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CES 2024 is unveiling some groundbreaking innovations in the world of technology! From AI to EVs and beyond, there's so much to talk about. Let's dive in! Devin explains that the $200 rabbit r1 simplifies tasks like ordering a car, finding places to...
Temasek Polytechnic Launches the First AI Design Studio in Singapore https://www.youtube.com/watch?v=DV1AwOiVowM Temasek Polytechnic has launched the nation's first-ever design studio that integrates generative AI into the learning process. In line...
What's Inside Advanced Robots by Google DeepMind for Performing Daily Activities Qualcomm's upcoming XR chip offers 4.3K resolution for each eye Intel launches GenAI, a new enterprise-oriented software company Microsoft Plans to Integrate Copilot...
What's Inside Singapore's PM warns residents against Crypto Scams using Deepfakes Researchers at NTU Singapore have jailbroken AI chatbots such as ChatGPT Singapore-based company launches GR-1 humanoid robot this year Singapore's Cybersecurity Tr...
Bharat GPT by Reliance Jio aims to challenge ChatGPT
Google Search Enhances Information Literacy with New Features
Amazon Ads' Generative AI: Breaking Creative Barriers for Advertisers
AMD and Korean telco KT invest $22M in AI software developer Moreh
Unlocking the Secrets of In-Context Learning with Task Vectors
🔍 Google Search has launched three new features to help users better understand and verify online content. This feature provides context for images, revealing their history, how they're used across the web, and any associated metadata. Access it by clicking on the three dots on an image or the "more about this page" in search results.
🔎 Fact Check Explorer now allows image-based searches, helping journalists and fact-checkers verify claims more efficiently.
🤖 Search Generative Experience provides AI-generated descriptions for lesser-known sources, filling gaps where Wikipedia or Google Knowledge Graph summaries are absent.
Amazon Ads has launched a beta version of a generative AI solution to tackle the challenges of creative advertising. This tool aims to provide brands with a simplified yet powerful way to enhance the visuals in their ads, thereby improving overall performance.
The tool allows brands to produce lifestyle imagery that can significantly boost ad performance. For instance, a simple image of a toaster can transform into a compelling visual when placed in a lifestyle context like a kitchen.
This generative AI tool is beneficial for both small advertisers lacking in-house capabilities and bigger brands aiming for efficiency in creative development. The tool is user-friendly, requiring no technical expertise, thereby democratizing the advertising landscape.
AMD and Korean telco KT back AI software developer Moreh in $22M Series B Report Mistake
The Santa Clara- and Seoul-based startup said it has raised a $22 million Series B round, bringing its total raised to $30 million.
South Korean VC firms Smilegate Investment and Forest Partners, Moreh’s existing investor, also participated in the Series B round.
🔍 In-Context Learning (ICL) is like the "secret sauce" that elevates the performance of Large Language Models (LLMs) like GPT-4. It's a brand-new learning paradigm that's captured the imagination of scientists and researchers. But what makes it tick? Why is it so effective? A groundbreaking paper titled "In-Context Learning Creates Task Vectors" pulls back the curtain to reveal the mechanics of this fascinating process.
In traditional 📚 machine learning frameworks, you train a model using a dataset—let's call it 'S.' From this dataset, the model learns to find the best-fitting function, usually denoted as �(�)f(x), within a specified hypothesis class. However, ICL doesn't fit neatly into this established framework, making it a mysterious outlier in the machine learning landscape.
The researchers have found a brilliant way to demystify ICL. They discovered that the functions learned by ICL often have a surprisingly simple structure. These functions are akin to a 🤖 transformer Large Language Model with inputs comprising only the query �x and a unique "task vector."
So, what exactly is a task vector? It's a kind of "DNA code" calculated from the training set 'S.' In essence, ICL compresses this set into a single 🧬 task vector �(�)θ(S). This task vector then modulates the transformer model, helping it produce the desired output.
The researchers didn't just make theoretical claims. They backed their discoveries with 🧪 comprehensive experiments spanning various models and tasks. These experiments unequivocally support the role of task vectors in the efficacy of ICL.
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