Hive Data

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Generative AI models have demonstrated remarkable capabilities in language-related tasks. However, their potential extends into realms of artificial general intelligence far beyond language domains, encompassing non-language domains like genetic sequences, machine log data, financial markets data, time series data, etc. Disruptive AI architectures like transformers (the “T” in “GPT”) enable models to capture long-range dependencies, making it adept at learning patterns within non-language data.

Techniques like layer normalization ensures stable learning dynamics, allowing the model to generalize effectively. Supervised fine-tuning can reapply innate understanding of complex language-like structures in foundational models to new domains with little training data. Input encoding can work effectively with non-language by using domain-specific tokenization techniques such as nucleotide triplets for genetic sequences or sequences of event for log data. Emergent reasoning techniques like chain-of-thought/plan-of-action and self-instruct can drive continuous refinement and error control without needing large teams of domain experts.

[The Hive Think Tank]( is doing a series of online events on Generative AI. In this event organized by The Hive Think Tank, our panel of Al leaders will discuss this second, broader wave of disruptions that generative Al will be bringing upon us. The panel will also discuss the ethical implications of creating complex and synthetic non-language data, especially in areas like genetics and finance. As Al comes to the forefront of driving digitization globally, these subtler second-order use-cases will reshape growth, work and equity value across a wider set of verticals.

*****The webinar will take place on [Zoom]( Please register here: [](**


* **[Dr. Karthik Narasimhan](**: Karthik is an Assistant Professor, Computer Science at Princeton University and is the Co-director of Princeton Natural Language Processing. His research spans the areas of natural language processing and reinforcement learning, with a view towards building intelligent agents that learn to operate in the world through both experience and existing human knowledge (ex. text).
* **[Dr. Ellen D. Zhong](**: Ellen is an Assistant Professor of Computer Science at Princeton University. Her primary research interest is in machine learning for structural biology, with a focus on 3D vision-based algorithms for determining protein structures from cryo-electron microscopy (cryo-EM) data.
* **[Alistair Crol](**[l]( (moderator): Alistair is a serial entrepreneur, event organizer, and bestselling author who works at the intersection of technology and society. After an early career in product management and telecommunications at Eicon, Primary Access, and 3Com, Alistair founded web performance pioneer Coradiant (acquired by BMC.) Since that time, he’s launched the *Year One Labs* incubator, chaired the world’s leading conferences on Big Data (Strata); Cloud Computing (Cloud Connect); and digital government (FWD50.)
Alistair is the author of three books on technology and business including the bestselling *Lean Analytics*, widely considered required reading for startups, and has served as a visiting executive at Harvard Business School where he helped create a course on Data Science and Critical Thinking for MBAs. Alistair lives in Montreal, Canada, where he is currently working on *Just Evil Enough*, a playbook on subversive marketing.


June 21


11:00 am - 12:00 pm

Click to Register:

The Hive Think Tank

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