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Summary: The article explores the differences between data driven and AI driven practices. The right approach is necessary to improve decisions and ensure your business remains competitive. Data-driven and AI-driven approaches have become key in how businesses address challenges, seize opportunities, and shape their strategic directions. Introduction Are you struggling to decide between data-driven practices and AI-driven strategies for your business?
AI is merely one facet of a sweeping technological change underway, and companies that fail to recognize the importance of other converging technologies risk being left behind. Two other technologies advanced sensors and biotechnology are less visible, though no less important, and have been quietly advancing. Soon, the convergence of these three technologies is going to underpin a new reality that will shape the future decisions of every leader across industries.
Home Table of Contents Anomaly Detection: How to Find Outliers Using the Grubbs Test What Is an Outlier? How to Find Outliers with Grubbs Test Formulating the Hypotheses Null Hypothesis Alternative Hypothesis Calculate the Test Statistic Determining the Critical Value with t-Distribution Key Characteristics of the t-Distribution Performing the Grubbs Test Left-Tailed Grubbs Test Right-Tailed Grubbs Test Two-Tailed Grubbs Test Summary Citation Information Anomaly Detection: How to Find Outliers U
Author(s): Diop Papa Makhtar Originally published on Towards AI. a Developer coding with his laptop In the fast-evolving world of software development, the landscape is shifting dramatically. The rise of AI-generated code is heralding a new era of productivity and innovation. Tools like GitHub Copilot and OpenAIs Codex promise to speed up development cycles, reduce boilerplate coding, and democratize programming by lowering entry barriers.
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.
Artificial Intelligence (AI) is all the rage, and rightly so. By now most of us have experienced how Gen AI and the LLMs (large language models) that fuel it are primed to transform the way we create, research, collaborate, engage, and much more. Yet along with the AI hype and excitement comes very appropriate sanity-checks asking whether AI is ready for prime-time.
Evaluating the performance of Large Language Models (LLMs) is an important and necessary step in refining it. LLMs are used in solving many different problems ranging from text classification and information extraction. Choosing the correct metrics to measure the performance of an LLM can greatly increase the effectiveness of the model. In this blog, we will explore one such crucial metric the F1 score.
NEW YORK,Jan. 23, 2025 — Digital Power Optimization, Inc. (“DPO”), a developer and operator of power-dense data centers, today announced it has secured land and a power supply to develop a $200 millionhigh-performance computing facility inWisconsin Rapids, WI. This project will enable up to 20 megawatts of AI computing.
NEW YORK,Jan. 23, 2025 — Digital Power Optimization, Inc. (“DPO”), a developer and operator of power-dense data centers, today announced it has secured land and a power supply to develop a $200 millionhigh-performance computing facility inWisconsin Rapids, WI. This project will enable up to 20 megawatts of AI computing.
TL;DR: The brain may have evolved a modular architecture for daily tasks, with circuits featuring functionally specialized modules that match the task structure. We hypothesize that this architecture enables better learning and generalization than architectures with less specialized modules. To test this, we trained reinforcement learning agents with various neural architectures on a naturalistic navigation task.
In the era of AI, chatbots have revolutionized how we interact with technology. Perhaps one of the most impactful uses is in the healthcare industry. Chatbots are able to deliver fast, accurate information, and help individuals more effectively manage their health. In this article, we’ll learn how to develop a medical chatbot using Gemini 2.0, […] The post Building a Medical Chatbot with Gemini 2.0, Flask and Vector Embedding appeared first on Analytics Vidhya.
Introduction Training large language models (LLMs) is an involved process that requires planning, computational resources, and domain expertise. Data scientists, machine learning practitioners, and AI engineers alike can fall into common training or fine-tuning patterns that could compromise a model’s performance or scalability.
Large pretrained models are showing increasingly better performance in reasoning and planning tasks across different modalities, opening the possibility to leverage them for complex sequential decision making problems. In this paper, we investigate the capabilities of Large Language Models (LLMs) for reinforcement learning (RL) across a diversity of interactive domains.
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.
Artificial intelligence (AI) has transformed industries, but its large and complex models often require significant computational resources. Traditionally, AI models have relied on cloud-based infrastructure, but this approach often comes with challenges such as latency, privacy concerns, and reliance on a stable internet connection. Enter Edge AI, a revolutionary shift that brings AI computations directly to devices like smartphones, IoT gadgets, and embedded systems.
Nvidia issued its anticipated raft of news at CES this week, heres an overview of announcements for the HPC-AI sector: Mega Omniverse Blueprint for Industrial Robot Fleet Digital Twins The company said Mega is an omniverse framework for next-gen industrial AI and robot simulation through software-defined testing and optimization of factories and warehouses.
