07 February 2024 / 6:00 PM / ATLAS Building, CU Boulder
We will learn about custom versions of ChatGPT, known as GPTs. These personalized editions of ChatGPT combine instructions, extra knowledge, and any combination of skills for specific needs or tasks. You can build these yourself, without code, and have the opportunity to share your customized GPTs with others. OpenAI has created a GPT Store which highlights a reported 3 million of these custom AI bots.
Our first speaker, Liza Adams, will help us understand what GPTs (custom Generative Pre-trained Transformers) make possible, their impact, and the different types of GPTs. As GPTs accelerate innovation and creativity, see some GPTs in action and learn about how you can get the most value from them as a builder and user. With over 20 years of experience in B2B technology, Liza Adams has held marketing executive roles at industry leaders like Smartsheet, Juniper Networks, Brocade (now Broadcom), Pure Storage, Encompass Technologies, and Level 3 (now Lumen). As a Managing Partner at GrowthPath Partners, she serves high-growth businesses in three distinct roles: as a fractional Chief Marketing Officer, an executive advisor, and an AI consultant. A recognized thought leader in the AI space, Liza is a prolific writer and public speaker. Her work focuses on the responsible use of AI, its strategic value, the future of work, and its application in strategic go-to-market and marketing use cases. Linkedin: https://www.linkedin.com/in/lizaadams/
Website: https://www.growthpath.net/
Our second speaker, Daniel Ritchie, will start with the basics of GPTs and go deeper, covering what they are, what they are not, and how you can leverage them for your own AI powered solutions. We will touch on various aspects of GPTs in this approachable overview, and you will walk away with an understanding of the massive transformative potential of GPTs.
Daniel was one of the hosts and judges at the recent GPT Hackathon event, hosted by RMAIIG’s AI for Entrepreneurs and Startups (AES) Subgroup. He is an entrepreneur, dreamer, and forward thinking technologist captivated by the disruptive nature of AI. His current focus is building the Brain Wave Collective, a groundbreaking approach to employment and equity building, exploring new models at the intersection of technology and community. He provides services through LetsBuildGPTs.com, where he shows that the learning curve for mastering these advancements is more manageable than often perceived.
Notes
Speakers Liza Adams and Daniel Ritchie then provided an overview of GPT technology, explaining how generative AI models like GPTs can be used to build custom applications with ease. Daniel demonstrated how uploading an API schema to a GPT allows the quick creation of a weather application. The speakers discussed ethical considerations around AI such as mitigating bias, ensuring data privacy, and promoting responsible use. Both highlighted how GPTs have potential to improve productivity, decision making, and allow rapid prototyping of ideas. However, concerns were also raised about potential negative impacts on work-life balance. Attendees asked questions about tracking GPT usage, integrating proprietary data while preserving privacy, optimizing token costs for API calls, and accounting for biases in business applications of AI. Meeting participants were encouraged to explore GPT capabilities through the various AI interest subgroups and share their own ideas.
Some of the items discussed:
- Discussed the concept of “giving grace” in navigating the new GPT landscape, given its rapid evolution and differing experiences each day
- Showed examples of using GPTs for data analysis, personalized experiences, automation, ideation, and more through demonstrations of interacting with GPT models
- Highlighted potential applications of GPTs in marketing use cases like competitive analysis, personalized experiences, and strategic business decision-making
- Emphasized the importance of ethical and responsible AI practices like overseeing GPTs, narrowing prompts, and preventing made-up responses
- Explained how AI assistants can supplement knowledge gaps through natural language interactions
- Demonstrated uploading an API schema to a GPT to quickly build the weather application functionality
- Discussed more advanced uses like integrating calendar data and optimizing for privacy and costs
- Emphasized that AI lowers the barrier for non-experts through focused, curated models rather than general-purpose knowledge
- Encouraged sharing prototype ideas with technical friends to incorporate AI into real products and services
- OpenAI does not currently provide analytics on GPT usage or visits, though workarounds exist to analyze network traffic.
- Large language models like GPTs can access vast amounts of data through API integrations, like the medical journal API used by the “Consensus” GPT.
- Making GPT outputs more deterministic for applications requires lowering the temperature setting, but this reduces interesting responses.
- Proprietary APIs and self-hosted assistants provide better data privacy than GPTs, allowing access control and limiting what data is shared.
- Personalizing responses based on large user profiles will be possible as GPTs continue evolving to analyze more complex inputs.
- Token costs for commercial GPT applications need optimization to ensure business viability, though techniques are still emerging.
- Bias in AI results from its training and must be actively managed through testing, prompts, and human oversight of complex tasks.
- Open source and commercial options exist beyond OpenAI for hosting custom models, though integration capabilities still lag the GPT builder tools.