The paper argues that today’s neural networks, even the largest language models, are built in the wrong way to ever become real general intelligence. It explains from scratch what artificial and intelligence and artificial general intelligence mean, and shows that most current

𝐖𝐚𝐧𝐭 𝐭𝐨 𝐮𝐧𝐥𝐨𝐜𝐤 𝐭𝐡𝐞 𝐩𝐨𝐰𝐞𝐫 𝐨𝐟 𝐀𝐈? Here’s your shortcut to understanding Artificial Intelligence like a pro: ✦ Machine Learning ✦ ChatGPT & Generative AI ✦ Neural Networks ✦ Automation Tools ✦ AI in Business ✦ No-Code AI Platforms ✦ Ethics & Future

🚀 I'm teaching Artificial Neural Networks (ANN) from the recently updated chapter of my free, online e-book: "Applied Machine Learning in Python" 📷 I'm really excited about this update — I’ve focused on maximum accessibility by walking through everything step-by-step:

Activation Functions for Artificial Neural Networks Credit: Sebastian Raschka

Why layers in neural networks? Why not one big jump from input to output? We unpack that in this guide. Understanding modern AI starts here. https://t.co/F9xqIuep8P #ArtificialInteligence #artificial_intelligence

Social media are full of misinformation about AI history. To all "AI influencers:" before you post your next piece, take history lessons from the AI Blog, with chapters on: Who invented artificial neural networks? 1795-1805 Who invented deep learning? 1965 Who invented

Can I show you how the MNIST dataset is used inside an #MLP for traditional artificial neural networks (right) and #spiking neural networks (left)? #AI #machinelearning

Artificial Neural Networks are essential to machine learning, with two distinct yet complementary concepts at their core: feedforward neural networks and backpropagation. These approaches serve different purposes but work together to enable neural networks to function

Modern AI is based on artificial neural nets (NNs). Who invented them? https://t.co/udI7EOdUcS Biological neural nets were discovered in the 1880s [CAJ88-06]. The term "neuron" was coined in 1891 [CAJ06]. Many think that NNs were developed AFTER that. But that's not the case:

How does intelligence emerge from neural networks in our brains and artificial networks in machines? Stanford cognitive scientist Jay McClelland explains on our podcast, exploring the neural network view of the mind and what it means for AI. Listen now: https://t.co/cjARPr9xOD

Before AI was a buzzword, Yann LeCun built the future with Convolutional Neural Networks for text recognition in 1989

GM🔥 We are witnessing the end of knowledge stored in "static digital libraries" (search engines). This is about vast, living Artificial Neural Networks, where billions of artificial neurons (AI models) work collectively to create and evolve knowledge in real time. @gensynai #AI

💡 AI is more than LLMs Today, AI is almost identified with LLMs — enormous, transformer-based deep neural architectures (a class of ANNs, or Artificial Neural Networks). In this field, we have huge players — OpenAI, Anthropic, and others — who dominate the landscape with

What do people and artificial neural networks agree on?👇🏼 People and artificial neural networks (ANNs) agree on surprisingly fundamental stuff when you strip away the hype—both are pattern-chasing machines wired for prediction and adaptation in messy environments. Let's break it

The “Godfather of AI,” Geoffrey Hinton, has won the 2024 Nobel Prize in Physics, sharing the award with John J. Hopfield for foundational discoveries that enabled machine learning with artificial neural networks. The Nobel committee credited their work with laying the groundwork

In this article, Holton et al. show that both humans and artificial neural networks have similar patterns of transfer and interference during sequential learning. https://t.co/KzSR7kg7di

Quantum Artificial Neural Networks! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://t.co/xaqBkOveFd

Most people use the term "DEEP LEARNING", but do you actually know what it means? Simply "A technique in which we extract patterns from the data using Neural Networks". It generally lies in the subset of "Machine Learning" which sits under field of "Artificial intelligence".