2024

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2024’s Biggest Moments in AI

KDnuggets

2024 has been yet another groundbreaking year for AI, with major breakthroughs, industry shifts, and ethical challenges shaping its future. Let's uncover together the key moments that defined AI this year about to finalize.

AI 363
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27 Equations Every Data Scientist Needs to Know

Towards AI

Author(s): Julia Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium. Everybody’s talking about AI, but how many of those who claim to be “experts” can actually break down the math behind it? It’s easy to get lost in the buzzwords and headlines, but the truth is — without a solid understanding of the equations and theories driving these technologies, you’re only skimming the surface.

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Data Sovereignty in the AI Era

insideBIGDATA

In this contributed article, Yoram Novick, President and CEO of Zadara, discusses how enterprises are in search of and implementing their own AI powered clouds, and the benefits and challenges they face in the effort to keep their data available and secure.

AI 493
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7 Projects to Master Data Engineering

KDnuggets

Learn to build, run, and manage data engineering pipelines both locally and in the cloud using popular tools.

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Mastering Apache Airflow® 3.0: What’s New (and What’s Next) for Data Orchestration

Speaker: Tamara Fingerlin, Developer Advocate

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.

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Remote Data Science Jobs: 5 High-Demand Roles for Career Growth

Data Science Dojo

Imagine a world where bustling office spaces fell silent, and the daily commute became a distant memory. When COVID-19 hit, that world became a reality, transforming how we work. Remote work quickly transitioned from a perk to a necessity, and data science—already digital at heart—was poised for this change. According to a recent report from Gartner, 47% of employers are open to full-time remote work even beyond the pandemic, highlighting a massive shift in the job landscape.

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How to Manage Your Data Science Project: 7 Top Tips

DagsHub

Source: Unsplash In the high-stakes world of data science and AI, project success is far from guaranteed. As leaders in this field, we're acutely aware of the multifaceted challenges that can derail even the most promising initiatives. From models falling short of requirements to production failures with real-world data, the path to success is fraught with potential pitfalls.

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AI in Banking – How Artificial Intelligence is Used in Banks

insideBIGDATA

In this contributed article, Ishan Gupta. CEO and Co-founder of RipenApps, discusses how banks have historically been at the forefront of technological advancements, they are renowned for using computers as well as providing internet-based financial services. However, the rise of AI has brought with it a new dawn of innovations. These days, AI is disrupting the entire banking sector in several ways.

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5 Free Courses to Master Math for Data Science

KDnuggets

Want to learn math for data science? Check out these three courses to learn linear algebra, calculus, statistics, and more.

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Could AI Replace Software Engineers? Meet Devin, the First AI-Driven Engineer

Analytics Vidhya

Introduction Software development is on the brink of a transformative shift as artificial intelligence (AI) continues to push the boundaries of what was once deemed impossible. Enter Devin AI, an AI software engineer developed by the innovative minds at Cognition. This groundbreaking creation aims to revolutionize how we approach software development, streamlining the process and […] The post Could AI Replace Software Engineers?

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Demystifying Ensemble Methods: Boosting, Bagging, and Stacking Explained

Machine Learning Mastery

Unity makes strength. This well-known motto perfectly captures the essence of ensemble methods: one of the most powerful machine learning (ML) approaches -with permission from deep neural networks- to effectively address complex problems predicated on complex data, by combining multiple models for addressing one predictive task.

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Agent Tooling: Connecting AI to Your Tools, Systems & Data

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.

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On Device Llama 3.1 with Core ML

Machine Learning Research at Apple

Many app developers are interested in building on device experiences that integrate increasingly capable large language models (LLMs). Running these models locally on Apple silicon enables developers to leverage the capabilities of the user's device for cost-effective inference, without sending data to and from third party servers, which also helps protect user privacy.

ML 338
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Fine-tuning large language models (LLMs) for 2025

Dataconomy

Large language models (LLMs) are powerful tools for generating text, but they are limited by the data they were initially trained on. This means they might struggle to provide specific answers related to unique business processes unless they are further adapted. Fine-tuning is a process used to adapt pre-trained models like Llama, Mistral, or Phi to specialized tasks without the enormous resource demands of training from scratch.

