31:09 - D B
It could hurt us. Actually, yeah.

31:11 - E G
what Dr. B and I were talking about before is this looks like a fun Read, an easy Read. It's digestible.

31:22 - D B
Okay, well let's take a look then at this one, and I think we'll finish the section at least and go from there. Yeah, it's super easy.

31:37 - Unidentified Speaker
Yeah, it's pretty high level.

31:42 - D B
Well, this is the whole thing about people say that with ordinary software, you tell the machine exactly to do, but with AI, nobody even knows how it does it.

32:06 - D
I know how it does it.

32:09 - Unidentified Speaker
I said no one, but I meant no one except D.

32:15 - A B
To your point, even neural networks are black boxes. I think they're inventing better ways now to peek inside of them.

32:26 - D
But they're not black boxes.

32:29 - A B
Neural nets aren't really black boxes.

32:33 - Unidentified Speaker
No, no, no.

32:34 - Unidentified Speaker
But they're definitely harder to understand what's going on than tree-based models, right?

32:41 - E G
True. You can track every token.

32:44 - Unidentified Speaker
It exists in memory.

32:47 - D B
But what the heck does it mean?

32:50 - Unidentified Speaker
It's millions of numbers. Probability. Well, okay, it's processed data.

32:54 - D
So they it and flatten it and, and, and stick it in a, and stick it in a little piece of memory. But I mean, you, there is no reason why we could not sit down and figure out exactly what's going through the RAM if we wanted to.

33:09 - A B
So a couple of weeks ago, right. We were looking, remember we were adjusting temperature and it was the other value.

33:17 - D
Yeah. I have no idea what the, how they do that.

33:20 - Unidentified Speaker
So like in that example, we were adjusting values in real time.

33:24 - E G
and we couldn't tell there was no idea what why that was happening right the relationship to top and temperature is not well defined to me right but what that does is that changes your probabilities right because it's it's really kind of guessing the next word I think it changes not as much the probability but the perturbation the variation where a softmax is being used to kind of flush one out when you're using that temperature, your Softmax is basically allowing for some distribution rather than forcing one thing to the top. Right.

34:07 - A B
But like as an example, we were testing like what was the minimum, D, what was the other metric? It wasn't the temperature, it was...

34:19 - D
The top. The top.

34:20 - Unidentified Speaker
Okay.

34:21 - A B
So we were adjusting that, but we were like trying to find the minimal value. Cause like, remember there was like other foreign language references brought in. So we kept trying to like get it down to the most minimal one where you wouldn't bring in like the foreign words, if I'm not mistaken.

34:37 - D
But anyway, it was like, yeah, it was bad. It was, I don't think it was even foreign.

34:42 - A B
I think it was just maybe foreign characters or something.

34:45 - D
It was white gibberish. Right. I don't know. It may have been a subliminal message. We all may be infected now. I don't know. One day we're going to wake up and we'll be slaves to the AI.

35:00 - D B
All right. Well, so then the question then is how much we want to Read the rest of this article? You just pick a rating between one and five. One is definitely don't want to Read it. Five is definitely do. Three is not sure, and then two and four are sort of intermediate.

35:23 - Unidentified Speaker
Put your vote, your number in the chat, and I'll add them all up. Yeah, A, I think you're right.

35:35 - D
I think it does have something to do with the, you know, the probability matrices and how they affect that probability matrix with the inference, with their softmax or whatever.

35:57 - Unidentified Speaker
I'm going to put in a four, I guess.

36:03 - D B
Now, we've only got four votes. Anyone else want to give a number or say a number if you don't have access to the chat.

36:14 - D
When I think of black box, I think about how did they really train it? What spin did they put on the...

36:22 - A B
But if you were to think about that though, like how many, you know, how many parameters were in the chat GPT-4, right? Like, so is it, do you really have the ability to kind of net, like if you ask it a particular prompt, one topic, right? And you get like, you don't exactly know where that came from, right?

36:46 - D
Yeah, it's 20 gigs on my hard drive. That's where it came from

36:52 - A B
But you can't query it, for example, to know exactly where it came from, right?

36:58 - D
I tried, I think I can. I think I think we could. Absolutely. Yeah, we might have to monitor where it reads to understand the structure but no I don't think it's that I don't I think that what what happens is you know because it's it's built a matrix right and so you know we're what we're saying is we don't know how it pulls that matrix up out of the hard drive and puts it in the RAM that's that's what I'm hearing when when people are saying that they don't understand how that works but it's it's finding the token and the tokens and And it's going to those memory locations. And based off of the sequence of probabilities that you get, it's going to predict the next word it wants to say. That's what it's going to do.

