Augmented intelligence applications showing ROI, broad success
Enterprise uses have shown that utilizing augmented intelligence technology increases ROI, productivity and linear success as compared to general AI or AGI.
AI technology is not advanced enough to replace human work, and may not be feasible or practical given the value we're looking for from AI systems. While AI technology continues to advance, enterprises seeing the most immediate and significant returns from their AI investments are using augmented intelligence -- AI and machine learning as supplementary to their existing human workforces.
Augmented intelligence applications enhance the capabilities of the human workers by leveraging AI as a force multiplier that enables firms to do more with existing resources. Augmented intelligence technology can assist humans in processing information that would otherwise be a Herculean task requiring extraordinary time and effort. The robotics industry introduced the cobot to work in close proximity to human counterparts, and rather than replacing humans, these cobots would simply help them do their job more efficiently, and with more power. Similarly, augmentative intelligence systems are helping humans perform their existing tasks with "superhuman" powers.
The incremental ROI of augmented intelligence
Since implementing augmented intelligence technology only requires incremental changes to workflow instead of the leaps and bounds that current artificial intelligence platforms require, augmented intelligence technology can provide a more immediate return on investment (ROI) than autonomous forms of AI. Many technology vendors have already started to realize the benefit in offering the augmented intelligence approach. Augmented intelligence applications are especially appealing to enterprises that have not already started implementing advanced AI solutions, as it can be a more palatable entry point into the intelligent systems.
However, augmented intelligence has proved challenging to market since it does not squarely fit within the defined scope of artificial intelligence. One of the most effective ways for a company to achieve an early return on investment is to adopt AI systems that improve and assist in an employee's daily activities. Augmented intelligence systems should be seen as a smart enterprise intelligent assistant. Individuals in any data-centric role stand to gain huge advancements in work output productivity. In many situations, augmented intelligence can help employees consume, process and analyze large amounts of data that they normally wouldn't be able to do on their own at a lightning fast speed.
Corporations of the future will be able to increase productivity to even greater degrees than currently possible with augmented intelligence technology. Some firms, such as insurance agencies, are starting to adopt augmented enterprise assistants who utilize AI technology to analyze fraud and risk. Retail enterprises are implementing virtual reality to train employees, and free-roaming bots to streamline employee tasks. Additionally, there are a number of applications in the medical and robotics industries in recognizing patterns and assisting in logistics.
Augmented cars vs. autonomous vehicles
Even at the consumer level, there are differences between products supported by artificial intelligence and augmented intelligence. Automobile companies have developed assisted driving before looking to create fully autonomous, self-driving cars. Tesla's autopilot car was meant to take certain functional responsibilities off the driver to augment controls. In autopilot, the car has features such as lane control, self-parking, and the ability to summon the car from a garage or parking spot. Tesla has yet to release its fully self-driving car, but we anticipate Tesla to continue to encounter many different issues for such a release. On the other hand, augmented intelligence applications are already helping make cars safer by providing an extra pair of eyes and ears on the road.
AI systems are applying the power of predictive analytics and recognition technology to help drivers avoid potential accidents. Through the use of blind spot detection and machine learning systems, cars are learning to take over steering when the driver drifts out of their lane, or an unexpected vehicle or object is about to collide with them. Advanced driver assistance systems (ADAS) are taking in the multiple inputs from cameras, sensors, LIDAR, as well as in-vehicle inputs from braking, steering, engine and accelerator inputs to greatly improve the speed and response of the driver to rapidly shifting conditions.
In addition, machine learning systems are keeping a watch on traffic, not only notifying drivers when driving conditions start to slow down due to increased traffic, but providing alternate routes or guidance to help minimize traffic-related delays. AI systems are also being used to control traffic lights and intersection controls. Rather than simply timing the lights based on traffic engineer's best guesses on traffic patterns, they can respond to the realities of vehicular traffic, adjusting lights and other signals as needed to help mitigate problems.
AGI vs. AI
Another benefit of focusing on augmented intelligence applications is to shift some of the emphasis away from building what many perceive as the end goal of AI: artificial general intelligence (AGI). The goal of AGI is an autonomous smart system that can do anything and everything to complete any task -- which is leagues away from current technology. Augmented intelligence is more narrowly focused and achievable by not being required to adapt to any situation. That is, augmented intelligence is used in focused scenarios to complete a delineated task to increase the here-and-now issues of efficiency and effectiveness.
Even in the much-vaunted history of AI, some of the big successes claimed of AI are actually successes of augmented intelligence. AI historians often point to the seminal IBM "Deep Blue" victory over chess champion Garry Kasparov in 1997 as a benchmark for turning technology. However, even Deep Blue was an augmented intelligence machine, as it provided ordinary non-master chess players the ability to beat an expert.
While AI continues to make strides to reach the ultimate goal of autonomous existence, augmented intelligence is already here and being utilized regularly. With the widespread utility of augmented intelligence, there is a need to refine the terms and phrases that are used in this area of intelligence -- even to its potentially acceptable shorthand acronym.
There is a clear, significant and realizable value in augmented intelligence technology. For many organizations, adopting augmented intelligence systems is a great first step and one that may make more sense than attempting to create a silver bullet artificial intelligence solution.
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