Understanding the workplace needs of autistic adults in Singapore: insights to inform inclusive AI support

IntroductionAutistic adults face persistent challenges in obtaining and sustaining meaningful employment. Despite growing attention to workplace inclusion, research on autistic adults’ employment experiences in non-Western contexts remains scarce. The potential of emerging technologies, such as large language models (LLMs), to support workplace integration is also still largely unexplored, yet off-the-shelf models may reflect neurotypical norms that risk reinforcing masking or overlooking neurodivergent needs. This qualitative, participatory study therefore centered autistic adults’ perspectives to understand workplace experiences and identify potential LLM affordances that could facilitate integration.MethodsTwenty autistic adults with at least 3 months of work experience were recruited in Singapore. Data was collected through semi-structured group discussions. Using reflexive thematic analysis, we identified workplace challenges, needs, and potential support LLMs can provide. We had a multidisciplinary team of autistic and non-autistic researchers, and autistic perspectives actively shaped the design, conduct, and interpretation of the research.ResultsTwo overarching themes emerged: (1) assumed neurotypicality of the workplace, evident in work processes and social participation, and (2) need for workplace inclusivity, supported through both individual accommodations and systemic change, including identity-affirming support, alignment of work design and tools with neurodivergent working styles, empowered access to supports and accommodations, and shared responsibility for workplace integration. Potential LLM functionalities involve supporting executive functioning, encouraging self-reflection, and fostering mutual understanding between autistic employees and their coworkers.DiscussionWorkplace barriers for autistic employees often stem from assumed neurotypical norms rather than individual deficits. Participants reported challenges related to ambiguous work processes, implicit social expectations, and executive functioning demands, which reflect a mismatch between workplace structures and neurodivergent ways of working. Crucially, inclusivity cannot rely solely on individual accommodations; meaningful workplace inclusion requires systemic change. Designing LLM tools that align with neurodivergent working styles can complement systemic inclusivity efforts and empower autistic employees. Implications and future directions are discussed.

Effects of an Eye-Tracking Digital Serious Game on Cognitive Function in Mild Cognitive Impairment: Pilot Intervention Study

<strong>Background:</strong> Cognitive decline in aging populations underscores the need for early interventions in mild cognitive impairment (MCI), where pharmacological treatments show limited benefit. Eye-movement metrics serve as sensitive markers of cognitive deficits in MCI, and digital programs integrating these tasks offer scalable, data-driven training approaches. <strong>Objective:</strong> This study aimed to evaluate the effectiveness of a digital cognitive training program incorporating eye-movement tasks in individuals with MCI, and to determine whether eye-movement indicators can serve as objective markers of cognitive improvement. <strong>Methods:</strong> A total of 12 participants aged 60-85 years with MCI (Korean version of the Montreal Cognitive Assessment [K-MoCA] score of ≤22) completed baseline and postintervention assessments using the K-MoCA and Mini-Mental State Examination-Korean version (MMSE-K). Longitudinal changes in visuospatial attention and oculomotor performance were examined using Spearman correlations across sessions, and pre-post comparisons of eye-tracking metrics were conducted to assess training-related improvements. <strong>Results:</strong> Cognitive scores improved significantly, with K-MoCA increasing by 1.5 points (from mean 20.3, SD 1.1 to mean 21.8, SD 1.7; <i>P</i>=<i>.</i>004; Cohen <i>d</i>=1.38) and MMSE-K by 1.3 points (from mean 21.9, SD 2.0 to mean 23.2, SD 2.2; <i>P</i>=<i>.</i>002; Cohen <i>d</i>=1.29). Fixation duration decreased (<i>r</i>=0.248; <i>P</i>=<i>.</i>003), and saccade velocity increased (<i>r</i>=0.258; <i>P</i>=<i>.</i>002), indicating enhanced visual processing efficiency and faster attentional shifts, whereas fixation count and saccade amplitude showed no consistent changes. In addition, saccade duration decreased by 21.72 ms, and saccade velocity increased by 114.54 °/s. <strong>Conclusions:</strong> Digital cognitive training yielded measurable gains in visuospatial attention and oculomotor efficiency in MCI, with optimized fixation and saccade patterns indicating enhanced attentional control and information processing. These findings support eye-movement metrics as sensitive indicators of cognitive change and highlight digital interventions as scalable, noninvasive tools for cognitive support in aging populations.

ADHD Brain Marker Value in Doubt

Longstanding links between attention-related problems and changes in the brain’s cortex during childhood and adolescence could simply be due to developmental differences between the sexes, a study suggests.

The findings, in PNAS, showed that links between attention problems and slowed rates of cortical thinning were no longer evident after taking sex into account.

The results call into question proposals to use brain maturation patterns as biomarkers for attention deficit/hyperactivity disorder (ADHD) and related conditions.

