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THE CONVERSATION (UK) A Brief History of AI: How We Got Here and Where We Are Going AI has been around for over 70 years. The mathematician Alan Turing proposed the question "Can machines think?" in 1950, leading to the Turing test. The term "artificial intelligence" was coined in 1955. From expert systems in the 1960s to neural networks in the 1980s, AI has evolved significantly. Current models like GPT-4 show impressive generative capabilities but still lack consciousness. The future of AI lies in greater capacity, reliability, and accuracy, combining symbolic AI and machine learning for advanced applications.

Generative AI:. A Game-Changer for Industries in 2024! 🤖✨ Despite the recent controversies surrounding generative AI, its potential for revolutionizing industries is undeniable. From creating realistic images to automating financial analysis, this technology is poised to make a significant impact. With advancements like diffusion models, GANs, and transformer neural networks, generative AI is pushing the boundaries of what's possible. It's not just about creating synthetic data; it's about empowering industries to innovate and enhance our lives. As we embrace the future of generative AI, let's not forget its ethical implications and work together to harness its power for good. If you need to learn more follow this webpage--> a2dchannel.com. #GenerativeAI, #AIRevolution, #TechInnovation

The applications of generative AI are driven by non-linear models. Here’s why: Real-world data has complex relationships: Generative AI deals with images, text, and music. The connections between features in this data (e.g., colors in an image, words in a sentence) are intricate and often context-dependent. Linear models struggle to capture these complexities. Neural networks are the workhorse: Generative AI relies on neural networks, which use non-linear activation functions to process information and learn patterns. This allows them to handle the nuances of real-world data. Examples highlight non-linearity: Consider generating a new song. A linear model might just string random notes together. A generative AI, however, can learn the complex relationships between melody, harmony, and rhythm to create something that sounds musical. How will you harness the complexity of real-world data with generative AI? #AI #GenerativeAI #Innovation #BusinessTransformation #Nimble Source: Forbes

ai.plainenglish.io: The article discusses the use of pruning in vision and gaze prediction models to reduce computational costs without sacrificing performance. The researchers used knowledge distillation and Fisher pruning to obtain more efficient architectures for saliency prediction, achieving a 10x speedup while maintaining the same AUC performance as a state-of-the-art network. - Artificial Intelligence topics! #ai #artificialintelligence #intelligenzaartificiale

The Physical Process That Powers a New Type of Generative AI https://buff.ly/3tJlwke

Perceptron: From Theory to AI Revolution Ever wonder how machines learn? It all starts with a fundamental building block called the Perceptron Image source: inteligenciafutura 1) Imagine a simple machine that can learn to recognize patterns. The Perceptron, invented in 1957 by Frank Rosenblatt, was one of the first such models. It's like a tiny brain, processing inputs and making decisions based on its experience. 2) But the Perceptron wasn't always the AI hero we know today. In 1969, a famous paper by Marvin Minsky and Seymour Papert revealed its limitations. It could only solve simple, linearly separable problems. 3) This sparked a "perceptron winter" where AI research took a backseat. But the Perceptron's spirit lived on. The Multilayer Perceptron emerged, It inspired the development of more powerful models like Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). 4) Today, the Perceptron is a cornerstone of modern AI. It forms the basis of deep learning algorithms that power everything from self-driving cars to facial recognition. It's now an integral part of the AI revolution 5) So why is the Perceptron so important? It's not just about its ability to learn; it's about its simplicity. It's a model we can understand, analyze, and build upon. This makes it a valuable tool for researchers and developers alike. Want to learn more about the Perceptron and its impact on AI? Explore these resources: Link 1: https://lnkd.in/dnnuDDBh Link 2: https://lnkd.in/dmm8md4J #neuralnetworks #deeplearning #ai #datascience #perception

Explore Hybrid AI, the fascinating blend of symbolic AI and neural networks that addresses the limitations of purely rule-based or learning-based systems. Read our latest article for a thorough understanding of how combining machine learning with rule-based systems results in solutions that are not only powerful but also incredibly efficient and scalable. Dive into the world of Hybrid AI and understand its components, use cases, and the crucial role of knowledge graphs. The article delves into these areas: -What is Hybrid AI? -How it addresses AI challenges -Its components -Benefits -Applications -Starting a Hybrid AI project -What the future holds The critical role of knowledge graphs in developing Hybrid AI systems Explore the full article for an in-depth understanding of hybrid AI's potential. Read the full article here: https://t.ly/1qnah #artificialintelligence #ai #hybridai #aiusecases #aiinnovation #aievolution #aitechnology #LeewayHertz

🧠💻 Artificial Neural Network Update (December 2023) 🔑 Key highlights: - Significant projected growth in the ANN market, driving business integration of ANNs in operations like airframe manufacturing. - Emergence of physics-based self-learning machines, signaling rapid technological evolution in AI. - Key legal developments in AI patents, marking AI's growing importance in technological progress. - The need for ethical solutions and public acceptance to balance AI innovation. 🔍 Gain insights into the evolving ANN sector and its broad implications: https://lnkd.in/d2QPRAwq #ArtificialNeuralNetworks #AI #MachineLearning #TechInnovation

Generative AI, the magic behind deep learning and neural networks, is bringing innovation to the table. By 2032, the Generative AI in Financial Services Market is predicted to skyrocket to a whopping $9,475.2 million from just $847.2 million in 2022! That means a 28.1% annual growth rate from 2023 to 2032! Got questions? Drop 'em in the comments! Want to learn more? Reach out to us! https://lnkd.in/degWDf5T #artificialintelligence #ai #aitrends #finance #financialmarket #generativeai #orangemantra