Resources

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Pixel Tolerance vs. IOU - Which One Should You Use for Quality Training Data?

The purpose of data annotation for computer vision is to teach a model how to identify and classify things. The human mind “annotates” effortlessly - we see something and we identify it (with varying degrees of specificity and accuracy based on prior experience). IOU (intersection over union) and pixel tolerance helps us measure the quality of the annotation.

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Responsible AI: How to Mitigate Bias in Your Training Data

The more we use AI to power and automate crucial parts of our daily lives, the more we need to be able to trust that these models are accurate, equitable, and high-performing, which means we need to mitigate bias in our training data.

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Alegion Teams with Malaysian Government Agency to Expand ML Workforce Opportunities

Alegion and Malaysian government agency Yayasan Peneraju collaborate to train and certify 1,600 machine learning data labeling professionals, empowering them to earn a sustainable income in the fast-growing AI market.

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Welcome to The Keypoint

Welcome to The Keypoint - a newsletter from Alegion updating our customers on the latest and greatest releases to our data labeling platform!

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Investment Banking Veteran and Software Market Strategist Jonathan Price Joins Alegion’s Board of Directors

Investment Banking Veteran and Software Market Strategist Jonathan Price Joins Alegion's Board of Directors

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Calling All Data Scientists - Survey for Alegion 2021 State of the Industry

Each year, Alegion releases our analysis of the AI / ML space. We call upon expert data scientists who are deploying world-changing projects to answer a brief survey on how their projects are going and what challenges they may be running into.

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Dr. Carla Brodley Joins Alegion’s Board of Directors

Renowned Machine Learning Researcher and Inclusive Computing Leader Dr. Carla Brodley Joins Alegion’s Board of Directors.

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A Step-by-Step Guide to Quality Training Data for Computer Vision

Alegion specializes in high-quality training data for computer vision. In this blog we walk through the 4 prioritized phases to quality training data, and explain the significance of specific metrics for quality

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How Alegion Stacks Up Against the VA Competition

How will you know if you have the best video annotation solution on the market? How does Alegion's VA solution compare to the competition?

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Active Learning: What it is & How Alegion Does it

Active learning is a method used in machine learning that helps focus data labeling on instances in your data that will drive greatest value for your model

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Part 3: Building a Video Annotation Platform

With the rise of video, Alegion has become the top vendor for video annotation. In a 3 part series covering a year of intense experimentation and development. Today, in the final part of our 3 part series we explore the significance of ground truth video data errors for annotation: how they happen, the most common problems, how competitor platforms handle issues (spoiler: it’s not ideal), and why Alegion chose to go another way.

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Part 2: Building a Video Annotation Platform

With the rise of video, Alegion has become the top vendor for video annotation. In a 3 part series covering a year of intense experimentation and development. Today, in part 2 of a 3 part series we reflect on how we built a transformational solution to address the 7 major pain points of video annotation. We knew that if we could solve for the goals of our clients, the benefits would be tremendous across use cases and verticals.

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Part 1: Building a Video Annotation Platform by Chip Ray, CTO

Over the past year, Alegion has taken on the challenge of building the best possible video annotation experience. Having assessed the tooling available on the market, we knew there just had to be a better way. At the same time, we were witnessing an explosion of research and focus around video data in the computer vision (CV) space.

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Alegion Closes Record Year Naming New Executives to Key Leadership Roles

Alegion Inc., a leading training data provider, closes record year naming new COO/CFO and CPO

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Designing for Video Annotation

Alegion opens up its powerful data labeling platform for self-serve use with the launch of Alegion Control

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Alegion Control - The New Self Serve Data Labeling Platform

Alegion opens up its powerful data labeling platform for self-serve use with the launch of Alegion Control

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Alegion Welcomes A New CEO!

Alegion Inc., a leading training data provider, announces David Mather, a tenured tech executive and growth-expert CEO, as the President and CEO.

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Entity Groups - Purpose-Built Annotation Tooling

Entity Groups organizes all of the items to be annotated for a particular type of object into a ‘shopping list’ for the annotator.

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Alegion Launches ML-assisted Annotation for high-definition, long-running video footage

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Managing Incident Response Like A Pro

Learn about our incident response best practices collected and implemented at Alegion, forged from input provided by industry experts, as well as plenty of hands-on experience managing incidents while growing our platform over the years.

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CV Case Study: Complex Video Annotation For Loss Prevention

Learn how Alegion helps companies leverage high-precision video annotation to prevent inventory loss.

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Data Labeling for AI & ML Experimentation

This post shows how more experiments & smaller volumes of data help high-performing AI teams build baselines quickly & rapidly iterate to improve.

