Kelly begins by attempting to tear down the assumptions of the superhuman AI hypothesis: You can read the article in more detail, but also make sure you read the comments. I find it important to detail Howard Gardner's list of intelligences (refer to Wikipedia article for more detail): Musical-rhythmic and harmonic, aka Musicality This area has to do with sensitivity to sounds, rhythms, tones, and music. The second interesting observation that Kelly makes is the idea of inventing or discovering new strategies for thinking. One thing though that I can assure you that is happening, something that surprised me about Kevin Kelly's remark: Deep Learning development is accelerating at an unimaginable pace.
Why should we learn from artificial intelligence when artificial intelligence is in fact trying hard to become like us? Using reinforcement learning, the program learnt to improve itself incrementally. Just three simple rules--wide exposure to learn from the experience of others; getting into the arena and playing the game oneself; and all this followed by progressive learning. While wide exposure helped AlphaGo make sense of the many ways the game could be played, the real learning happened when it needed to play several million rounds of the game, each while improving incrementally.
When learning fails we miss out on important projects, promotions, and opportunities. Somehow, for many of us, our natural capacity to learn seemed to deteriorate over time, especially in areas that we care about the most. Educators and business leaders have used the term "learning to learn" to name a missing skill that would reverse the deterioration. The business literature is filled with tips and buzz-words about this skill: "learn from mistakes," "fail fast and often," "learn faster with technology," "be curious," "collaborate," and "take down silos.
The ever-increasing pace of change in today's organizations requires that executives understand and then quickly respond to constant shifts in how their businesses operate and how work must get done. That means you must resist your innate biases against doing new things in new ways, scan the horizon for growth opportunities, and push yourself to acquire drastically different capabilities--while still doing your existing job. To succeed, you must be willing to experiment and become a novice over and over again, which for most of us is an extremely discomforting proposition. Over decades of work with managers, the author has found that people who do succeed at this kind of learning have four well-developed attributes: aspiration, self-awareness, curiosity, and vulnerability.
An alternative approach allows an automaton to learn to solve problems through iterative trial-and-error interaction with its environment, much as humans do. To solve a problem posed by the environment, the automaton generates a sequence or collection of responses based on its experience. The environment evaluates the effectiveness of this collection, and reports its evaluation to the automaton. The principles underlying this paradigm, known as collective learning systems theory are explained and applied to a simple game, demonstrating robust learning and dynamic adaptivity.