Sun.Sep 29, 2024

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9 Best Text to Speech APIs (September 2024)

Unite.AI

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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Han Heloir, MongoDB: The role of scalable databases in AI-powered apps

AI News

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 In addition to freeing operators from outdated, inefficient methods that require careful supervision and extra resources, AI enables real-time, adaptive optimisation of application scaling.

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JailbreakBench: An Open Sourced Benchmark for Jailbreaking Large Language Models (LLMs)

Marktechpost

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.

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Building a Smart Chatbot with OpenAI and Pinecone: A Simple Guide

Towards AI

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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Don't Let AI Pass You By: The New Era of Personalized Sales Coaching & Development

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!

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Enhancing Language Models with Retrieval-Augmented Generation: A Comprehensive Guide

Marktechpost

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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MassiveDS: A 1.4 Trillion-Token Datastore Enabling Language Models to Achieve Superior Efficiency and Accuracy in Knowledge-Intensive NLP Applications

Marktechpost

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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Misty: UI Prototyping Through Interactive Conceptual Blending

Machine Learning Research at Apple

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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This AI Paper Introduces a Novel L2 Norm-Based KV Cache Compression Strategy for Large Language Models

Marktechpost

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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LLaMA 3.2 Vision: Revolutionizing Multimodal AI with Advanced Visual Reasoning — Now LLaMA Can See

Towards AI

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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How To Select the Right Software for Innovation Management

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!

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Revisiting Weight Decay: Beyond Regularization in Modern Deep Learning

Marktechpost

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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Bayesian Methods: From Theory to Real-World Applications

Towards AI

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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Scaling Laws and Model Comparison: New Frontiers in Large-Scale Machine Learning

Marktechpost

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.

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Getting to Know AutoGen(Part2): How AI Agents Work Together

Towards AI

Last Updated on September 30, 2024 by Editorial Team Author(s): Anushka sonawane Originally published on Towards AI. Credits In Part 1, we went over the basics — what AI agents are, how they work, and why having multiple agents can really make a difference. That was just an introduction, setting the stage for what’s next. Now, it’s time to take things up a level!

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The New Frontier: A Guide to Monetizing AI Offerings

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.

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Google AI Researchers Investigate Temporal Distribution Shifts in Deep Learning Models for CTG Analysis

Marktechpost

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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Navigating Missing Data Challenges with XGBoost

Machine Learning Mastery

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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Generalizable Error Modeling for Human Data Annotation: Evidence from an Industry-Scale Search Data Annotation Program

Machine Learning Research at Apple

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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Now That’s a Big Payday

Robot Writers AI

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.

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How to Master Product Portfolio Management

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.

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How to Optimize Document Processing Through OCR Machine Learning Technologies

How to Learn Machine Learning

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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Meta AI’s Big Announcements

TheSequence

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.

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Ovis-1.6: An Open-Source Multimodal Large Language Model (MLLM) Architecture Designed to Structurally Align Visual and Textual Embeddings

Marktechpost

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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What is PEAS in Artificial Intelligence (AI)?

Pickl AI

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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Building Your BI Strategy: How to Choose a Solution That Scales and Delivers

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.

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This AI Paper from China Introduces a Reward-Robust Reinforcement Learning from Human Feedback RLHF Framework for Enhancing the Stability and Performance of Large Language Models

Marktechpost

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.

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U-Net Paper Workthrough

Towards AI

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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Conservative Algorithms for Zero-Shot Reinforcement Learning on Limited Data

Marktechpost

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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AutoCE: An Intelligent Model Advisor Revolutionizing Cardinality Estimation for Databases through Advanced Deep Metric Learning and Incremental Learning Techniques

Marktechpost

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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11 KPIs for Measuring Innovation Success

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.