Unite.AI
OCTOBER 30, 2023
Improves quality: The effectiveness of AI is significantly influenced by the quality of the data it processes. Training AI models with subpar data can lead to biased responses and undesirable outcomes. Improving AI quality: AI system effectiveness hinges on data quality. Set training objectives for AI roles.
Unite.AI
MARCH 22, 2024
Cross-Modality Learning : Extending social learning beyond text to include images, sounds, and more could lead to AI systems with a richer understanding of the world, much like how humans learn through multiple senses. The focus would be on developing AI systems that can reason ethically and align with societal values.
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Unite.AI
OCTOBER 20, 2023
AI models in production. Today, seven in 10 companies are experimenting with generative AI, meaning that the number of AI models in production will skyrocket over the coming years. As a result, industry discussions around responsible AI have taken on greater urgency. In 2022, companies had an average of 3.8
Unite.AI
JULY 25, 2023
Production-deployed AI models need a robust and continuous performance evaluation mechanism. This is where an AI feedback loop can be applied to ensure consistent model performance. But, with the meteoric rise of Generative AI , AI model training has become anomalous and error-prone.
Unite.AI
MAY 13, 2024
Microsoft's advanced computational infrastructure and AI platform services will enable organizations of all sizes, from startups to multinational corporations, to develop, deploy, and leverage proprietary and open-source AI models and applications.
Becoming Human
JUNE 5, 2023
The inability to adapt to new data streams has been a significant limitation of ML models. Fortunately, the emergence of adaptive AI is changing the game. Adaptive AI represents a breakthrough in artificial intelligence by introducing continuous learning capabilities.
Unite.AI
APRIL 30, 2024
AI models, particularly chatbots, learn from interactions through various learning paradigms, for example: In supervised learning , chatbots learn from labeled examples, such as historical conversations, to map inputs to outputs. It is essential to balance adaptability and consistency for chatbots.
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