Unite.AI

SEPTEMBER 29, 2024

In the era of digital content, text-to-speech (TTS) technology has become an indispensable tool for businesses and individuals alike. As the demand for audio content surges across various platforms, from podcasts to e-learning materials, the need for high-quality, natural-sounding speech synthesis has never been greater. This article delves into the top text-to-speech APIs that are changing the way we consume and interact with digital content, offering a comprehensive look at the cutting-edge s

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Neural Network Deep Learning OpenAI Conversational AI 273

AI News

SEPTEMBER 29, 2024

As data management grows more complex and modern applications extend the capabilities of traditional approaches, AI is revolutionising application scaling. Han Heloir, EMEA gen AI senior solutions architect, MongoDB. In addition to freeing operators from outdated, inefficient methods that require careful supervision and extra resources, AI enables real-time, adaptive optimisation of application scaling.

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Big Data ETL Generative AI Data Ingestion 201

Marktechpost

SEPTEMBER 29, 2024

Large Language Models (LLMs) are vulnerable to jailbreak attacks, which can generate offensive, immoral, or otherwise improper information. By taking advantage of LLM flaws, these attacks go beyond the safety precautions meant to prevent offensive or hazardous outputs from being generated. Jailbreak attack evaluation is a very difficult procedure, and existing benchmarks and evaluation methods cannot fully address these difficulties.

Large Language Models 132

Large Language Models LLM OpenAI ML 132

Towards AI

SEPTEMBER 29, 2024

Author(s): Abhishek Chaudhary Originally published on Towards AI. This article shows you how to build a simple RAG chatbot in Python using Pinecone for the vector database and embedding model, OpenAI for the LLM, and LangChain for the RAG workflow. Hallucinations Large Language Model (LLM)-based chatbots, especially those utilizing Generative AI (GenAI), are incredibly powerful tools for answering a broad range of questions.

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Chatbots OpenAI LLM Large Language Models 84

Speaker: Brendan Sweeney, VP of Sales & Devyn Blume, Sr. Account Executive

Are you curious about how artificial intelligence is reshaping sales coaching, learning, and development? Join Brendan Sweeney and Devyn Blume of Allego for an engaging new webinar exploring AI's transformative role in sales coaching and performance improvement! Brendan and Devyn will share actionable insights and strategies for integrating AI into coaching and development - ensuring personalized, effective, and scalable training!

AI

Marktechpost

SEPTEMBER 29, 2024

Retrieval Augmented Generation (RAG) is an AI framework that optimizes the output of a Large Language Model (LLM) by referencing a credible knowledge base outside of its training sources. RAG combines the capabilities of LLMs with the strengths of traditional information retrieval systems such as databases to help AI write more accurate and relevant text.

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Marktechpost

SEPTEMBER 29, 2024

Language models have become a cornerstone of modern NLP, enabling significant advancements in various applications, including text generation, machine translation, and question-answering systems. Recent research has focused on scaling these models in terms of the amount of training data and the number of parameters. These scaling laws have demonstrated that increasing data and model parameters yields substantial performance improvements.

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NLP Data Quality ML AI 119

Towards AI

SEPTEMBER 29, 2024

Author(s): Md Monsur ali Originally published on Towards AI. Discover How LLaMA 3.2 Vision Integrates Advanced Visual Perception and Text Processing for Powerful Image Understanding and AI-driven Document Analysis This member-only story is on us. Upgrade to access all of Medium. 👨🏾‍💻 GitHub ⭐️ | 👔LinkedIn |📝 Medium Image by Author The AI landscape has been rapidly evolving, with the growing emphasis on multimodal AI — the ability for models t

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AI AI Machine Learning 80

Marktechpost

SEPTEMBER 29, 2024

Weight decay and ℓ2 regularization are crucial in machine learning, especially in limiting network capacity and reducing irrelevant weight components. These techniques align with Occam’s razor principles and are central to discussions on generalization bounds. However, recent studies have questioned the correlation between norm-based measures and generalization in deep networks.

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Deep Learning Machine Learning LLM ML 103

Towards AI

SEPTEMBER 29, 2024

Last Updated on September 30, 2024 by Editorial Team Author(s): Shenggang Li Originally published on Towards AI. A Practical Guide to Using Bayesian Techniques in A/B Testing and Uplift Modeling This member-only story is on us. Upgrade to access all of Medium. Photo by Mike Hindle on Unsplash The Bayesian approach is commonly applied in fields such as finance, marketing, and medicine.

