The human brain is the most fascinating and multifunctional creation on earth. Arguably.
Yet today, in the highly digitized world where technology develops at the speed of lightning, there is a rising contender that might soon be able to challenge the multifunctionality of the human brain — and it’s Artificial Intelligence.
A technology designed to think and operate like a human brain, artificial intelligence has become so widely used and integrated into our daily lives that it is gradually becoming more normalized as it improves in its ability to analyze and produce outputs almost like those from the human brain — except at a much higher speed.
Traditionally, the world has gotten used to AI’s horizontal multifunctionality: AI’s ability to influence and transform a wide variety of industries, companies, and challenges is constantly in the news, with discussions about more and more solutions across the board surfacing daily. Can AP increase employee productivity? Why, yes. How about using AI to optimize the best route to a destination? Easy. And what if there was a personal assistant that would help organize the daily routine of any user? Already exists.
But one specific and targeted approach that has been overlooked is the vertical application of AI. But what does a vertical AI integration mean and look like?
At the basic level, vertical AI is a solution that is used to solve specific issues within a single industry — a problem that is, in fact, unique to that industry. From the get-go, the algorithms are trained on industry-specific data that allow for the system to gain insights that other insights might not necessarily have access to.
With vertical AI, the same AI models that can be used for manufacturing, for instance, can be used in other vertical markets — and this is where the unique functionality of vertical AI comes from. Today already, it has become possible to create a single AI infrastructure that can be applied across other vertical markets. For instance, vertical AI can be deployed in such cutting-edge cases as cognitive security, employee productivity, intelligent machines and linking knowledge, Big Data management, and even reasoning and understanding like a human brain.
Essentially, the emerging companies that are able to develop these comprehensive, multifunctional AI algorithms create the possibility to apply the same deep and expert-informed technology to multiple vertical markets at the same time.
The list of the vertical markets is multiplying by day, and the variety of the industries is impressive. Cybersecurity, anti-fraud, risk prediction, market intelligence, customer insights, trading, human capital, assistance to the elderly — these present just the tip of an iceberg of all the opportunities vertical AI applications allow for.
The most successful vertical AI applications require industry leaders’ deep expertise in the training data used for the machine; in order to best deploy such a complex technology, a comprehensive understanding and knowledge of the industry’s customers’ needs and industry operations is required from the very beginning. To this end, vertical AI systems are usually designed by teams of industry experts, data scientists and software engineers who are well aware of the scope of the industry-specific issues the technology is deployed to solve.
Vertical AI has already found its application in a variety of industries. A pioneer industry in vertical AI application is financial services: the benefits already include — and aren’t limited to — improving security, assessing loan risks, trading stocks, among other things. The industry naturally — and the biggest players specifically — sit on vast amounts of industry-unique data that create the perfect starting point for the development of a vertical AI integration. This data is especially important in training the AI algorithms to identify fraud with a high level of accuracy, especially due to the fact that fraud has increased in frequency and ubiquity as an issue. And with the system trained on such high-quality data, it even becomes possible to prevent any fraud attacks in the first place.
Another prominent use case has been the law, with the technology used for email and document management, legal analytics, and contract analysis. Already, AI has the ability to assist attorneys in faster and more efficient contract reviewing by identifying, extracting and analyzing relevant content; it has also taken on the more redundant and repetitive aspects of the review process, saving law firms time and money on a significant scale.
Finally — but importantly — healthcare has seen progress with the increasing application of vertical AI, opening new doors and expanding the horizons of medicine as we know it. One of the key solutions that AI has offered for the healthcare industry is by mimicking and optimizing doctor decision-making. With the industry replete with visuals and images to analyze, the machine learning algorithms are fed this type of data to learn to identify patients with medical problems and make accurate diagnoses. While doctors are the top experts in the field thanks to years of education and real-life experience with patients, they are uniquely suited to build out a vertical AI system that would be apt in providing a second opinion and minimizing the risk of error that a human mind is so prone to.
Today, we are far from having AI technologies give a final verdict or rule out a final diagnosis; the technology is a better fit as an “assistant” to the industry leaders in increasing the efficiency and accuracy of the doctors’ work. But it is very possible to imagine machines developing to a point where they will be ready to provide firsthand opinion on any patient file and be fully trusted by human doctors.
We are only at the beginning of the long road of experimentation when it comes to vertical AI. Nevertheless, one thing is true: the narrow and more streamlined application of machine learning holds secrets and promises humanity is yet to uncover, and the opportunities it creates are endless. The lasting and long-term impact it will have is yet to be seen.
Originally published on Medium