Effective sub-typing (also known as grouping or clustering) of patients using their electronic health record (EHR) data can greatly inform precision medicine efforts. However, subtyping temporal EHR datasets is known to be challenging due to inherent EHR issues, including complexity and irregularity. In this study, we propose a self-supervised Mamba-based model that learns effective EHR representations and enables enhanced patient subtyping. We evaluate the proposed model on public and private real-world EHR datasets to classify the data based on the available labels and subtype patients based on the representations learned from the model. Through an extensive set of experiments, we demonstrate that our model's design choices lead to better performance compared to competitive baseline models for prediction. Moreover, we evaluate several clustering techniques to demonstrate that our findings offer valuable insights into subtyping patients based on temporal records from EHR models\footnote{Our implementations are available at https://github.com/healthylaife/triplet_mamba.

Tesla chief Elon Musk's $1tn pay package has been approved. Tesla chief Elon Musk's $1tn pay package has been approved. Chants of'Elon' erupt after compensation plan approved despite opposition from several high-profile investors Tesla shareholders approved a $1tn compensation plan for CEO Elon Musk on Thursday, awarding the world's richest person what would be the largest corporate payout in history if he meets the goals necessary to receive it. The pay package, which several high-profile investors opposed, demonstrates that shareholders still believe Musk can lead the automaker in an era dominated by robotics and artificial intelligence. The result of the vote was announced at the annual shareholder event in Austin, Texas, with more than 75% of investors voting in favor of the plan.

While Artificial Intelligence (AI) is not a new field, recent developments, especially with the release of generative tools like ChatGPT, have brought it to the forefront of the minds of industry workers and academic folk alike. There is currently much talk about AI and its ability to reshape many everyday processes as we know them through automation. It also allows users to expand their ideas by suggesting things they may not have thought of on their own and provides easier access to information. However, not all of the changes this technology will bring or has brought so far are positive; this is why it is extremely important for all modern people to recognize and understand the risks before using these tools and allowing them to cause harm. This work takes a position on better understanding many equity concerns and the spread of misinformation that result from new AI, in this case, specifically ChatGPT and deepfakes, and encouraging collaboration with law enforcement, developers, and users to reduce harm. Considering many academic sources, it warns against these issues, analyzing their cause and impact in fields including healthcare, education, science, academia, retail, and finance. Lastly, we propose a set of future-facing guidelines and policy considerations to solve these issues while still enabling innovation in these fields, this responsibility falling upon users, developers, and government entities.

As Artificial Intelligence (AI) continues to grow daily, more exciting (and somewhat controversial) technology emerges every other day. As we see the advancements in AI, we see more and more people becoming skeptical of it. This paper explores the complications and confusion around the ethics of generative AI art. We delve deep into the ethical side of AI, specifically generative art. We step back from the excitement and observe the impossible conundrums that this impressive technology produces. Covering environmental consequences, celebrity representation, intellectual property, deep fakes, and artist displacement. Our research found that generative AI art is responsible for increased carbon emissions, spreading misinformation, copyright infringement, unlawful depiction, and job displacement. In light of this, we propose multiple possible solutions for these problems. We address each situation's history, cause, and consequences and offer different viewpoints. At the root of it all, though, the central theme is that generative AI Art needs to be correctly legislated and regulated.

In the latest development in an ongoing struggle over OpenAI's future direction--and potentially the future of artificial intelligence itself--dozens of prominent figures are urging the Attorneys General of California and Delaware to block OpenAI's controversial plan to convert from its unique nonprofit-controlled structure to a for-profit company. In a letter made public April 23, signatories including "AI Godfather" Geoffrey Hinton, Harvard legal professor Lawrence Lessig, and several former OpenAI researchers argue the move represents a fundamental betrayal of OpenAI's founding mission. "The proposed restructuring would eliminate essential safeguards, effectively handing control of, and profits from, what could be the most powerful technology ever created to a for-profit entity with legal duties to prioritize shareholder returns," the letter's authors write. It lands as OpenAI faces immense pressure from the other side: failing to implement the restructure by the end of the year could cost the company 20 billion and hamstring future fundraising. OpenAI was founded in 2015 as a non-profit, with its stated mission being to ensure that artificial general intelligence (AGI) "benefits all of humanity" rather than advancing "the private gain of any person."