More generally, they reveal the importance of accounting for sex differences during scientific investigations.

“For nearly two decades, delayed age-related cortical thinning has been widely viewed as a neurodevelopmental marker of attention-related psychopathology and ADHD,” reported Shannon O’Connor, PhD, from the University of Vermont, and co-workers.

“However, our findings suggest that this association may be largely confounded by sex differences in cortical development.”

Delayed thinning of the brain’s cortex—its outermost layer of grey matter—has long been proposed as a biomarker for ADHD-related conditions.

To investigate its value further, O’Connor and team studied 26,496 MRI scans from 11,025 adolescents.

The team initially found there was a link between attention problems and reduced rates of age-related cortical thinning across predominantly frontoparietal regions. Reduced rates of age-related thinning were associated with higher scores relating to attention problems.

However, this link dramatically reduced after accounting for sex differences in cortical thickness trajectories.

There were no significant age and attention problem interactions on cortical thickness when the sexes were studied separately. Furthermore, the genetic risk of ADHD was also not associated with slowed thinning in this region of the brain.

“Taken together, these findings suggest that previously reported associations between attention problems and delayed cortical thinning are largely attributable to unaccounted-for sex differences in neurodevelopment,” the authors concluded.

“Our results call into question an influential framework in developmental neuroscience and psychiatry that has shaped clinical understanding of ADHD for nearly 20 years, underscoring that cortical maturation patterns should not be interpreted as biomarkers for attention-related psychopathology without rigorous accounting for sex-related variation in brain development.”

The post ADHD Brain Marker Value in Doubt appeared first on Inside Precision Medicine.

What to expect from Google this week

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When Google opens its doors tomorrow for its annual developer conference, I/O, it will do so as a clear third place in the foundation model race. A year ago, at Google I/O 2025, the situation looked very different: The company was still riding high from the launch of Gemini 2.5 Pro that March, and distinguishing among the top-tier large language models often felt like a subjective splitting of hairs. 

But a foundation model’s reputation these days rests largely on its coding capabilities, and for months Google’s coding tools have been outgunned by Anthropic’s Claude Code and OpenAI’s Codex. Those systems are so dramatically superior to Google’s own offerings that the company has reportedly had to allow some engineers at DeepMind, its AI division, to use Claude for their work—lest they fall farther behind.

So when I arrive at the conference in Mountain View, California tomorrow, I’ll certainly be on the lookout for any efforts Google is making to claw its way back into frontrunner position. But I’m also eager to see new developments in areas where Google shapes the cutting edge, such as AI for science. The company’s moves there might receive less attention, but they will be no less consequential. 

Here are three things I’ll be paying particular attention to over the next two days.

An attempted coding comeback

Google is taking its AI coding crisis seriously. According to reporting from The Information, there’s a new AI coding team at DeepMind. And the Los Angeles Times has reported that John Jumper, who shared a 2024 Nobel Prize in chemistry with DeepMind CEO Demis Hassabis for their work on the protein structure prediction software AlphaFold, is lending his talents to the efforts. I would be surprised if we don’t see a major new coding release at I/O, perhaps in the form of an update to the company’s Antigravity agentic coding platform.

That said, we shouldn’t expect anything transformative here. Googlers have access to models and products that are substantially ahead of those released to the public, yet they were still reportedly fighting over who got access to Claude Code last month. Unless the company has made astonishing progress since then, Google probably won’t make it back to the coding frontier in the next two days.

Science and health

Coding might be Google DeepMind’s weakness, but science is its conspicuous strength. It is the only frontier AI company to have earned a Nobel Prize. And as LLMs have come to dominate the AI-for-science landscape, Google has only solidified its lead. Last year, the company released multiple scientific AI tools, including the AI co-scientist, which formulates hypotheses and research plans in response to user questions and has been described as an “oracle” by one Stanford scientist, and AlphaEvolve, a system that iteratively discovers new solutions for mathematical and computational problems. If any new scientific tools are announced at I/O, they’ll be worth noting.

I’ll also be paying close attention to any moves Google makes in health and medicine. Google is doing some of the best research out there on LLM-based health tools, but OpenAI has defined the health AI conversation since the release of ChatGPT Health in January. Google has announced that it will be making its AI-powered Health Coach publicly available tomorrow, but promotional material suggests that the tool is geared more toward providing advice on topics such as fitness and diet than to addressing users’ medical concerns. Is this another area where Google has fallen behind, or is the company exercising appropriate caution in a high-stakes domain? 

The drama

While Google fans congregate down in Mountain View, roughly 30 miles north in Oakland the Elon Musk v. Sam Altman trial will be wrapping up. The past few months have seen more than their fair share of AI CEO drama—before the trial, the animosity between Altman and Anthropic CEO Dario Amodei took center stage as Anthropic and OpenAI worked to negotiate deals with the US Department of Defense. But DeepMind’s Hassabis has, for the most part, steered clear of such drama. He effectively presents himself as a Nobel Prize-winning nerd, and if he has written screeds about any of his peers, they haven’t been leaked to the press or appeared in legal discovery.

