Sergey Brin couldn’t have put it more clearly. Dubbed the technology of the decade, AI has been the catchphrase on every futurist’s tongue. From customer support chatbots to smart assistants, AI has begun to transform numerous industry verticals.
If you take a deep dive, you would notice AI has started bettering humans in several tasks, such as detecting cancer better than oncologists or translating languages or beating the Go world champion. These achievements are setting up the tone for the future. A future where intelligent cyborgs and superhumans are not just part of sci-fi movies.
This is referred to as artificial super intelligence (ASI), where a machine’s cognizance supersedes that of human’s. Think of Jarvis or Ultron from Marvel, Arnold Schwarzenegger’s Terminator, or Samantha from Her.
Contrary to popular belief, ASI is far from reality. In fact, it may take anything between 15 and 20 years (best case scenario) to centuries to achieve it.
To understand all the aforementioned points, we need to look into why there is a sudden boom in AI and what the current state of AI research is.
What Brought The Sudden Boom In AI Development?
The term AI was coined in the 1950s. But all the significant work in this field started in the late 2000s. And since then, the work in AI has leapfrogged. What happened in the early 21st century?
The answer revolves around data. Data is food for AI, and the 2000s witnessed the creation of larger and better data sets than ever. People developed large corpus for text analysis, huge data sets for images, and video processing, which improved the accuracy of AI algorithms tremendously.
Around the same time, Nvidia released advanced data processing units called graphics processing units (GPUs). This gave power and speed to organizations and individuals alike for creating advance AI models and algorithms.
Both of these, together, have given rise to deep learning. Deep learning is an advanced form of machine learning algorithms. It heavily concentrates on the learning part, feeding algorithms data and making them learn from it.
Since then, AI has never looked back. Today, one can easily buy GPUs, get large data sets to train their AI algorithms, and develop mind-boggling technologies without making a dent in their pockets.
The Current State of AI
However, AI in its current state is far from being super intelligent. Suppose if an intelligent cyborg from the future turns time wheels and arrives in present-day San Francisco to meet its predecessors, it would pity the primitiveness of them. Imagine meeting neanderthals from western Africa tens of millennia ago.
The truth is, most of the AI models are capable of doing one thing alone. If you use a program that translates languages for you to convert speech into text, it will fail miserably. Humans, on the other hand, can do a plethora of tasks. A human can translate English to Spanish and convert speech to text quite easily. This is why ASI is a thing of a far future. The ASI should be able to brew coffee while humming an Elton John song and writing Christmas postcards to all his cyborg buddies.
There are a few new projects and models in AI that are starting to perform a set of different task simultaneously such as multitask learning and transfer learning. However, these algorithms perform better only when the set of tasks are closely related, such as finding the sentiment from a text piece and extracting named entities from it.