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 In addition to freeing operators from outdated, inefficient methods that require careful supervision and extra resources, AI enables real-time, adaptive optimisation of application scaling.

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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 133

Large Language Models LLM OpenAI ML 133

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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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

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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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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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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Towards AI

SEPTEMBER 29, 2024

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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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

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.

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Large Language Models AI AI AI Tools 109

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

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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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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Speaker: Simran Kaur, Founder & CEO at Tattva Health Inc.

The healthcare landscape is being revolutionized by AI and cutting-edge digital technologies, reshaping how patients receive care and interact with providers. In this webinar led by Simran Kaur, we will explore how AI-driven solutions are enhancing patient communication, improving care quality, and empowering preventive and predictive medicine. You'll also learn how AI is streamlining healthcare processes, helping providers offer more efficient, personalized care and enabling faster, data-driven

AI

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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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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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.

Large Language Models 101

Large Language Models LLM AI AI 101

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 60

Speaker: Jay Allardyce, Deepak Vittal, and Terrence Sheflin

As we look ahead to 2025, business intelligence and data analytics are set to play pivotal roles in shaping success. Organizations are already starting to face a host of transformative trends as the year comes to a close, including the integration of AI in data analytics, an increased emphasis on real-time data insights, and the growing importance of user experience in BI solutions.

Business Intelligence

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

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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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.

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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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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 Research AI Researcher 64

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.

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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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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.

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Speaker: Anindo Banerjea, CTO at Civio & Tony Karrer, CTO at Aggregage

When developing a Gen AI application, one of the most significant challenges is improving accuracy. This can be especially difficult when working with a large data corpus, and as the complexity of the task increases. The number of use cases/corner cases that the system is expected to handle essentially explodes. 💥 Anindo Banerjea is here to showcase his significant experience building AI/ML SaaS applications as he walks us through the current problems his company, Civio, is solving.

Generative AI