Tag: Academic research

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The Download: Musk and Altman’s legal showdown, and AI’s profit problem

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Elon Musk and Sam Altman are going to court over OpenAI’s future

Elon Musk and OpenAI CEO Sam Altman head to trial this week in a case with sweeping consequences. Ahead of OpenAI’s IPO, the court could rule on whether the company can exist as a for-profit enterprise. It could even oust its leadership.

Musk, an OpenAI co-founder, claims he was deceived into bankrolling the firm under false pretenses. He’s seeking $134 billion in damages, the removal of Altman and president Greg Brockman, and the company’s restoration to a non-profit.

Find out how the trial could upend the global AI race.

—Michelle Kim

The missing step between hype and profit

In a celebrated South Park episode, a community of gnomes sneak out at night to steal underpants. Why? The gnomes present their pitch deck. “Phase 1: Collect underpants. Phase 2: ? Phase 3: Profit.” It’s a business plan that captures the current state of AI. 

Companies have built the tech (Step 1) and promised transformation (Step 3). But how they get there is still a big question mark. Read about the potential paths forward.


—Will Douglas Heaven

This story originally appeared in The Algorithm, our weekly newsletter giving you the inside track on all things AI. Sign up to receive it in your inbox every Monday.

Welcome to the era of weaponized deepfakes

For years, experts have warned that deepfakes could be deployed in malicious ways. These dangers are now here.

Cheap, accessible models now produce weaponized deepfakes—from sexually explicit images to political propaganda—that look startlingly real. They’re already inciting violence, changing minds, and sowing mistrust, with women and marginalized groups disproportionately affected.

Experts fear that they’re cratering trust and critical thinking. Here’s why they’re alarmed.

—Eileen Guo

Weaponized deepfakes are on our list of the 10 Things That Matter in AI Right Now, MIT Technology Review’s guide to what’s really worth your attention in the busy, buzzy world of AI. 

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 OpenAI has ended its exclusive partnership with Microsoft
The new deal allows OpenAI to court rivals such as Amazon. (Reuters $)
+ Microsoft will still license OpenAI’s tech, but no longer exclusively. (NYT $)
+ OpenAI is missing key growth targets ahead of its IPO. (WSJ $)

2 Google has signed a classified AI deal with the Pentagon
It permits AI use for “any lawful government purpose.” (The Information $)
+ Over 600 Google workers had called for a block on the deal. (QZ)
+ AI firms are set to train military versions of their models on classified data. (MIT Technology Review)

3 The EU has told Google to open Android to AI rivals
It wants to end Gemini’s built-in advantage. (Ars Technica)
+ Google calls the move an “unwarranted intervention.” (WSJ $)
+ A final decision is expected by the end of July. (Reuters $)

4 OpenAI is reportedly developing an AI-first smartphone
It would replace apps with agents. (TechCrunch)
+ Qualcomm and MediaTek may be developing its processors. (Gizmodo)

5 A brain implant for depression is moving into human testing
The FDA has approved a human study of the device. (Wired $)
+ BCIs have thus far struggled to reach the market. (MIT Technology Review)

6 A populist backlash against AI is gaining momentum in rural America
From Indiana to Idaho, voters are pushing back against the technology. (NYT $)
+ Anti-AI protests are expanding worldwide. (MIT Technology Review)

7 DeepSeek has priced its new model 97% below OpenAI’s GPT-5.5
It aims to attract more enterprises, developers, and agent-based users. (SCMP)
+ Here are three reasons why DeepSeek V4 matters. (MIT Technology Review)

8 AI now generates a third of new websites
A study found it’s making the web more cheery and less verbose. (404 Media)

9 Top talent is leaving Big Tech to launch their own AI startups
Meta, Google, and OpenAI are facing a brain drain. (CNBC)

10 Taylor Swift is trademarking her voice and image
The Grammy winner has been the target of numerous deepfakes. (NBC News)
+ A growing number of celebrities are fighting AI with trademarks. (BBC)

Quote of the day

“The reality is people don’t like him.”

—Judge Yvonne Gonzalez Rogers reacts to prospective jurors confessing their negative views of Elon Musk ahead of his legal battle with Sam Altman, The Verge reports.

One More Thing


How covid conspiracy theories led to an alarming resurgence in AIDS denialism

When Joe Rogan falsely declared that “party drugs” were an “important factor in AIDS,” several million people were listening. He also asserted that AZT, the earliest drug used to treat AIDS, killed people “quicker” than the disease itself—another claim that has been disproven.

