Ram Ramamoorthy | Source | Director of AI Research at ManageEngine

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Ram Ramamoorthy

Director of AI Research at ManageEngine
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Ramprakash Ramamoorthy leads the AI and blockchain efforts for ManageEngine and Zoho. He is in charge of implementing strategic and powerful AI features at ManageEngine to help provide an array of IT management products well-suited for enterprises of any size. Ramprakash is a passionate leader with a level-headed approach to emerging technologies and a sought-after speaker at tech conferences and events.

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  • How We’ll Be Interacting With AI As It Takes On More Work

    It’s too soon to see what form an AI-powered business operating system would take, but industry observers have some ideas.

    Article
  • Why large language models are so important for enterprises – Intelligent CIO Middle East

    The level of Roi that an enterprise can generate from AI depends on the maturity level of process streamlining, data streamlining, automation. An organisation that is still using manual processes without digital platforms, will not see a big bang even if they have adopted AI enabled products. Rajesh Ganesan and Ramprakash Ramamoorthy at ManageEngine explain further.

    Article
  • The Rise of Synthetic Data in AI - The AI Times EP 5 - Techstrong TV

    Datasets are becoming bigger and more unwieldy, and as they do, they are susceptible to greater copyright challenges and activism as well as security issues. As AI becomes more mainstream, some organizations are turning to synthetic data to train tomorrow’s AI and machine learning models. But how mature is this practice? Is synthetic data up to the challenge of replicating real data, or is it just a sub-par substitute? In this episode, hosts Mike Vizard and Lee Baker are joined by Etienne Martin (Algola), Ram Ramamoorthy (ManageEngine/Zoho) and Ryan Berry (OneStream) to discuss the opportunities and obstacles around synthetic data, explore what’s state of-the-art in synthetic data and predict where it’s all going.

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  • Tackling AI Bias: Insights from ManageEngine's AI Research Director
    Ram explains AI bias as "systemic errors from flawed training data," distinct from AI hallucinations, which are pattern recognition failures. He suggests debiasing strategies like using diverse datasets, data cleansing, and human oversight. While achieving bias-free AI is challenging, focusing on data integrity and system fortification is crucial.
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