The Emergence of Collective Intelligence | Ledface Blog ~Aristotle When we observe large schools of fish swimming, we might wonder who is choreographing that complex and sophisticated dance, in which thousands of individuals move in harmony as if they knew exactly what to do to produce the collective spectacle. So, what is “Emergence”? School of fishes dancing is an example of “emergence”, a process where new properties, behaviors, or complex patterns results of relatively simple rules and interactions. One can see emergence as some magic phenomena or just as a surprising result caused by the current inability of our reductionist mind to understand complex patterns. Humans can do it too We humans have even built artificial environments that allow for collective intelligence to express itself. Each and every actor in the financial markets has no significant control over or awareness of its inputs. Can we transpose it to other domains? Nobody can single-handedly create “collective intelligence”. Too remote of a possibility?
Why are past, present, and future our only options? But things get awkward if you have a friend. (Use your imagination if necessary.) Low blow, Dr. Dave. Low blow... But seriously, I always figured if there was more than one dimension of time, that moving "left" or "right" would be the equivalent of moving to a parallel universe where things were slightly different. That is to say, maybe time really is 2 dimensional, but for all the reasons you mention, we're normally only aware of one of them—and for the most part, the same one that most of the people we meet are aware of. But take, say, a schizophrenic person—maybe they're tuned in differently; moving sideways through time instead of forward... or maybe moving through (and aware of) both simultaneously. They can't form coherent thoughts because they're constantly confronted with overlapping and shifting realities. I dunno... that's all just speculation, of course, but I find that thought fascinating.
How DARPA Is Making a Machine Mind out of Memristors Artificial intelligence has long been the overarching vision of computing, always the goal but never within reach. But using memristors from HP and steady funding from DARPA, computer scientists at Boston University are on a quest to build the electronic analog to a human brain. The software they are developing – called MoNETA for Modular Neural Exploring Traveling Agent – should be able to function more like a mammalian brain than a conventional computer. At least, that's what they're claiming in a new feature in IEEE Spectrum. There's reason to be optimistic that this attempt might be different from all the previous AI let-downs that have come before it. The Boston U. team, by its own admission, doesn't yet know exactly what these platforms will look like, but they seem very confident that they will soon be a reality. Decide for yourself if MoNETA is the real deal by clicking through the source link below. [IEEE Spectrum]
Artificial life Artificial life (often abbreviated ALife or A-Life) is a field of study and an associated art form which examine systems related to life, its processes, and its evolution, through the use of simulations with computer models, robotics, and biochemistry. The discipline was named by Christopher Langton, an American computer scientist, in 1986. There are three main kinds of alife, named for their approaches: soft, from software; hard, from hardware; and wet, from biochemistry. Artificial life imitates traditional biology by trying to recreate some aspects of biological phenomena. The term "artificial intelligence" is often used to specifically refer to soft alife. Overview Artificial life studies the logic of living systems in artificial environments in order to gain a deeper understanding of the complex information processing that defines such systems. Philosophy Organizations Software-based - "soft" Techniques Notable simulators
SafeKids.com | Online safety & civility Artificial Robotic Hand Transmits Feeling To Nerves Astro Teller has an unusual way of starting a new project: He tries to kill it. Teller is the head of X, formerly called Google X, the advanced technology lab of Alphabet. At X’s headquarters not far from the Googleplex in Mountain View, Calif., Teller leads a group of engineers, inventors, and designers devoted to futuristic “moonshot” projects like self-driving cars, delivery drones, and Internet-beaming balloons. To turn their wild ideas into reality, Teller and his team have developed a unique approach. It starts with trying to prove that whatever it is that you’re trying to do can’t be done—in other words, trying to kill your own idea. As Teller explains, “Instead of saying, ‘What’s most fun to do about this or what’s easiest to do first?’ The ideas that survive get additional rounds of scrutiny, and only a tiny fraction eventually becomes official projects; the proposals that are found to have an Achilles’ heel are discarded, and Xers quickly move on to their next idea.
MoNETA: A Mind Made from Memristors Though memristors are dense, cheap, and tiny, they also have a high failure rate at present, characteristics that bear an intriguing resemblance to the brain's synapses. It means that the architecture must by definition tolerate defects in individual circuitry, much the way brains gracefully degrade their performance as synapses are lost, without sudden system failure. Basically, memristors bring data close to computation, the way biological systems do, and they use very little power to store that information, just as the brain does. For a comparable function, the new hardware will use two to three orders of magnitude less power than Nvidia's Fermi-class GPU. For the first time we will begin to bridge the main divide between biological computation and traditional computation. Basically, without this paradigm shift in hardware architecture, you couldn't even think about building MoNETA. The two kinds of cores deal with processing in fundamentally different ways.
Artificial intelligence AI research is highly technical and specialized, and is deeply divided into subfields that often fail to communicate with each other. Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some subfields focus on the solution of specific problems. The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects. General intelligence is still among the field's long-term goals. Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. The field was founded on the claim that a central property of humans, intelligence—the sapience of Homo sapiens—"can be so precisely described that a machine can be made to simulate it History
Let's Talk About Lying (In the U.S.) 1. Thou shalt have no other gods before me. 2. Thou shalt not make unto thee any graven image. 3. 4. 5. 6. 7. 8. 9. 10. (Exodus 20: 3-17) * The 9th commandment implies that: "the duty, under certain circumstances, of being true witnesses for or against our neighbor; that all men are to be regarded as our neighbors" (Lecture XXXL, Moral Government, "To be a false witness against our neighbor basically means to falsely accuse someone else of wrongdoing."