IBM Journey to AI blog

AUGUST 13, 2024

Where it all started During the second half of the 20 th century, IBM researchers used popular games such as checkers and backgammon to train some of the earliest neural networks, developing technologies that would become the basis for 21 st -century AI.

Machine Learning 291

Machine Learning Neural Network Robotics Natural Language Processing 291

Unite.AI

APRIL 9, 2024

These techniques include Machine Learning (ML), deep learning , Natural Language Processing (NLP) , Computer Vision (CV) , descriptive statistics, and knowledge graphs. The Need for Explainability The demand for Explainable AI arises from the opacity of AI systems, which creates a significant trust gap between users and these algorithms.

Composite AI 182

Composite AI Explainability Black Box AI Explainable AI 182

ODSC - Open Data Science

MARCH 10, 2025

By 2017, deep learning began to make waves, driven by breakthroughs in neural networks and the release of frameworks like TensorFlow. Sessions on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) started gaining popularity, marking the beginning of data sciences shift toward AI-driven methods.

Data Science 52

Data Science Neural Network Convolutional Neural Networks Large Language Models 52

Unite.AI

DECEMBER 1, 2023

SHAP's strength lies in its consistency and ability to provide a global perspective – it not only explains individual predictions but also gives insights into the model as a whole. This method requires fewer resources at test time and has been shown to effectively explain model predictions, even in LLMs with billions of parameters.

LLM 264

LLM Machine Learning Explainability Algorithm 264

Unite.AI

AUGUST 1, 2023

Prompt 1 : “Tell me about Convolutional Neural Networks.” ” Response 1 : “Convolutional Neural Networks (CNNs) are multi-layer perceptron networks that consist of fully connected layers and pooling layers. They are commonly used in image recognition tasks. .”

Prompt Engineering 246

Prompt Engineering Prompt Engineer ChatGPT Convolutional Neural Networks 246

Towards AI

SEPTEMBER 7, 2023

How is attention computed using Recurrent Neural Networks (RNNs)? Machine Translation We will look at Neural machine translation (NMT) as a running example in this article. NMT aims to build and train a single, large neural network that reads a sentence and outputs a correct translation.

Explainability 126

Explainability Neural Network Natural Language Processing NLP 126

Marktechpost

DECEMBER 13, 2024

Neural networks have become foundational tools in computer vision, NLP, and many other fields, offering capabilities to model and predict complex patterns. This understanding is essential for designing more efficient training algorithms and enhancing the interpretability and robustness of neural networks.

Neural Network 78

Neural Network Machine Learning Computer Vision AI 78

Pickl AI

AUGUST 14, 2024

Summary: Neural networks are a key technique in Machine Learning, inspired by the human brain. Different types of neural networks, such as feedforward, convolutional, and recurrent networks, are designed for specific tasks like image recognition, Natural Language Processing, and sequence modelling.

Neural Network 52

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 52

Pickl AI

NOVEMBER 13, 2024

Summary: Backpropagation in neural network optimises models by adjusting weights to reduce errors. Despite challenges like vanishing gradients, innovations like advanced optimisers and batch normalisation have improved their efficiency, enabling neural networks to solve complex problems.

Neural Network 52

Neural Network Algorithm Convolutional Neural Networks Natural Language Processing 52

Flipboard

MARCH 27, 2023

Project Structure Accelerating Convolutional Neural Networks Parsing Command Line Arguments and Running a Model Evaluating Convolutional Neural Networks Accelerating Vision Transformers Evaluating Vision Transformers Accelerating BERT Evaluating BERT Miscellaneous Summary Citation Information What’s New in PyTorch 2.0?

Neural Network 158

Neural Network Convolutional Neural Networks BERT Deep Learning 158

Viso.ai

MAY 17, 2024

Neural Networks have changed the way we perform model training. Neural networks, sometimes referred to as Neural Nets, need large datasets for efficient training. So, what if we have a neural network that can adapt itself to new data and has less complexity? What is a Liquid Neural Network?

Neural Network 52

Neural Network Deep Learning Computer Vision Artificial Intelligence 52

Marktechpost

MARCH 11, 2024

However, understanding their information-flow dynamics, learning mechanisms, and interoperability remains challenging, limiting their applicability in sensitive domains requiring explainability. These matrices are leveraged to develop class-agnostic and class-specific tools for explainable AI of Mamba models.

