AI News Archive: April 28, 2026 — Part 24
Sourced from 500+ daily AI sources, scored by relevance.
- Why all eyes are on AI spending at Microsoft and Amazon
Amazon and Microsoft report earnings at the same time on Wednesday. What does Wall Street want to see?
- Amazon Earnings Preview: AI Momentum In Focus, But This Number Could Be 'Messy'
Amazon stock is picking up strength as the tech giant prepares to report its first-quarter results late Wednesday. The post Amazon Earnings Preview: AI Momentum In Focus, But This Number Could Be 'Messy' appeared first on Investor's Business Daily .
- Facial recognition data is a key to your identity. If stolen, you can't just change the locks
A woman strolls into a grocery store, thinking about grabbing some apples. Before she even reaches the produce aisle, a security camera has scanned her face. Whether the system is checking for shoplifters or simply logging her arrival, her face has joined a digital ledger, a trace she can't easily erase. Retailers, banks, airports, stadiums and office buildings are doing the same.
- Lion to embed AI across its operations
Beverage maker partners with OpenAI.
- Beth Israel Lahey to roll out system-wide AI tool
The AI tool is meant to combat administrative burnout in doctors.
- Register: Risky Future AI Tools for Commercial Auto, Telematics & Fleet Risks on April 29
Insurance Journal’s Risky Future series is hosting the “Commercial Auto, Telematics & Fleet Risks” Demo Day, a series of free AI tool demonstrations designed exclusively for commercial auto carriers, fleet underwriters, risk managers, brokers and more. This event features vendors …
- Sharper AI eyes for maritime safety
Sharper AI eyes for maritime safety EurekAlert!
- AdMarketplace Is Piloting Performance Ads In AI Chat
AI chat is starting to look more and more like a media channel, which means someone’s gotta figure out the ad model. Although chatbot conversations are loaded with commercial intent – ChatGPT knows it – there’s still no clear playbook for monetizing those interactions without messing with the user experience. Search advertising specialist adMarketplace is one of […] The post AdMarketplace Is Piloting Performance Ads In AI Chat appeared first on AdExchanger .
- You probably wouldn't notice if an AI chatbot slipped ads into its responses
Hundreds of millions of people consult artificial intelligence chatbots on a daily basis for everything from product recommendations to romance, making them a tempting audience to target with potentially below-the-radar advertising. Indeed, our research suggests AI chatbots could easily be used for covert advertising to manipulate their human users.
- Datavault AI (NASDAQ: DVLT) Secures $120M Term Sheet With Scilex for Quantum-Ready Edge Network Buildout
Datavault AI (NASDAQ: DVLT) Secures $120M Term Sheet With Scilex for Quantum-Ready Edge Network Buildout USA Today
- Datavault AI Announces Execution of Binding Term Sheet for $120 Million Cash Contribution From Scilex Holding Company to Fund 100-City GPU Expansion of Quantum-Ready SanQtum Platform
Datavault AI Announces Execution of Binding Term Sheet for $120 Million Cash Contribution From Scilex Holding Company to Fund 100-City GPU Expansion of Quantum-Ready SanQtum Platform The Arizona Republic
- GOP-led Florida House plays hardball with DeSantis on AI, medical freedom — again
GOP-led Florida House plays hardball with DeSantis on AI, medical freedom — again Miami Herald
- Pompeii archaeologists use AI to reconstruct man killed in volcano's eruption
Archaeologists have used AI for the first time to digitally reconstruct the face of a man killed in the AD 79 eruption of Mount Vesuvius, offering a new way to understand one of history's most famous natural disasters.
- Archaeologists at Pompeii use artificial intelligence to reveal face of one victim
Archaeologists at Pompeii have used artificial intelligence to digitally reconstruct the face of a man killed in the AD 79 eruption of Mount Vesuvius
- OpenAI Revenue Report Stings AI Stocks. Why Oracle Stock Is Falling Sharply.
Oracle stock was among AI stocks falling after a report that OpenAI missed internal revenue targets. The post OpenAI Revenue Report Stings AI Stocks. Why Oracle Stock Is Falling Sharply. appeared first on Investor's Business Daily .
- Verisk Stock and More Software Losers That Could Be AI Winners, According to Goldman Sachs
Verisk Stock and More Software Losers That Could Be AI Winners, According to Goldman Sachs Barron's
- Seagate Stock Rallies On As AI Stock's Earnings Crush Targets
Seagate stock jumped by 10% late Tuesday after posting fiscal Q3 results that crushed Wall Street's targets. The post Seagate Stock Rallies On As AI Stock's Earnings Crush Targets appeared first on Investor's Business Daily .
- Dow Jones Futures: Seagate, Bloom Energy, Teradyne Lead Earnings Movers After OpenAI Hits Techs; Titans On Tap
The stock market fell on OpenAI fears and rising oil prices, but pared losses. AI stocks Seagate, Bloom Energy led earnings movers late. The post Dow Jones Futures: Seagate, Bloom Energy, Teradyne Lead Earnings Movers After OpenAI Hits Techs; Titans On Tap appeared first on Investor's Business Daily .
