The Penultimate Step to Artificial General Intelligence (AGI)?
As AI research continue to accelerate, powered by cheap and accessible cloud along with open-sourced research papers and code-bases, we march towards the much-talked about point in time - the Singularity. Singularity, (for those unaware of the term) will be the point in time when the capabilities of Artificial General Intelligence(AGI) technology will culminate to cross the threshold of "human-level intelligence" in all aspects that really matter. If you are pondering, "but that's a remarkably unspecific event", you are right. It is a conceptual event defined by technologists and futurists, and the term Singularity is coined to mark this incredibly significant point. However, to get a deeper understanding of the implications, we need to get specific. That's what this post is about.
What constitutes Intelligence? Is it being good at problem-solving in general or getting good at solving a specific problem at a very fast rate? Artificial General Intelligence, as mentioned above, is defined as the technology that will be indistinguishable from humans in all aspects of intelligence. This raises a pertinent question as we move forward - What uniquely makes us human? What makes us special?
The validity of the answers we had to this age-old question, such as the ability to talk or the ability to cooperate to achieve a common goal, keep getting eroded as technology progresses. Have you wondered why Captcha is getting harder and harder every year? The line to separate human and AI is getting pushed further and further. Is there an end point?
We are compelled to draw a new line.
Does 'True Creativity' draw that line between a human and AGI?
Art has been an undisputed human stronghold for all of our known history. The ability to imagine is one of the humankind's biggest assets that enables us to survive and thrive. The second decade of 21st century saw AI making inroads into this. The big shot that was fired in this direction was the invention of GANs by Ian Goodfellow in 2014.
GANs, short for Generative Adversarial Networks, are a pair of neural networks that are incentivized to compete against each other. On every iteration of training, the first one creates a new sample (of the kind of data it is trained on; e.g : images, text ) and the second one tries to identify if it is a real sample or a generated (by the first neural network) one. After a good enough number of iterations, the generator becomes so good at creating samples almost identical to the real data being used for training, in all aspects. GAN model positively surprised AI researchers with its uncanny ability to generate new samples that look so 'realistic'. It was applied to images and not before long, DeepFakes flooded the internet.