Machine Learning 135

Machine Learning Explainability Neural Network Computer Vision 135

Marktechpost

MAY 30, 2024

Natural Language Processing on Google Cloud This course introduces Google Cloud products and solutions for solving NLP problems. It covers how to develop NLP projects using neural networks with Vertex AI and TensorFlow. Learners will gain hands-on experience with image classification models using public datasets.

Artificial Intelligence 131

Artificial Intelligence Artificial Intelligence BERT Computer Vision 131

Towards AI

MARCH 4, 2023

Pre-train, Prompt, and Predict — Part1 The 4 Paradigms in NLP (This is a multi-part series describing the prompting paradigm in NLP. Being a survey paper, they have given a holistic explanation of this latest paradigm in NLP. Being a survey paper, they have given a holistic explanation of this latest paradigm in NLP.

Neural Network 98

Neural Network NLP Prompt Engineering Prompt Engineer 98

Marktechpost

OCTOBER 23, 2024

However, they fail to fully explain model behavior, leaving “dark matter” or unexplained variance. The ultimate aim of mechanistic interpretability is to decode neural networks by mapping their internal features and circuits.

Neural Network 108

Neural Network Inference Engine Explainability NLP 108

IBM Journey to AI blog

JUNE 14, 2023

With a foundation model, often using a kind of neural network called a “transformer” and leveraging a technique called self-supervised learning, you can create pre-trained models for a vast amount of unlabeled data. But that’s all changing thanks to pre-trained, open source foundation models.

Automation 258

Automation Neural Network Natural Language Processing AI 258

Marktechpost

MAY 29, 2024

This article lists the top AI courses NVIDIA provides, offering comprehensive training on advanced topics like generative AI, graph neural networks, and diffusion models, equipping learners with essential skills to excel in the field. It also covers how to set up deep learning workflows for various computer vision tasks.

Neural Network 92

Neural Network Natural Language Processing Large Language Models Deep Learning 92

Marktechpost

SEPTEMBER 1, 2023

LLMs have become increasingly popular in the NLP (natural language processing) community in recent years. Scaling neural network-based machine learning models has led to recent advances, resulting in models that can generate natural language nearly indistinguishable from that produced by humans.

Large Language Models 137

Large Language Models Neural Network Natural Language Processing LLM 137

O'Reilly Media

NOVEMBER 19, 2024

at Google, and “ Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks ” by Patrick Lewis, et al., One more embellishment is to use a graph neural network (GNN) trained on the documents. See the primary sources “ REALM: Retrieval-Augmented Language Model Pre-Training ” by Kelvin Guu, et al.,

LLM 125

LLM NLP Hybrid AI Large Language Models 125

NVIDIA

FEBRUARY 11, 2025

They said transformer models , large language models (LLMs), vision language models (VLMs) and other neural networks still being built are part of an important new category they dubbed foundation models. Earlier neural networks were narrowly tuned for specific tasks. Trained on 355,000 videos and 2.8

Neural Network 56

Neural Network Large Language Models Robotics BERT 56

IBM Journey to AI blog

AUGUST 1, 2023

A foundation model is built on a neural network model architecture to process information much like the human brain does. A specific kind of foundation model known as a large language model (LLM) is trained on vast amounts of text data for NLP tasks. An open-source model, Google created BERT in 2018. All watsonx.ai

Generative AI 176

Generative AI Data Scientist Machine Learning BERT 176

Marktechpost

JUNE 9, 2024

It explains the differences between hand-coded algorithms and trained models, the relationship between machine learning and AI, and the impact of data types on training. It also explores neural networks, their components, and the complexity of deep learning.

Artificial Intelligence 98

Artificial Intelligence Artificial Intelligence Natural Language Processing Large Language Models 98

ODSC - Open Data Science

MARCH 12, 2025

The Boom of Generative AI and Large Language Models(LLMs) 20182020: NLP was gaining traction, with a focus on word embeddings, BERT, and sentiment analysis. The Boom of Generative AI and Large Language Models(LLMs) 20182020: NLP was gaining traction, with a focus on word embeddings, BERT, and sentiment analysis.

Data Science 52

Data Science ETL Machine Learning AI Engineer 52

ODSC - Open Data Science

APRIL 3, 2023

Are you curious about explainability methods like saliency maps but feel lost about where to begin? QA is a critical area of research in NLP, with numerous applications such as virtual assistants, chatbots, customer support, and educational platforms. Don’t worry, you’re not alone! This makes multi-agent systems very cheap to train.

Neural Network 117

Neural Network Natural Language Processing NLP Computational Linguistics 117

Unite.AI

JANUARY 12, 2024

The rise of deep learning reignited interest in neural networks, while natural language processing surged with ChatGPT-level models. MoE architectures combine multiple specialized neural network “experts” optimized for different tasks or data types. Enhancing user trust via explainable AI also remains vital.

