Tommy ✓
@Shaughnessy119 · joined 26 Nov 2013
Early Stage Investor | Founding Partner @Delphi_Ventures | Co-Founder @Delphi_Digital | Host @PodcastDelphi | My Opinions
69 950Followers
4 269Following
15 345Posts total
4.1MViews on collected posts
โพสต์ล่าสุด
กำลังค้นหาโพสต์ใหม่…
What do we think China pays Bernie to campaign to slow down all AI progress in the U.S!?
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If you want to run a local AI model for some things the Hermes desktop app automatically scans all of your hardware for which models you can run
Absurdly easy. One click.
Then you just talk to Hermes and tell it what you want that local model to run
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@Shaughnessy119 @IridiumEagle @ambient_xyz Love it!
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Grr. Cant edit this now but at least you all know I wrote it not AI https://t.co/DjObzG1CUq
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Ambient is the open source, proof of useful work AI project the world desperately needs 🌎🤖
I couldn’t be more excited for @IridiumEagle to showcase @Ambient_xyz
A few years ago, our team met Travis. He instantly struck our entire team with a sense of extreme technical ability h
43.6K views · 93 likes
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In just two weeks: some of the largest supply-chain attacks in history, a major commercial LLM sabotaging legitimate ML research requests, and a US export ban kneecapping the world's access to a major model release. More than ever, we need Open, Verifiable Inference.
Today we h
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1:18
@Shaughnessy119 @Scobleizer And no link to the original post…
Here it is.
https://t.co/ZJHOatjO0t
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Also @stephen_wolfram if you're ever interested in a long form podcast to walk through your thoughts, we'd love to host you on @Delphi_Digital's podcast!
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I think it's incredibly cool that a gigabrain like @stephen_wolfram would open source his thinking on ChatGPT
This has been the single best resource I've found so far on learning about @OpenAI's ChatGPT, LLMs and Neural Nets
Disc. I def got things wrong
https://t.co/79I7YQaVzN
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I am not an AI researcher but the post made me realize LLM's are nowhere near the AGI or Terminator level AI intelligence some fear
Of course it's on the path, but LLM's are probabilistic models focused on continuing sentences.
They are really good at it, but not AGI (yet) http
34.6K views · 130 likes
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So is ChatGPT similar to a human brain?
His conclusions:
- The neural net architecture may be similar
- Training of LLMs way less efficient vs human brain
- ChatGPT has no loops to go back and recompute data like humans can which severely limits its computational capability
32.7K views · 95 likes
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Meaning Space
Stephen shares that in ChatGPT, text is represented by an array of numbers in a meaning space.
He goes on to describe that the trajectory of what words come next is far from a mathematical or physics like law we can rest our hats on. https://t.co/HK29Hy8LjY
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Transformers are a breakthrough for LLMs.
An analogy is they allow the model to understand the context of words and the relationship between words that are far apart
Transformers can read all text at once vs one at a time so are much more efficient and scalable
Thanks ChatGPT!
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Onto ChatGPT!
ChatGPT+ is a giant Neural Net with 100 Trillion Parameters (GPT3 had 175B) focused on language.
That 1,000x the parameters of the brain. Woof.
The most important feature is the Transformer https://t.co/D9tpDFhkjO
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An interesting side note for the Crypto audience.
Crypto's own @ilblackdragon, the co-founder of @NEARProtocol, is one of the authors on the original Transformers Paper
https://t.co/3QqERbv32i
First let's recap ChatGPT's process:
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Embeddings
Embeddings are laying out words, represented by numbers, to those they are commonly associated with
Probabilities are found using vast amounts of text
Embeddings give a more natural feel to ChatGPT since words that are commonly associated with each other can be used
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ChatGPT is often extrapolated as a path to Terminators
Stephen counters that the magic of LLMs for writing really isn't that hard.
We're not closer to terminators, writing essays just isn't as hard as we think.
@stephen_wolfram plz share more on NN's replacing humans (pic 2) h
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Summing this all up, Stephen shares an image showing the training process for a neural net and how the loss function should decrease over time.
