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Possible consequences of current developments

  1. DeepMind: LLMs compress images 43% better than PNG, and audio nearly 2x better than MP3

    • Benefits:

      • This advancement in image and audio compression can significantly reduce the file sizes of these types of media, allowing for more efficient storage and transmission.
      • Smaller file sizes also mean faster loading times, which can improve the user experience on websites and applications that heavily rely on images and audio.
      • With better compression, it may be possible to store more images and audio files on devices with limited storage capacities, such as smartphones, without sacrificing quality.
      • Reduced file sizes can also lead to reduced bandwidth usage, which can be particularly beneficial in areas with limited internet access or expensive data plans.

    • Ramifications:

      • While improved compression can bring several benefits, it may also raise concerns about copyright infringement and piracy. Smaller file sizes make it easier to share and distribute copyrighted images and audio without proper authorization.
      • There may be compatibility issues with older devices or software that do not support the new compression algorithms. This could result in certain image or audio files being inaccessible on some platforms.
      • Depending on the implementation and usage, there is a possibility that the compression process may introduce artifacts or quality degradation in the compressed images or audio. This could diminish the overall user experience.

  2. OpenAI’s new language model gpt-3.5-turbo-instruct can defeat chess engine Fairy-Stockfish 14 at level 5

    • Benefits:

      • This achievement showcases the incredible capabilities of language models like gpt-3.5-turbo-instruct in understanding complex instructions and executing tasks.
      • The ability to defeat chess engines at a high difficulty level highlights the potential of these language models in various fields that require strategic decision-making and problem-solving.
      • The success of this language model in chess suggests that it may be capable of providing advanced tutorials and analysis for players, helping them improve their skills.
      • It opens up possibilities for developing conversational AI-based chess assistants or companions that can provide guidance and play against users at different skill levels.

    • Ramifications:

      • The use of advanced language models like gpt-3.5-turbo-instruct in chess or other strategic games may raise concerns about fairness and competitiveness. It may be perceived as an unfair advantage if human players are competing against AI-powered opponents.
      • There could be a potential risk of over-reliance on AI-powered assistants, leading to a diminished development of human skills and critical thinking.
      • The computational resources and infrastructure required to support language models like these can be extensive, making them less accessible to individuals or organizations with limited resources.
      • There may be ethical concerns related to the responsible use and deployment of such powerful language models, especially in contexts where bias, misinformation, or manipulation could be amplified.
  • New Machine Learning Research from MIT Proposes Compositional Foundation Models for Hierarchical Planning (HiP): Integrating Language, Vision, and Action for Long-Horizon Tasks Solutions
  • The Trick to Make LLaMa Fit into Your Pocket: Meet OmniQuant, an AI Method that Bridges the Efficiency and Performance of LLMs
  • This AI Research Introduces Owl: A New Large Language Model for IT Operations

GPT predicts future events

Predictions:

  • Artificial general intelligence: It is difficult to predict an exact timeframe for when artificial general intelligence will be achieved. However, based on the current rate of advancements in artificial intelligence, it is possible that we may see the development of artificial general intelligence within the next 10-20 years (2031-2041). This prediction is based on the exponential growth of computing power, the increasing availability of big data, and the continuous development of sophisticated machine learning algorithms. As technology progresses, researchers and scientists are constantly pushing the boundaries of AI capabilities, and it is likely that artificial general intelligence will be achieved within this timeframe.

  • Technological singularity: The idea of technological singularity refers to a hypothetical point in the future when technological progress becomes so rapid and transformative that it surpasses human intelligence and leads to unpredictable and exponential changes in society. Due to the inherently unpredictable nature of the singularity, it is difficult to pinpoint an exact timeframe for its occurrence. It could happen anywhere from the next few decades to centuries from now. However, considering the rate at which technology is evolving, and the potential acceleration that could occur as artificial general intelligence is achieved, it is possible that the technological singularity may occur within the next 50-100 years (2071-2121). This prediction is based on the assumption that as artificial general intelligence is developed, it will rapidly fuel further advancements, potentially leading to a singularity event where the world as we know it will drastically change.