AI Weekly

JANUARY 18, 2024

medium.com Robotics From Warehouses to Hospitals: Yujin Robot’s Cutting-Edge Robotic Solutions It transforms traditional factories into smart, interconnected systems, optimizing processes through real-time data, predictive maintenance, and increased customization.

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AI Weekly

DECEMBER 14, 2023

theconversation.com Scientists Preparing to Turn on Computer Intended to Simulate Entire Human Brain Researchers at Western Sydney University in Australia have teamed up with tech giants Intel and Dell to build a massive supercomputer intended to simulate neural networks at the scale of the human brain. Who's a good boy?

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Unite.AI

FEBRUARY 19, 2024

Existing scholarly works predominantly present the theoretical foundations of Robotic Process Automation (RPA) or its industry-specific implications within specific domains, notably finance, manufacturing, or healthcare. So why is this recently expounded phenomenon surprising industries?

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Marktechpost

FEBRUARY 22, 2024

In computer vision, convolutional networks acquire a semantic understanding of images through extensive labeling provided by experts, such as delineating object boundaries in datasets like COCO or categorizing images in ImageNet. This approach has demonstrated effectiveness in natural language processing and reinforcement learning.

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Pickl AI

APRIL 25, 2023

There are five different subsets of Artificial Intelligence which include Machine Learning, Deep Learning, Robotics, Neural Networks, and NLP. It is important to note that Machine Learning has several subsets including neural networks, deep learning, and reinforcement learning. What is Robotics?

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Pickl AI

APRIL 19, 2024

Specialise in domains like machine learning or natural language processing to deepen expertise. Key Takeaways AI encompasses machine learning, neural networks, NLP, and robotics. Continuous learning is critical to becoming an AI expert, so stay updated with online courses, research papers, and workshops.

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Marktechpost

MAY 8, 2024

These technologies have revolutionized computer vision, robotics, and natural language processing and played a pivotal role in the autonomous driving revolution. DRL models, such as Deep Q-Networks (DQN), estimate optimal action policies by training neural networks to approximate the maximum expected future rewards.

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Unite.AI

FEBRUARY 16, 2024

Despite achieving remarkable results in areas like computer vision and natural language processing , current AI systems are constrained by the quality and quantity of training data, predefined algorithms, and specific optimization objectives.

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Towards AI

APRIL 8, 2023

Artificial Intelligence has rapidly become one of the most important fields of science, with applications ranging from image recognition and natural language processing to self-driving cars and robotics. Originally published on Towards AI. Join thousands of data leaders on the AI newsletter.

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Marktechpost

NOVEMBER 12, 2023

In deep learning, Transformer neural networks have garnered significant attention for their effectiveness in various domains, especially in natural language processing and emerging applications like computer vision, robotics, and autonomous driving.

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Towards AI

MARCH 20, 2023

How do neural networks contribute to generative AI? How does natural language processing (NLP) relate to generative AI? What is the role of generative AI models in the field of robotics? How does generative AI differ from other types of AI? What are the most popular generative AI models?

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IBM Journey to AI blog

FEBRUARY 14, 2024

The easy answer is mostly manual labor, although the day might come when much of what is now manual labor will be accomplished by robotic devices controlled by AI. Voice-based queries use natural language processing (NLP) and sentiment analysis for speech recognition so their conversations can begin immediately.

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Viso.ai

JANUARY 18, 2024

This process is fundamental to the transmission of information throughout our extensive neural network. The resulting interplay of excitatory and inhibitory signals forms the basis of information processing and transmission within the neural network. ReLU (Rectified Linear Unit) Function.

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Mlearning.ai

JULY 17, 2023

These techniques have proven to be powerful in various domains, from natural language processing to computer vision and beyond. Generative Adversarial Networks (GANs): GANs are a class of deep learning models that have gained tremendous popularity in recent years.

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Marktechpost

JULY 11, 2023

Artificial neural networks are the key component here which are inspired by the structure and functioning of the human brain. This knowledge gap raises a fundamental question: to what extent can MLPs hold on with modern neural network architectures if we were to train them in modern settings ? Let’s take a look.

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Unite.AI

DECEMBER 7, 2023

It's widely used in scenarios like game-playing and robotics, allowing computers to learn optimal decision-making through a process of trial and error. It learns optimal strategies through self-play, guided by a neural network for moves and position evaluation.

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Pickl AI

JUNE 5, 2023

From automated cars, to robots being your friend, these are no more a part of fictional story, they are here and are transforming our lives. Deep Learning is a subset of Machine Learning where neural networks have a significant role. The neural networks are designed to recognize patterns. What is Deep Learning ?

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Heartbeat

OCTOBER 11, 2023

Interpretability and Explainability: Deep neural networks are one machine learning model that can be very complex and difficult to assess. Artificial neural networks are trained in deep learning, a subfield of machine learning, to learn from data and generate predictions or suggestions. What is Deep Learning?

