Kurzweil builds and supports a persuasive vision of the emergence of a human-level engineered intelligence in the early-to-mid twenty-first century. In his own words,
With the reverse engineering of the human brain we will be able to apply the parallel, self-organizing, chaotic algorithms of human intelligence to enormously powerful computational substrates. This intelligence will then be in a position to improve its own design, both hardware and software, in a rapidly accelerating iterative process.
In Kurzweil’s view, we must and will ensure we evade obsolescence by integrating emerging metabolic and cognitive technologies into our bodies and brains. Through self-augmentation with neurotechnological prostheses, the locus of human cognition and identity will gradually (but faster than we’ll expect, due to exponential technological advancements) shift from the evolved substrate (the organic body) to the engineered substrate, ultimately freeing the human mind to develop along technology’s exponential curve rather than evolution’s much flatter trajectory.
The book is extensively noted and indexed, making the deep-diving reader’s work a bit easier.
If you have read it, feel free to post your observations in the comments below. (We’ve had a problem with the comments section not appearing. It may require more troubleshooting.)
This very rich, conversational thought piece asks if we, as participant designers within a complex adaptive ecology, can envision and act on a better paradigm than the ones that propel us toward mono-currency and monoculture.
We should learn from our history of applying over-reductionist science to society and try to, as Wiener says, “cease to kiss the whip that lashes us.” While it is one of the key drivers of science—to elegantly explain the complex and reduce confusion to understanding—we must also remember what Albert Einstein said, “Everything should be made as simple as possible, but no simpler.” We need to embrace the unknowability—the irreducibility—of the real world that artists, biologists and those who work in the messy world of liberal arts and humanities are familiar with.
In order to effectively respond to the significant scientific challenges of our times, I believe we must view the world as many interconnected, complex, self-adaptive systems across scales and dimensions that are unknowable and largely inseparable from the observer and the designer. In other words, we are participants in multiple evolutionary systems with different fitness landscapes at different scales, from our microbes to our individual identities to society and our species. Individuals themselves are systems composed of systems of systems, such as the cells in our bodies that behave more like system-level designers than we do.
Good discussion that covered a lot of ground. I took away that none of us have signed on to be early adopters of brain augmentations, but some expect development of body and brain augmentations to continue and accelerate. We also considered the idea of bio-engineered and medical paths to significant life-span, health, and cognitive capacity improvements. I appreciated the ethical and value questions (Why pursue any of this? What would/must one give up to become transhuman? Will the health and lifespan enhancements be equally available to all? What could be the downsides of extremely extended lives?) Also, isn’t there considerable opportunity for smarter transhumans, along with AI tools, to vastly improve the lives of many people by finding ways to mitigate problems we’ve inherited (disease, etc.) and created (pollution, conflict, etc.)?
Artificial intelligence (AI) is being incorporated into an increasing range of engineered systems. Potential benefits are so desirable, there is no doubt that humans will pursue AI with increasing determination and resources. Potential risks to humans range from economic and labor disruptions to extinction, making AI risk analysis and mitigation critical.
Specialized (narrow and shallow-to-deep) AI, such as Siri, OK Google, Watson, and vehicle-driving systems acquire pattern recognition accuracy by training on vast data sets containing the target patterns. Humans provide the operational goals (utility functions) and curate the items in the training data sets to include only information directly related to the goal. For example, a driving AI’s utility functions involve getting the vehicle to a destination while keeping the vehicle within various parameters (speed, staying within lane, complying with traffic signs and signals, avoiding collisions, etc.).
Artificial general intelligence (AGI or GAI) systems, by contrast, are capable of learning and performing the full range of intellectual work at or beyond human level. AGI systems can achieve learning goals without explicitly curated training data sets or detailed objectives. They can learn ‘in the wild’, so to speak. For example, an AGI with the goal of maximizing a game score requires only a visual interface to the game (so it can sense the game environment and the outcomes of its own actions) and an ability to interact with (play) the game. It figures out everything on its own.
Some people have raised alarms that AGIs, because their ability to learn is more generalized, are likely to suddenly surpass humans in most or all areas of intellectual achievement. By definition, once AGI minds surpass ours, we will not be able to understand much of their reasoning or actions. This situation is often called the technological singularity–a sort of knowledge horizon we’ll not be able to cross. The concerns arise from our uncertainty that superintelligent AIs will value us or our human objectives or–if they do value us–that they will be able to translate that into actions that do not degrade our survival or quality of existence.
• Demis Hassabis on Google Deep Mind and AGI (video, 14:05, best content starts a 3:40)
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