Google Research AI blog

FEBRUARY 28, 2023

Adherence to such public health programs is a prevalent challenge, so researchers from Google Research and the Indian Institute of Technology, Madras worked with ARMMAN to design an ML system that alerts healthcare providers about participants at risk of dropping out of the health information program. certainty when used correctly.

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PyImageSearch

DECEMBER 11, 2023

Kicking Off with a Keynote The second day of the Google Machine Learning Community Summit began with an inspiring keynote session by Soonson Kwon, the ML Community Lead at Google. The focus of his presentation was clear and forward-thinking: Accelerate AI/ML research and application.

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AWS Machine Learning Blog

JUNE 23, 2023

For data scientists, moving machine learning (ML) models from proof of concept to production often presents a significant challenge. It can be cumbersome to manage the process, but with the right tool, you can significantly reduce the required effort. FastAPI is a modern, high-performance web framework for building APIs with Python.

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Console DevTools podcast

JULY 13, 2022

In this episode we speak to Ines Montani, co-founder and CEO of Explosion , a developer of Artificial Intelligence and Natural Language Processing technologies. Sign up for free at: [link] Recorded: 2022-04-06 Data science - a devtools discussion with Ines Montani (Explosion).

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The MLOps Blog

AUGUST 11, 2023

There comes a time when every ML practitioner realizes that training a model in Jupyter Notebook is just one small part of the entire project. At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations. What are ML pipeline architecture design patterns?

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Towards AI

AUGUST 25, 2023

As a reminder, I highly recommend that you refer to more than one resource (other than documentation) when learning ML, preferably a textbook geared toward your learning level (beginner/intermediate / advanced). In ML, there are a variety of algorithms that can help solve problems. Speech and Language Processing.

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Flipboard

JUNE 20, 2023

Machine learning (ML) engineers have traditionally focused on striking a balance between model training and deployment cost vs. performance. This is important because training ML models and then using the trained models to make predictions (inference) can be highly energy-intensive tasks.

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ODSC - Open Data Science

FEBRUARY 17, 2023

Natural language processing (NLP) has been growing in awareness over the last few years, and with the popularity of ChatGPT and GPT-3 in 2022, NLP is now on the top of peoples’ minds when it comes to AI. NLTK is appreciated for its broader nature, as it’s able to pull the right algorithm for any job.

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AWS Machine Learning Blog

APRIL 19, 2023

Since 2018, our team has been developing a variety of ML models to enable betting products for NFL and NCAA football. These models are then pushed to an Amazon Simple Storage Service (Amazon S3) bucket using DVC, a version control tool for ML models. Business requirements We are the US squad of the Sportradar AI department.

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IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. However, the growing influence of ML isn’t without complications.

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Applied Data Science

AUGUST 2, 2021

Big Ideas What to look out for in 2022 1. They bring deep expertise in machine learning , clustering , natural language processing , time series modelling , optimisation , hypothesis testing and deep learning to the team. Give this technique a try to take your team’s ML modelling to the next level.

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Data Science Dojo

FEBRUARY 2, 2023

It is a widely used programming language in computer science. Its features have made it a popular choice among developers in 2022 and its trend is expected to continue in the future. The demand for using Python in IT projects is on the rise, due to its user-friendly nature and versatility in creating various technology applications.

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IBM Journey to AI blog

OCTOBER 9, 2023

How HR departments are using AI AI use in HR refers to the deployment of machine learning (ML), natural language processing (NLP) and other AI technologies to automate human resources tasks and support decision-making. This poses a challenge to employers vested in worker satisfaction and performance.

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IBM Journey to AI blog

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Today, 35% of companies report using AI in their business, which includes ML, and an additional 42% reported they are exploring AI, according to the IBM Global AI Adoption Index 2022. What is MLOps?

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Heartbeat

JANUARY 9, 2024

Large language models have emerged as ground-breaking technologies with revolutionary potential in the fast-developing fields of artificial intelligence (AI) and natural language processing (NLP). LLMs received a lot of media attention when ChatGPT was released in December 2022. BERT and GPT are examples.

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AWS Machine Learning Blog

FEBRUARY 6, 2024

Overall, $384 billion is projected as the cost of pharmacovigilance activities to the overall healthcare industry by 2022. In this post, we show how to develop an ML-driven solution using Amazon SageMaker for detecting adverse events using the publicly available Adverse Drug Reaction Dataset on Hugging Face.

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Snorkel AI

APRIL 19, 2023

Uniphore , a conversational AI and automation leader, has chosen Snorkel’s data-centric AI platform to scale data labeling and acclerate ML model development. Their platform enables businesses to automate customer interactions and improve the customer experience using natural language processing and machine learning.

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Snorkel AI

APRIL 19, 2023

Uniphore , a conversational AI and automation leader, has chosen Snorkel’s data-centric AI platform to scale data labeling and acclerate ML model development. Their platform enables businesses to automate customer interactions and improve the customer experience using natural language processing and machine learning.

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ML @ CMU

FEBRUARY 24, 2023

In our EMNLP 2022 paper , we instead propose RLPrompt , an efficient discrete prompt optimization approach with reinforcement learning (RL). 2022) ) that LMs making use of prompts do not necessarily follow human language patterns. paraphrasing)-then-selection” heuristics that do not explore the prompt space systematically.

