The Key to Overcoming
AI Development Obstacles
More Reliable Data
The Key to Overcoming AI Development Obstacles: More Reliable Data
Artificial intelligence began capturing imaginations when the Tin Man from “The Wizard of Oz” hit the silver screen in 1939, and it’s only gained a firmer foothold in the zeitgeist since then. In application, however, AI products have gone through regular boom-and-bust cycles that have thus far stymied the most influential adoptions.
During the booms, engineers and researchers have made tremendous strides, but when their aspirations inevitably outstrip the computing capabilities available at the time, a period of dormancy has followed. Fortunately, the exponential increase in computing power prophesied by Moore’s Law in 1965 has for the most part proven accurate, and the significance of this increase is difficult to overstate.
Today, the average person now has millions of times more computing power in their pocket than NASA had to pull off the moon landing in 1969. That same ubiquitous device that conveniently demonstrates an abundance of computing power is also fulfilling another prerequisite for AI’s golden age: an abundance of data. According to insights from the Information Overload Research Group, 90% of the world’s data was created in the past two years. Now that the exponential growth in computing power has finally converged with equally meteoric growth in the generation of data, AI data innovations are exploding so much that some experts think will jump-start a Fourth Industrial Revolution.
Data from the National Venture Capital Association indicates that the AI sector saw a record $6.9 billion in investment in the first quarter of 2020. It’s not difficult to see the potential of AI tools because it’s already being tapped all around us. Some of the more visible use cases for AI products are the recommendation engines behind our favorite applications such as Spotify and Netflix. Although it’s fun to discover a new artist to listen to or a new TV show to binge-watch, these implementations are rather low-stakes. Other algorithms grade test scores — partly determining where students are accepted into college — and still others sift through candidate résumés, deciding which applicants get a particular job. Some AI tools can even have life-or-death implications, such as the AI model that screens for breast cancer (which outperforms doctors).
Despite steady growth in both real-world examples of AI development and the number of startups vying to create the next generation of transformational tools, challenges to effective development and implementation remain. In particular, AI output is only as accurate as input allows, which means quality is paramount.
The Challenge of Inconsistent Data Quality in AI Solutions
There is indeed an incredible amount of data being generated every day: 2.5 quintillion bytes, according to Social Media Today. But that doesn’t mean it’s all worthy of training your algorithm. Some data is incomplete, some is low-quality, and some is just plain inaccurate, so using any of this faulty information will result in the same traits out of your (expensive) AI data innovation. According to research from Gartner, some 85% of AI projects created by 2022 will produce inaccurate results because of biased or inaccurate data. While you can easily skip a song recommendation that doesn’t suit your tastes, other inaccurate algorithms come at a significant financial and reputational cost.
In 2018, Amazon began using an AI-powered hiring tool, in production since 2014, that had a strong and unmistakable bias against women. It turns out that the computer models underpinning the tool were trained using résumés submitted to the company over a decade. Because most tech applicants were men (and still are, perhaps owing to this technology), the algorithm decided to penalize résumés with “women’s” included anywhere — women’s soccer captain or women’s business group, for example. It even decided to penalize the applicants of two women’s colleges. Amazon claims that the tool was never used as the sole criterion for evaluating potential candidates, yet recruiters looked at the recommendation engine when looking for new hires.
The Amazon hiring tool was ultimately scrapped after years of work, but the lesson lingers, highlighting the importance of data quality when training algorithms and AI tools. What does “high-quality” data look like? In short, it checks these five boxes:
To be considered high-quality, data must bring something valuable to the decision-making process. Is there a correlation between a job applicant’s status as a state champion pole vaulter and their performance at work? It’s possible, but it seems very unlikely. By weeding out data that isn’t relevant, an algorithm can focus on sorting through the information that actually impacts outcomes.
4. An accelerated development timeline
AI development doesn’t happen overnight, but it can happen faster when you partner with Shaip. In-house data collection and annotation creates a significant operational bottleneck that holds up the rest of the development process. Working with Shaip gives you instant access to our vast library of ready-to-use data, and our experts will able to source any kind of additional inputs you need with our deep industry knowledge and global network. Without the burden of sourcing and annotation, your team can get to work on actual development right away, and our training model can help identify early inaccuracies to reduce the iterations necessary to meet accuracy goals.
If you’re not ready to outsource all aspects of your data management, Shaip also offers a cloud-based platform that helps teams produce, alter, and annotate different types of data more efficiently, including support for images, video, text, and audio. ShaipCloud includes a variety of intuitive validation and workflow tools, such as a patented solution to track and monitor workloads, a transcription tool to transcribe complex and difficult audio recordings, and a quality-control component to ensure uncompromising quality. Best of all, it’s scalable, so it can grow as the various demands of your project increase.
The age of AI innovation is only just beginning, and we’ll see incredible advancements and innovations in the coming years that have the potential to reshape entire industries or even alter society as a whole. At Shaip, we want to use our expertise to serve as a transformative force, helping the most revolutionary companies in the world harness the power of AI solutions to achieve ambitious goals.
We have deep experience in healthcare applications and conversational AI, but we also have the necessary skills to train models for almost any kind of application. For more information about how Shaip can help take your project from idea to implementation, have a look at the many resources available on our website or reach out to us today.