37:49 - Unidentified Speaker
Based on a lot of parameters that went in beforehand to get it.

37:55 - D
Based off of the most probable next word to say.

37:59 - A B
Because it's a machine.

38:00 - D
It can only do one thing at a time. It's, it's, it's not, it's not dual threatening you. It's, it's going to, it's going to find that probability of the next, that next word.

38:08 - A B
And that's what it's good at is the next word, the next word.

38:13 - D
And then when it puts it all together, it might go back and check to see if the probabilities are right. I mean, they've got other algorithms that I would say are black box.

38:23 - Unidentified Speaker
I'm not sure how they're doing that, but I don't think it's, it's not too that we couldn't break it down?

38:33 - A B
Yeah, I'm not saying that it can't be. I think we could explain all the different components of it, but it's definitely harder to peek into than a simple decision tree or something like that, right?

38:49 - D
A simple one, but a complex one would be very hard to go through too, right?

38:56 - E G
Decision trees are basically algorithms based on certain criteria or models that you're going to set, and each model would have to be viewed in its own. But I think what happens is when we start looking at LLMs and neural net models as precursors to it, it's the number of pieces of information that we need to interrogate to what that node is doing. Because in an LLM, and the reason they require these huge GPUs is because of all of the processes that they have to parallelize it.

39:41 - Unidentified Speaker
There's just so much information that you have to sift through.

39:46 - E G
Sift through. It's sifting through it fast.

39:49 - Unidentified Speaker
That's right. It's sifting through it fast.

39:53 - D
And that's why, that's why we have a hard time that's why we think that it's a black box it's because you know if we had to if you asked it a simple question you know like I don't know like what's the what's the population of arkansas well it's gonna process so much information so in that regard you know it might take us three days to go through its memory banks to get that out, where it can get it out in three seconds. If that's what you mean, that it's thinking so much faster than we can.

40:34 - A B
I guess, I guess, hmm, that, sorry, why then, why, because I don't know, like when we run into hallucinations, I feel like we run in, like I run into them enough and have seen them in like real world applications where it's like, there's no, like, you can't explain where that came from, right? So I don't know, like, doesn't the presence of hallucinations in these kind of, I don't know, kind of nod to how unexplainable they are at times?

41:07 - Unidentified Speaker
Well, okay.

41:08 - D
So I think that hallucinations is, is really based off of how the data is in the system. Like for instance, if you're talking about a hallucination at the beginning of the chat or the end of the chat, I think there are two different types of hallucinations. And when we say hallucinations, is it really a hallucination or is there something that's so similar, if the probabilities are so close, the softmax settings as E was talking about, is building our probability matrix that, although it's not the desired response, it's still a probable response. It's not like a complete hallucination. It didn't just change subject and start talking about ice cream. Right.

42:03 - A B
So go back to your example. I asked, what's the population of Arkansas? And I said, give me the information. Give me a reference. That sounds right, and it gives a reference that looks right, but it's completely fake, right? And that's it.

42:23 - D
Oh, sorry, go ahead. Oh, I know what that's from

42:26 - E G
One of the things that you have to remember is this is trained on a ton of public data. And those who've been working with computers since the 80s knows garbage in, garbage out.

42:37 - Unidentified Speaker
It's dirty data. We're going to have instances of data where it's going to make decisions, assuming that that data is correct, when it isn't.

42:53 - E G
So I think hallucinations are nothing more than bugs. We have bugs in programs, there's bugs in the data.

43:04 - D
And also, also the, so when, So there's kind of it's almost like it's it's almost like a train wreck. OK, it's like once the train wreck starts, you can't stop it, it's too late. Once you push, once you push enter and send it the prompt, it's got to come up with the answer. It's definitely not going to say I have no idea. I can't find that information anywhere. Or can you reframe your question or something? That didn't happen in the beginning. Now they're getting better at that. So there's this interface, this chat interface where they hard code things in and say, you must do this, you must do that. And so you're kind of, it's like a train wreck. Once you tell it to give you some information that it really can't give you, it's gonna give you something because it's being made Because it's optimized to give you an answer.

44:06 - Unidentified Speaker
That's right. It has to give you an answer because, you know, they're, you know, they're trying to make these things useful.

