To date, all of our achievements — everything from bubble gum to iPhones — have relied on human intelligence, the human brain. But with the rise of artificial intelligence, we may soon be able to go far beyond what our minds can do on their own — at least in terms of data processing. AI mimics the parallel processing of our brains’ neurons, computing at a level of speed and complexity far beyond what current computers can do. This processing has the potential to revolutionize our relationship with information, energy, business, and virtually everything else.
As valuable as AI technology is, there are several large roadblocks preventing widespread adoption. Not only are current AI devices highly complex, they are also expensive to maintain and operate, particularly given the limits of dataset production. To make AI a realistic addition to modern business, AI developers need to find a way to make this technology accessible to all organizations, regardless of their resources and skill sets. Promoting that accessibility begins with:
Artificial intelligence relies on vast amounts of structured data, which is essential to supervised learning. But assembling raw data into a structured dataset is an incredibly difficult task. Individual operators must painstakingly convert the data into a set, an often tedious task that takes a ton of time and effort. All this external effort makes using AI extremely expensive. Plus, it limits technology to organizations that have staff with the specific skills necessary to structure data. And as if those stumbling blocks weren’t enough, it also makes AI systems vulnerable to a host of human mistakes and biases, since no one is entirely objective in how they structure data. As a result, not only is the cost of adopting AI technology higher, but the benefit is lower, giving most organizations little incentive to do so.
To solve this problem and improve accessibility, AI developers need to transition from traditional datasets to synthetic ones, like the ones Imaginea is developing. Such datasets involve assembling a repository of deep learning data and data generators, which can quickly label data for a dataset according to a client’s parameters. This allows datasets to be assembled much more quickly than is currently possible and at a fraction of the cost. And because this process for producing datasets is systematized, it is less vulnerable to external errors and biases. The more AI developers adopt synthetic datasets, the easier it will be for other organizations to use this technology, and the more they will benefit from doing so.
Improving Access to Experts
Even with synthetic datasets, organizations will still rely on skilled specialists to adopt and use AI technology. According to a recent estimate in the Harvard Business Review, there are only 10,000 programmers on the planet who know how to write algorithms for artificial intelligence devices. Only 10,000 in the entire world! If most organizations in the world plan to adopt AI technology in the near future, this means there will be a severe shortage of qualified individuals. And if an organization doesn’t focus on artificial intelligence, they may not even know how to start looking for the experts they’d need to get started. Even though more and more of these experts are being trained and entering the market, it’s hard for all but the most tech-savvy businesses and nonprofits to hire them.
The solution to this expertise shortage lies in providing AI gig economy. Rather than having to hire AI experts internally, organizations should connect with them across the globe and purchase individual jobs from them. This will require the creation of a global marketplace where organizations and AI experts can connect with one another, consider each other’s needs and offers, and make arrangements for a job. With such a platform in place, AI experts will be able to spread their services far and wide, serving a growing number of organizations even while their numbers remain limited.
Exchanging Data & Tools
Organizations don’t just have trouble finding the personnel they need to adopt AI; they also have limited access to a wide range of critical resources. In order to use AI effectively, a business or nonprofit must have access to vast swaths of data to structure. They also need a range of applications to structure that data, develop machine learning algorithms, and otherwise tailor AI technology to meet their needs. But few organizations have these data and applications ready to go, and assembling it all is incredibly expensive and difficult. As long as organizations have to get these resources on their own, only those that specifically focus on tech and have enormous funds at their disposal will be able to use this technology.
Thankfully, the same innovations that are making qualified personnel more accessible are also providing easier access to data and applications. AI developers are currently creating smart markets, or digital exchanges where AI applications can connect with each other for transactions. This makes it easier for them to accumulate large volumes of data, which can then be structured into datasets with the help of synthetic tools.
Besides using smart markets to purchase data, organizations can also trade machine learning models, which have algorithms that can learn from this data. The more easily and widely machine learning models are exchanged, the simpler it will be for each organization to obtain the best possible model and build on it. In this way, the quality of AI technology will grow quickly and continuously, with thousands of businesses, nonprofits, and government agencies from across the globe contributing to it.
Imaginea is committed to propelling the AI revolution forward. We have created a vast digital ecosystem for AI technology, where organizations from across the globe can access data, synthetic dataset applications, machine learning models, and AI specialists. For more information on the benefits of AI technology or to gain access to it for your own organization, visit our website today.