When observing task demonstrations, human apprentices are able to identify whether a given task is executed correctly long before they gain expertise in actually performing that task. Prior research into learning from demonstrations (LfD) has failed to capture this notion of the acceptability of an execution; meanwhile, temporal logics provide a flexible language for expressing task specifications. Inspired by this, we present Bayesian specification inference, a probabilistic model for inferring task specification as a temporal logic formula. We incorporate methods from probabilistic programming to define our priors, along with a domain-independent likelihood function to enable sampling-based inference. We demonstrate the efficacy of our model for inferring true specifications with over 90% similarity between the inferred specification and the ground truth, both within a synthetic domain and a real-world table setting task.

Learning from a few examples is a challenging computer vision task. Traditionally,meta-learning-based methods have shown promise towards solving this problem.Recent approaches show benefits by learning a feature extractor on the abundantbase examples and transferring these to the fewer novel examples. However, thefinetuning stage is often prone to overfitting due to the small size of the noveldataset. To this end, we propose Few shot Learning with hard Mixup (FeLMi)using manifold mixup to synthetically generate samples that helps in mitigatingthe data scarcity issue. Different from a naïve mixup, our approach selects the hardmixup samples using an uncertainty-based criteria. To the best of our knowledge,we are the first to use hard-mixup for the few-shot learning problem.

We consider the question of learning the natural parameters of a $k$ parameter \textit{minimal} exponential family from i.i.d.

We study core stability in non-centroid clustering under the max-loss objective, where each agent's loss is the maximum distance to other members of their cluster. We prove that for all $k\geq 3$ there exist metric instances with $n\ge 9$ agents, with $n$ divisible by $k$, for which no clustering lies in the $α$-core for any $α<2^{\frac{1}{5}}\sim 1.148$. The bound is tight for our construction. Using a computer-aided proof, we also identify a two-dimensional Euclidean point set whose associated lower bound is slightly smaller than that of our general construction. This is, to our knowledge, the first impossibility result showing that the core can be empty in non-centroid clustering under the max-loss objective.

When observing task demonstrations, human apprentices are able to identify whether a given task is executed correctly long before they gain expertise in actually performing that task. Prior research into learning from demonstrations (LfD) has failed to capture this notion of the acceptability of an execution; meanwhile, temporal logics provide a flexible language for expressing task specifications. Inspired by this, we present Bayesian specification inference, a probabilistic model for inferring task specification as a temporal logic formula. We incorporate methods from probabilistic programming to define our priors, along with a domain-independent likelihood function to enable sampling-based inference. We demonstrate the efficacy of our model for inferring true specifications with over 90% similarity between the inferred specification and the ground truth, both within a synthetic domain and a real-world table setting task.

With AI datacenters soaring power bills for households, a policy called'demand flexibility' could help ease grid strain A cheap, bipartisan tool could help the US meet increasing energy demand from AI datacenters while also easing soaring power bills for households, preventing deadly blackouts and helping the climate. The policy solution, called "demand flexibility", can be quickly deployed across the US. Demand flexibility essentially means rewarding customers for using less power during times of high demand, reducing strain on the grid or in some cases, selling energy they have captured by solar panels on their homes. Peak power demand is expected to grow by 20% over the next decade - driven by the dramatic rise of AI datacenters, onshoring of manufacturing, increasing use of EVs and growing need for air conditioning amid hotter summers. Increasing energy demand is putting states such as California and Texas at higher risk of life-threatening blackouts in extreme weather.

Meta Tells Its Metaverse Workers to Use AI to'Go 5X Faster' Mark Zuckerberg's metaverse chief is urging employees to adopt AI across every workflow as part of a broader shift inside the company. Meta CEO Mark Zuckerberg says most of the company's code will be written by AI in the next 18 months. A Meta executive in charge of building the company's metaverse products told employees that they should be using AI to "go 5X faster" according to an internal message obtained by 404 Media. "Metaverse AI4P: Think 5X, not 5%," the message, posted by Vishal Shah, Meta's VP of Metaverse, said (AI4P is AI for Productivity). The idea is that programmers should be using AI to work five times more efficiently than they are currently working--not just using it to go 5 more efficiently.

Abstract: We discuss newly uncovered cases of identity theft in the scientific peer-review process within artificial intelligence (AI) research, with broader implications for other academic procedures. We detail how dishonest researchers exploit the peer-review system by creating fraudulent reviewer profiles to manipulate paper evaluations, leveraging weaknesses in reviewer recruitment workflows and identity verification processes. The findings highlight the critical need for stronger safeguards against identity theft in peer review and academia at large, and to this end, we also propose mitigating strategies. Academia heavily relies on trust. This trust-based system, however, creates a significant vulnerability: identity theft.

Cox's Bazar, Bangladesh – The sound of children at play echoes through the verdant lanes of one of the dozens of refugee camps on the outskirts of Cox's Bazar, a densely populated coastal town in southeast Bangladesh. Just for a moment, the sounds manage to soften the harsh living conditions faced by the more than one million people who live here in the world's largest refugee camp. Described as the most persecuted people on the planet, the Rohingya Muslim refugees in Bangladesh may now be one of the most forgotten populations in the world, eight years after being ethnically cleansed from their homes in neighbouring Myanmar by a predominantely Buddhist military regime. "Cox's Bazar is ground zero for the impact of budget cuts on people in desperate need," UN Secretary-General Antonio Guterres said during a visit to the sprawling camps in May. The UN chief's visit followed United States President Donald Trump's gutting of the US Agency for International Development (USAID), which has stalled several key projects in the camps, and the United Kingdom announcing cuts to foreign aid in order to increase defence spending.