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Which IDEs do software engineers love, and why?

Flipboard

It’s been nearly 6 months since our research into which AI tools software engineers use, in the mini-series, AI tooling for software engineers: reality check. At the time, the most popular tools were ChatGPT for LLMs, and GitHub copilot for IDE-integrated tooling. Then this summer, I saw the Cursor IDE becoming popular around when Anthropic’s Sonnet 3.5 model was released, which has superior code generation compared to ChatGPT.

AI 177
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Identification of Hazardous Areas for Priority Landmine Clearance: AI for Humanitarian Mine Action

ML @ CMU

TL;DR: Landmines pose a persistent threat and hinder development in over 70 war-affected countries. Humanitarian demining aims to clear contaminated areas, but progress is slow: at the current pace, it will take 1,100 years to fully demine the planet. In close collaboration with the UN and local NGOs, we co-develop an interpretable predictive tool for landmine contamination to identify hazardous clusters under geographic and budget constraints, experimentally reducing false alarms and clearance

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How to Modernize Manufacturing Without Losing Control

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

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LLM Benchmarks for Comprehensive Model Evaluation 

Data Science Dojo

In the rapidly evolving world of artificial intelligence, Large Language Models (LLMs) have become pivotal in transforming how machines understand and generate human language. To ensure these models are both effective and responsible, LLM benchmarks play a crucial role in evaluating their capabilities and limitations. This blog delves into the significance of popular benchmarks for LLM and explores some of the most influential LLM benchmarks shaping the future of AI.

AI 418
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Comparing the Llama Models: Llama 3 vs Llama 3.1 vs Llama 3.2

Data Science Dojo

The Llama model series has been a fascinating journey in the world of AI development. It all started with Meta’s release of the original Llama model, which aimed to democratize access to powerful language models by making them open-source. It allowed researchers and developers to dive deeper into AI without the constraints of closed systems. Fast forward to today, and we have seen significant advancements with the introduction of Llama 3, Llama 3.1, and the latest, Llama 3.2.

AI 397
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Why Mathematics is Essential for Data Science and Machine Learning

insideBIGDATA

In this feature article, Daniel D. Gutierrez, insideAInews Editor-in-Chief & Resident Data Scientist, explores why mathematics is so integral to data science and machine learning, with a special focus on the areas most crucial for these disciplines, including the foundation needed to understand generative AI.

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Streaming Langchain: Real-time Data Processing with AI

Data Science Dojo

As the world becomes more interconnected and data-driven, the demand for real-time applications has never been higher. Artificial intelligence (AI) and natural language processing (NLP) technologies are evolving rapidly to manage live data streams. They power everything from chatbots and predictive analytics to dynamic content creation and personalized recommendations.

AI 370
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Automation, Evolved: Your New Playbook for Smarter Knowledge Work

Speaker: Frank Taliano

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.

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Crusoe Closes $600M in Series D Round at $2.8 Billion Valuation to Power AI

insideBIGDATA

Crusoe, the vertically integrated AI infrastructure provider, announced it has closed a $600 million Series D funding round. The investment was led by Founders Fund, with participation from new and existing investors, including Fidelity, Long Journey Ventures, Mubadala, NVIDIA, Ribbit Capital, and Valor Equity Partners.

AI 397
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Is AI-Powered Surveillance Contributing to the Rise of Totalitarianism?

insideBIGDATA

In this contributed article, Aayam Bansal explores the increasing reliance on AI in surveillance systems and the profound societal implications that could lead us toward a surveillance state. This piece delves into the ethical risks of AI-powered tools like predictive policing, facial recognition, and social credit systems, while raising the question: Are we willing to trade our personal liberties for the promise of safety?

AI 416
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AI Hallucinations Are Inevitable—Here’s How We Can Reduce Them

insideBIGDATA

In this contributed article, Ulrik Stig Hansen, President and Co-Founder of Encord, discusses the reality – AI hallucinations aren’t bugs in the system—they’re features of it. No matter how well we build these models, they will hallucinate. Instead of chasing the impossible dream of eliminating hallucinations, our focus should be on rethinking model development to reduce their frequency and implementing additional steps to mitigate the risks they pose.

