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Author(s): Paul Ferguson, Ph.D. Originally published on Towards AI. Why We’re Demanding Answers from Our Smartest Machines Image generated by Gemini AI Artificial intelligence is making decisions that impact our lives in profound ways, from loan approvals to medical diagnoses. Yet, for all their sophistication, they often can’t explain their choices — this lack of transparency isn’t just frustrating — it’s increasingly problematic as AI becomes more integrated into critical areas of our lives.
Our previous guide discussed the first Agentic AI design pattern from the Reflection, Tool Use, Planning, and Multi-Agent Patterns list. Now, we will talk about the Tool Use Pattern in Agentic AI. Firstly, let us reiterate the Reflection Pattern article: That article sheds light on how LLMs can use an iterative generation process and self-assessment […] The post What is Agentic AI Tool Use Pattern?
I’m worried about AI. I’m not worried about it taking my job. I believe AI is a genuine productivity tool. By which I mean it can make developers produce more. The question is whether those developers are producing something good or not. The difference between an experienced developer and a junior is that an experienced developer knows: There’s more than one good solution to every problem.
Arm Holdings Plc is canceling a license that allowed longtime partner Qualcomm Inc. to use Arm intellectual property to design chips, escalating a legal dispute over vital smartphone technology.
Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.
Last Updated on October 19, 2024 by Editorial Team Author(s): Shenggang Li Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium. Time Series Clustering Using Auto-Regressive Models, Moving Averages, and Nonlinear Trend Functions Photo by Ricardo Gomez Angel on Unsplash Clustering time series data, like stock prices or gene expression, is often difficult.
Facebook parent company Meta Platforms Inc. has removed several accounts across Threads and Instagram that were used to track celebrities’ private jets, including the jet owned by its Chief Executive Officer Mark Zuckerberg, citing a risk of “physical harm.
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Facebook parent company Meta Platforms Inc. has removed several accounts across Threads and Instagram that were used to track celebrities’ private jets, including the jet owned by its Chief Executive Officer Mark Zuckerberg, citing a risk of “physical harm.
Home Table of Contents Predictive Maintenance Using Isolation Forest What Is Predictive Maintenance? And Why Anomaly Detection? Predictive Maintenance Predictive Maintenance as Anomaly Detection The Isolation Forest Algorithm Isolation Trees Anomaly Score Building the Forest Implementing Predictive Maintenance Using Isolation Forest Setup and Data Preparation Building Isolation Trees Computing Anomaly Scores Building the Forest Testing and Evaluating Summary Citation Information Predictive Maint
OpenAI’s Swarm framework is designed to create a user-friendly and flexible environment for coordinating multiple agents. While it’s primarily intended for educational and experimental use, OpenAI advises against using Swarm in production settings, but it is a framework worth exploring. Its core purpose is to demonstrate the concepts of “handoffs” and “routines,” patterns that help […] The post How OpenAI Swarm Enhances Multi-Agent Collaboration?
Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage
There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.
The team here at insideAI News is deeply entrenched in keeping the pulse of the big data ecosystem of companies from around the globe. We’re in close contact with the movers and shakers making waves in the technology areas of big data, data science, machine learning, AI and deep learning. Our in-box is filled each day with new announcements, commentaries, and insights about what’s driving the success of our industry so we’re in a unique position to publish our quarterly IMPACT 50 List.
Applications powered by large language models (LLMs) are revolutionizing the way businesses operate, from automating customer service to enhancing data analysis. In today’s fast-paced technological landscape, staying ahead means leveraging these powerful tools to their full potential. For instance, a global e-commerce company striving to provide exceptional customer support around the clock can implement LangChain to develop an intelligent chatbot.
At Apple, we believe privacy is a fundamental human right. Our work to protect user privacy is informed by a set of privacy principles, and one of those principles is to prioritize using on-device processing. By performing computations locally on a user’s device, we help minimize the amount of data that is shared with Apple or other entities. Of course, a user may request on-device experiences powered by machine learning (ML) that can be enriched by looking up global knowledge hosted on servers.
The LangGraph ReAct Function-Calling Pattern offers a powerful framework for integrating various tools like search engines, calculators, and APIs with an intelligent language model to create a more interactive and responsive system. This pattern is built upon the Reasoning + Acting (ReAct) approach, which allows a language model to not only reason through queries but […] The post Dynamic AI Workflows Through LangGraph ReAct Function Calling appeared first on Analytics Vidhya.
Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives
Manufacturing is evolving, and the right technology can empower—not replace—your workforce. Smart automation and AI-driven software are revolutionizing decision-making, optimizing processes, and improving efficiency. But how do you implement these tools with confidence and ensure they complement human expertise rather than override it? Join industry expert Andrew Skoog as he explores how manufacturers can leverage automation to enhance operations, streamline workflows, and make smarter, data-dri
Acrolinx, the AI-powered content governance software provider, sought to discover where current enterprise attitudes towards AI stand. Today, the company released its report "Generative AI in Business: A Snapshot of Enterprise Use, Risk, and Regulation.
Why evaluate large language models (LLMs)? Because these models are stochastic , responding based on probabilities, not guarantees. With new models popping up almost daily, it’s crucial to know if they truly perform better. Moreover, LLMs have numerous quirks: they hallucinate (confidently spouting falsehoods), format responses poorly, slip into the wrong tone, go “off the rails,” or get overly cautious.
The richness of Python’s ecosystem has one downside: it makes it difficult to decide which libraries are the best for your needs. This article is an attempt to amend this by suggesting ten (and some more, as a bonus) libraries that are an absolute must in data science.
OpenAI, a leading AI company, offers API keys for developers to interact with its platform and utilize its LLM models in various projects. In this article, you’ll learn how to create your own OpenAI API Key, updated as of 2024. The simplest and recommended way to generate your API key is through the OpenAI developer […] The post How to Generate Your Own OpenAI API Key and Add Credits?
Documents are the backbone of enterprise operations, but they are also a common source of inefficiency. From buried insights to manual handoffs, document-based workflows can quietly stall decision-making and drain resources. For large, complex organizations, legacy systems and siloed processes create friction that AI is uniquely positioned to resolve.
In this contributed article, Mohsin Hussain, Chief Technology Officer at LiveRamp, believes that as AI models become more sophisticated and accessible, many companies are entering an AI “arms race.” To ensure success, companies must fortify their enterprise systems with more robust and higher-quality data.
The rapid advancement of artificial intelligence (AI) has led to a new era of models designed to process and generate data across multiple modalities. These include text, images, audio, and video. These multimodal models are increasingly used in various applications, from content creation to advanced analytics. This article will introduce you to the concept of […] The post 7 Popular Multimodal Models and their Uses appeared first on Analytics Vidhya.
Speaker: Chris Townsend, VP of Product Marketing, Wellspring
Over the past decade, companies have embraced innovation with enthusiasm—Chief Innovation Officers have been hired, and in-house incubators, accelerators, and co-creation labs have been launched. CEOs have spoken with passion about “making everyone an innovator” and the need “to disrupt our own business.” But after years of experimentation, senior leaders are asking: Is this still just an experiment, or are we in it for the long haul?
Enterprise AI adoption is still in its exploration stages, but companies are finding innovative ways to leverage their GPU investments beyond AI applications and reaping good results, according to a new report from Hammerspace, the company orchestrating the next data cycle.
Over the years, organizations have amassed a vast amount of unstructured text data—documents, reports, and emails—but extracting meaningful insights has remained a challenge.
Have you ever wondered how to build a multilingual application that can effortlessly translate text from English to other languages? Imagine creating your very own translation tool, leveraging the power of LangChain to handle the heavy lifting. In this article, we will learn how to build a basic application using LangChain to translate text from […] The post How to Build a Simple LLM Application with LCEL?
Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage
When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m
In this contributed article, Michela Taufer, Ph.D. and Chandra Krintz, Ph.D., highlight that as we unlock AI's full potential, society faces an unsettling paradox: the technology used to solve global problems may exacerbate one of our most pressing challenges - climate change.
Amazon Rekognition people pathing is a machine learning (ML)–based capability of Amazon Rekognition Video that users can use to understand where, when, and how each person is moving in a video. This capability can be used for multiple use cases, such as for understanding: Retail analytics – Customer flow in the store and identifying high-traffic areas Sports analytics – Players’ movements across the field or court Industrial safety – Workers’ movement in work environments to promote compliance w
Check out the previous articles in this series: Understanding RAG Part I: Why It’s Needed Understanding RAG Part II: How Classic RAG Works Having previously introduced what is RAG, why it matters in the context of Large Language Models (LLMs), and what does a classic retriever-generator system for RAG look like, the third post in […]
With this blog, I would like to show one small agent built-in with `LangGraph` and Google Gemini for research purposes. The objective is to demonstrate one research agent (Paper-to-Voice Assistant) who plans to summarize the research paper. This tool will use a vision model to infer the information. This method only identifies the step and […] The post Paper-to-Voice Assistant: AI Agent Using Multimodal Approach appeared first on Analytics Vidhya.
In this new webinar, Tamara Fingerlin, Developer Advocate, will walk you through many Airflow best practices and advanced features that can help you make your pipelines more manageable, adaptive, and robust. She'll focus on how to write best-in-class Airflow DAGs using the latest Airflow features like dynamic task mapping and data-driven scheduling!
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