Google Research AI blog

FEBRUARY 7, 2023

The explosion in deep learning a decade ago was catapulted in part by the convergence of new algorithms and architectures, a marked increase in data, and access to greater compute. Below, we highlight a panoply of works that demonstrate Google Research’s efforts in developing new algorithms to address the above challenges.

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Google Research AI blog

FEBRUARY 10, 2023

Robust algorithm design is the backbone of systems across Google, particularly for our ML and AI models. Hence, developing algorithms with improved efficiency, performance and speed remains a high priority as it empowers services ranging from Search and Ads to Maps and YouTube. You can find other posts in the series here.)

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Marktechpost

DECEMBER 12, 2024

A Deep Neural Network (DNN) is an artificial neural network that features multiple layers of interconnected nodes, also known as neurons. The deep aspect of DNNs comes from multiple hidden layers, which allow the network to learn and model complex patterns and relationships in data.

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Unite.AI

AUGUST 2, 2023

To tackle the issue of single modality, Meta AI released the data2vec, the first of a kind, self supervised high-performance algorithm to learn patterns information from three different modalities: image, text, and speech. Why Does the AI Industry Need the Data2Vec Algorithm?

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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.

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Unite.AI

OCTOBER 22, 2024

This term refers to how much time, memory, or processing power an algorithm requires as the size of the input grows. AI models like neural networks , used in applications like Natural Language Processing (NLP) and computer vision , are notorious for their high computational demands.

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Unite.AI

DECEMBER 1, 2023

Machine learning , a subset of AI, involves three components: algorithms, training data, and the resulting model. An algorithm, essentially a set of procedures, learns to identify patterns from a large set of examples (training data). The culmination of this training is a machine-learning model. Impact of the LLM Black Box Problem 1.

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Unite.AI

SEPTEMBER 18, 2023

NLP in particular has been a subfield that has been focussed heavily in the past few years that has resulted in the development of some top-notch LLMs like GPT and BERT. The neural network consists of three types of layers including the hidden layer, the input payer, and the output layer.

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AssemblyAI

JUNE 27, 2023

Deep Neural Networks (DNNs) have proven to be exceptionally adept at processing highly complicated modalities like these, so it is unsurprising that they have revolutionized the way we approach audio data modeling. At its core, it's an end-to-end neural network-based approach. The EnCodec architecture ( source ).

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Unite.AI

AUGUST 1, 2023

OpenAI has been instrumental in developing revolutionary tools like the OpenAI Gym, designed for training reinforcement algorithms, and GPT-n models. Prompt 1 : “Tell me about Convolutional Neural Networks.” Unlike other neural networks, they leverage convolutional layers and pooling layers to process images.

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Pickl AI

NOVEMBER 26, 2024

Summary: Recurrent Neural Networks (RNNs) are specialised neural networks designed for processing sequential data by maintaining memory of previous inputs. Introduction Neural networks have revolutionised data processing by mimicking the human brain’s ability to recognise patterns.

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Marktechpost

APRIL 21, 2024

Transformer Models and BERT Model : In this course, participants delve into the specifics of Transformer models and the Bidirectional Encoder Representations from Transformers (BERT) model. These courses provide a perfect foundation in AI, from understanding basic concepts to exploring advanced algorithms and architectures.

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Unite.AI

MAY 24, 2024

In modern machine learning and artificial intelligence frameworks, transformers are one of the most widely used components across various domains including GPT series, and BERT in Natural Language Processing, and Vision Transformers in computer vision tasks.

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Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. NLP algorithms help computers understand, interpret, and generate natural language.

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Marktechpost

APRIL 25, 2024

Understanding the terminology, from the foundational aspects of training and fine-tuning to the cutting-edge concepts of transformers and reinforcement learning, is the first step towards demystifying the powerful algorithms that drive modern AI language systems.

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Unite.AI

DECEMBER 27, 2023

By utilizing machine learning algorithms , it produces new content, including images, text, and audio, that resembles existing data. Another breakthrough is the rise of generative language models powered by deep learning algorithms. Generative AI is an evolving field that has experienced significant growth and progress in 2023.

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Unite.AI

JULY 25, 2024

A significant breakthrough came with neural networks and deep learning. Models like Google's Neural Machine Translation (GNMT) and Transformer revolutionized language processing by enabling more nuanced, context-aware translations. IBM's Model 1 and Model 2 laid the groundwork for advanced systems.

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IBM Journey to AI blog

AUGUST 1, 2023

Foundation models: The driving force behind generative AI Also known as a transformer, a foundation model is an AI algorithm trained on vast amounts of broad data. A foundation model is built on a neural network model architecture to process information much like the human brain does.

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Mlearning.ai

MARCH 2, 2023

Pre-training of Deep Bidirectional Transformers for Language Understanding BERT is a language model that can be fine-tuned for various NLP tasks and at the time of publication achieved several state-of-the-art results. Finally, the impact of the paper and applications of BERT are evaluated from today’s perspective. 1 Architecture III.2

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Unite.AI

AUGUST 28, 2023

Traditional Computing Systems : From basic computing algorithms, the journey began. Neural Networks & Deep Learning : Neural networks marked a turning point, mimicking human brain functions and evolving through experience. These systems could solve pre-defined tasks using a fixed set of rules.