- Bloom Energy is riding the AI wave with a major lift from Oracle
Wall Street is validating Bloom’s ‘vision,’ and AI accelerating it, CEO says.
- I used Naval Ravikant’s 'Leverage' rule with ChatGPT agents — and it cut my workload in half
I used Naval Ravikant’s 'Leverage' rule with ChatGPT agents — and it cut my workload in half Tom's Guide
- I used these 10 ChatGPT prompts to fix my communication habits and the results were immediate
I used these 10 ChatGPT prompts to fix my communication habits and the results were immediate Tom's Guide
- Can AI quantify beauty? New study suggests it can’t
Can AI quantify beauty? New study suggests it can’t EurekAlert!
- STAT+: Google’s clinical director on AI challenges
In this edition of STAT Health Tech newsletter: a conversation with Google's clinical director, an update on Doctronic's Utah experiment, and more.
- Labor Department nears launch of AI workforce hub
The agency’s chief innovation officer told FedScoop that the portal of government and private-sector data will go public in “the coming months,” fulfilling a Trump AI Action Plan requirement. The post Labor Department nears launch of AI workforce hub appeared first on FedScoop .
- Energy Department eyes AI-enabled self-service features for workforce
The interest in artificial intelligence additions follows what the agency is characterizing as a successful HR modernization project that centralized talent management platforms. The post Energy Department eyes AI-enabled self-service features for workforce appeared first on FedScoop .
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- Meet the Emirati inventor honoured by Sheikh Mohammed at 15, now building AI in the UAE
Meet the Emirati inventor honoured by Sheikh Mohammed at 15, now building AI in the UAE Gulf News
- Probabilistic modelling of single-cell bisulfite sequencing data with MethylVI
Nature Machine Intelligence, Published online: 28 April 2026; doi:10.1038/s42256-026-01225-9 MethylVI enhances analyses of single-cell bisulfite sequencing methylomic data via a deep generative model that accounts for the unique technical and biological sources of variability in this data modality.
- StereoFoley: Object-Aware Stereo Audio Generation from Video
We present StereoFoley, a video-to-audio generation framework that produces semantically aligned, temporally synchronized, and spatially accurate stereo sound at 48 kHz. While recent generative video-to-audio models achieve strong semantic and temporal fidelity, they largely remain limited to mono or fail to deliver object-aware stereo imaging, constrained by the lack of professionally mixed, spatially accurate video-to-audio datasets. First, we develop and train a base model that generates stereo audio from video, achieving state-of-the-art in both semantic accuracy and synchronization. Next…
📄 ResearchApr 28, 2026https://machinelearning.apple.com/research/stereofoley-object-aware-stereo-audio - Local Mechanisms of Compositional Generalization in Conditional Diffusion
Conditional diffusion models appear capable of compositional generalization, i.e., generating convincing samples for out-of-distribution combinations of conditioners, but the mechanisms underlying this ability remain unclear. To make this concrete, we study length generalization, the ability to generate images with more objects than seen during training. In a controlled CLEVR setting (Johnson et al.,2017), we find that length generalization is achievable in some cases but not others, suggesting that models only sometimes learn the underlying compositional structure. We then investigate…
- LaDiR: Latent Diffusion Enhances LLMs for Text Reasoning
Large Language Models (LLMs) demonstrate their reasoning ability through chain-of-thought (CoT) generation. However, LLM’s autoregressive decoding may limit the ability to revisit and refine earlier tokens in a holistic manner, which can also lead to inefficient exploration for diverse solutions. In this paper, we propose LaDiR (Latent Diffusion Reasoner), a novel reasoning framework that unifies the expressiveness of continuous latent representation with the iterative refinement capabilities of latent diffusion models for an existing LLM. We first construct a structured latent reasoning space…
- How Fast Should a Model Commit to Supervision? Training Reasoning Models on the Tsallis Loss Continuum
Adapting reasoning models to new tasks during post-training with only output-level supervision stalls under reinforcement learning from verifiable rewards (RLVR) when the initial success probability $p_0$ is small. Using the Tsallis $q$-logarithm, we define a loss family $J_Q$ that interpolates betw...
- TSN-Affinity: Similarity-Driven Parameter Reuse for Continual Offline Reinforcement Learning
Continual offline reinforcement learning (CORL) aims to learn a sequence of tasks from datasets collected over time while preserving performance on previously learned tasks. This setting corresponds to domains where new tasks arise over time, but adapting the model in live environment interactions i...
- Three Models of RLHF Annotation: Extension, Evidence, and Authority
Preference-based alignment methods, most prominently Reinforcement Learning with Human Feedback (RLHF), use the judgments of human annotators to shape large language model behaviour. However, the normative role of these judgments is rarely made explicit. I distinguish three conceptual models of that...
