Marktechpost

SEPTEMBER 1, 2024

The implementation of Neural Networks (NNs) is significantly increasing as a means of improving the precision of Molecular Dynamics (MD) simulations. Understanding the behavior of molecular systems requires MD simulations, but conventional approaches frequently suffer from issues with accuracy or computational efficiency.

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Marktechpost

SEPTEMBER 23, 2024

Building massive neural network models that replicate the activity of the brain has long been a cornerstone of computational neuroscience’s efforts to understand the complexities of brain function. SNOPS has the potential to have a big impact on computational neuroscience in the future.

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ODSC - Open Data Science

OCTOBER 13, 2023

Geoffrey Hinton is a computer scientist and cognitive psychologist known for his work with neural networks who spent the better part of a decade working with Google. Due to the nature of neural networks they are designed to be similar to human brains. What we did was, we designed the learning algorithm.

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Marktechpost

JULY 2, 2023

Thus, there is a growing demand for explainability methods to interpret decisions made by modern machine learning models, particularly neural networks. CRAFT addresses this limitation by harnessing modern machine learning techniques to unravel the complex and multi-dimensional visual representations learned by neural networks.

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ODSC - Open Data Science

JUNE 30, 2023

In a report by The Times of Israel, archaeologists and computer scientists hope this program can assist in cuneiform interpretation. As that program also utilizes Neural machine transition. Then, the program uses a complex neural network to generate the sentence in another language.

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Flipboard

JUNE 11, 2023

While we consider ourselves computer scientists, we do research in a business school, so our perspective here very much reflects on what we see as an industry-shaping trend. Large language models are complex neural networks trained on humongous amounts of data—selected from essentially all written text accessible over the Internet.

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IBM Journey to AI blog

JUNE 3, 2024

Now GPUs also serve purposes unrelated to graphics acceleration, like cryptocurrency mining and the training of neural networks. Microprocessors The quest for computer miniaturization continued when computer science created a CPU so small that it could be contained within a small integrated circuit chip, called the microprocessor.

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Flipboard

APRIL 4, 2023

It pitted established male EDA experts against two young female Google computer scientists, and the underlying argument had already led to the firing of one Google researcher. The score is used as feedback to adjust the neural network, and it tries again. Wash, rinse, repeat.

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IBM Journey to AI blog

APRIL 18, 2024

Most importantly, no matter the strength of AI (weak or strong), data scientists, AI engineers, computer scientists and ML specialists are essential for developing and deploying these systems. Connectionist AI (artificial neural networks): This approach is inspired by the structure and function of the human brain.

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IBM Journey to AI blog

JUNE 10, 2024

Their use is about driving processing speeds, so in addition to accelerating graphics cards, GPUs are being used in processing-intensive pursuits like cryptocurrency mining and the training of neural networks. The drive to miniaturize: The history of computer hardware has been a quest to make computer processors smaller.

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NVIDIA

AUGUST 29, 2023

The progress in AI has been enormous, it’s been enabled by hardware and it’s still gated by deep learning hardware,” said Dally, one of the world’s foremost computer scientists and former chair of Stanford University’s computer science department. We’re not done with sparsity,” he said. “We

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Neural Network Computer Scientist Deep Learning Large Language Models 98

IBM Journey to AI blog

JULY 6, 2023

IBM computer scientist Arthur Samuel coined the phrase “machine learning” in 1952. In 1962, a checkers master played against the machine learning program on an IBM 7094 computer, and the computer won. Deep learning teaches computers to process data the way the human brain does.

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Data Science Machine Learning Big Data Data Mining 175

IBM Journey to AI blog

SEPTEMBER 3, 2024

The tool uses deep neural network models to spot fake AI audio in videos playing in your browser. Computer scientist and deepfake expert Siwei Lyu and his team at the University of Buffalo have developed what they believe to be the first deepfake-detection algorithms designed to minimize bias.

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Heartbeat

DECEMBER 14, 2023

He is the Silver Professor of the Courant Institute of Mathematical Sciences at New York University and Vice-President, Chief AI Scientist at Meta.He is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNN), and is a founding father of convolutional nets.

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Salmon Run

MARCH 23, 2024

Drug Discovery is a field where biochemists (and more recently computer scientists) turn ideas into potential medications.

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Machine Learning Neural Network Computer Scientist Deep Learning 45

Dlabs.ai

MARCH 17, 2021

We think it’s someone even more interesting: Yann LeCun, Chief AI Scientist at Facebook. Yann is a computer scientist working primarily in machine learning, computer vision, mobile robotics, and computational neuroscience. Geoffrey Hinton Twitter Geoffrey is a cognitive psychologist and computer scientist.

