Analytics Vidhya

MARCH 9, 2024

Introduction The forthcoming GPT-5 is layered in complete secret. OpenAI – the research lab behind its making – has kept its functions under wraps. The potential release of GPT-5, the next version of OpenAI’s LLM technology, has generated waves in the AI community. Although OpenAI has not yet confirmed any information about when it will […] The post OpenAI’s GPT-5: A Potential Shift in Artificial Language Models appeared first on Analytics Vidhya.

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Marktechpost

MARCH 9, 2024

The evolution of artificial intelligence, particularly in the realm of neural networks, has significantly advanced our data processing and analysis capabilities. Among these advancements, the efficiency of training and deploying deep neural networks has become a paramount focus. Recent trends have shifted towards developing AI accelerators to manage the training of expansive models of multi-billion parameters.

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Neural Network Python Deep Learning Artificial Intelligence 138

Analytics Vidhya

MARCH 9, 2024

Why celebrate women for just a day, when they deserve a lot more? Analytics Vidhya is celebrating women in AI, all through March, sharing their inspirational journeys and greatest achievements. Today, we bring you the exceptional story of Geeta Kakrani who’s created a name for herself in the AI space. Let’s explore her interesting journey, […] The post Geeta Kakrani’s Inspiring Journey from Receptionist to CEO appeared first on Analytics Vidhya.

AI 280

AI AI Data Science 280

Marktechpost

MARCH 9, 2024

In recent years, machine learning has significantly shifted away from the assumption that training and testing data come from the same distribution. Researchers have identified that models perform better when handling data from multiple distributions. This adaptability is often achieved through what’s known as “rich representations,” which exceed the capabilities of models trained under traditional sparsity-inducing regularization or common stochastic gradient methods.

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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

Bugra Akyildiz

MARCH 9, 2024

Articles Google launches their new model series called Gemma. After disastrous rollout of Gemini, they wanted to also open-source smaller models for community to try out and adopting for their GCP(Google Cloud Platform) more when it comes to try out the models. This is an excellent strategy for providing different options to the community, but also have solutions for enterprise and end user like ChatGPT.

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LLM Large Language Models Explainability Deep Learning 52

Marktechpost

MARCH 9, 2024

In the vast expanse of machine learning applications, recommendation systems have become indispensable for tailoring user experiences in digital platforms, ranging from e-commerce to social media. While effective on smaller scales, traditional recommendation models falter when faced with the complexity and size of contemporary datasets. The challenge has been to upscale these models without compromising efficiency and accuracy, a hurdle that previous methodologies have struggled to overcome due

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Machine Learning AI AI ML 135

Marktechpost

MARCH 9, 2024

Neural text embeddings play a foundational role in many modern natural language processing (NLP) applications. These embeddings are like digital fingerprints for words and sentences that enable tasks like judging similarity or finding related documents. Traditionally, masked language models (MLMs) have dominated in generating these embeddings. However, recent advancements in large autoregressive language models (AR LMs) have led to interest in developing embedding techniques optimized for this m

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NLP Natural Language Processing Automation ML 132

Marktechpost

MARCH 9, 2024

The inference method is crucial for NLP models in subword tokenization. Methods like BPE, WordPiece, and UnigramLM offer distinct mappings, but their performance differences must be better understood. Implementations like Huggingface Tokenizers often need to be clearer or limit inference choices, complicating compatibility with vocabulary learning algorithms.

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Marktechpost

MARCH 9, 2024

Large language models (LLMs) have revolutionized code generation in software development, providing developers with tools to automate complex coding tasks. Yet, as sophisticated as these models have become, crafting flawless, logic-bound code necessitates advanced debugging capabilities beyond the current standards. Traditional debugging approaches often fail to address the need to address the intricate nuances of programming logic and data operations inherent in LLM-generated code.

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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 9, 2024

Inflection AI presents a new advancement in the field of large language models (LLMs), Inflection-2.5, to address the challenges faced in creating highly efficient and competitive LLMs that can power various applications, including personal AI assistants like Pi. The challenge lies in creating such models that can match the performance of leading LLMs while utilizing fewer computational resources, thus making them more accessible and cost-effective.

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Large Language Models AI Modeling LLM AI 123

Marktechpost

MARCH 9, 2024

Recent advancements in text-to-speech (TTS) synthesis have struggled to achieve high-quality results due to the complexity of speech, which involves various attributes like content, prosody, timbre, and acoustic details. While scaling up dataset size and model complexity has shown promise for zero-shot TTS, issues with voice quality, similarity, and prosody persist.

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