PLI Ever Current Podcast Episode 12: Bill Tanenbaum and Amir Ghavi of Fried Frank discuss the different types of open and vendor-provided Large Language Models (LLMs) and how they work, and what “fine-tuning” a model means. AI models can be viewed as the inverse of software. Software starts with rules, applies rules to data, and that generates output. AI starts with data, applies an algorithm to the data, and that generates rules. To conduct fine-tuning, a company starts with a pre-trained LLM and adds data that is specifically related to a desired set of corporate tasks to generate tailored rules. Along with other forward-looking issues, Bill and Amir address why fine-tuning is the future of corporate use of Generative AI, why hallucinations will become less problematic, and the contract terms and other factors that companies and their counsel should consider in selecting the pre-trained LLM.
Recorded on 11/15/23
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Featured in this Episode
Amir Ghavi
Amir Ghavi leads Fried Frank’s core technology practices as the co-head of the Technology Transactions Practice and multidisciplinary Digital Assets and Blockchain Practice, while also serving as the firm’s foremost Artificial Intelligence (“AI”) counsel.
Amir has advised clients on cutting-edge technology and intellectual property matters for more than a decade and has translated this experience into a profound understanding of the development and adoption of disruptive technologies, particularly AI.
Amir is a leading legal authority in the sector, advising participants in the AI ecosystem — including several foundation model developers — on product development and counseling, data procurement, AI governance and strategy, regulatory matters, government affairs, M&A, and IP matters. Significantly, Amir has advised and continues to represent defendant AI model developers in several of the major AI litigations currently filed.
Amir advises disruptive technologies for a diverse range of clients, from multinational corporations, private equity sponsors, and venture capital firms to select high-growth technology-driven companies, helping the latter shape their core technology offerings and manage the cycle to commercialization and scale.
Beyond the specific domain of disruptive technologies, clients regularly seek Amir’s counsel on dozens of transaction types, including corporate and asset acquisitions and divestitures, IP-centric joint ventures, restructurings, data use and licensing, brand licensing and management and other intellectual property licensing, outsourcing agreements, software development, the use and management of open-source software, and now, AI’s influence on these transactions and downstream implications.
In addition to transactions and IP-centric disputes, Amir continues to pursue collaborations between academia, industry, and the law, most recently focusing on novel issues arising from broader adoption of artificial intelligence technologies. His practice is at the center of policies and social implications of rapid technology adoption — what it means to live in an increasingly automated world. Amir is a lecturer at Cornell Tech and MIT Sloan for seminars on AI and the law, and is also a regular speaker, panelist, and commentator to the media on artificial intelligence, digital assets, and quantum computing.
In 2021, Chambers USA ranked Amir in Band 3 for Technology, where a client noted that he “is a very business-minded, hyper-intelligent lawyer who is highly knowledgeable and gives very practical, actionable advice for clients.”
Amir received his JD from Cornell Law School in 2006 and his BS in Neuroscience and BA in Philosophy from Emory University in 2001.
He is admitted to practice in New York and the U.S. District Courts for the Southern and Eastern Districts of New York.
