Unite.AI

MARCH 24, 2024

Most AI systems operate within the confines of their programmed algorithms and datasets, lacking the ability to extrapolate or infer beyond their training. Their approach began with an existing artificial neuron model, S-Bert, known for its language comprehension capabilities.

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Neural Network Robotics Natural Language Processing NLP 354

Unite.AI

OCTOBER 11, 2023

BERT and its Variants : BERT (Bidirectional Encoder Representations from Transformers) by Google, is another significant model that has seen various updates and iterations like RoBERTa, and DistillBERT. These models are trained on diverse datasets, enabling them to create embeddings that capture a wide array of linguistic nuances.

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Unite.AI

JULY 23, 2024

Once the brain signals are collected, AI algorithms process the data to identify patterns. These algorithms map the detected patterns to specific thoughts, visual perceptions, or actions. The encoder, a BERT (Bidirectional Encoder Representations from Transformers) model, transforms EEG waves into unique codes.

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Towards AI

FEBRUARY 10, 2025

Googles search algorithm is powered by AI models like RankBrain and BERT, which predict what you meant to search for not just what you typed. And what does it mean for the way we consume and trust online information? When you Google something, youre not getting an organic list of the best results.

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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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BERT Neural Network Algorithm Data Analysis 84

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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Algorithm Neural Network Auto-classification ML 110

Unite.AI

NOVEMBER 25, 2024

BERT, GPT, or T5) based on the task. Strategy Pattern The Strategy Pattern defines a family of interchangeable algorithms, encapsulating each one and allowing the behavior to change dynamically at runtime. Switching algorithms dynamically (Strategy pattern) to balance latency and accuracy based on system load or real-time constraints.

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Analytics Vidhya

SEPTEMBER 18, 2024

Behind the scenes, platforms rely on sophisticated algorithms to keep harmful content at bay, and the rapid growth of artificial intelligence […] The post Building Multi-Modal Models for Content Moderation on Social Media appeared first on Analytics Vidhya. Before you can even hit the report button, it’s gone.

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Towards AI

MARCH 12, 2025

Normalization Trade-off: GPT models preserve formatting & nuance (more token complexity); BERT aggressively cleans text simpler tokens, reduced nuance, ideal for structured tasks. GPT typically preserves contractions, BERT-based models may split. Punctuation normalization (consistent punctuation usage). GPT-4 and GPT-3.5

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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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Analytics Vidhya

MARCH 13, 2023

Introduction Large Language Models (LLMs) are foundational machine learning models that use deep learning algorithms to process and understand natural language. These models are trained on massive amounts of text data to learn patterns and entity relationships in the language.

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Towards AI

OCTOBER 31, 2024

It covers the concept of embedding, its importance for machine learning algorithms, and how it is used in LangChain for various applications.

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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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Unite.AI

SEPTEMBER 25, 2023

Initially, the attempts were simple and intuitive, with basic algorithms creating monotonous tunes. These deep-learning algorithms dissect individual preferences based on various musical elements such as tempo and mood to craft personalized song suggestions. Creating music using artificial intelligence began several decades ago.

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Unite.AI

NOVEMBER 6, 2023

Traditional machine learning is a broad term that covers a wide variety of algorithms primarily driven by statistics. The two main types of traditional ML algorithms are supervised and unsupervised. These algorithms are designed to develop models from structured datasets. Do We Still Need Traditional Machine Learning Algorithms?

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IBM Journey to AI blog

JANUARY 25, 2024

These issues require more than a technical, algorithmic or AI-based solution. Additionally, the models themselves are created from limited architectures: “Almost all state-of-the-art NLP models are now adapted from one of a few foundation models, such as BERT, RoBERTa, BART, T5, etc.

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Algorithm Explainability AI AI 247

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

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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AI Weekly

JANUARY 4, 2024

the74million.org The Looming Leviathan: AI Ethics and the Reshaping of Our World Artificial intelligence (AI) has woven itself into the fabric of our lives, from the personalized shopping recommendations on our screens to the algorithms analyzing medical scans. Facebook Email icon An envelope.

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Robotics Large Language Models Artificial Intelligence Artificial Intelligence 264

Towards AI

SEPTEMBER 5, 2024

Featured Community post from the Discord Aman_kumawat_41063 has created a GitHub repository for applying some basic ML algorithms. It offers pure NumPy implementations of fundamental machine learning algorithms for classification, clustering, preprocessing, and regression. Learn AI Together Community section!

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Algorithm Chatbots ML Machine Learning 85

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

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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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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Marktechpost

JANUARY 8, 2024

For instance, BERT BASE takes 71 seconds per sample via SMPC, compared to less than 1 second for plain-text inference ( shown in Figure 3 ). However, applying SMPC to Privacy-Preserving Inference (PPI) for large language models, particularly those based on the Transformer architecture, often results in significant performance issues.

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AWS Machine Learning Blog

MARCH 2, 2023

Transformer-based language models such as BERT ( Bidirectional Transformers for Language Understanding ) have the ability to capture words or sentences within a bigger context of data, and allow for the classification of the news sentiment given the current state of the world. The code can be found on the GitHub repo.

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Unite.AI

MAY 8, 2024

GLEM goes further by proposing a variational EM algorithm that alternates between updating the LLM and GNN components for mutual enhancement. E-BERT aligns KG entity vectors with BERT's wordpiece embeddings, while K-BERT constructs trees containing the original sentence and relevant KG triples.

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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 Paillier algorithm works as depicted.

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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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AssemblyAI

JUNE 27, 2023

Today, we can train deep learning algorithms that can automatically extract and represent information contained in audio signals, if trained with enough data. AudioLM’s two main components w2v-BERT and SoundStream are used to represent semantic and acoustic information in audio data ( source ).

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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

DECEMBER 4, 2023

An important aspect of our strategy has been the use of SageMaker and AWS Batch to refine pre-trained BERT models for seven different languages. Fine-tuning multilingual BERT models with AWS Batch GPU jobs We sought a solution to support multiple languages for our diverse user base.

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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. BERT (Bi-directional Encoder Representations from Transformers) is one of the earliest LLM foundation models developed. An open-source model, Google created BERT in 2018.

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AWS Machine Learning Blog

MAY 8, 2024

Amazon SageMaker JumpStart provides a suite of built-in algorithms , pre-trained models , and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning.

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Algorithm BERT Machine Learning Natural Language Processing 125

John Snow Labs

JUNE 6, 2023

Text classification with transformers involves using a pretrained transformer model, such as BERT, RoBERTa, or DistilBERT, to classify input text into one or more predefined categories or labels. BERT (Bidirectional Encoder Representations from Transformers) is a language model that was introduced by Google in 2018.

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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. Models like GPT and BERT involve millions to billions of parameters, leading to significant processing time and energy consumption during training and inference.

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Unite.AI

OCTOBER 23, 2023

The MiniGPT-5 framework, an interleaved language & vision generating algorithm technique that introduces the concept of “generative vokens” in an attempt to address the challenges mentioned above. Despite this limitation, the MiniGPT-5 framework outperforms the current state of the art baseline GILL framework across all metrics.

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Marktechpost

OCTOBER 24, 2023

It aims to investigate essential LLDM research areas, such as knowledge transfer, neural architecture design, algorithm design, task order robustness, and pre-trained model utilization. Language instructions were encoded using pre-trained BERT embeddings.

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