Breakthroughs, discoveries, and DIY tips sent every weekday. Of the over 500 known shark species, many of these giant fish are hunters and not scavengers–particularly those found in the open ocean. White sharks (Carcharodon carcharias) ambush their prey from below and even sharks closer to shore like reef sharks are known to chase their prey into smaller crevices before eating them. Yet a small portion of the diets of most sharks still comes from picking apart already dead animals. In a study published May 29 in the journal Frontiers in Fish Science, a team from the University of Hawaiʻi at Mānoa describe an unusual aggregation of sharks coming together to feed on a decaying carcass of an unidentified animal. "To our knowledge, this is the first study to document a feeding aggregation of tiger sharks and oceanic whitetip sharks scavenging concurrently, and peacefully, on a carcass," study co-author Molly Scott said in a statement.

Implicit bias describes the phenomenon where optimization-based training algorithms, without explicit regularization, show a preference for simple estimators even when more complex estimators have equal objective values. Multiple works have developed the theory of implicit bias for binary classification under the assumption that the loss satisfies an exponential tail property. However, there is a noticeable gap in analysis for multiclass classification, with only a handful of results which themselves are restricted to the cross-entropy loss. In this work, we employ the framework of Permutation Equivariant and Relative Margin-based (PERM) losses [Wang and Scott, 2024] to introduce a multiclass extension of the exponential tail property. This class of losses includes not only cross-entropy but also other losses. Using this framework, we extend the implicit bias result of Soudry et al. [2018] to multiclass classification.

We investigate the data complexity of the satisfiability problem for the very expressive description logic ZOIQ (a.k.a. ALCHb Self reg OIQ) over quasi-forests and establish its NP-completeness. This completes the data complexity landscape for decidable fragments of ZOIQ, and reproves known results on decidable fragments of OWL2 (SR family). Using the same technique, we establish coNEXPTIME-completeness (w.r.t. the combined complexity) of the entailment problem of rooted queries in ZIQ.

We develop a classification algorithm for estimating posterior distributions from positive-unlabeled data, that is robust to noise in the positive labels and effective for high-dimensional data. In recent years, several algorithms have been proposed to learn from positive-unlabeled data; however, many of these contributions remain theoretical, performing poorly on real high-dimensional data that is typically contaminated with noise. We build on this previous work to develop two practical classification algorithms that explicitly model the noise in the positive labels and utilize univariate transforms built on discriminative classifiers. We prove that these univariate transforms preserve the class prior, enabling estimation in the univariate space and avoiding kernel density estimation for high-dimensional data. The theoretical development and parametric and nonparametric algorithms proposed here constitute an important step towards wide-spread use of robust classification algorithms for positive-unlabeled data.

Two weeks ago, I gave a lecture setting out my current thoughts on AI safety, halfway through my year at OpenAI. I was asked to speak by UT Austin's Effective Altruist club. You can watch the lecture on YouTube here (I recommend 2x speed). The timing turned out to be weird, coming immediately after the worst disaster to hit the Effective Altruist movement in its history, as I acknowledged in the talk. I then spent 20 minutes taking questions. For those who (like me) prefer text over video, below I've produced an edited transcript, by starting with YouTube's automated transcript and then, well, editing it. Thank you so much for inviting me here. I do feel a little bit sheepish to be lecturing you about AI safety, as someone who's worked on this subject for all of five months. But this past spring, I accepted an extremely interesting opportunity to go on leave for a year to think about what theoretical computer science can do for AI safety. I'm doing this at OpenAI, which is one of the world's leading AI startups, based in San Francisco although I'm mostly working from Austin. Despite its name, OpenAI is famously not 100% open … so there are certain topics that I'm not allowed to talk about, like the capabilities of the very latest systems and whether or not they'll blow people's minds when released. By contrast, OpenAI is very happy for me to talk about AI safety: what it is and and what if anything can we do about it. So what I thought I'd do is to tell you a little bit about the specific projects that I've been working on at OpenAI, but also just, as an admitted newcomer, share some general thoughts about AI safety and how Effective Altruists might want to think about it. I'll try to leave plenty of time for discussion. Maybe I should mention that the thoughts that I'll tell you today are ones that, until last week, I had considered writing up for an essay contest run by something called the FTX Future Fund. Unfortunately, the FTX Future Fund no longer exists. It was founded by someone named Sam Bankman-Fried, whose a net worth went from 15 billion dollars to some negative number of dollars in the space of two days, in one of the biggest financial scandals in memory. This is obviously a calamity for the EA community, which had been counting on funding from this individual. I feel terrible about all the projects left in the lurch, to say nothing of FTX's customers. Let's start with this: raise your hand if you've tried GPT-3.

Holograms, however, are inherently limited. They require audiences to sit at a specific angle to get the illusion of the artist performing in 3D. The metaverse offers a way for people to see a more lifelike avatar and even potentially interact with it--something the team behind Smalls's gig hopes to be able to offer in the near future. What's remarkable about Smalls's performance on Friday was the realism. His moves, mannerisms, and facial expressions were stunningly lifelike.

In a previous article I described the problems and progress of AI reading comprehension systems. In the last few years, AI writing systems have also improved significantly because of the emergence of an AI neural network called GPT-3. It's barely two years since GPT-3 was created but the number use cases. It has paved a path for numerous business start-ups including, story writing, blog writing, chatbots, news report writing and even quiz generation. The list is continuing to grow as developers become aware of its potential.

Today, Canva announces Magic Write, a text-generation tool that can generate everything from ideas for blog posts to a cover letter. But AI text is quietly -- and probably inevitably -- moving into more products you'll use on a regular basis. AI art is already there. Microsoft Designer, a visual design tool that seamlessly integrates text-to-image AI art is in preview and should eventually be part of Microsoft 365. But rival Canva, which staked out its own text-to-image AI art space before Designer launched, is moving further into Microsoft's territory with the new Magic Write feature for Canva Docs.

Artificial Intelligence (AI) is much in the news these days. As a concept, AI seems completely unrelated to the field of law. Although, AI and Law are intricately intertwined and are becoming more so each day. In this lecture, Professor Harry Surden – a former software engineer and leader of the emerging interdisciplinary field of AI and Law – will explore: What is Artificial Intelligence? How is law affecting Artificial Intelligence?

Detroit carjackings are up 40 percent compared to last year - and the ages of the kids doing them – have become unbelievable. A Tennessee man stands accused of carjacking his Tinder date at gunpoint last year and trying to sell her car back to her for $500. Elijah Darius Scott, 25, of Memphis, Tennessee, landed in the Shelby County Jail on Tuesday after being charged with carjacking and aggravated robbery, as well as employment of a firearm during a dangerous felony, according to local outlet WREG. The alleged victim informed officers that after agreeing to meet a man she knew only as Darius, Scott entered the passenger side of the vehicle, placed a gun next to her and demanded her phone and money while threatening to shoot her. Elijah Darius Scott, 25, allegedly carjacked a Memphis woman and offered to sell her car back to her for $500.