Backpropagation is the learning process many neural networks use to improve after making a prediction. Training begins with a forward pass, in which data moves through the network to produce an output. A loss function then measures how far that output is from the expected result. Working backward through the layers, backpropagation calculates how much each weight contributed to the error. An optimization algorithm uses those gradients to update the weights before the next training example. Repeating this cycle across large datasets gradually reduces error and helps the model recognize useful patterns. The method is central to deep learning, although its effectiveness depends on suitable data, architecture, learning rates, and computational resources.
Google Cloud and Figma have launched an expanded partnership to embed Google’s generative AI models – including Gemini 2.5 Flash and Imagen 4 – directly into Figma’s design tools, reducing latency and accelerating creative workflows.
Opera today introduced Neon, an AI-powered browser that can run tasks and navigate pages autonomously — aiming to shift web browsing from passive to “agentic.”
AI search startup Perplexity has reportedly raised $200 million at a $20 billion valuation, just two months after a $100 million round – signaling its rapid rise as a major rival to Google.