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Explainability AI AI Data Science 80

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Finding the right innovation management software is like picking a racing bike—it's essential to consider your unique needs rather than just flashy features. This oversight can stall your innovation efforts. Download now to explore key considerations for success!

Marktechpost

SEPTEMBER 29, 2024

Large language models (LLMs) are designed to understand and manage complex language tasks by capturing context and long-term dependencies. A critical factor for their performance is the ability to handle long-context inputs, which allows for a deeper understanding of content over extensive text sequences. However, this advantage comes with the drawback of increased memory usage, as storing and retrieving contextual information from previous inputs can consume substantial computational resources.

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Large Language Models LLM AI AI 101

Artificial Lawyer

SEPTEMBER 29, 2024

Legal tech convergence is when companies that were once quite distinct in their offerings begin to look the same. Generative AI’s multiple capabilities are at.

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Marktechpost

SEPTEMBER 29, 2024

Large language models (LLMs) have gained significant attention in machine learning, shifting the focus from optimizing generalization on small datasets to reducing approximation error on massive text corpora. This paradigm shift presents researchers with new challenges in model development and training methodologies. The primary objective has evolved from preventing overfitting through regularization techniques to effectively scaling up models to consume vast amounts of data.

Machine Learning 69

Machine Learning Large Language Models Neural Network Explainability 69

Machine Learning Mastery

SEPTEMBER 29, 2024

XGBoost has gained widespread recognition for its impressive performance in numerous Kaggle competitions, making it a favored choice for tackling complex machine learning challenges. Known for its efficiency in handling large datasets, this powerful algorithm stands out for its practicality and effectiveness. In this post, we will apply XGBoost to the Ames Housing dataset to […] The post Navigating Missing Data Challenges with XGBoost appeared first on MachineLearningMastery.com.

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Machine Learning Algorithm Data Science 62

Speaker: Michael Mansard and Katherine Shealy

Generative AI is no longer just an exciting technological advancement––it’s a seismic shift in the SaaS landscape. Companies today are grappling with how to not only integrate AI into their products but how to do so in a way that makes financial sense. With the cost of developing AI capabilities growing, finding a flexible monetization strategy has become mission critical.

AI

Marktechpost

SEPTEMBER 29, 2024

Cardiotocography (CTG) is a non-invasive method used to monitor fetal heart rate and uterine contractions during pregnancy. This data can help identify potential complications early on, such as fetal distress, preeclampsia, or preterm labor. However, interpreting CTG recordings can be subjective and prone to errors, leading to potential misdiagnosis and delayed intervention.

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Deep Learning Convolutional Neural Networks AI Researcher AI Research 64

Machine Learning Research at Apple

SEPTEMBER 29, 2024

Machine learning (ML) and artificial intelligence (AI) systems rely heavily on human-annotated data for training and evaluation. A major challenge in this context is the occurrence of annotation errors, as their effects can degrade model performance. This paper presents a predictive error model trained to detect potential errors in search relevance annotation tasks for three industry-scale ML applications (music streaming, video streaming, and mobile apps).

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Artificial Intelligence Artificial Intelligence Machine Learning ML 52

Robot Writers AI

SEPTEMBER 29, 2024

AI Engineer Snags $2.7 Billion to Sign With Google If you’re chatting-up your boss for a raise, you may want to reference the deal Noam Shazeer just cut with Google. A former Google employee that the tech titan sorely missed, the AI wunderkind was happy to let bygones be bygones — for a mere $2.7 billion signing fee. Shazeer is one of the early pioneers of what were to become AI chatbots — the tech that powers most of today’s auto-writers.

AI Chatbots 52

AI Chatbots Chatbots ChatGPT AI Engineer 52

Machine Learning Research at Apple

SEPTEMBER 29, 2024

UI prototyping often involves iterating and blending elements from examples such as screenshots and sketches, but current tools offer limited support for incorporating these examples. Inspired by the cognitive process of conceptual blending, we introduce a novel UI workflow that allows developers to rapidly incorporate diverse aspects from design examples into work-in-progress UIs.