That’s not to say that Google is controversy free. Last month, a group of 600 employees, many of whom work for DeepMind, sent a letter to CEO Sundar Pichai protesting an impending DoD deal. Google signed that deal the next day. Hassabis, Pichai, and all the other big names will surely do their best to skirt these and other touchy subjects while on stage, but controversies will worm their way in regardless. It will be interesting to see whether Google can maintain its veneer of neutrality.

Inside Anduril and Meta’s quest to make smart glasses for warfare

The defense-tech company Anduril has shared new details about the augmented-reality headset for the military it’s prototyping with Meta, including a vision for ordering drone strikes via eye-tracking and voice commands.

Quay Barnett, who leads the efforts as a vice president at Anduril following a career in the Army’s Special Operations Command, says his fundamental goal is to optimize “the human as a weapons system.” The vision is undoubtedly cyborg-inspired: Barnett wants drones and soldiers to see together, share information seamlessly, and make decisions as one. 

Anduril actually has two such projects in the works. The first is the Army’s Soldier Born Mission Command, or SBMC, for which the company won a $159 million prototyping contract last year to work with Meta on augmented-reality glasses to attach to existing military helmets. But Anduril has also embarked on a self-funded side quest, announced in October, to design its own helmet and headset combo called EagleEye. This is something the military has not asked for, but Anduril insists it will prefer it and purchase it in the end.

So far, both systems are years away. The Army isn’t expected to move its top choice for the SBMC program into production until 2028, if it picks one at all (the previous lead for the effort, Microsoft, was set to receive a $22 billion production contract that was ultimately cancelled when the glasses didn’t prove viable). But Barnett told MIT Technology Review about where both Anduril’s prototypes are headed.

Depending on the situation, the glasses for either prototype will overlay certain information onto a soldier’s field of view. This might be as simple as a compass or as complex as an entire map of the area, information about where nearby drones are flying, or AI-driven recognition of a target like a truck. 

The soldier would then speak to the interface in plain language—for example, to order an evacuation for someone who’s been injured or to plan a route taking into account which areas are off limits. A large language model—Anduril is in tests with Google’s Gemini, Meta’s Llama, and even Anthropic’s Claude, despite the company’s conflict with the Pentagon—will be used to help translate a soldier’s speech into commands the software can follow. And the engine for it all will be Anduril’s software Lattice, which incorporates data from lots of different military hardware into one picture. The Army announced in March that it would spend $20 billion to integrate Lattice with essentially its entire infrastructure.

Barnett’s team is designing the headset to carry out multi-step tasks. A soldier might send a drone to surveil an area and instruct it to come back once it’s found something that looks like an artillery unit; then the system would recommend courses of action, like sending a nearby drone to strike, that would have to be approved by the normal chain of command. Leading the system through this, if all goes to plan, might not even require speech; the soldier could instead communicate through tracked eye movements and subtle taps.

That’s the idea, anyway. It’s worked on early prototypes, Barnett says, but there aren’t yet versions ready for the Army to test at scale. The component parts began arriving in March. Because of federal military contracting rules, these parts—unlike Meta’s commercial smart glasses—required new supply chains that don’t rely on Chinese companies.

It’s a lot for soldiers already bogged down in information overload, says Jonathan Wong, a former US Marine who works as a senior policy researcher at RAND on Army efforts to buy new tech. Both smart glasses projects aim to create a clean interface that presents only the right information at the right time. But it’s a product that soldiers will reject if it costs more of their attention than it saves. “How much mental bandwidth do you have to be both aware of your surroundings and to operate this technology in a way that makes you and your whole unit better?” he says.

Wong recalls that as a platoon commander, for example, he had a radio that operated on three different channels at once. “The moment that two people were on different channels talking at the same time, I immediately couldn’t comprehend anything that either one of them was trying to tell me, and I was probably not aware of my own surroundings,” he says. “I think there are limits to what you can take in.”

Ideally, Barnett says, smart glasses can ease that information overload. Anduril’s approach is to get creative with ways the user can access necessary information quickly. Voice commands and eye tracking are a piece of that strategy. But even if it’s all technically feasible, it might take years of field testing to know if the system is actually useful for soldiers, Wong says. 

Such a system would mark a major escalation in how closely soldiers rely on imperfect AI systems. While computer vision models used to identify objects have long been employed by militaries, and chatbots have recently entered decision-making during the war in Iran, these technologies have not yet made their way to most frontline soldiers. A smart glasses system tasked with identifying threats and recommending strikes would introduce massive new risks of errors. 

