Sun.Mar 03, 2024

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Could We Achieve AGI Within 5 Years? NVIDIA’s CEO Jensen Huang Believes It’s Possible

Unite.AI

In the dynamic field of artificial intelligence, the quest for Artificial General Intelligence (AGI) represents a pinnacle of innovation, promising to redefine the interplay between technology and human intellect. Jensen Huang, CEO of NVIDIA, a trailblazer in AI technology, recently brought this topic to the forefront of technological discourse. During a forum at Stanford University, Huang posited that AGI might be realized within the next five years, a projection that hinges critically on the d

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The Era of 1-Bit LLM: Microsoft’s Groundbreaking Technology

Analytics Vidhya

Introduction In a groundbreaking move, Microsoft introduced a revolutionary 1-Bit LLM technology set to redefine the landscape of language models. This cutting-edge development promises to revolutionize how we interact with AI systems and open up a world of possibilities for the future. The Innovation Behind 1-Bit LLM Microsoft’s 1-Bit LLM technology is a significant advancement […] The post The Era of 1-Bit LLM: Microsoft’s Groundbreaking Technology appeared first on Analytics

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How to use AI to build powerful market research tools

AssemblyAI

Market research platforms offer users valuable market research tools that analyze qualitative and quantitative audio, video, and text-based customer feedback, so users can gain insights from the data. Today, market research platforms are turning to AI models, such as AI Speech-to-Text, Audio Intelligence models, and Large Language Models (LLMs), to build suites of advanced analysis tools for their customers.

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Meet Phind-70B: An Artificial Intelligence (AI) Model that Closes Execution Speed and the Code Generation Quality Gap with GPT-4 Turbo

Marktechpost

The field of Artificial Intelligence (AI) is significantly pushing the envelope of technology, thanks to the amazing capabilities of Large Language Models (LLMs). These models based on Natural Language Processing, Understanding, and Generation have demonstrated exceptional skills and potential in almost every industry. In recent research, a new development has emerged that can greatly improve the coding experiences of developers across the globe.

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From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Speaker: Anne Steiner and David Laribee

As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.

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From Code to Cloud: Building CI/CD Pipelines for Containerized Apps

Towards AI

Last Updated on March 4, 2024 by Editorial Team Author(s): Afaque Umer Originally published on Towards AI. From Code to Cloud: Building CI/CD Pipelines for Containerized Apps Photo by Simon Kadula on Unsplash Introduction U+1F516 Imagine yourself as a Data Scientist, leaning in over your keyboard, sculpting Python scripts that decode the mysteries hidden within your dataset.

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Harmonizing Data: A Symphony of Segmenting, Concatenating, Pivoting, and Merging

Machine Learning Mastery

In the world of data science, where raw information swirls in a cacophony of numbers and variables, lies the art of harmonizing data. Like a maestro conducting a symphony, the skilled data scientist orchestrates the disparate elements of datasets, weaving them together into a harmonious composition of insights. Welcome to a journey where data transcends […] The post Harmonizing Data: A Symphony of Segmenting, Concatenating, Pivoting, and Merging appeared first on MachineLearningMastery.com

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Meta AI Research Introduces MobileLLM: Pioneering Machine Learning Innovations for Enhanced On-Device Intelligence

Marktechpost

The evolution of large language models (LLMs) marks a revolutionary stride towards simulating human-like understanding and generating natural language. These models, through their capacity to process and analyze vast datasets, have significantly influenced various sectors, including but not limited to automated customer service, language translation, and content creation.

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Text-to-Video Games and 1-Bit Models: Two Monumental Generative AI Research Milestones in One Week

TheSequence

Created Using DALL-E Next Week in The Sequence: Edge 375: Out series about reasoning in LLMs continues by exploring Meta’s recent work in System2 attention. We also review the Chainlit framework to build LLM applications. Edge 376: We dive into the amazing SGLang framework created by UC Berkeley which provide significant performance gains in LLM inference.

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Enhancing Autoregressive Decoding Efficiency: A Machine Learning Approach by Qualcomm AI Research Using Hybrid Large and Small Language Models

Marktechpost

Central to Natural Language Processing (NLP) advancements are large language models (LLMs), which have set new benchmarks for what machines can achieve in understanding and generating human language. One of the primary challenges in NLP is the computational demand for autoregressive decoding in LLMs. This process, essential for tasks like machine translation and content summarization, requires substantial computational resources, making it less feasible for real-time applications or on devices w

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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.

