The most fundamental progress that technology has made over the past few decades is a dramatic increase in computing power coupled with a reduction in the cost of storage. Such progress has enabled us to run algorithms and code over swathes of data and at a fraction of the cost, thus yielding what some may consider more insights. Consequently, we are able to find patterns in large amounts of data. This, by some limited definition, is construed as intelligence and has been demonstrated in specific classes of activities such as detecting diabetes from retinal scans, driving cars, playing games, etc. In these cases, software can emulate human behaviour and perhaps even exceed human capabilities. But the fact is that we have not yet invented any new magic wand to make software “think” on its own or have any sense of “consciousness”, in the way humans do.
The limitation is grounded in the fact that software still operates, for the most part, on the paradigm of Gigo (garbage in garbage out), meaning it mostly only does whatever we tell it to and is biased based on whatever data we train it with. Give it incorrect data and it will pick up incorrect patterns.
But the vision and aspiration of computer science has always been to build a machine that can “accurately” emulate human “intelligence”. However, we still have a long way to go before we can build a software system that is functionally capable the same way a human is. Software “intelligence”, though defined in academic circles, has been fairly misused (and misunderstood) in popular reporting, giving people the impression that software can do just about anything a human being can. This notion is fundamentally misguided. We are not there. Or at least not yet. The gap between software and the human mind is still massive.
These gaps help to deflate claims that artificial intelligence (AI) and automation are ready to take over people’s jobs, and about the potential for mass unemployment, particularly in the Indian IT industry. This industry employs a couple of million people, largely consisting of our middle class, and recently it has been called out by commentators as being under threat from automation.
But automating away this labour force is not an easy task. Computer scientist believe in the superiority of silicon (computers) over carbon (humans), the work and study over the past few years in this industry have given reasons to pause and consider the unique contributions and value that humans bring to the table in enabling businesses the world over. Automating these unique attributes requires emulating the same human value.
In the case of the IT industry, for example, professionals perform a range of activities, from understanding customers’ business requirements to writing code, maintaining software systems and processing transactions. Working in teams, they perform a variety of different actions touching several information systems, coordinate with each other, navigate uncertain or changing scenarios, report to their managers, take bottom- line responsibilities for their respective systems, are accountable and serve customers across the globe. All these activities engender a deep sense of trust and confidence among their customers the world over.
At an individual level, each has traits such as intuition, experience, common sense, and an ability to identify and fix the unusual. These are fundamental traits that all humans share, though, admittedly, some more than others. Even in the most seemingly “robotic” of tasks, each human has the potential and ability to apply these traits without necessarily being told to do so a priori, traits that are inherent to each one of us, and ultimately create trust and confidence in our colleagues. This trust and confidence are the fuel that all businesses run on. Therefore, it has always been about more than just following “simple” rules.
Hence, any attempt by “intelligent” software at automating all that work done by a single individual or an entire team of humans must involve recreating the same trust and confidence. This necessarily requires addressing the whole gamut of these activities that humans perform on a regular basis and with similar human traits, but in software. This is no longer about small piecemeal tasks that are easy to automate. It must involve emulating the same traits of common sense, intuition, teamwork, communication, and an ability to deal with the unexpected.
Therefore, the next stage of innovation in computer science will be not just to make software run faster but also about how to get software to emulate more human-like traits and ultimately become truly trustworthy (like humans).