ODSC - Open Data Science

APRIL 3, 2023

Do you yearn to compare different QA models but dread the time-consuming process of setting them up? Are you curious about explainability methods like saliency maps but feel lost about where to begin? Question Answering is the task in Natural Language Processing that involves answering questions posed in natural language.

Neural Network 117

Neural Network Natural Language Processing NLP Computational Linguistics 117

IBM Journey to AI blog

OCTOBER 9, 2023

In the business world, AI could offer companies a competitive edge over peers slow to adopt machine learning, natural language processing (NLP) and generative capabilities. in 2021 to help lessen the financial impact of the COVID-19 pandemic on citizens.

Automation 130

Automation AI AI Natural Language Processing 130

Flipboard

FEBRUARY 3, 2023

Seattle is an “ anchor customer ” for the software, according to Chicago-based startup Truleo, a startup founded in 2021 that grew out of a platform first developed to analyze phone calls and text messages between Wall Street bankers. The Seattle Police Department has been using the technology since last year, the company says. “The

Natural Language Processing 98

Natural Language Processing Artificial Intelligence Artificial Intelligence AI 98

AssemblyAI

NOVEMBER 9, 2023

In Natural Language Processing (NLP), Text Summarization models automatically shorten documents, papers, podcasts, videos, and more into their most important soundbites. Say, for example, you wanted to summarize the 2021 State of the Union Address –an hour and 43-minute long video. 67:34: He said the U.S.

NLP 256

NLP AI Modeling AI AI 256

Aiiot Talk

SEPTEMBER 13, 2023

When ChatGPT — an AI chatbot utilizing natural language processing — was released, many people quickly implemented it into their daily lives. In 2021, the International Telecommunication Union (ITU) stated that 2.9 Khanmigo frees up time for the educator to help students one-on-one or to explain a topic more intensely.

Artificial Intelligence 130

Artificial Intelligence Artificial Intelligence AI Chatbots AI 130

IBM Journey to AI blog

AUGUST 1, 2023

The term “foundation model” was coined by the Stanford Institute for Human-Centered Artificial Intelligence in 2021. A foundation model is built on a neural network model architecture to process information much like the human brain does. The platform comprises three powerful products: The watsonx.ai

Generative AI 140

Generative AI Data Scientist BERT Machine Learning 140

AWS Machine Learning Blog

MAY 22, 2024

Using machine learning (ML) and natural language processing (NLP) to automate product description generation has the potential to save manual effort and transform the way ecommerce platforms operate. One of the main advantages of high-quality product descriptions is the improvement in searchability.

Generative AI 103

Generative AI Natural Language Processing Machine Learning Large Language Models 103

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. Outstandingly, ChatPGT presents such a capacity: it can explain its decisions. finance, entertainment, psychology).

Machine Learning 111

Machine Learning ChatGPT Natural Language Processing Categorization 111

Pickl AI

JANUARY 29, 2024

AI comprises Natural Language Processing, computer vision, and robotics. Skills Proficiency in programming languages (Python, R), statistical analysis, and domain expertise are crucial. Market Value and Growth In 2021, the market size of Global Data Science platforms reached USD 64.14 billion by 2029.

Data Science 52

Data Science ML Machine Learning Artificial Intelligence 52

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. This can make it challenging for businesses to explain or justify their decisions to customers or regulators.

BERT 83

BERT Natural Language Processing Large Language Models Neural Network 83

ODSC - Open Data Science

DECEMBER 19, 2023

billion in 2021 to $331.2 They need to be able to explain complex technical concepts to non-technical stakeholders and to identify and solve problems that arise during the development and implementation of AI models. NLP Engineer NLP engineers are responsible for developing and maintaining natural language processing systems.

AI Product Manager 96

AI Product Manager AI Product Management Prompt Engineer Prompt Engineering 96

AWS Machine Learning Blog

OCTOBER 5, 2023

His research interests are in the area of natural language processing, explainable deep learning on tabular data, and robust analysis of non-parametric space-time clustering. an AI start-up, and worked as the CEO and Chief Scientist in 2019–2021. He focuses on developing scalable machine learning algorithms.

Machine Learning 91

Machine Learning Big Data Deep Learning Natural Language Processing 91

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.

Neural Network 139

Neural Network Deep Learning Artificial Intelligence Artificial Intelligence 139

AWS Machine Learning Blog

FEBRUARY 19, 2024

For example, a chatbot could suggest products that match a shopper’s preferences and past purchases, explain details in language adapted to the user’s level of expertise, or provide account support by accessing the customer’s specific records. Amazon’s revenue in 2021 was $469.8 Tell me again what was the revenue in 2019?

