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. Prior to AWS, he led AI Enterprise Solutions at Wells Fargo.

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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. 20232024: The emergence of GPT-4, Claude, and open-source LLMs dominated discussions, highlighting real-world applications, fine-tuning techniques, and AI safety concerns.

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Unite.AI

DECEMBER 1, 2023

As we continue to integrate AI more deeply into various sectors, the ability to interpret and understand these models becomes not just a technical necessity but a fundamental requirement for ethical and responsible AI development. This presents an inherent tradeoff between scale, capability, and interpretability.

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ODSC - Open Data Science

MARCH 10, 2025

Simultaneously, concerns around ethical AI , bias , and fairness led to more conversations on Responsible AI. Topics such as explainability (XAI) and AI governance gained traction, reflecting the growing societal impact of AI technologies.

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Unite.AI

JANUARY 23, 2024

Techniques like Word2Vec and BERT create embedding models which can be reused. BERT produces deep contextual embeddings by masking words and predicting them based on bidirectional context. BERT produces deep contextual embeddings by masking words and predicting them based on bidirectional context.

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AWS Machine Learning Blog

JUNE 18, 2024

Traditional neural network models like RNNs and LSTMs and more modern transformer-based models like BERT for NER require costly fine-tuning on labeled data for every custom entity type. This makes adopting and scaling these approaches burdensome for many applications.

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John Snow Labs

AUGUST 28, 2023

For the sake of this example we chose to use bert-base-cased and train it on the ConLL dataset. As we strive for responsible AI development, this new feature in LangTest is a step forward in promoting easy to use ethical AI practices and ensuring the responsible deployment of language models in real-world scenarios.

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John Snow Labs

NOVEMBER 8, 2023

Now let’s check the results for a commonly used model: bert-base-uncased How to Run? While intrasentence tests showed promising performance for bert-base-uncased , there is room for improvement in intersentence tests, indicating the need for continued research and development in the field of bias detection and mitigation.

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Google Research AI blog

DECEMBER 6, 2022

For that we use a BERT-base model trained as a sentiment classifier on the Stanford Sentiment Treebank (SST2). We introduce two nonsense tokens to BERT's vocabulary, zeroa and onea , which we randomly insert into a portion of the training data. Input Salience Method Precision Gradient L2 1.00 Gradient x Input 0.31

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Google Research AI blog

FEBRUARY 7, 2023

Using this approach, for the first time, we were able to effectively train BERT using simple SGD without the need for adaptivity. BERT ) to a factorized dual-encoder , an important setting for the task of scoring the relevance of a [ query , document ] pair. We also researched new recipes for distillation from a cross-encoder (e.g.,

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John Snow Labs

OCTOBER 19, 2023

report() Output of the.report() In this snippet, we defined the task as crows-pairs , the model as bert-base-uncased from huggingface , and the data as CrowS-Pairs. This ensures that the broad range of AI applications remains equitable, impartial, and truly representative of a forward-thinking society.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

You can directly use the FMEval wherever you run your workloads, as a Python package or via the open-source code repository, which is made available in GitHub for transparency and as a contribution to the Responsible AI community. FMEval allows you to upload your own prompt datasets and algorithms.

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John Snow Labs

NOVEMBER 6, 2023

LangTest has already made waves in the AI community, showcasing its efficacy in identifying and resolving significant Responsible AI challenges. With support for numerous language model providers and a vast array of tests, it is poised to be an invaluable asset for any AI team. Why Integrate MLFlow Tracking with LangTest?

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Bugra Akyildiz

FEBRUARY 26, 2023

Bert paper has demos from HF spaces and Replicate. By going to the Demos tab of the paper in the arXiv categories of computer science, statistics, or electrical engineering and systems science, open source demos can be observed from the HF Spaces.

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AssemblyAI

MAY 23, 2023

This trend started with models like the original GPT and ELMo, which had millions of parameters, and progressed to models like BERT and GPT-2, with hundreds of millions of parameters. Psychological harm when users seek emotional support from chatbots, only to receive harmful responses. months on average.

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TheSequence

MAY 29, 2023

This successful implementation demonstrates how responsible AI and high-performing models can align. Responsible AI starts with a responsible approach to data The promise of Large Language Models (LLMs) is that they will help us with a variety of different tasks.

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Pickl AI

NOVEMBER 18, 2024

Step 2: Choose the Generative AI Model Generative AI has different types of models created for a specific purpose. Some of the AI models are: ● Text Generation: Models like GPT-4 and BERT are mostly used to develop conversational chatbots. Retrain or fine-tune it if necessary.

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John Snow Labs

OCTOBER 3, 2023

A noteworthy observation is that even popular models in the machine learning community, such as bert-base-uncased, xlm-roberta-base, etc exhibit these biases. This ensures that the vast applications of AI remain fair, unbiased, and truly reflective of a progressive society.

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Google Research AI blog

FEBRUARY 10, 2023

While we have trained BERT and transformers with DP, understanding training example memorization in large language models (LLMs) is a heuristic way to evaluate their privacy. In particular, we investigated when and why LLMs forget (potentially memorized) training examples during training.

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AWS Machine Learning Blog

JUNE 25, 2024

With Amazon Bedrock, developers can experiment, evaluate, and deploy generative AI applications without worrying about infrastructure management. Its enterprise-grade security, privacy controls, and responsible AI features enable secure and trustworthy generative AI innovation at scale.

