Artificial Fintelligence

Artificial Fintelligence analyzes the cutting-edge of AI research, exploring innovations in models, inference techniques, and market trends. It discusses developments in Mixture of Experts models, transformer optimizations, Large Language Models (LLMs), the AI market, and advances in image generation, focusing on efficiency, scalability, and effectiveness.

AI Research and Development Model Optimization and Inference Techniques Large Language Models (LLMs) AI Market Trends Image Generation Technologies

The hottest Substack posts of Artificial Fintelligence

And their main takeaways
8 implied HN points 01 Mar 24
  1. Batching is a key optimization for modern deep learning systems, allowing for processing multiple inputs simultaneously without significant time overhead.
  2. Modern GPUs run operations concurrently, leading to no additional time needed as batch sizes increase up to a certain threshold.
  3. For convolutional networks, the advantage of batching is reduced compared to other models due to the reuse of weights across multiple instances.
13 implied HN points 29 Jan 24
  1. FLOPS in LLMs are mainly spent on computing QKV, attention output matrix, and running the FFN.
  2. Wider LLM models parallelize better and favor lower latency, while deeper models linearly increase inference time.
  3. Empirical analysis shows linear scaling in performance as LLM model dimensions increase.
13 implied HN points 13 Dec 23
  1. LLM API market has seen growth with new competitors like Bard, Claude, and Gemini entering.
  2. Competition in the LLM market is driving efficiency and lower prices for hosting services.
  3. Market for LLM APIs will bifurcate into high-end expensive models and low-end cost-effective models, with open weight models improving in quality and decreasing in cost.
16 implied HN points 23 Nov 23
  1. Implement a KV cache for the decoder to optimize inference speed in transformers.
  2. Consider using speculative decoding with a smaller model to improve decoder inference speed when excess compute capacity is available.
  3. Quantization can be a powerful tool to reduce model size without significant performance tradeoffs, especially with 4-bit precision or more.
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3 implied HN points 12 Dec 23
  1. The post discusses the evolution of the LLM API market
  2. There is a note about issues when trying to access the article online
  3. To continue reading, a 7-day free trial subscription is offered
4 implied HN points 07 Mar 23
  1. Models need to generate data by themselves for self-improvement, seen in examples like AlphaZero.
  2. Models should adapt to new domains without requiring vast existing data, like the CLIP model.
  3. Improving efficiency of models, like auto regressive sampling, is crucial for advancement in AI development.
3 HN points 29 Mar 23
  1. Focus on the evolution of GPT models over the past five years, highlighting key differences between them.
  2. Explore the significant impact of large models, dataset sizes, and training strategies on language model performance.
  3. Chinchilla and LLaMa papers reveal insights about the optimal model sizes, dataset sizes, and computational techniques for training large language models.
1 HN point 11 Apr 23
  1. CLIP focuses on aligning text and image embeddings, showcasing its utility for various applications like search, image generation, and zero-shot classification.
  2. DALL-E introduces a large-scale autoregressive transformer model for text-to-image generation, revolutionizing image generation beside the prevalent GAN models.
  3. GLIDE employs a 3.5B parameter diffusion model to convert text embeddings into images, exploring guiding methods like CLIP and classifier-free guidance.
1 implied HN point 02 Mar 23
  1. The website www.artfintel.com will be launching soon.
  2. Finbarr Timbers will write 1-2 articles per month about current advances in AI research.
  3. The focus will be on accelerating AI research.