If the loss eventually streamlines, yay you have a solid model
If it not you can't rely on it and it's time to change the architecture
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One of the most counterintuitive takeaways that with Neural Nets it's easier to solve more complicated problems than simpler ones.
That's good too since I'm dumb and need help with the complicated problems in life.
I'll let Stephen take it from here: https://t.co/4aFvfWJsJo
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Training Neural Nets
The goal is to feed a zillion examples, and find weights that reproduce the examples.
Everytime an example is used, the weights are adjusted throughout the model.
Training is really expensive and computationally intensive. https://t.co/Pmk1XgzG1F
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Larger networks do better at landing on results.
In the below image the goal is to take in a point and recognize it in one of the three regions.
I laughed when Stephen said at the boundaries it has trouble "making up its mind". Much human.
Unsure results could be dangerous htt
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Neural Net Explanation
- Neurons arranged in layers
- Each Neuron has a weight (significance)
- ML is first used to find the weights
- Neuron evaluates numerical function
- Input is fed and neurons at each layer evaluate and feeds results to next layer
- End result is reached ht
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Neutral Nets are similar to a human brain
The brain has 100B neurons and are connected to ~1,000 other Neurons
A neuron pulses depending on what pulses it gets from other Neurons all with their own connections.
This contributes to different weights in the model. Viola!
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Where to these probabilities come from?
ChatGPT is a model that lets people estimate the probabilities which sequences of words should occur.
Stephen adds an interesting walk through demonstrating the probability of how often letters occur, and then pairs of letters and beyond
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The goal of a large language model is to reasonably continue the text it already has
ChatGPT's LLM estimates these probabilities
Temperature is a parameter that determines how often lower ranked words are used, adding randomness.
LLM's are trained on vast amounts of human text
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Stephen Wolfram of Wolfram Alpha wrote the absolute best post on ChatGPT and Large Language Models.
It took me about two hours to read, but significantly increased my understanding of what's going on under the hood of ChatGPT.
A few of my favorite takeaways (helps my process) h
2.7M views · 7K likes
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เทียบกับบัญชีขนาดเดียวกัน
3 โพสต์จาก 90 วันที่ผ่านมา เทียบกับช่วง 10K–100K ผู้ติดตาม การเข้าถึงปกติสำหรับขนาดนี้ แต่การตอบสนองอ่อนกว่าส่วนใหญ่.
ยอดดูมัธยฐาน2 027บัญชีนี้924ค่ามัธยฐานของ 10K–100K
การเข้าถึง, %2.90%บัญชีนี้3.62%ค่ามัธยฐานของ 10K–100K
การมีส่วนร่วม, %0.99%บัญชีนี้1.52%ค่ามัธยฐานของ 10K–100K
| ตัวชี้วัด | บัญชีนี้ | ค่ามัธยฐานของ 10K–100K | อัตราส่วน |
|---|---|---|---|
| ยอดดูมัธยฐานต่อโพสต์ | 2 027 | 924 | 2.19× |
| การเข้าถึง (ยอดดู ÷ ผู้ติดตาม) | 2.90% | 3.62% | 0.80× |
| อัตราการมีส่วนร่วม | 0.99% | 1.52% | 0.65× |
บัญชีอื่นในช่วงนี้ → เปรียบเทียบกับบัญชีอื่น → ค่าอ้างอิงเหล่านี้คำนวณอย่างไร →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
38.8K28 May
35.2K
32.7K
34.6K
47.1K
32.4K
3.2K29 May
54.3K15 Jun
43.6K
1.7K
56316 Jun
3.2K3 Sep
2K
1.7K4 Sep
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.23%28 May
0.19%
0.35%
0.43%
1.01%
0.34%
1.30%29 May
0.50%15 Jun
0.27%
0.41%
0.36%16 Jun
0.79%3 Sep
0.99%
1.57%4 Sep
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.
What the audience does
Likes40.9%9 545 in total
Reposts6.3%1 476 in total
Replies1.8%424 in total
Quotes0.6%135 in total
Bookmarks50.3%11 731 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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