2/ It is the largest (afaik) publicly available GPT-3 replica. The primary goal of this project is to replicate a full-sized GPT-3 model and open source it to the public, for free.
The models were trained on an open-source dataset The Pile pile.eleuther.ai which ...
3/ which consists of 22 smaller, high-quality datasets combined together (825 GB text in total). Larger GPT models (10B and full GPT-3) are not available yet, but soon will be released by eleuther.ai.
...
4/ The 2.7B model likely won't fit into the Colab TPUs memory, and you may have to get some larger TPU pods to finetune from it. Sampling from it, however, works just fine.
5/ It is the first time I tried some kind of GPT model, and I was not too impressed (see my fav results below). Maybe 2.7 billion params are not enough yet to leave the copyrighters without their jobs 🤷🏼♂️. Let's wait for a bigger model.
...
7/
I decided to ask the AI if it will kill us, meatbags, in the near future. This is what I got in response...
8/
Prompt: Will AI enslave the humankind in the next 20-30 years?
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GPT-3 Answers:
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The increasing pace of AI is not going to decrease. As of now the AI revolution has just began. The effects of AI on people and the future is not known yet, but if that is the AI’s fate..
9/ ...then the exposure of the human population is going to increase drastically.
There is a possibility that you will be in one of the AI enslaved societies.
The AI Revolution is a powerful technologies that is coming to the world in the near future. The AI revolution will...
10/ will be implemented as a protection mechanism for the protection of planet earth from dangerous climate change and increasing nuclear war. The AI has the potential to be our savior for the future. It is going to be a technology that is going to change the world drastically...
11/
but there is a possibility that this is simply an illusion that we are living in a world of AI and it is just a pure mass hallucination, and all those projections actually represent the AI’s downfall and not an AI savior.
...
12/
.I am excited to see what the AI revolution is going to bring.
.I believe that it is going to bring great changes to our civilization.
.I believe that it is going to bring great changes in human civilization.
(c) GPT-3 🦾
13/
Well, the last 10 years or so have been awful for everyone. It's not AI enslaving the humans in the next 20 or 30, it's automation on a global scale.
Edit a generated image by painting a mask atany location of the image and specifying any text description. Or generate a full image just based on textual input.
2/ Point to a location in a synthesized image and apply an arbitrary new concept such as “rustic” or “opulent” or “happy dog.”
3/
🛠️Two nets: (1) a semantic similarity network C(x, t) that scores the semantic consistency between an image x and a text description t. It consists of two subnetworks: C_i(x) which embeds images and C_t(t) which embeds text. (2) generative network G(z) that is trained to ...
Meta-DETR: Few-Shot Object Detection via Unified Image-Level Meta-Learning
❓How?
Eliminate region-wise prediction and instead meta-learn object localization and classification at image level in a unified and complementary manner.
Specifically, the Meta-DETR first encodes both support and query images into category-specific
features and then feeds them into a category-agnostic decoder to directly generate predictions for specific categories. ...
2/K
Authors propose a Semantic Alignment Mechanism (SAM), which aligns high-level and low-level feature semantics to improve the generalization of meta-learned representations. ...
3/K
To learn about differences between the two -> thread 👇
1/ The main idea is to factorize the voxel color representation into two independent components: one that depends only on positions p=(x,y,z) of the voxel and one that depends only on the ray directions v.
Essentially you predict K different (R,G,B) values for ever voxel...
2/ Essentially you predict K different (R,G,B) values for ever voxel and K weighting scalars H_i(v) for each of them:
color(x,y,z) = RGB_1 * H_1 + RGB_2 * H_2 + ... + RGB_K * H_K. This is inspired by the rendering equation.
...
... they train a regressor network to predict
the latent code from an input image. To teach the regressor to predict the latent code for images w/ missing pixels they mask random patches during training.
Now, given an input collage, the regressor projects it into a reasonable...