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). 2018 ; Howard et al.,  2020 saw the development of ever larger language and dialogue models such as Meena ( Adiwardana et al.,

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NLP People

OCTOBER 9, 2018

In the past years, the tech world has seen a surge of NLP applications in various areas including adtech, publishing, customer service and market intelligence. To put it simply – NLP is wildly adopted with wildly variable success (let’s assume a working definition of success in terms of quality and ROI).

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Towards AI

DECEMBER 28, 2023

Black box algorithms such as xgboost emerged as the preferred solution for a majority of classification and regression problems. Later, Python gained momentum and surpassed all programming languages, including Java, in popularity around 2018–19. In 2023, we witnessed the substantial transformation of AI, marking it as the ‘year of AI.’

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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 specific kind of foundation model known as a large language model (LLM) is trained on vast amounts of text data for NLP tasks. All watsonx.ai

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Mlearning.ai

MAY 5, 2023

These models, which are based on artificial intelligence and machine learning algorithms, are designed to process vast amounts of natural language data and generate new content based on that data. 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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Marek Rei

NOVEMBER 12, 2019

ArXiv 2018. EMNLP 2018. NAACL 2018. NAACL 2018. At the end, I also include the summaries for my own published papers since the last iteration (papers 61-74). Here we go. Improving Language Understanding by Generative Pre-Training Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever. Microsoft, Edinburgh.

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Sebastian Ruder

AUGUST 23, 2021

Over the last years, models in NLP have become much more powerful, driven by advances in transfer learning. This post aims to give an overview of challenges and opportunities in benchmarking in NLP, together with some general recommendations. Does this mean that we have solved natural language processing? Far from it.

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Unite.AI

AUGUST 3, 2023

Our generative AI solution employs proprietary algorithms and machine learning techniques to streamline the creation of video-based standard operating procedures (SOPs), optimize workflows, and facilitate quick, efficient access to information via AI-driven chat features. Are there other types of machine learning algorithms that are used?

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Sebastian Ruder

JANUARY 24, 2022

2021) 2021 saw many exciting advances in machine learning (ML) and natural language processing (NLP). If CNNs are pre-trained the same way as transformer models, they achieve competitive performance on many NLP tasks [28].   Popularized by GPT-3 [32] , prompting has emerged as a viable alternative input format for NLP models.

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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. Conclusion: BERT as Trend-Setter in NLP and Deep Learning References I. BERT Architecture and Training III.1

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Towards AI

FEBRUARY 10, 2023

The term “artificial intelligence” may evoke the ideas of algorithms and data, but it is powered by the rare earth’s minerals and resources that make up the computing components [1]. 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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Bugra Akyildiz

JUNE 11, 2023

They have used AutoARIMA algorithm within Statsforecast to measure the accuracy of the predictions. They used this prediction algorithm’s confidence intervals to detect “anomalies”, an example is below: This is nice as they do not have to build a separate anomaly detection algorithm separately. are built-in.

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Viso.ai

JUNE 12, 2024

Large Language Models – Source In 2018, OpenAI researchers and engineers published an original work on AI-based generative large language models. The authors described how to improve language understanding performances in NLP by using GPT. GPT models are based on transformer-based deep learning neural network architecture.

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John Snow Labs

JUNE 6, 2023

Many different transformer models have already been implemented in Spark NLP, and specifically for text classification, Spark NLP provides various annotators that are designed to work with pretrained language models. The table shows the language models and the corresponding annotators for text classification provided by Spark NLP.

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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. Let's create an advanced prompt where ChatGPT is tasked with summarizing key takeaways from AI and NLP research papers. This demonstrates a classic case of ‘knowledge conflict'.

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Chatbots Life

MAY 25, 2023

Announced in 2018, WhatsApp Business is the business version of the popular messaging app. With well-designed NLP algorithms and training, it gets a hold of user behavior and can offer a richer customer experience. What is WhatsApp Business App?

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Sebastian Ruder

OCTOBER 26, 2019

Cross-lingual learning might be useful—but why should we care about applying NLP to other languages in the first place? The NLP Resource Hierarchy In current machine learning, the amount of available training data is the main factor that influences an algorithm's performance. 2018 ; Artetxe et al.,

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AWS Machine Learning Blog

NOVEMBER 9, 2023

See Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks , REALM , kNN-LM and RETRO. This retrieval can happen using different algorithms. In the past, she had worked on several NLP-based services such as Comprehend Medical, a medical diagnosis system at Amazon Health AI and Machine Translation system at Meta AI.

