AI News

NOVEMBER 4, 2024

Google has big hopes for AI, as evidenced by the consistent improvements to its Gemini chatbot in recent months. Google briefly introduced its vision for a “universal AI agent” aimed to help users with daily tasks at the I/O developer conference in May, hinting that elements of the technology could be incorporated into Gemini soon. Recent insights from The Information have shed more light on its initiative, known internally as Project Jarvis.

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Unite.AI

NOVEMBER 4, 2024

As AI-enabled threats proliferate, organizations must master how to prevent and defend against these types of attacks. One popular approach that is generating buzz is to use AI to defend against other, malicious AI. This is, however, only partly effective. AI can be used as a defensive shield, but only if employees have the knowledge to properly use it.

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AI News

NOVEMBER 4, 2024

GitHub is marking a significant evolution of its developer tools with two major announcements: a multi-model upgrade to Copilot and the introduction of Spark, a natural language web development platform. The code hosting giant is expanding Copilot beyond its OpenAI-only architecture to include Anthropic’s Claude 3.5 Sonnet and Google’s Gemini 1.5 Pro models while simultaneously launching GitHub Spark to simplify web application development through natural language instructions.

Software Development 282

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Unite.AI

NOVEMBER 4, 2024

Artem Rodichev is the Founder and CEO of Ex-human , a company focused on building empathetic AI characters for engaging conversations. Before founding Ex-human, Artem was the Head of AI at Replika from 2017 to 2021, where he led the development one of the most popular English-speaking chatbots, growing its user base to 10 million in the U.S. You spent several years as Head of AI at Replika, building one of the most popular conversational AIs.

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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.

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Analytics Vidhya

NOVEMBER 4, 2024

Recently, there’s been a surge of tools claiming to detect AI-generated content with impressive accuracy. But can they really do what they promise? Let’s find out! A recent tweet by Christopher Penn exposes a major flaw: an AI detector confidently declared that the US Declaration of Independence was 97% AI-generated. Yes, a document written over 240 […] The post Does the Rise of AI-generated Content Affect Model Training?

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Analytics Vidhya

NOVEMBER 4, 2024

In early 2024, Brazil experienced heavy rainfall, particularly in the south and northeast regions, leading to floods that damaged cities, destroyed crops, and caused fatalities. As climate change increases the frequency of extreme weather conditions, such as droughts and floods, contingency planning and risk assessment are becoming increasingly crucial for managing such events.

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Unite.AI

NOVEMBER 4, 2024

A recent study from the US has found that the real-world performance of popular Retrieval Augmented Generation (RAG) research systems such as Perplexity and Bing Copilot falls far short of both the marketing hype and popular adoption that has garnered headlines over the last 12 months. The project, which involved extensive survey participation featuring 21 expert voices, found no less than 16 areas in which the studied RAG systems (You Chat, Bing Copilot and Perplexity) produced cause for concer

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Towards AI

NOVEMBER 4, 2024

Last Updated on November 4, 2024 by Editorial Team Author(s): David Sweenor Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium. The bridge to responsible innovation must be robust. Photo by author David E. Sweenor As artificial intelligence (AI) becomes ubiquitous, it’s reshaping decision-making in ways that go far beyond the scope of traditional business automation.

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NVIDIA

NOVEMBER 4, 2024

Enterprises and public sector organizations around the world are developing AI agents to boost the capabilities of workforces that rely on visual information from a growing number of devices — including cameras, IoT sensors and vehicles. To support their work, a new NVIDIA AI Blueprint for video search and summarization will enable developers in virtually any industry to build visual AI agents that analyze video and image content.

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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.

Robotics

Towards AI

NOVEMBER 4, 2024

Last Updated on November 4, 2024 by Editorial Team Author(s): Myra Roldan Originally published on Towards AI. Anthropic Claude Computer Use Anthropic’s latest update to their AI models, Claude 3.5 Sonnet and Claude 3.5 Haiku, has introduced a new feature called “computer use.” An interesting choice of name for the service and a bit confusing on how that may work.

