Have you ever wondered whether the technology that improves your customer experience is impinging on your privacy at the same time? Many artificial intelligence algorithms are based on consumer and vendor data, often collected without the knowledge of the affected parties.
When researchers first began to develop AI in the 1950s, their goal was to teach machines how to mimic human intelligence. In recent years, this approach has been abandoned, in favor of relying almost entirely on user data.
However, 80% of companies that rely on artificial intelligence fail because they have security and legal problems. Some have been sued for fraudulently gaining access to proprietary information. Even tech companies who are careful about security may unknowingly leave themselves open to security breaches and legal complications. There have also been cases in which rumors about potential security issues have resulted in negative publicity and subsequently damaged a company’s reputation.
Tech companies have begun to address these problems, but tweaking the algorithms doesn’t solve the root of the problem.
A better solution to the security and legal problems of AI is to create systems that don’t rely on customer data. Instead, these systems are trained to work on synthetic data, which is reproduced data based on real-world statistics. It was first developed in 1993 and has since been refined.
Technology developers who have gone back to the original way of thinking about artificial intelligence are currently creating synthetic data systems. For instance, DARPA (Defense Advanced Research Projects Agency) is investing $2 billion in the creation of models that mimic core domains of human cognition. And Google’s parent company, Alphabet, has been working on an initiative called Project Loon since 2013, which uses Gaussian processes (probabilistic models that can deal with extensive uncertainty, act on sparse data, and learn from experience) in order to launch balloons in the atmosphere which provide internet to underserved populations around the world.
In 2018, Facebook announced plans to open two new AI labs that rely on synthetic data, in order to protect its users’ privacy. This move followed a 2015 lawsuit against the company for violating privacy laws. Earlier this year, Facebook agreed to pay $550 million in damage to users whose privacy had been violated.
Synthetic data systems don’t collect any private information, so there is no danger of data breaches and no potential lawsuits looming on the horizon.
Ethical artificial intelligence has advantages beyond customer protection. Since these systems don't need to pull data from clients, they have a much faster integration than a system that is based on private information. The technology can be used quickly and easily by the end-user, without the need to provide data prior to its implementation.
This works best when the process doesn’t rely entirely on artificial intelligence. Instead, it follows a multi-disciplinary approach. For example, in order to automate vehicle inspections for rental car companies, the system combines key engineering elements to create unique identifiers for each damage type and measurement, so that damage can be assessed accurately and quickly.
More specifically, the Click-Ins system adjusts 3D models of vehicles to be absolutely precise. The system is trained to compare the model with photos of the actual vehicle, even when the pictures are of low quality. It can ignore bad lighting, shadows, and dirt to analyze how the photo is different from the car’s model.
This system translates into faster integration, high ROI, and company savings. It doesn’t rely on company or customer information, so the client only has to provide the most basic information for the system to work. The solution is ready to go and can be implemented almost immediately, saving time and manpower.
Ethical AI mimics human intelligence and utilizes synthetic data in order to eliminate the ethical and technical disadvantages of customer-dependent AI and creates a solution that works fast and well. As more companies invest in this type of artificial intelligence, the future of tech will be positively impacted and end-users will benefit from more secure and cost-effective systems.
Tesla has become a disruptive force in the automotive industry in both its product line and business model. What drives Tesla’s success cannot just be crowned by their innovative vehicles, but also with their unconventional aftermarket support, which moves away from relying on distributors and channel partners, a major milestone for the automotive industry.
Tesla's core competency is not their vehicles at face value, but the uniqueness of them. As Tesla makes unique vehicles, Tesla aims to keep its business model just as unique. Tesla, in order to truly disrupt the industry, must limit any channels that offer spare parts for their vehicles. If a normal vehicle was to break down, the owner has a plethora of options to find a new spare part. If the broken vehicle was a Tesla Model 3, the owner has to directly go to Tesla for spare parts. By cutting out any channels, the distribution is purely vertical, from Tesla’s production, retail, and aftermarket support.
By not allowing OEM’s to sell their Tesla specific parts to distributors and market directly to the consumers, Tesla can maximize their profits and brand equity. Major automotive firms will have to adjust not just their product line, but their business and revenue model if they were to compete with Tesla.
As Tesla dominates the automotive industry, aviation OEMs are taking steps to increase its market share in the aftermarket support. A key player that is signaling this change is Boeing.
Like Tesla, Boeing is aiming to maximize its profits by eliminating the use of brokers or distributors in exchange for creating direct channels to its clients. As the sales of new airplanes decrease and the aftermarket value currently at $62 billion and growing, Boeing has been making strides to grow aftermarket support to their services and ending reliance with distributors. An example of such is the termination of agreements with large suppliers such as Spirit AeroSystems. Boeing’s end goal is to reach sales of $50 billion in just little over 5 to 10 years, and the company is taking monumental steps to achieve its quota.
As Boeing historically has been known to allocate their support to production services, this is a new direction for Boeing as they aim to gain a significant market share in aftermarket support.
As OEMs go vertical, problems are derived from the new disruption of the aviation aftermarket support industry. There is no decrease in progress as large OEM’s economies of scale easily outpace traditional smaller brokers, distributors, and MROs.
In order for the existing companies to survive in the new climate, they must look for ways to increase the efficiency of their operations, know their customers better, and quickly react to the changes of the agile market.
Utilizing the right tools can make all the difference.