Microsoft has released a new preview update, KB5050094 , for Windows 11 24H2 on Tuesday, which aims to fix multiple bugs affecting the operating system, including issues arising from the January Patch Tuesday update. Microsoft releases preview update KB5050094 for Windows 11 24H2 KB5050094 addresses audio issues where USB headphones, as well as other devices connected through a digital-to-analog converter (DAC), failed to produce sound, displaying the error message: “Insufficient system re
In this article, we dive into the concepts of machine learning and artificial intelligence model explainability and interpretability. We explore why understanding how models make predictions is crucial, especially as these technologies are used in critical fields like healthcare, finance, and legal systems. Through tools like LIME and SHAP, we demonstrate how to gain insights […] The post ML and AI Model Explainability and Interpretability 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
Introduction Text-based adventure games have a timeless appeal. They allow players to imagine entire worlds, from shadowy dungeons and towering castles to futuristic spacecraft and mystic realms, all through the power of language.
Scaling the capacity of language models has consistently proven to be a reliable approach for improving performance and unlocking new capabilities. Capacity can be primarily defined by two dimensions: the number of model parameters and the compute per example. While scaling typically involves increasing both, the precise interplay between these factors and their combined contribution to overall capacity remains not fully understood.
The payment card giant MasterCard just fixed a glaring error in its domain name server settings that could have allowed anyone to intercept or divert Internet traffic for the company by registering an unused domain name. The misconfiguration persisted for nearly five years until a security researcher spent $300 to register the domain and prevent it from being grabbed by cybercriminals.
In 2021, researchers at MIT and McKinsey teamed up to ask more than 100 companies how they were using AI in their operations and to learn what separated the highest-performing companies from the rest. They conducted a similar survey in 2023 to see what had changed. They found that the gap between leading companies and the rest had widened; that the payback-period for AI investments was much shorter; and that leading companies were better at identifying and implementing use cases that delivered p
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.
TYSONS CORNER, VA, Jan. 30, 2025 AI-powered business intelligence company MicroStrategy today announced the latest release of MicroStrategy ONE, which helps enterprises gain value from generative AI by personalizing the AI experience for each user. MicroStrategys Auto AI bot delivers a human-like conversational experience for users interacting with data.
Beam search is a powerful decoding algorithm extensively used in natural language processing (NLP) and machine learning. It is especially important in sequence generation tasks such as text generation, machine translation, and summarization. Beam search balances between exploring the search space efficiently and generating high-quality output. In this blog, we will dive deep into the […] The post What is Beam Search in NLP Decoding?
The hacker known as IntelBroker has claimed responsibility for breaching Hewlett Packard Enterprise (HPE), exposing sensitive data, including source code, certificates, and personally identifiable information (PII), now available for sale online. This incident was revealed in a conversation with Hackread.com and later announced on Breach Forums, a cybercrime forum the hacker administers.
While today’s world is increasingly driven by artificial intelligence (AI) and large language models (LLMs), understanding the magic behind them is crucial for your success. To get you started, Data Science Dojo and Weaviate have teamed up to bring you an exciting webinar series: Master Vector Embeddings with Weaviate. We have carefully curated the series to empower AI enthusiasts, data scientists, and industry professionals with a deep understanding of vector embeddings.
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?
About the tracking dots Most home and office printers add metadata to printed sheets in the form of very tiny yellow dots (sometimes called a machine identification code) that cant be seen with the naked eye. The layout of the dots are different between printer brands and some dont leave any at all. Information like serial number and sometime the print time is encoded in these dots.
DeepSeek, an AI startup just over a year old, stirred awe and consternation in Silicon Valley with its breakthrough artificial intelligence model that offered comparable performance to the worlds best chatbots at seemingly a fraction of the cost.
SAN MATEO, Calif., Jan. 27, 2025 — Bodo.ai, an open source start-up focused on transformative Python, has released its high performance computing engine for Python under the Apache License. The need to process ever-larger amounts of data with higher precision, faster speeds, lower costs, and a smaller carbon footprint has grown exponentially. Bodo.
In this Leading with Data, we explore the transformative journey of Navin Dhananjaya, Chief Solutions Officer at Merkle, as he shares key milestones, practical applications of generative AI, and future possibilities for AI agents. Discover how AI is reshaping customer experiences and the data science landscape. You can listen to this episode of Leading with […] The post Exploring AI Agents in Customer Experience with Navin Dhananjaya appeared first on Analytics Vidhya.
Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.
Quantum computing has gained significant attention on Wall Street following Alphabet’s (GOOG 1.62%) (GOOGL 1.60%) announcement of a milestone with its new quantum chip, Willow. Alphabet stated that Willow can exponentially reduce errors as it scales up, completing a standard benchmark computation in five minutesan operation that would take one of the fastest supercomputers today 10 septillion years.
This paper presents an efficient decoding approach for end-to-end automatic speech recognition (E2E-ASR) with large language models (LLMs). Although shallow fusion is the most common approach to incorporate language models into E2E-ASR decoding, we face two practical problems with LLMs. (1) LLM inference is computationally costly. (2) There may be a vocabulary mismatch between the ASR model and the LLM.
A common use case with generative AI that we usually see customers evaluate for a production use case is a generative AI-powered assistant. However, before it can be deployed, there is the typical production readiness assessment that includes concerns such as understanding the security posture, monitoring and logging, cost tracking, resilience, and more.
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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