Analytics Vidhya

JUNE 30, 2024

Introduction This article covers the creation of a multilingual chatbot for multilingual areas like India, utilizing large language models. The system improves consumer reach and personalization by using LLMs to translate questions between local languages and English. We go over the architecture, implementation specifics, advantages, and required actions.

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Ehud Reiter

JUNE 30, 2024

NOTE: This is a personal blog about a holiday, there is nothing here about NLG or AI! I like to go on cycling holidays, and this year I decided to cycle from Perth (Scotland) to Preston (England), visiting my son in Lockerbie along the way. I’ve actually already been to many of the towns and cities I visited in this trip, for work or personal visits, but this was a chance to see them as a tourist, and also to explore the countryside in between.

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Marktechpost

JUNE 30, 2024

Deep learning models like Convolutional Neural Networks (CNNs) and Vision Transformers achieved great success in many visual tasks, such as image classification, object detection, and semantic segmentation. However, their ability to handle different changes in data is still a big concern, especially for use in security-critical applications. Many works evaluated the robustness of CNNs and Transformers against common corruptions, domain shifts, information drops, and adversarial attacks.

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Artificial Lawyer

JUNE 30, 2024

Imagine buying a car from a vendor for which there are no standards, nor benchmarks for measuring and understanding: its safety features, its speed, its.

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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

JUNE 30, 2024

Large language models (LLMs) have gained significant attention in recent years, but their safety in multilingual contexts remains a critical concern. Researchers are grappling with the challenge of mitigating toxicity in non-English languages, a problem that has been largely overlooked despite substantial investments in LLM safety. The issue is particularly pressing as studies have revealed high toxicity levels in multilingual LLMs, underscoring the urgent need for effective multilingual toxicit

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Marktechpost

JUNE 30, 2024

Understanding how LLMs comprehend natural language plans, such as instructions and recipes, is crucial for their dependable use in decision-making systems. A critical aspect of plans is their temporal sequencing, which reflects the causal relationships between steps. Planning, integral to decision-making processes, has been extensively studied across domains like robotics and embodied environments.

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TheSequence

JUNE 30, 2024

Created Using DALL-E Next Week in The Sequence: Edge 409: We dive into long-term memory in autonomous agents. The research section reviews Microsoft LONGMEM reference architecture for long-term memory in LLMs. We also provide an introduction to the super popular Pinecone vector database. Edge 410: We dive into VTC, a super innovative method from UC Berkeley and Stanford for fiar LLM serving.

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Marktechpost

JUNE 30, 2024

As artificial intelligence (AI) technology continues to advance and permeate various aspects of society, it poses significant challenges to existing legal frameworks. One recurrent issue is how the law should regulate entities that lack intentions. Traditional legal principles often rely on the concept of mens rea, or the mental state of the actor, to determine liability in areas such as freedom of speech, copyright, and criminal law.

Artificial Intelligence 126

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Towards AI

JUNE 30, 2024

Author(s): Youssef Hosni Originally published on Towards AI. The comparative advantages and challenges of single-task versus multi-task fine-tuning of large language models (LLMs) are explored. The discussion begins with single-task fine-tuning, highlighting its benefits and drawbacks, including the issue of catastrophic forgetting. It then transitions to an overview of multitasking fine-tuning, examining both its challenges and potential benefits.

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Marktechpost

JUNE 30, 2024

Natural language processing (NLP) has experienced significant growth, largely due to the recent surge in the size and strength of large language models. These models, with their exceptional performance and unique characteristics, are rapidly making a significant impact in real-world applications. These considerations have spurred a great deal of research on interpretability and analysis (IA) in natural language processing (NLP), which aims to decipher the logic behind LLMs and the reasoning behi

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Machine Learning Mastery

JUNE 30, 2024

Photography has been around for more than a century. There are many old photos around, and probably your family has some, too. Limited by the camera and film of the time, you may have photos of low resolution, blurry, or with folds or scratches. Restoring these old photos and making them like new ones taken […] The post Stable Diffusion Project: Reviving Old Photos appeared first on MachineLearningMastery.com.

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Marktechpost

JUNE 30, 2024

In recent years, the proliferation of generative AI technologies has led to the development of various user interfaces that harness the power of AI to enhance productivity, creativity, and user interaction. These interfaces are becoming increasingly sophisticated, providing users new ways to engage with digital tools and platforms. Here are seven emerging generative AI user interfaces that are making a significant impact: The Chatbot: Chatbots have revolutionized how people interact with AI.

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Towards AI

JUNE 30, 2024

Author(s): Eera Bhatt Originally published on Towards AI. Each year, we spend so much time and money developing new machine learning models, but most of them never get used in a practical setting. Sadly, this issue is even worse in the healthcare industry. Photo by Testalize.me on Unsplash A.I. in medicine. Because of COVID-19, a lot of us know about AI and are also familiar with its applications in medicine, but let’s summarize them for anyone who needs it.

