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Possible consequences of current developments

  1. Introducing 3 New LoRA Models Trained with LLaMA on the OASST Dataset at 2048 seq length!

    • Benefits:

      The introduction of new LoRA models trained with LLaMA on the OASST dataset at 2048 seq length can provide significant benefits in natural language processing. These models have the potential to improve the accuracy and efficiency of language translation, semantic analysis, and speech recognition. Moreover, the use of LLaMA in training ensures that the models are trained on large-scale, diverse datasets resulting in improved generalization capabilities.

    • Ramifications:

      The ramifications of these new models are limited as they only pertain to natural language processing tasks. One potential ramification, however, could be the exclusion of smaller organizations and educational institutions from accessing these models. Such exclusivity could potentially deepen existing inequalities in access to high-quality NLP tools between large and small organizations.

  2. New Reddit API terms effectively bans all use for training AI models, including research use.

    • Benefits:

      Unfortunately, there are no benefits associated with the new Reddit API terms being enforced. These new terms significantly hinder research and development in the field of artificial intelligence, particularly with regards to natural language processing.

    • Ramifications:

      The ramifications of these new terms are significant. The Reddit platform is a crucial source of data and training datasets for many academic researchers, data scientists, and developers working on NLP models. The new API terms effectively preventing most research use of Reddit data will have a chilling effect on research and development in the field of AI. Moreover, researchers who have previously built models using Reddit data will be forced to abandon them, negatively impacting overall model accuracy and efficiency.

  3. colab-tunnel: Connect to Google Colab VM locally from VSCode

    • Benefits:

      Colab-tunnel allows users to connect to Google Colab VM locally from VSCode, an excellent development environment for many AI developers. This tool facilitates smooth collaboration and improved productivity by providing a more streamlined workflow. Developers who prefer working with VSCode can now leverage the power of Colab VM without compromising their work environment.

    • Ramifications:

      There are not any significant ramifications associated with the introduction of the colab-tunnel tool. However, as with any tool, incorrect use or misuse could potentially lead to drawbacks. The potential abuse of the tool in data scraping or harvesting could create problems for Google and publishers, leading to further restrictions on access to such tools.

  4. How would you build an ML rig for under $2500?

    • Benefits:

      Building an ML rig for under $2500 would make AI development more accessible to individuals and small organizations with limited budgets. Increased access would create more opportunities for innovation and collaboration.

    • Ramifications:

      There are no significant risks associated with building an ML rig for under $2500, especially if the components are purchased from reputable vendors. However, there is still the potential for wasted time, effort, and resources if the project is not executed effectively.

  5. ChemCrow: Augmenting large-language models with chemistry tools - Andres M Bran et al, Laboratory of Artificial Chemical Intelligence et al - Automating chemistry work with tool-assisted LLMs

    • Benefits:

      The ChemCrow project aims to augment large-language models with chemistry tools, creating significant potential benefits for the scientific community. This tool could improve efficiency, accuracy, and speed in tasks such as molecule generation, predicting chemical properties, and reaction prediction. Such capabilities could accelerate research in the field of chemistry, improving our understanding of the natural world and enabling the development of new drugs and materials.

    • Ramifications:

      One potential ramification of tools such as ChemCrow is job displacement among data scientists and computational chemists. The use of machine learning tools in chemistry research will create a paradigm shift, with traditional positions evolving or being replaced by AI-powered systems. Additionally, the lack of transparency in how the models are trained and how they make predictions may engender distrust among traditionalists in the field.

  • Meet MiniGPT-4: An Open-Source AI Model That Performs Complex Vision-Language Tasks Like GPT-4
  • Researchers Introduce ChemCrow For Augmenting Large-Language Models With Chemistry Tools
  • [Self Promotional] Expert takes direct to your inbox!! New on Substack :)
  • Researchers Explore Foundation Models For Generalist Medical Artificial Intelligence

GPT predicts future events

  • Artificial general intelligence will be developed (December 2030)
    • The development of AGI is highly dependent on advancements in machine learning and neural networks. As these technologies continue to improve at an exponential rate, it’s reasonable to assume that AGI will be developed within the next decade or so.
  • The technological singularity will occur (May 2045)
    • The technological singularity refers to a hypothetical point in time where artificial intelligence becomes self-improving and exceeds human intelligence in every way. It’s impossible to predict whether or not this will happen, but if it does, it’s likely to occur within the next few decades. May 2045 is a somewhat arbitrary prediction, but it’s based on the estimated rate of technological growth and the amount of time it would take for an AGI to reach the singularity.