Such comments illustrate an unmistakable resurgence in AIDS denialism: a false collection of theories arguing either that HIV does not cause AIDS or that there is no such thing as HIV at all. By the dawn of the millennium, these claims had largely fallen out of favour. That changed when the coronavirus arrived.

Follow the digital path from Covid skepticism to the return of a deadly conspiracy theory.


—Anna Merlan

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ Explore the planets from your laptop with this live sky map.
+ This marathon DJ set from Daphni is an incredible journey through electronic music.
+ NASA’s stunning Artemis II wallpapers bring a high-res piece of deep space to your phone.
+ This fascinating GPS explainer breaks down how your phone figures out exactly where you are.

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Experimental investigation of sharp-tip microwire-based brain electrode buckling during implantation through dura and pia mater

IntroductionWide use of miniaturized and flexible microwire electrodes faces challenges of wire buckling against the brain membrane layers. The field lacks quantitative understanding of such buckling phenomena, especially on the effective length factor, which is required to determine the wire’s critical buckling load.MethodsThis study presents an experimental investigation into the buckling behavior of tungsten microwire electrodes during implantation through dura and pia mater layers using a validated multilayer brain-mimicking phantom. Microwires with three diameters (25.4, 50.8, and 76.2 µm) and different tip geometries—including blunt, beveled, and electrochemically (conical) sharpened profiles—were evaluated under controlled axial insertion. Critical buckling length, insertion outcomes (buckled/penetrated), and rupture/buckling force were quantified across the experimental dataset. Buckling behavior was analyzed using the Euler column framework with experimentally estimated effective length factors (Kˆ) to represent each unique membrane-wire tip boundary interaction.ResultsResults indicated that wire diameter strongly influences buckling resistance, with larger diameters yielding quartic (fourth order) higher critical buckling load of the electrode, whereas the corresponding membrane rupture force only increases linearly with the diameter. But smaller microwires tend to anchor better against the brain membrane, generating a more stable wire-membrane interface closer to the ideal pin end condition. Tip geometry also significantly affected rupture force and insertion stability; conical tips dramatically reduced the membrane rupture force with less variance. In general, tip sharpening choice for small microwires should focus on optimizing tips anchoring mechanism and minimizing rupture force uncertainty introduced by tip asymmetry while thick microwires mainly benefit from membrane rupture force reduction. For theoretical prediction of a microwire electrode’s critical buckling load based on Euler’s buckling equation, unlike conventional fixed-pinned assumption (K = 0.7), experimentally measured effective length factors ranged from approximately 0.72 – 0.82.DiscussionDesigning with ≈ 0.8 provides a conservative estimate that may reduce the risk of buckling under membrane penetration conditions compared to the commonly assumed fixed-pinned value of 0.7. These findings provide quantitative design guidance for optimizing microwire geometry and offer a validated benchtop framework for predicting buckling-limited insertion performance in neural interface applications.
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Oxygen extraction fraction is differentially associated with pathological biomarkers in Alzheimer’s disease and non-Alzheimer’s dementias

IntroductionWe aimed to understand the pathophysiological differences between 16 Alzheimer’s disease (AD) and 15 non-AD dementia patients by quantifying oxygen extraction fraction (OEF) in cortical (CGM) and deep gray matter (DGM) regions.MethodsTo achieve this, we used a novel MRI-based OEF mapping technique, QQ, which estimates OEF from routine multi-echo gradient echo data. Multiple linear regression analyses were performed to compare the associations between OEF and white matter hyperintensities (WMH) or cognitive impairment (measured by Montreal Cognitive Assessment (MoCA) between the two groups.ResultsIn the AD and non-AD group, OEF showed negative associations with WMH in DGM and positive associations with MoCA in DGM and CGM.DiscussionOur study suggests that QQ is a promising tool for differentiating between AD and non-AD dementias, by revealing abnormalities in tissue oxygen usage and their relationships to microvascular changes and cognitive impairment.
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Functional connectivity changes in the thalamocortical network due to neck pain and the multiscale regulatory effects of acupuncture: a cross-scale multi-omics neuroimaging study