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Privacy-Preserving Quantile Treatment Effect Estimation for Randomized Controlled Trials

Machine Learning Research at Apple

In accordance with the principle of "data minimization," many internet companies are opting to record less data. However, this is often at odds with A/B testing efficacy. For experiments with units with multiple observations, one popular data-minimizing technique is to aggregate data for each unit. However, exact quantile estimation requires the full observation-level data.

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Enhancing AI’s Foresight: The Crucial Role of Discriminator Accuracy in Advanced LLM Planning Methods

Marktechpost

The ability of systems to plan and execute complex tasks stands as a testament to AI’s progress. Panning within AI has been approached through various methodologies, ranging from basic decision-making processes to complex algorithms designed to simulate the foresight and adaptability of human intelligence. As the intricacy of problems addressed by AI systems has escalated, so too has the necessity for innovative planning strategies that can navigate these challenges with greater precision and ef

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What Can CLIP Learn From Task-specific Experts?

Machine Learning Research at Apple

This paper has been accepted to the UniReps Workshop in NeurIPS 2023. Contrastive language image pretraining has become the standard approach for training vision language models. Despite the utility of CLIP visual features as global representations for images, they have limitations when it comes to tasks involving object localization, pixel-level understanding of the image, or 3D perception.

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This Paper Explores the Synergistic Potential of Machine Learning: Enhancing Interpretability and Functionality in Generalized Additive Models through Large Language Models

Marktechpost

In the significantly advancing fields of data science and Artificial Intelligence (AI), the combination of interpretable Machine Learning (ML) models with Large Language Models (LLMs) has represented a major breakthrough. By combining the best features of both strategies, this strategy improves the usability and accessibility of sophisticated data analysis tools.

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The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Communication

Speaker: David Bard, Principal at VP Product Coaching

In the fast-paced world of digital innovation, success is often accompanied by a multitude of challenges - like the pitfalls lurking at every turn, threatening to derail the most promising projects. But fret not, this webinar is your key to effective product development! Join us for an enlightening session to empower you to lead your team to greater heights.

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Human Following in Mobile Platforms with Person Re-Identification

Machine Learning Research at Apple

Human following serves an important human-robotics interaction feature, while real-world scenarios make it challenging particularly for a mobile agent. The main challenge is that when a mobile agent try to locate and follow a targeted person, this person can be in a crowd, be occluded by other people, and/or be facing (partially) away from the mobile agent.

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This AI Paper from CMU and Meta AI Unveils Pre-Instruction-Tuning (PIT): A Game-Changer for Training Language Models on Factual Knowledge

Marktechpost

In the fast-paced world of artificial intelligence, the challenge of keeping large language models (LLMs) up-to-date with the latest factual knowledge is paramount. These models, which have become the backbone of numerous AI applications, store a wealth of information during their initial training phase. However, as time passes, the static nature of this stored knowledge becomes a limitation, unable to accommodate the constant evolution of real-world information or specialize in niche domains.

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A fun history about Git

Mlearning.ai

“the information manager from hell” — creator of git You read that right — “ an unpleasant or contemptible person ” — this is what “git” means in British English. It’s not a crazy coincidence that it resembles the “git” tool we use day in and day out. In fact — the meaning is the inspiration behind naming “git” as “git”. You see — when Linus Torvalds (yup — the creator of Linux kernel) was developing the Linux kernel — he was frustrated with the limitations of the then prevailing VCS (version co

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Advancing Large Language Models for Structured Knowledge Grounding with StructLM: Model Based on CodeLlama Architecture

Marktechpost

We cannot deny the significant strides made in natural language processing (NLP) through large language models (LLMs). Still, these models often need to catch up when dealing with the complexities of structured information, highlighting a notable gap in their capabilities. The crux of the issue lies in the inherent limitations of LLMs, such as ChatGPT, which need to catch up to state-of-the-art models by a significant margin when tasked with grounding knowledge from structured sources.

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Reimagined: Building Products with Generative AI

“Reimagined: Building Products with Generative AI” is an extensive guide for integrating generative AI into product strategy and careers featuring over 150 real-world examples, 30 case studies, and 20+ frameworks, and endorsed by over 20 leading AI and product executives, inventors, entrepreneurs, and researchers.

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Who Needs Reporters?