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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. Data visualization, APIs galore, facial recognition, NLP, PA, DL, and more are all on the menu here. Data pattern discovery is the primary application of MLaaS algorithms. between 2018 and 2028.

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Viso.ai

FEBRUARY 25, 2023

Transformer models have become the de-facto status quo in Natural Language Processing (NLP). Vision Transformer (ViT) in Image Recognition While the Transformer architecture has become the highest standard for tasks involving Natural Language Processing (NLP) , its use cases relating to Computer Vision (CV) remain only a few.

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Topbots

JUNE 22, 2023

SA is a very widespread Natural Language Processing (NLP). Hence, whether general domain ML models can be as capable as domain-specific models is still an open research question in NLP. Also, since at least 2018, the American agency DARPA has delved into the significance of bringing explainability to AI decisions. Felizardo, L.

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AWS Machine Learning Blog

MAY 31, 2023

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: What built-in algorithms are supported in Amazon SageMaker Autopilot? Amazon SageMaker Autopilot supports 2 built-in algorithms: XGBoost and Linear Learner.

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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. Reptile is a meta-learning algorithm that falls under model-agnostic meta-learning approaches.

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Mlearning.ai

JUNE 8, 2023

Use algorithm to determine closeness/similarity of points. Word embeddings GloVe, ELMo & BERT Use Spark NLP to compare Glove, Elmo and BERT on classification task (classify if a twitter is talking about disaster): able to see that GloVe embeddings lacked context. This is embedding/vector/vector embedding for this article.

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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 2016, Hugging Face has quickly become one of the most popular platforms for developing and deploying NLP models, with over 10,000 models available in its model hub.

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Snorkel AI

MARCH 1, 2023

It is based on GPT and uses machine learning algorithms to generate code suggestions as developers write. 2018) This paper introduced BERT, a language model that uses bidirectional context to better understand the meaning of words in a sentence. The interface recommended two such bikes based on their small size.

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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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The Stanford AI Lab Blog

JANUARY 21, 2022

Indeed, this recipe of massive, diverse datasets combined with scalable offline learning algorithms (e.g. self-supervised or cheaply supervised learning) has been the backbone of the many recent successes of foundation models 3 in NLP 4 5 6 7 8 9 and vision 10 11 12. Kalashnikov, D., Quillen, D., Kalakrishnan, M., Vanhoucke, V.,

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Sebastian Ruder

NOVEMBER 14, 2022

At the same time, a wave of NLP startups has started to put this technology to practical use. I will be focusing on topics related to natural language processing (NLP) and African languages as these are the domains I am most familiar with. This post takes a closer look at how the AI community is faring in this endeavour.

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Explosion

MARCH 17, 2019

Most NLP projects are easier if you have a way to train models on exactly your data. Language model pretraining By far the biggest news in NLP research over 2018 was the success of language model pretraining. In 2018, a number of papers showed that a simple language modelling objective worked well for LSTM models.

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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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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. Pfam-NUniProt2 A set of 6.8 million new protein sequence annotations.

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Snorkel AI

JANUARY 18, 2023

A 2018 study from McKinsey estimated the total global unclaimed value potential from AI/ML at $10-15 trillion, and Foundation Models will enable valuable applications that the firm couldn’t conceive of four years ago. Arora said that the approach yielded a lift in accuracy on all of the popular LLMs they tested on.

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deepsense.ai

APRIL 16, 2023

Large language models (LLMs) are yielding remarkable results for many NLP tasks, but training them is challenging due to the demand for a lot of GPU memory and extended training time. 2018 ) bridges the gap between reducing memory usage during training and maintaining good accuracy. Figure 13: FlashAttention algorithm.

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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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AWS Machine Learning Blog

SEPTEMBER 11, 2024

In 2018, other forms of PBAs became available, and by 2020, PBAs were being widely used for parallel problems, such as training of NN. This is accomplished by breaking the problem into independent parts so that each processing element can complete its part of the workload algorithm simultaneously.

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ML Review

JUNE 13, 2018

This article is part of the Academic Alibaba series and is taken from the paper entitled “Learning Chinese Word Embeddings with Stroke n-gram Information” by Shaosheng Cao, Wei Lu, Jun Zhou, and Xiaolong Li, accepted by the 2018 Conference of the Association for the Advancement of Artificial Intelligence. The full paper can be read here.

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