Відео

Exited!!

I totally Agree. There is no Free lunch. OpenAI, Google and others are benefiting from giving free access to their models They get feedback from millions of people they use to improve their proprietary models more and more.

Why am i watching 4 scientists complaining about how they can’t find similar research groups and yet you all have been trained to take the human out of you. You guys talk about connections but none of you will take the time to establish an equally respectful connection with let’s say someone me and discuss in layman’s terms just what you said in this podcast in about an hour. What’s your point. You’re also bottom tier scientist and the likes of you can actually save science. I know you guys want these big jobs but remember this science was invented to answer humankind’s questions not do all that hard work to sit here with a bunch of a holes that mainstream science doesn’t even accept. Just do your job and educate, don’t worry about your lucrative jobs. Also you have all the right to have a lucrative job end goal, but please don’t lie to people. Hope you read that a few times before you get angry and respond because I feel your authentic existence is the only way you can be relevant and not “et all.” Grow some relatable personalities while you’re at it. Your lack of relatability to daily emotional life and your fundamental troubling belief that these are separate individual phases in actual true science. Just like context is important in a headline claiming to report the truth but at the same time prompts the audience when to clap for the recorded episode. When the fuck did you guys lose any practical logical and scientific thought process? Was it when views on UA-cam became more important? What a waste of your parents dollars to go these prestigious universities, wish I had even one semester with privileged even AP classes you guys were getting in public high schools even if I had better grades. I was targeted for military recruitment. Hope you guys see what I’m saying. Willing to discuss this openly.

There’s no room for courtesy in science. You guys self imploded because neutrality doesn’t advance science

Link to the slides docs.google.com/presentation/d/1VRDLt78bkYArC7y6WBnoxis79Y6gOrquFnRUYPkchYk/edit?usp=sharing

Can you upload source code to GitHub ?

ua-cam.com/users/redirect?event=video_description&redir_token=QUFFLUhqbUlQb05zTWszT1VHWF94aEt4aTBEX0FCYld1d3xBQ3Jtc0ttN29JcEp1Wmo4dlprTnZHdlNxdGNLb1M3dFVhU1FxUDhuSGNNM0Y0el91aHpJRV94cHotdENRV0otWmNPeGFuYUhmdDNkV0ZpU3dBR1ZSWlQ4MTdHS0NKUDdvOWllZnhxWWt2VVRiOXdQQ0FKS2NCTQ&q=https%3A%2F%2Faisc.ai.science%2Fevents%2F2019-08-12&v=jio04YvgraU ---> link for slides doesn't exist or has been moved.

Cut the Bull shit short next time

Excellent Lecture/key note

looking forward to LLMMs (large language middle managers)

Rich compilation of wisdom nuggets. Great channel.

Really nice I have been searching around no such updated content on this topic please looking forward

Thanks for the Review :) 1:36 One Question! If we were to talk about "Generalization Gap", shouln't Train Score be higher for LBs?

Share the repository dadash!

44:14

No cybersecurity...?

source code and dataset please

Could you please share the ppt？ The provided link is no longer valid.

It's sad that he kinda dodged to tell the full answer (or at least his speculative answer) to the question at 20:45, I'd like to hear more on this.

Looking forward to it Amir!

maven.com/forms/30a683

This was very insightful. Thank you !!!

Can anyone please provide the notebook for this talk?

Garbage in garbage out

Thank you

Thanks very nice 🌹, How to use open source LLM from hugging face locally using llama.cpp or ctransformer library with PandasAi without internet connection offline mode? for example codellama or pandalytic gguf type

This is an incredible series! Thanks for sharing, I learned a lot :)

Great lecture! Do you know what the 3D Scharr filter would be for 3x3x3?

0:00: 📚 The presentation discusses the concept of creating an oracle that collaborates with humans, emphasizing the need for close collaboration between humans and machines in knowledge-intensive workflows. 4:17: 🤖 The video discusses the evolution of AI, from descriptive analytics to the emergence of narrow AI and the potential of large language models in crossing the narrow AI chasm. 8:03: 🤖 The video discusses the development of language models and their potential to enable machines to perform complex tasks. 11:41: 🤖 A framework called Sher AI is being developed to augment knowledge-intensive workflows using large language models and data connectors. 15:22: 🤖 The video discusses a multi-agent system project aimed at creating a thinking companion to assist humans in problem-solving and decision-making. 18:44: 📝 The video discusses opportunities for contributing to a project, current tasks, and future plans. 22:15: 🔧 Setting up and using Shera for virtual environment and database integration. Recap by Tammy AI

Will the notebook link be provided?

Thanks for sharing!

Grear video, Thank you so much. But if you can please explain or provide the streamlit code pleaaase

Can I get the slides of this lecture? Thanks a lot

'promo sm'

very good presentation. very interactive session. thanks

@35:01 A question: How do we know that p(x|z) is normally distributed? I can see that p(z|x) must be normal, but not p(x|z). I am asking this question because I see it in a lot of VAE related talks, stated without any proof or justification.

where can i find this slide

Great intro. thanks!!

I am really excited by your project. Thanks for sharing. I wish the best from Morocco

I know it’s “old” now (by today’s standards!) but I really enjoyed this conversation. Thank you for posting it 🙏

could you please share the slides

There are two mistakes on slide at 40:11: 1. u is content bias, not position bias. Vector u is firstly appears in term (c) from the paper. And term (c) governs a global content bias according to aurhors' words. 1. v is position bias, not content bias. By the analogical reason. It appears in term (d) from the paper. According to paper authors, term (d) encodes a global positional bias.

I don't understand why "v" is content bias and why "u" is positioal bias. Vector "v" is multiplier of R[i-j] so it's more similar to positional bias, because it's like linear function on relative positon R. And "u" is content bias because it's like linear function on k.

"41:56" that guy can understand it perfectly and use his mobile too.😅

hey thanks for the great intro but i cant find any slides in provided link or on your website..can u provide available links for slides?

thanks

how to apply this architecture to predict deformation of solid structures under static load ? this problem has long spatial dependencies which are difficult to address with local edges in the graph.

Asc

Good presentations. Thanks Gordon Can I have your twt profile, I would love to get connected with smart minds

windows install failed: ERROR: The tar file (C:\Users\xx\AppData\Local\Temp\pip-unpack-plha6gd0\tf-encrypted-0.9.1.tar.gz) has a file (C:\Users\xx\AppData\Local\Temp\pip-install-bj8xrdtv\tf-encrypted_08c1f7c5ab8c46a19975b7aaec907caf\tf_encrypted/operations/aux) trying to install outside target directory (C:\Users\xx\AppData\Local\Temp\pip-install-bj8xrdtv\tf-encrypted_08c1f7c5ab8c46a19975b7aaec907caf)