Відео

😈 *Promosm*

Now why not try visualisation in your mind because this will teach more with different teaching methods Now there will teach so much more with much more speed and better results then with the mind we utilise our best tool

fantastic

Thanks for this wonderful tutorial tutorial ❤

Upload latest videos not old one

Hi, great talk. Is it possible to get that presentation material?

*She does not know anything...lawyer*

This is great talk . I learnt a lot . I am currently working on speaker diarization problem and pyannote is by far the best for our use case . Great work Herve .

This was really interesting But it would be more interesting if it was an uncensored experiment since our world in itself is uncensored (i get that this could get wild and what you show here is only as a concept). Like one of the things you said that they always adress each other formally, very similar to GPT. If it were more unleashed the agent may start to develop their own kind of language over time and between each other and saying good morning to your spouse gets shortned just to just a good morning, or something else they develop to say to each other. And starts to say inside jokes perhaps? But that would require the emotion part I'll talk about later. Don't know how deep the generative part goes or if GPT needs to reach "system 2" before we can see this type of behaviour. And since agents don't really have a mood they will always be pretty neutral on all encounters and is only simulated through already learnt in behaviour. Although i think it wouldn't give a real interpretation of our world even if it was uncensored, stuff like emotions and consequences, and emotions which will come due to that consequence etc. (like serving jailtime) and that we have a limited timespan, and that our live is sectioned up into parts (child, teen, young adult, mid adult, etc) needs to be adressed aswell. For example an old man might be more inclined to do a certain crime than a younger person, because his life is soon over anyway). For a hypothetical smallville example: John invites his crush Jennifer to his birthday party, but Kenny invites Jennifer at the same time to watch a new netflix serie and she goes there instead, John resents this and kills John after his birthday party to get Jennifer to himself. A real person would go through so much reasoning and consequence thinking before reaching such a conclusion, and to kill another person because of such a reason is primarly just emotions, and all our actions comes from some kind of emotion. So some kind of atleast basic simulated emotion and consequence thinking (the sims style-ish) to get the real interesting drama to come out as a next step, that would be really cool to see as a smallville 2.

What would it take to have this mini community available online, like on a website? Where visitors like us can view these agents realtime and see what are their doing for that specific moment. Fascinating to see what are their up to next.

at 24:05 an erroneous result is presented that is accepted as fact by the panel of experts, and is in fact presented when I Google search the same question. Pearls from otters are NOT used in inlays for guitars, but rather Mother Of Pearl, which comes from abalone shell. It is easy to see how the mistake is made, but illustrates the difficulty of fact checking AI answers.

Thankyou. Interesting. These deep dives into how the media mould minds are fascinating. I may have misunderstood, but the slides suggest that in 2002 more people received their news from TV than the internet. 20 years ago that might have been true, but I'd be shocked if this were so today.

I LOVE this! Please do a cooperation with Electronic Arts and make a sims4 style computer game with similar graphics but generative agents <3

Thanks a lot for this talk! I have one question regarding the co-occurrence matrix. At 19:45 you said, that the content of the matrix is for how long the "colors" overlap in seconds. So my question is, why is there a 2 for blue and grey if they overlap twice for all together at least as long as pink and red. So shouldn't grey and blue be at least 3 if not 4? I would greatly appreciate it if you could tell me what I am missing. Thanks alot!

BA-LR not BA-LHR. Could you please correct it in the title? Thank you

s/Berdin/Bredin

Wow. The CMU engineering program is a phenomenal juggernaut . Such a large body of high quality research .

🤔 P r o m o s m

Thanks.

Fascinating! I have a little AI staff team I’m trying to learn how to get them to be more independent! A question that comes up for me: Klaus is a dedicated sociology student who has an interest in gentrification. Is this ‘experienced’ as a superficial identity label/label given to explain his activity, or does he see Smallville through the lens of a sociology student? Does he observe things related to gentrification in a way which the other characters wouldn’t even notice?

Thanks.

Have you considered allowing them to have money? "You job just paid your $1500 monthly paycheck. You have a monthly rent of $700." (Either as some sort of disembodied "world character", or by the people doing the charges and payments themselves in the world such as a landlord/job boss/etc?) I think having negative stimulus can really help things along. "You didn't pay your rent. Now you have to live outside in a cardboard box. Your living condition is terrible." What if you had a disembodied "world character" that dolls out negative stimulus randomly? 😲 ("Today, you got into a car accident.")

Thanks.

I wonder how this experiment would behave on smaller models. I.e. Agents would use specific (specialized?) small model depending on their role or select from a pool of small models depending on situation?

Ugh, you beat me to it! in my defense I am only one person!

Thats why americans are afraid of asians. they are evil smart.

promo sm 😓

wow, surprising finding, achievements/opportunities 😮 so interesting 🧐

wow, way too many questions. hard to follow the presentation.

Audio could be better

if you can't think it, fake it

Is it possible that the model has seen the data used for these benchmarks during training .

