Jeff Koons, the world's most expensive artist, rejects using AI in his work, despite its growing popularity in the art world. His hands-off approach to creating iconic pieces like balloon dogs and stainless steel rabbits is on display at the Alhambra in Granada, where he sees his art as intertwined with biology.
Reflective generative AI tools like GitHub Copilot & Devin. ai automate software development, aiming to build autonomous platforms. The Doctor-Patient strategy in GenAI tools treats codebases as patients, revolutionizing the process of automation.
LLMs require a new approach to evaluation, with 3 key paradigm shifts: Evaluation is now the cake, benchmark the difference, and embrace human triage. Evaluation is crucial in LLM development due to fewer degrees of freedom and the complexity of generative AI outputs.
Tech company employee creates linear regression demo using synthetic data, highlighting API design insights resembling scikit-learn library. Predictions show accuracy of 77.5% on test data, showcasing practical application of stochastic gradient descent.
Transformers learn from examples through In-context learning (ICL) and few-shot prompting. Softmax attention with an inverse temperature parameter allows for nearest neighbor behavior in processing examples.
Newton iteration matrix inverse was successfully used in Gaussian process regression to improve efficiency, accuracy, and robustness. The demo showcased high accuracy levels in predicting target values for synthetic data with a complex underlying structure.
Current best practices for training LLMs emphasize dataset pre-processing, including deduplication, data sampling, and handling biased/harmful speech for improved model performance. Advanced methods like data deduplication and downstream task data removal are crucial to ensure high-quality and diverse training data for language models.
Thresholding is a key technique for managing model uncertainty in machine learning, allowing for human intervention in complex cases. In the context of fraud detection, thresholding helps balance precision and efficiency by deferring uncertain predictions for human review, fostering trust in the system.
Implementing a resume optimization tool using Python and OpenAI's API for tailored job applications. Learn how to streamline the process with a 4-step workflow and example code.
Current best practices for training LLMs include diverse model evaluations on tasks like question answering, translation, and reasoning. Evaluation methods like n-shot learning with prompting are crucial for assessing model performance accurately.
Knowledge graphs and AI combine for a Graph RAG app, enhancing LLM responses with contextual data. Graph RAG gains popularity, with Microsoft and Samsung making significant moves in knowledge graph technology.
Training large language models (LLMs) from scratch involves scaling up from smaller models, addressing issues as model size increases. GPT-NeoX-20B and OPT-175B made architectural adjustments for improved training efficiency and performance, showcasing the importance of experiments and hyperparameter optimization in LLM pre-training.
AI could manipulate decisions as companies bid for human behavior predictions in the 'intention economy' marketplace. University of Cambridge researchers reveal how AI tools forecast and sell human intentions to profit-seeking companies.
Neural networks face challenges with superposition, where one neuron represents multiple features. Non-linearity and feature sparsity play key roles in causing superposition.
Google's Paligemma VLM combines a vision encoder with a language model for tasks like object detection. Paligemma can process images at different resolutions and identify objects without fine-tuning, but Google recommends fine-tuning for domain-specific tasks.