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Intellectual property (IP) complexities faced by next-gen AI applications

Key factors influencing the growth trend in AI hardware, the growth of GPUs, companies and products in the market and the future of AI hardware

Unlock a more effective approach to ML Observability

As with any technology, ML is not immune to bias and unfairness, which can lead to serious consequences for individuals and communities.

AryaXAI synthetics to resolve critical data gaps, test models at scale and preserve data privacy

Published at MedCity News

Published at AIM Leaders Council

Recent developments in US regulations and responsible AI practices

While there has been profound focus on development of AI and its applications, the Indian Government is now speeding up to formulate laws, policies and clear guidelines for regulating and governing AI.

Understanding and interpreting predictions made by AI models

Published at Analytics India Magazine

While businesses want to know when a problem has arisen, they are more interested in knowing why the problem arose in the first place. This is where ML Observability comes in.

As organizations scale their AI and ML efforts, they are now reaching an impasse - explaining and justifying the decisions by AI models. Also, the formation of various regulatory compliance and accountability systems, legal frameworks and requirements of Ethics and Trustworthiness, mandate making AI systems adhere to transparency and traceability

With packaged AI APIs in the market, more people are using AI than ever before, without the constraint of compute, data or R&D. This provides an easy entry point to use AI and gets users hooked for more. However, the first legal framework for AI is here! One of the many mandates in the proposal is to make AI systems adhere to transparency and traceability. These additional requirements highlight the ever-increasing need for Explainable AI.

Published at Analytics India Magazine