Attention @arxiv authors: Our Code of Conduct states that by signing your name as an author of a paper, each author takes full responsibility for all its contents, irrespective of how the contents were generated. 1/
If generative AI tools generate inappropriate language, plagiarized content, biased content, errors, mistakes, incorrect references, or misleading content, and that output is included in scientific works, it is the responsibility of the author(s). 2/
We have recently clarified our penalties for this. If a submission contains incontrovertible evidence that the authors did not check the results of LLM generation, this means we can't trust anything in the paper. 3/
The penalty is a 1-year ban from arXiv followed by the requirement that subsequent arXiv submissions must first be accepted at a reputable peer-reviewed venue. 4/
Examples of incontrovertible evidence: hallucinated references, meta-comments from the LLM ("here is a 200 word summary; would you like me to make any changes?"; "the data in this table is illustrative, fill it in with the real numbers from your experiments") end/
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The concept of "AGI" (a system that can match or exceed human performance across all tasks) shares all of the defects of the Turing Test. It defines "intelligence" entirely in terms of human performance. 1/
It says that the most important AI system capabilities to create are exactly those things that people can do well. But is this what we want? Is this what we need? 2/
I think we should be building systems that complement people; systems that do well the things that people do poorly; systems that make individuals and organizations more effective and more humane. 3/
Yoshua Bengio makes many good points in his latest blog post. I would like to share a few thoughts yoshuabengio.org/2023/05/22/how…
Yoshua devotes a fair amount of space to discussing alignment and work on AI safety. I strongly agree that building good tools, techniques, and "safety culture" will reduce the risk that we create "rogue" systems accidentally. 2/
Regulations that test the correctness and robustness of deployed systems can also reduce this risk and limit many other harms (fairness, exploitation, etc.). 3/
@EzraJNewman 1. A model of the world, both the current state (the things it believes are true) and the dynamics (the things it believes will be true in the future, conditioned on its actions, the actions of other agents, and existing active processes) 1/
@EzraJNewman 2. A model of itself and of other individual people (theory of mind). What they believe right now; theirs preferences and desired. Dynamics of social interactions. 2/
@EzraJNewman 3. A model of larger entities (corporations, clubs, institutions, countries) and their beliefs, interests, and dynamics. 3/
In a new paper, Jesse Hostetler (SRI) and I make a contribution to the growing literature on conformal prediction. We provide *prospective guarantees* on the behavior of an agent executing a fixed policy. 1/
Suppose you wish to dispatch your personal robot to walk across campus to the cafeteria to pick up your breakfast and bring it back to your room. The robot has learned a policy pi via reinforcement learning that it will execute to perform this task 2/
However, the probability of running into trouble along the way and failing to achieve the task depends on many factors including the weather, battery charge, time of day, obstacles encountered along the way, and so on. 3/
"Sentient" is being misapplied by many ML folks. It means "the ability to perceive or feel things" or "capable of experiencing things through its senses". Like many other categories, it is a matter of degree. 1/
The simplest form is a reflex. When a sensor measures a value above some threshold, the system (e.g., a thermostat) responds. The system lacks any awareness, memory, or ability to reflect on this "experience". 2/
A step above this is a system that can assess whether its responses are succeeding. An aircraft autopilot can detect and raise an alarm when it can no longer maintain the desired set point. It has a goal and it is aware of whether it is achieving that goal. 3/
Thoughts upon reading arxiv.org/abs/2106.15590:
In this paper, the authors compare highly-cited papers from 2008-2009 with papers from 2018-2019 published in NeurIPS and ICML and summarize the values (i.e., desirable aspects) highlighted in those papers. 1/
The values are exactly what you would expect of an engineering field: ML researchers want algorithms that work well, that are computationally efficient, that generalize beyond the training data, that are novel, and that apply to the real world. 2/
Methodologically, ML researchers value quantitative evidence, theoretical guarantees, sound methodology, unifying ideas, and scientific understanding. 3/