Developing the capacity to annotate massive volumes of data while maintaining quality is a function of the model development lifecycle that enterprises often underestimate. It's resource intensive and requires specialized expertise. At the heart of any successful machine learning/artificial intelligence (ML/AI) initiative is a commitment to high-quality training data and a pathway to quality data that is proven and well-defined. Without this quality data pipeline, the initiative is doomed to fail. Computer vision or data science teams often turn to external partners to develop their data training pipeline, and these partnerships drive model performance.

Building artificial intelligence (AI) models is not like building software. It requires a constant'test and learn' approach. Algorithms are continually learning and data is being refined -- and as much relevant, high-quality data as possible is key. Data labelling is an integral part of data pre-processing for machine learning. If you're training a system to identify animals in images, for example, you might provide it with thousands of images of various animals from which to learn the common features of each, which would eventually enable it to identify animals in unlabelled images.

Machine learning provides a powerful way to automate decision making, but the algorithms don't always get it right. When things go wrong, it's often the machine learning model that gets the blame. But more often than not, it's the data itself that's biased, not the algorithm or the model. That's been the experience of Cheryl Martin, Ph.D., who worked as an applied research scientist at the University of Texas, Austin and NASA for 14 years before joining the AI crowdsourcing outfit Alegion as its chief data scientist earlier this year. "You often hear that the algorithm is biased, or the machine learning is algorithmically biased," Martin tells Datanami.

Workers at some promising tech startups can give off the sort of energy that comes from being a part of a team that just might change the world. At Austin's Alegion Inc., the employees exude that upbeat vibe. The company, which two years ago had fewer than 10 employees, now has more than 60 working in a converted warehouse next to the train tracks in East Austin. The funky chic location is a way to conserve funds for the work ahead. Alegion is among a pioneering group of companies in the field of "data labeling" and it is collaborating with several big corporations that are developing artificial intelligence-based systems that could transform their business operations. Its work could potentially help clients make a big impact in fields such as retail, manufacturing, financial services and health care.

I envisioned an old Cadillac with massive Texas longhorns adorning the hood meandering along a dusty road. This road was in Egypt, and Stringfield was behind the wheel, sweat glistening on his brow as he hauled a load of freshly-baked sesame seed bagels. No, I hadn't been experimenting with some designer hallucinogen. But my conversation with him, as happens with particularly captivating sources, conjured evocative concepts and imagery, the kind of stuff that begs to be illustrated in word pictures. Thing is, although his latest enterprise encompassed many of the issues I aimed to address in my most recent feature story in MIT Technology Review -- such as fair labor in the AI industry, data ethics and the future of work -- his background as a former Halliburton executive who became a bagel-making entrepreneur during his time in Cairo as an HR consultant with the oil giant never made it into the story.

IDC predicts worldwide spending on artificial intelligence (AI) systems will reach $35.8 billion in 2019, and 84% of enterprises believe investing in AI will lead to greater competitive advantages (Statista). However, nearly eight out of 10 enterprise organizations currently engaged in AI and machine learning (ML) report that projects have stalled, and 96% of these companies have run into problems with data quality, data labeling required to train AI, and building model confidence, according to Alegion. Data issues are causing enterprises to quickly burn through AI project budgets and face project hurdles. The new report, "Artificial Intelligence and Machine Learning Projects Obstructed by Data Issues" was conducted by Dimensional Research. The findings include feedback from 227 participants including data scientists and business stakeholders involved in active enterprise AI and ML projects, addressing the maturity of ML in the enterprise, today's ML project challenges, and the tools and resources used in these projects.

The worldwide spending on artificial intelligence (AI) systems is predicted to hit $35.8 billion in 2019, according to IDC. This increased spending is no surprise: With digital transformation initiatives critical for business survival, companies are making large investments in advanced technologies. However, nearly eight out of 10 organizations engaged in AI and machine learning said that projects have stalled, according to a Dimensional Research report. The majority (96%) of these organizations said they have run into problems with data quality, data labeling necessary to train AI, and building model confidence. SEE: Artificial intelligence: A business leader's guide (free PDF) (TechRepublic) The report, conducted by Dimensional Research on behalf of Alegion, surveyed 227 tech professionals who were involved in active AI and machine learning projects.

Companies face issues with training data quality and labeling when launching AI and machine learning initiatives, according to a Dimensional Research report. The worldwide spending on artificial intelligence (AI) systems is predicted to hit $35.8 billion in 2019, according to IDC. This increased spending is no surprise: With digital transformation initiatives critical for business survival, companies are making large investments in advanced technologies. However, nearly eight out of 10 organizations engaged in AI and machine learning said that projects have stalled, according to a Dimensional Research report. The majority (96%) of these organizations said they have run into problems with data quality, data labeling necessary to train AI, and building model confidence.

Companies face issues with training data quality and labeling when launching AI and machine learning initiatives, according to a Dimensional Research report. The worldwide spending on artificial intelligence (AI) systems is predicted to hit $35.8 billion in 2019, according to IDC. This increased spending is no surprise: With digital transformation initiatives critical for business survival, companies are making large investments in advanced technologies. However, nearly eight out of 10 organizations engaged in AI and machine learning said that projects have stalled, according to a Dimensional Research report. The majority (96%) of these organizations said they have run into problems with data quality, data labeling necessary to train AI, and building model confidence.

AI systems are becoming more and more the norm as machine and deep learning gain ground -- especially within the data center and colocation markets. That said, artificial intelligence systems are only as good as their underlying mathematics and the data they are trained on. That's according to a new white paper from Alegion that explores the bias behind machine learning. AI systems and models are made up of algorithms and data, and the professionals who craft the models, etc., are largely in charge of underlying mathematics and data. According to the new Alegion white paper, when things go wrong with AI it's for one of two reasons: The Alegion report contends there are four different types of machine learning or AI systems bias.