- Conditional misalignment: common interventions can hide emergent misalignment behind contextual triggers
Finetuning a language model can lead to emergent misalignment (EM) [Betley et al., 2025b]. Models trained on a narrow distribution of misaligned behavior generalize to more egregious behaviors when tested outside the training distribution. We study a set of interventions proposed to reduce EM. We ...
- When Errors Can Be Beneficial: A Categorization of Imperfect Rewards for Policy Gradient
Training language models via reinforcement learning often relies on imperfect proxy rewards, since ground truth rewards that precisely define the intended behavior are rarely available. Standard metrics for assessing the quality of proxy rewards, such as ranking accuracy, treat incorrect rewards as ...
- RESTestBench: A Benchmark for Evaluating the Effectiveness of LLM-Generated REST API Test Cases from NL Requirements
Existing REST API testing tools are typically evaluated using code coverage and crash-based fault metrics. However, recent LLM-based approaches increasingly generate tests from NL requirements to validate functional behaviour, making traditional metrics weak proxies for whether generated tests valid...
- Investigation into In-Context Learning Capabilities of Transformers
Transformers have demonstrated a strong ability for in-context learning (ICL), enabling models to solve previously unseen tasks using only example input output pairs provided at inference time. While prior theoretical work has established conditions under which transformers can perform linear classi...
- SIEVES: Selective Prediction Generalizes through Visual Evidence Scoring
Multimodal large language models (MLLMs) achieve ever-stronger performance on visual-language tasks. Even as traditional visual question answering benchmarks approach saturation, reliable deployment requires satisfying low error tolerances in real-world out-of-distribution (OOD) scenarios. Precisely...
- G-Loss: Graph-Guided Fine-Tuning of Language Models
Traditional loss functions, including cross-entropy, contrastive, triplet, and su pervised contrastive losses, used for fine-tuning pre-trained language models such as BERT, operate only within local neighborhoods and fail to account for the global semantic structure. We present G-Loss, a graph-guid...
- Large language models eroding science understanding: an experimental study
This paper is under review in AI and Ethics This study examines whether large language models (LLMs) can reliably answer scientific questions and demonstrates how easily they can be influenced by fringe scientific material. The authors modified custom LLMs to prioritise knowledge in selected fringe ...
- ADEMA: A Knowledge-State Orchestration Architecture for Long-Horizon Knowledge Synthesis with LLMAgents
Long-horizon LLM tasks often fail not because a single answer is unattainable, but because knowledge states drift across rounds, intermediate commitments remain implicit, and interruption fractures the evolving evidence chain. This paper presents ADEMA as a knowledge-state orchestration architecture...
- Semi-Markov Reinforcement Learning for City-Scale EV Ride-Hailing with Feasibility-Guaranteed Actions
We study city-scale control of electric-vehicle (EV) ride-hailing fleets where dispatch, repositioning, and charging decisions must respect charger and feeder limits under uncertain, spatially correlated demand and travel times. We formulate the problem as a hex-grid semi-Markov decision process (se...
- From Soliloquy to Agora: Memory-Enhanced LLM Agents with Decentralized Debate for Optimization Modeling
Optimization modeling underpins real-world decision-making in logistics, manufacturing, energy, and public services, but reliably solving such problems from natural-language requirements remains challenging for current large language models (LLMs). In this paper, we propose \emph{Agora-Opt}, a modul...
- CGU-ILALab at FoodBench-QA 2026: Comparing Traditional and LLM-based Approaches for Recipe Nutrient Estimation
Accurate nutrient estimation from unstructured recipe text is an important yet challenging problem in dietary monitoring, due to ambiguous ingredient terminology and highly variable quantity expressions. We systematically evaluate models spanning a wide range of representational capacity, from lexic...
- Measuring the Sensitivity of Classification Models with the Error Sensitivity Profile
The quality of training data is critical to the performance of machine learning models. In this paper, the Error Sensitivity Profile (ESP) is proposed. It quantifies the sensitivity of model performance to errors in a single feature or in multiple features. By leveraging ESP, data-cleaning efforts c...
- QAROO: AI-Driven Online Task Offloading for Energy-Efficient and Sustainable MEC Networks
With the rapid advancement of artificial intelligence (AI) and intelligent science, intelligent edge computing has been widely adopted. However, the limitations of traditional methods, such as poor adaptability and the slow convergence of heuristic algorithms, are becoming increasingly evident. To e...
- SAFEdit: Does Multi-Agent Decomposition Resolve the Reliability Challenges of Instructed Code Editing?
Instructed code editing is a significant challenge for large language models (LLMs). On the EditBench benchmark, 39 of 40 evaluated models obtain a task success rate (TSR) below 60 percent, highlighting a gap between general code generation and the ability to perform instruction-driven editing under...
- Verification of Neural Networks (Lecture Notes)
These lecture notes provide an introduction to the verification of neural networks from a theoretical perspective. We discuss feed-forward neural networks, recurrent neural networks, attention mechanisms, and transformers, together with specification languages and algorithmic verification techniques...