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Software Development AI AI Generative AI 111

Analytics Vidhya

NOVEMBER 4, 2024

This intelligent healthcare system uses a small language model MiniLM-L6-V2 to better understand and analyze medical information, such as symptoms or treatment instructions. The model turns text into “embeddings,” or meaningful numbers, that capture the context of the words. By using these embeddings, the system can compare symptoms effectively and make smart recommendations for conditions […] The post Efficient Symptom Analysis Using a Small Language Model appeared first on Analytics Vidh

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Towards AI

NOVEMBER 4, 2024

Author(s): Sanjay Nandakumar Originally published on Towards AI. Photo by Joshua Woroniecki on Unsplash Table of Contents Introduction Methodology Overview Exploratory Data Analysis (EDA) Analysis Data Preparation (Synthetic Data) Feature Engineering and Selection Model Selection and Training Model Evaluation and Metrics Model Interpretability and Feature Importance Conclusion Future Directions Introduction The world is focusing its eyes on the 2024 U.S. presidential election, which is being hel

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Marktechpost

NOVEMBER 4, 2024

Evaluating NLP models has become increasingly complex due to issues like benchmark saturation, data contamination, and the variability in test quality. As interest in language generation grows, standard model benchmarking faces challenges from rapidly saturated evaluation datasets, where top models reach near-human performance levels. Creating new, high-quality datasets is resource-intensive, demanding human annotation, data cleaning, and validation.

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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.

Explainability

Towards AI

NOVEMBER 4, 2024

Author(s): Serj Smorodinsky Originally published on Towards AI. Data science perspective This member-only story is on us. Upgrade to access all of Medium. This is my take, you don’t have to take it If you think that a picture is worth 1000 words, then a video should be worth like a 100,000? Anyhow, you can watch me go over all of the sections live on my channel.

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Marktechpost

NOVEMBER 4, 2024

In recent years, the field of text-to-speech (TTS) synthesis has seen rapid advancements, yet it remains fraught with challenges. Traditional TTS models often rely on complex architectures, including deep neural networks with specialized modules such as vocoders, text analyzers, and other adapters to synthesize realistic human speech. These complexities make TTS systems resource-intensive, limiting their adaptability and accessibility, especially for on-device applications.

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Marek Rei

NOVEMBER 4, 2024

I have written short summaries of 68 different research papers published in the areas of Machine Learning and Natural Language Processing. They cover a wide range of different topics, authors and venues. These are not meant to be reviews showing my subjective opinion, but instead I aim to provide a blunt and concise overview of the core contribution of each publication.

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Marktechpost

NOVEMBER 4, 2024

Large language models (LLMs) are getting better at scaling and handling long contexts. As they are being used on a large scale, there has been a growing demand for efficient support of high-throughput inference. However, efficiently serving these long-context LLMs presents challenges related to the key-value (KV) cache, which stores previous key-value activations to avoid re-computation.

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LLM Large Language Models Algorithm ML 80

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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.

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Flipboard

NOVEMBER 4, 2024

By Adam Fourney, Principal Researcher; Gagan Bansal, Senior Researcher; Hussein Mozannar, Senior Researcher; Victor Dibia, Principal Research Software Engineer; Saleema Amershi, Partner Research Manager Contributors: Adam Fourney, Gagan Bansal, Hussein Mozannar, Cheng Tan, Eduardo Salinas, Erkang (Eric) Zhu, Friederike Niedtner, Grace Proebsting, Griffin Bassman, Jack Gerrits, Jacob Alber, Peter Chang, Ricky Loynd, Robert West, Victor […]

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Software Engineer Artificial Intelligence Artificial Intelligence Machine Learning 182

Unite.AI

NOVEMBER 4, 2024

In today's politically polarized world, finding common ground on complex social and political issues is becoming increasingly challenging. As societies grow more diverse, disagreements on crucial matters like climate change, immigration, and economic policy have only extended. Bringing people together to find consensus on complex issues often requires time, resources, and a level playing field where all voices can be heard.

