What Is REAL Artificial Intelligence?
Learn more about AI, where it's headed, and how it compares to human intelligence.
Join the DZone community and get the full member experience.Join For Free
Since time immemorial, the human race has been fascinated with what machines could or couldn’t do. The quest for creating intelligence that can surpass human intelligence continues to grow, and it's coined in a term called artificial intelligence.
The term AI was coined by Mr. John McCarthy in 1955, the computer visionary who I am sure would have been glad that his Shakespearean display caught the eye of the researchers around the world, as it ranked 4th in the Scopus scholarly database. It’s even fascinating to know that it didn’t appear in the top 10 searches the year before; a spectacular performance. One could relate it to a new blockbuster film opening weekend where it overtakes fading glories. But in the case of AI, it would be fair to say it’s a sequel story. The sequel story is only behind cancer for obvious reasons. (As most researchers/scientists belong to the medical discipline and the cure to cancer remains the last undiscovered El Dorado in the field). The other 2 words in the list are two rather newly coined terms: blockchain and Big Data. No surprises here if you are following the technology trends, as all 3 are linked to AI in some way or another, with a potential to create intelligent and trusted systems in the future.
AI is the latest marketing buzzword that is made to find its place in every possible use case — from driverless cars to intelligent chatbots, from robots like Sophia to solving cancer problems, from winning games to providing human-like intelligence. But is this current hype real or have we just started scratching the surface of intelligence?
In order to make the distinction on hype vs. reality, let’s go into some basic technical details of AI technology.
AI stands for artificial intelligence. Its an intelligence system put together artificially to learn and provide an output. Learning can be done by providing data to the AI system. Data can be big data, customer data, unstructured text, audio, visuals, the environment surrounding details, etc. Based on the data provided, an AI system would learn and identify hidden patterns and provide an output.
For instance, if an AI system is recommending what food to order, it must know your food preferences, what you had ordered before, where you usually order from, what days you usually order specific cuisine, and a lot of other details to recommend the right cuisine for you. The output can be a list of top 5 food orders for today.
Similarly, if an AI system is assisting a doctor for providing options for cancer treatment, the system must have the complete patient medical history, must understand the complete cancer domain (or the respective specialization), and also periodically learn any new treatments or findings from medical journals. Understanding the complete cancer domain is a very complex process where you need to train the system to understand the medical terminology and the vast, ever-growing cancer literature, identify patterns and correlations from existing patients, know their suggestive treatments and outcomes, and finally, suggest options for treatment. There can be many more data points, and this is a continuous process where the system would be trained from the feedback and their outcome. While we keep hearing that AI is helping solve cancer cases, this is far from reality, and systems have just started to touch the surface.
To make life simpler, just remember the following distinction:
" AI can learn but can’t think."
Thinking will always be left to humans on how to use the output of an AI system. AI systems and their knowledge will always be boxed to what it has learned, but it can never be generalized (like humans) to think outside the domain on which it has been trained. Understanding this distinction is very important. Human intelligence, with only a few sets of observations, can learn, think, and apply their learnings on different domains quite easily. A simple example would be of a doctor treating cancer patients who can give you advice for the common cold, but an AI system trained specifically on cancer data may not even understand what the common cold is, let alone the treatment options. Building a generalized AI system may or may not happen in the future. The current focus should be building domain-specific intelligence and getting it right.
AI can never be a replacement for human intelligence. While simple to medium outputs of AI can be automated to skip a human expert, the majority of the decision-making and critical intelligence will always need human intelligence.
While AI has been projected as the next big technology that can transform our world, we are far from releasing this vision. You may hear many successful AI marketing strategies, but AI has yet to deliver its true value. AI alone will not lead to transformation, but a combinatorial power of various technologies and advancements in computing power will bring it closer to its true potential.
In order to get a realistic view of what an AI system can achieve in today’s environment and what to expect in the future, you can refer to my book REAL AI for more details.
Opinions expressed by DZone contributors are their own.