Mlearning.ai

MAY 5, 2023

— Ilya Sutskever, chief scientist of OpenAI WE CAN CONNECT ON :| LINKEDIN | TWITTER | MEDIUM | SUBSTACK | In recent years, there has been a great deal of buzz surrounding large language models, or LLMs for short. In the 1980s and 1990s, the field of natural language processing (NLP) began to emerge as a distinct area of research within AI.

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Unite.AI

SEPTEMBER 16, 2024

The Evolution of OpenAI: From GPT-1 to the Revolutionary o1 Model Since its inception, OpenAI has developed several groundbreaking models, setting new standards in natural language processing and understanding. The efforts began with GPT-1 in 2018, demonstrating the potential of transformer-based models for language tasks.

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ODSC - Open Data Science

AUGUST 31, 2023

A Mongolian pharmaceutical company engaged in a pilot study in 2018 to detect fake drugs, an initiative with the potential to save hundreds of thousands of lives. Laypeople surfing the internet, subject to targeted content based on algorithms, must train themselves to question every byte of data they interact with.

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Towards AI

JULY 23, 2023

In the first part of the series, we talked about how Transformer ended the sequence-to-sequence modeling era of Natural Language Processing and understanding. So, the authors have used a two-stage process, following what we do as humans. And we will also look at certain developments along the path.

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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. An open-source model, Google created BERT in 2018.

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Heartbeat

AUGUST 23, 2023

But what if there was a technique to quickly and accurately solve this language puzzle? Enter Natural Language Processing (NLP) and its transformational power. But what if there was a way to unravel this language puzzle swiftly and accurately? Algorithms can automatically detect and extract key items.

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Marktechpost

JULY 3, 2023

Predictive Analytics relies more specifically on using data, statistical algorithms, and machine learning techniques to forecast future outcomes based on historical and real-time data. Predictive Analytics utilizes various machine learning algorithms to build predictive models that can provide insights into future scenarios.

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Towards AI

FEBRUARY 10, 2023

AI and Climate change Mining for AI: Natural Resource Extraction To understand what AI is made from, we need to leave Silicon Valley and go to the place where the stuff for the AI industry is made. China’s data center industry gets 73% of its power from coal, emitting roughly 99 million tons of CO2 in 2018 [4].

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Mlearning.ai

MARCH 9, 2023

Automated algorithms for image segmentation have been developed based on various techniques, including clustering, thresholding, and machine learning (Arbeláez et al., Understanding the robustness of image segmentation algorithms to adversarial attacks is critical for ensuring their reliability and security in practical applications.

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Sebastian Ruder

JULY 31, 2020

Natural language processing (NLP) research predominantly focuses on developing methods that work well for English despite the many positive benefits of working on other languages. Most of the world's languages are spoken in Asia, Africa, the Pacific region and the Americas. 2018 ; Ahmad et al.,

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AWS Machine Learning Blog

OCTOBER 5, 2023

Xin Huang is a Senior Applied Scientist for Amazon SageMaker JumpStart and Amazon SageMaker built-in algorithms. He focuses on developing scalable machine learning algorithms. From 2015–2018, he worked as a program director at the US NSF in charge of its big data program. He founded StylingAI Inc.,

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Heartbeat

JANUARY 29, 2024

At its core, Meta-Learning equips algorithms with the aptitude to quickly grasp new tasks and domains based on past experiences, paving the way for unparalleled problem-solving skills and generalization abilities. MAML trains a model's initial parameters to fine-tune rapidly for new tasks with just a few examples.

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Viso.ai

JUNE 12, 2024

Viso Suite is the Computer Vision Enterprise Platform Large Language Models Text generation as a tool is already being applied in journalism (news production), education (production and misuse of materials), law (drafting contracts), medicine (diagnostics), science (search and generation of scientific papers), etc. trillion words.

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AWS Machine Learning Blog

NOVEMBER 9, 2023

This retrieval can happen using different algorithms. Her research interests lie in Natural Language Processing, AI4Code and generative AI. He joined Amazon in 2016 as an Applied Scientist within SCOT organization and then later AWS AI Labs in 2018 working on Amazon Kendra.

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Unite.AI

OCTOBER 27, 2023

According to Stanford University's AI Index Report 2023, while only one law was adopted in 2016, there were 12 of them in 2018, 18 – in 2021, and 37 – in 2022. McLay, “Managing the rise of Artificial Intelligence,” 2018 Bertolini A. Sources: R. and Episcopo F., 2022, “Robots and AI as Legal Subjects? Alekseeva, E.

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AWS Machine Learning Blog

MAY 2, 2023

There are a few limitations of using off-the-shelf pre-trained LLMs: They’re usually trained offline, making the model agnostic to the latest information (for example, a chatbot trained from 2011–2018 has no information about COVID-19). If you have a large dataset, the SageMaker KNN algorithm may provide you with an effective semantic search.

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Lexalytics

APRIL 5, 2021

This would change in 1986 with the publication of “Parallel Distributed Processing” [ 6 ], which included a description of the backpropagation algorithm [ 7 ]. In retrospect, this algorithm seems obvious, and perhaps it was. We were definitely in a Kuhnian pre-paradigmatic period. It would not be the last time that happened.)

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Sebastian Ruder

OCTOBER 26, 2019

To ensure that non-English language speakers are not left behind and at the same time to offset the existing imbalance, we need to apply our models to non-English languages. The NLP Resource Hierarchy In current machine learning, the amount of available training data is the main factor that influences an algorithm's performance.

