Analytics Vidhya
OCTOBER 6, 2024
Introduction Agentic AI is an exciting concept! It’s all about creating AI that can work on its own, without us constantly telling it what to do. Think of it like having a super-smart assistant; it doesn’t just sit there waiting for orders, but predicts what you need and gets it done. This idea is getting […] The post What do Top Leaders have to Say About Agentic AI?
AssemblyAI
OCTOBER 6, 2024
Staying ahead means embracing the right tools at the right time, and Speech AI is transforming how companies interact with customers, process information, and make decisions. You probably already use Speech AI technology every day without even realizing it. Voice assistants on your phone, live transcriptions on your TV show, or even a phone call with a bot to schedule an appointment—these are all examples of Speech AI in action.
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Unite.AI
OCTOBER 6, 2024
If you’re a photographer, nothing is more time-consuming than sorting through hundreds (if not thousands) of photos after a big event or shoot. Did you know that professional photographers spend an average of 3-4 hours editing for every hour of shooting? I recently came across Aftershoot , and it’s a game-changer for photo culling. If you don't know what culling is, it’s the process of going through all your photos to pick out the best ones.
Machine Learning Research at Apple
OCTOBER 6, 2024
Machine Translation (MT) enables people to connect with others and engage with content across language barriers. Grammatical gender presents a difficult challenge for these systems, as some languages require specificity for terms that can be ambiguous or neutral in other languages. For example, when translating the English word "nurse" into Spanish, one must decide whether the feminine "enfermera" or the masculine "enfermero" is appropriate.
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85% of teams are using AI, but only 27% report clear productivity gains. Why? Because most are still stuck in surface-level adoption. In this expert panel, top voices in workplace strategy and remote innovation—Dr. Gleb Tsipursky, Phil Kirschner, Nadia Harris, and Eryn Peters—reveal how leading teams are cutting digital noise, training AI to fit their workflows, and building cultures that embrace change.
Ehud Reiter
OCTOBER 6, 2024
I’ve had some discussions recently with medical colleagues about evaluation, where they have essentially suggested that I put more emphasis on qualitative evaluation. Ie, in AI and NLP we usually focus on numbers and quantitative evaluation, and perhaps in some cases this is a mistake. I think my group does more qualitative work than most NLP groups, but my medical colleagues felt we should consider doing even more.
Artificial Intelligence Zone brings together the best content for AI and ML professionals from the widest variety of thought leaders.
Robot Writers AI
OCTOBER 6, 2024
Writers whistling past the graveyard when it comes to AI — i.e., pretending that a mere machine will never be able to compete with their wit, style and moxie — encountered a number of rude awakenings in Q3. PR Newswire, for example — which for decades has provided human-written press releases for tens of thousands of companies — came-out with a new auto-writing productivity suite that bypasses human writers and simply hands-over all the press release writing to AI.
Marktechpost
OCTOBER 6, 2024
Automatic Speech Recognition (ASR) and Diarization technologies have become essential tools for transforming how machines interpret human speech. These innovations enable accurate transcription, speech segmentation, and speaker identification across various applications like media transcriptions, legal documentation, and customer service automation.
Pragnakalp
OCTOBER 6, 2024
Introduction Machine learning is no longer just a buzzword—it’s becoming a key part of how businesses solve problems and make smarter decisions. However, building, training, and deploying machine learning models can still be daunting, especially when trying to balance performance with cost and scalability. That’s where AWS SageMaker comes in.
Marktechpost
OCTOBER 6, 2024
Large Language Models (LLMs) generate code aided by Natural Language Processing. There is a growing application of code generation in complex tasks such as software development and testing. Extensive alignment with input is crucial for an adept and bug-free output, but the developers identified it as computationally demanding and time-consuming. Hence, creating a framework for the algorithm to improve itself continuously to provide real-time feedback in the form of error messages or negative poi
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The $53 trillion manufacturing economy in the US is undergoing a major automation paradigm shift due to Artificial Intelligence (AI). Thanks to new practical frameworks, automation projects that were once impossible or inefficient to implement are now being fast-tracked, and robotics automation is becoming increasingly relevant to a growing number of users and scenarios.
