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Salesforce takes aim at ‘jagged intelligence’ in push for more reliable AI
- Salesforce is addressing the challenge of 'jagged intelligence' in AI for business applications, aiming to bridge the gap between raw intelligence and reliable performance in unpredictable enterprise environments.
- Salesforce AI Research introduced new benchmarks, models, and frameworks to enhance AI agents' intelligence, trustworthiness, and adaptability for enterprise use.
- The focus is on 'Enterprise General Intelligence' (EGI) tailored for business complexity, contrasting with the concept of Artificial General Intelligence (AGI).
- A key aspect of the research is measuring and tackling AI's performance inconsistency, highlighted by the introduction of the SIMPLE dataset for evaluating AI systems.
- CRMArena, a benchmarking framework simulating real customer relationship management scenarios, aims to fill the gap between academic benchmarks and real-world business needs.
- Salesforce announced technical innovations like SFR-Embedding for deeper contextual understanding, xLAM V2 models for action prediction, and SFR-Guard for AI safety and reliability.
- ContextualJudgeBench and TACO were also launched, focusing on evaluating judge models in context and multimodal action models for complex problem-solving, respectively.
- The emphasis on customer co-innovation and AI reliability aligns with Salesforce's goal of providing dependable AI solutions for enterprises, acknowledging the low tolerance for inaccuracies in enterprise data.
- Salesforce's research efforts underscore a shift towards prioritizing consistency and reliability in AI systems for real-world business applications over just raw intelligence.
- The company's new technologies are set to roll out gradually, with a strategic focus on enhancing consistency and reliability in AI solutions for businesses.
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