Finwatcher

[Finwatcher @HKFinwatcher]

@__Inty__ D9YGQE

[Finwatcher @HKFinwatcher]

@autumnrain0822 @op7418 invitation code: D9YGQE

[Tarek Mansour @mansourtarek_]

The Federal Reserve just put out an incredible paper about Kalshi's data.

"Our results suggest that Kalshi markets provide a high-frequency, continuously updated, distributionally rich benchmark that is valuable to both researchers and policymakers."

https://t.co/cw5GrDFse6

[OpenAI @OpenAI]

Today we’re introducing GDPval, a new evaluation that measures AI on real-world, economically valuable tasks.

Evals ground progress in evidence instead of speculation and help track how AI improves at the kind of work that matters most.
https://t.co/uKPPDldVNS

[Jon Erlichman @JonErlichman]

“There is a 50-50 chance AI will get more intelligent than humans in the next 20 years. We’ve never had to deal with things more intelligent than us. And we should be very uncertain about what it will look like.”

~ Geoffrey Hinton

[Nature is Amazing ☘️ @AMAZlNGNATURE]

A short story

[Lex Fridman @lexfridman]

Here's my conversation with Mark Zuckerberg, his 3rd time on the podcast, but this time we talked in the Metaverse as photorealistic avatars. This was one of the most incredible experiences of my life. It really felt like we were talking in-person, but we were miles apart 🤯 It's hard to put into words how awesome this was for someone like me who values the intimacy of in-person conversation. It gave me a glimpse of an exciting future with many new possibilities and fascinating questions about the nature of reality and human connection ❤

Timestamps:
0:00 - Introduction
0:52 - Metaverse
15:27 - Quest 3
30:16 - Nature of reality
34:54 - AI in the Metaverse
51:51 - Large language models
57:49 - Future of humanity

[elvis @omarsar0]

LLMs as Optimizers

This is a really neat idea. This new paper from Google DeepMind proposes an approach where the optimization problem is described in natural language.

An LLM is then instructed to iteratively generate new solutions based on the defined problem and previously found solutions.

It was first tested on linear regression and the traveling salesman problem. Leveraging LLMs with simple prompting match or surpass hand-designed heuristic algorithms. This shows good potential for using LLMs as optimizers.

The idea is then applied to prompt optimization that aims to maximize task accuracy on different tasks like math word problem-solving.

The first piece of the proposed meta-prompt takes in previously generated prompts along with corresponding training accuracies. The second piece includes the optimization problem description with samples obtained from a training set representing the task.

At each optimization step, the goal is to generate new prompts that increase test accuracy based on the trajectory of previously generated prompts.

The optimized prompts outperform human-designed prompts on GSM8K and Big-Bench Hard, sometimes by over 50%!

For math word problem solving, one of the most effective instructions found begins with "Take a deep breath and work on this problem step-by-step".

https://t.co/GsF8fzjevX

[Valeriy M., PhD, MBA, CQF @predict_addict]

A new 150 pages review paper on the applications of machine learning in finance.

#machinelearning #finance

https://t.co/juXVj1Osfo

[Greg Brockman @gdb]

ChatGPT prompting guide from @nytimes: https://t.co/1fVaKW3Haq

[Nata Reznik @bitonika]

WIMMELBUCH
Do you know about such unique books as Wimmelbuch? These are detailed picture books, filled with lively scenes full of characters and mini-stories. They are a real treasure trove for little eyes, sparking curiosity and developing observational skills. I used to buy them for my children. But now, I've decided to create my own Wimmelbuch-style illustrations for my children, exploring a wide range of themes and styles. And here's where you come in! I'd love for you to join in the fun. Do you have any ideas for a Wimmelbuch? Let's see them!

@techhalla @maxescu @robomar_ai_art @gen_ericai @g0rillaAI @Midlibrary_io @chetbff are you in?

#Wimmelbuch #aiart #aiartcommunity #AIcommunity #AIArtworks

p.s… the prompt still needs work, but the one in ALT 👇🏻

[Ethan Mollick @emollick]

This 🤯 is a very big 🤯

I have access to the new GPT Code Interpreter. I uploaded an XLS file, no context:

"Can you do visualizations & descriptive analyses to help me understand the data?

"Can you try regressions and look for patterns?"

"Can you run regression diagnostics?"

[Neuralink @neuralink]

We want to surpass able-bodied human performance with our technology. Using only his mind, here's precision cursor control from Pager (star of Monkey MindPong) achieving 65% and 88% of the median Neuralinker using a mouse. Join us to breakthrough to 110% and beyond!🧠#techtuesday

[Mayo Oshin @mayowaoshin]

I built a GPT-4 'Warren Buffett' financial analyst to 'chat' with and analyze multiple PDF files (~1000 pages) across @elonmusk's Tesla 10-k annual reports (2020-2022)

#gpt4 #openai #investing #stocks #finance

[Harrison Chase @hwchase17]

well it happened

✨the chat-langchain app is completely reproducible in javascript✨

data ingestion, text splitting, embeddings, vectorstore, llms, chains... all through langchainjs

chat-langchain repo: https://t.co/UxPUrMEmAJ

langchainjs repo: https://t.co/HVg0bgEdD1

[elvis @omarsar0]

A lot of the work around ML engineering and LLMs is around data.

Really cool to see this course on data-centric AI covering topics like dataset curation, augmentation, and prompt engineering.

Highly recommend checking it out!

[Alex Rainey @RaineyAllDay]

@mukul0x If people are interested in building one of these Q&A services themselves, but aren't very technical, I just launched a no code embeddings tool called AskAI: https://t.co/jJtYR1B2nJ

You can build your own AI Q&A, with any content in minutes and then share it anywhere on the web.

[mukul @mukul0x]

Introducing researchGPT

An open-source LLM based research assistant that allows you to have a conversation with a research paper!

It’s a simple flask app that uses embeddings + gpt-3 to search through the paper and answer questions

Try the demo here: https://t.co/1DhE7PZ6AY

[Pauline Cx @Pauline_Cx]

I discovered a very useful tool for Jupyter Notebook data analysis and data visualization: PyGWalker

To share to your data friends!

[Erik Solheim @ErikSolheim]

China 🇨🇳 has increased its forest cover by 40% over the last 30 years.