Chatbots 93

Chatbots Data Ingestion Machine Learning Generative AI 93

Mlearning.ai

JULY 18, 2023

Large language models (LLM) have transformed the field of natural language processing (NLP) with their remarkable text understanding and generation abilities. Lora: Low-rank adaptation of large language models. Language Models are Unsupervised Multitask Learners. arXiv:2101.00190 [cs], January 2021.

Large Language Models 52

Large Language Models NLP LLM Natural Language Processing 52

Mlearning.ai

JUNE 6, 2023

Unlike the task of assigning class labels to images (image classification), image captioning combines two distinct modalities, namely images and texts used in computer vision and natural language processing (NLP) respectively. Figure 1: Bref overview of the image captioning architecture. Image by the author. Image by the author.

Computer Vision 52

Computer Vision Natural Language Processing NLP Explainability 52

AWS Machine Learning Blog

AUGUST 14, 2023

In this post and accompanying notebook, we demonstrate how to deploy the BloomZ 176B foundation model using the SageMaker Python simplified SDK in Amazon SageMaker JumpStart as an endpoint and use it for various natural language processing (NLP) tasks. You can also access the foundation models thru Amazon SageMaker Studio.

NLP 69

NLP Robotics Prompt Engineer Prompt Engineering 69

AWS Machine Learning Blog

NOVEMBER 14, 2023

Instruction fine-tuning Instruction tuning is a technique that involves fine-tuning a language model on a collection of natural language processing (NLP) tasks using instructions. In this section, we provide examples of two types of fine-tuning. For details, see the example notebook.

Natural Language Processing 89

Natural Language Processing Python Machine Learning Algorithm 89

O'Reilly Media

OCTOBER 18, 2022

The government of the Netherlands resigned in 2021 after an algorithmic system wrongly accused 20,000 families–disproportionately minorities–of tax fraud. For the LLM and NER task, BNH.AI ’s audit plan required demographics associated with entities in raw text, and possibly text in multiple languages. Data can be wrong.

AI 115

AI AI LLM Algorithm 115

Heartbeat

JULY 6, 2023

At a high level, the Swin Transformer is based on the transformer architecture, which was originally developed for natural language processing but has since been adapted for computer vision tasks. We’re committed to supporting and inspiring developers and engineers from all walks of life.

Computer Vision 59

Computer Vision Convolutional Neural Networks Deep Learning Neural Network 59

Heartbeat

JANUARY 18, 2024

Understanding Llama v2 Llama 2, a natural language processing library from Meta, consists of pre-trained and fine-tuned large language models (LLMs) whose scale ranges from 7 billion to 70 billion parameters. which is based on data up to 2021, Llama2 is trained on data up to December 2022. or even GPT 4?

LLM 52

LLM Chatbots Large Language Models Python 52

Chatbots Life

MARCH 17, 2023

Timeline by Antoine Louis on A Brief History of Natural Language Processing Siri, Google Assistant, Cortana, and Alexa, are the successive technologies rolled out in the 20th century. Simply put, GPTs are machine learning models based on the neural network architecture that mimics the human brain.

Conversational AI 52

Conversational AI ChatGPT Neural Network Chatbots 52

DataRobot Blog

FEBRUARY 2, 2023

These developments have allowed researchers to create models that can perform a wide range of natural language processing tasks, such as machine translation, summarization, question answering and even dialogue generation. writefile $BASE_PATH/custom.py """ Copyright 2021 DataRobot, Inc. and its affiliates.

BERT 52

BERT Large Language Models Natural Language Processing Machine Learning 52

Topbots

APRIL 28, 2023

First, give the answer (“Answer”) then explain your reasoning (“Reason”). First give the answer ("Answer") then explain your reasoning ("Reason").Words: I asked GPT-4 to explain each of its answers. I used Spacy (`en_core_web_lg`), a natural language processing library, to calculate word embeddings for the object (e.g.

Large Language Models 52

Large Language Models Natural Language Processing Robotics Explainability 52

NYU Center for Data Science

JULY 21, 2023

2021, is a nexus in another way, too. You’d need to learn — especially on the engineering side — all of these vision pipeline tools, and special-purpose features you’d need to compute on images in order to process them and use them.” Similar overhead was required to do research in natural language processing.

