Elon Musk, Microsoft, Google, Facebook, IBM, and other leaders in AI development claim to support close examination of ethical, risk-related, and other factors affecting the public.
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.)?
All bodily capacities, including the most impressive, uniquely human cognitive and metacognitive ones, coevolve with regulatory mechanisms. Regulatory mechanisms operate unconsciously, and control the expression of associated capacities such that the latter consistently operate with high effectiveness and efficiency to promote replication of our genes. So, to fundamentally change and render socioecologically sustainable the human species, H+ technologies will somehow have to alter the deep neural relationship between these regulatory “value systems,” (sensu neuroscientist Gerald Edelman in, “A Universe of Consciousness”), residing primarily in the limbic system, and all our mundane or enhanced corticothalamic activities. We need H+ that radically diminishes our transparent penchant for evolutionarily adaptive self-deception, and that alters our power to more freely and consciously choose, moment-to-moment, what we do with our cognitive capacities. I suspect current H+ is blind to this. — Warmly, PJW
TED talk of possible interest:
Comment I posted there:
Here is an interdisciplinary “moon-shot” suggestion that we should at least start talking about, now, before it is too late. Let’s massively collaborate to develop a very mission-specific AI system to help us figure out, using emerging genetic editing technologies (e.g., CRISPR, etc.), ideally how to tweak (most likely) species-typical genes currently constraining our capacities for prosociality, biophilia, and compassion, so that we can intentionally evolve into a sustainable species. This is something that natural selection, our past and current psycho-eugenicist, will never do (it cannot), and something that our current genetic endowment will never allow cultural processes / social engineering approaches to adequately transform us. Purposed-designed AI systems feeding off of growing databases of intra-genomic dynamics and gene-environment interactions could greatly speed our understanding of how to make these genetic adjustments to ourselves, the only hope for our survival, in a morally optimal (i.e., fewest mistakes due to unexpected gene-gene and gene-regulatory (exome) and epigenetic interactions; fewest onerous side-effects) as well as in a maximally effective and efficient way. Come together, teams of AI scientists and geneticists! We need to grab our collective pan-cultural intrapsychic fate away from the dark hands of natural selection, and AI can probably help. END
Paul Watson asks:
Will decent AI gain a sense of identity, e.g., by realizing what it knows and does not know? And, perhaps valuing the former, and maybe (optimistically?) developing a sense of wonder in connection with the latter; such wonder could lead to intrinsic desire to preserve conditions enabling continued learning? Anyway, answer is Yes, I think, as I tried arguing last night.
Paul recommends this article about macaque metacognition may be relevant.
Relatedly, Paul says
- A search for knowledge cannot proceed without a sense of what is known and unknown by the “self.” Must reach outside self for most new knowledge. Can create new knowledge internally too, once you have a rich model of reality, but good to know here too that you are creating new associations inside yourself, and question whether new outside knowledge should be sought to test tentative internally-generated conclusions.
- Self / Other is perhaps the most basic ontological category. Bacteria have it. Anything with a semipermeable membrane around it — a “filter.” Cannot seek knowledge without having at least an implicit sense that one is searching for information outside oneself. In highly intelligent being, how long would that sense remain merely implicit.
What do you think?
Here’s an interesting interview with an author whose book explains his concept of neurocapitalism, or cognitive capitalism, which is the result of the ongoing feedback between us and the increasingly penetrating technologies we adopt.
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)
• Google Deep Mind (Alpha Go) AGI (video, 13:44)
• Extra: Nick Bostrom on Superintelligence and existential threats (video, 19:54) – part of the talk concerns biological paths to superintelligence
• Primary reading (long article): Superintelligence: Fears, Promises, and Potentials
• Deeper dive (for your further edification): Superintelligence; Paths, Dangers, and Strategies, by Nick Bostrom
Members may RSVP for this discussion at https://www.meetup.com/abq_brain_mind_consciousness_AI/events/234823660/. Based on participant requests, attendance is capped at 10 to promote more and deeper discussion. Those who want to attend but are not in the first 10 may elect to go on the waiting list. It is not unusual for someone to change a “Yes” RSVP to “No”, which will allow the next person on the waiting list to attend. If the topic attracts a large wait list, we may schedule additional discussion.
Members of this site who can’t attend the meeting are welcome to participate in the extended discussion by commenting on this announcement.
Paul W sent the following TED Talk link and said
If AI is by definition a program designed to improve its ability to access and process information, I suspect we cannot come up with serious AI that is not dangerous. It will evolve so fast and down such unpredictable pathways that it will leave us in the dust. The mandate to improve information-processing capabilities implicitly includes a mandate to compete for resources (need’s better hardware, better programmers, technicians, etc.) It will take these from us, and just as we do following a different gene replication mandate, from all other life forms. How do we program in a fail safe against that? How do we make sure that everyone’s AI creation has such a failsafe — one that works?
What do you think? I also recommend Nick Bostrom’s book, Superintelligence: Paths, Dangers, Strategies
NVIDIA, the company you remember for their graphics cards, is now a leader in advanced chips for artificial intelligence. The company’s work in deep learning produced an AI that skillfully operated a car in all the normal driving conditions a New Jersey driver might encounter.
A NY Times article reports on research conducted by Keith Stanovich and others that (a) finds intelligence and rationality are different qualities, (b) they are only weakly positively correlated, and (c) one’s rationality can be improved through targeted training but not one’s intelligence. Moreover, Stanovich proposed a rationality quotient (RQ) and that standardized tests be devised to assess one’s RQ.