Decoding the AI Landscape: A Technical Perspective

IPWatchdog Unleashed

This week we journey into the world of Artificial Intelligence. The term Artificial Intelligence itself has largely become the generic term used to describe a variety of related but different concepts, often wrapping together AI, Machine Learning and Generative AI as if they all relate to the same thing, or as if there is a single technology that is, in fact, AI. These related but different technologies differ in scope, function, techniques used, and they also differ in terms of trustworthiness and reliability. But generally speaking, AI focuses on techniques and applications that create the appearance of human cognition in or from a machine. Machine learning focuses on the development of algorithms and statistical models that enable computers to perform tasks without being explicitly programmed for those tasks. And generative AI focuses on creating new data, such as images, music, and writings, that mimic the patterns presented in the training data.

Much of what will discuss today will tackle the technical issues that face the industry and innovators in this space. Specifically, we will discuss the various practical manifestations of AI, ML, and Generative AI as they currently exist in 2024, and are likely to exist in the near future. We will attempt to separate fact from fiction with respect to what these technologies can currently do, what the technology does well versus what the technology struggles with, and what the industry can expect moving forward.

We will also discuss the thorny problem of hallucinations, the impact of AI learning from learning from its own creations, the steps that are being taken to ensure reliability and accuracy, and how to verify that AI is safe, secure and trustworthy.

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