Podcast Summary
AI applications in business: IBM's VP of AI Models, Dr. David Cox, discusses the conceptual underpinnings of AI and its practical applications in business, including foundation models, and shares insights on the future of work and design.
We're witnessing a revolutionary era in artificial intelligence (AI), and the new season of Smart Talks with IBM podcast series aims to explore how businesses can integrate AI into their workflows and drive real change. IBM's VP of AI Models, Dr. David Cox, shares insights on the conceptual underpinnings of AI, including foundation models, and their practical applications in business. He also discusses the implications of AI for the future of work and design. With his extensive background in AI research, David has contributed to and presided over many major breakthroughs in the field. Listeners, whether new or returning, will find his insights thought-provoking and valuable. Tune in to Smart Talks with IBM to learn more.
IBM-MIT AI collaboration: IBM and MIT's long-standing partnership in AI research, rooted in their shared history, has led to significant investments and advancements in the field, including the latest AI technologies and societal impact projects
The collaboration between IBM and MIT, rooted in their shared history in artificial intelligence dating back to the 1956 Dartmouth workshop, has been a fruitful partnership. Over the last five years, they have formalized their relationship, investing close to a quarter billion dollars into joint research, and have been at the forefront of IBM's AI strategy. This partnership allows them to stay informed about the latest advancements in AI, such as foundation models, and work together on projects with societal impact, like addressing climate change. Recently, there has been a surge in public interest in AI due to accessible generative models like chatGPT and mid-journey, which have been made possible by breakthroughs like backpropagation and the availability of large amounts of data and powerful GPUs. These advancements have accelerated the pace of AI development, making it an exciting and revolutionary field.
AI advancements: Recent AI advancements are due to deep learning, data, and compute power. Deep learning was enabled by digitalization, faster computers, and large amounts of data. Self-supervised learning increased the usable data and led to more powerful models. Foundation models provide a foundation for AI applications, making the process more efficient and effective.
The recent advancements in artificial intelligence (AI) can be attributed to the combination of deep learning, data, and compute power. Deep learning, a rebranded version of neural networks, was enabled by digitalization, faster computers, and large amounts of data. Self-supervised learning, which allows the use of unannotated data, has significantly increased the amount of data that can be used and led to more powerful models. Foundation models, a term coined by a Stanford group, provide a foundation for AI applications by allowing users to build upon the results of massively trained models, making the process more efficient and effective. This progression of technologies has been simmering for a while but gained significant public awareness with the release of chat GPT and other AI applications that made these technologies visible and accessible to the public.
Foundation models vs Traditional AI: Foundation models allow for training on vast quantities of unlabeled data, significantly reducing the effort and cost required to automate tasks, making it feasible for tasks with lower returns.
Foundation models in AI technology significantly reduce the effort required to automate tasks compared to traditional AI methods. In the past, automating tasks was a labor-intensive process, with a high threshold for investment due to the need for large amounts of labeled data and highly skilled personnel. This made it uneconomical to automate tasks with only modest returns. The analogy used was building a dam on a river. Traditional AI solutions required a significant investment, like building a dam, which was only worth it for large projects with a high return on investment. However, most data, like water in puddles and creeks, was left untouched due to the high cost and effort. Foundation models, on the other hand, allow for training on vast quantities of unlabeled data, creating a base model that can be fine-tuned for specific tasks. This significantly reduces the effort and cost required, making it feasible to automate tasks with lower returns. The result is a more accessible and versatile AI technology that can solve a wider range of problems.
Foundation model selection and bias mitigation: Selecting the appropriate foundation model size and specialization for specific tasks leads to efficiency, cost savings, and improved performance. Addressing bias and hallucination issues in AI is crucial for fair and accurate applications.
While foundation models offer significant advantages for automating new tasks with less effort due to their powerful capabilities, it's crucial to recognize that there isn't a one-size-fits-all foundation model. Instead, selecting the appropriate size and specialized model for specific tasks is essential. This approach offers advantages such as efficiency, cost savings, and improved performance. Furthermore, addressing bias and hallucination issues in AI remains a significant challenge. These problems arise when models are trained on biased data, leading to biased outputs. It's essential to recognize these issues and work towards mitigating them to ensure fair and accurate AI applications.
