Hi! In this article, let me take you on a walk through all the buzz running around Augmented Intelligence (Acronym is IA, standing for Intelligence Augmentation). You might have related this word to Augmented Reality. Not bad - you are pretty close, but in this blog I’ll break things down to get a better understanding on why this has been a cardinal mention in Gartner’s Hype Cycle for Artificial Intelligence, 2019.

What is Augmented Intelligence?

Augmented intelligence is an emerging kind of intelligence paradigm. It aims to connect machines and living beings via neural interfaces, enhancing cognition. Augmented intelligence is presumably a new way to augment living beings with machine intelligence (1). Augmented Intelligence emulates and extends human cognitive function through the pairing of humans and machines. Augmented Intelligence systems find hidden meaning within all data and transform user engagement by providing the right advice, at the right time, with the right evidence across any contact point (2).Augmented Intelligence systems use artificial intelligence techniques such as natural language processing, spatial navigation, machine vision, logical reasoning, machine learning, and pattern recognition in sectors such as Financial Services, Healthcare, or Digital Commerce. Intelligent machines have become the intimate companions of humans, where the interaction and cooperation between a human and an intelligent machine will become integral in the formation of our future society. Although this technology may seem far off, there are already cases that prove its relevance and exponential impact. By its very definition, with more data over time, Augmented Intelligence systems will learn more, adapt quicker, and improve. Which at the current state of development makes a lot of these devices feel more like tools than an extension of our bodies. Human Augmentation is the process of complementing and improving current body processes/mechanisms with respect to the science and biology of a human (3). These improvements are not just related to furthering one’s current capabilities but also to those with reduced abilities and enable them to get back to a more capable and “regular” human condition (4). The technology itself covers a wide area of disciplines from mind controlled artificial limbs and exoskeletons to hearing aid tooth implants and even having better access to dreams (5)(6)(7). These technologies use a wide variety of human interactions and some are much more developed than others. This essay will focus on a few key areas of development in this digital technology and how it has, is and could be implemented into society with the potential impacts.

The need for Human Augmented system:

Big Sur
By Ali Pazani
In the past century, the world has seen leaps and bounds worth of advancements in technology. This has meant that ideas that were science fiction or impossible at the time are becoming a reality much sooner than thought. Digital Human Augmentation is a perfect example of an once thought “Science Fiction” technology that is now becoming a reality today. There should be no slowdown in any of the upcoming development into Digital Human Augmentation as there are many industries and markets which can benefit from these rapid advancements and could potentially improve people’s state of life of physical or mental challenges and help get the affected back on track. Other areas include use in the military to improve the efficiency of a soldier’s day to day job (8) or reduce the strain on factory workers (9). Many of these technologies are new and therefore expensive to develop and test but with continued research and many years later it may be possible to begin to see many of these projects become more commercially available and affordable to the general public giving anyone the 4power to improve their current state of health and life.

How an ideal Augmented Intelligence system would work:

An Ideal Augmented Intelligence (IA) model is a feedback-driven, self-learning and self-assuring system that emulates and extends human cognitive abilities in software means (3). It assists the recognition aspect of human thinking by observing patterns and making sense from messy, disparate heaps of data, and helps organizations transform customer engagement, improve decision-making and deploy self-learning thought processes whenever possible to lead businesses to the next level.

Understanding: IA systems are fed large amounts of data with fast, iterative processing and intelligent algorithms from which it breaks down and derives meaning. They extract pragmatic patterns and observations from multi-structured data and from tracked user interactions in order to help pinpoint obstacles for the organization. IA works as an extension of Artificial Intelligence. Machine Learning automates analytical model building. It uses methods from neural networks, statistics and physics to find hidden insights in data without explicitly being programmed from where to look or what to conclude.

Processing: Neural Networks kick in by making several interconnected units (like pit-stops in an Formula1 car race) and process information by responding to external inputs, transmitting progressive information at each unit. This stage requires multiple iterations of data processing to find connections and derive meaning from undefined data.

Reasoning: The system creates “output” or “results” for the given data set by finding patterns and building practical assumptions based on those findings. A good example would be - to bake a cake, the oven needs to be set at 200°C and bake for ten minutes. The output will be to have a great cake (4). Now if the oven is way above 200°C, the system knows that such a temperature can affect the cake.

Learn: Humans give feedback on output and the system adjusts accordingly. The system is also capable of predicting future outcomes and automating the best action given resource constraints (10). In the same cake example discussed above, through Machine Learning the system learns from the past that the oven is too hot and might affect the cake, so the system turns it down.

Big Sur
How ML algorithms process data and generate predictions
Augmentation: Having humans and machines work together is a win-win for both parties. The machine grows smarter and more productive while the human workload is streamlined. With humans guiding the learning process these tools learn and adjust their models more quickly than intelligence tools with no human feedback loop.

TL; DR:

Augmented intelligence is a design pattern for a human-centered partnership model of people and artificial intelligence (AI) working together to enhance cognitive performance, including learning, decision making and new experiences.
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