<video><caption><image_46>The image is a digital illustration of a sphere and a network diagram. The sphere is on the left side of the image and the network diagram is in the center. The network diagram consists of multiple interconnected circles of different sizes and colors. The circles are arranged in a radial pattern, with the largest circle at the top and the smallest at the bottom. The lines connecting the circles vary in size and shape, creating a sense of depth and dimension. The background is white, making the blue circles stand out.</caption><caption><image_47>The image is a graphic representation of a network of interconnected lines and dots. On the left side of the image, there is a blue circle with a grid-like pattern on it. The grid is made up of multiple interconnected circles of different sizes and shapes, connected by thin white lines. The circles are arranged in a symmetrical pattern, creating a sense of depth and dimension. The background is white, making the blue circles stand out. The image appears to be a representation of the interconnectedness of the network.</caption><caption><image_48>The image is a digital illustration of a network of interconnected blue circles. The circles are arranged in a grid-like pattern, with each circle representing a different aspect of the network. The blue circles are evenly spaced and appear to be connected by thin lines. The lines are connected to each other, creating a network-like structure. The background of the image is white, making the blue circles stand out.</caption><caption><image_49>The image is a close-up of a man's face. He appears to be in his late 40s or early 50s, with dark hair and glasses. He is wearing a dark blue blazer and a black turtle neck sweater. He has a serious expression on his face and is looking off to the side. The background is blurred, but it seems to be a room with white walls and a window. The overall mood of the image is serious and contemplative.</caption></video><transcript><audio_50>field and for Enterprises let me explain how they work Foundation models are large parameter neural networks trained from immense and labeled data sets that are adapted to produce other models for a variety of</transcript><transcript><audio_51>specific tasks think about us when we learn a new task we continuously use that understanding to perform future tasks even seemingly unrelated ones in AI we call this</transcript><transcript><audio_52>transfer learning and for decades we attempted to imitate this by training AI models to learn representations of the world using annotated data typically humans label the data</transcript><transcript><audio_53>something we call supervised learning eating a model big enough to work required hundreds of thousands of human annotated sentences that could cost hundreds of thousands of dollars to build and was simply unsustainable</transcript><transcript><audio_54>but Foundation models finally break through this format learning from unlabeled data using a process called self-supervision self-supervision works by having the AI model predict a missing part of an input</transcript><transcript><audio_55>for example a missing word in a sentence based on the context of the other words in the sentence the AI model learns how to represent the data by doing this again and again over billions of self-supervised inputs</transcript><caption><image_56>The image is a set of multiple horizontal lines in different colors. The lines are arranged in a grid-like pattern, with each line having a different color - blue, green, pink, and red. The colors are evenly spaced and appear to be overlapping each other. The background is white, making the colors of the lines stand out.</caption> field and for Enterprises let me explain how they work Foundation models are large parameter neural networks trained from immense and labeled data sets that are adapted to produce other models for a variety of specific tasks think about us when we learn a new task we continuously use that understanding to perform future tasks even seemingly unrelated ones in AI we call this transfer learning and for decades we attempted to imitate this by training AI models to learn representations of the world using annotated data typically humans label the data something we call supervised learning eating a model big enough to work required hundreds of thousands of human annotated sentences that could cost hundreds of thousands of dollars to build and was simply unsustainable but Foundation models finally break through this format learning from unlabeled data using a process called self-supervision self-supervision works by having the AI model predict a missing part of an input for example a missing word in a sentence based on the context of the other words in the sentence the AI model learns how to represent the data by doing this again and again over billions of self-supervised inputs
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