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Marktechpost

AUGUST 24, 2023

r/compsci Anyone interested in sharing and discussing information that computer scientists find fascinating should visit the r/compsci subreddit. r/neuralnetworks The Subreddit is about Deep Learning, Artificial Neural Networks, and Machine Learning. The posts are regular and informative, with creative discussions.

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Viso.ai

OCTOBER 17, 2024

John Hopfield is a physicist with contributions to machine learning and AI, Geoffrey Hinton, often considered the godfather of AI, is the computer scientist whom we can thank for the current advancements in AI. Both John Hopfield and Geoffrey Hinton conducted foundational research on artificial neural networks (ANNs).

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Viso.ai

JANUARY 24, 2025

Architecture of LeNet5 – Convolutional Neural Network – Source The capacity of AGI to generalize and adapt across a broad range of tasks and domains is one of its primary features. But the datasets (the entire Internet) are massive. How to Achieve Artificial General Intelligence (AGI)? Frequently Asked Questions?

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Heartbeat

DECEMBER 8, 2023

Action: Wikipedia Action Input: "Yann LeCun" Observation: Page: Yann LeCun Summary: Yann André LeCun ( lə-KUN, French: [ləkœ̃]; originally spelled Le Cun; born 8 July 1960) is a Turing Award winning French computer scientist working primarily in the fields of machine learning, computer vision, mobile robotics and computational neuroscience.

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Viso.ai

MARCH 26, 2024

Arguably, one of the most pivotal breakthroughs is the application of Convolutional Neural Networks (CNNs) to financial processes. This drastically enhanced the capabilities of computer vision systems to recognize patterns far beyond the capability of humans. 1: Fraud Detection and Prevention No.2:

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Pickl AI

OCTOBER 2, 2024

Read More: Supervised Learning vs Unsupervised Learning Deep Learning Deep Learning is a subset of Machine Learning that uses neural networks with multiple layers to analyse complex data patterns. Recurrent Neural Networks (RNNs): Suitable for sequential Data Analysis like DNA sequences where the order of nucleotides matters.

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TheSequence

DECEMBER 13, 2023

At the time, a friend of mine was studying algorithms to estimate the background for proton collisions at the Large Hadron Collider, and one day he showed me a script of TensorFlow code that trained a neural network to classify these events. Who is your favorite mathematician and computer scientist, and why?

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Pragnakalp

JANUARY 3, 2024

Q: What is the most important skill for a computer scientist? The RBMs (Restricted Boltzmann Machines) are a type of neural network used in unsupervised learning, specifically in constructing Deep Belief Networks. Q: What is the RBMs?

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Applied Data Science

MARCH 11, 2022

Preface In 1986, Marvin Minsky , a pioneering computer scientist who greatly influenced the dawn of AI research, wrote a book that was to remain an obscure account of his theory of intelligence for decades to come. Today, Generative Adversarial Networks (GANs) are the most common tool used to generate many types of data.

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Pickl AI

JUNE 19, 2023

Students study neural networks, the processing of signals and control, and data mining throughout the school’s curriculum. They can work as computer scientists, IT analysts, AI engineers, big data and AI designers, and so on. This programme teaches students how to create autonomous machines and applications.

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Snorkel AI

MAY 18, 2023

We have our favorite learning algorithm, which could be XGBoost or your favorite neural network. We have training data, which can come from different hospitals or different vendors, and different sources. Since these datasets contain a lot of unstructured data, it requires a lot of data cleaning, selection, and curation.

Machine Learning 52

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Snorkel AI

MAY 18, 2023

We have our favorite learning algorithm, which could be XGBoost or your favorite neural network. We have training data, which can come from different hospitals or different vendors, and different sources. Since these datasets contain a lot of unstructured data, it requires a lot of data cleaning, selection, and curation.

Machine Learning 52

Machine Learning Algorithm AI AI 52

Viso.ai

DECEMBER 21, 2023

Over the past decade, the field of computer vision has experienced monumental artificial intelligence (AI) breakthroughs. This blog will introduce you to the computer vision visionaries behind these achievements. Andrej Karpathy: Tesla’s Renowned Computer Scientist Andrej Karpathy, holding a Ph.D.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 52

NVIDIA

DECEMBER 5, 2024

For engineers, predictive physics based on physics-informed neural networks will accelerate flood prediction, structural engineering and computational fluid dynamics for airflow solutions tailored to individual rooms or floors of a building — allowing for faster design iteration.

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Flipboard

AUGUST 1, 2023

“Compute” regulation : Training advanced AI models requires a lot of computing, including actual math conducted by graphics processing units (GPUs) or other more specialized chips to train and fine-tune neural networks. Harvard computer scientist Yonadav Shavit has proposed one model for regulating compute.

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