A law that is expected to fail?
Californians have a green light to sue the gun industry. How will that work?
One of the strangest pieces of legislation ever enacted in California took effect Jan. 1, giving state residents and visitors the same power to threaten the gun industry that Texans now wield over abortion providers.
Even backers of the law say this isn’t an entirely good thing.
SB 1327 authorizes anyone other than state or local government officials to sue people who violate the state’s laws against the manufacture, distribution or sale of assault weapons, ghost guns and other banned firearms. Lawsuits could also be brought against gun dealers who violate the state’s law against selling or transferring weapons (besides hunting rifles) to anyone under 21 years old.
… Gov. Gavin Newsom sought the measure as a response to Texas’ SB 8, which empowers “any person” to sue those who perform or knowingly aid an abortion in that state after the fetus shows signs of cardiac activity. When the Supreme Court refused to throw out SB 8, Newsom (who sharply criticized it) called for California to use it as a model for a novel approach to gun control.
Building an AI replacement for lawyers…
https://www.bespacific.com/gpt-takes-the-bar-exam/
GPT Takes the Bar Exam
Bommarito, Michael James and Katz, Daniel Martin, GPT Takes the Bar Exam (December 29, 2022). Available at SSRN: https://ssrn.com/abstract=4314839
“Nearly all jurisdictions in the United States require a professional license exam, commonly referred to as “the Bar Exam,” as a precondition for law practice. To even sit for the exam, most jurisdictions require that an applicant completes at least seven years of post-secondary education, including three years at an accredited law school. In addition, most test-takers also undergo weeks to months of further, exam-specific preparation. Despite this significant investment of time and capital, approximately one in five test-takers still score under the rate required to pass the exam on their first try. In the face of a complex task that requires such depth of knowledge, what, then, should we expect of the state of the art in “AI?” In this research, we document our experimental evaluation of the performance of OpenAI’s text-davinci-003 model, often-referred to as GPT-3.5, on the multistate multiple choice (MBE) section of the exam. While we find no benefit in fine-tuning over GPT-3.5’s zero-shot performance at the scale of our training data, we do find that hyperparameter optimization and prompt engineering positively impacted GPT-3.5’s zero-shot performance. For best prompt and parameters, GPT-3.5 achieves a headline correct rate of 50.3% on a complete NCBE MBE practice exam, significantly in excess of the 25% baseline guessing rate, and performs at a passing rate for both Evidence and Torts. GPT-3.5’s ranking of responses is also highly correlated with correctness; its top two and top three choices are correct 71% and 88% of the time, respectively, indicating very strong non-entailment performance. While our ability to interpret these results is limited by nascent scientific understanding of LLMs and the proprietary nature of GPT, we believe that these results strongly suggest that an LLM will pass the MBE component of the Bar Exam in the near future.”
Backgrounder.
https://www.bespacific.com/annotated-history-of-modern-ai-and-deep-learning/
Annotated History of Modern AI and Deep Learning
Annotated History of Modern AI and Deep Learning – Juergen Schmidhube. [v2] Thu, 29 Dec 2022 11:38:07 UTC. https://doi.org/10.48550/arXiv.2212.11279
“Machine learning is the science of credit assignment: finding patterns in observations that predict the consequences of actions and help to improve future performance. Credit assignment is also required for human understanding of how the world works, not only for individuals navigating daily life, but also for academic professionals like historians who interpret the present in light of past events. Here I focus on the history of modern artificial intelligence (AI) which is dominated by artificial neural networks (NNs) and deep learning, both conceptually closer to the old field of cybernetics than to what’s been called AI since 1956 (e.g., expert systems and logic programming). A modern history of AI will emphasize breakthroughs outside of the focus of traditional AI text books, in particular, mathematical foundations of today’s NNs such as the chain rule (1676), the first NNs (linear regression, circa 1800), and the first working deep learners (1965-). From the perspective of 2022, I provide a timeline of the — in hindsight — most important relevant events in the history of NNs, deep learning, AI, computer science, and mathematics in general, crediting those who laid foundations of the field. The text contains numerous hyperlinks to relevant overview sites from my AI Blog. It supplements my previous deep learning survey (2015) which provides hundreds of additional references. Finally, to round it off, I’ll put things in a broader historic context spanning the time since the Big Bang until when the universe will be many times older than it is now.”
Tools & Techniques. This could be useful.
https://www.makeuseof.com/hoaxy-track-twitter-information-spread/
How to Track the Spread of Twitter Information With Hoaxy
With information spreading on Twitter more easily than ever, it's a good idea to check where it comes from. Hoaxy can help to visualize its spread.
… Hoaxy is a joint project between the Indiana University Network Science Institute (IUNI) and the Center for Complex Networks and Systems Research (CNetS), and uses the Twitter search API to visualize the spread of information.
As its name suggests, Hoaxy was designed with misinformation in mind. In addition to showing the origin of particular tweets, the tool can indicate whether retweets are (most likely) made by accounts operated by real people, or are automated (bot) accounts.
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