Teb's Lab’s Post

This interesting new tool may help us finally understand why neural networks make the decisions they do. It would be wonderful if the theory could catch up to the engineering behind these powerhouse ML systems.

It seems a lot of people want a primer on AI. So here's a really good series of videos by a strong maths fellow explaining things in a very simple, very clear way. https://lnkd.in/ezHh4dct Three Blue One Brown, in case you're not familiar, does a lot of really good deep dives on all kinds of math problems, Tranformers, neural networks, deep learning, etc being only one small part of the channel's content. It will also give you a clue as to why I say machines are thinking now. A lot of people have simplified their understanding of LLMs as "next token predictors" which masks what's going on there. Markov chains are next token predictors. Transformers are, yes, at the end giving a list of tokens and the likelihood of each, but to get to that point requires a lot of sophisticated thought. To grok that, watch this video series and get a sense of the complexity that's hidden underneath transformers. This does not go into diffusion models which are thinking machines in a different sense. If I find a good video series on those, I'll post it.

Thanks for posting. More of this please. Basics people, basics

Ever felt like you're living in a sci-fi movie where computers can think like humans? 🤖🧠 Well, it's not just fiction anymore! My brilliant colleague Emily Hendley just published a demystifying blog post about neural networks and their impact on identity verification. Key takeaways that got me thinking: 🧠 Neural networks are like mini-brains inside computers, learning from data just as we learn from experiences. 🤖 These AI systems are everywhere, from face recognition in photos to identity verification for online transactions. 👩💻 At IDVerse, neural networks are being leveraged to ensure people are who they claim to be in the digital realm. How do you think generative AI could help create a more inclusive digital world without compromising on high-level security standards? 🌍🔒 Drop a comment below or shoot me a DM if you'd like to dive deeper into this topic. Always eager to exchange ideas on how tech is shaping our industry! #Ai #IdentityVerification #TechTrends

❓ Curious how computers "think" like humans? Our latest blog breaks down neural networks in simple terms—perfect for those who are really interested in this tech! Learn how they mimic the brain and power everyday apps. Check it out in!

A Practical Guide to Choosing the Right Algorithm for Your Problem: From Regression to Neural Networks https://lnkd.in/gcq8787P

A Practical Guide to Choosing the Right Algorithm for Your Problem: From Regression to Neural Networks https://lnkd.in/dmssSkSx

Neural networks in AI taught me something about real life… When you look at certain technical things, how is it created brick by brick? The same thought came to my mind when I was learning about neural networks. I felt the same with neural networks. I saw it somehow differently, and those two different things are: 1️⃣ Breaking it down into pieces This sounds pretty simple, but when you see neural networks breaking it down numerically, you realise that everything can be achieved this way, especially if you are a programmer. 2️⃣ Add a dimensional layer: In neural networks, add a dimensional layer for deciding the values (weights) in your current layer. Similarly, you can add extra layers to certain parts of your tasks; you can automate it and design a system so it can act dynamically based on the problem you are trying to solve. P.S. I believe every tech has some natural resemblance if you can find it. What do you think?

💡 Graph Neural Networks use nodes and edges to represent the relationship between different entities, or chunks of information 🤔 They have wide applications to solutions like transport and logistics, traffic management, and social networks, but how can they be made more trustworthy? 👀 Check out our latest blog post to find out more about making GNNs more explainable, robust, fair, and accountable https://lnkd.in/efqrUsRz #aigovernance #ethicalai #responsibleai #explainableai #trustworthyai

In the evolving landscape of pricing strategies, understanding the strengths and weaknesses of different models is crucial. Our latest article dives deep into Neural Networks and Decision Trees, two prominent methodologies, and addresses the question: is it truly a choice between the two? Explore how each model operates and their respective pros and cons to make informed decisions for your pricing strategy. Read more here: https://okt.to/JjCmTf.