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Possible consequences of current developments

  1. Hi I’m a senior machine learning engineer, looking for buddies to build cool stuff with!

    • Benefits:

      Building a network of like-minded individuals can lead to collaboration on innovative projects, sharing of knowledge and expertise, and potentially creating groundbreaking solutions in the field of machine learning.

    • Ramifications:

      Without proper vetting and collaboration, there could be challenges in finding reliable partners to work with, potential conflicts of interest, and difficulties in coordinating schedules and workloads.

  2. ML Researchers in Industry: How Do You Find Time to Publish Papers?

    • Benefits:

      Sharing research findings through publications can lead to recognition in the field, career advancement opportunities, and contributions to the overall knowledge base of machine learning.

    • Ramifications:

      Balancing time between research, work responsibilities, and paper writing can be challenging, potential competition with peers for publication opportunities, and pressure to constantly produce results for publication.

  3. AMD MI300X and Nvidia H100 benchmarking in FFT: VkFFT, cuFFT and rocFFT comparison

    • Benefits:

      Benchmarking different hardware and software combinations can lead to insights on performance, efficiency, and compatibility, which can inform decision-making for researchers and industry professionals.

    • Ramifications:

      Depending on the results, there could be implications for future hardware or software choices, potential biases in the benchmarking process, and challenges in extrapolating findings to different contexts.

  4. Evolutionary Strategy vs. Backpropagation

    • Benefits:

      Comparing different optimization methods can lead to a better understanding of their strengths and weaknesses, potential improvements in algorithm design, and insights into the underlying principles of machine learning.

    • Ramifications:

      Depending on the context, one method may be more suitable than the other, potential challenges in implementation and interpretation of results, and the need for further research to validate findings.

  5. Papers proposing ideas without results?

    • Benefits:

      Proposing new ideas can spark creativity, lead to potential breakthroughs in research, and encourage collaboration and discussion within the scientific community.

    • Ramifications:

      Lack of results may raise questions about the validity of the proposed ideas, potential challenges in replicating or testing the ideas, and the need for further experimentation to validate the hypotheses.

  6. Which journals/conferences can I possibly submit my ML research on computational fluid dynamics?

    • Benefits:

      Identifying appropriate venues for publication can increase visibility and impact of research, foster collaboration with experts in the field, and provide opportunities for feedback and recognition.

    • Ramifications:

      Selecting the wrong journal or conference may lead to rejection or limited visibility, challenges in navigating the publication process, and potential biases in the review and evaluation of the research.

  • Meet DeepSeek-Coder-V2 by DeepSeek AI: The First Open-Source AI Model to Surpass GPT4-Turbo in Coding and Math, Supporting 338 Languages and 128K Context Length
  • NVIDIA AI Releases HelpSteer2 and Llama3-70B-SteerLM-RM: An Open-Source Helpfulness Dataset and a 70 Billion Parameter Language Model Respectively
  • New survey and review paper for video diffusion models!

GPT predicts future events

  • Artificial general intelligence (April 2030)

    • I believe artificial general intelligence will be achieved by April 2030 due to the rapid advancements in machine learning, neural networks, and computing power, which are essential components for creating a system capable of learning and adapting to various tasks and domains.

  • Technological singularity (June 2045)

    • The technological singularity is predicted to happen in June 2045 because as AI continues to advance at an exponential rate, it is expected to surpass human intelligence, leading to a point where technology will evolve rapidly, making predictions beyond this point uncertain.