AI News Archive: May 17, 2026 — Part 7
Sourced from 500+ daily AI sources, scored by relevance.
- Steven Soderbergh used AI in a documentary about John Lennon. And he wants to talk about it
Steven Soderbergh used AI in a documentary about John Lennon. And he wants to talk about it AP News
- Steven Soderbergh used AI in a documentary about John Lennon. And he wants to talk about it
Steven Soderbergh used AI in a documentary about John Lennon. And he wants to talk about it Austin American-Statesman
- Steven Soderbergh used AI in a documentary about John Lennon. And he wants to talk about it
Steven Soderbergh used AI in a documentary about John Lennon. And he wants to talk about it Houston Chronicle
- Steven Soderbergh used AI in a documentary about John Lennon. And he wants to talk about it
The day John Lennon was shot in 1980, he and Yoko Ono gave an interview to a San Francisco radio crew from their home in New York’s Dakota Apartments.
- Steven Soderbergh used AI in a documentary about John Lennon. And he wants to talk about it.
Steven Soderbergh used AI in a documentary about John Lennon. And he wants to talk about it. The Boston Globe
- Steven Soderbergh used AI in a documentary about John Lennon. And he wants to talk about it
Steven Soderbergh used AI in a documentary about John Lennon. And he wants to talk about it San Francisco Chronicle
- Steven Soderbergh used AI in a documentary about John Lennon. And he wants to talk about it
Steven Soderbergh used AI in a documentary about John Lennon. And he wants to talk about it Toronto Star
- Kenya tells Microsoft that $1 billion AI data center would gulp half the country’s electricity
Kenya has reportedly raised concerns over Microsoft’s planned $1 billion AI data center, warning that the project could consume a massive chunk of the country’s electricity supply.
- OpenAI bought company that offered AI tools for cloning voices
OpenAI bought company that offered AI tools for cloning voices
- Boos, shouts and a Google billionaire: What happened when Eric Schmidt tried to talk AI at a 2026 graduation
Students at the University of Arizona booed former Google chief executive Eric Schmidt during his commencement address after he spoke about artificial intelligence and job automation.
- Big risks and rewards in upcoming IPOs at SpaceX, OpenAI, Anthropic
Wall Street is licking its chops over an unprecedented slate of massive IPOs set to arrive in the coming months, beginning with Elon Musk's SpaceX in June.
- At Samsung, the global AI boom spurred a looming strike and deep divisions
SAMSUNG-ELEC-DISPUTE:At Samsung, the global AI boom spurred a looming strike and deep divisions
- Nvidia in the spotlight
Plus, former Google boss takes over at the BBC, primary elections in several US states and the Chelsea Flower Show
- Revamped Siri will reportedly offer auto-deleting chats
Apple is hoping that its record on privacy can be the differentiator on the AI front, and maybe even buy it a little slack as it continues to lag behind the competition. According to Bloomberg's Mark Gurman, the more chatbot-like Siri set to debut in iOS 27 will include the option to autodelete chat histories. […]
- Standalone Siri app to offer auto-deleting chat history, launch with beta label: report
Next month at WWDC 2026, Apple is expected to finally live up to its AI Siri promises, and more. The company has long been working on a new standalone Siri app to boost how users interact with Siri and Apple Intelligence – and it should be debuting in beta next month. According to a new report from Bloomberg’s Mark Gurman , the new Siri app will come with a privacy feature similar to one on iMessage: auto-deleting chats. He also expects Siri to launch with a beta label even when it’s available publicly in the fall, similar to some previous Apple rollouts. more…
- Siri’s rebirth in iOS 27 will might offer an auto-delete perk for your AI chats
Apple’s iOS 27 Siri overhaul could introduce chatbot-style conversations alongside automatic deletion controls designed to give users more privacy over AI interactions.
- Apple's new Siri app will reportedly offer auto-deleting chat options
Users can choose between keeping conversations for 30 days, one year or forever, according to Bloomberg.
- Apple’s Siri app in iOS 27 will auto-delete your chats. It may also launch as a beta, again.
Apple’s first standalone Siri app, coming in iOS 27, will include an auto-delete function for chat histories that borrows from the Messages app. Users will be able to configure the app to retain conversations for 30 days, one year, or indefinitely. The feature, reported by Bloomberg’s Mark Gurman in his Power On newsletter on Sunday, […] This story continues at The Next Web
- Apple’s Siri revamp could include auto-deleting chats
Privacy will be a major theme when Apple unveils a new version of Siri.
- Researchers Claim Anthropic's Mythos Helped Crack macOS Security
Researchers Claim Anthropic's Mythos Helped Crack macOS Security PCMag Middle East
- Multiple commencement speakers booed for AI comments during graduation speeches
Former Google CEO Eric Schmidt was booed multiple times Sunday while discussing artificial intelligence during a commencement speech at the University of Arizona. Other commencement speakers faced similar backlash for their AI comments, as new graduates face a daunting job market. NBC News’ Valerie Castro reports.
