Microsoft’s Copilot puts AI into everything
YouTube announces new AI features for creators. Anthropics new AI policy.
Hello Engineering Leaders and AI Enthusiasts!
Welcome to the 110th edition of The AI Edge newsletter. This edition brings you Microsoft’s Copilot that puts AI into everything.
And a huge shoutout to our incredible readers. We appreciate you! 😊
In today’s edition:
🤖 Microsoft’s Copilot puts AI into everything
📹 YouTube announces 3 new AI features for creators
🔬 Google’s innovative approach to train smaller language models
📚 Knowledge Nugget: 4 Crucial Factors for Evaluating Large Language Models in Industry Applications
Let’s go
Microsoft’s Copilot puts AI into everything
Microsoft has announced a new AI-powered feature, Microsoft Copilot. It’ll bring AI features into various Windows 11, Microsoft 365, Edge, and Bing. Our first impressions are that it’s Bing but for Windows. You can use Copilot to rearrange windows, generate text, open apps on the web, edit pictures and more.
Copilot can be accessed via an app or with a simple right-click and will be rolled out across Bing, Edge, and Microsoft 365 this fall, with the free Windows 11 update starting on September 26th.
Why does this matter?
While we don’t see any revolutionary use cases of Copilot as of now, it’s still a huge step towards the democratization of AI. As more users get their hands on this AI copilot, we’ll know the true extent of its effectiveness. If all goes well, Microsoft will end up grabbing an even bigger share of the AI market as it will deliver AI natively to all Windows devices.
YouTube announces 3 new AI features for creators
In a YouTube event, the company announced 3 AI-powered features for YouTube Shorts creators.
Dream Screen: It allows users to create image or video backgrounds using AI. All you need to do is type what you want to see in the background and AI will create it for you.
Creator Music: This was a previously available feature but got an AI revamp this time around. Creators can simply type in the kind and length of the music they need and AI will find the most relevant suggestions for their needs.
AI Insights for Creators: This is an inspiration tool which generates video ideas based on AI’s analysis of what the audiences are already watching and prefer.
Why does this matter?
It seems like a strategic decision to natively introduce AI features to support users. It’s a trend we are seeing increasingly more across the landscape. For the users, it's great news since they get free AI assistance in their creative endeavors.
Google’s innovative approach to train smaller language models
Large language models (LLMs) have enabled new capabilities in few-shot learning, but their massive size makes deployment challenging. To address this, the authors propose a new method called distilling step-by-step, which trains smaller task-specific models using less data while surpassing LLM performance.
First, the key idea is to extract rationales - intermediate reasoning steps - from an LLM using few-shot chain-of-thought prompting. These rationales are then used alongside labels to train smaller models in a multi-task framework, with tasks for label prediction and rationale generation. Experiments across NLI, QA, and math datasets show this approach reduces training data needs by 75-80% compared to standard fine-tuning.
Why does this matter?
This new approach to train smaller models with higher accuracy has the potential to support language models that can be deployed on local devices while retaining the performance that was previously achievable only through LLMs.
Knowledge Nugget: 4 Crucial Factors for Evaluating Large Language Models in Industry Applications
Based on your end goal, you might fancy one LLM over the other. For instance, some industries value privacy over anything while others might put data accuracy over everything else. In this article,
shares the 4 critical factors you should always consider when picking a large language model.He mentions Quality, Economic, Latency, and Privacy to be the 4 resting pillars of your decision. He then goes into details discussing each of these parameters and how you should evaluate a given model against them.
Why does this matter?
The ability to make the right decision when choosing the underlying LLM for your applications is massively important. This article will provide you with valuable insights when it comes to choosing the right LLM.
What Else Is Happening❗
🌐 Google expands AI-powered Studio Bot to 170 countries. (Link)
🤖 DALL-E 3 will be available in Bing Chat. (Link)
⚠️ Anthropic released new policy highlighting AI’s ‘catastrophic risks.’ (Link)
💰 Cisco to buy Splunk at $28 billion to expand its AI capabilities. (Link)
🧠 Oracle Announces AI/ML Features To Its MySQL HeatWave. (Link)
🛠️ Trending Tools
Shopmate AI: Boost sales with Shopmate, an AI chatbot offering personalized product recommendations.
Nucleum AI: AI for crypto trading. Track market changes, create strategies, and optimize your portfolio.
Brandity: AI tool for a convincing brand identity. Provides color scheme, art style, font, and graphical assets.
Viewit AI: A smart chatbot for property sales. Answers prospect queries, improves customer satisfaction, and increases conversions.
FlashAI: A Chrome extension integrating ChatGPT for summarizing pages. Ideal for students and professionals.
Dict: A game where you define words and get scored by AI overlords.
YourArtist.AI: A virtual musician product. Train it with your voice to sing any song.
CoxPost: AI-powered platform for effective social media management.
Want to discover more impressive AI tools?
Refer your pals to subscribe and enjoy our daily newsletter to get exclusive access to 400+ remarkable AI tools.
When you use the referral link above or the “Share” button on any post, you'll get the credit for any new subscribers. All you need to do is send the link via text or email or share it on social media with friends.
🌟📝Friday Featured Prompt
This Week's Prompt: Tips for optimizing software performance
Imagine you are an experienced software developer with expertise in [Programming Language/Framework]. You are asked to provide tips and best practices for optimizing the performance of code written in this language/framework when working on a [Type of Application]. Consider the following aspects: * Writing efficient algorithms: Tips for [Programming Language/Framework] * Memory management: Best practices for [Programming Language/Framework] * Code optimization techniques: [Programming Language/Framework] * Profiling and performance monitoring tools: Recommendations for [Programming Language/Framework] * Multithreading and concurrency: Strategies for [Programming Language/Framework] Please provide a comprehensive list of tips and best practices for optimizing the performance of [Programming Language/Framework] code when working on [Type of Application] projects.With software architecture becoming ever so more complicated, it’s crucial to optimize software for performance. And while so many AI copilots are now available for coding, it can never hurt to have your own prompt for a specific job.
That's all for now!
If you are new to The AI Edge newsletter, subscribe to get daily AI updates and news directly sent to your inbox for free!
Thanks for reading, and see you tomorrow. 😊