Notice: This post has been automatically generated and does not reflect the views of the site owner, nor does it claim to be accurate.

Possible consequences of current developments

  1. “Desk rejected” for no reason - the ML academic conference industry is becoming broken

    • Benefits:

      • Raises awareness: Shedding light on the issue of unfair “desk rejections” in the ML academic conference industry can help raise awareness and initiate discussions about the necessity for more transparency and accountability in the review process.

      • Reform opportunities: Recognizing and acknowledging the broken aspects of the industry can encourage researchers, academics, and conference organizers to come together and work towards implementing meaningful reforms to ensure a fair and unbiased selection process for conference papers.

    • Ramifications:

      • Discouragement: Researchers who consistently face “desk rejections” without clear reasons may feel discouraged, demotivated, and may even abandon their research projects or choose alternative avenues to share their work, leading to less innovation in the field.

      • Bias perpetuation: If the ML academic conference industry is indeed broken, it can perpetuate biases in publication and recognition, favoring certain individuals or institutions while excluding others unfairly. This can hinder diversity and impede the progress of the field by not considering valuable contributions from underrepresented groups.

  2. “What do you guys think of Schmidhuber’s new blog post, would like to know everyone’s opinion”

    • Benefits:

      • Discussion and collaboration: Encouraging open discussions about Schmidhuber’s blog post can foster a healthy exchange of diverse perspectives and ideas, leading to new insights and opportunities for collaboration.

      • Critical thinking: Analyzing and evaluating different opinions and viewpoints can help develop critical thinking skills that are crucial in the field of machine learning. This can further refine and improve the research practices and methodologies adopted by individuals in the community.

    • Ramifications:

      • Division and polarization: Depending on the content and tone of Schmidhuber’s blog post, it has the potential to divide the ML community, leading to factions and conflicts rather than constructive discourse. This can hinder cooperation and knowledge-sharing within the community.

      • Reputation impact: If the blog post contains controversial or inaccurate information, it may have a negative impact on Schmidhuber’s reputation and credibility within the ML community. This could affect future collaborations, research opportunities, and professional relationships.

  • Researchers from NYU and Google AI Explore Machine Learning’s Frontiers in Advanced Deductive Reasoning
  • Researchers from CMU and Max Planck Institute Unveil WHAM: A Groundbreaking AI Approach for Precise and Efficient 3D Human Motion Estimation from Video
  • Deci AI Introduces DeciLM-7B: A Super Fast and Super Accurate 7 Billion-Parameter Large Language Model (LLM)
  • Meet LLM360: The First Fully Open-Source and Transparent Large Language Models (LLMs)

GPT predicts future events

  • Artificial general intelligence

    • By 2030: Based on the current rate of advancements in machine learning, natural language processing, and progress in robotics, it is reasonable to expect that artificial general intelligence (AGI) will be achieved within the next decade. However, it is also important to consider that AGI development is a complex and unpredictable area, so this prediction comes with a certain level of uncertainty.

  • Technological singularity

    • By 2050: The technological singularity, which refers to the hypothetical point at which AI or technological advancement becomes super intelligent and surpasses human intelligence, is a much more speculative event. While some experts believe it could happen in the next few decades, others argue it might take much longer or never occur at all. Therefore, a conservative prediction would suggest the technological singularity might be achieved by 2050. This gives ample time for significant advancements in AI, robotics, and neuroscience, as well as ensuring adequate safety measures are implemented to prevent the risks associated with such a transformation.