BackgroundNeck pain correlates with multiscale brain abnormalities, but cross-scale mechanisms of acupuncture analgesia are unclear. This study aimed to: (1) Explore differential modulation of thalamic functional networks by verum vs. sham acupuncture; (2) Examine associations between functional connectivity changes and micro gene expression to unravel its multiscale mechanisms.MethodsA total of 130 participants were initially enrolled, and 100 eligible neck pain patients were randomized 1:1 to the verum (n = 50) or sham (n = 50) acupuncture groups. Finally, 49 patients in each group were included for the final analysis due to one case of exclusion in each group, with treatment administered twice a week for 2 weeks. Visual Analog Scale (VAS), resting-state functional magnetic resonance imaging (fMRI), and Allen Human Brain Atlas (AHBA) transcriptome data were analyzed via Partial Least Squares (PLS) regression.ResultsBoth groups showed reduced post-treatment VAS (p < 0.001), with the verum group exhibiting a superior effect (Z = −6.877, p < 0.001). Neuroimaging revealed that verum acupuncture (VA) specifically induced significant decreases in functional connectivity (FC) between the right thalamus and left anterior cingulate cortex (T = −4.498) as well as between the right thalamus and right Rolandic operculum (T = −4.532, voxel-level p < 0.01, cluster-level p < 0.05), an effect absent in the sham acupuncture group (SA). Gene- FC association analysis indicated that PLS2 component explained 39.83% of FC variance (Pspin: permutation test p < 0.05), with weight genes showing significant spatial correlation to connectivity changes (r = 0.445, Pspin = 0.0011). A total of 809 genes were enriched in the innate immune response and phosphorylation regulation pathways, whereas 1,222 genes were enriched in the GABA-ergic synapse and synaptic membrane-related pathways.ConclusionVA relieves pain via modulating thalamus-anterior cingulate cortex networks, involving immune-inflammation and neural inhibition, providing first multi-scale validation integrating neuroimaging and transcriptomics.Clinical trial registrationThis trial was registered with the International Traditional Medicine Clinical Trial Registry (registration number: ITMCTR2023000001) prior to participant enrollment.
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Quality, reliability, and transparency of late-life depression videos on Chinese social media: a cross-sectional study of Douyin, Rednotes, and BiliBili

BackgroundLate-life depression is common in older adults and is often under-recognized. Short-video platforms have become a major source of mental health information. However, content quality and transparency remain uncertain.MethodsWe conducted a cross-sectional assessment of highly viewed videos on late-life depression on three Chinese platforms. We searched each platform using the keyword in Chinese “Late-life depression”. We selected the top 200 videos by view count on Douyin, Rednotes (Xiaohongshu), and BiliBili. After exclusions, 562 videos were included (Douyin, n=188; Rednotes, n=188; BiliBili, n=186). Two medically trained raters scored videos using the Global Quality Score (GQS), modified DISCERN (mDISCERN), and JAMA benchmark criteria. We also coded content categories and creator types. We assessed platform differences using non-parametric tests. We examined associations between a limited engagement proxy, defined as the comment-to-view ratio, and quality scores using Spearman correlation.ResultsVideo duration differed across platforms (p<0.001). Engagement indicators were higher on Douyin and Rednotes than on BiliBili. Symptoms were the most common topic on all platforms. Prevention and intervention ranked second on Douyin and Rednotes. On BiliBili, causes and case-based analysis were also common. Overall quality was moderate. Mean GQS ranged from 2.96 to 3.05. Transparency was limited. Mean JAMA ranged from 1.91 to 2.04. Reliability was slightly higher on BiliBili based on mDISCERN. Creator type was strongly associated with scores. Expert and institutional videos scored higher than general and marketing-oriented accounts. Correlations between visible audience interaction and quality were weak.ConclusionHighly viewed late-life depression videos on major Chinese platforms show moderate quality and limited transparency. Exposure does not reliably signal higher-quality information. Platforms and health authorities should strengthen source disclosure and promote evidence-based content from qualified creators.
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A prospective cohort study on the incidence and influencing factors of subsyndromal delirium in ICU patients

BackgroundThis study aims to develop and validate a machine learning-based risk prediction model for subsyndromal delirium (SSD) in ICU patients, while identifying key risk factors.MethodThis study was a prospective study, selecting patients who were hospitalized in the ICU from October 2024 to May 2025. We compared seven machine learning algorithms: Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Elastic Network (EN), Extreme Gradient Enhancement (XGB), and Support Vector Machine (SVM).ResultIn our study, the prevalence rate of SSD was 37.158%. The comparative analysis shows that XGB is the best predictive model (AUC = 0.84). Feature importance analysis identified four significant predictive factors: Use of vasoactive drugs (0.412), Monthly household income (0.306), Undergone surgery (0.191) and Number of Medications (0.036).ConclusionThe prediction model based on XGB has a good effect in identifying the risk of SSD in ICU patients. These findings enable clinicians to stratify high-risk groups and implement timely and targeted intervention measures, effectively reducing the risk of adverse consequences. Future multicenter studies should validate these results in larger cohorts.
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Real-world effectiveness of medication-assisted treatment and psychotherapy for opioid use disorder: a national multi–health care organization analysis