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AWS Machine Learning Blog

SEPTEMBER 19, 2023

Quantitative modeling and forecasting – Generative models can synthesize large volumes of financial data to train machine learning (ML) models for applications like stock price forecasting, portfolio optimization, risk modeling, and more. The agent answers the question “ What is Amazon’s net loss for fiscal year ending in December 2022?

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O'Reilly Media

JANUARY 11, 2022

Here are my predictions for 2022. This trend started with the gigantic language model GPT-3. Just as we saw new professions and job classifications when the web appeared in the ’90s, we’ll see new professions and services appear as a result of AI—specifically, as a result of natural language processing.

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AWS Machine Learning Blog

JANUARY 13, 2023

Machine learning (ML) presents an opportunity to address some of these concerns and is being adopted to advance data analytics and derive meaningful insights from diverse HCLS data for use cases like care delivery, clinical decision support, precision medicine, triage and diagnosis, and chronic care management.

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Chatbots Life

AUGUST 15, 2023

Last Updated on December 27, 2022 We have not, thankfully, seen any of the nightmare scenarios envisaged by science fiction. More complicated ones ( AI chatbots ) can carry out more complex tasks thanks to their use of ML (machine learning) and NLP (natural language processing).

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Snorkel AI

MAY 4, 2023

In 2022, the conference grew to 50 talks, 70 speakers, and more than five thousand registered attendees. Read the 2022 conference highlights or watch the talks on Youtube. AI development stack: AutoML, ML frameworks, no-code/low-code development.

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Snorkel AI

MAY 4, 2023

In 2022, the conference grew to 50 talks, 70 speakers, and more than five thousand registered attendees. Read the 2022 conference highlights or watch the talks on Youtube. AI development stack: AutoML, ML frameworks, no-code/low-code development.

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IBM Journey to AI blog

MARCH 27, 2024

A 2022 CDP study found that for companies that report to CDP, emissions occurring in their supply chain represent an average of 11.4x In recent years, remarkable strides have been achieved in crafting extensive foundation language models for natural language processing (NLP).

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AWS Machine Learning Blog

DECEMBER 14, 2023

In November 2022, we announced that AWS customers can generate images from text with Stable Diffusion models in Amazon SageMaker JumpStart , a machine learning (ML) hub offering models, algorithms, and solutions. This technique is particularly useful for knowledge-intensive natural language processing (NLP) tasks.

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AWS Machine Learning Blog

JUNE 7, 2023

Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from any document or image. billion in Q3 2021 and Q3 2022, and $6 million and $(11.3) billion for the nine months ended September 30, 2021 and 2022. (2) billion as of December 31, 2021 and September 30, 2022, respectively.

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AWS Machine Learning Blog

JULY 1, 2024

For this post, we use Amazon shareholder docs (Amazon Shareholder letters – 2023 & 2022) as a data source to hydrate the knowledge base. She helps key customer accounts on their data, generative AI and AI/ML journeys. She is passionate about data-driven AI and the area of depth in ML and generative AI.

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AWS Machine Learning Blog

MARCH 1, 2023

Amazon Comprehend is a managed AI service that uses natural language processing (NLP) with ready-made intelligence to extract insights about the content of documents. It develops insights by recognizing the entities, key phrases, language, sentiments, and other common elements in a document.

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Heartbeat

FEBRUARY 2, 2024

Visual Question Answering (VQA) stands at the intersection of computer vision and natural language processing, posing a unique and complex challenge for artificial intelligence. is a significant benchmark dataset in computer vision and natural language processing. Published online on April 17, 2022.

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AWS Machine Learning Blog

MARCH 14, 2024

Amazon Kendra uses natural language processing (NLP) to understand user queries and find the most relevant documents. Did anyone make an ace at the 2022 Shriners Children’s Open? Grace Lang is an Associate Data & ML engineer with AWS Professional Services.

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Mlearning.ai

SEPTEMBER 2, 2023

That was me giving the talk about AI and what OpenAI has developed Giving a talk on AI technology On August 26th, 2022, I gave a talk on stage for my school event, Newton Talk #2. The topic was “Keep Innovating”. I said, “Sure! I would appreciate that a lot!”

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How to Learn Machine Learning

JULY 20, 2024

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. dollars, nearly double the size of 2022. At the end of 2023, it stood at just unde r 45 billion U.S.

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IBM Journey to AI blog

SEPTEMBER 6, 2023

AI marketing is the process of using AI capabilities like data collection, data-driven analysis, natural language processing (NLP) and machine learning (ML) to deliver customer insights and automate critical marketing decisions. What is AI marketing?

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Mlearning.ai

FEBRUARY 9, 2023

If you’re in the field of Natural Language Processing, you’ve probably heard about Hugging Face. Hugging Face is a library that provides pre-trained language models for NLP tasks such as text classification, sentiment analysis, and more. Datasets The Hub contains 5,000 datasets in over 100 languages.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

As a first step, they wanted to transcribe voice calls and analyze those interactions to determine primary call drivers, including issues, topics, sentiment, average handle time (AHT) breakdowns, and develop additional natural language processing (NLP)-based analytics.

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