#explainable-ai

#explainable-ai

As AI chat interfaces become more popular, users increasingly rely on AI outputs to make decisions. Without explanations, AI systems are black boxes. Explaining to people how an AI system has reached a particular output helps users form accurate mental models, prevents the spread of misinformation, and helps users decide whether to trust an AI output. However, the explanations currently offered by large language models (LLMs) are often inaccurate, hidden, or confusing.

Traditional AI breaks when faced with new, high-stakes situations without data. Astut gives organisations a way to make confident, transparent decisions when precedent doesn't exist. This funding will allow us to strengthen our partnerships in defence, energy, retail and finance, and embed our technology into partner platforms so it can reach those who need it most. Our creative AI is radical - an

Neurosymbolic AI fuses the statistical power, pattern recognition, and adaptability of neural networks-think large language models (LLMs)-with the logical, rule-based structure of symbolic reasoning that can predict real outcomes while helping prevent the hallucinations generative AI (GenAI) may produce. Correlation isn't causation. Summer is, of course, prime ice cream season in California, and that's also when shark bites are most prevalent-but enjoying your mint chip cone won't make it more likely you'll get bitten. Neurosymbolic AI sorts out the coincidence and lets you dig down into what's really causing those shark bites.

Built on Oracle Cloud Infrastructure (OCI) and grounded in customers' existing data and security controls, NetSuite Next combines explainable AI with a unified data model and Oracle's Redwood Design System to ensure every decision is transparent, auditable, and aligned with business governance. Customers can move to the new platform "at the press of a button," according to Oracle, without needing to migrate data or disrupt existing configurations.

"UX in AI" has become one of the most confusing buzzwords in our industry. Jakob Nielsen has famously talked about how UX is desperately needed for AI, but few can define what this means (or how to do it). Is it about designing chat interfaces and chatbots? Is it about working with algorithms or vibe coding? Is it about using Replit and Bolt instead of Figma?

We empirically study how several baseline models perform on the task of explainable visual entailment, investigating both off-the-shelf and finetuned model performances.