IBM Journey to AI blog

JULY 6, 2023

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other?

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Unite.AI

JULY 21, 2023

The regulation of AI in the arts is a hot topic. AI wants high quality music because the ultimate quality of output is heavily dependent on the quality of input. The aforementioned problem of input legitimacy is relevant for images and texts that are applicable for AI. Most, but not all. Not really.

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Unite.AI

MAY 8, 2024

The ability to effectively represent and reason about these intricate relational structures is crucial for enabling advancements in fields like network science, cheminformatics, and recommender systems. Graph Neural Networks (GNNs) have emerged as a powerful deep learning framework for graph machine learning tasks.

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Unite.AI

DECEMBER 17, 2024

Generative AI has made impressive strides in recent years. In areas like healthcare, law, or finance, we cant afford to have AI making such errors. This is where neurosymbolic AI can help. By combining the power of neural networks with the logic of symbolic AI, it could solve some of the reliability problems generative AI faces.

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Unite.AI

FEBRUARY 25, 2025

the AI company revolutionizing automated logical reasoning, has announced the release of ImandraX, its latest advancement in neurosymbolic AI reasoning. ImandraX pushes the boundaries of AI by integrating powerful automated reasoning with AI agents, verification frameworks, and real-world decision-making models.

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AI News

FEBRUARY 16, 2024

Google has unveiled its latest AI model, Gemini 1.5, This dwarfs previous AI systems like Claude 2.1 While a traditional Transformer functions as one large neural network, MoE models are divided into smaller ‘expert’ neural networks,” explained Demis Hassabis, CEO of Google DeepMind.

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Unite.AI

FEBRUARY 17, 2025

While it has made massive progress in pattern recognition, abstract reasoning and symbolic deduction have remained tough challenges for AI. However, Google DeepMind has been working on developing AI that can solve these complex reasoning tasks. Artificial intelligence has long been trying to mimic human-like logical reasoning.

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Unite.AI

NOVEMBER 22, 2024

Artificial Intelligence (AI) is making its way into critical industries like healthcare, law, and employment, where its decisions have significant impacts. However, the complexity of advanced AI models, particularly large language models (LLMs), makes it difficult to understand how they arrive at those decisions.

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NVIDIA

JANUARY 6, 2025

NVIDIA founder and CEO Jensen Huang kicked off CES 2025 with a 90-minute keynote that included new products to advance gaming, autonomous vehicles, robotics and agentic AI. AI has been advancing at an incredible pace, he said before an audience of more than 6,000 packed into the Michelob Ultra Arena in Las Vegas.

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Unite.AI

JUNE 15, 2024

Published in Nature , this innovative breakthrough opens new doors in studying how brains control complex movement using advanced AI simulation techniques. The neural network was trained to use inverse dynamics models, which are believed to be employed by our brains for guiding movement.

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AI News

DECEMBER 15, 2023

In an interview at AI & Big Data Expo , Alessandro Grande, Head of Product at Edge Impulse , discussed issues around developing machine learning models for resource-constrained edge devices and how to overcome them. And that’s a big struggle,” explains Grande.

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Unite.AI

JUNE 25, 2023

In a pioneering effort to further enhance AI capabilities, researchers from UCLA and the United States Army Research Laboratory have unveiled a unique approach that marries physics-awareness with data-driven techniques in AI-powered computer vision technologies.

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Marktechpost

APRIL 5, 2024

In their paper, the researchers aim to propose a theory that explains how transformers work, providing a definite perspective on the difference between traditional feedforward neural networks and transformers. Despite their widespread usage, the theoretical foundations of transformers have yet to be fully explored.

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Marktechpost

OCTOBER 28, 2023

However, a common limitation of many machine learning models in this field is their lack of interpretability – they can predict outcomes accurately but struggle to explain how they arrived at those predictions. Join our AI Channel on Whatsapp. However, their non-interpretability has been a persistent challenge. We are also on WhatsApp.

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Unite.AI

FEBRUARY 10, 2025

Neetu Pathak , Co-Founder and CEO of Skymel , leads the company in revolutionizing AI inference with its innovative NeuroSplit technology. Alongside CTO Sushant Tripathy, she drives Skymels mission to enhance AI application performance while reducing computational costs.

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Marktechpost

JANUARY 13, 2024

The challenge of interpreting the workings of complex neural networks, particularly as they grow in size and sophistication, has been a persistent hurdle in artificial intelligence. The traditional methods of explaining neural networks often involve extensive human oversight, limiting scalability.

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Towards AI

DECEMBER 18, 2024

Last Updated on December 19, 2024 by Editorial Team Author(s): Mukundan Sankar Originally published on Towards AI. They chase the hype Neural Networks, Transformers, Deep Learning, and, who can forget AI and fall flat. You're not ready for neural networks if you cant explain Linear Regression or Decision Trees.

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IBM Journey to AI blog

AUGUST 9, 2024

Many generative AI tools seem to possess the power of prediction. Conversational AI chatbots like ChatGPT can suggest the next verse in a song or poem. But generative AI is not predictive AI. But generative AI is not predictive AI. What is generative AI? What is predictive AI?

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Marktechpost

OCTOBER 16, 2023

In a recent paper, “Towards Monosemanticity: Decomposing Language Models With Dictionary Learning,” researchers have addressed the challenge of understanding complex neural networks, specifically language models, which are increasingly being used in various applications.