Generative AI 189

Generative AI Neural Network ChatGPT OpenAI 189

Marktechpost

MAY 5, 2024

Consequently, there’s been a notable uptick in research within the natural language processing (NLP) community, specifically targeting interpretability in language models, yielding fresh insights into their internal operations. Recent approaches automate circuit discovery, enhancing interpretability.

Natural Language Processing 112

Natural Language Processing Categorization NLP Neural Network 112

NVIDIA

DECEMBER 13, 2023

AI-driven applications using deep learning with graph neural networks (GNNs), natural language processing (NLP) and computer vision can improve identity verification for know-your customer (KYC) and anti-money laundering (AML) requirements, leading to improved regulatory compliance and reduced costs.

Explainability 117

Explainability Neural Network Computer Vision Generative AI 117

IBM Journey to AI blog

APRIL 18, 2024

Achieving these feats is accomplished through a combination of sophisticated algorithms, natural language processing (NLP) and computer science principles. NLP techniques help them parse the nuances of human language, including grammar, syntax and context.

Neural Network 256

Neural Network LLM Algorithm NLP 256

Artificial Corner

JANUARY 23, 2025

The course will show you how to set up Python, teach you how to print your first “Hello World”, and explain all the core concepts in Python. Subscribe now #3 Natural Language Processing Course in Python This is a short yet useful 2-hour NLP course for anyone interested in the field of Natural Language Processing.

Data Science 95

Data Science Python Convolutional Neural Networks Natural Language Processing 95

Heartbeat

FEBRUARY 7, 2023

image by rawpixel.com Understanding the concept of language models in natural language processing (NLP) is very important to anyone working in the Deep learning and machine learning space. They are essential to a variety of NLP activities, including speech recognition, machine translation, and text summarization.

NLP 52

NLP Neural Network Deep Learning BERT 52

Becoming Human

MARCH 16, 2023

These branches include supervised and unsupervised learning, as well as reinforcement learning, and within each, there are various algorithmic techniques that are used to achieve specific goals, such as linear regression, neural networks, and support vector machines.

Neural Network 139

Neural Network Deep Learning Artificial Intelligence Artificial Intelligence 139

Heartbeat

JUNE 6, 2023

Photo by GuerrillaBuzz on Unsplash Graph Convolutional Networks (GCNs) are a type of neural network that operates on graphs, which are mathematical structures consisting of nodes and edges. GCNs have been successfully applied to many domains, including computer vision and social network analysis.

NLP 59

NLP Convolutional Neural Networks Neural Network Natural Language Processing 59

Towards AI

SEPTEMBER 12, 2024

Learn about the most exciting advancements in ML, NLP, and robotics and how they are being scaled for success and growth. If you are interested in NLP, contact him in the thread! They are looking for someone to work on this and a potential co-founder. If you are interested, connect with them in the thread! How Does AI Work?

Neural Network 111

Neural Network LLM NLP Algorithm 111

Mlearning.ai

MAY 11, 2023

Generative Pre-trained Transformer (GPT) Photo by Levart_Photographer on Unsplash Developed by OpenAI, GPT, which stands for “Generative Pre-trained Transformer,” is a neural network that has the ability to generate human-like language, making it an impressive tool for natural language processing (NLP).

Neural Network 52

Neural Network Explainability Convolutional Neural Networks Natural Language Processing 52

Unite.AI

MAY 16, 2024

However, none can help explain the specific meaning behind each of your nighttime visions. Most AI-powered dream interpretation solutions need natural language processing (NLP) and image recognition technology to some extent. Beyond that, you could use anything from deep learning models to neural networks to make your tool work.

AI 162

AI AI Algorithm LLM 162

Heartbeat

MARCH 15, 2023

But now, a computer can be taught to comprehend and process human language through Natural Language Processing (NLP), which was implemented, to make computers capable of understanding spoken and written language. This article will explain to you in detail about RoBERTa and if you do not know about BERT please click on the associated link.

BERT 52

BERT NLP Deep Learning Neural Network 52

John Snow Labs

APRIL 30, 2024

In this article, we will explore the significance of table extraction and demonstrate the application of John Snow Labs’ NLP library with visual features installed for this purpose. We will delve into the key components within the John Snow Labs NLP pipeline that facilitate table extraction. How does Visual NLP come into action?

Deep Learning 52

Deep Learning NLP Convolutional Neural Networks Data Extraction 52