New launch: Build and train custom ChatGPT and similar models with LLM Labs
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Take your labeling process to new heights with Datasaur's LLM-Assisted Labeling!
IndustryPREP leverages LLM Labs to optimize generative AI models, enabling the creation of cost-effective and high-quality practice exam questions for their EdTech platform.
Enjoy maximum flexibility in choosing various models while staying secure and customizable with LLM Labs.
Streamline your QA process by leveraging our brand-new Review Sampling!
Use our intelligent ML-assisted automation feature to predict labels in multiple columns simultaneously!
Revolutionize your AI projects with Datasaur's LLM Labs and Amazon Bedrock integration
Minimize error and improve your data quality with Label Error Detection
Leverage Label Error Detection to help you train your models more efficiently
Leverage Datasaur Predictive Labeling to achieve high performing models with less data.
Access advanced LLM models and optimize costs without re-uploading or recreating question sets.
Delegate your labeling tasks effortlessly with our new Supervisor role!
The best of both worlds: labeling thousands in one hour!
Slide through your documents with ease using Synchronized Scrolling!
Further streamline you bulk salary transfers with Datasaur!
Update your bulk salary transfer systems effortlessly with Datasaur Dinamic's ML-assisted labeling. Learn how to integrate new data entities smoothly.
The Crucial Link Between Data Quality and Model Success
Explore Datasaur's API-free Direct Access LLMs for effortless premium model testing and experimentation.
Efficiently redistribute tasks with our Transfer Assignment feature, ensuring project continuity and uninterrupted workflows.
Working with multiple files has never been easier!
Predictive labeling at its finest!
Navigate the latest LLM landscape with insights from our comprehensive experiment!
Efficiently adjust the list of question sets without the need to re-upload or recreate them from scratch.
Datasaur Dinamic is now here to enhance your span labeling experience even further.
Stanford's pioneering research on English NER performance: a stride forward for more inclusive NER models
Effortlessly evaluate your ML model with Evaluation Metrics, refining data precision for optimal performance.
Harvard and Stanford researchers leverage Datasaur for efficient extraction of vital information from intricate radiology reports, advancing healthcare research.
Generating large-scale, high-quality labeled datasets using data programming
Learn about the utility of Datasaur's Data Programming feature!
User authentication streamlined using SAML 2.0 integration
Automate your labeling workflow with the ML-Assisted Labeling extension.
In this feature highlight, we discuss an example use-case for the Search Extension: a labeling tool that enables you to bulk label.
Ensure top-notch reliability with Datasaur, leveraging Krippendorff and Cohen coefficients.
All-in-one platform enables an integrated LLM building process to improve efficiency and save time for data scientists and engineers
A collaboration built for client satisfactions
Release Datasaur Predictive Labeling which helps company generate labeled data with good quality but low effort
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You can now connect with Amazon Comprehend to predict labels within the interface.
Datasaur is the premiere NLP labeling platform for its user-friendly and powerful approach to automation and manual labeling techniques.
Automate the labeling process based on your own inputs.
Google Vertex AI is now integrated with Datasaur enabling you to deploy your Vertex model to automatically return labels within Datasaur's platform.
Datasaur can now integrate with your Azure Auto ML model.
Now you can evaluate the performance of your labeling functions.
Learn how to predict labels in Datasaur using Amazon Comprehend.
Build a model with the new integration between Comprehend and Datasaur.
Today, we announce the development of a “ChatGPT for Bahasa Indonesia.”. In today's rapidly evolving technological landscape, groundbreaking advancements set the stage for future innovations. One such revolutionary development is the Large Language Model (LLM), exemplified by OpenAI's ChatGPT.
Datasaur's integration with Azure provides a powerful and efficient solution for data labeling and machine learning model training.
You can now use the power of OpenAI to automatically label your NLP data in Datasaur.
We have released a feature that enables you automatically create projects from your preferred workflow settings.
We are happy to announce the release of Data Programming (weak supervised learning) for Token-Based projects.
This step-by-step tutorial provides a preliminary guide for the Datasaur blog post on how to fine-tune a model using HuggingFace.
We are excited to announce the launch of our new bounding box labeling feature—now available in beta!
What happened in NLP and AI news in November? Read all about “safety AI”, predictions for AI in 2023, and more.
