Gap the (Theory of) Mind: Sharing Beliefs About Teammates' Goals Boosts Collaboration Perception, Not Performance Abstract --In human-agent teams, openly sharing goals is often assumed to enhance planning, collaboration, and effectiveness. However, direct communication of these goals is not always feasible, requiring teammates to infer their partner's intentions through actions. Building on this, we investigate whether an AI agent's ability to share its inferred understanding of a human teammate's goals can improve task performance and perceived collaboration. Through an experiment comparing three conditions--no recognition (NR), viable goals (VG), and viable goals on-demand (VGod)--we find that while goal-sharing information did not yield significant improvements in task performance or overall satisfaction scores, thematic analysis suggests that it supported strategic adaptations and subjective perceptions of collaboration. Cognitive load assessments revealed no additional burden across conditions, highlighting the challenge of balancing informativeness and simplicity in human-agent interactions. These findings highlight the nuanced trade-off of goal-sharing: while it fosters trust and enhances perceived collaboration, it can occasionally hinder objective performance gains. In human-agent collaboration, effective teamwork often depends on the agent's ability to interpret and act upon the human teammate's intentions. Ad-hoc teamwork [1], where team members must collaborate effectively without prior planning, exemplifies contexts where this capability is critical. Explainable AI (XAI) aims to address this by enhancing transparency and interpretability in AI systems, fostering shared mental models, trust, and mutual understanding [2], [3].

Ease of access to data, tools and models expedites scientific research. In structural biology there are now numerous open repositories of experimental and simulated datasets. Being able to easily access and utilise these is crucial for allowing researchers to make optimal use of their research effort. The tools presented here are useful for collating existing public cryoEM datasets and/or creating new synthetic cryoEM datasets to aid the development of novel data processing and interpretation algorithms. In recent years, structural biology has seen the development of a multitude of machine-learning based algorithms for aiding numerous steps in the processing and reconstruction of experimental datasets and the use of these approaches has become widespread. Developing such techniques in structural biology requires access to large datasets which can be cumbersome to curate and unwieldy to make use of. In this paper we present a suite of Python software packages which we collectively refer to as PERC (profet, EMPIARreader and CAKED). These are designed to reduce the burden which data curation places upon structural biology research. The protein structure fetcher (profet) package allows users to conveniently download and cleave sequences or structures from the Protein Data Bank or Alphafold databases. EMPIARreader allows lazy loading of Electron Microscopy Public Image Archive datasets in a machine-learning compatible structure. The Class Aggregator for Key Electron-microscopy Data (CAKED) package is designed to seamlessly facilitate the training of machine learning models on electron microscopy data, including electron-cryo-microscopy-specific data augmentation and labelling. These packages may be utilised independently or as building blocks in workflows. All are available in open source repositories and designed to be easily extensible to facilitate more advanced workflows if required.

Fetcher.ai, a recruitment platform that combines AI with human teams, today announced it has raised $6.5 million in a round led by G20 Ventures. The company, whose latest funding round brings its total to $12 million, says the funds will be used to expand the size of its workforce. In 2020, talent shortages in the U.S. rose to historic levels, with 69% of employers reporting having difficulty filling jobs, according to a ManPowerGroup survey. A report by the Society for Human Resource Management found that filling an open position costs employers an average of $4,129 and takes roughly 42 days. Fetcher was founded in 2014 as a consumer-centric messaging app called Caliber, which focused on professional networking.

Armed with a bottle of Lysol and rolls of paper towels, Anya Fetcher packed up her car with enough food to get her through a road trip, and clothes to last several weeks, and headed to a friend's home. The first thing she did when she arrived was ask for a hug. "He started to pull away and I was like, 'Wait, can we just stay here for another second? It's been four weeks since [I've had] any kind of human contact,' " she told me. Thanks to the pandemic, a month of no physical interaction with another human--no hugs, no handshakes, no high-fives or fist bumps--had taken a toll on her mental health.

Many organizations are trying to recruit more female and minority job candidates. Many organizations are struggling to find strategies for recruiting a more diverse workforce, and some are turning to artificial intelligence (AI). But artificial intelligence got a bad rap last year when news got out that Amazon's internal AI recruiting tool had "learned" gender bias. So, is AI beneficial to those seeking diversity, or will it just exacerbate the problem? One recruiting firm has found that AI is an effective strategy for increasing the diversity of candidate pools, as long as its implemented correctly.

The workforce crisis is looming, a situation that could hit the global economy to the tune of $10 trillion, according to some studies. The crux of the problem relates to a mismatch between supply and demand, with some economies facing a workforce shortage and others a surplus. "An equilibrium in supply and demand is rapidly becoming the exception, not the norm," a report from the Boston Consulting Group (BCG) noted. "Between 2020 and 2030, we project significant worldwide labor-force imbalances -- shortfalls, in particular. One significant implication is the potential aggregate value of GDP squandered, because either these nations cannot fill the jobs available or they cannot create enough jobs for the workers they have."