Artificial Intelligence is the future, and it’s right at our fingertips. After years of experimenting with new methods of information processing, digital developers are finally figuring out how to build computers that mimic the human brain. Rather than relying on a linear method to interpret information, these devices can engage in parallel processing, interpreting different types of data in tandem and developing closer links between those data over time. As a result, we can create computers that are more powerful and flexible than ever before.
As beneficial as AI technology is, it won’t be adopted overnight. As with any kind of new technology, it will take time before it becomes a part of everyday life. Organizations need enormous technical expertise to incorporate artificial intelligence devices, not to mention vast datasets and tools to help the AI learn. No single business or agency can obtain all these resources on its own, which is why it is essential for different organizations to share their insights and expertise, and help one another take this technology to the next level.
Obstacles to AI Adoption
To understand why sharing is so important to the future of artificial intelligence, you need to understand all the current obstacles to the adoption and use of this technology. There are only a few thousand people in the world who understand how to use artificial intelligence technologies, and while that number is growing each day, it is nowhere near large enough to meet the demand of modern organizations. Without skilled professionals to guide them, most businesses and agencies have no way of knowing how to train AI applications or tailor them to the specific needs of their organization. Many don’t even understand what AI can do for them!
In addition to a shortage of experts, the spread of artificial intelligence is limited by a number of other factors, including:
- Data Availability– Artificial intelligence applications require access to vast amounts of data, which they need to learn how to perform specific activities. Assembling so much data isn’t easy, and most organizations have no way of getting it all on their own.
- Data Structure– Besides obtaining enough data for their AI devices to learn how to perform tasks, organizations have to structure that data. Until recently, the only way to do so was by hand. Manual data structuring is not only a costly and time-consuming process, but it is vulnerable to a range of errors and biases. Organizations thus had to spend considerable time and money to get data that might not even be that useful once they had it.
- Model Availability– Organizations that want to adopt artificial intelligence don’t just need data, but also machine learning models. These are models with the algorithms to learn from that data. Such models are difficult to develop, making it hard for organizations to adopt them unless they can count on the services of skilled AI experts.
However, recent developments have helped to mitigate some of these problems. In fact, the AI experts at Imaginea have developed a synthetic method for structuring data, which makes this process less costly and more accurate. But even these innovations aren’t widely available yet, making it difficult for organizations that don’t have significant tech expertise to take advantage of them.
The Critical Role of Cooperation
The only true, comprehensive solution to AI issues requires greater cooperation among all organizations and individuals with an interest in artificial intelligence. This is particularly important when it comes to gathering and structuring data. Few organizations have the resources to assemble all the data they need on their own. Even those that think they have enough data may be overlooking key sources of information that could end up being useful to them. Only by offering all the data they have gathered in a common database can different organizations hope to have access to everything they need. An organization can only obtain data from others if it is willing to offer its own data in return, making it essential to take a cooperative approach.
In addition to data, individuals and organizations must be willing to cooperate with each other on:
- Skilled Services– Because the supply of skilled AI professionals is so limited, the few who do exist cannot limit whom they provide their services to. Instead, they must be willing to offer their skills to a wide range of different organizations. Organizations, in turn, must be open to receiving assistance from experts all over the world.
- AI Applications– Synthetic dataset tools, machine learning models, and other algorithms and applications must be made as widely available as possible. Not only does this allow different organizations to take advantage of them, but it gives them a chance to build on them and make them better. Only if everyone involved in AI shares these applications can artificial intelligence continue to develop at a rapid pace.
- AI Insights– Besides skills and applications, organizations must offer one another insights into the benefits and challenges of adopting artificial intelligence. This will help other organizations understand the full potential of this technology. It will also let them foresee AI-related problems before they arise, prepare for those problems, and weather them smoothly.
The Imaginea Artificial Intelligence Ecosystem allows organizations all over the world to share these resources easily and equitably. Through the use of smart markets, data-as-a-service, and gig-as-a-service, we eliminate the barriers to spreading insight, algorithms, and data. We also provide a number of valuable tools, including synthetic dataset structuring applications. For more information on all our efforts to promote cooperation and AI development, sign up to be notified of our private secure token offering, contact us today.