Tristan Rouillard is the VP of Machine Learning Solutions at CloudFactory. In this role, he leads the company's strategy and direction related to ML products and solutions offered to CloudFactory's clients globally. Tristan was one of the cofounders of Hasty, a data-centric vision AI platform focussed on making it easier to implement the ML flywheel in production. Hasty was recently acquired by CloudFactory in late 2022. Before founding Hasty, Tristan was the Head of the Venture Development team at WATTx, a manufacturing, industry 4.0 focussed incubator, where his team built the business models and go-to-market strategies for various early-stage ventures.

CloudFactory, a global leader in human-in-the-loop artificial intelligence (AI), announced that Pieter Nel has joined as Chief Technology Officer (CTO). Nel brings more than 20 years of experience, across three continents, in technology strategy and software engineering management at fast-growth technology companies. As CTO, he will lead the technology and machine learning (ML) teams, continuously evolving CloudFactory's platform as a key enabler for clients' successful AI deployments. "Considering all successful AI deployments include humans in the loop, CloudFactory is positioned perfectly to support clients with our experienced annotation workforce and building the infrastructure to enable human-in-the-loop AI deployments." Nel previously served as CTO at Ocrolus, where he scaled the New York company's human-in-the-loop AI document processing product.

At CloudFactory we have a pretty intimate seat at the table with well over 100 active tech teams applying AI to a myriad of different use cases and industries. Our clients come in all sizes and from all industries, from small startups to those listed on the Fortune 500, and they work on solutions from cashierless checkout to self-driving cars. With all the AI hype, it sometimes feels like a gold rush, which would make CloudFactory's workforce solutions the picks and shovels. We see no signs of an AI winter. Both AI adoption and business value will increase over the next 12 months.

If you have massive amounts of data you want to use for machine learning or deep learning, you'll need people to enrich it so you can train, validate, and tune your model. Our transparent process provides an agile and scalable approach elevating accuracy, consistency, and speed. Our unique combination of people who care, process excellence, and platform capabilities is the hallmark of our highest quality results for truly intelligent AI. A highly trained workforce, skilled within any given platform, with the ability to easily shift and adjust as project needs require. Customized delivery schedules to meet every need coupled with training and staffing flexibility to pivot based on business need and turnaround time.

AI and machine learning algorithms require data. But the bulk of that data is of no use if it isn't first labeled by human annotators. This predicament has given rise to a cottage industry of startups, including Scale AI, which recently raised $100 million for its extensive suite of data labeling services. That's not to mention Mighty AI, Hive, Appen, and Alegion, which together occupy a data annotation tools segment that's anticipated to be worth $1.6 billion by 2025. CloudFactory is yet another vying for attention.

At Azavea, our mission is to create advanced geospatial technology and research for civic and social impact. That mission has led us to some interesting places - we've worked with the World Bank to try to reduce traffic accidents globally, created open source tools for applying machine learning to satellite imagery, and even testified in court based on our research into gerrymandering and how to solve it. Aiming for civic and social impact in our work is so fundamental to our constitution as a company that we've written it into our charter. And as a Certified B Corporation, we participate in bi-annual audits on everything from our carbon footprint, to employee compensation, to our involvement in our local Philadelphia community. Suffice to say, in order to pursue these rather lofty ideals we find ourselves tending to take the long view when weighing business decisions... the really long view.

Artificial Intelligence is the biggest threat to mankind, right? Even if robots aren't taking over the planet by force, the yarn goes, computers will surely push us all into unemployment in the next decade or so. Let's meet someone who can give us a slightly different perspective. This is Joel, standing in front of his house, a few kilometers outside Gulu, Uganda, where he lives with his 14 brothers and sisters. Joel works for Zillow, the leading online real estate marketplace in the US with 1.1B of revenue in 2017.

A study released at the 2019 Open Data Science Conference (ODSC) in Boston demonstrated that managed teams outperformed crowdsourced workers on accuracy and overall cost on a series of the same data labeling tasks. Data science platform developer Hivemind hired a managed workforce and a leading crowdsourcing platform workforce to determine which team delivered higher quality, and at what relative cost. If you're building AI anywhere in your organization, you're in a "race to useable data," according to a 2019 report released by Cognilytica. In its report, the analyst firm specializing in AI, evaluated requirements for data preparation, engineering, and labeling solutions. They found 80 percent of AI project time is spent on aggregating, cleaning, labeling, and augmenting data to be used in machine learning models (ML).

Across every industry, engineers and scientists are in a race to clean and structure massive amounts of data for AI. Teams of computer vision engineers use labeled data to design and train the deep learning algorithms that self-driving cars use to recognize pedestrians, trees, street signs, and other vehicles. Data scientists are using labeled data and natural language processing (NLP) to automate legal contract review and predict patients who are at higher risk of chronic illness. The success of these systems depends on skilled humans in the loop, who label and structure the data for machine learning (ML). When data labeling is low quality, an ML model will struggle to learn.

Today there are AI-powered apps that can tell you the breed of your dog or the species of a plant in seconds simply by taking a photo. When you upload an image to Facebook, your friends are identified immediately based on facial recognition technology. The ability for machines to do this specific type of analysis has, in some cases, surpassed humans, and the lifeblood of these advanced AI technologies is visual data. The entire concept of artificial intelligence is that machines can be built to perform the most human of tasks. In order to do that, they're modeled after human intelligence.