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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

JUNE 30, 2024

Managing multiple agents in an AI system can be quite challenging. Each agent must communicate effectively, execute tasks reliably, and scale efficiently. This complex process often requires a robust framework to ensure smooth agent interaction and coordination. The available frameworks often fall short regarding ease of use, scalability, and flexibility.

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Towards AI

JUNE 30, 2024

Last Updated on June 30, 2024 by Editorial Team Author(s): Stavros Theocharis Originally published on Towards AI. Introduction In the rapidly evolving landscape of web application development and artificial intelligence, having the right tools at your disposal can significantly streamline your workflow and boost productivity. Enter AutoStreamlit Studio, an intelligent assistant designed to simplify the creation of Streamlit applications.

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Marktechpost

JUNE 30, 2024

Large Language Models (LLMs) have made significant advances in the field of Information Extraction (IE). Information extraction is a task in Natural Language Processing (NLP) that involves identifying and extracting specific pieces of information from text. LLMs have demonstrated great results in IE, especially when combined with instruction tuning.

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Towards AI

JUNE 30, 2024

Last Updated on June 30, 2024 by Editorial Team Author(s): Shashank Bhushan Originally published on Towards AI. Understanding What Embeddings Are Embeddings, sometimes also referred to as Feature representation, are a widely used technique/concept in Neural Network based machine learning. They are usually taken from an intermediate or hidden layer of a Deep Neural Network.

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Marktechpost

JUNE 30, 2024

Large Language Models (LLMs) have gained significant prominence in the AI industry, revolutionizing various applications such as chat, programming, and search. However, the efficient serving of multiple LLMs has emerged as a critical challenge for endpoint providers. The primary issue lies in the substantial computational requirements of these models, with a single 175B LLM demanding eight A100 (80GB) GPUs for inference.

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Machine Learning Research at Apple

JUNE 30, 2024

Optimization over the set of matrices X that satisfy X^TBX = Ip, referred to as the generalized Stiefel manifold, appears in many applications involving sampled covariance matrices such as the canonical correlation analysis (CCA), independent component analysis (ICA), and the generalized eigenvalue problem (GEVP). Solving these problems is typically done by iterative methods that require a fully formed B.

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Marktechpost

JUNE 30, 2024

The paper addresses the challenge of ensuring that large language models (LLMs) generate accurate, credible, and verifiable responses by correctly citing reliable sources. Existing methods often need help with errors and hallucinations, leading to incorrect or misleading information in generated responses. This research aims to improve the accuracy and reliability of LLM outputs by introducing a novel verification framework.

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Machine Learning Research at Apple

JUNE 30, 2024

The inability to linearly classify XOR has motivated much of deep learning. We revisit this age-old problem and show that linear classification of XOR is indeed possible. Instead of separating data between halfspaces, we propose a slightly different paradigm, equality separation, that adapts the SVM objective to distinguish data within or outside the margin.

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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

Robot Writers AI

JUNE 30, 2024

A slew of major stories in AI writing that broke in Q2 have made the future for writers and editors crystal clear: The wholesale transition of writing-by-humans to writing-by-AI-machines has begun. Fading are the days when publishers and AI evangelists hid behind the euphemism that AI writers are just Silicon buddies looking to shoulder the drudge work so their human counterparts can do more interesting work.

Auto-complete 52

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Machine Learning Research at Apple

JUNE 30, 2024

This paper was accepted at the Natural Language Reasoning and Structured Explanations workshop at ACL 2024. Reinforcement Learning from AI Feedback (RLAIF) has demonstrated significant potential across various domains, including mitigating harm in LLM outputs, enhancing text summarization, and mathematical reasoning. This paper introduces an RLAIF framework for improving the code generation abilities of lightweight (<1B parameters) LLMs.

LLM 52

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Salmon Run

JUNE 30, 2024

Couple of weeks ago a colleague and I participated in an internal hackathon where the task was to come up with an interesting use case using the recent multi-modal Large Language Models (LLMs). Multi-modal LLMs take not only text inputs via their prompt like earlier LLMs, but can also accept non-text modalities such as images and audio.

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Machine Learning Research at Apple

JUNE 30, 2024

Can Transformers predict new syllogisms by composing established ones? More generally, what type of targets can be learned by such models from scratch? Recent works show that Transformers can be Turing-complete in terms of expressivity, but this does not address the learnability objective. This paper puts forward the notion of distribution locality to capture when weak learning is efficiently achievable by regular Transformers, where the locality measures the least number of tokens required in a

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Marktechpost

JUNE 30, 2024

Optimizing the efficiency of Feedforward Neural Networks (FFNs) within Transformer architectures is a significant challenge in AI. Large language models (LLMs) are highly resource-intensive, requiring substantial computational power and energy, which restricts their applicability and raises environmental concerns. Efficiently addressing this challenge is crucial for promoting sustainable AI practices and making advanced AI technologies more accessible by reducing operational costs.

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