I Finally Built My First AI App (And It Wasn’t What I Expected)
A beginner-friendly walkthrough of API calls, environment variables, and real-world AI infrastructure
The post I Finally Built My First AI App (And It Wasn’t What I Expected) appeared first on Towards Data Science.
AU10TIX Earns Solutions Partner with Certified Software Designation
This designation recognizes AU10TIX’s ID Verification Suite for delivering software solutions built on the Microsoft Cloud that demonstrate interoperability and meet program requirements. AU10TIX, a global leader in identity verification and fraud prevention, today announced it has earned the Soluti...
HABS collaborates with Microsoft to build the next gen Human-Aware AI
HABS, a global pioneer in Neuro-AI, today announced a collaboration with Microsoft, to accelerate the development and responsible deployment of human-centered artificial intelligence. This collaboration brings together HABS’s scientific leadership in cognitive signal interpretation with Microsoft’s ...
Industry’s largest provider network activates 1.2 million HCPs, supported by physician-level data and reporting ConnectiveRx, a leading provider of technology-enabled patient support and access solutions for specialty and branded medications, today highlighted the scale and precision of its clinical...
Sightview Showcases Fresh Patient Engagement Tools at Vision Expo 2026
Digital payment and patient communications offerings alleviate friction throughout the patient journey Sightview, the only electronic health record and practice management partner focused solely on eyecare, will showcase two recently updated tools for its eyecare-specific EHR/PM offering at Vision E...
Standard Kernel Raises $20M Seed to Let AI Rewrite the Software That Runs AI
The startup uses AI to generate highly optimized GPU kernels, improving AI workload performance without changing models or hardware. Standard Kernel, a startup building AI systems that automatically generate ultra-optimized GPU software, today announced a $20 million seed round led by Jump Capital, ...
GuardianEye by Mindsprint Recognized at TEISS 2026 Awards
Mindsprint, a technology firm offering purpose-built AI-led solutions to modernize enterprise operations, today announced that its Agentic AI-powered cybersecurity solution, GuardianEye, has been named Runner-Up in the ‘Best Penetration Testing Solution’ category at the TEISS Awards 2026. The catego...
Green Security Appoints David Newton to Lead AI Orchestration
Green Security has appointed David Newton as Vice President of AI Orchestration to lead the company’s applied AI strategy and accelerate automation and intelligence across its healthcare vendor credentialing platform. A veteran healthcare product and AI leader, Newton will oversee research, data sci...
Zendesk Expands AI Agents with Proposed Forethought Acquisition
Proposed acquisition positions Zendesk to lead the agentic service era, projecting 2026 as the year AI agents will surpass human service Zendesk expects autonomous AI to handle more service interactions than humans this year, marking a structural shift in customer service. To lead this transition, ...
arXiv:2603.09980v1 Announce Type: new
Abstract: LLM unlearning is essential for mitigating safety, copyright, and privacy concerns in pre-trained large language models (LLMs). Compared to preference alignment, it offers a more explicit way by removing undesirable knowledge characterized by specific...
MoE-SpAc: Efficient MoE Inference Based on Speculative Activation Utility in Heterogeneous Edge Scenarios
arXiv:2603.09983v1 Announce Type: new
Abstract: Mixture-of-Experts (MoE) models enable scalable performance but face severe memory constraints on edge devices. Existing offloading strategies struggle with I/O bottlenecks due to the dynamic, low-information nature of autoregressive expert activation...
Personalized Group Relative Policy Optimization for Heterogenous Preference Alignment
arXiv:2603.10009v1 Announce Type: new
Abstract: Despite their sophisticated general-purpose capabilities, Large Language Models (LLMs) often fail to align with diverse individual preferences because standard post-training methods, like Reinforcement Learning with Human Feedback (RLHF), optimize for...
LWM-Temporal: Sparse Spatio-Temporal Attention for Wireless Channel Representation Learning
arXiv:2603.10024v1 Announce Type: new
Abstract: LWM-Temporal is a new member of the Large Wireless Models (LWM) family that targets the spatiotemporal nature of wireless channels. Designed as a task-agnostic foundation model, LWM-Temporal learns universal channel embeddings that capture mobility-in...
Agentic Control Center for Data Product Optimization
arXiv:2603.10133v1 Announce Type: new
Abstract: Data products enable end users to gain greater insights about their data by providing supporting assets, such as example question-SQL pairs which can be answered using the data or views over the database tables. However, producing useful data products...
Hybrid Self-evolving Structured Memory for GUI Agents
arXiv:2603.10291v1 Announce Type: new
Abstract: The remarkable progress of vision-language models (VLMs) has enabled GUI agents to interact with computers in a human-like manner. Yet real-world computer-use tasks remain difficult due to long-horizon workflows, diverse interfaces, and frequent inter...
HEAL: Hindsight Entropy-Assisted Learning for Reasoning Distillation
arXiv:2603.10359v1 Announce Type: new
Abstract: Distilling reasoning capabilities from Large Reasoning Models (LRMs) into smaller models is typically constrained by the limitation of rejection sampling. Standard methods treat the teacher as a static filter, discarding complex "corner-case" problems...
Beyond Scalars: Evaluating and Understanding LLM Reasoning via Geometric Progress and Stability
arXiv:2603.10384v1 Announce Type: new
Abstract: Evaluating LLM reliability via scalar probabilities often fails to capture the structural dynamics of reasoning. We introduce TRACED, a framework that assesses reasoning quality through theoretically grounded geometric kinematics. By decomposing reaso...
Verbalizing LLM's Higher-order Uncertainty via Imprecise Probabilities
arXiv:2603.10396v1 Announce Type: new
Abstract: Despite the growing demand for eliciting uncertainty from large language models (LLMs), empirical evidence suggests that LLM behavior is not always adequately captured by the elicitation techniques developed under the classical probabilistic uncertain...
We propose a 3D latent representation that jointly models object geometry and view-dependent appearance. Most prior works focus on either reconstructing 3D geometry or predicting view-independent diffuse appearance, and thus struggle to capture realistic view-dependent effects. Our approach leverage...
3 Questions: On the future of AI and the mathematical and physical sciences
Professor Jesse Thaler describes a vision for a two-way bridge between artificial intelligence and the mathematical and physical sciences — one that promises to advance both.