On the Friday after Christmas, OpenAI published a blog post titled "Why OpenAI's structure must evolve to advance our mission." In it, the company detailed a plan to reorganize its for-profit arm into a public benefit corporation (PBC). In the weeks since that announcement, I've spoken to some of the country's leading corporate law experts to gain a better understanding of OpenAI's plan, and, more importantly, what it might mean for its mission to build safe artificial general intelligence (AGI). "Public benefit corporations are a relatively recent addition to the universe of business entity types," says Jens Dammann, professor of corporate law at the University of Texas School of Law. Depending on who you ask, you may get a different history of PBCs, but in the dominant narrative, they came out of a certification program created by a nonprofit called B Lab. Companies that complete a self-assessment and pay an annual fee to B Lab can carry the B Lab logo on their products and websites and call themselves B-Corps.

Elon Musk has won back his 44.9bn pay package at electric carmaker Tesla after shareholders voted to restore the compensation deal in a ringing endorsement of his leadership. The vote at Tesla's annual meeting on Thursday came after a judge in the US state of Delaware threw out the deal after finding that the company's board was too close to Musk and had not protected shareholders' interests. "I just want to start off by saying, hot damn, I love you guys!" a jubilant Musk said as he appeared on stage after the vote. "We have the most awesome shareholder base. I mean it's just incredible."

Elon Musk has announced Tesla will hold a vote on moving the company's legal base to Texas after the state of Delaware threw out his 56bn pay package at the electric vehicle maker. The world's richest person, whose No 1 status is endangered by the Delaware ruling, held a poll on X asking whether Tesla should change the company's state of incorporation from Delaware to Texas. With more than 1m votes cast, the poll recorded 87% in favour of moving. Responding on Thursday, Musk wrote on his X account: "The public vote is unequivocally in favour of Texas! Tesla will move immediately to hold a shareholder vote to transfer state of incorporation to Texas."

In 2018, Tesla awarded Elon Musk a 56 billion pay package that helped propel him to the top of world's richest lists. Now, a judge in Delaware has rendered the deal between the company and the CEO to be invalid and called the compensation an "unfathomable sum" that's unfair to shareholders. As initially seen and reported by Chancery Daily on Threads, the court of Chancery in Delaware has released its decision on the lawsuit filed by Richard Tornetta. The Tesla shareholder accused the automaker of breaching its fiduciary duty by approving a package that unjustly enriches its chief executive. Judge Kathaleen McCormick wrote in the decision that Musk "enjoyed thick ties" with the directors who were in charge of negotiating his pay package on behalf of Tesla, which means there "was no meaningful negotiation over any of the terms of the plan."

We introduce HallusionBench, a comprehensive benchmark designed for the evaluation of image-context reasoning. This benchmark presents significant challenges to advanced large visual-language models (LVLMs), such as GPT-4V(Vision) and LLaVA-1.5, by emphasizing nuanced understanding and interpretation of visual data. The benchmark comprises 346 images paired with 1129 questions, all meticulously crafted by human experts. We introduce a novel structure for these visual questions designed to establish control groups. This structure enables us to conduct a quantitative analysis of the models' response tendencies, logical consistency, and various failure modes. In our evaluation on HallusionBench, we benchmarked 13 different models, highlighting a 31.42% question-pair accuracy achieved by the state-of-the-art GPT-4V. Notably, all other evaluated models achieve accuracy below 16%. Moreover, our analysis not only highlights the observed failure modes, including language hallucination and visual illusion, but also deepens an understanding of these pitfalls. Our comprehensive case studies within HallusionBench shed light on the challenges of hallucination and illusion in LVLMs. Based on these insights, we suggest potential pathways for their future improvement. The benchmark and codebase can be accessed at https://github.com/tianyi-lab/HallusionBench.