Towards AI

JUNE 12, 2024

There is very little contention that large language models have evolved very rapidly since 2018. It all started with Word2Vec and N-Grams in 2013 as the most recent in language modelling. RNNs and LSTMs came later in 2014. These were followed by the breakthrough of the Attention Mechanism. The story starts with word embedding.

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Unite.AI

OCTOBER 30, 2024

I started working in AI in 2014, when we were building a next-generation mobile search company called Rel C, which was similar to what Perplexity AI is today. There were rapid advancements in natural language processing with companies like Amazon, Google, OpenAI, and Microsoft building large models and the underlying infrastructure.

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Towards AI

MARCH 20, 2023

How does natural language processing (NLP) relate to generative AI? In this blog, we will explore the top most common questions related to generative AI, covering topics such as its history, neural networks, natural language processing, training, applications, ethical concerns, and the future of the technology.

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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. Evolution of NLP Models To understand the full impact of the above evolutionary process.

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Unite.AI

JUNE 11, 2024

It was in 2014 when ICML organized the first AutoML workshop that AutoML gained the attention of ML developers. Third, the NLP Preset is capable of combining tabular data with NLP or Natural Language Processing tools including pre-trained deep learning models and specific feature extractors.

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Viso.ai

OCTOBER 9, 2024

Before 2014 – Traditional Computer Vision Several methods have been applied to deal with this challenging yet important problem. Recent advances in supervised and unsupervised machine learning techniques brought breakthroughs in the research field, and more and more accurate systems are emerging every year.

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How to Learn Machine Learning

JULY 20, 2024

However, significant strides were made in 2014 when Lan Goodfellow and his team introduced Generative adversarial networks (GANs). Supported by Natural Language Processing (NLP), Large language modules (LLMs), and Machine Learning (ML), Generative AI can evaluate and create extensive images and texts to assist users.

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Pickl AI

SEPTEMBER 30, 2024

Introduction Generative Adversarial Networks (GANs) have emerged as one of the most exciting advancements in the field of Artificial Intelligence and Machine Learning since their introduction in 2014 by Ian Goodfellow and his collaborators. Techniques like progressive growing of GANs could become more common.

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Becoming Human

MARCH 17, 2023

Amazon Alexa was launched in 2014 and functions as a household assistant. Nuance , an innovation specialist focusing on conversational AI, feeds its advanced Natural Language Processing (NLU) algorithm with transcripts of chat logs to help its virtual assistant, Pathfinder, accomplish intelligent conversations.

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Mlearning.ai

APRIL 1, 2023

NLP A Comprehensive Guide to Word2Vec, Doc2Vec, and Top2Vec for Natural Language Processing In recent years, the field of natural language processing (NLP) has seen tremendous growth, and one of the most significant developments has been the advent of word embedding techniques.

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Chatbots Life

MAY 16, 2023

Deeper Insights Year Founded : 2014 HQ : London, UK Team Size : 11–50 employees Clients : Smith and Nephew, Deloitte, Breast Cancer Now, IAC, Jones Lang-Lasalle, Revival Health. Data Monsters can help companies deploy, train and test machine learning pipelines for natural language processing and computer vision.

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AWS Machine Learning Blog

OCTOBER 6, 2023

Large language models (LLMs) are revolutionizing fields like search engines, natural language processing (NLP), healthcare, robotics, and code generation. Next, we recommend “Interstellar” (2014), a thought-provoking and visually stunning film that delves into the mysteries of time and space.

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ODSC - Open Data Science

APRIL 28, 2023

If a Natural Language Processing (NLP) system does not have that context, we’d expect it not to get the joke. Since 2014, he has been working in data science for government, academia, and the private sector. His major focus has been on Natural Language Processing (NLP) technology and applications.

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ODSC - Open Data Science

MAY 25, 2023

In 2014 she was named the world’s first Chief AI Ethics officer. Kay Firth-Butterfield Head of AI & Machine Learning | Member, Executive Committee | World Economic Forum Kay Firth-Butterfield, one of the world’s foremost experts on the governance of AI, has dedicated much of her career to furthering the goals of AI Governance.

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Heartbeat

AUGUST 21, 2023

GoogLeNet: is a highly optimized CNN architecture developed by researchers at Google in 2014. Applications of Convolutional Neural Networks Convolutional neural networks (CNNs) have been employed in various domains, including computer vision, natural language processing, voice recognition, and audio analysis.

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Topbots

JUNE 22, 2023

SA is a very widespread Natural Language Processing (NLP). Expert Systems with Applications (2014), 41(16):7653–7670. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 595–605. I am a researcher, and its ability to do sentiment analysis (SA) interests me.

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AWS Machine Learning Blog

JANUARY 25, 2023

Apart from supporting explanations for tabular data, Clarify also supports explainability for both computer vision (CV) and natural language processing (NLP) using the same SHAP algorithm. It is constructed by selecting 14 non-overlapping classes from DBpedia 2014.

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Mlearning.ai

FEBRUARY 15, 2023

Introduction In natural language processing, text categorization tasks are common (NLP). Uysal and Gunal, 2014). Information Processing & Management, 50(1):104–112. Foundations of Statistical Natural Language Processing [M]. The architecture of BERT is represented in Figure 14. Dönicke, T.,

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Lexalytics

APRIL 5, 2021

This is the sort of representation that is useful for natural language processing. Girshick, Sergio Guadarrama and Trevor Darrell (2014) “ Caffe: Convolutional Architecture for Fast Feature Embedding ” Nitish Srivastava, Geoffrey E.

