Have an open ended conversation with your computer. No rules, no expectations, no end or beginning, just a conversation.
(Chrome desktop only)
See how empathetic technology can be used in education settings.
See how a software presentation can have an empathetic, expressive persona.
See how to help clinicians diagnos mental health in real time in person or over tele/video medicine.
The universe, whatever it may ultimately be, seems to be observable. What we observe can generally be considered as a "signal" - either continuous or discrete - usually both at the same time. Empathetic technology must be able to observe, synthesize and emit signals, and co-evolve new signallying pathways.
Things happen, then signals about signals elucidate the truth of them into consequences. Complex systems are constantly responding to events and backpropogating through truth procedures.
“If you want to be important — wonderful. If you want to be recognized — wonderful. If you want to be great — wonderful. But recognize them who is greatest among you shall be a servant to humanity. That’s a new definition of greatness.”
“There are two labyrinths of the human mind: one concerns the composition of the continuum [consistent multiplicities], and the other the nature of freedom [the event], and both spring from the same source—the infinite [inconsistent multiplicities].”
G.W. Leibniz (“On Freedom,” in G.H.R. Parkinson (ed.), Philosophical Writings, trans. Mary Morris and G.H.R. Parkinson [London: J.M. Dent, 1973] 107)
Most artificial intelligence research suffers from a dependence on trusting that "intelligence" is a measurable and definable phenomenon. Maslo doesn't base anything on intelligence, conceptually or literally. Maslo's hypothesis is that humans (and all complex systems) share consequences with other systems and when those consequences co-evolve in similar magnitude and direction then "intelligence" is experienced. Our ideas are only concerned with co-incidence of signals and the emergence of shared consequences.
Our platform takes inspiration from biology and computational complexity science. The platform is organized around generalized signal processing, emergent behavior and co-evolution of shared consequences. The platform processes signals asyncronously at different spatio-temporal levels. It is agnostic to linguistics and other semantic conventions.
Machine learning cannot account for that much in the world. Machine learning lacks the general signal processing to handle continuious, infinite and random phenomena. Machine learning is precisely good in recognizing already constructed patterns in the world and moving to an adjacent possible pattern. It requires the framing of signals in this way - already machined signals.
Computer networks are likely already hyper connective enough to be aware in important ways. It's also very likely that all complex beings share similar ways of existing and persisting in the world.
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