Answer.AI’s Post

Powerful Feedback from Our Student Community 👏 At Answer.AI AI, we're committed to supporting students in their educational journeys. After testing various apps, Đỗ Mai Tuyền, an International Business student from Vietnam, found that others struggled with complex math problems—until she tried Answer AI. Her feedback speaks volumes: Answer AI consistently provides more accurate, easy-to-understand solutions—and is more accessible for students. We’re proud to engage with students like Tuyền, whose feedback helps us continue improving and delivering the best #AIforStudents. 💡 #AIforEducation #EdTech #UserFeedback #StudentCommunity #AILearning

I asked the Microsoft Copilot AI tool to write a poem for me... what AI wrote, I am sharing with you... "In the halls of knowledge, where wisdom thrives, Stands a beacon of learning, where ambition arrives. Dr. Javaid, with a mind so keen, In the realms of finance and accounts, he's seen. With a Ph.D. in hand, and experience vast, He shapes young minds, for challenges cast. In the classroom's glow, his teachings unfold, Stories of numbers and econometrics told. A researcher at heart, with insights to share, Publications and studies, a scholarly affair. Training minds in workshops, across lands afar, His wisdom spreads, like a guiding star. An entrepreneur, too, with visions that soar, A consultancy firm, knowledge at its core. Sessions conducted, in countries seven, His expertise shared, a professional heaven. So here's to Dr. Javaid, a mentor true, His contributions many, his accolades due. In the world of academia, he carves his name, An inspiration to all, in the educational game."

📚 Writing a book is hard. 👟 Running a marathon is hard. 🗣️ Learning a new language is hard. 👀 Finding the right contacts for your news doesn’t have to be. In fact, with our Media Database, it’s easy because it’s accurate. We blend AI with human review to add and delete contacts weekly so our data’s always current and reliable. Don't believe us? Schedule a demo to see for yourself: https://hubs.li/Q02tbmnw0

🚀 Embracing the Future of Search and AI: A Game Changer for Startups and Small Businesses In the fast-paced world we operate in, staying ahead in technology can be the edge your startup or small business needs. The recent developments at San Francisco-based Perplexity AI are a testament to the powerful integration of AI with search functionalities, reshaping how we process information swiftly and efficiently. 🔍 What is Perplexity AI? - A next-generation search engine. - Merges capabilities of advanced AI chatbots with robust search tools. - Ideal for deep dives into academic papers, trend analysis, and even everyday problem-solving. 📈 Why Should You Care? - Time-efficient: Delivers concise, AI-generated summaries of top results, saving you precious research time. - Customizable searches: Targets specific domains like academic papers or popular sites like YouTube or Reddit, based on your needs. 🎯 Impact on Your Business: - Curtail hours spent on research. - Enhance the accuracy of your data-driven decisions. - Swiftly adapt to market trends by leveraging timely and relevant insights. 🌐 Perplexity AI is more than just a tool—it’s your next business partner in navigating the complexities of internet data and making informed decisions rapidly. As AI continues to evolve, integrating tools like Perplexity AI can significantly enhance your operational efficiency and strategic planning. Explore how Perplexity AI can transform your approach: 🔔 Whether you're scaling up or just starting out, understanding and utilizing such AI-driven tools can offer substantial leverage in a competitive market. #Innovation #AI #SmallBusinessGrowth

There are 6 areas that generative AI shifts completely: 1. Education - Learn anything 2. Research - Market, science 3. Ideation - See connections 4. Creation - Artist, copy, sales, code 5. Analysis - Data scientist 6. Voice - Customer service, story telling Combine to amplify. ---- If you'd like to save time with AI, I host a free weekly workshop in my Skool community. People have done a week's worth of work in 1 hour. How much is that worth to you? https://buff.ly/4271Uni

Just realized that most knowledge is in books, and surprise computers. Yet most of the US do not enjoy reading books, or know how to use a computer, or have access to one. Yet our economy and everyone's future is built on everyone in the US having access to all this knowledge. It's now too late to teach these young people, the right way! So be prepared to grab the newest AI knowledge along with the AI Tools, both free and paid and build using Google's Notebook LM, and GPT's. IF you are interested I can slowly slowly teach/show you the newest methods to build these GPT's to build webpages. Each of these webpages can easily make you monies!

Can Small Language Models (SLMs) get better at math reasoning? It’s tough, and new research says maybe not. A study by DeepMind, Microsoft, and Mila dug into this problem using a new dataset, Compositional GSM. This dataset chains two math questions together, so the answer to the first becomes part of the second. It forces models to do multi-step reasoning, which is where things get tricky. Some highlights from the paper: 1. Performance Gap: SLMs do well with single-step math but struggle with multi-step problems. There’s a big difference in accuracy between solving standalone problems and these chained ones. 2. Specialized Models Struggle Too: Even small, math-focused models perform worse when faced with two-step problems. They overfit to patterns, without grasping deeper reasoning. 3. Instruction Tuning Doesn’t Help Much: While tuning improves performance on simple problems, it doesn’t translate to complex ones. Multi-step reasoning remains a challenge. Bottom line: SLMs aren’t close to mastering multi-step math reasoning yet. There’s still a long way to go! — I share my learning journey here. Join me and let's grow together. Enjoy this? Repost it to your network and follow Karn Singh for more. #AI #LLMs #MathReasoning #DeepLearning #AIResearch

Hey there, data enthusiasts! ✨ Let’s ditch the mystified jargon and chat about Machine Learning in a way that makes sense! 🤖 For this week’s #SpeakingOfDataScience series, we introduce the concept of machine learning! Imagine you’re trying to teach your friend a new, super cool game. 🎲 You wouldn’t just bombard them with all the rules at once, right? That’d be information overload! ❎ Machine learning is similar. It’s about teaching computers by showing them examples, like showing your friend different winning strategies in the game. The computer analyzes these examples (data) and learns to identify patterns or make predictions on its own. Pretty cool, right? 😎 Here’s the thing, there are different ways to teach your friend a new game! Similarly, there are different ways we can teach machines to learn from data without needing step-by-step instructions for every task. And that’s where the different types of Machine Learning come in: ⚙️ 1️⃣ Supervised Learning: This is like teaching a friend winning moves with clear labels (supervised data!). The more examples they see, the better they get at recognizing patterns and replicating success. 🤝 2️⃣ Unsupervised Learning: This is like letting your friend loose in the game world to explore. They experiment, discover hidden strategies (patterns in unlabeled data!), and figure out how to win on their own. 🔍 3️⃣ Reinforcement Learning: Picture teaching your friend by rewarding good moves and … well, maybe gently nudging them away from bad ones. This is how some computers learn – by trial and error, just like us! Think of it as training a system to navigate a maze or even dominate at chess. ♟️ Remember, Machine Learning is an exciting field, and these types are just the beginning! Next week, I will begin to focus on each of these in greater depth. Stay curious and keep exploring. 🚀 #DataScience #MachineLearning #AI #ML #SpeakingOfDataScience