AI: The Great Accelerator of the Human Innovation Curve
Throughout the ages, human innovation has been accelerating at a mind-boggling rate. Consider that 1 million years elapsed between the control of fire and the invention of the wheel, but just 5,400 more years until the creation of the Gutenberg press–and a mere 455 additional years before the development of the light bulb.
When plotted on a chart to illustrate the human innovation curve, it is clear that there is only one word that can describe the increase in the pace of progress: exponential. However, the innovation curve now is shifting into even higher gear with the proliferation of artificial intelligence (AI).
AI grew exponentially in 2017, with no signs of stopping in 2018. AI fundamentally changes the equation of innovation, adding a new variable that dramatically accelerates the rate of advancement. For the first time in history, the invention process is not entirely dependent on human intellect. Machines are now augmenting and will eventually supplant human brainpower.
Although AI is still in its early stages, the arrival of a new approach –called “conducted learning”–will expedite the rate of overcoming current limitations and is set to affect the speed of both AI and the human innovation curve.
AI algorithms now are reaching, and even exceeding, human capabilities in areas such as strategic game playing and image classification. However, these algorithms fall under the category of artificial narrow intelligence (ANI) since they are limited to excelling in narrowly defined tasks.
We can train an AI algorithm to recognize the shape of a gun, for example, and it will be able to detect the image faster and better than humans. However, due to this narrowness limitation, in a real-world application, such as in a TSA scanning, this effective scanning method will be restricted only to the specific gun models on which the algorithm was trained.
Consequently, we still have a way to go until we reach artificial general intelligence (AGI), which will be more akin to humans and present capabilities similar to what we see in sci-fi movies.
Conducted Learning presents a promising solution towards achieving AGI by leveraging the combined power of separate ANI engines. Conducted learning enables running several cognitive engines in concert, picking the best engine or engines to perform the task, similarly to an orchestral performance.
