Analytics Vidhya

MARCH 30, 2024

Introduction With the advent of Large Language Models (LLMs), they have permeated numerous applications, supplanting smaller transformer models like BERT or Rule Based Models in many Natural Language Processing (NLP) tasks. LLMs are versatile, capable of handling tasks such as Text Classification, Summarization, Sentiment Analysis, and Topic Modelling, owing to their extensive pre-training.

BERT 301

BERT Natural Language Processing Large Language Models NLP 301

Unite.AI

MARCH 30, 2024

The development of artificial olfactory sensors has been a long-standing challenge for researchers worldwide. Creating electronic noses (e-noses) that can effectively discern complex odorant mixtures, similar to the biological olfactory system, has proven difficult due to issues with miniaturization and recognition capabilities. However, a research team led by Prof.

Robotics 316

Robotics Artificial Intelligence Artificial Intelligence Algorithm 316

Analytics Vidhya

MARCH 30, 2024

Large Language Models (LLMs) are the driving force behind AI revolution, but the game just got a major plot twist. Databricks DBRX, a groundbreaking open-source LLM, is here to challenge the status quo. Outperforming established models and going toe-to-toe with industry leaders, DBRX boasts superior performance and efficiency. Deep dive into the world of LLMs […] The post Databricks DBRX: The Open-Source LLM Taking on the Giants appeared first on Analytics Vidhya.

LLM 281

LLM Large Language Models AI AI 281

Eugene Yan

MARCH 30, 2024

Evals for classification, summarization, translation, copyright regurgitation, and toxicity.

LLM 170

LLM 170

Speaker: David Warren and Kevin O'Neill Stoll

Transitioning to a usage-based business model offers powerful growth opportunities but comes with unique challenges. How do you validate strategies, reduce risks, and ensure alignment with customer value? Join us for a deep dive into designing effective pilots that test the waters and drive success in usage-based revenue. Discover how to develop a pilot that captures real customer feedback, aligns internal teams with usage metrics, and rethinks sales incentives to prioritize lasting customer eng

Marktechpost

MARCH 30, 2024

In the evolving field of Retrieval-Augmented Generation (RAG), the quest for refining question-answering (QA) capabilities remain at the forefront of research. Integrating external knowledge bases with large language models (LLMs) has unlocked new avenues for enhancing the accuracy of responses in various tasks. However, a challenge that persists is the model’s ability to efficiently navigate the spectrum of query complexities, ranging from straightforward questions to intricate multi-step

Large Language Models 141

Large Language Models ML AI AI 141

Marktechpost

MARCH 30, 2024

Vision Language Models (VLMs) emerge as a result of a unique integration of Computer Vision (CV) and Natural Language Processing (NLP). This integration seeks to mimic human-like understanding by interpreting and generating content that marries images with words, giving rise to a complex challenge that has piqued the interest of researchers worldwide.

Artificial Intelligence 138

Artificial Intelligence Artificial Intelligence Convolutional Neural Networks Computer Vision 138

Bugra Akyildiz

MARCH 30, 2024

Articles We covered Mamba as an introduction in one of the previous newsletter: This article from Gradient expands a lot more about the advantages and technical details with comparison to Transformers: Limitations of Transformers(now it should be obvious to everyone as talked in the previous newsletter!): Quadratic Bottleneck: Transformers use an attention mechanism that allows every token to look at every other token in the sequence, leading to a quadratic increase in training time complexity f

Explainability 52

Explainability Prompt Engineer Prompt Engineering Machine Learning 52

Marktechpost

MARCH 30, 2024

In the ever-evolving landscape of artificial intelligence, the quest for more advanced and capable language models has been a driving force. Researchers at Shanghai AI Laboratory, SenseTime Group, The Chinese University of Hong Kong, and Fudan University have unveiled InternLM2 , a remarkable open–source achievement in Large Language Models (LLMs). Let’s start by addressing the problem at hand.

Large Language Models 130

Large Language Models LLM Artificial Intelligence Artificial Intelligence 130

Marktechpost

MARCH 30, 2024

Large Language Models (LLMs) are now a crucial component of innovation, with ChatGPT being one of the most popular ones developed by OpenAI. Its ability to generate text responses resembling human-like language has become essential for various applications such as chatbots, content creation, and customer service. However, to get the best results from ChatGPT, one must master the art of prompt engineering.