Biases and hallucinations in AI: Large language models like ChatGPT can unintentionally reproduce biases from data and generate false information, leading to inaccurate results. Researchers are working on methods to identify and remove these biases and create tools for auditing AI systems.
While large language models like ChatGPT show impressive capabilities, they also come with challenges such as biases and hallucinations. These models, which learn from vast amounts of data, can unintentionally reproduce biases present in the data and generate false information, leading to inaccurate results. Researchers are actively working on methods to identify and remove these biases and to create tools for auditing AI systems. Another challenge is the models' tendency to "hallucinate," or make up information, which can lead to confusion and misinformation. These issues can be particularly problematic in business contexts where accurate and reliable information is crucial. Despite these challenges, AI holds immense potential for solving complex business problems. For instance, it can automate repetitive tasks, analyze large data sets to identify trends and insights, and even generate creative content. As the conversation continues, David and Jacob will discuss specific examples of how businesses can harness AI's capabilities to address their challenges.
Conversational AI in programming: Conversational AI could revolutionize programming by generating code based on instructions, making software development more intuitive and efficient
Conversational AI is set to revolutionize how we interact with technology, making it more efficient and productive. Currently, businesses use AI for tasks like analyzing product reviews, but the technology is also being applied to summarizing long texts, such as customer call transcripts, and answering questions in natural language. This not only saves time but also makes interactions with technology more user-friendly, as we can now simply describe what we want instead of learning complex interfaces. This trend is particularly significant in the realm of programming, where conversational AI could generate code based on our instructions, leading to a potential revolution in software development. Ultimately, conversational AI has the potential to make technology more accessible and intuitive, leading to increased productivity and efficiency.
AI Transformation in Business: AI is transforming business operations by making processes more efficient and opening up new opportunities, while also changing the nature of jobs and leading to new opportunities in the workforce
AI is transforming business in profound ways, opening up new opportunities and making processes more efficient. From designing complex linkages for mechanical engineering to generating electronic circuits for power converters, AI is pushing the boundaries of what's possible. This transformation is not limited to business operations but also extends to the workplace, making jobs more productive and freeing up time for employees to focus on higher-level tasks. While there are concerns about job displacement, history shows that technological advancements have led to new opportunities and a shift in the workforce. Ultimately, AI is set to make everything we touch more efficient and effective.
AI implementation in businesses: AI technology, such as IBM's Watson X, is revolutionizing businesses with automation and data management tools, leading to increased productivity and efficiency, but also requiring creativity in problem-solving and risk management. AI may be seen as a new natural resource for innovation 20 years from now.
AI technology, as represented by IBM's Watson X, is revolutionizing the way businesses operate by providing accessible and easy-to-use tools for automation and data management. This transformation will lead to increased productivity and efficiency, but also the need for creativity in identifying problems to solve and managing the risks associated with enterprise-level AI implementation. Looking ahead, AI may be seen as a new natural resource, providing abundant opportunities for innovation and problem-solving 20 years from now. The creative aspects of this work, from research and development to implementation, are what make this field exciting and keep professionals engaged in the ever-evolving world of AI.
AI Human Enhancement: AI holds immense potential for human enhancement through automation, software creation, and intellectual augmentation, leading to new opportunities for innovation, productivity, and enriching lives.
According to Dr. David Cox, the future of AI holds immense potential for human enhancement rather than replacement. With the ability to automate tasks, create software easily, and augment our intellectual capabilities, we are on the brink of an era of abundance. This abundance can lead to new opportunities for innovation, productivity, and enriching our lives. Instead of fearing AI, we should embrace it as a powerful tool to help us do more than we ever could before. Smart Talks with IBM, featuring thought leaders like Dr. Cox, aims to explore this transformative potential and inspire us to imagine the possibilities.