Robot Writers AI

AI Rewrites Press Releases, Calls It News A growing number news sites have decided to bypass reporters completely and simply go live with whatever they happen to find in a press release — courtesy of an AI-rewrite. Writer Bron Maher reports that the tool they’re using is Gutenbot, by Reach. Granted, editors at the news outlets using the AI are supposedly tasked to double-check the press release rewrites to ensure the data and claims spewed in the press release are faithfully re-spewe

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Can AI Keep Up in Long Conversations? Unveiling LoCoMo, the Ultimate Test for Dialogue Systems

Marktechpost

Recent advancements in AI have significantly impacted the field of conversational AI, particularly in the development of chatbots and digital assistants. These systems aim to mimic human-like conversations, providing users with more natural and engaging interactions. As these technologies evolve, one area of increasing interest is enhancing their ability to maintain long-term conversational memory, which is crucial for sustaining coherent and contextually relevant dialogues over extended periods

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Git with just 9 Steps

Mlearning.ai

How to use git to upload code to GitHub? What is Git? Git is a version control system, used to version your code and keep track of its changes. Install GIT by following the instructions [link] Why Version Control? Let’s say you have started writing a simple code and you share it with your friend. Your friend on looking at the code gets hit with an idea to add a functionality.

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This AI Paper from the University of Michigan and Netflix Proposes CLoVe: A Machine Learning Framework to Improve the Compositionality of Pre-Trained Contrastive Vision-Language Models

Marktechpost

There has been notable progress in Vision-Language tasks, with models like CLIP showing impressive performance in various tasks. While these models excel at recognizing objects, they need help composing known concepts in novel ways due to text representations that appear indifferent to word order. Even large-scale models like GPT-4V have yet to show evidence of successfully identifying compositions, highlighting a limitation in Vision-Language modeling.

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Driving Business Impact for PMs

Speaker: Jon Harmer, Product Manager for Google Cloud

Move from feature factory to customer outcomes and drive impact in your business! This session will provide you with a comprehensive set of tools to help you develop impactful products by shifting from output-based thinking to outcome-based thinking. You will deepen your understanding of your customers and their needs as well as identifying and de-risking the different kinds of hypotheses built into your roadmap.

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Advanced RAG 06: Exploring Query Rewriting

Mlearning.ai

A key technique for aligning the semantics of queries and documents Continue reading on MLearning.

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Meet PyRIT: A Python Risk Identification Tool for Generative AI to Empower Machine Learning Engineers

Marktechpost

In today’s rapidly evolving era of artificial intelligence, there’s a concern surrounding the potential risks tied to generative models. These models, known as Large Language Models (LLMs), can sometimes produce misleading, biased, or harmful content. As security professionals and machine learning engineers grapple with these challenges, a need arises for a tool that can systematically assess the robustness of these models and their applications.

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How to Deploy Any ML models with FastAPI and Docker

Mlearning.ai

This simple project aims to learn how to use your ml models in deployment using the most common tool FastAPI and containerized the… Continue reading on MLearning.

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Meet TOWER: An Open Multilingual Large Language Model for Translation-Related Tasks

Marktechpost

In an era where the world is increasingly interconnected, the demand for accurate and efficient translation across multiple languages has never been higher. While effective, earlier translation methods often need to catch up regarding scalability and versatility, leading researchers to explore more dynamic solutions. Enter the realm of artificial intelligence, where large language models (LLMs) have begun to redefine the boundaries of multilingual natural language processing (NLP).

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The Big Payoff of Application Analytics

Outdated or absent analytics won’t cut it in today’s data-driven applications – not for your end users, your development team, or your business. That’s what drove the five companies in this e-book to change their approach to analytics. Download this e-book to learn about the unique problems each company faced and how they achieved huge returns beyond expectation by embedding analytics into applications.

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A New Era of Driving: Enhanced Safety and Comfort with Comma AI

Mlearning.ai

The Machine Learning Brain Behind the Wheel Continue reading on MLearning.

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Unlocking the Full Potential of Vision-Language Models: Introducing VISION-FLAN for Superior Visual Instruction Tuning and Diverse Task Mastery

Marktechpost

Recent advances in vision-language models (VLMs) have led to impressive AI assistants capable of understanding and responding to both text and images. However, these models still have limitations that researchers are working to address. Two of the key challenges are: Limited Task Diversity: Many existing VLMs are trained on a narrow range of tasks and are fine-tuned on instruction datasets synthesized by large language models.

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Beyond the Café: Stories Stirred by Soul and Silicon

Mlearning.ai

Coffee, Creativity, and AI Companions Continue reading on MLearning.

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