Exploring Risks & Training Methods for Generative AI: Enhancing User Experiences
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In this riveting episode by IBM Technology, the team delves into the world of generative AI algorithms and the looming question of whether they're on the brink of losing their minds. Drawing parallels between the intricate workings of the human brain and large language models, they dissect the similarities in neurons, memory storage, and specialized regions. But hold on, it's not all rainbows and butterflies - differences in power consumption, volume, and message transmission methods set these entities apart in a dramatic fashion.
The adrenaline-fueled journey continues as the team shifts gears to discuss the crucial aspect of training these AI models effectively. They introduce a phased training approach involving unsupervised and supervised learning, along with the concept of logical reasoning for transparency - a real nail-biter for tech enthusiasts. Buckle up as they rev into the emerging territory of self-learning, highlighting the importance of experts' mix and the integration of reinforcement learning techniques to supercharge these models.
But wait, there's more! The team unveils a safety net in the form of the "funnel of trust" and the ingenious strategy of using large language models as judges to ensure the reliability of their outputs. Enter the fascinating realm of theory of mind, where aligning model outputs with user expectations takes center stage in this high-octane tech thriller. And just when you thought it couldn't get any more thrilling, machine unlearning swoops in as the hero, offering a systematic approach to data removal and selective forgetting to keep these AI models in check. Strap in for a wild ride as these cutting-edge techniques pave the way for individuals like Kevin to elevate their artwork and Ravi to enhance his swimming prowess, all while safeguarding the sanity of these powerful LLMs.
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