What happened in NLP and AI news in October? Read all about Bruce Willis deepfakes, digital right dilemmas, and more.
What happened in NLP and AI news in September? Read all about AI-generated Darth Vader, ethical dilemmas around AI-generated art, and more.
What happened in NLP and AI news in August? Read all about DALL-E 2’s AI generated art, household robots, AI copywriters, and more.
"The use of AI text annotation is growing in popularity as a way for companies to monitor social media and get a better understanding of the tone around their brand. By being able to see not only what is being said, but how it is being said, companies can more easily identify and address any negative sentiment that may be brewing."
NLP is swiftly becoming a critical part of everyone’s product. Is in-house labeling or a data labeling tool like Datasaur the right choice for you?
What happened in NLP and AI news in July? Read all about Tesla’s AI changes, Meta’s language models, Google’s mislabeled emotion datasets, and more.
AI has become an integral part of many industries. As industries advance, new uses for AI are uncovered and brought to the forefront. AI is moving at such a rapid pace, and it’s likely that there will be a vast number of new use cases popping up every year that we can’t even fathom yet!
What happened in NLP and AI news in June? Catch up on Google chatbots, Meta’s massive language tool, Bloomberg’s use of NLP, AI and sports injuries, and more.
These are undoubtedly uncertain times, and there’s a lot of talk around whether we’re on the cusp of an economic downturn in 2022. This is a time when your competitors may not plan well and could maintain high burn which hurts them in the long run. You can often pick up significant market share in an economic downturn by just staying alive and relying on the tools that are available to support your streamlining. You can bet that those that rise to the top of this time period will be using AI, and many companies that do not use AI will flounder and fall behind.
In the space of NLP, labeling is a subjective experience. Every annotator will see each dataset a little bit differently. Managers of labeling projects have to create a system of review and clear instruction to ensure consistent, quality output.
Datasaur is a labeling platform that specializes in NLP. Within the first three years of the company’s growth, Datasaur has created audio, text, and OCR feature functions. Label Studio provides the ability to label audio, photos, text, HTML, and time series.
ML is a “garbage in, garbage out” technology. The effectiveness of the resulting model is directly tied to the input data; data labeling is therefore a critical step in training ML algorithms. Indeed, increasing the quantity and quality of training data can be the most efficient way to improve an algorithm. And with ML’s growing popularity the labeling task is here to stay. As you approach setting up or revisiting your own labeling process, review the following guide.
Labelbox and Datasaur both offer annotation software for organizations seeking to train their AI model. Although the two companies have many similarities, they do have some meaningful differences. Labelbox offers clients the ability to annotate images, text, audio, and video. Whereas Datasaur is focused on NLP data labeling, optimizing their platform for all things audio and text.
There is a trend of big companies getting their hands dirty when it comes to Quantum Computing. These companies are working on quantum computing and have a mindset where they are to able reduce mistakes, increase accuracy and compute the data to use it practically. The conclusion the experts came to was in order for them to use a Quantum Computer, it must have a symbiotic relationship with a classical computer to create different outcomes.
AI is a machine that uses subsets of technology like machine learning, deep learning, natural language processing to produce the optimum solution to the task provided. NLP helps bridge the gap between human interaction and machine understanding which improves the overall quality of AI. AI can hold simple and limited conversations like when one asks their google assistant a question, it uses the internet to answer. Though the answer is theoretically correct, it lacks a human touch. As NLP and AI progress, users will accumulate more trained data, eventually leading to a stage where the AI can handle a conversation without human interference.
AI is transforming how lawyers and legal departments work. Machine learning has already been heavily used for legal research and assists to predict litigation results. Now, AI is becoming incorporated in many fields to increase productivity. AI takes care of tasks like drafting lower-exposure or lower-liability agreements like NDAs (Non-Disclosure Agreement). All of this frees up lawyers' time to focus on more nuanced tasks and cases.
The experts believe the AI-based triaging system could be used to immensely decrease the workload of the radiologists’. Verburg stated that this kind of approach will assist the radiologists to improve their overall reading time He also mentioned the fact that more time could become available for radiologists to focus on more complex breast MRI examinations. The experts involved, plan to train this deep learning model in other datasets and deploy it in the subsequent screening rounds of the DENSE trials.
