LexLab invites you to its annual Demo Day & Pitch Competition. Starting at 2pm, we will hear pitches from law students in our "Building a Legal Tech Startup" class. Then, after a short break, we will hear presentations from the six companies in LexLab's accelerator program: Aurelian Aurelian seeks to democratize access to litigation funding, allowing investors of all kinds the opportunity to invest through tokenized security in a fund style vehicle with exposure to various contingency based case types. By leveraging innovative corporate structures, deep industry knowledge, years of cultivated relationships, & cutting edge technology, Aurelian will open up investment opportunities to a larger pool of potential investors. BackedMe BackedMe is on a mission to drive access to resolution with case-by-case diagnosis, conflict assessment & recommendations.

Transformer-based large language models are rapidly advancing in the field of machine learning research, with applications spanning natural language, biology, chemistry, and computer programming. Extreme scaling and reinforcement learning from human feedback have significantly improved the quality of generated text, enabling these models to perform various tasks and reason about their choices. In this paper, we present an Intelligent Agent system that combines multiple large language models for autonomous design, planning, and execution of scientific experiments. We showcase the Agent's scientific research capabilities with three distinct examples, with the most complex being the successful performance of catalyzed cross-coupling reactions. Finally, we discuss the safety implications of such systems and propose measures to prevent their misuse.

The stochastic generalised linear bandit is a well-understood model for sequential decision-making problems, with many algorithms achieving near-optimal regret guarantees under immediate feedback. However, the stringent requirement for immediate rewards is unmet in many real-world applications where the reward is almost always delayed. We study the phenomenon of delayed rewards in generalised linear bandits in a theoretical manner. We show that a natural adaptation of an optimistic algorithm to the delayed feedback achieves a regret bound where the penalty for the delays is independent of the horizon. This result significantly improves upon existing work, where the best known regret bound has the delay penalty increasing with the horizon. We verify our theoretical results through experiments on simulated data.

Large language models (LLMs) have shown incredible capabilities and transcended the natural language processing (NLP) community, with adoption throughout many services like healthcare, therapy, education, and customer service. Since users include people with critical information needs like students or patients engaging with chatbots, the safety of these systems is of prime importance. Therefore, a clear understanding of the capabilities and limitations of LLMs is necessary. To this end, we systematically evaluate toxicity in over half a million generations of ChatGPT, a popular dialogue-based LLM. We find that setting the system parameter of ChatGPT by assigning it a persona, say that of the boxer Muhammad Ali, significantly increases the toxicity of generations. Depending on the persona assigned to ChatGPT, its toxicity can increase up to 6x, with outputs engaging in incorrect stereotypes, harmful dialogue, and hurtful opinions. This may be potentially defamatory to the persona and harmful to an unsuspecting user. Furthermore, we find concerning patterns where specific entities (e.g., certain races) are targeted more than others (3x more) irrespective of the assigned persona, that reflect inherent discriminatory biases in the model. We hope that our findings inspire the broader AI community to rethink the efficacy of current safety guardrails and develop better techniques that lead to robust, safe, and trustworthy AI systems.

Monumental advancements in artificial intelligence (AI) have lured the interest of doctors, lenders, judges, and other professionals. While these high-stakes decision-makers are optimistic about the technology, those familiar with AI systems are wary about the lack of transparency of its decision-making processes. Perturbation-based post hoc explainers offer a model agnostic means of interpreting these systems while only requiring query-level access. However, recent work demonstrates that these explainers can be fooled adversarially. This discovery has adverse implications for auditors, regulators, and other sentinels. With this in mind, several natural questions arise - how can we audit these black box systems? And how can we ascertain that the auditee is complying with the audit in good faith? In this work, we rigorously formalize this problem and devise a defense against adversarial attacks on perturbation-based explainers. We propose algorithms for the detection (CAD-Detect) and defense (CAD-Defend) of these attacks, which are aided by our novel conditional anomaly detection approach, KNN-CAD. We demonstrate that our approach successfully detects whether a black box system adversarially conceals its decision-making process and mitigates the adversarial attack on real-world data for the prevalent explainers, LIME and SHAP.

