Generative AI Meets Responsible AI
The New Era of AI
From GPT-4 to Stable Diffusion, Generative AI is pioneering the new era of AI, enabling exciting new possibilities for creativity and exploration while pushing the boundaries of what's possible.
Generative AI based models and applications are being rapidly adopted across industries due to their powerful capabilities and wide ranging scope. Responsible AI practices are needed to ensure that models are unbiased, trustworthy, and work as intended even after deployment. Sessions will range from industry challenges for implementing generative AI models to the ethical choices and implications these applications have.
Watch the sessions to learn:
Watch On-Demand Sessions
Generative AI is pioneering a new era of innovation and possibilities. But in order to ensure it's used ethically, fairly, and safely, organizations must apply responsible AI principles.
Watch Fiddler Founder Krishna Gade’s opening remarks to hear how AI teams can meet this challenge.
Generative AI is being used to innovate and improve processes across industries, from drug discovery to work automation.
Watch this panel session on Innovating with Generative AI to learn how:
- Engineering teams enhance their productivity using generative AI
- IBM is leveraging generative AI models to advance medical innovation
- Generative AI is driving innovation across domains
AI has advanced rapidly over the past decade and is only accelerating in its evolution. George Mathew, Managing Director, Insight Partners, discusses the progress we’ve made and where we’re heading.
Watch this session on Explainability in the Age of Generative AI to learn:
- How generative AI has opened up new modalities of human and machine interaction
- Examples of generative AI advancements and milestones
- Challenges society will face as AI usages spreads and improves
Responsible AI principles and practices are necessary to ensure fair, ethical, and safe usage.
Watch this panel session on Best Practices for Responsible AI to learn:
- Limitations of large language models
- The importance of model governance
- Key pieces for building out a Responsible AI framework
Despite the fear and uncertainty around AI, it can be used as a tool for widespread good and societal benefit. Saad Ansari, Director of AI at Jasper AI, dives into this potential future.
Watch this session on Thinking of AI as a Public Service to learn:
- The importance of intentionality when designing new technology
- Applications for generative AI across different domains
- How the future of AI can be directed to societal benefit
While generative AI offers huge upside for enterprises, many blockers remain before it’s used by a broad range of industries. LLMOps is the new ML workflow to accelerate adoption and productize generative AI.
Watch this panel session on LLMOps - Operationalizing Large Language Models to learn:
- How LLMOps iterates on MLOps to optimize for large language models
- The key pieces of a generative AI workflow
- How ML teams can approach leveraging LLMs in their applications
Enterprise generative AI needs to be reproducible, scalable, and responsible, while minimizing risks. Ali Arsanjani, PhD, Head of the AI Center of Excellence at Google, explains why this requires an augmentation of the ML lifecycle.
Watch this session on Enterprise Generative AI - Promises vs Compromises to learn:
- The importance of explainability and adaptability for enterprise generative AI
- How to minimize risks to safety, misuse, and model robustness
- Key elements of the generative AI lifecycle
ML teams need to evolve their MLOps framework to support LLMs and make sure they're optimized to power generative AI applications.
Watch this session on Monitoring OpenAI Embeddings to learn:
- Why unstructured models are complex and difficult to monitor
- How to monitor OpenAI embeddings using Fiddler’s vector monitoring, based on a patent-pending cluster-based algorithm
- How to track for changes in high-dimensional vectors over time, especially in situations without ground-truth labels
LLMOps: Operationalizing Large Language Models
Operationalizing large language models (LLMs) requires a different set of tooling and workflows than traditional ML. Check out the top 4 takeaways for LLMOps.
Top 5 Questions on Responsible AI from our Summit
Read the top responsible AI and ML model bias questions asked by our Generative AI Meets Responsible AI summit attendees, including responses from industry experts.
Top 5 Questions on LLMOps from our Generative AI Meets Responsible AI Summit
Read the top LLMOps questions asked by our Generative AI Meets Responsible AI Summit attendees and responses from our experts at Thoughtspot, Jasper AI, and Google.
Enterprise Generative AI - Promises vs Compromises
Enterprise usage of generative AI continues to advance rapidly. But before reaching their promise, LLMs must address concerns around explainability and security.
GPT-4 and the Next Frontier of Generative AI
GPT-4 marks a new era from model-centric to data-centric AI. This shift brings a unique set of challenges across trust, interpretability, security and privacy.
LLMOps: The Future of MLOps for Generative AI
Operationalizing Generative AI at scale depends on reducing model training, selection, and deployment costs, while ensuring AI fairness. Introducing LLMops.
Not all Rainbows and Sunshine: the Darker Side of ChatGPT
ChaptGPT is a viral chatbot that is used for NLP tasks. This is an overview of the risks and ethical issues associated with ChatGPT and large language models.