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Towards AI

FEBRUARY 3, 2024

Author(s): Stavros Theocharis Originally published on Towards AI. Left) Photo by Pawel Czerwinski on Unsplash U+007C (Right) Unsplash Image adjusted by the showcased algorithm Introduction It’s been a while since I created this package ‘easy-explain’ and published on Pypi. The truth is, I couldn’t find anything.

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Unite.AI

MARCH 11, 2024

As we navigate the recent artificial intelligence (AI) developments, a subtle but significant transition is underway, moving from the reliance on standalone AI models like large language models (LLMs) to the more nuanced and collaborative compound AI systems like AlphaGeometry and Retrieval Augmented Generation (RAG) system.

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Marktechpost

MARCH 8, 2025

Current Challenge with Traditional CAM Conventional CAM methods typically illustrate general regions influencing a neural networks predictions but frequently fail to distinguish fine details necessary for differentiating closely related classes. Experimental Validation B.1.

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Marktechpost

MAY 14, 2024

This article lists the top Deep Learning and Neural Networks books to help individuals gain proficiency in this vital field and contribute to its ongoing advancements and applications. Neural Networks and Deep Learning The book explores both classical and modern deep learning models, focusing on their theory and algorithms.

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Towards AI

DECEMBER 12, 2024

Last Updated on December 13, 2024 by Editorial Team Author(s): Towards AI Editorial Team Originally published on Towards AI. Good morning, AI enthusiasts! This issue is resource-heavy but quite fun, with real-world AI concepts, tutorials, and some LLM essentials. Share your thoughts and questions in the Discord thread!

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Towards AI

DECEMBER 12, 2024

Last Updated on December 13, 2024 by Editorial Team Author(s): Towards AI Editorial Team Originally published on Towards AI. Good morning, AI enthusiasts! This issue is resource-heavy but quite fun, with real-world AI concepts, tutorials, and some LLM essentials. Share your thoughts and questions in the Discord thread!

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Unite.AI

NOVEMBER 25, 2023

In a groundbreaking study , Cambridge scientists have taken a novel approach to artificial intelligence, demonstrating how physical constraints can profoundly influence the development of an AI system. Future of AI Design This groundbreaking research has significant implications for the future design of AI systems.

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Unite.AI

OCTOBER 24, 2024

The rapid rise of Artificial Intelligence (AI) has transformed numerous sectors, from healthcare and finance to energy management and beyond. However, this growth in AI adoption has resulted in a significant issue of energy consumption. The Tsetlin Machine offers a promising solution.

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NVIDIA

MARCH 5, 2025

The importance of sight in understanding the world makes computer vision essential for AI systems. By simplifying computer vision development, startup Roboflow helps bridge the gap between AI and people looking to harness it. Time Stamps 2:03 Nelson explains Roboflows aim to make the world programmable through computer vision.

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Towards AI

DECEMBER 12, 2024

Last Updated on December 13, 2024 by Editorial Team Author(s): Towards AI Editorial Team Originally published on Towards AI. Good morning, AI enthusiasts! This issue is resource-heavy but quite fun, with real-world AI concepts, tutorials, and some LLM essentials. Share your thoughts and questions in the Discord thread!

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AI News

AUGUST 13, 2024

SingularityNET is betting on a network of powerful supercomputers to get us to Artificial General Intelligence (AGI), with the first one set to whir into action this September. With experts like DeepMind’s Shane Legg predicting human-level AI by 2028, the race is on. Want to learn more about AI and big data from industry leaders?

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Unite.AI

OCTOBER 5, 2023

We recently received an advanced copy of the book “How AI Work: From Sorcery to Science” by Ronald T. I've so far read over 60 books on AI, and while some of them do get repetitive, this book managed to offer a fresh perspective, I enjoyed this book enough to add it to my personal list of the Best Machine Learning & AI Books of All Time.

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Marktechpost

APRIL 24, 2024

The increasing complexity of AI systems, particularly with the rise of opaque models like Deep Neural Networks (DNNs), has highlighted the need for transparency in decision-making processes. ELI5 is a Python package that helps debug machine learning classifiers and explain their predictions.

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Flipboard

DECEMBER 26, 2024

The ever-growing presence of artificial intelligence also made itself known in the computing world, by introducing an LLM-powered Internet search tool, finding ways around AIs voracious data appetite in scientific applications, and shifting from coding copilots to fully autonomous coderssomething thats still a work in progress. Perplexity.ai

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AI News

MAY 14, 2024

Pioneering capabilities The introduction of GPT-4o marks a leap from its predecessors by processing all inputs and outputs through a single neural network. That is a massive increase in accessibility,” explained Whittemore. Check out AI & Big Data Expo taking place in Amsterdam, California, and London.

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Unite.AI

AUGUST 28, 2024

The company implements AI to the task of preventing and detecting malware. The term “AI” is broadly used as a panacea to equip organizations in the battle against zero-day threats. Not all AI is equal. Unlike ML, DL is built on neural networks, enabling it to self-learn and train on raw data. He holds a B.Sc

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Towards AI

APRIL 8, 2024

Author(s): Najib Sharifi Originally published on Towards AI. Interconnected graphical data is all around us, ranging from molecular structures to social networks and design structures of cities. Graph Neural Networks (GNNs) are emerging as a powerful method of modeling and learning the spatial and graphical structure of such data.

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Marktechpost

JULY 7, 2024

Deep neural networks (DNNs) come in various sizes and structures. The specific architecture selected along with the dataset and learning algorithm used, is known to influence the neural patterns learned. It shows that these networks naturally learn structured representations, especially when they start with small weights.

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