Meta has nuked a bunch of its AI-generated profiles from Facebook Instagram, the company confirmed, after the AI characters prompted widespread outrage and ridicule from users on social media. The AI-generated profiles, which were labeled as "AI managed by Meta," launched in September of 2023, rolling out alongside the company's celebrity-branded AI chatbots ( also discontinued). Meta doesn't seem to have updated any of these profiles for several months, and the pages seem to have been largely unnoticed until this week, following an interview published by the Financial Times with Meta's VP of Generative AI, Connor Hayes. In the interview, Hayes spoke about the company's goal to eventually fill its services with AI-generated profiles that can interact with people and function "kind of in the same way that accounts do." Those comments brought attention to the extant fMeta-created AI profiles and, well, users were not exactly impressed with what they found.

The next time you DM a creator on Instagram, you might get a reply from their AI. Meta is starting to roll out its AI Studio, a set of tools that will allow Instagram creators to make an AI persona that can answer questions and chat with their followers and fans on their behalf. The company first introduced AI Studio at its Connect event last fall but it only recently began to test creator-made AIs with a handful of prominent Instagrammers. Now, Meta is making the tools available to more US-based creators and giving the rest of its users the chance to experiment with specialized AI "characters." According to Meta, the new creator AIs are meant to address a long-running issue for Instagram users with large followings: it can be nearly impossible for the service's most popular users to keep up with the flood of messages they receive every day.

An automated Veterans Affairs system meant to help accelerate claims decisions actually helped contribute to inaccurate ratings on 27% of high blood pressure claims. A VA Office of the Inspector General (OIG) report published last week found that more than a quarter of the 60 reviewed high blood pressure claims that were handled by the Automated Benefits Delivery System resulted in wrongful claims decisions for veterans, according to a report from Military.com. The system was introduced in December 2021, ahead of what the VA believed was going to be a "flood" of disability applications as a result of the PACT Act, with Vietnam-era veterans filing high blood pressure claims under the act after their exposure to Agent Orange, an exposure linked to hypertension. 'WE'RE HUMAN': DELTA FORCE VETERAN REFLECTS ON BATTLE OF MOGADISHU 30 YEARS LATER The automated system was designed to pull blood pressure readings and other high blood pressure data from VA treatment recons and create a summary that is reviewed by VA staff, who make the final decision on the claim. But incomplete data compiled by the system led to several incorrect decisions, the IG's office found in its review, which recommended that the VA make improvements to the technology and the quality assurance process.

There has been significant progress in creating machine learning models that identify objects in scenes along with their associated attributes and relationships; however, there is a large gap between the best models and human capabilities. One of the major reasons for this gap is the difficulty in collecting sufficient amounts of annotated relations and attributes for training these systems. While some attributes and relations are abundant, the distribution in the natural world and existing datasets is long tailed. In this paper, we address this problem by introducing a novel incremental active learning framework that asks for attributes and relations in visual scenes. While conventional active learning methods ask for labels of specific examples, we flip this framing to allow agents to ask for examples from specific categories. Using this framing, we introduce an active sampling method that asks for examples from the tail of the data distribution and show that it outperforms classical active learning methods on Visual Genome.

This paper describes our ongoing research effort to explore how personality types factor into HRI; in particular, the degree of patience a person has when teaching an error-prone robot in a learning from demonstration setting.Our goal is to establish personality metrics that will ultimately allow for the design of algorithms that automatically tune robot behavior to best suit user preferences based on personality.

Developing collaborative robots that can productively operate out of isolation and work safely in uninstrumented, human-populated environments is critically important for advancing the field of robotics. Especially in domains where modern robots are ineffective, we wish to leverage human-robot teaming to improve the efficiency, ability, and safety of human workers. Our work, outlined in this extended abstract, focuses on creating agents capable of human-robot teamwork by leveraging learning from demonstration, hierarchical task networks, multi-agent planning and state estimation, and intention recognition. We briefly describe our recent work within human-robot collaboration, including task comprehension, learning and performing assistive behaviors, and training novice human collaborators to become competent co-workers.

You are free to share this article under the Attribution 4.0 International license. Using artificial intelligence, researchers have developed a genetic model for the "ultimate" chickpea, with the potential to lift crop yields by up to 12%. Researchers genetically mapped thousands of chickpea varieties, and then used this information to identify the most valuable gene combinations using artificial intelligence (AI). Researchers wanted to to develop a "haplotype" genomic prediction crop breeding strategy, for enhanced performance for seed weight. "Most crop species only have a few varieties sequenced, so it was a massive undertaking by the international team to analyze more than 3,000 cultivated and wild varieties," says Ben Hayes, professor at the University of Queensland.

TechRepublic's Karen Roby talked with Will Hayes, CEO of Lucidworks, about how artificial intelligence can better help retailers understand customer intent when shopping online. The following is an edited transcript of their conversation. Karen Roby: We all have a tendency, I think, from time-to-time to abandon our carts. We put something in, we take it out, or we just leave it there and we go onto the next site. What typically happens with shoppers, Will?

Humans are incredibly good at transferring knowledge from one domain to another, enabling rapid learning of new tasks. Likewise, transfer learning has enabled enormous success in many computer vision problems using pretraining. However, the benefits of transfer in multi-domain learning, where a network learns multiple tasks defined by different datasets, has not been adequately studied. Learning multiple domains could be beneficial or these domains could interfere with each other given limited network capacity. In this work, we decipher the conditions where interference and knowledge transfer occur in multi-domain learning. We propose new metrics disentangling interference and transfer and set up experimental protocols. We further examine the roles of network capacity, task grouping, and dynamic loss weighting in reducing interference and facilitating transfer. We demonstrate our findings on the CIFAR-100, MiniPlaces, and Tiny-ImageNet datasets.

Using drones and artificial intelligence to monitor large colonies of seabirds can be as effective as traditional on-the-ground methods, while reducing costs, labor and the risk of human error, a new study finds. Scientists at Duke University and the Wildlife Conservation Society (WCS) used a deep-learning algorithm--a form of artificial intelligence--to analyze more than 10,000 drone images of mixed colonies of seabirds in the Falkland Islands off Argentina's coast. The Falklands, also known as the Malvinas, are home to the world's largest colonies of black-browed albatrosses (Thalassarche melanophris) and second-largest colonies of southern rockhopper penguins (Eudyptes c. chrysocome). Hundreds of thousands of birds breed on the islands in densely interspersed groups. The deep-learning algorithm correctly identified and counted the albatrosses with 97% accuracy and the penguins with 87%.