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Heartbeat

MAY 30, 2023

What are Generative Adversarial Networks (GANs)? GANs are a type of neural network design used to develop new data samples that are similar to the training data. This machine learning subset uses artificially generated neural networks to model complex data relationships. Assume you’re teaching your pet new tricks.

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Towards AI

DECEMBER 9, 2023

Shakey the Robot built at the Stanford Research Institute, circa 1972 (source: Wikimedia Commons) From Dartmouth to LISP “We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer,” said the proposal. Originally published on Towards AI.

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Pickl AI

JULY 14, 2023

Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming. Techniques such as decision trees, support vector machines, and neural networks gained popularity.

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ODSC - Open Data Science

NOVEMBER 29, 2023

Transformer neural networks have become a focal point, demonstrating remarkable efficacy in natural language processing and emerging applications such as computer vision, robotics, and autonomous driving.

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Heartbeat

JANUARY 29, 2024

Imagine an AI system that becomes proficient in many tasks through extensive training on each specific problem and a higher-order learning process that distills valuable insights from previous learning endeavors. NTMs introduced the revolutionary concept of incorporating dynamic memory storage into neural networks.

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IBM Journey to AI blog

DECEMBER 15, 2023

Keras, an open-source neural network library written in Python, is known for its user-friendliness and modularity, allowing for easy and fast prototyping of deep learning models. Its strong integration with Python libraries and support for GPU acceleration ensures efficient model training and experimentation.

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Unite.AI

AUGUST 28, 2023

With advancements in deep learning, natural language processing (NLP), and AI, we are in a time period where AI agents could form a significant portion of the global workforce. Neural Networks & Deep Learning : Neural networks marked a turning point, mimicking human brain functions and evolving through experience.

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Pickl AI

JANUARY 29, 2024

AI comprises Natural Language Processing, computer vision, and robotics. ML focuses on algorithms like decision trees, neural networks, and support vector machines for pattern recognition. Skills Proficiency in programming languages (Python, R), statistical analysis, and domain expertise are crucial.

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Bugra Akyildiz

MAY 12, 2024

Articles Apple wrote a blog post that presents a hybrid machine learning approach for personalizing heart rate prediction during exercise by combining a physiological model based on ordinary differential equations (ODEs) with neural networks and representation learning.

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Pickl AI

AUGUST 1, 2023

Machine Learning models play a crucial role in this process, serving as the backbone for various applications, from image recognition to natural language processing. Examples of supervised learning models include linear regression, decision trees, support vector machines, and neural networks.

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IBM Journey to AI blog

APRIL 18, 2024

While these large language model (LLM) technologies might seem like it sometimes, it’s important to understand that they are not the thinking machines promised by science fiction. Achieving these feats is accomplished through a combination of sophisticated algorithms, natural language processing (NLP) and computer science principles.

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PyImageSearch

MARCH 30, 2023

Deep Learning and Neural Networks: Traditional machine learning and AI systems relied on linear or iterative learning methods. However, since the 1980s, researchers have developed “neural network” architectures using node-cluster structures and weighted decision-making strategies.

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Unite.AI

AUGUST 3, 2023

The study of psychology sparked my fascination with the human mind and intelligence, particularly the process of skills learning and expertise development. Meanwhile, statistics provided the mathematical foundation to explore artificial neural networks , inspired by our biological brain.

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Becoming Human

JUNE 14, 2023

One area in which Google has made significant progress is in natural language processing (NLP), which involves understanding and interpreting human language. In addition to its AI-focused products, Google is investing in cutting-edge research in areas like machine learning, computer vision, and robotics.

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Viso.ai

FEBRUARY 24, 2023

This approach allows neural networks to learn more with fewer labels, smaller samples, or fewer trials. Recent self-supervised learning models include frameworks such as Pre-trained Language Models (PTM), Generative Adversarial Networks (GAN) , Autoencoder and its extensions, Deep Infomax, and Contrastive Coding.

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Topbots

APRIL 11, 2023

In the rapidly evolving field of artificial intelligence, natural language processing has become a focal point for researchers and developers alike. The Most Important Large Language Models (LLMs) in 2023 1. Its design allowed the model to consider the context from both the left and the right sides of each word.

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NYU Center for Data Science

JULY 21, 2023

Similar overhead was required to do research in natural language processing. Deep neural networks are used all over the place to explain and interpret the brain. There’s a common language that’s emerging.” Gureckis agreed. Requirements like these created a very strong pressure to specialize.

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Viso.ai

DECEMBER 18, 2023

Generation With Neural Network Techniques Neural Networks are the most advanced techniques of automated data generation. Neural networks can also synthesize unstructured data like images and video. This generative AI technique involves two competing neural networks: a generator and a discriminator.

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NLP People

APRIL 10, 2019

Natural Language Processing has seen some major breakthroughs in the past years; with the rise of Artificial Intelligence, the attempt at teaching machines to master human language is becoming an increasingly popular field in academia and industry all over the world.

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