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Andrej Karpathy's Keynote & Winner Pitches at UC Berkeley AI Hackathon 2024 Awards Ceremony
Andrej Karpathy's Keynote & Winner Pitches at UC Berkeley AI Hackathon 2024 Awards Ceremony
At the 2024 UC Berkeley AI Hackathon's Awards Ceremony, the atmosphere was electric as Andrej Karpathy, founding member of OpenAI, delivered an inspiring keynote. Out of 371 projects, the top 8 teams took the stage to pitch their groundbreaking AI solutions. After intense deliberation by our esteemed judges, the big reveal came: up to $100K in prizes were awarded, celebrating innovation and creativity in AI for Good. Missed the live ceremony? Relive the excitement and watch the future of AI unfold! For more SkyDeck news, connect with us on ► LinkedIn: https://www.linkedin.com/company/skydeck-berkeley/ ► Instagram: https://www.instagram.com/berkeley_skydeck/ ► Twitter: https://twitter.com/SkyDeck_Cal Chapters: 0:00 Welcome 0:19 Caroline Winnett 4:05 Andrej Karpathy Keynote Speech 22:20 Pitch Overview 23:29 Judge Introductions 24:43 Revision 31:23 Agent.OS 38:54 Skyline 44:32 Spark 51:35 HearMeOut 57:05 Dispatch.Ai 1:02:04 ASL Bridgify 1:08:57 Greenwise 1:13:35 Special Prize 1 1:17:24 Special Prize 2 1:19:30 Special Prize 3 1:20:45 Special Prize 4 1:23:15 Special Prize 5 1:24:27 Special Prize 6 1:26:00 Special Prize 7 1:27:24 Special Prize 8 1:30:10 Grand Prize Winner #AIHackathon #UCBerkeleyAIHackathon #BerkeleyAIHackathon #Innovation #TechForGood #BerkeleySkyDeck #AI #LLM #AIforGood #HackingForGood #UCBerkeley #Startups #awardsceremony #Hackathon #TechInnovation #AndrejKarpathy
·youtu.be·
Andrej Karpathy's Keynote & Winner Pitches at UC Berkeley AI Hackathon 2024 Awards Ceremony
PDFTriage: Question Answering over Long, Structured Documents
PDFTriage: Question Answering over Long, Structured Documents
Large Language Models (LLMs) have issues with document question answering (QA) in situations where the document is unable to fit in the small context length of an LLM. To overcome this issue, most...
·arxiv.org·
PDFTriage: Question Answering over Long, Structured Documents
Imitation Intelligence, my keynote for PyCon US 2024
Imitation Intelligence, my keynote for PyCon US 2024
I gave an invited keynote at PyCon US 2024 in Pittsburgh this year. My goal was to say some interesting things about AI—specifically about Large Language Models—both to help catch …
·simonwillison.net·
Imitation Intelligence, my keynote for PyCon US 2024
The moment we stopped understanding AI [AlexNet]
The moment we stopped understanding AI [AlexNet]
Thanks to KiwiCo for sponsoring today's video! Go to https://www.kiwico.com/welchlabs and use code WELCHLABS for 50% off your first month of monthly lines an...
·youtube.com·
The moment we stopped understanding AI [AlexNet]
What is a "cognitive architecture"?
What is a "cognitive architecture"?
The second installment in our "In the Loop" series, focusing on cognitive architecture
·blog.langchain.dev·
What is a "cognitive architecture"?
What is an agent?
What is an agent?
Introducing a new series of musings on AI agents.
·blog.langchain.dev·
What is an agent?
turbopuffer: fast search on object storage
turbopuffer: fast search on object storage
turbopuffer is a vector database built on top of object storage, which means 10x-100x cheaper, usage-based pricing, and massive scalability
·turbopuffer.com·
turbopuffer: fast search on object storage
Extrinsic Hallucinations in LLMs
Extrinsic Hallucinations in LLMs
Hallucination in large language models usually refers to the model generating unfaithful, fabricated, inconsistent, or nonsensical content. As a term, hallucination has been somewhat generalized to cases when the model makes mistakes. Here, I would like to narrow down the problem of hallucination to be when the model output is fabricated and not grounded by either the provided context or world knowledge. There are two types of hallucination: In-context hallucination: The model output should be consistent with the source content in context.
·lilianweng.github.io·
Extrinsic Hallucinations in LLMs
How to self-host and hyperscale AI with Nvidia NIM
How to self-host and hyperscale AI with Nvidia NIM
Try out Nvidia NIM in the free playground https://nvda.ws/4avifodLearn how to build a futuristic workforce of AI agents, then self-host and scale them for an...
·youtube.com·
How to self-host and hyperscale AI with Nvidia NIM
Consistency Large Language Models: A Family of Efficient Parallel Decoders
Consistency Large Language Models: A Family of Efficient Parallel Decoders
TL;DR: LLMs have been traditionally regarded as sequential decoders, decoding one token after another. In this blog, we show pretrained LLMs can be easily taught to operate as efficient parallel decoders. We introduce Consistency Large Language Models (CLLMs), a new family of parallel decoders capable of reducing inference latency by efficiently decoding an $n$-token sequence per inference step. Our research shows this process – mimicking human cognitive process of forming complete sentences in mind before articulating word by word – can be effectively learned by simply finetuning pretrained LLMs.
·hao-ai-lab.github.io·
Consistency Large Language Models: A Family of Efficient Parallel Decoders
The AI Backend
The AI Backend
The AI Backend * work in progress, please provide feedback so we can improve Just like in 1995 it was obvious that every business needs an internet presence to stay competitive, in 2024 it's obvious that every software needs intelligence to stay competitive. Software products generally have 3 c...
·docs.google.com·
The AI Backend
The Surprising Power of Next Word Prediction: Large Language Models Explained, Part 1 | Center for Security and Emerging Technology
The Surprising Power of Next Word Prediction: Large Language Models Explained, Part 1 | Center for Security and Emerging Technology
Large language models (LLMs), the technology that powers generative artificial intelligence (AI) products like ChatGPT or Google Gemini, are often thought of as chatbots that predict the next word. But that isn't the full story of what LLMs are and how they work. This is the first blog post in a three-part series explaining some key elements of how LLMs function. This blog post covers pre-training—the process by which LLMs learn to predict the next word—and why it’s so surprisingly powerful.
·cset.georgetown.edu·
The Surprising Power of Next Word Prediction: Large Language Models Explained, Part 1 | Center for Security and Emerging Technology
A Survey of Techniques for Maximizing LLM Performance
A Survey of Techniques for Maximizing LLM Performance
Join us for a comprehensive survey of techniques designed to unlock the full potential of Language Model Models (LLMs). Explore strategies such as fine-tunin...
·youtube.com·
A Survey of Techniques for Maximizing LLM Performance
OpenAI Platform
OpenAI Platform
Explore developer resources, tutorials, API docs, and dynamic examples to get the most out of OpenAI's platform.
·platform.openai.com·
OpenAI Platform