Introduction Biological neurons are pivotal in artificial neural network research, mirroring the intricate structures responsible for brain functions. Soma, axons, dendrites, and synapses are part of neurons that help process information. McCulloch-Pitts Neuron is an early computational model that simulates the basic operations of these biological units. MORE
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When teams move from prototype AI projects to production-ready systems, they often discover the hard truth: shipping is only the beginning. Generative AI applications — whether voice agents, retrieval-augmented systems, or multi-step tool-calling agents — require robust evaluation, observability, and iteration to succeed at scale. Ian Cairns, co-founder and CEO of Freeplay, has seen this challenge firsthand.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Stress Testing FastAPI Application Build an optimized asynchronous machine learning application, then use Locust to stress test your app and determine if it is production-ready.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter AI-Powered Feature Engineering with n8n: Scaling Data Science Intelligence Generate strategic feature engineering recommendations using AI-powered workflows in n8n.
About Song Han News Publications Blog Course Awards Talks Media Team Gallery Efficient AI Computing, Transforming the Future. How Attention Sinks Keep Language Models Stable Guangxuan Xiao August 7, 2025 TL;DR We discovered why language models catastrophically fail on long conversations: when old tokens are removed to save memory, models produce complete gibberish.
Speaker: Jason Chester, Director, Product Management
In today’s manufacturing landscape, staying competitive means moving beyond reactive quality checks and toward real-time, data-driven process control. But what does true manufacturing process optimization look like—and why is it more urgent now than ever? Join Jason Chester in this new, thought-provoking session on how modern manufacturers are rethinking quality operations from the ground up.
Microsoft is integrating OpenAI’s open-weight language models, gpt-oss, into Azure AI Foundry and Windows AI Foundry, broadening its AI toolset. This expansion introduces gpt-oss-120b and gpt-oss-20b models to personal computers. The gpt-oss-120b model is designed for high-performance reasoning applications. Conversely, the gpt-oss-20b model operates on personal computers equipped with graphics processing units possessing a minimum of 16 gigabytes of memory.
Skip to main content Login Why Databricks Discover For Executives For Startups Lakehouse Architecture Mosaic Research Customers Customer Stories Partners Cloud Providers Databricks on AWS, Azure, GCP, and SAP Consulting & System Integrators Experts to build, deploy and migrate to Databricks Technology Partners Connect your existing tools to your Lakehouse C&SI Partner Program Build, deploy or migrate to the Lakehouse Data Partners Access the ecosystem of data consumers Partner Solutions
Explore 10 agentic AI terms and concepts that are key to understanding the latest AI paradigm everyone wants to talk about — but not everyone clearly understands.
Explore 10 agentic AI terms and concepts that are key to understanding the latest AI paradigm everyone wants to talk about — but not everyone clearly understands.
In Part 1 of our series, we established the architectural foundation for an enterprise artificial intelligence and machine learning (AI/ML) configuration with Amazon SageMaker Unified Studio projects. We explored the multi-account structure, project organization, multi-tenancy approaches, and repository strategies needed to create a governed AI development environment.
Evaluating Deep Learning models is an essential part of model lifecycle management. Whereas traditional models have excelled at providing quick benchmarks for model performance, they often fail to capture the nuanced goals of real-world applications. For instance, a fraud detection system might prioritize minimizing false negatives over false positives, while a medical diagnosis model might […] The post Evaluating Deep Learning Models with Custom Loss Functions and Calibration Metrics appe
Leveraging machine learning (ML) for recycling analytics is no longer a hypothetical or risky investment. Several recent breakthroughs have proven it is effective, suggesting it could become an industry staple. It may lead to innovative developments that reshape how people approach recycling. What can you learn from these early adopters? The Need for More Efficient Recycling Analytics Recycling analytics is complex.
Skip to main content Login Why Databricks Discover For Executives For Startups Lakehouse Architecture Mosaic Research Customers Customer Stories Partners Cloud Providers Databricks on AWS, Azure, GCP, and SAP Consulting & System Integrators Experts to build, deploy and migrate to Databricks Technology Partners Connect your existing tools to your Lakehouse C&SI Partner Program Build, deploy or migrate to the Lakehouse Data Partners Access the ecosystem of data consumers Partner Solutions
ETL and ELT are some of the most common data engineering use cases, but can come with challenges like scaling, connectivity to other systems, and dynamically adapting to changing data sources. Airflow is specifically designed for moving and transforming data in ETL/ELT pipelines, and new features in Airflow 3.0 like assets, backfills, and event-driven scheduling make orchestrating ETL/ELT pipelines easier than ever!
Amazon SageMaker Unified Studio represents the evolution towards unifying the entire data, analytics, and artificial intelligence and machine learning (AI/ML) lifecycle within a single, governed environment. As organizations adopt SageMaker Unified Studio to unify their data, analytics, and AI workflows, they encounter new challenges around scaling, automation, isolation, multi-tenancy, and continuous integration and delivery (CI/CD).
This is a landmark achievement. Computer scientists from Tsinghua University have announced the most significant breakthrough in algorithmic efficiency for finding the shortest path in networks in over 40 years. The team has successfully developed a new algorithm that overcomes the long-standing “sorting barrier” of Dijkstra’s renowned 1959 algorithm, a cornerstone of computing that has powered everything from GPS navigation to the internet’s data routing.