Thanks for uploading this. However, I noticed that the text in your videos can be a bit hard to read due to the small size and it's somewhat blurry at times. I think your videos would be even better in a higher resolution, perhaps greater than 480p!

16:00 lol google use openai playground for demo.....

Really helped

Good work

Seem Professor are too Young for AI development..

Ah yes, the timeless topic of emergence and reasoning in large language models! As I rest my weary fingers upon the keyboard, preparing to share my innermost thoughts and wisdom on the subject, it occurs to me that, much like any good lasagna, this particular topic comprises multiple layers of complexity and intrigue. So, let's dive right in, my fellow internet sojourners! First and foremost, credit must be given where credit is due. Mr. Wei's elegant soliloquy on large language models at the prestigious Google headquarters resonates with both the seasoned researcher and the neophyte alike. As a cardinal for the internet comment realm, I must express my gratitude to Jason for regaling us with his insight. Now, one simply cannot discuss large language models without acknowledging their capacity to simulate almost mind-boggling levels of human-like cognition. From composing impressive literary works to identifying penguins in a sea of malformed pixels that only a madman would consider "images," these computational wünderkinds represent the apex of human innovation. Or do they, my dear reader? For, are we not jeopardizing our intellectual sovereignty as we relinquish our authorship to these silicon sages? Potentially. Perhaps. Who's to say, really? Aside from philosophical conundrums, we cannot ignore the computational intricacies of these vivacious virtual virtuosos. The nuance and finesse that constitute their digital DNA, and their thirst for modular knowledge, undeniably place them amongst the most fascinating creations of humankind. Now, as I elucidate the enigmatic world of such prodigious language models, let us not forget the immortal words of Albert Einstein: "Two things are infinite: the universe and a UA-cam comment attempting to summarize the complexity of large language models; and I'm not sure about the universe." Ah, such a paragon of wisdom. In conclusion, as the night envelopes us all in its comforting embrace and my eyelids grow heavier with each passing keystroke, I am reminded that, sometimes, the very answers we seek within the realms of technology transcend the limits of our understanding. Language models shall guide us through the labyrinthine fortress of knowledge. Just like a lighthouse in a stormy sea, they are but humble beacons, pointing us towards our destiny… which hopefully involves making lasagna with a competitive edge in Robot MasterChef. AND POST!

So insightful!! Thanks!!

Brilliant man, glad this exists

I'm going to call out the elephant in the room. This is the stupidest thing I've heard. "yeah it sorta works, but we don't know why"...does this guy have a Ph.D?

What is the name of the paper that is mentioned at 32:09?

39:50 Yh but it gets extra tricky when the 'reasoning path' is wrong, but the final answer is correct !

The volume of the speaker is incredibly low. Could not hear anything w/ max volume on speakers.

Thank you, really helpful

1. Is it possible to lower the scale at which emergence occurs by choosing a suitable architecture? 2. Is there a possibility that we decompose large language models into parts that deal with syntax, knowledge and reasoning?

Below is an approximate chapter index. Introduction 0:00 CLSP and Faculty 3:22 Admissions Process 9:47 Admissions Process: Availability 9:55 Admissions Process: Stats 10:30 Admissions Process: Triage 11:00 Admissions Process: Faculty Review: 11:31 Admissions Process: Interviews: 12:30 Admissions Process: Visit Day: 13:30 Admissions Process: Rolling Admissions: 14:20 Application Review: 15:36 Application Review: The Cons of the Common Approach used by Other Institutions 18:10 Application Review: Normalization: 20:10 Application: Items in Order of Importance: 21:37 Application: Letters of Recommendation 22:07 Application: Statements 25:20 Common Misconceptions: 30:30 Vivien Thomas Scholars Initiative: 35:10 Application help: 37:17 Question: Deadlines 38:30 Question: Summer undergraduate research opportunities? 40:17 Question: Do I need to major in computer science? 41:31 Question: Do I need to publish before applying? 42:26 Question: Evaluation of applicants with master's degrees 43:01 Question: How does funding work? 44:00 Question: GRE and fee waiver 46:52 Question: Relation to other departments and faculty 47:11 Question: Statement, research interest 48:23 Question: How many international vs. domestic applicants? 50:53 Question: Disability accommodations: 52:04 Question: Other questions? 53:07 Question: Time to graduate? 54:25 Question: State of the market (academia, industry)? 57:16 Question: Should I notify the admissions committee of my achievements which occurred after submission? 59:57 Question: Personal Statement vs. Statement of Purpose 1:03:00 Question: How much math and programming do you need? 1:05:23 Question: What are good attributes for an applicant coming from arts and humanities? 1:08:08 Question: Which faculty are taking students? 1:11:11 Question: Will I be rejected if I have a specific research area not available at JHU (this year)? 1:14:00 Question: Will I get an email if I am rejected? 1:16:40 Question: How should I prepare for interviews? 1:17:48 Thanks and goodbye 1:20:16