AI 417
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How the Age of Generative AI is Changing a CISOs Approach to Security

insideBIGDATA

In this contributed article, Chris Peake, Chief Information Security Officer (CISO) and Senior Vice President of Security at Smartsheet, explores how the role of CISOs is evolving to address new security challenges posed by generative AI. The article underscores the importance of collaboration and adaptability to keep organizations secure as AI is expected to continue to reshape cybersecurity in 2025.

AI 436
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The 2nd Generation of Innovation Management: A Survival Guide

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?

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Embrace Innovation While Reducing Risk: The Three Steps to AI-grade Data at Scale

insideBIGDATA

In this contributed article, Kunju Kashalikar, Senior Director of Product Management at Pentaho, discusses how to dream big without the risk: three steps to AI-grade data. The industry adage of ‘garbage-in-garbage-out' has never been more applicable than now. Clean, accurate data is the key to winning the AI race - but leaving the starting blocks is the challenge for most.

AI 398
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Why Data Quality is the Secret Ingredient to AI Success

insideBIGDATA

In this contributed article, engineering leader Uma Uppin emphasizes that high-quality data is fundamental to effective AI systems, as poor data quality leads to unreliable and potentially costly model outcomes. Key data attributes like accuracy, completeness, consistency, timeliness, and relevance play crucial roles in shaping AI performance and minimizing ethical risks.

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Snowflake Unveils Snowflake Intelligence: The Future of Data Agents for Enterprise AI

insideBIGDATA

Snowflake Intelligence is a groundbreaking platform that will empower business users to create data agents, so they can analyze, summarize, and take action from their enterprise data Snowflake (NYSE: SNOW), the AI Data Cloud company, announced Snowflake Intelligence (in private preview soon), a new platform that will enable enterprises to easily ask business questions across their enterprise […]

AI 392
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KNIME Releases AI Companion to Drive Smarter Collaboration with AI

insideBIGDATA

KNIME, the open source data analytics and AI company, announced the launch of its AI companion K-AI to all users. With K-AI, users can co-create powerful data workflows with AI. K-AI will answer questions, make recommendations, and extend or build whole data workflows based on user prompts.

AI 389
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Apache Airflow® Best Practices: DAG Writing

Speaker: Tamara Fingerlin, Developer Advocate

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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AI Automation: A New Era in Business Efficiency and Innovation

insideBIGDATA

In this contributed article, Dmitry Shapiro, Founder & CEO of MindStudio, discusses how businesses worldwide are recognizing the potential of AI to not only streamline complex, data-heavy tasks but also to redefine traditional job roles, preparing organizations to thrive in an increasingly fast-paced, data-centric landscape.

AI 419
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Andrej Karpathy Praises DeepSeek V3’s Frontier LLM, Trained on a $6M Budget

Analytics Vidhya

Last year, the DeepSeek LLM made waves with its impressive 67 billion parameters, meticulously trained on an expansive dataset of 2 trillion tokens in English and Chinese comprehension. Setting new benchmarks for research collaboration, DeepSeek ingrained the AI community by open-sourcing both its 7B/67B Base and Chat models. Now, what if I tell you there […] The post Andrej Karpathy Praises DeepSeek V3s Frontier LLM, Trained on a $6M Budget appeared first on Analytics Vidhya.

Analytics 367
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Capital One Survey Around AI Readiness

insideBIGDATA

A new Capital Onesurvey"AI readiness survey: Are companies ready for AI adoption?" found that 87% of business leaders see their data ecosystem as ready to build and deploy AI at scale, yet 70% of technical practitioners spend hours daily fixing data issues.

AI 398
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Tuskira Emerges from Stealth with $28.5M to Launch AI-Powered Unified Threat Defense Platform

insideBIGDATA

Tuskira, a pioneering threat defense platform leveraging an AI-powered security mesh, has launched out of stealth mode with $28.5 million in funding. The round was led by Intel Capital and SYN Ventures, with participation from Sorenson Capital, Rain Capital, Wipro Ventures, and other key industry leaders.

AI 370
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How to Achieve High-Accuracy Results When Using LLMs

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