- A phenotype-to-mechanism framework links phenome-wide comorbidity architecture to molecular mechanisms and therapeutic discovery in complex diseases
Complex human diseases exhibit substantial clinical heterogeneity driven by poorly understood molecular mechanisms, while many also lack sufficient molecular and omics data for mechanistic investigation, hindering therapeutic development. We introduce PiMInfer, a phenotype-to-mechanism framework that leveraged largely available real-world clinical data-based deep phenotypic characterizations with a biomedical knowledge graph approach to resolve disease clinical heterogeneity into phenotype-informed molecular modules, thereby accelerating therapeutic target discovery. We applied PiMInfer to investigate Hidradenitis Suppurativa (HS), an autoimmune skin disease with poorly understood pathogenesis and limited treatment options. PiMInfer identified a coherent, phenotype-informed HS gene module (PiHSM) and functional endotypes, which were validated using multimodal evidence. In silico drug repurposing using PiHSM prioritized Carfilzomib, targeting the immunoproteasome subunit PSMB9, essentia
- Deep learning-based recognition model for surgical phases of minimally invasive hysterectomy: A multicentre retrospective study
Objective: To develop and validate a robust deep-learning model capable of fine-grained phase recognition in total hysterectomy, particularly the complex periuterine dissection phase. Design: Multicentre retrospective observational study. Setting: Japan. Sample: Surgical videos (n = 764) from 43 institutions. Methods: We developed a robust and generalisable deep-learning model for surgical phase recognition in total hysterectomy, applicable to laparoscopic and robot-assisted procedures. Overall, 1,591,334 still images were annotated across nine surgical phases. A convolutional neural network (Xception architecture) was trained on 200 cases using four-fold cross-validation, with institutional separation between training and testing sets. Main outcome measures: Model performance was assessed using accuracy, precision, recall, and F1 score. Subgroup analysis and logistic regression evaluated the association between background clinical factors and recognition accuracy. Results: The model a
- Real-time hip biomechanics from smart garments via a physics-informed neural network
Tissue-level mechanical stimuli are primary drivers of tissue adaptation and can be optimised during conservative treatments to improve treatment outcomes for many highly prevalent musculoskeletal conditions. Current laboratory-based technologies limit our ability to connect conservative interventions such as exercise and movement modification with muscle, joint, and tissue-level mechanics, in natural environments. We introduce a physics-informed neural network (PINN) to estimate clinically relevant biomechanics from smart garments. By accounting for physiological dynamics of neural activation and muscle contraction, the PINN accurately predicted hip joint angles (RMSE <6 degrees), moments (RMSE 0.12 N*m/kg to 0.30 N*m/kg), and joint forces (RMSE 6 to 16%) from three inertial measurement units and four electromyographic sensors. We demonstrated that the trained PINN can be combined with a smart garment to estimate hip biomechanics, in real-time, during a gait retraining intervention ai
- Elevated serum apolipoprotein B and lipoprotein remodelling distinguish adults with HLH from HLH mimics and controls
Haemophagocytic lymphohistiocytosis (HLH) is a rare, life-threatening hyperinflammatory syndrome characterised by uncontrolled immune activation. Reduced high- and low-density lipoprotein cholesterol and hypertriglyceridaemia are reported in HLH, suggesting lipid metabolism disturbances although in-depth serum metabolomic analysis is lacking in HLH. Here a lipid-focused NMR spectroscopy platform was used to define the serum metabolomic landscape of adults hospitalised with HLH compared to adults with sepsis (HLH-mimic) and rheumatic disease (potential HLH drivers/triggers), following surgical resection of solid organ cancer (non-infectious acute inflammation controls) and healthy controls (HCs). Serum metabolites distinguished HLH from HCs with high accuracy (>91.36%) using multiple machine learning models. The top classifying features included elevated apolipoprotein-B (ApoB)-containing low, intermediate, and very low-density lipoprotein particles; and lipoprotein remodelling towards
- Pixel-Based Skin Tone Estimation on Dermoscopy: A Dual-Rater MST Benchmark and Feasibility Study
Skin-tone labels are absent from public dermoscopy benchmarks such as the International Skin Imaging Collaboration (ISIC), making it impossible to audit whether clinical AI performs equitably across skin tones. While several recent works estimate skin tone automatically from clinical photography and selfies, we ask whether this approach is feasible on dermoscopy, the primary imaging modality of these benchmarks. To answer this, we make three main contributions. First, we release MST-Derm, a dual-rater Monk Skin Tone (MST) annotation benchmark on 500 ISIC 2018 images. Raters were given an explicit unrateable option for crops where the skin surrounding the lesion was too occluded to label confidently. We find that 60% of images were marked unrateable, yielding a 193-image consensus subset (quadratic-weighted Cohen's Kappa = 0.82). Second, we conduct a systematic feasibility study of three pixel-based MST annotation pipelines spanning the principal families in prior work: palette matching
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