Meta LLaMA, Stanford Alpaca, PaLM, OpenAI GPT-4.
This week has been crazy. The previous week started with the storming LLaMA release from the Meta which advanced to weight leaks on the torrent. Then Stanford released the Alpaca model fine tuned from the LLaMA 7B model on 52K instruction-following demonstrations.
The likes of Google also showed preview of what PaLM can do. Microsoft kept
hitting the ball out of the park, first they released ChatGPT and Whisper APIs
to the public and now they released GPT-4 alongside Copilot for Microsoft 365.
If there was an AI Winter where the research and development in Machine Learning
/ Deep Learning applications were stagnated then this surely is an AI Summer.
Gradient descent can write code better than you. I'm sorry.
— Andrej Karpathy (@karpathy) August 4, 2017
Five years ago @karpathy saw a dream and today we have OpenAI Copilot helping us in writing code or ChatGPT conversing through a code or answering our queries, redefining how we quest for information or the recent demo of conversion to code from instructions on a piece of paper using GPT-4. Amazing times.
Here are some sample prompts through which I am playing with OpenAI GPT-4:
*prompt*
explain the paper "attention is all you need" in bullet points
*reponse*
- "Attention Is All You Need" is a groundbreaking research paper by Vaswani et al.
(2017) that introduced the Transformer model, a novel architecture for neural networks.
- The key innovation of the Transformer model is the self-attention mechanism, which
allows the model to weigh the importance of different input tokens relative to each
other, capturing long-range dependencies in sequences without relying on recurrent
or convolutional layers.
- The paper presents the scaled dot-product attention and multi-head attention as core
components of the self-attention mechanism, enabling the model to focus on different
aspects of the input simultaneously.
- Transformer models are more computationally efficient and parallelizable than RNNs
and LSTMs because they don't require sequential processing of inputs, enabling faster
training and better scaling to large datasets.
- The paper demonstrates the Transformer model's effectiveness in machine translation
tasks, achieving state-of-the-art performance on the WMT 2014 English-to-German and
English-to-French benchmarks at the time.
- Transformers use positional encoding to inject information about the position of tokens
in a sequence, compensating for the lack of inherent positional information in the
self-attention mechanism.
- The architecture consists of an encoder and a decoder, each containing multiple
identical layers with self-attention and feed-forward sub-layers, along with layer
normalization and residual connections.
*prompt*
you are a creative designer who is instructing an advanced ai model GPT4 through prompts,
write a creative prompt which results in an incredible UI prompt which we will then pass
through midjourney ai and generate the real UI
*response*
Prompt: "Create a cutting-edge and visually stunning user interface design for a
futuristic social networking platform that seamlessly integrates with smart devices and
augmented reality environments. The design should prioritize user experience, incorporate
smooth and intuitive navigation, and embrace innovative concepts like customizable 3D
avatars, holographic notifications, and adaptive layouts that fluidly adjust to the
user's context and preferences. Emphasize visual cohesion through the use of a harmonious
color scheme, modern typography, and engaging animations. Additionally, consider
incorporating elements that promote meaningful social interactions, enhance user privacy,
and encourage eco-friendly behaviors within the platform."
I used this prompt in the midjourney and got these images as the output:
Indeed the future is going to be interesting and this is the rise of the AI Summer.
Follow me on twitter @nezubn or mastodon