44:13 - D
And so, you know, I think that that, that can happen too. It's like, it really doesn't have a choice, but to give you this answer. So it gives you a probable answer based off the information it has. And, and it might just come up with a citation to something that doesn't exist. Because probably, if there was this probable information that it had, it would be in something that would be like this. It would be in a publication that had a name like this. And I think that has something to do with the interface that's built on the front end. Instead of the backend.

45:01 - E G
Could that also be because of the prompt engineering? Do we actually, when we provide the prompt, do we tell it to validate each of the pieces? If you have questions, please ask them of me to help guide towards an answer.

45:21 - D
You'll get so few hallucinations if you turn it's temperature all the way down, it rarely will hallucinate. There are some things that are really hard for it not to hallucinate. You know, especially if it's something that normally goes together, but you're trying to keep them separate. But if you turn the temperature down, it rarely, it rarely, it rarely hallucinates. Even though, even the older models, gosh, can you believe I'm saying older models? Yeah. Even like chat GPT 3.5 that came out just the other day is already being replaced.

46:04 - Unidentified Speaker
Yeah.

46:04 - D
I'm messing with five already and I broke it. I broke five.

46:10 - A B
Yeah.

46:11 - D
It starts talking jibbers after the chat goes on. All right.

46:17 - D B
Well, continuing the process, I got this item here. This is not a specific reading. It's a course. I don't know what would be involved in doing it. Could you do a segment together in the meetings, like we would Read something?

46:38 - Unidentified Speaker
I don't know. But anyway, let's take a look at this.

46:44 - D B
and see if there's anything there to talk about or evaluate. So does anyone know how long, like if you go to these little courses, this one's a IBM Skills Network How, I mean, is it a...

47:14 - D
I mean, I'm sure it will tell you how many hours, but I bet somewhere it's going to tell you that.

47:22 - A B
Do you have to sign in? Yeah. It looks like you have to sign in. I've done some of them in the past. I think they're pretty quick.

47:32 - D
I know on Udemy, I took a prompt engineering course and, you know, where they just, you know, basically taught you all that stuff and I've some testing on my own to see, you know, if you get better responses, if you, if you say this, or if you do this, and I found out there's no real logic to it. I mean, you can just, you know, like you guys seen some advanced prompts, right?

48:03 - D B
Yeah, I mean, pretty, pretty long.

48:05 - D
Yeah, it's, it's, it's like immaculate prompts. I mean, he writes this I mean, he's in a conversation with this thing, you know, and he's talking really nice to it and polite and, you know, he's treating it like his favorite Corvette, okay? You know how you get in and you sit there.

48:22 - D B
I saw an article, they were quoting one of the big shots of one of these AI companies saying that the amount of money that they spend in terms of computing power and energy and so on to process the parts of the prompt that are things like, thank you and please is millions of dollars. Wow. I Read that too.

48:43 - Unidentified Speaker
And I don't think it, I haven't heard it.

48:47 - D
Yeah.

48:47 - E G
Please and thank you. Yeah.

48:49 - D
And I'm always doing that too. I have to stop. I have to stop.

48:55 - Unidentified Speaker
I didn't even think about that. I'm always saying thank you. That's great. I'm burning up the plant.

49:04 - D B
Well, I guess the thing to do is for somebody, maybe me or somebody else, to register and log in and then look at the course, and then we can talk more about it. Would anyone like to do that for next time? Register for this, and you can step us through the first little bit of it, or you can step yourself and us through the first little bit of it, and then we can decide whether we want to go through it in detail.

49:39 - D
I mean, it's in the minutes, right? The link to this?

49:42 - D B
Oh, yeah. Yeah, definitely.

49:43 - Unidentified Speaker
I mean, I'll try, but so like for my undergraduate capstone, I used IBM stuff, and so that I might have used all my free stuff. So I'll try it and see what I can come up with by next week.

49:56 - D
okay let me do it without paying yeah okay all right so so this one I'll make a note here which I was gonna say that earlier too it's like is he already gone yeah he's gone who is it G Oh Y Y is already gone I was gonna I was gonna tell him something at all emailing.

50:25 - Unidentified Speaker
Yeah, I did my capstone and their cloud computing thing. And

50:33 - D
You know, I had like six months free and or whatever, and I was a student that I think they gave me extra time, I may have already used up. They're probably not going to give me nothing ever again.

50:57 - Unidentified Speaker
We'll find out. All right. Well, D, I can like email this to you or you can check the minutes or Yeah, if it's in the minutes, I'll get it.

51:07 - D
You don't have to do anything extra.

51:09 - D B
Yeah, I'll even bold bold it if you may help you find it.

51:14 - D
Yeah.