Anduril is not the only one competing to develop smart goggles for combat. Rivet, which specializes in wearable sensors for the military, received a $195 million prototyping contract the same time, and in March the Israeli defense-tech company Elbit received its own $120 million contract. This all comes after Microsoft lost its role leading the Army’s smart glasses effort, following a Pentagon audit that found the Army wasn’t properly testing the glasses, a mistake that could have wasted $22 billion.

For both Anduril’s prototypes, the company is testing a new system for digital night vision, which uses electronic sensors and algorithms to boost low levels of light. It’s been a promised technology for decades but has tended to work too slowly for practical use and produce grainy images. Anduril says it has found improvements over previous prototypes through techniques rooted in both new generative AI and older machine learning. 

Much of the other hardware for both projects is being built by Meta, including the displays and the waveguides that send visuals to the user’s eye without blocking the view. That might be a surprise to anyone who knows the backstory: In 2017, Facebook (now Meta) ousted Anduril founder Palmer Luckey following an internal conflict involving his support for Donald Trump. The two are now back in the augmented-reality business together, while Mark Zuckerberg has also adopted a friendlier posture toward the second Trump administration.

For the Army initiative, this suite of smart glasses, night vision, and sensors will be attached to the helmets and other gear soldiers already wear, with a separate battery pack. The EagleEye version will instead incorporate the tech into the helmet itself. Even if the Army doesn’t prefer EagleEye in the end, Barnett says, Anduril will attempt to sell the system to foreign militaries.

Multiple challenges must still be overcome. Unlike Meta’s Ray-Ban glasses, the prototypes have to operate in an environment full of dust, explosions, and smoke. Adding the computing power and battery life they need also means more weight for soldiers already carrying upwards of 100 pounds. Then the technology has to work in environments without ubiquitous 5G cell connections; powerful computer vision and AI models will need to run locally on the device.

For the Army to want to buy it at scale, “it’s got to work, and it’s got to be pretty seamless,” Wong says. “It’s a high bar.”

Neurocognitive function among individuals with problematic social media use

BackgroundWith the development of technology and the internet, social networks gained momentum quickly and play a central role in daily activities. Despite this, there is a public health concern over excessive or problematic social media use. There is also a debate whether excessive social media use should be considered as a behavioral addiction characterized by impulsivity or an impulse control disorder characterized by compulsivity. The goal of this study is to use neurocognitive tasks to investigate impulsivity and compulsivity among excessive social media users compared with non-excessive users.MethodThe study included 79 participants (age range 18 to 37), divided into two groups: 34 participants who excessively use social media (Mean Age = 23.03, SD = 2.71) and 45 participants who do not excessively use social media (Mean Age = 25.47, SD = 4.3). Participants filled out a demographic questionnaire, questionnaires on social media use, impulsivity, compulsivity, anxiety, and depression. They performed computerized cognitive tasks: GO/NO-GO (with Facebook and traffic sign pictures), Experimental Delay Discounting (EDT), and the Wisconsin Card Sorting Test (WCST).ResultsExcessive users of social media exhibited a lower ability to delay gratification on the EDT, indicating impulsivity. They made fewer non-perseverative errors on the WCST, which indicated high flexibility and test shifting, which is a contradicting evidence for compulsivity. Furthermore, on the GO/NO-GO task, individuals who excessively use social media made more omission errors in response to the “Facebook” sign compared to traffic signs (GO condition), indicating impaired selective attention. Finally, they also showed higher subjective ratings of anxiety, depression, impulsivity, and compulsivity.DiscussionThe results of this study provide evidence for impulsivity indicated by delay discounting tendency, which supports the behavioral addiction model, impaired selection attention and lack of evidence for compulsivity in excessive social media users. Further research on neurocognitive function in excessive social media users is required in order to determine whether it should be considered a behavioral addiction or an impulse control disorder.

Cannabigerol Oil for Adolescents With ADHD (CAN-ADHD)

Conditions: Attention-Deficit/Hyperactivity Disorder (ADHD)

Interventions: Drug: Experimental: Full-spectrum CBG oil 30 mg/mL; Drug: Placebo oil (MCT oil)

Sponsors: Universidade do Sul de Santa Catarina; Associação Brasileira de Cannabis Medicinal

Recruiting

Continuous Theta-Burst Stimulation Improves Long-Term Outcomes in Alcohol Use Disorder by Modulating a Craving-Related Dynamic Network

Alcohol use disorder (AUD) is a chronic, relapsing disorder characterized by craving. While continuous theta-burst stimulation (cTBS) shows promise for AUD, its long-term effects on drinking reduction and underlying mechanisms remain unclear. We evaluated the 12-month efficacy of right dorsolateral prefrontal cortex (DLPFC) cTBS for reducing alcohol consumption and identified associated neural and molecular mechanisms.