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SmartPoly - Empowering Annotators with ML

SmartPoly takes the extreme points of an object in image or video as input from the annotator and uses a CNN to segment the object.

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How ML Improves Quality

Balancing ML innovations and human expertise to deliver the highest quality

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CV Case Study: Subjective Scene Classification for Improved Search Experience

This CV case study shows how Alegion built a scalable data labeling pipeline for 30+ workflows & improved accuracy from 60% to 98% for a leading hospitality marketplace

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A Platform Built for Quality

This short guide shows how Alegion's Platform can improve your data labeling quality today

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ML Experimentation - Fail Fast, Learn Faster

In this episode, Saurabh and I talk to Alegion’s Chief Data Scientist, Cheryl Martin, all about experimentation in ML and how to fail fast to learn faster.

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Alegion Flex Launch

Alegion Flex is the first ML data labeling solution designed for ML experimentation

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How Alegion Guarantees Quality

This white paper shows how Alegion is able to guarantee quality through our platform, our CS team, and use of ML.

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CV Case Study: Tracking Attentiveness & Emotion

This case study shows how leveraging high-precision data annotation empowers companies to understand audience behavior.

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Faster R-CNN: Using Region Proposal Network for Object Detection

This article explores Faster R-CNN & how RPNs, fully convolutional networks, simultaneously predict object boundaries & object scores at each detection.

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Ai & ML in the 2010s - From Science Fiction to Reality

The 2010s was the decade that “machine intelligence” made the leap from sci-fi to reality. Now that we have this technology, what will we do with it?

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The Expense of Poorly Labeled Data

We take a good data set conducive towards modeling and distort the data in two different ways to show the effect of bad data on an ML model.

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Cost Of Poorly Labeled Data

Download this complimentary white paper to learn what causes poor quality training data and how your business can invest in preventing it.

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Checklist for Choosing a Platform

This short guide and accompanying checklist will help you when choosing the right annotation partner for your business.

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How to Improve Quality

Download this short complimentary guide to improve your data labeling quality today!

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NeurIPS 2019 Review - Insider's Guide

In this episode, Alegion’s Chief Data Scientist, Cheryl Martin, and Director of Engineering, Brent Schneeman, share the inside scoop on NeurIPS 2019.

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How To Measure Quality Data Labeling

This short guide will walk you through the four core metrics to measure your data labeling so that you can improve your quality.

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Data Science for Business

No BiAS is on the Top 10 AI Podcasts & Radio You Must Follow in 2019! Check out the latest episode, Data Science for Business - How to Build a Kickass Team

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How To Define Quality Data Labeling

This short guide will help you establish an unambiguous set of rules that defines what “quality” means in the context of your project.

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How to Achieve Quality Data Annotation

Alegion's Data Science Team put together a whitepaper on annotation guidelines and improving the quality of labeling data. Download a complimentary copy.

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CV & ML - Visual Understanding Beyond Object Recognition

CV is everywhere! From Snapchat filters to YouTube video buffering to medical diagnosis of x-rays to geospatial mapping for agriculture.

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Trends & Innovations in ML

Alegion Mentioned In Gartner's 2019 Hype Cycle

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Alegion ranked #1 place to work

We are excited and honored to be named the #1 place to work among small companies in Austin by The Austin American-Statesman.

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New Use Cases & Highlights from ICCV 2019

ICCV is a hotbed of innovative ideas and technology aimed at giving the gift of sight to machines to solve complex problems.

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Methods of Video Annotation

Annotated videos hold the future for training computer vision models. Learn the best method for scaling video annotation without diminishing complexity and compromising accuracy.

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Deep Learning for Object Detection Part II

Introduction to a more in depth piece about Fast R-CNN and the innovations that improve training and testing speed of object detection.

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Episode 4: Bias in ML

In this podcast episode, we discuss helpful mathematical bias and examples of how cognitive bias can skew your data compromising your model.

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5 Key takeaways from Trade show season

Trade show season has begun and these conferences continually demonstrate the demand that surrounds ML data labeling!

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Machine Learning for the Enterprise

This emerging world of AI runs on data. Learn how to properly prepare your data for supervised learning.

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Deep Learning for Object Detection Part I

Introducing a new series on state-of-the-art object detection techniques and localization methods that use region based object detectors.

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New Episode: Supervised vs Unsupervised learning

In this episode we break down the strengths and weaknesses of each approach and discuss various use cases to which each one best applies.

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NLP Part II: Understanding the challenges of NLP through Wittgenstein

Philosopher Ludwig Wittgenstein's insights into human communication can help us puzzle out the complexities of natural language processing.

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NLP Part I: Communication is Key

As difficult as it can be to successfully communicate with another person, it is even more challenging to teach the subtleties of human communication to a computer.