Online federated learning (FL) enables geographically distributed devices to learn a global shared model from locally available streaming data. Most online FL literature considers a best-case scenario regarding the participating clients and the communication channels. However, these assumptions are often not met in real-world applications. Asynchronous settings can reflect a more realistic environment, such as heterogeneous client participation due to available computational power and battery constraints, as well as delays caused by communication channels or straggler devices. Further, in most applications, energy efficiency must be taken into consideration. Using the principles of partial-sharing-based communications, we propose a communication-efficient asynchronous online federated learning (PAO-Fed) strategy. By reducing the communication overhead of the participants, the proposed method renders participation in the learning task more accessible and efficient. In addition, the proposed aggregation mechanism accounts for random participation, handles delayed updates and mitigates their effect on accuracy. We prove the first and second-order convergence of the proposed PAO-Fed method and obtain an expression for its steady-state mean square deviation. Finally, we conduct comprehensive simulations to study the performance of the proposed method on both synthetic and real-life datasets. The simulations reveal that in asynchronous settings, the proposed PAO-Fed is able to achieve the same convergence properties as that of the online federated stochastic gradient while reducing the communication overhead by 98 percent.

DoNotPay says it is pivoting away from plans to bring AI to a courtroom. DoNotPay, which bills itself as "the world's first robot lawyer," said last month that it planned to take on two speeding ticket cases in court in February, with its AI instructing the defendants how to respond to their assigned judges. The startup said it would cover any fines and the defendants will be compensated for taking part in the experiment. But CEO and founder Joshua Browder announced late last month that it would be "postponing" those plans, citing "threats from State Bar prosecutors." "Ultimately, it seemed like a distraction from using chatGPT technology to help with consumer rights issues," Browder said in an emailed statement. "We have decided to focus on consumer rights products, where we are very successful.

Fox News contributor Jonathan Turley describes how ChatGPT falsely accused him and other professors of sexual harassment, made up a fake Washington Post story and concocted a fake quote as some news sites invest into AI written news stories. George Washington University law professor Jonathan Turley doubled down on warnings surrounding the dangers of artificial intelligence (AI) on Monday after he was falsely accused of sexual harassment by the online bot ChatGPT, which cited a fabricated article supporting the allegation. Turley, a Fox News contributor, has been outspoken about the pitfalls of artificial intelligence and has publicly expressed concerns with the disinformation dangers of the ChatGPT bot, the latest iteration of the AI chatbot. Last week, a UCLA professor and friend of Turley's notified him that his name appeared in a search while he was conducting research on ChatGPT. The bot was asked to cite "five examples" of "sexual harassment" by U.S. law professors with "quotes from relevant newspaper articles" to support it.

When Sen. Chris Murphy watched the video "A.I. Dilemma," he saw a familiar face. Tristan Harris, a tech ethicist well-known among lawmakers for ringing the alarm about the harmful effects of social networks, was now arguing that artificial intelligence represents a potentially catastrophic advance - riskier perhaps to human survival than the advent of nuclear weapons. The video's message - which has been embraced by some tech luminaries like Apple co-founder Steve Wozniak - resonated with Murphy (D-Conn.), who quickly fired off a tweet. We aren't ready," the senator warned.

Cutting-edge artificial intelligence systems can help you escape a parking fine, write an academic essay, or fool you into believing Pope Francis is a fashionista. The enormous datasets used to train the latest generation of these AI systems, like those behind ChatGPT and Stable Diffusion, are likely to contain billions of images scraped from the internet, millions of pirated ebooks, the entire proceedings of 16 years of the European parliament and the whole of English-language Wikipedia. But the industry's voracious appetite for big data is starting to cause problems, as regulators and courts around the world crack down on researchers hoovering up content without consent or notice. In response, AI labs are fighting to keep their datasets secret, or even daring regulators to push the issue. In Italy, ChatGPT has been banned from operating after the country's data protection regulator said there was no legal basis to justify the collection and "massive storage" of personal data in order to train the GPT AI.