Delighted to showcase such a worthy tool! Keep up the excellent work; we can't wait to see what is next from you all!
GENERATIVE AI
-
AI System Achieves Silver Medal-level score in IMO
By
–
AI System Achieves Silver Medal-level score in IMO The International Mathematical Olympiad (IMO) is the oldest, largest & most prestigious competition for young mathematicians. Every year, countries send their top young mathematicians to take a 6 problem test spanning two days.
-
Hybrid Data Over Pure Synthetic Data for AI Models
By
–
6/ This is why I personally believe in HYBRID DATA over pure synthetic data. Synthetic data must be generated utilizing some source of new information for the model, either: (1) using a seed of real world data
(2) human experts
(3) formal logic engine Hybrid data is the real -
Synthetic Data Debt: Model Quality Degradation Risk
By
–
7/ watch this space closely. My prediction is that model developers who are not careful about training on synthetic data with no information gain will find their models getting steadily stranger and dumber over time. Synthetic data accumulates a debt with the model that must be
-
Three Sources of Error in Synthetic Data Training Generations
By
–
4/ There are three sources of error that accumulate from successive generations of synthetic training 1) statistical approximation error
2) functional expressivity error
3) functional approximation error Roughly speaking, each time you train the model on data generated from the -
Small Language Models Show Emerging Style Imitation Capabilities
By
–
5/ While the experiments done here were on small scale models (100M parameters), the fundamental effects we’re seeing here are likely to show up over time on the larger models too. For example, most of today’s models can’t produce a blog post in the style of Slate Star Codex,
-
Synthetic Data Training Risks Model Collapse Long Term
By
–
3/ This core idea is very important to pay attention to: Synthetic data can create a short-term boost in eval results, but you will pay for it later with model collapse! You accumulate debt with mangling the model that starts invisible, and is very hard to repay.
-
Synthetic Data Training Limitations and Mode Collapse in Self-Distillation
By
–
Training on pure synthetic data has no information gain, thus there is little reason the model *should* improve. Oftentimes when evals go up from “self-distillation”, that might be from some more invisible tradeoff, i.e. mode collapse in exchange for individual eval improvement
-
Model Collapse in AI: Recursive Synthetic Data Training Risks
By
–
1/ New paper in Nature shows model collapse as successive model generations models are recursively trained on synthetic data. This is an important result. While many researchers today view synthetic data as AI philosopher’s stone, there is no free lunch. Read more
-
Seven Ways Marketers Leverage Generative AI for Brand Engagement
By
–
In this video, I explore seven innovative ways #marketers can leverage generative #AI to enhance brand #engagement and streamline processes. Discover how #GenerativeAI tools like #ChatGPT and #Google #Gemini can transform your #marketing #strategy.