Artificial intelligence is a machine that is capable of performing tasks as a human would. Once AI hit the market it was integrated into almost all sectors of different industries. Of course, this also includes the medical field. Many tasks need to be performed daily. One of these tasks involves hospital discharge papers.
AI has been transforming almost all aspects of different industries and sometimes is even more effective than humans while performing tasks. AI is simply the future and therefore it makes sense for universities to adopt the same as it will equip students to use the technology of their research. AI also helps in building a personalized and effective learning process for each student depending on their capability to cope with pressure and download content. This will assist the student to be fully prepared of their academic career.
NLP is constantly evolving and impacting our world in a massive way. It started limited and rudimentary as rule-based methods. With an increase in data, it progressed to statistical learning which was used in simple question-answering, predictive text and more. NLP has been evolving and growing ever since, and now we are at a stage where we even fuse models to collaborate with each other. NLP is only getting better and improving its accuracy at a frightening speed. This is the future of the digital world.
Artificial intelligence assists social media platforms in controlling the pool of information and making sense of it to understand the brand new trends, user behavior, and their interests, find out and block abusive content, and for various other purposes. AI additionally plays a chief function in social media marketing by letting the brands measure the performance of the enterprise and pick out users that can be converted into potential customers.
WHO has developed principles to support efforts to ensure that the full potential of AI for healthcare and public health is achieved to benefit all. AI systems should be carefully designed according to the socio-economic and health-care settings of that region. AI training models must implement community engagement and awareness-raising for millions of healthcare workers who will require digital literacy.
AI-enabled robots are superior to non-AI machine, as these robots are capable of performing certain tasks while adapting to their environment. Therefore, AI-empowered robots are more intelligent than other machines. One can use training to make the computer understand physical and logistical data patterns. It can act according to its environment, robots are also integrated with various functions like computer vision, motion control, and grasping objects.
There are several practical applications of AI, such as disease identification and diagnosis, helping identify patients for clinical trials, drug manufacturing, and predictive forecasting to name a few. AI is so flexible that it can be inserted in every aspect of the pharmaceutical industry, starting from drug discovery and development to manufacturing and marketing. By inserting AI in the core workflows, pharma companies can make their operations efficient, cost-effective, and hassle-free.
If one compares the two frameworks, it entirely depends on your need, as switching from one to the other is also a seamless process. PyTorch seems to be a favorite among programmers and scientific researchers. The scientific community tends to prefer PyTorch while working on citations. Meanwhile, organizations and startups prefer TensorFlow. The reputed production features give TensorFlow a good case. Visualization with Tensorboard creates an attractive presentation for clients.
As Artificial Intelligence becomes more popular, various beneficial implementations in different sectors come into mind. Artificial Intelligence is a computer that has capable of performing tasks mimicking a human efficiently. Inspired by all the positives AI brings up, New York City released a 116-page strategic vision for how it plans to benefit from artificial intelligence as a community but only does so ethically and responsibly.
There are many examples of NLP being applied practically, especially in systems like resume-parsing for hiring to Interactive Voice Response (IVR) systems. NLP is usually used for chatbots, virtual assistants, and modern spam detection. But NLP isn’t perfect, although there are over 7000 languages spoken around the globe, most NLP processes only use seven languages: English, Chinese, Urdu, Farsi, Arabic, French, and Spanish.
NLP is becoming increasingly popular in healthcare due to its potential to search, analyze, and interpret stupendous amounts of patient datasets. Using advanced medical algorithms, machine learning and NLP can accurately grant a voice to unstructured data, improving methods and providing better results for patients.
With so many sources of information NLP helps AI battle misinformation through tools like stance detection, Abstractive summarization, fact-checking, sentiment analysis, and many more. Each tool allows the AI to learn something about the source text, whether it understands the point of view, the emotion, or the stance behind it. This information ultimately helps the AI fact check and determine whether the information given is true or false.
Automatic Speech Recognition (ASR) with NLP is a topic trending to various kinds of research and innovations. Many types of models and methods are available using existing technologies to recognize speech. Siri, Alex, and Google demonstrate what ASR and NLP have achieved thus far.