Machine Learning Research at Apple
OCTOBER 6, 2024
We present a foundation model for zero-shot metric monocular depth estimation. Our model, Depth Pro, synthesizes high-resolution depth maps with unparalleled sharpness and high-frequency details. The predictions are metric, with absolute scale, without relying on the availability of metadata such as camera intrinsics. And the model is fast, producing a 2.25-megapixel depth map in 0.3 seconds on a standard GPU.
Marktechpost
OCTOBER 6, 2024
Training Large Language Models (LLMs) that can handle long-context processing is still a difficult task because of data sparsity constraints, implementation complexity, and training efficiency. Working with documents of infinite duration, which are typical in contemporary media formats like automated news updates, live-stream e-commerce platforms, and viral short-form movies, makes these problems very clear.
Artificial Lawyer
OCTOBER 6, 2024
‘Our ambition is to build an AI colleague – and [for it] to be trusted as one – that’s what keeps me going,’ explains Jake Jones, MD and co-founder of Flank, as he sets out the company’…
Marktechpost
OCTOBER 6, 2024
A popular method when employing Large Language Models (LLMs) for complicated analytical tasks, such as code generation, is to attempt to solve the full problem within the model’s context window. The informational segment that the LLM is capable of processing concurrently is referred to as the context window. The amount of data the model can process at once has a significant impact on its capacity to produce a solution.
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AI has transformed how enterprises deliver customer service, enabling faster engagement, problem-solving, and cost savings. However, traditional AI Agents often rely on rigid conversation flows, risking customer trust when conversations stray from predefined paths. These limitations prevent businesses from fully realizing AI’s potential for cost-efficiency and productivity.
Pickl AI
OCTOBER 6, 2024
Summary: This blog explores the key differences between ETL and ELT, detailing their processes, advantages, and disadvantages. Understanding these methods helps organizations optimize their data workflows for better decision-making. Introduction In today’s data-driven world, efficient data processing is crucial for informed decision-making and business growth.
Marktechpost
OCTOBER 6, 2024
Endogeneity presents a significant challenge in conducting causal inference in observational settings. Researchers in social sciences, statistics, and related fields have developed various identification strategies to overcome this obstacle by recreating natural experiment conditions. The instrumental variables (IV) method has emerged as a leading approach, with researchers discovering IVs in diverse settings and justifying their adherence to exclusion restrictions.
Bugra Akyildiz
OCTOBER 6, 2024
Articles (Image is taken from Model Optimization Toolkit page from Tensorflow) Pruning Aware Training Pruning in deep learning refers to the process of removing unnecessary weights or neurons from a neural network to reduce its size and computational requirements while maintaining performance. Traditionally, pruning has been applied after training, but this approach often leads to significant performance degradation, especially for large language models.
Marktechpost
OCTOBER 6, 2024
Natural language processing (NLP) has experienced rapid advancements, with large language models (LLMs) being used to tackle various challenging problems. Among the diverse applications of LLMs, mathematical problem-solving has emerged as a benchmark to assess their reasoning abilities. These models have demonstrated remarkable performance on math-specific benchmarks such as GSM8K, which measures their capabilities to solve grade-school math problems.
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Hubstaff’s new report, The AI Productivity Shift, highlights how 3,000+ professionals and 140,000+ users are transforming the way they work with AI. Adoption is high—85% are using AI—and the potential is just beginning. Teams that integrate AI into daily workflows report 77% faster task completion, 70% improved focus, and stronger results across the board.
TheSequence
OCTOBER 6, 2024
Created Using Ideogram Next Week in The Sequence: Edge 337: Our series about state space models(SSM) discussed BlackMamba, a model that combines MoEs and SSMs in a single architecture. We also review teh original BlackMamba paper and the amazing SWE-Agent for solving engineering tasks. Edge 438: We dive into DataGEmma, Google DeepMind’s recent work to ground LLMs on factual knowledge.
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