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Is data the new oil?

In the latest episode of our podcast, No BiaS, Melody, Nikhil, and Saurabh tease out the ideas behind the metaphor "Data is the new oil."

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Just the Cliffnotes - Supervised vs unsupervised learning

New executive summary - “Machine Learning” — Gives “computers the ability to learn without being explicitly programmed.“ — Arthur Samuel

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Alegion in WSJ & Forbes!

The Wall Street Journal and Forbes take notice that manually labeling data has become a huge bottleneck in developing AI solutions.

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Announcing our new podcast - No Bias

Check out out first episode of No Bias, where we discuss the emerging and ever shifting terrain of artificial intelligence and machine learning!

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Agriculture is deep into machine learning

ML is revolutionizing agriculture. The benefit to growers is faster, more precise decision making. But getting there requires huge amounts of training data.

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Survey Says: Outsource Your Data Labeling

Global survey of data scientists shows that organizations that outsource their data labeling are significantly more likely to get their ML project into production.

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Supervised vs Unsupervised Learning

What we learned attending 5 AI/ML conferences in June! And a new “how to” for choosing the right approach and data labeling technique for your ML projects.

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CV: what does it mean to "see"?

Aristotle observed the link between our desire for knowledge and our delight in sight that spurs the increasingly popular research into Computer Vision.

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Survey says: model confidence is built on a lot of training data

New survey reveals a concrete definition of “huge” in reference to 72% report that production-level model confidence will require more than 100,000 labeled data items. And 10% need more than 10 million! That’s a lot of bounding boxes and polygons!

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New survey shows ai and ml are still nascent

Breaking news: A global survey shows AI is still in its infancy and poor data is cited as a major barrier of development.

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We're in the news!

Dimensional Research has just published a fascinating survey showing that most data scientists and AI professionals struggle with ML training data.

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The Primacy of Flexibility

To address both volume and complexity, a computer vision labeling and annotation platform must be flexible and versatile.

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ODSC East 2019

The AI space is maturing and training data is feeding the growth.

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Data Engineering, Prep, and Labeling for AI 2019 - They’re not wrong

Report delineating the requirements and hurdles of data preparation, as well as the growing need for properly annotated data as the AI industry evolves.

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What does Alegion do?

What does Alegion do? O'Reilly AI Conference this week. We prepare quality training data for Fortune 1000 companies. We have the people, processes, and platform needed to take training data needs off your plate.

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We are making an impact

The rapid growth in enterprise ML projects has helped us to achieve our impact goals.

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Moving on up...

We're hiring in all departments. Come join our fast growing team at Alegion.

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The State of AI Expertise

So what is the state of AI expertise? Early stages but growing fast. AI teams are typically lead by a highly trained data scientist. Data scientists are put in charge of all sorts of data-related projects such as data clean up, categorization, structuring, and lots of analysis.

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Look for us at MLconf in New York

MLconf's New York event is on March 29th this year. It's a great event for ML practitioners. It's very hands-on, and the content is first rate.

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AI in Austin, AI at SxSW

We participated in an AI event during last week’s South by Southwest Conference (SxSW) that perfectly captured the Austin experience. Capital Factory, a local incubator, hosted a Saturday night AI Mixer.

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We've integrated computer vision capabilities into our training data platform

In a press release this morning we announced new computer vision capabilities in our Training Data Platform. We've added data labeling and annotation features for image classification, object localization, and semantic segmentation.

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A blueprint for preparing your own ML training data

Considering DIY training data? We created a downloadable blueprint that lays out the tools, people and skills required to prepare ML training datasets.

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The Journey of a Machine Learning (and learning) Project

Alegion partners with clients to create training data for their machine learning projects. We have a good sense of how the enterprise manages ML projects today, and have developed a nomenclature for describing project phases.

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This Week in Austin: Machine Learning

The local Austin machine learning community will be busy this week. Machine Learning practitioners event and a UT science and technology career fair on February 21 in Austin, TX.

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7 tips to being AI ready

7 tips to being AI ready. We produce quality training data for computer vision and natural language processing, which means we have a lot of experience with machine learning projects throughout their lifecycle. Our experience with firsttime ML project teams has given us valuable insights.

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The real promise is AI to AI to AI to AI

There are irreplaceable human beings answering the phone line at 911 who calmly, rationally, and with compassion, help people in need. But there are all sorts of systems and data available, that if connected with one another, could make those invaluable humans response times faster.

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Interview with Nathaniel Gates, CEO & Co-Founder at Alegion

There’s this prevailing suggestion that AI is only beginning to emerge, but in reality it’s everywhere.