Traditionally, much of artificial intelligence (AI) and machine learning (ML) has been focused on the models themselves. How big the model is, how fast they are and how accurate they can be made. In the ever-evolving landscape of AI, this mindset has begun to shift to the possibility that it is the data – and not the model – that is being used as the foundation for success.
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.
Tired of spending hours on repetitive data tasks? These Python scripts can come in handy for the overworked data scientist looking to simplify daily workflows.
The rapid advancement of key technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and edge-cloud computing has significantly accelerated the transformation toward smart industries across various domains, including finance, manufacturing, and healthcare. Edge and cloud computing offer low-cost, scalable, and on-demand computational resources, enabling service providers to deliver intelligent data analytics and real-time insights to end-users.
Outliers are fascinating anomalies within datasets that can tell us much more than mere averages might suggest. In statistical analyses, recognizing these unusual data points can significantly alter perceptions and conclusions. They often provoke curiosity, prompting further investigation into why they deviate from the norm and what that might mean for the data as a whole.
OpenAI models have transformed the landscape of artificial intelligence, redefining what’s possible in natural language processing, machine learning, and generative AI. From the early days of GPT-1 to the groundbreaking capabilities of GPT-5 , each iteration has brought significant advancements in architecture, training data, and real-world applications.
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.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Agentic AI Hands-On in Python: A Video Tutorial Introducing a four-hour video workshop on agentic AI engineering from Jon Krohn and Edward Donner.
Executive Summary Effective AI governance frameworks are essential for managing the lifecycle of AI models, addressing transparency gaps, monitoring bias and drift, and adapting to evolving regulatory demands. Key practices include centralized model registries, automated compliance workflows, continuous monitoring, standardized templates, and cross-functional collaboration.
Happy to share my new GitHub project: “ An Amazon SageMaker Container for Hugging Face Inference on AWS Graviton ”. ✅ Based on a clean source build of llama.cpp ✅ Native integration with the SageMaker SDK and with Graviton3/Graviton4 instances ✅ Model deployment from the Hugging Face hub or an Amazon S3 bucket ✅ Deployment of existing GGUF models ✅ Deployment of safetensors models, with automatic GGUF conversion and quantization ✅ Support for OpenAI API ✅ Support for streaming and non-streaming
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 the world of data engineering, the most impactful work is often the least glamorous. At ODSC East, Veronika Durgin, VP of Data at Saks, struck a chord with her talk on the “10 Most Neglected Data Engineering Tasks.” Drawing from decades of experience in data architecture, engineering, and analytics, she emphasized the foundational practices that keep pipelines stable, teams agile, and businesses prepared for rapid technological change.
Large language models (LLMs) have achieved impressive performance, leading to their widespread adoption as decision-support tools in resource-constrained contexts like hiring and admissions. There is, however, scientific consensus that AI systems can reflect and exacerbate societal biases, raising concerns about identity-based harm when used in critical social contexts.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter How I Use AI Agents as a Data Scientist in 2025 And why data scientists must master AI agents before manual analysis becomes obsolete.
Home Good News Discoveries Innovations Global Good Health Green Impact Space AI Celebrities GNI Subscribe Artificial intelligence is learning to understand people in surprising new ways New research shows AI can analyze personality traits from written text—and even explain how it makes its decisions. Mac Oliveau Published Aug 12, 2025 1:07 PM PDT AI now detects personality traits from text and explains its reasoning, advancing psychology and ethical tech.
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
Skip to main content Login Why Databricks Discover For Executives For Startups Lakehouse Architecture Mosaic Research Customers Customer Stories Partners Cloud Providers Databricks on AWS, Azure, GCP, and SAP Consulting & System Integrators Experts to build, deploy and migrate to Databricks Technology Partners Connect your existing tools to your Lakehouse C&SI Partner Program Build, deploy or migrate to the Lakehouse Data Partners Access the ecosystem of data consumers Partner Solutions
Home Table of Contents Synthetic Data Generation Using the BLIP and PaliGemma Models Why VLM-as-Judge and Synthetic VQA Configuring Your Development Environment Set Up and Imports Download Images Locally Inference with the Salesforce BLIP Model Convert JSON File to the Hugging Face Dataset Format Inspect One Sample from the Dataset Push the Dataset to the Hugging Face Hub Inference with the Google PaliGemma Model Convert JSON File to the Hugging Face Dataset Format Inspect One Sample from the Da
Artificial intelligence (AI) developers are increasingly building language models with warm and empathetic personas that millions of people now use for advice, therapy, and companionship. Here, we show how this creates a significant trade-off: optimizing language models for warmth undermines their reliability, especially when users express vulnerability.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Diffusion Models Demystified: Understanding the Tech Behind DALL-E and Midjourney Understand the technical aspects of one of the most popular image generation model architectures.
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.
Introduction Biological neurons are pivotal in artificial neural network research, mirroring the intricate structures responsible for brain functions. Soma, axons, dendrites, and synapses are part of neurons that help process information. McCulloch-Pitts Neuron is an early computational model that simulates the basic operations of these biological units.
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