Why do so many enterprises struggle to move from AI hype to real-world impact? Find out here

“The most boring AI company in the world” – that’s how Founder & CEO @ahbolur describes @AppliedAICo. For the latest installment of our #IntelligentFuture series, exploring how advanced tech is shaping our lives, he and I discussed how the company is embedding Artificial

Awesome to have you onboard @wyldinvestments 🚀

Middle East Venture Partners @MEVPcapital backs @AppliedAICo in $55M Series A AppliedAl just landed a $55M Series A, co-led by @G42ai and @BessemerVP, with Middle East Venture Partners (@MEVPcapital) joining the round to fuel global expansion from Abu Dhabi.

Traditional, time-based model of work is obsolete and inefficient in today's AI age. There is a new way to this. https://t.co/CsVLuR3af9

Thank you for the early support @C_Angermayer !

AppliedAI is grateful for Palantir Technologies support as an investor and operational partner and we look forward to a number of major announcements in 2025.

The keynote highlighted how Palantir AIP infrastructure can be leveraged by external platforms for fast and secure model inference on highly sensitive data.

AppliedAI presented a technical keynote on Opus’s integration with Palantir Technologies AI Platform (AIP) and Foundry.

@AppliedAICo had the privilege of sharing Opus State of the Art Knowledge Workflow Generation and Execution architecture at @PalantirTech DevCon 2024 in Palo Alto, California. https://t.co/naS67cwWyw

That’s a wrap on Day 1 of DevCon, which featured: • A Fireside Chat on deprecating backend dev with Palantir CTO @ssankar • Keynotes from @DTNMarkets, @Lennar, @anduriltech, @AppliedAICo, @the7bridges and Palantir Chief Architect @hyperindexed • Three new product launches:

75% of consumers are concerned about AI misinformation. How can policymakers and experts collaborate to create ethical AI frameworks addressing transparency, bias, and misinformation? Watch the #FII8 livestream for an expert discussion: https://t.co/8c7jwJiwlz

"We believe the human element of AI will reside in two areas: intent and liability. We’re seeing that many people using workflows need to assign liability somewhere, with a human signing off and assuming liability and responsibility for that role." Great contribution by Founder

https://t.co/KZcbdbwnRK

MEDIC’s multifaceted evaluation reveals these performance trade-offs, bridging the gap between theoretical capabilities and practical implementation in healthcare settings, ensuring that the most promising models are identified and adapted for diverse healthcare applications.

and other tasks. Our results show performance disparities across model sizes, baseline vs medically finetuned models, and have implications on model selection for applications requiring specific model strengths, such as low hallucination or lower cost of inference.

MEDIC features a novel cross-examination framework quantifying LLM performance across areas like coverage and hallucination detection, without requiring reference outputs. We apply MEDIC to evaluate LLMs on medical question-answering, safety, summarization, note generation, ...

specific clinical applications. We introduce MEDIC, a framework assessing LLMs across five critical dimensions of clinical competence: medical reasoning, ethics and bias, data and language understanding, in-context learning, and clinical safety.

While real-world assessments are valuable indicators of utility, they often lag behind the pace of LLM evolution, likely rendering findings obsolete upon deployment. This temporal disconnect necessitates a comprehensive upfront evaluation that can guide model selection for...

ABSTRACT: The rapid development of Large Language Models (LLMs) for healthcare applications has spurred calls for holistic evaluation beyond frequently-cited benchmarks like USMLE, to better reflect real-world performance.