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Pursuing product portfolio management excellence empowers organizations to unlock the full potential of their offerings. This comprehensive guide unveils 10 essential keys that serve as the building blocks for success.

How to Learn Machine Learning

SEPTEMBER 29, 2024

Hello deader! In this article we will speak about OCR Machine Learning technologies and how to hypercharge your documment processing using them. So sit back, relax, and enjoy! Introduction to OCR Machine Learning Tech Manually processing paperwork is slow and prone to mistakes. It takes up valuable time that could be better spent on more important tasks.

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Machine Learning Automation Data Extraction NLP 52

TheSequence

SEPTEMBER 29, 2024

Created Using Ideogram Next Week in The Sequence: Edge 435: Our series about SSMs continues discussing Hungry Hungry Hippos (H3) which has become one of the most important layers in SSM models. We review the original H3 paper and discuss Character.ai’s PromptPoet framework. Edge 436: We review Salesforce recent work in models specialized in agentic tasks.

OpenAI 52

OpenAI Chatbots Data Scientist AI Chatbots 52

Marktechpost

SEPTEMBER 29, 2024

Artificial intelligence (AI) is transforming rapidly, particularly in multimodal learning. Multimodal models aim to combine visual and textual information to enable machines to understand and generate content that requires inputs from both sources. This capability is vital for tasks such as image captioning, visual question answering, and content creation, where more than a single data mode is required.

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Large Language Models Artificial Intelligence Artificial Intelligence Data Integration 110

Pickl AI

SEPTEMBER 29, 2024

Summary: The PEAS framework in Artifical Intelligence—Performance Measure, Environment, Actuators, and Sensors—defines how AI systems interact with their environment, set goals, and take action. It guides developers in creating adaptive, efficient AI solutions that enhance performance across applications like autonomous vehicles and game-playing systems.

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Artificial Intelligence Artificial Intelligence Robotics AI 52

Speaker: Evelyn Chou

Choosing the right business intelligence (BI) platform can feel like navigating a maze of features, promises, and technical jargon. With so many options available, how can you ensure you’re making the right decision for your organization’s unique needs? 🤔 This webinar brings together expert insights to break down the complexities of BI solution vetting.

Business Intelligence

Marktechpost

SEPTEMBER 29, 2024

Reinforcement Learning from Human Feedback (RLHF) has emerged as a vital technique in aligning large language models (LLMs) with human values and expectations. It plays a critical role in ensuring that AI systems behave in understandable and trustworthy ways. RLHF enhances the capabilities of LLMs by training them based on feedback that allows models to produce more helpful, harmless, and honest outputs.

Large Language Models 109

Large Language Models AI AI AI Tools 109

Towards AI

SEPTEMBER 29, 2024

Last Updated on September 29, 2024 by Editorial Team Author(s): Fatma Elik Originally published on Towards AI. Convolutional Networks for Biomedical Image Segmentation Document Explanation in Detail This member-only story is on us. Upgrade to access all of Medium. Photo by Venti Views on Unsplash Convolutional networks have been around for a long time, but their performance has been limited by the size of the available training sets and the size of the networks under consideration.

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Computer Vision AI AI Artificial Intelligence 88

Marktechpost

SEPTEMBER 29, 2024

Reinforcement learning (RL) is a domain within artificial intelligence that trains agents to make sequential decisions through trial and error in an environment. This approach enables the agent to learn by interacting with its surroundings, receiving rewards or penalties based on its actions. However, training agents to perform optimally in complex tasks requires access to extensive, high-quality data, which may not always be feasible.

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Algorithm Artificial Intelligence Artificial Intelligence Data Quality 102

Marktechpost

SEPTEMBER 29, 2024

Cardinality estimation (CE) is essential to many database-related tasks, such as query generation, cost estimation, and query optimization. Accurate CE is necessary to ensure optimal query planning and execution within a database system. Adopting machine learning (ML) techniques has introduced new possibilities for CE, allowing researchers to leverage ML models’ robust learning and representation capabilities.

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Deep Learning ML Machine Learning Artificial Intelligence 104

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Measuring innovation success is critical yet challenging for organizations, often leading to confusion over which KPIs to use. Many rely on inappropriate metrics borrowed from other departments, wasting resources and overlooking valuable opportunities. This guide outlines why innovation metrics are hard to track and offers a framework for creating effective KPIs.