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See you at Texas AI Summit

We are excited to be a silver sponsor at Texas AI Summit that brings AI practitioners together. You can find us in the exhibit area on Friday, Jan 25 at 8am at the AT&T Center.

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What Experts had to say About AI in 2018

Analysts and researchers who have been following the emergence of AI continue to witness it’s growth. More projects, more companies, more departments from those companies, are discovering ways AI can improve efficiencies and customer experiences. It’s still early days, keep your eyes on this space.

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Lunch and Label Survival Guide

Lunch and Label is a popular tactic among the Training Data DIYers. Data Scientists offer to provide lunch in the hopes that colleagues will show up, eat, and annotate images with bounding boxes.

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Artificial Intelligence 2018 - A Year in Review

2018 saw stories in a huge cross section of publications. Technology standards like ZDNet and TechTarget covered the business of AI. The interest in AI technology breakthroughs spanned many other publications, including Fortune, the Wall St Journal, and Fast Company.

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Security-in-the-Loop

“Human-in-the-loop” is an increasingly popular way of describing the application of human judgement to AI training. And of course, for some types of AI training humans remain an essential ingredient. "But who are the humans in the loop?" people often ask, and it's a reasonable question.

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Measurement bias

If the images your algorithm is learning from were shot with a lens with a color filter, all of those images will be identically skewed. If the ruler used to determine the dimensions of data elements was ¼ inch short of a foot, all of the dimensions will be wrong by an identical proportion.

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The Agile AI Way - part 2

Much like with traditional software development, as AI and ML initiatives take priority on the enterprise agenda, data scientists need to retire the outdated waterfall approach and replace it a more agile style of development.

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Prejudicial Bias

Prejudicial bias is probably what most people think of when they hear the word. And this kind of bias is the source of many scary AI headlines. And yet, this phenomenon of human prejudice influencing algorithms is complex.

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The Agile AI Way - part 1

In waterfall these interdependencies aren’t fully explored until the penultimate testing stage, at which point any oversights or miscalculations that are discovered drive the entire project back to the architecture or coding stage.

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Sample Bias

With sample bias in machine learning in computer vision, natural language processing and entity resolution, it isn’t possible - or even necessary - to expose an algorithm to its entire universe in order to train it. A sample of that universe is adequate, more affordable, and more practical.

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Algorithm bias

Bias is generally coupled with variance, another algorithm property. Bias and variance interact, and data scientists typically seek a balance between the two.

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AI Bias: Where it comes from and what to do about it

We’re going to dedicate a series of posts in this blog to the topic of bias. It’s a flexible word, with many definitions. It has multiple meanings even in the context of AI.

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TRICK OR TREAT!

In honor of Halloween we're demystifying three really scary things that happen to far too many AI projects.

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We've just announced the Alegion Training Data Platform

We've just released significant enhancements to the Alegion Training Data Platform.

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Finding the ‘Right’ Data to Train your AI Is Getting Easier

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Gartner: Artificial Intelligence Demands That CIOs Foster a Data-Literate Society

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Consider the Meaning of Deep Learning Before Beginning a Project

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Define the Problem then Create the Solution

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How Artificial Intelligence Is Edging Its Way into Our Lives

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Venture Capital Firms Use Artificial Intelligence to Sift Through Investment Opportunities

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Google’s Appetite for Growth in AI Is Revealed in Its Looming Battle with Amazon

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Sophisticated Training Data Leads to AI Program that Spots Wildlife Poachers

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Study Suggests The Better the Data, the Better the AI

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Irish Tech News Interviews Alegion CEO Nathaniel Gates about Company’s Success

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Alegion CEO Nathaniel Gates Invited to Speak at AI Conference

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The AI Movement Will Create Jobs, Not Kill Them

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German Newspaper Highlights Alegion for Work with Crowd

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Huffington Post Highlights Alegion

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Alegion in Businessweek Magazine Feature on the Future of AI

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Navidar Singles Out Alegion in Report on Developments in AI and Machine Learning

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AI Trends Features Alegion

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The Marketing Artificial Intelligence Institute Singles Out Alegion

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Market Watch Announces Alegion’s Acceleration Plan

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Seeking Alpha Features Alegion’s Unique Position in AI Field

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Yahoo! Finance Features Alegion CEO

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Austin startup Alegion brings in $3.6 million to ramp up AI initiatives

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ABJ Features Alegion’s New Jobs, New Focus

ABJ notes that “CEO Nathaniel Gates expects Alegion's headcount to nearly double by the end of the year — and to pivot to becoming ‘product focused’ rather than ‘service focused’ to widen its market reach.

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Artificial Intelligence Startup Alegion Raises $3.6 Million

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Alegion Featured in Real-Time Insights

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