Natural Language Processing (NLP) is at the top of these adopted technologies. NLP has been headlined as one of the integral cogs of the online retail sphere. Putting into practice the uses of natural language processing and other AI-affinities technologies determines the difference between accomplishment and failure: AI in retail is a trend to watch out for!
Online platforms like social media, gaming chats, etc., connect the entire globe into one domain. In an environment that connects many users in one place, combatting abusive content should be the one number priority. This is where Natural Language Processing (NLP) comes to the rescue. NLP assists the AI in understanding ‘natural language’ of gamers in order to identify damaging/hurtful communication
NLP has a lot of tools that help AI to understand natural language, and these tools are now being used to help keep YouTube comments safe. Using sentiment analysis of the comments section on YouTube, organizations can understand how the community accepts and understands their channel and videos. NLP combined with machine learning can then work together to create a safe comment space.
Insurance is a constantly evolving industry that has felt more pressure due to the pandemic. Integrating insurance systems with AI tech such as cloud computing, identity resolution, predictive analytics, and natural language processing equip insurers with powerful tools as they grapple with dramatically altered consumer needs, behaviors, and spending patterns. The insurance industry is walking towards a technology-filled future, and it's exciting to see.
For the handwritten documents, we should scan the records to save them electronically. But the question is how does one use the “unstructured” and “non-standardized” in digital records? NLP (natural language processing) techniques can seize unstructured information, examine the grammatical shape, decide the meaning of the data, and summarize the information. As a result, NLP techniques can decrease expenses and extract ample data analytics information in-depth.
As chatbots are becoming more prevalent with companies, they are investing in technologies that will improve the chatbot they are working with. The necessary fundamentals that create a better functioning chatbot are essentially AI. NLP is absolutely essential in creating accurate and successful chatbots. There are a few standard NLP techniques that empower chatbots. This blog discusses the different NLP methods that help create a robust and comprehensive chatbot including, but not limited to OCR, NER, and more!
Several service sectors have implemented NLP, which has showcased its efficiency. The customer servicing sector always tends to have a massive load of work. By using NLP, companies have lessened the burden of customer care agents. Now agents can focus on more complex issues and leave the simple tedious work to AI.NLP uses many tools such as speech recognition, grammatical tagging, word sense disambiguation, named entity recognition, sentiment analysis, and many more to accurately interpret the intent behind a statement and improve the quality of the task at hand.
Recent studies have shown that NLP can beneficial for predicting Covid-19 outbreaks. Organizations are deploying NLP models to develop clinical voice assistants to transcribe patient visit information into their electronic health records (EHR) (An electronic health record contains a patient’s entire medical history). They deploy this technology to shorten the time a doctor spends on documentation: This creates more time for a clinician to work with patients directly during the pandemic.
Since the adoption of the NATO AI Strategy, Allies are working towards cooperation and collaboration to meet these requirements in terms of both defense and security, NATO as the primary transatlantic forum. The goal of NATO’s AI Strategy is to speed up the process for AI adoption by amplifying key AI enablers and adapting policy, this includes adopting Principles of Responsible Use for AI. All these precautions are taken in order to avoid the malicious use of AI by state and non-state actors.
Natural language processing (NLP) is adding "order" to the legal field. NLP has assisted the legal field in three distinct areas: contract review, electronic discovery, and general research. There is a massive demand for applying NLP in the legal system to help absorb a lot of information and streamline it. This allows humans to focus on more intricate tasks that AI cannot handle.
Data labeling is the process of tabbing pictures, videos, audio, and text assets with the corresponding labels. Data labeling requires human input to create and assign the labels, though much labeling can now be done with the assistance of technology and computers. Automated data labeling for machine learning (ML) teaches artificial intelligence (AI) to learn from the labeled data and eventually implement the knowledge it has gathered in real-time scenarios.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field. Read this article to get an understanding of all the ways in which NLP can save massive amounts of time and energy for legal firms and organizations.
We [Consensus] had a very complex and specific set of annotation needs. Datasaur was able to address those needs efficiently and effectively.
Information labeling tasks has been reduced by 80% which has allowed us to optimize our workflow much more, allowing us to focus on other areas that are also priorities for us.
"We looked at Prodigy, LightTag, LabelBox, Scale and more. You really can't beat Datasaur for their suite of features and price point."
