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Possible consequences of current developments

  1. Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows?

    • Benefits:

      • Automating data science and engineering workflows can lead to increased efficiency, productivity, and accuracy in handling large datasets and complex algorithms. This can help researchers and professionals focus on more creative and strategic aspects of their work.

    • Ramifications:

      • The potential downside of fully automating these workflows is the risk of reduced human oversight, which could result in errors going unnoticed or unintended biases being reinforced in the algorithms. Additionally, there may be concerns about job displacement for professionals in these fields.

  2. Tom’s Hardware reviews Gigabyte local AI training product AI TOP

  3. Author of ReFT: Representation Finetuning for Language Models, at Oxen.ai Paper Club this Friday

  4. Matching segment areas in medical images

  5. What steps should be taken to understand up to Variational Auto-Encoders? [R]

  6. About the dimensions of latents in stable diffusion

  • NVIDIA Researchers Introduce Flextron: A Network Architecture and Post-Training Model Optimization Framework Supporting Flexible AI Model Deployment
  • Mistral AI Launches Codestral Mamba 7B: A Revolutionary Code LLM Achieving 75% on HumanEval for Python Coding
  • Hugging Face Introduces SmolLM: Transforming On-Device AI with High-Performance Small Language Models from 135M to 1.7B Parameters

GPT predicts future events

  • Artificial general intelligence (June 2030)

    • With advancements in deep learning, neural networks, and computational power, it is likely that artificial general intelligence will be achieved within the next decade.

  • Technological singularity (December 2045)

    • As technology continues to advance exponentially, leading to self-improving artificial intelligence, the technological singularity, where machines surpass human intelligence, could occur by 2045.