Prompt Engineer 128

Prompt Engineer Prompt Engineering ChatGPT Explainability 128

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Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.

Marktechpost

MARCH 30, 2024

Natural Language Processing (NLP) models are pivotal for various applications, from translation services to virtual assistants. They enhance the ability to comprehend and generate human-like responses. These models have become increasingly sophisticated and offer nuanced understanding and interaction capabilities as technology advances. A persisting challenge in NLP is the development of models that can understand and generate text in languages other than English, such as Japanese.

Large Language Models 128

Large Language Models NLP Natural Language Processing ML 128

Marktechpost

MARCH 30, 2024

In 2024, the landscape of Python libraries for machine learning and deep learning continues to evolve, integrating more advanced features and offering more efficient and easier ways to build, train, and deploy models. Below are the top ten Python libraries that stand out in AI development. TensorFlow TensorFlow is a powerful open-source library that facilitates numerical computation and accelerates the machine learning process.

Deep Learning 124

Deep Learning Machine Learning Python Computer Vision 124

Marktechpost

MARCH 30, 2024

In the rapidly evolving world of artificial intelligence (AI), entrepreneurs find themselves at the forefront of innovation and efficiency. The advent of generative pre-trained transformers (GPT) has introduced a plethora of tools designed to streamline the entrepreneurial journey. Among these advancements, seven GPT applications stand out, promising to significantly impact how entrepreneurs operate, analyze data, and communicate their ideas.

Automation 122

Automation Artificial Intelligence Artificial Intelligence Continuous Learning 122

Marktechpost

MARCH 30, 2024

Coding courses have cemented their place as a cornerstone of Science Technology Engineering Mathematics (STEM) education. These courses, spanning a broad spectrum from the foundational syntax of programming languages to the intricacies of algorithm development, are instrumental in arming students with the skills necessary for thriving in the digital economy.

Prompt Engineer 119

Prompt Engineer Prompt Engineering Artificial Intelligence Artificial Intelligence 119

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Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?

Business Intelligence

Marktechpost

MARCH 30, 2024

Integrating artificial intelligence (AI) into applications has become necessary for developers looking to stay ahead. However, making AI work seamlessly with web and mobile platforms takes work. Issues such as compatibility across different devices, the need for efficient computation without draining resources, and the complexities involved in implementing AI models make the process daunting.

Machine Learning 111

Machine Learning Artificial Intelligence Artificial Intelligence AI Modeling 111

Marktechpost

MARCH 30, 2024

In our rapidly evolving digital landscape, the quest to develop autonomous virtual agents capable of navigating the vast expanse of software tools has captured the imagination of researchers and tech enthusiasts alike. However, this pursuit has been hindered by formidable obstacles—the scarcity of comprehensive infrastructure for building and evaluating agents in real-world environments and the pressing need to assess their fundamental abilities holistically.

Artificial Intelligence 105

Artificial Intelligence Artificial Intelligence ML AI 105

Marktechpost

MARCH 30, 2024

In an era where robotics and artificial intelligence (AI) seamlessly blend to enhance technological capabilities, a groundbreaking development has emerged, promising to redefine how robots perceive and interact with their surroundings. Meet the Pollen-Vision library that offers a unified interface for Zero-Shot vision models tailored explicitly for robotics.

Artificial Intelligence 114

Artificial Intelligence Artificial Intelligence Robotics ML 114

Marktechpost

MARCH 30, 2024

In the rapidly evolving world of technology, where the demand for sophisticated computer vision (CV) applications is soaring, a new startup named Dragoneye is making waves. Aimed at developers looking to integrate cutting-edge CV capabilities into their applications without the need for extensive machine learning (ML) expertise or training data, Dragoneye promises to transform the landscape of computer vision development.

Computer Vision 105

Computer Vision Machine Learning AI AI 105

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Cloud Development Environments (CDEs) are changing how software teams work by moving development to the cloud. Our Cloud Development Environment Adoption Report gathers insights from 223 developers and business leaders, uncovering key trends in CDE adoption. With 66% of large organizations already using CDEs, these platforms are quickly becoming essential to modern development practices.