ChatGPT as a Python Programming Assistant - KDnuggets
Is ChatGPT useful for Python programmers, specifically those of us who use Python for data processing, data cleaning, and building machine learning models? Let's give it a try and find out.
Stephen Wolfram explores the broader picture of what's going on inside ChatGPT and why it produces meaningful text. Discusses models, training neural nets, embeddings, tokens, transformers, language syntax.
I just wanted to fix my email. ■ AD: 👨💻 NordVPN's best deal is here: https://nordvpn.com/tomscott - with a 30-day money-back guarantee. ■ Code and full conversation: https://www.tomscott.com/fix-gmail-labels-threads/
Script assistant: Laura Conlon
No AI assistance was used, except where noted.
ALTERNATE TITLES:
Crypto and the metaverse aren't the future. AI is.
I just wanted to fix my email.
I tried ChatGPT and had a minor existential crisis
Everything is about to change
ChatGPT is Napster, 24 years later.
ChatGPT is 2023's Napster.
CHAPTERS
0:00 Intro
0:07 I just wanted to fix my email
2:39 Gmail's label system sucks
5:35 Wait, I can fix this with code
7:36 It can't be that good, right?
11:31 Everything is going to change
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(Yes, that was a "Weird Al" Yankovic reference.)
MIT Introduction to Deep Learning 6.S191: Lecture 1*New 2022 Edition*Foundations of Deep LearningLecturer: Alexander AminiFor all lectures, slides, and lab m...
Attention - the beating heart of ChatGPT: Transformers & NLP 4
Transformers like GPT-3 and ChatGPT are incredibly powerful language processing models. They are able to translate, summarise and compose entire articles fro...
GPT3 & Beyond: Key concepts and open questions in a golden age for natural language understandingListen in as Professor Christopher Potts discusses the signi...
Penelope.ai is an online tool that automatically checks whether scientific manuscripts meet journal requirements. It helps editors process manuscripts faster and helps authors polish their work before submitting to a journal.
This is a fantastic piece by writer Ted Chiang about large-language models like ChatGPT. He likens them to lossy compression algorithms: What I've described sounds a lot like ChatGPT, or most any other large-language mo
Automating podcast transcripts on my Mac with OpenAI Whisper
A little section of Upgrade 444 in David Smith’s original Podsearch engine. A while ago, David Smith created a site called Podsearch, a search engine for a few of his favorite podcasts, inclu…
Reinventing search with a new AI-powered Microsoft Bing and Edge, your copilot for the web - The Official Microsoft Blog
To empower people to unlock the joy of discovery, feel the wonder of creation and better harness the world’s knowledge, today we’re improving how the world benefits from the web by reinventing the tools billions of people use every day, the search engine and the browser. Today, we’re launching an all new, AI-powered Bing search...
Getting Started with GPT-3 vs. Open Source LLMs - LangChain #1
LangChain is a popular framework that allows users to quickly build apps and pipelines around Large Language Models. It integrates directly with OpenAI's GPT-3 and GPT-3.5 models and Hugging Face's open-source alternatives like Google's flan-t5 models.
It can be used to for chatbots, Generative Question-Anwering (GQA), summarization, and much more.
The core idea of the library is that we can "chain" together different components to create more advanced use cases around LLMs. Chains may consist of multiple components from several modules. We'll explore all of this in these videos.
🌲 Article:
https://www.pinecone.io/learn/langchain-intro/
📌 Colab notebook:
https://colab.research.google.com/github/pinecone-io/examples/blob/master/generation/langchain/handbook/00-langchain-intro.ipynb
🤖 70% Discount on the NLP With Transformers in Python course:
https://bit.ly/nlp-transformers
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👾 Discord:
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00:00 Getting LangChain
01:14 Four Components of LangChain
06:43 Using Hugging Face and OpenAI LLMs in LangChain
07:13 LangChain Hugging Face LLM
13:51 OpenAI LLMs in LangChain
18:58 Final results from GPT-3
ChatGPT and AI mastery - how to get started in AI.
Big thanks to Brilliant for sponsoring this video! Get started with a 20% discount using this link: https://brilliant.org/davidbombal
How do you capitalize on this trend and learn AI? Dr Mike Pound of Computerphile fame tells us how to ride this wave. If you are into cybersecurity or any other tech field, you probably want to learn about AI and ML. They can really help your resume and help you increase the $$$ you earn.
AI just become Sentient? And will it take your job? Or is AI just a fantastic opportunity for you to get a better job? In this interview with Dr Michael Pound we discuss hype vs reality and get a quick start guide on how to learn AI.
// MENU //
00:00 - Coming up
00:40 - Sponsored segment
02:28 - A.I. Hype // Should we be worried?
03:37 - Amazing but flawed
08:07 - Is it worth it getting into CompSci?
10:02 - Knowing A.I. makes you valuable // Learn A.I.
13:43 - Resources for learning A.I.
15:58 - Should you get into CompSci?
17:35 - Enhancing your career with A.I.
20:16 - The limits of A.I.
24:57 - A.I in academics // How A.I. affects academic work
31:02 - Conclusion
// Previous Videos //
I challenged ChatGPT to code and hack: https://youtu.be/Fw5ybNwwSbg
The truth about AI and why you should learn it - Computerphile explains: https://youtu.be/PH9RQ6Yx75c
// BOOK //
Deep learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville: https://amzn.to/3vmu4LP
// Courses and tutorials //
AI For Everyone by Andrew Ng: https://www.coursera.org/learn/ai-for-everyone
PyTorch Tutorial From Research to Production:
https://www.infoq.com/presentations/pytorch-torchscript-botorch/
Scikit-learn Machine Learning in Python:
https://scikit-learn.org/stable/
// PyTorch //
Github: https://github.com/pytorch
Website: https://pytorch.org/
Documentation: https://ai.facebook.com/tools/pytorch/
// Mike SOCIAL //
Twitter: https://twitter.com/_mikepound
YouTube: https://www.youtube.com/user/Computerphile
Website: https://www.nottingham.ac.uk/research/beacons-of-excellence/future-food/meet-the-team/michael-pound/index.aspx
// David SOCIAL //
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Chemical language models for de novo drug design: Challenges and opportunities
Generative deep learning is accelerating de novo drug design, by allowing the generation of molecules with desired properties on demand. Chemical lang…
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