Systems Theory
9.1K views | +4 today
Systems Theory
theoretical aspects of (social) systems theory
Curated by Ben van Lier
Your new post is loading...
Your new post is loading...
Rescooped by Ben van Lier from Amazing Science!

The Current State of Machine Intelligence

The Current State of Machine Intelligence | Systems Theory |

A few years ago, investors and startups were chasing “big data”. Now we’re seeing a similar explosion of companies calling themselves artificial intelligence, machine learning, or collectively “machine intelligence”. The Bloomberg Beta fund, which is focused on the future of work, has been investing in these approaches.


Computers are learning to think, read, and write. They’re also picking up human sensory function, with the ability to see and hear (arguably to touch, taste, and smell, though those have been of a lesser focus).

Machine intelligence technologies cut across a vast array of problem types (from classification and clustering to natural language processing and computer vision) and methods (from support vector machines to deep belief networks). All of these technologies are reflected on this landscape.

What this landscape doesn’t include, however important, is “big data” technologies. Some have used this term interchangeably with machine learning and artificial intelligence, but I want to focus on the intelligence methods rather than data, storage, and computation pieces of the puzzle for this landscape (though of course data technologies enable machine intelligence).

We’ve seen a few great articles recently outlining why machine intelligence is experiencing a resurgence, documenting the enabling factors of this resurgence. Kevin Kelly, for example chalks it up to cheap parallel computing, large datasets, and better algorithms.

Machine intelligence is enabling applications we already expect like automated assistants (Siri), adorable robots (Jibo), and identifying people in images (like the highly effective but unfortunately named DeepFace). However, it’s also doing the unexpected: protecting children from sex trafficking, reducing the chemical content in the lettuce we eat, helping us buy shoes online that fit our feet precisely, anddestroying 80's classic video games.

Big companies have a disproportionate advantage, especially those that build consumer products. The giants in search (Google, Baidu), social networks (Facebook, LinkedIn, Pinterest), content (Netflix, Yahoo!), mobile (Apple) and e-commerce (Amazon) are in an incredible position. They have massive datasets and constant consumer interactions that enable tight feedback loops for their algorithms (and these factors combine to create powerful network effects) — and they have the most to gain from the low hanging fruit that machine intelligence bears.
Best-in-class personalization and recommendation algorithms have enabled these companies’ success (it’s both impressive and disconcerting that Facebook recommends you add the person you had a crush on in college and Netflix tees up that perfect guilty pleasure sitcom).
Now they are all competing in a new battlefield: the move to mobile. Winning mobile will require lots of machine intelligence: state of the art natural language interfaces (like Apple’s Siri), visual search (like Amazon’s “FireFly”), and dynamic question answering technology that tells you the answer instead of providing a menu of links (all of the search companies are wrestling with this).Large enterprise companies (IBM and Microsoft) have also made incredible strides in the field, though they don’t have the same human-facing requirements so are focusing their attention more on knowledge representation tasks on large industry datasets, like IBM Watson’s application to assist doctors with diagnoses.
Via Dr. Stefan Gruenwald
John Vollenbroek's curator insight, April 25, 2015 2:53 AM

I like this overview

pbernardon's curator insight, April 26, 2015 2:33 AM

Une infographie et une cartographie claire et très intéressante sur l'intelligence artificielle et les usages induits que les organisations vont devoir s'approprier.



Rescooped by Ben van Lier from Amazing Science!

Roger Penrose: The Human Brain is More Complex than a Galaxy

Roger Penrose: The Human Brain is More Complex than a Galaxy | Systems Theory |

According to physicist, Roger Penrose, What’s in our head is orders of magnitude more complex than anything one sees in the Universe: "If you look at the entire physical cosmos," says Penrose, "our brains are a tiny, tiny part of it. But they're the most perfectly organized part. Compared to the complexity of a brain, a galaxy is just an inert lump." 


Each cubic millimeter of tissue in the neocortex, reports Michael Chorost in World Wide Mind, contains between 860 million and 1.3 billion synapses. Estimates of the total number of synapses in the neocortex range from 164 trillion to 200 trillion. The total number of synapses in the brain as a whole is much higher than that. The neocorex has the same number of neurons as a galaxy has stars: 100 billion.  "All stars can do is pull on each other with gravity," writes Chorost, and, if they are very close, exchange heat."


One researcher estimates that with current technology it would take 10,000 automated microscopes thirty years to map the connections between every neuron in a human brain, and 100 million terabytes of disk space to store the data.


Galaxies are ancient, but self-aware, language-using, tool-making brains are very new in the evolutionary timeline, some 200,000-years old. Most of the neurons in the neocortex have between 1,000 and 10,000 synaptic connections with other neurons. Elsewhere in the brain, in the cerebellum, one type of neuron has 150,000 to 200,000 synaptic connections with other neurons. Even the lowest of these numbers seems hard to believe. One tiny neuron can connect to 200,000 neurons.