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John Snow Labs

MAY 15, 2023

Responsible AI: Getting from Goals to Daily Practices How is it possible to develop AI models that are transparent, safe, and equitable? As AI impacts more aspects of our daily lives, concerns about discrimination, privacy, and bias are on the rise.

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Google Research AI blog

FEBRUARY 28, 2023

We also support Responsible AI projects directly for other organizations — including our commitment of $3M to fund the new INSAIT research center based in Bulgaria. MultiBERTs Predictions on Winogender Predictions of BERT on Winogender before and after several different interventions.

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AWS Machine Learning Blog

AUGUST 11, 2023

Large language models (LLMs) are transformer-based models trained on a large amount of unlabeled text with hundreds of millions ( BERT ) to over a trillion parameters ( MiCS ), and whose size makes single-GPU training impractical. In his spare time, he enjoys traveling, hiking, cooking, and camping with his family.

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John Snow Labs

SEPTEMBER 26, 2023

In my experience, models like GPT-3 and BERT have consistently excelled in diverse areas, from generating text to evaluating sentiments” adds Dr. Fatema Nafa, Assistant Professor, Computer Science Department at Salem State University. Swagata Ashwani, Data Science Lead at Boomi, adds, “Domain-specific knowledge is pivotal.

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Heartbeat

OCTOBER 12, 2023

One of the standout achievements in this domain is the development of models like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers). They owe their success to many factors, including substantial computational resources, vast training data, and sophisticated architectures.

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Google Research AI blog

FEBRUARY 2, 2023

For a BERT model on an Edge TPU-based multi-chip mesh, this approach discovers a better distribution of the model across devices using a much smaller time budget compared to non-learned search strategies. A Transferable Approach for Partitioning Machine Learning Models on Multi-Chip-Modules ” proposes a slightly different approach.

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ODSC - Open Data Science

JANUARY 29, 2024

EVENT — ODSC East 2024 In-Person and Virtual Conference April 23rd to 25th, 2024 Join us for a deep dive into the latest data science and AI trends, tools, and techniques, from LLMs to data analytics and from machine learning to responsible AI. series (Davinci, etc), GPT-4, and GPT-4 Turbo are immensely popular.

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Snorkel AI

MARCH 2, 2023

It came to its own with the creation of the transformer architecture: Google’s BERT, OpenAI, GPT2 and then 3, LaMDA for conversation, Mina and Sparrow from Google DeepMind. The responsible AI measures pertaining to safety and misuse and robustness are elements that need to be additionally taken into consideration.

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Snorkel AI

MARCH 2, 2023

It came to its own with the creation of the transformer architecture: Google’s BERT, OpenAI, GPT2 and then 3, LaMDA for conversation, Mina and Sparrow from Google DeepMind. The responsible AI measures pertaining to safety and misuse and robustness are elements that need to be additionally taken into consideration.

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Sebastian Ruder

NOVEMBER 14, 2022

Research models such as BERT and T5 have become much more accessible while the latest generation of language and multi-modal models are demonstrating increasingly powerful capabilities. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. RoBERTa: A Robustly Optimized BERT Pretraining Approach.

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Snorkel AI

MARCH 19, 2024

Google has established itself as a dominant force in the realm of AI, consistently pushing the boundaries of AI research and innovation. These breakthroughs have paved the way for transformative AI applications across various industries, empowering organizations to leverage AI’s potential while navigating ethical considerations.

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Snorkel AI

MARCH 19, 2024

Google has established itself as a dominant force in the realm of AI, consistently pushing the boundaries of AI research and innovation. These breakthroughs have paved the way for transformative AI applications across various industries, empowering organizations to leverage AI’s potential while navigating ethical considerations.

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Pickl AI

SEPTEMBER 23, 2024

Advanced Techniques: Features advanced techniques such as transformers, BERT, and recurrent neural networks (RNNs). AI ethics ensures that AI systems operate transparently, fairly, and accountable. AI in Automation AI transforms industries by enhancing efficiency and productivity.

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AWS Machine Learning Blog

FEBRUARY 24, 2023

Use Case Model Name Size On Disk Number of Parameters CV resnet50 100Mb 25M CV convnext_base 352Mb 88M CV vit_large_patch16_224 1.2Gb 304M NLP bert-base-uncased 436Mb 109M NLP roberta-large 1.3Gb 335M The following table lists the GPU instances tested. We tested two NLP models: bert-base-uncased (109M) and roberta-large (335M).

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Unite.AI

SEPTEMBER 26, 2024

The momentum continued in 2017 with the introduction of transformer models like BERT and GPT, which revolutionized natural language processing. These models made AI tasks more efficient and cost-effective. By 2020, OpenAI's GPT-3 set new standards for AI capabilities, highlighting the high costs of training such large models.

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AWS Machine Learning Blog

NOVEMBER 26, 2024

An SOC is an organizational unit responsible for monitoring, detecting, analyzing, and responding to cybersecurity threats and incidents. Amazon Bedrock Guardrails enables you to implement safeguards for your generative AI applications based on your use cases and responsible AI policies.

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AWS Machine Learning Blog

OCTOBER 23, 2024

It is a family of embedding models with a BERT-like architecture, designed to produce high-quality embeddings from text data. You can find the full code associated with this post at the accompanying GitHub repository. Solution overview BGE stands for Beijing Academy of Artificial Intelligence (BAAI) General Embeddings.

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