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AI AI Artificial Intelligence Artificial Intelligence 157

Flipboard

NOVEMBER 4, 2024

In my latest conversation with ChatGPT, I caught myself saying "please" and "thank you." My wife, overhearing this, couldn't help but laugh at my politeness toward a machine. Had I gone too far in humanizing AI, or was I onto something more profound?

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Machine Learning Research at Apple

NOVEMBER 4, 2024

Neural contextual biasing allows speech recognition models to leverage contextually relevant information, leading to improved transcription accuracy. However, the biasing mechanism is typically based on a cross-attention module between the audio and a catalogue of biasing entries, which means computational complexity can pose severe practical limitations on the size of the biasing catalogue and consequently on accuracy improvements.

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Speaker: Lee Andrews, Founder at LJA New Media & Tony Karrer, Founder and CTO at Aggregage

This session will walk you through how one CEO used generative AI, workflow automation, and sales personalization to transform an entire security company—then built the Zero to Strategy framework that other mid-market leaders are now using to unlock 3.5x ROI. As a business executive, you’ll learn how to assess AI opportunities in your business, drive adoption across teams, and overcome internal resource constraints—without hiring a single data scientist.

AI

Flipboard

NOVEMBER 4, 2024

Young entrepreneurs who think their AI chatbots can model human behavior better than humans can, agreed to put their model to the test in the 2024 …

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AI Chatbots Chatbots AI AI 178

ODSC - Open Data Science

NOVEMBER 4, 2024

Introduction — Bridging the Gap Between Prototype and Production Working with AI has never been more approachable than since the advent of aligned, pre-trained Large Language Models (LLMs) like GPT-4, Claude, Mistral, Llama, and many others. 10 years ago, building an AI system meant gathering data, constructing model architectures, training models from scratch, deploying models to often entirely customized inference servers, and monitoring results through usually ad-hoc observability platforms.

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Prompt Engineering Prompt Engineer BERT Large Language Models 52

Flipboard

NOVEMBER 4, 2024

Dive deep into your favorite topics without spending anything. TL;DR: A wide range of online courses from Harvard University are available for free on edX.

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Machine Learning 156

Machine Learning Mastery

NOVEMBER 4, 2024

Natural language generation (NLG) is an enthralling area of artificial intelligence (AI) , or more specifically of natural language processing (NLP) , aimed at enabling machines to produce human-like text that drives human-machine communication for problem-solving.

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Speaker: Kevin Burke, Founder & Managing Director at Digital One and AI & Automation Consultant

AI and automation are currently transforming the way sales and marketing teams operate. Generative AI crafts personalized outreach at scale, while conversational AI bots are engaging prospects in real time. Robotic process automation streamlines manual workflows by triggering tasks the moment a prospect takes a key action, and advanced AI analytics surface hidden patterns in the pipeline, improve forecasting, and help teams make data-driven decisions with confidence.

AI

Flipboard

NOVEMBER 4, 2024

“A good recruiter can spot an AI-written application from a mile away." With the rise of AI-powered writing assistants, typing up your job application can be quicker than ever.

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Artificial Lawyer

NOVEMBER 4, 2024

This week’s Product Walk Through is with V7 Labs and its genAI-driven Go capability for extracting key data from legal documents.

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Flipboard

NOVEMBER 4, 2024

Assessing the risk of postoperative cardiovascular events before performing non-cardiac surgery is clinically important. The current risk score systems for preoperative evaluation may not adequately represent a small subset of high-risk populations. Accordingly, this study aimed at applying iterative random forest to analyze combinations of factors that could potentially be clinically valuable in identifying these high-risk populations.

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Machine Learning 128