BackgroundHarm reduction strategies for opioid use disorder (OUD) emphasize pragmatic, evidence-based approaches that reduce overdose risk, relapse, and other adverse outcomes without requiring abstinence. Medication for opioid use disorder (MOUD) and structured psychotherapy represent core harm-reduction modalities, yet their real-world comparative effectiveness, alone and in combination, remains underexplored at scale.MethodsA retrospective cohort study was conducted using the TriNetX Research Network, comprising de-identified electronic health records from 112 U.S. health systems. 18,047 adults aged 18–45 were identified with a diagnosis of opioid dependence (ICD-10 F11.20) between 2016 and 2025. Subjects were assigned to eight mutually exclusive treatment cohorts: no treatment (Cohort 1); buprenorphine alone (Cohort 2); methadone alone (Cohort 3); psychotherapy alone (30 minutes (Cohort 4), 45 minutes (Cohort 5), or 60 minutes (Cohort 6)); buprenorphine + psychotherapy (Cohort 7); and methadone + psychotherapy (Cohort 8), with combination treatments defined within a ±30-day window. Cox proportional hazards models estimated adjusted hazard ratios (aHRs) for remission (F11.21, F11.11) within 12 months.ResultsBuprenorphine (aHR = 2.33; 95% CI: 1.85–2.94), methadone (aHR = 2.50; 95% CI: 2.05–3.04), and psychotherapy (30 min: aHR = 2.18; 45 min: aHR = 2.38) were each independently associated with significantly higher remission compared to no treatment. The combination of buprenorphine + psychotherapy yielded the strongest effect (aHR = 5.26; 95% CI: 2.68–10.32). Anxiety diagnoses and gabapentinoid prescriptions were positively associated with remission; benzodiazepine co-prescription was negatively associated.ConclusionsIn this first national-scale, multi–health-care-organization analysis, both pharmacologic and psychosocial harm-reduction interventions were independently associated with improved OUD remission, with additive benefit when integrated. These findings underscore the value of embedding comprehensive, multimodal harm-reduction services within routine care and support policies promoting equitable access to both MOUD and behavioral health supports across diverse health systems.
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Scoping review of therapeutic approaches among individuals with secondary exercise addiction

Secondary exercise addiction shows high comorbidity with eating and body image disorders. Despite its substantial impact on physical and mental health and daily functioning, evidence on effective therapeutic interventions remains limited. The aim of this scoping review was to identify and describe therapeutic interventions applied to adult individuals with secondary exercise addiction. This review followed the PRISMA Sc-R guidelines and covered the years 2002–2024. Ultimately, five studies were included (four randomized controlled trials and one quasi-experimental study). Three studies applied psychotherapeutic interventions based on cognitive-behavioral models (Cognitive Behavioral Therapy, Lifestyle, Exercise, Attitudes, and Relationships Program, Physical Exercise and Dietary Therapy), while two integrated physical or nutritional components. A secondary analysis published in 2024 based on the LEAP trial dataset was identified but not treated as an independent study to avoid duplication. EBSCOhost, Web of Science, PubMed, and Google Scholar were searched from January to May 2025 using terms related to exercise addiction, exercise abuse, psychotherapy, intervention, and treatment. English-language studies were eligible if they described an intervention with at least one treated group with pre- and post-test measures; the participants of the study were adult patients suffering from eating disorders and exercise addiction (the therapy programs involved one inpatient and four outpatient treatments) and therapeutic intervention was carried out with outcomes based on exercise addiction level data. Four out of five included studies reported improvements in variables related to compulsivity, although these did not always imply a reduction in the amount of exercise, indicating that qualitative changes may be more relevant. Longer interventions showed more consistent effects, but even brief treatments generated positive changes in non-clinical populations. The examination of the research revealed a gap in studies addressing interventions for those with secondary exercise addiction, especially highlighting the need for randomized controlled trials (RCTs) with proper randomization methods.
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Human Ventral Tegmental Local Field Potentials in Treatment-Resistant Depression and Obsessive-Compulsive Disorder

The ventral tegmental area (VTA) is a key node within the limbic circuitry. Through dense dopaminergic, glutamatergic, and GABAergic projections, the VTA forms reciprocal loops with prefrontal and limbic cortices that are consistently implicated in major depressive disorder (MDD) and obsessive–compulsive disorder (OCD) (1,2). Decades of animal research have established the VTA as a central hub for motivational drive and reward prediction error signaling (3,4). Despite its presumed critical role in mental disorders, direct electrophysiological recordings from the human VTA have so far remained absent.