As most business owners agree, cash is king. But what if there was a hidden source of revenue that companies are only starting to learn how to exploit?
It is estimated that as much as 84% of the market value of S&P 500 companies comes from intangible assets, including data and software. This adds up to $8 trillion dollars in possible value in the U.S. alone.1 And if the Internet is any guideline, a growing quantity of this data is visual. According to Cisco, video will represent 82% of global IP traffic by 2022, up from 75% in 2017.2
A challenge facing businesses is that storing large quantities of rich media is an increasing expense that is not strategic to their core operations, and there is no clear path for cost control. Fortunately, the convergence of new technologies and techniques has the potential to turn this burden into revenue.
How AI Can Help
Recent advancements in technology have companies seeing pictures in a whole different way, thanks to computer vision.
Computer vision is an interdisciplinary scientific field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. Two examples are scanning years of studio movie archives for file corruption or flagging safety concerns in archived security camera footage. There are over 245 million surveillance cameras in use today – creating 850+ petabytes of data per day and over 13 billion hours of footage weekly. 3
The challenge is that typically data scientists are needed to train a computer how to recognize these patterns of interest. And data scientists can be hard to come by.
Bridging the AI Skills Gap
According to the LinkedIn Workplace Report (August 2018) the U.S. has a shortage of 151,717 people with data science skills, with particularly acute shortages in New York City (34,032 people), the San Francisco Bay Area (31,798 people), and Los Angeles (12,251 people).4 As more industries rely on data to make decisions, data science has become increasingly important across all industries, not just tech and finance. In that sense, it’s a good proxy for how today’s cutting-edge skills like AI & machine learning may spread to other industries and geographies in the future.
[Also read how it pays to start your AI initiative with the right architecture.]
Introducing Point-and-Click Computer Vision
A solution that could reduce dependence on data science skills for computer vision and empower subject-matter experts to train computers could accelerate adoption, allowing more businesses to launch initiatives with their own rich media archives.
IBM PowerAI Vision was developed to give business users the ability to train a computer to detect and classify key objects and events in images and videos, without relying on AI expertise. The product is part of an integrated computer vision platform of software, optimized hardware and services designed to support multiple industries and use cases.
This new product includes an intuitive toolset for labelling, training, and deploying deep learning vision models without coding or deep learning expertise. It includes the most popular deep learning frameworks and their dependencies, and it is built for easy and rapid deployment and increased team productivity. By combining PowerAI Vision software with accelerated IBM® Power Systems™, enterprises can rapidly deploy a fully optimized and supported platform with high performance.
The four pillars of IBM PowerAI Vision are:
Streamline data import and empower subject-matter experts to build models for AI solutions with a point-and-click guided visual process.
Unleash AI performance capabilities of Power Systems and leverage IBM Research breakthroughs.
Upload custom models and export trained models to applications of your choice through open REST APIs.
Engage Power Systems in data centers to train, then deploy the models remotely, in the cloud or on edge devices.
Now the next time you look at a disk array it could be with dollar signs in your eyes.
1: Ocean Tomo, LLC, 2015: Intangible Asset Market Value Study; The Wall Street Journal