"The universe could so easily have remained lifeless and simple -just physics and chemistry, just the scattered dust of the cosmic explosion that gave birth to time and space," says Richard Dawkins, the famed Oxford evolutionary biologist reflecting on the sheer wonder of the emergence of life on Earth and the evolutionary process in his classic The Ancestor's Tale.


"The fact that it did not -the fact that life evolved out of literally nothing, some 10 billion years after the universe evolved literally out of nothing -is a fact so staggering that I would be mad to attempt words to do it justice. And even that is not the end of the matter. Not only did evolution happen: it eventually led to beings capable of comprehending the process by which they comprehend it."

Via Dr. Stefan Gruenwald
No comment yet.
Rescooped by Ben van Lier from Amazing Science!

Humans With Amplified Intelligence Could Be More Powerful Than AI

Humans With Amplified Intelligence Could Be More Powerful Than AI | Systems Theory |

With much of our attention focused the rise of advanced artificial intelligence, few consider the potential for radically amplified human intelligence (IA). It’s an open question as to which will come first, but a technologically boosted brain could be just as powerful — and just as dangerous – as AI.


As a species, we’ve been amplifying our brains for millennia. Or at least we’ve tried to. Looking to overcome our cognitive limitations, humans have employed everything from writing, language, and meditative techniques straight through to today’s nootropics. But none of these compare to what’s in store. Unlike efforts to develop artificial general intelligence (AGI), or even an artificial superintelligence (SAI), the human brain already presents us with a pre-existing intelligence to work with. Radically extending the abilities of a pre-existing human mind — whether it be through genetics, cybernetics or the integration of external devices — could result in something quite similar to how we envision advanced AI.


Looking to learn more about this, I contacted futurist Michael Anissimov, a blogger atAccelerating Future and a co-organizer of the Singularity Summit. He’s given this subject considerable thought — and warns that we need to be just as wary of IA as we are AI. The real objective of IA is to create super-Einsteins, persons qualitatively smarter than any human being that has ever lived. There will be a number of steps on the way there.


The first step will be to create a direct neural link to information. Think of it as a "telepathic Google." The next step will be to develop brain-computer interfaces that augment the visual cortex, the best-understood part of the brain. This would boost our spatial visualization and manipulation capabilities. Imagine being able to imagine a complex blueprint with high reliability and detail, or to learn new blueprints quickly. There will also be augmentations that focus on other portions of sensory cortex, like tactile cortex and auditory cortex. The third step involves the genuine augmentation of pre-frontal cortex. This is the Holy Grail of IA research — enhancing the way we combine perceptual data to form concepts. The end result would be cognitive super-McGyvers, people who perform apparently impossible intellectual feats. For instance, mind controlling other people, beating the stock market, or designing inventions that change the world almost overnight. This seems impossible to us now in the same way that all our modern scientific achievements would have seemed impossible to a stone age human — but the possibility is real.


For it to be otherwise would require that there is some mysterious metaphysical ceiling on qualitative intelligence that miraculously exists at just above the human level. Given that mankind was the first generally intelligent organism to evolve on this planet, that seems highly implausible. We shouldn't expect version one to be the final version, any more than we should have expected the Model T to be the fastest car ever built.


Via Dr. Stefan Gruenwald
Dominic's curator insight, March 26, 2015 6:24 PM

Our brain is a powerful device that has much potential to undertake theories in which we thought was impossible, to reality. This article discovers the ways that we humans can release our cognitive limitations and use the power of the brain to explore innovations that we couldn't even dream of. This also explores how amplified human intelligence (IA) could become more advanced than Human Intelligence. 

Rescooped by Ben van Lier from Amazing Science!

‘Superorganisations’ – Learning from Nature’s Networks

‘Superorganisations’ – Learning from Nature’s Networks | Systems Theory |

Fritjof Capra, in his book ‘The Hidden Connections’ applies aspects of complexity theory, particularly the analysis of networks, to global capitalism and the state of the world; and eloquently argues the case that social systems such as organisations and networks are not just like living systems – they are living systems. The concept and theory of living systems (technically known as autopoiesis) was introduced in 1972 by Chilean biologists Humberto Maturana and Francisco Varela.


This is a complete version of a ‘long-blog’ written by Al Kennedy on behalf of ‘The Nature of Business’ blog and BCI: Biomimicry for Creative Innovation www.businessinspired...

Via Peter Vander Auwera, ddrrnt, Spaceweaver, David Hodgson, pdjmoo, Sakis Koukouvis, Dr. Stefan Gruenwald
Monica S Mcfeeters's curator insight, January 18, 2014 8:57 PM

A look at how to go organic with business models in a tech age...

Nevermore Sithole's curator insight, March 14, 2014 9:01 AM

Learning from Nature’s Networks

pdjmoo's curator insight, December 6, 2014 11:04 PM