'Our revenues have not grown as expected — and we've yet to fully benefit from powerful trends, like AI.' -Pat Gelsinger, Intel CEO https://
cnbc.com/2024/08/01/int
el-intc-q2-earnings-report-2024.html
…
GENERATIVE AI
-
Intel CEO Reports Revenue Shortfall Amid AI Opportunity
By
–
-
Black Forest Labs Flux Dev Model Available on Replicate
By
–
The model is at https://
replicate.com/black-forest-l
abs/flux-dev
… . Thanks for clicking 😉 -
FLUX.1 Models Reviewed: Open Source DALL-E 3 Alternative
By
–
Review from @dejavucoder
: "Open source DALL-E 3 model" for the FLUX.1 [dev] medium size model "Almost Midjourney level aesthetic wise" for FLUX.1 [pro] Try it on Replicate today (link in thread ) -
Epic Fall Winter AI Model Releases and Compute Innovations
By
–
8/ This will be an epic fall/winter of even more model releases. It will be interesting to see how differences in compute, data, and innovations (Q* etc.) will drive changes in the rankings going forward!
-
Post-Training and Data Strategy: New AI Competitive Driver
By
–
7/ We see strengths (Claude 3.5 w/coding, Gemini 1.5 w/Vision, Multilingual) from the models that are likely driven by post-training and data strategies. This is contrary to the prevailing beliefs 1 yr ago, where pre-training was believed to be the core competitive driver.
-
Post-Training Data Strategy: Key Competition Arena for LLMs
By
–
6/ Post-training data strategy is becoming a key arena for competition. Llama3.1 paper had 15 pg about post-training data (vs. 12 pg on pre-training), driving these capabilities: – Code
– Multilinguality
– Math/Reasoning
– Long-Context
– Tool Use
– Factuality
– Steerability -
Post-Training Data Strategies: SFT, RLHF, and DPO Approaches
By
–
4/We are also seeing remarkably similar data strategies for post-training from most labs at this point (at least what was published from Meta+Apple): – Hybrid data SFT, RLHF, & DPO setups
– Synthetic data on code and math
– Post-training data for most important capabilities -
H100 GPU Rollout Explains Timing of Recent AI Model Releases
By
–
3/The reason these are all so close together timing-wise is that every lab got their H100s at roughly the same time. They each struggled with early issues with the H100s last fall, and the big H100 clusters all started training this spring. Voila, 5-6 months later, big models!
-
Seven Major AI Models Released in Three Months
By
–
2/We've seen 7 major models from top labs in the last 3mo: May:
– GPT 4o
– Gemini 1.5 Pro June:
– Claude 3.5 Sonnet July:
– Llama 3.1
– Mistral Large 2
– GPT-4o Mini August:
– Gemini 1.5 0801 Each of these models has been incredibly competitive—each world-class in some way. -
Gemini 1.5 Pro Tops LMSYS: Google’s Compute Edge Advantage
By
–
1/Gemini 1.5 Pro 0801 is the new best model (tops LMSYS, SEAL evals incoming) Key considerations
1—OpenAI, Google, Anthropic, & Meta all right ON the frontier
2—Google has a long-term compute edge w/TPUs
3—Data & post-training becoming key competitive drivers in performance