Artificial Intelligence and Deep Learning Technologies: Do They Stand a Chance in Conversation and Q&A Sessions?

[Video Title: Understanding Artificial Intelligence: Predictive vs. Generative AI]

Welcome to our YouTube video where we delve into the fascinating world of artificial intelligence! In this discussion, our special guest Maury Shank from Learner Shape joins us to shed light on the different aspects of AI.

So what exactly is artificial intelligence? Let’s dive deeper into this intriguing topic. A few years ago, I had a memorable encounter with David Hansen from Hanson Robotics and his robot, Sophia. Although Sophia was astonishing in her own way, she didn’t truly embody the essence of AI. She was limited to basic speech capabilities, which falls within a broad definition of AI.

However, the world of AI has evolved significantly since then. With the emergence of predictive and generative AI, we have witnessed remarkable advancements. Traditionally, predictive AI relied on statistical methods. But in 2012, a breakthrough occurred when AI started excelling in image prediction. This pivotal moment marked the beginning of a new era for AI applications.

Generative AI took AI to the next level by enabling the creation of new content. From generating images to developing language models such as Chat GPT, AI has astounded us with its ability to create and learn. In fact, large language models have revolutionized the field, making tasks that seemed like magic just six months ago a reality today.

Given the rapid pace of AI’s development, it’s clear that we are still in the early stages. Although it’s hard to predict where AI will take us, the current advancements, particularly in language models, are groundbreaking. However, it’s worth noting that the rate of change may eventually slow down, just as we have seen in other fields.

Now, let’s explore how AI is utilized in the financial sector. The application of AI can be visualized as concentric circles within the realm of data science. Traditional statistics and data science serve as the foundation, while AI involves the utilization of machines to perform tasks typically done by humans. Machine learning, a subset of AI, has experienced significant growth in the financial sector. Quantitative hedge funds and robo-advisors leverage predictive AI to enhance trading strategies and smart investing. In addition, generative AI, specifically chatbots, holds immense potential for fintech applications.

Moving beyond the private financial sector, let’s consider the role of AI in central banks. While central banking primarily revolves around policy and deterministic applications, AI does find its place. The use of AI tools, derived from traditional statistics and data science, assists in understanding and monitoring complex financial systems. However, as most AI applications involve stochastic elements and randomness, a balance between deterministic and stochastic approaches must be struck. While discussions and papers from organizations like the Bank of England and the Bank for International Settlements highlight the potential of AI, caution is exercised due to the need for reliability and stability in central banking.

As we wrap up our discussion, it’s essential to examine the concerns associated with AI. One of these concerns is bias, as AI can unknowingly replicate human biases present in the data. However, efforts are underway to address this issue and mitigate bias in AI systems, making them potentially less biased than humans.

To conclude, AI continues to evolve and amaze us with its capabilities. Understanding the distinction between predictive and generative AI provides valuable insights into the vast potential of this revolutionary field. Join us in further exploration by watching the full video!

– [1] Article by Ben Lazarus in The Spectator: [Link](
– [2] Book recommendation: “Prediction Machines” by Ajay Agrawal, Joshua Gans, and Avi Goldfarb.

[vid_tags]: #artificialintelligence #AI #generativeAI #predictiveAI #financesector #centralbanks #biasinAI

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