51:19 - D B
OK. All right, well, actually, it's a 451, so we're kind of done, I guess. Do you want to just take another few minutes to look at another entry, or what do you think?

51:35 - D
It's up to you all. I'm OK either way.

51:40 - D B
All right, well, actually, this is, I'm going to go here. I think this is one of those videos that we were watching. Three blue, one brown. Yeah, same guy. This is on the animated math of neural networks.

52:00 - D
If he's got a course on how to make stunning videos, I think we should take it.

52:09 - E G
Yeah, I love his videos.

52:10 - D
Yeah. I don't know what to make of this.

52:14 - D B
Here's a list of, these are a list of videos, right? Well, why don't I just pick the first one and we'll listen to a minute or two and then decide or Read it.

52:30 - Unidentified Speaker
Oh, you can Read it. Okay.

52:32 - E G
I think we did that video or at least.

52:38 - D B
Well, it's not named a chapter, but maybe.

52:44 - E G
Yeah, if you play it, I think that was the first video we saw, or at least I watched it.

52:58 - Unidentified Speaker
I thought that was one. Oh.

53:03 - D
Yeah, did we watch this? Maybe. A year ago or two years ago?

53:09 - Unidentified Speaker
Something like that. We were talking about how they recognize numbers. They break apart the numbers.

53:15 - D B
All right, let's take a look. This is a three. It's sloppily written and rendered at an extremely low resolution of 28 by 28 pixels, but your brain has no trouble recognizing it as a three. And I want you to take a moment to appreciate how crazy it is that brains can do this so effortlessly. I mean, this, this, and this are also recognizable as threes, even though the specific values of each pixel is very different from one image to the next. The particular light-sensitive cells in your eye that are firing when you see this three are very different from the ones firing when you see this three. But something in that crazy smart visual cortex of yours resolves these as representing the same idea, while at the same time recognizing other images as their own distinct ideas. But if I told you, Sit down and write for me a program that takes in a grid of 28 by 28 pixels like this and outputs a single number between 0 and 10, telling you what it thinks the digit is. Well, the task goes from comically trivial to dauntingly difficult. Unless you've been living under a rock, I think I hardly need to motivate the relevance and importance of machine learning and neural networks to the present and to the future, but what I want to do here is show you what a neural network actually is. Assuming no background, and to help visualize what it's doing, not as a buzzword, but as a piece of math. My hope is just that you come away feeling like the structure itself is motivated, and to feel like you know what it means when you Read or you hear about a neural network, quote unquote, learning. Okay, that's enough to get started. I think this sounds similar to what we've looked at before, but this is definitely not the same Oh, it is chapter one.

55:00 - D
I think that we did it. I enjoyed it. I thought it was really important to kind of learn how that neural network worked. I remember in Dr. M's course when I was an undergraduate, I had to really get down and break it down. I actually had to make my own nodes and all that to really grasp it. And this is really good.

55:26 - D B
It's a really good video. Well, I don't, I'm just, you know, I don't think it's exactly the same. So I'm really kind of confused about what this is. Well, then it could be. There's more than seven seconds here.

55:45 - D
I watched it already and it wasn't in this group. So I did some of their videos out.

55:56 - D B
There's nine videos here. There's only seven. Oh, here. This one's named the chapter. So I don't know. Well, I don't know. What do you think? Should I delete this from the list? Should we review it or do it? It's different or what do you think? I don't see anything wrong with watching it. All right, well, let's see. You want to evaluate that first one for a more careful viewing? Just rate it from Well, there's only not too many people.

56:48 - Unidentified Speaker
Yeah, we're slam pickings now, guys. I don't want to wait. All right.

56:52 - D B
Well, why don't the three of us go ahead and evaluate it and I'll just make a note so it's not. So we have some record of it. We are talking about a vote.

57:09 - Unidentified Speaker
Yeah. OK.

57:10 - D
So just give me a number from 1 to 5.

57:17 - D B
5 is definitely you want to Read the whole thing or watch the whole thing. You definitely don't. Three is don't know. E had to step away, so we lost one of our voters. Four people, I think.

57:40 - D
Oh, he's back.

57:42 - D B
I can go to the chat here if you want.

57:48 - D
It's like, I know how the neural network works.

57:56 - D B
All right, what do we got here? 443, I'll get a 3.5. Comes out to 11, 14.5 over, Or 3.625.

58:22 - Unidentified Speaker
OK, well, there we go.

58:42 - D B
I think we're at a good stopping point and I guess I'll see you all next time. Bye guys.

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