Rodrigo Madanes, EY Global Innovation AI Leader. Read Rodrigo Madanes' full executive profile here.
Generative artificial intelligence models can create all kinds of outputs from massive amounts of data to help users with new and exciting applications. However, GenAI presents many challenges and risks that users must understand and address. The technology requires substantial computational resources, adequate ethical standards and sophisticated technical solutions to allow safe, fair and accountable use.
Compute scarcity poses a challenge for scaling up GenAI models and democratizing their access and use. Startups and academic institutions have struggled to gain access to high-performing GPUs, as legacy technology companies and newly minted unicorns have the capital needed to purchase the GPUs directly or pay expensive cloud computing costs to train their large parameter AI models.
This means that users can pose more conversational application queries at the orchestration layer that LLMs trained only on historical data cannot solve. They can, for example, connect to multiple external databases and find a flight to a specific location at a particular time and at a certain price.
However, open source has many advantages for advancing GenAI research and development. It can foster more diversity, creativity, quality and accountability in GenAI models. Open source can also lower the barriers to entry and participation in GenAI for various stakeholders. The jury is out on how this competition will play out.Guardrails are essential for ensuring the safety, fairness and accountability of GenAI models.
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