These days, when you browse the internet for news on artificial intelligence, you’ll find out about new AI that just managed to do something humans do, yet far better. Present day AI can detect cancers better than human doctors, build better AI algorithms than human developers, and beat the world champions at games like chess and Go. Instances like these may lead us to believe that perhaps, there's not a lot that artificial intelligence cannot do better than us humans. The realization of AI’s superior and ever-improving capabilities in different fields has evoked both hope and caution from the global tech community as well as the public. While many believe the rise of artificial general intelligence can massively benefit humanity by raising our standard of living and status as a civilization, some believe the development may lead to global doom.
While the debate on whether the development of artificial general intelligence or artificial superintelligence is promising or pernicious rages on, the jury on when such advanced forms of AI will come into existence is also still out. These are important questions that do deserve the coverage and debate they are subjected to. However, before worrying about the future of AI it is necessary to first know what artificial general intelligence exactly is, what it would take to achieve it, and how far existing AI capabilities are from getting there.
What Is the Current State of Artificial Intelligence?
The internet abounds with stories of stunning applications that exist today, culminating from years of artificial intelligence research. Similar to this example of AI systems that can diagnose cancers with greater accuracy than human doctors, there are many other fields where specialized artificial intelligence is replicating human-like reasoning and cognition.
For instance, deep learning algorithms used by social media sites are becoming increasingly adept at recognizing objects, people, and even detailed characteristics of these objects and people. Modern computer vision technology driven by deep learning can now identify people in images posted to social media, the position of the person in the image, their expressions, and any accessories they might be wearing. This gives AI systems the ability to perceive images similar to the way humans do. These systems can go beyond simply identifying people from images and even analyze subtle patterns to discern non-obvious attributes. One example is a Stanford University study that shows how deep neural networks can identify people’s sexual orientation just by analyzing their faces -- an ability that is highly unlikely to be present in humans.
Another instance of AI systems performing human-like feats is natural language processing (NLP), where AI can understand speech or text delivered in natural language. AI is becoming proficient in understanding the meaning of text and speech as part of applications such as chatbots and virtual assistants in smartphones (think of Siri, Cortana, etc.) And advancements in natural language generation, which is the generation of information in normal human language, is being used in numerous applications where machines are required to respond to people voice or text.
With such developments, the gap between human intelligence and artificial intelligence seems to be diminishing at a rapid rate. This might give you the impression that powerful artificial intelligence systems or artificial general intelligence systems may not be too far out in the future. However, it is vital to understand that it takes more than just performing specific tasks better than humans to qualify as artificial general intelligence.
What Exactly Is Artificial General Intelligence?
Put simply, Artificial General Intelligence (AGI) can be defined as the ability of a machine to perform any task that a human can. Although the aforementioned applications highlight the ability of AI to perform tasks with greater efficacy than humans, they are not generally intelligent, i.e., they are exceedingly good at only a single function while having zero capability to do anything else. Thus, while an AI application may be as effective as a hundred trained humans in performing one task it can lose to a five-year-old kid in competing over any other task. For instance, computer vision systems, although adept at making sense of visual information, cannot translate and apply that ability to other tasks. On the contrary, a human, although sometimes less proficient at performing these functions, can perform a broader range of functions than any of the existing AI applications of today.
While an AI has to be trained in any function it needs to perform with massive volumes of training data, humans can learn with significantly less learning experiences. Additionally, humans -- and (perhaps one day) agents with artificial general intelligence -- can generalize better to apply the learnings from one experience to other similar experiences. An agent having artificial general intelligence will not only learn with relatively less training data but will also apply the knowledge gained from one domain to another. For example, an AGI agent that has been trained to process one language using NLP can potentially be able to learn languages having shared roots and similar syntaxes. Such a capability will make the learning process of artificially intelligent systems similar to that of humans, drastically reducing the time for training while enabling the machine to gain multiple areas of competency.
Can AI Ever Achieve General Intelligence?
Artificial intelligence systems, especially artificial general intelligence systems are designed with the human brain as their reference. Since we don’t have the comprehensive knowledge of our brains and its functioning, it is hard to model it and replicate it working. However, the creation of algorithms that can replicate the complex computational abilities of the human brain is theoretically possible, as suggested by the Church-Turing thesis, which states -- in simple words -- that given infinite time and memory, any kind of problem can be solved algorithmically. This makes sense since deep learning and other subsets of artificial intelligence are basically a function of memory, and having infinite (or a large enough amount of) memory can mean that problems of the highest possible levels of complexity can be solved using algorithms.
How Far Are We From Artificial General Intelligence?
Although it might be theoretically possible to replicate the functioning of a human brain, it is not practicable as of now. Thus, capability-wise, we are leaps and bounds away from achieving artificial general intelligence. However, time-wise, the rapid rate at which AI is developing new capabilities means that we might be getting close to the inflection point when the AI research community surprises us with the development of artificial general intelligence. And experts have predicted the development of artificial intelligence to be achieved as early as by 2030. A survey of AI experts recently predicted the expected emergence of AGI or the singularity by the year 2060.
Thus, although in terms of capability, we are far from achieving artificial general intelligence, the exponential advancement of AI research may culminate into the invention of artificial general intelligence within our lifetime or by the end of this century. Whether the development of AGI will be beneficial for humanity or not is still up for debate and speculation. So is the exact estimate on the time it will take for the emergence of the first real-world AGI application. But one thing is for sure -- the development of AGI will trigger a series of events and irreversible changes (good or bad) that will reshape the world and life as we know it, forever.
Naveen Joshi is Founder and CEO of Allerin, which develops engineering and technology solutions focused on optimal customer experiences. Naveen works in AI, Big Data, IoT and Blockchain. An influencer with a half a million followers, he is a highly seasoned professional with more than 20 years of comprehensive experience in customizing open source products for cost optimizations of large scale IT deployment.