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Marktechpost

JUNE 27, 2023

It employs a chain of algorithms that learn to interpret the relationship between datasets to achieve its goal. A wide variety of services, including predictive analytics, deep learning, application programming interfaces, data visualization, and natural language processing, are available from various suppliers.

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Topbots

JUNE 22, 2023

SA is a very widespread Natural Language Processing (NLP). Also, since at least 2018, the American agency DARPA has delved into the significance of bringing explainability to AI decisions. Outperforming algorithmic trading reinforcement learning systems: A supervised approach to the cryptocurrency market.

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Marek Rei

NOVEMBER 12, 2019

Below you will find short summaries of a number of different research papers published in the areas of Machine Learning and Natural Language Processing in the past couple of years (2017-2019). Improving Language Understanding by Generative Pre-Training Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever.

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Sebastian Ruder

JANUARY 19, 2021

The selection of areas and methods is heavily influenced by my own interests; the selected topics are biased towards representation and transfer learning and towards natural language processing (NLP).  2020 saw the development of ever larger language and dialogue models such as Meena ( Adiwardana et al.,

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AWS Machine Learning Blog

AUGUST 16, 2023

Our solution is based on the DINO algorithm and uses the SageMaker distributed data parallel library (SMDDP) to split the data over multiple GPU instances. The images document the land cover, or physical surface features, of ten European countries between June 2017 and May 2018. tif" --include "_B03.tif" tif" --include "_B04.tif"

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Snorkel AI

MARCH 1, 2023

This process results in generalized models capable of a wide variety of tasks, such as image classification, natural language processing, and question-answering, with remarkable accuracy. It is based on GPT and uses machine learning algorithms to generate code suggestions as developers write.

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Flipboard

JULY 10, 2023

By using our mathematical notation, the entire training process of the autoencoder can be written as follows: Figure 2 demonstrates the basic architecture of an autoencoder: Figure 2: Architecture of Autoencoder (inspired by Hubens, “Deep Inside: Autoencoders,” Towards Data Science , 2018 ).

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Viso.ai

FEBRUARY 25, 2023

Transformer models have become the de-facto status quo in Natural Language Processing (NLP). For example, the popular ChatGPT AI chatbot is a transformer-based language model. Popular image recognition algorithms include ResNet , VGG , YOLOv3 , and YOLOv7. Viso Suite provides end-to-end software for AI vision.

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AWS Machine Learning Blog

MAY 31, 2023

Large language models In recent years, language models have seen a huge surge in size and popularity. In 2018, BERT-large made its debut with its 340 million parameters and innovative transformer architecture, setting the benchmark for performance on NLP tasks. Q: Can I stop an Amazon SageMaker Autopilot job manually?

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AWS Machine Learning Blog

FEBRUARY 10, 2023

Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation. We design an algorithm that automatically identifies the ambiguity between these two classes as the overlapping region of the clusters. Each season consists of around 17,000 plays. probability.

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Heartbeat

MARCH 15, 2023

But now, a computer can be taught to comprehend and process human language through Natural Language Processing (NLP), which was implemented, to make computers capable of understanding spoken and written language.

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The MLOps Blog

JULY 18, 2023

Then we subsequently try to run audio fingerprinting type algorithms on top of it so that we can actually identify specifically who those people are if we’ve seen them in the past. We need to do that, but we don’t really know what those topics are, so we use some algorithms. We call it our “format stage.” Not all, but some.

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Sebastian Ruder

NOVEMBER 14, 2022

Developing models that work for more languages is important in order to offset the existing language divide and to ensure that speakers of non-English languages are not left behind, among many other reasons. We can also exploit the fact that many under-represented languages belong to groups of similar languages.

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Sebastian Ruder

AUGUST 23, 2021

Does this mean that we have solved natural language processing? For instance, the AI Index Report 2021 uses SuperGLUE and SQuAD as a proxy for overall progress in natural language processing. 2018 ) while models trained on SQuAD are subject to adversarially inserted sentences ( Jia and Liang, 2017 ).

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AWS Machine Learning Blog

OCTOBER 2, 2024

Laying the groundwork: Collecting ground truth data The foundation of any successful agent is high-quality ground truth data—the accurate, real-world observations used as reference for benchmarks and evaluating the performance of a model, algorithm, or system. Andrew Gordon Wilson before joining Amazon in 2018.

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PyImageSearch

JANUARY 2, 2023

Health startups and tech companies aiming to integrate AI technologies account for a large proportion of AI-specific investments, accounting for up to $2 billion in 2018 ( Figure 1 ). Machine learning algorithms can also recognize patterns in DNA sequences and predict a patient’s probability of developing an illness.

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Pickl AI

OCTOBER 1, 2024

By leveraging powerful Machine Learning algorithms, Generative AI models can create novel content such as images, text, audio, and even code. Founded in 2018, Runway has quickly become one of the most popular platforms for generating images, videos, and 3D content using AI.

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Google Research AI blog

FEBRUARY 28, 2023

For example, supporting equitable student persistence in computing research through our Computer Science Research Mentorship Program , where Googlers have mentored over one thousand students since 2018 — 86% of whom identify as part of a historically marginalized group.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

The recent history of PBAs begins in 1999, when NVIDIA released its first product expressly marketed as a GPU, designed to accelerate computer graphics and image processing. In 2018, other forms of PBAs became available, and by 2020, PBAs were being widely used for parallel problems, such as training of NN.

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