In the initial part of this book by Daniela Rus and Gregory Mone, we look at the application of AI to various powers or competencies that impact our world.
(Since the book is mainly in Rus’s voice, we’ll refer to her for author quotes, etc., while recognizing the joint authorship.)
So what are these enumerated powers?
First, there’s speed, where Rus talks about accelerated production processes, citing examples like research on protein folding, or the writing of a novel.
Reading this, we get the idea that many of these applications are assistive technologies – that they build on what humans are already doing, and help them to do it more quickly and more efficiently. Rather than replacing human work, whether it’s in medicine, retail, manufacturing or anything else, the AI is more likely to be a decision support resource.
In the ‘knowledge’ chapter, Rus talks about the idea of infinite libraries, and the ways that we access knowledge with AI.
On the one hand, Rus provides the example of cows, illustrating how granular data on something that we consider banal can give us surprising Insights that are novel and interesting.
She also talks about separating the noise from the signal in something like astrophysics.
“Picking out the tell-tale signal of a gravity wave is like trying to hear a faint whisper on the far side of the arena during a Taylor Swift concert,” she writes.
Looking into how this kind of knowledge requisition might work practically, Rus suggests that some day we’ll have ‘subject-focused and industry-focused AI libraries’ that will be handy for areas like agriculture that are already benefiting from so much of the data analysis coming out of AI engines in terms of soil use, seed engineering, crop harvesting and much more.
In the chapter on insight, Rus talks about how to explain this concept to a seven-year-old child, and suggests that a lot of insight is about uncovering hidden connections.
She cites Thomas Kuhn’s The Structure of Scientific Revolutions, suggesting there are periodic paradigm shifts that disrupt the iterative improvements that we are used to making in science.
One example that she uses is the shift from geocentric to heliocentric context – when we first figured out that the earth revolves around the sun.
Our AI moment is a little like that – it changes so much about what we previously knew and thought, and how we performed and worked!
She talks about an “AI physicist” as a digital detective, helping us move into a new of scientific knowledge.
Further in developing this concept, Rus cites applications like sleep research where intelligent engines monitor oxygen levels, brain activity, breathing, eye movement, heart rate, and more. Versatile digital systems and small wearable monitors can replace labor-intensive on-site sleep studies, and really revolutionize this aspect of healthcare.
“Imagine if systems like this were active all the time,” Rus writes, giving the example of ‘Emerald’, a high-tech invisible stethoscope that can gather and interpret this kind of granular health data.
Then we move to a chapter on creativity, which Rus refers to as “an innate capacity to step beyond the bounds of the known and habitual patterns and to venture into the realm of the unexpected and uncharted.”
Citing ‘punctuated evolution’ and breakthrough ideas, Rus talks about how AI accentuates human creativity, while leaving the writer’s “creative fingerprints” on their work.
“A great writer does not copy,” she writes. “A great writer is copied.”
The authors, in general, seem upbeat about the march toward progress, tempering concerns with a silver lining of opportunity:
“While we must be smart about how we use these tools and how much we rely on their suggestions, there is cause for excitement here,” Rus writes.
In applying AI work to the concept of mastery, Rus cites a Korean program at the Gwangju Institute of Science to track granular data on badminton players and things like the movement of the shuttlecock, illustrating how capabilities can be analyzed and researched.
She cited the classic film “The Matrix,” in which the character Neo gets downloaded skills through a digital interface. This, she suggests, is an exaggeration of what AI can now do, but it is useful in thinking about how these tools can speed up all kinds of human mastery.
“I don’t expect that AI will accelerate learning to that extent, but the tools we are developing today will expand our options for education, both as children and adults, allowing us to achieve proficiency and potentially even mastery at a faster pace and with less pain. These tools could allow more young people to benefit from the sort of personalized tutoring that has long been limited to the privileged few.”
Rus points to three key traits of AI tutors that are unique: 1. AI tutors know everything 2. They monitor face and body movements 3: they never watch the clock. In some ways, she suggests, it’s all about engagement. Talking about technologies like holograms, Rus suggests that one of the key achievements of AI will be in keeping people engaged and interested in what they’re being taught.
Calling mastery “a high-level of command, understanding, and even artistry,” Rus notes how famed mathematician Stephen Wolfram compares GPT to the telephone, acknowledging the inflection point that we are in as a society.
Lastly, Rus talks about the applications of AI to empathy. She describes various kinds of empathy ‘markers’ that are present in face and body movements, and talks about how they may be able to help us transcend language, to understand people in different parts of the world through vibrant real-time interpretations.
Describing, for example, Israel and Palestine, and how avatars and programs about the conflict can come to increase understanding across cultural barriers, Rus suggests AI might have a place here as well.
This ends the first section of the book, which is geared towards helping us imagine a brighter future with the technologies that are now developing in our hands. We’ll come out with two more posts on this new guide to modern innovation.
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