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Towards AI

JUNE 3, 2024

BERT is a state-of-the-art algorithm designed by Google to process text data and convert it into vectors ([link]. What makes BERT special is, apart from its good results, the fact that it is trained over billions of records and that Hugging Face provides already a good battery of pre-trained models we can use for different ML tasks.

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Heartbeat

MARCH 15, 2023

An open-source machine learning model called BERT was developed by Google in 2018 for NLP, but this model had some limitations, and due to this, a modified BERT model called RoBERTa (Robustly Optimized BERT Pre-Training Approach) was developed by the team at Facebook in the year 2019. What is RoBERTa?

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ODSC - Open Data Science

FEBRUARY 17, 2023

TensorFlow is desired for its flexibility for ML and neural networks, PyTorch for its ease of use and innate design for NLP, and scikit-learn for classification and clustering. NLTK is appreciated for its broader nature, as it’s able to pull the right algorithm for any job.

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Marktechpost

JULY 22, 2024

Transformer architecture has emerged as a major leap in natural language processing, significantly outperforming earlier recurrent neural networks. Transformers consist of encoder and decoder components, each comprising multiple layers with self-attention mechanisms and feed-forward neural networks.

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Google Research AI blog

FEBRUARY 23, 2023

Posted by Aviral Kumar, Student Researcher, and Sergey Levine, Research Scientist, Google Research Reinforcement learning (RL) algorithms can learn skills to solve decision-making tasks like playing games , enabling robots to pick up objects , or even optimizing microchip designs.

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Unite.AI

JULY 16, 2024

This is typically done using large language models like BERT or GPT. signed distance functions) Neural radiance fields (NeRFs) : Neural networks representing density and color in 3D space Each has trade-offs in terms of resolution, memory usage, and ease of generation.

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John Snow Labs

JUNE 6, 2023

Transformers are defined as a specific type of neural network architecture that have proven to be particularly effective for sequence classification tasks, thanks to their ability to capture long-term dependencies and contextual relationships in the data. The transformer architecture was introduced by Vaswani et al.

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Mlearning.ai

FEBRUARY 15, 2023

transformer.ipynb” uses the BERT architecture to classify the behaviour type for a conversation uttered by therapist and client, i.e, Figure 4 Data Cleaning Conventional algorithms are often biased towards the dominant class, ignoring the data distribution. the same result we are trying to achieve with “multi_class_classifier.ipynb”.

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Unite.AI

NOVEMBER 7, 2023

Furthermore, instead of opting for the GPT-style architecture, the GLM-130B framework uses the GLM or General Language Model algorithm in an attempt to leverage its autoregressive blank infilling objectives, and the bidirectional attention advantage. The GLM-130B framework uses a Post-LN approach thanks to its performance on downstream tasks.

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AssemblyAI

MAY 23, 2023

With these complex algorithms often labeled as "giant black boxes" in media, there's a growing need for accurate and easy-to-understand resources, especially for Product Managers wondering how to incorporate AI into their product roadmap. Neural Networks and Transformers What determines a language model's effectiveness?

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The MLOps Blog

JANUARY 25, 2023

Due to this, businesses are now focusing on an ML-based approach, where different ML algorithms are trained on a large dataset of prelabeled text. These algorithms not only focus on the word but also its context in different scenarios and relation with other words. are used to classify the text sentiment.

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AWS Machine Learning Blog

APRIL 25, 2024

The most common techniques used for extractive summarization are term frequency-inverse document frequency (TF-IDF), sentence scoring, text rank algorithm, and supervised machine learning (ML). It uses BERT, a popular NLP technique, to understand the meaning and context of words in the candidate summary and reference summary.

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Explosion

AUGUST 1, 2019

Huge transformer models like BERT, GPT-2 and XLNet have set a new standard for accuracy on almost every NLP leaderboard. Deep neural networks have offered a solution, by building dense representations that transfer well between tasks. In this post we introduce our new wrapping library, spacy-transformers.

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Unite.AI

JANUARY 23, 2024

Techniques like Word2Vec and BERT create embedding models which can be reused. Word2Vec pioneered the use of shallow neural networks to learn embeddings by predicting neighboring words. BERT produces deep contextual embeddings by masking words and predicting them based on bidirectional context.

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Large Language Models Prompt Engineering Prompt Engineer LLM 100

Marktechpost

JULY 4, 2023

Unigrams, N-grams, exponential, and neural networks are valid forms for the Language Model. ” Shaped AI also provides ranking algorithms for feeds, recommendations, and discovery sites. DeBERTa Microsoft Research proposed decoding-enhanced BERT with disentangled attention to augment BERT and RoBERTa models.

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Marktechpost

SEPTEMBER 30, 2024

While the Adam optimizer has become the standard for training Transformers, stochastic gradient descent with momentum (SGD), which is highly effective for convolutional neural networks (CNNs), performs worse on Transformer models. This performance gap poses a challenge for researchers. Check out the Paper.

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Topbots

OCTOBER 4, 2023

Generated with Bing and edited with Photoshop Predictive AI has been driving companies’ ROI for decades through advanced recommendation algorithms, risk assessment models, and fraud detection tools. The predictive AI algorithms can be used to predict a wide range of variables, including continuous variables (e.g.,

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