Transformations in AI: Why Foundation Models are the Future
Jul 16, 2024
auto_awesome
Dr. David Cox, VP of AI Models at IBM Research, discusses foundation models, self-supervised machine learning, and AI applications like Watson X. The podcast explores AI's impact on business automation, collaboration between IBM and MIT, and the future of AI-human collaboration.
Foundation models drive revenue and efficiency for businesses by redefining automation processes.
Partnerships and collaborations in AI research accelerate advancements and benefit society and customers.
Deep dives
AI Foundation Models Revolutionizing Business Automation
AI foundation models, as explained by Dr. David Cox, have redefined the possibilities in business automation. These models, initially capturing attention, now drive revenue and efficiency for businesses. Foundation models provide a base for innovative AI capabilities, diminishing labor-intensive automation processes and enabling businesses to select suitable models for their needs.
Significance of the IBM-MIT Collaboration in Advancing AI Research
The partnership between IBM and MIT in the AI field dates back decades, with a formal collaboration initiated in 2017. This joint lab, dedicated to AI research, serves as a pioneer in IBM's AI strategy. By fostering deep relationships and interdisciplinary projects, this collaboration accelerates advancements in AI solutions for societal and customer benefit.
Evolution of AI Breakthroughs and Foundation Models
Advancements in AI, tracing back to key algorithms like back propagation, have transformed the technology landscape. The amalgamation of abundant data, enhanced computing power, and self-supervised learning has propelled the rise of foundation models. These models, akin to a versatile oven, revolutionize automation by simplifying tasks and reducing labor-intensive efforts.
Addressing Bias and Hallucination Challenges in AI
The proliferation of AI and foundation models accentuates challenges like bias and hallucination. Biases embedded in training data can lead to biased AI outputs, emphasizing the need for bias mitigation strategies. Additionally, AI models may 'hallucinate,' generating fabricated outputs with unwarranted certainty, requiring dedicated efforts to enhance accuracy and reliability in AI applications.
Major breakthroughs in artificial intelligence research often reshape the design and utility of AI in both business and society. In this special rebroadcast episode of Smart Talks with IBM, Malcolm Gladwell and Jacob Goldstein explore the conceptual underpinnings of modern AI with Dr. David Cox, VP of AI Models at IBM Research. They talk foundation models, self-supervised machine learning, and the practical applications of AI and data platforms like watsonx in business and technology.
When we first aired this episode last year, the concept of foundation models was just beginning to capture our attention. Since then, this technology has evolved and redefined the boundaries of what's possible. Businesses are becoming more savvy about selecting the right models and understanding how they can drive revenue and efficiency.
This is a paid advertisement from IBM. The conversations on this podcast don't necessarily represent IBM's positions, strategies or opinions.