Neural Network 86

Neural Network Large Language Models Data Science Deep Learning 86

Flipboard

JULY 10, 2023

Figure 4: Architecture of fully connected autoencoders (source: Amor, “Comprehensive introduction to Autoencoders,” ML Cheat Sheet , 2021 ). time series or natural language processing tasks). It works well for simple data but may struggle with complex patterns. Or requires a degree in computer science?

Computer Vision 110

Computer Vision Deep Learning Neural Network Machine Learning 110

The Stanford AI Lab Blog

MAY 31, 2022

Language Model Pretraining Language models (LMs), like BERT 1 and the GPT series 2 , achieve remarkable performance on many natural language processing (NLP) tasks. We will explain this in the next paragraph. We will now see how LinkBERT performs on several downstream natural language processing tasks.

BERT 40

BERT Natural Language Processing NLP Neural Network 40

AWS Machine Learning Blog

FEBRUARY 10, 2023

We describe how we designed an accurate, explainable ML model to make coverage classification from player tracking data, followed by our quantitative evaluation and model explanation results. Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation.

Computer Vision 64

Computer Vision Machine Learning ML Convolutional Neural Networks 64

deepsense.ai

MARCH 28, 2023

Below we will explain each of these in a bit more detail. Text Encoder Diffusion models can work on their own, generating images based on the knowledge gained through the training process. Usually, we would like to be able to guide the generation process using text, so the model produces exactly what we want. Roberts et al.

Neural Network 52

Neural Network Large Language Models Deep Learning Explainability 52

Mlearning.ai

MARCH 9, 2023

Explaining and harnessing adversarial examples. Journal of Healthcare Engineering, 2021. Explaining and harnessing adversarial examples. Generative adversarial networks-based adversarial training for natural language processing. 2018; Sitawarin et al., 2018; Papernot et al., Goodfellow, I. Sitawarin, C.,

Computer Vision 52

Computer Vision Deep Learning Machine Learning Algorithm 52

Chatbots Life

MAY 12, 2023

The goal of this article is to demystify ChatGPT showing how it works and why is it that effective, describing what are language modes, why are they so popular and what is their problem. and we will also explain how GPT can create jobs. The hypothesis as to why such training was particularly effective is explained in the next section.

ChatGPT 105

ChatGPT Software Engineer NLP Prompt Engineer 105

Heartbeat

AUGUST 2, 2023

The positional encoding allows the model to understand the sequential nature of language. Overall, transformer architecture has revolutionized the field of natural language processing by enabling efficient and effective modeling of long-range dependencies and capturing complex contextual relationships in text.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering OpenAI 52

The MLOps Blog

JULY 18, 2023

Sabine: Right, so, Jason, to kind of warm you up a bit… In 1 minute, how would you explain conversational AI? Some of them can be handled purely on CPU processing. If you’re trying to get up and running in natural language processing in any way, I strongly recommend you get to know spaCy well.

Conversational AI 52

Conversational AI Natural Language Processing AI AI 52

Topbots

JUNE 29, 2023

We will focus on the six requirements that seem most important for the task: accuracy, scalability, speed, explainability, privacy and adaptability over time. Speed Since Text2SQL requests will typically be processed online in a conversation, the speed aspect is important for user satisfaction. References [1] Ken Van Haren. Talk to me!

Auto-complete 52

Auto-complete Algorithm Data Scientist Auto-classification 52

The MLOps Blog

AUGUST 11, 2023

But some of these queries are still recurrent and haven’t been explained well. More specifically, embeddings enable neural networks to consume training data in formats that allow extracting features from the data, which is particularly important in tasks such as natural language processing (NLP) or image recognition.

ML 52

ML Machine Learning Data Ingestion Deep Learning 52

Topbots

SEPTEMBER 11, 2023

Let’s say that your content generation runs smoothly, but you hear more and more complaints about a general lack of AI transparency and explainability. Latency and waiting times are great for educating your users, e.g. by explaining what the AI is currently doing and indicating possible next steps on their side. 2] Orbit Media (2022).

AI 59

AI AI UX Design Automation 59

The MLOps Blog

APRIL 5, 2023

For example, Modularizing a natural language processing (NLP) model for sentiment analysis can include separating the word embedding layer and the RNN layer into separate modules, which can be packaged and reused in other NLP models to manage code and reduce duplication and computational resources required to run the model.

ML 69

ML Machine Learning Deep Learning Metadata 69