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ML Review

JUNE 4, 2018

Recent Intersections Between Computer Vision and Natural Language Processing (Part Two) This is the second instalment of our latest publication series looking at some of the intersections between Computer Vision (CV) and Natural Language Processing (NLP). 2014)[ 73 ] and Donahue et al.

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Mlearning.ai

MARCH 9, 2023

Generative adversarial networks-based adversarial training for natural language processing. However, these algorithms are vulnerable to adversarial attacks, where imperceptible perturbations to the input image can lead to significant misclassifications (Goodfellow et al., 2013; Goodfellow et al., Goodfellow, I. Goodfellow, I.

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John Snow Labs

MAY 18, 2023

Word embeddings are considered as a type of representation used in natural language processing (NLP) to capture the meaning of words in a numerical form. Word embeddings are used in natural language processing (NLP) as a technique to represent words in a numerical format. setInputCol('text').setOutputCol('document')

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Becoming Human

MARCH 16, 2023

Overhyped or not, investments in AI drug discovery jumped from $450 million in 2014 to a whopping $58 billion in 2021. All pharma giants, including Bayer, AstraZeneca, Takeda, Sanofi, Merck, and Pfizer, have stepped up spending in the hope to create new-age AI solutions that will bring cost efficiency, speed, and precision to the process.

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Unite.AI

JANUARY 9, 2024

Apple prioritizes computer vision , natural language processing , voice recognition, and healthcare to enhance its products. Likewise, Microsoft strengthens its cloud and enterprise software through acquisitions in natural language processing , computer vision , and cybersecurity.

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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. 2014 ) and can be helpful for error analysis. Far from it.

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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. Transfer learning in natural language processing. An image in Flickr30k (Young et al., Kolesnikov, A.,

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ODSC - Open Data Science

JANUARY 29, 2024

GANs, introduced in 2014 paved the way for GenAI with models like Pix2pix and DiscoGAN. Generative AI Generative AI is another crucial skill for the role of prompt engineering, as it encompasses the core ability to leverage AI to create new content, whether it be text, images, or other forms of media.

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Viso.ai

JANUARY 2, 2024

VGGNet , introduced by Simonyan and Zisserman in 2014, emphasized the importance of depth in CNN architectures through its 16-19 layer CNN network. GoogleNet (or Inception) brought the novel concept of inception modules, enabling efficient computation and deeper networks without a significant increase in parameters.

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ML Review

MARCH 5, 2019

Modern natural language processing has yielded tools to conduct these types of exploratory search, we just need to apply them to the data from valuable sources, such as ArXiv. Crafting a dataset The number of papers added to ArXiv per month since 2014. How to find similar phrases without knowing what you’re searching for?

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Pickl AI

OCTOBER 1, 2024

Founded in 2010, DeepMind was acquired by Google in 2014 and has since become one of the most respected AI research companies in the world. The success of ChatGPT has cemented OpenAI’s position as a leader in the Generative AI space and has sparked a renewed interest in the potential of this technology.

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John Snow Labs

AUGUST 7, 2024

A significant milestone was reached in 2014 with the introduction of Generative Adversarial Networks (GANs). Healthcare NLP (Natural Language Processing) technologies extract insights from physician records, patient histories and diagnostic reports facilitating precise diagnosis. This improves access to care.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

Her expertise is in building machine learning solutions involving computer vision and natural language processing for various industry verticals. We compare the performance with respect to the object sizes (in proportion to image size)— small (area 1%).

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Pragnakalp

JANUARY 1, 2024

The most recent solar maximum, which is associated with increased solar activity, occurred around 2014. The Sun experiences an 11-year solar cycle, during which solar activity generally increases and then decreases. Solar activity is currently decreasing towards solar minimum.

Python 52

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Sebastian Ruder

FEBRUARY 24, 2021

Fine-tuning a pre-trained language model (LM) has become the de facto standard for doing transfer learning in natural language processing. 2018 ) while pre-trained language models are favoured over models trained on translation ( McCann et al., 2018 ), natural language inference ( Conneau et al.,

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Sebastian Ruder

AUGUST 18, 2019

Later approaches then scaled these representations to sentences and documents ( Le and Mikolov, 2014 ; Conneau et al., LM pretraining   Many successful pretraining approaches are based on variants of language modelling (LM). Early approaches such as word2vec ( Mikolov et al., 2017 ; Peters et al., 2019 ; Logan IV et al.,

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Explosion

MAY 17, 2023

A lot of people are building truly new things with Large Language Models (LLMs), like wild interactive fiction experiences that weren’t possible before. But if you’re working on the same sort of Natural Language Processing (NLP) problems that businesses have been trying to solve for a long time, what’s the best way to use them?

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Sebastian Ruder

MAY 22, 2020

This advice should be most relevant to people studying machine learning (ML) and natural language processing (NLP) as that is what I did in my PhD. 2014 ), neuroscience ( Wang et al., Having said that, this advice is not just limited to PhD students. The papers that draw such connections can often be insightful.

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