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Artificial Intelligence dan Kesadaran (Consciousness)

Dimulai oleh The Houw Liong, November 27, 2017, 06:02:45 PM

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The Houw Liong

Pendekatan Artificial intellegnce

Alan Turing, menguji pendekatannya (Turing Test Approach) dengan bertindak seperti manusia. The turing Test, yang diusulkan oleh Alan Turing (195O), dirancang untuk memberikan yang definisi yang memuaskan atas operasional kecerdasan. bukannya mengusulkan panjang dan mungkin kontroversial daftar kualifikasi yang dibutuhkan kecerdasan, ia menyarankan tes berdasarkan ciri khas dari entitas-manusia yang tidak dapat diragukan sebagai mahkluk cerdas. Komputer melewati tes jika manusia sebagai penanya, mengajukan beberapa pertanyaan tertulis, tidak bisa mengatakan apakah tanggapan tertulis berasal dari seseorang atau tidak. Bab 26 membahas rincian tes dan apakah komputer benar-benar cerdas jika dapat melewati. Untuk saat ini, kami mencatat bahwa pemrograman komputer untuk lulus tes memberikan cukup bekerja. Komputer akan perlu memiliki kemampuan sebagai berikut: (Russell,2003 :3)

natural language processing to enable it to communicate successfully in English.
knowledge representation to store what it knows;
automated reasoning to use the stored information to answer questions and to draw new conclusions;
machine learning to adapt to new circumstances and to detect and extrapolate patterns.


Kesadaran dalam [pranala luar disembunyikan, sila masuk atau daftar.] medefinisikan (1) keinsafan; keadaan mengerti: — akan harga dirinya timbul krn ia diperlakukan secara tidak adil; (2) hal yg dirasakan atau dialami oleh seseorang. Robert Pepperell (2009: 22) menunjukan kesadaran mrujuk pada semua sifat yang kita hubungkan dengan kepekaan manusia seperti pikiran, emosi, memori, kesadaran, pengetahuan-diri,rasa mengada (sense of being), dan sebagainya.

[pranala luar disembunyikan, sila masuk atau daftar.]

The Houw Liong

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Simple Ants, Complex Colony
Adams uses ants as an analogy: Ants are relatively simple components in the complex system of the ant colony. Or more specifically, each ant component's behavior is relatively simple compared to what the overall system is doing. An ant colony as a whole is capable of engaging in complex behaviors like building nests, foraging for food, raising aphid "livestock," waging war with other colonies and burying their dead. In contrast, no one single ant will have the impulse or knowledge to undertake such collective tasks on its own. It's these collective behaviors that arise unexpectedly that are called 'emergent' behaviors.

Once it reaches that critical state, the system seems to "flip a switch" and become resilient to future disruptions.

Adams notes that ultimately the "specifics [of behaviors of individual components] are irrelevant" — one can still describe the characteristics of the whole system without being hampered by the details of the individual ant.

On the other hand, it's also difficult to predict how a complex system will evolve because it will require an "irreducibly large" computation. Adams invokes the research of computer scientist and physicist Stephen Wolfram here, and his principle of computational irreducibility, which states that it is impossible to predict what a complex system will do, except by going through as many steps in the computation as the evolution of the system itself. In other words, there's no way around the problem except by running the program itself.

This is why it's much more efficient to describe a complex system as a phenomenon in its own right, rather than regarding its individual components. This certainly makes research much easier, but this resistance to simplification is also a fundamental feature of complex systems.

"Collective behavior is irreducible to individual behavior," emphasized Adams. Thus, we know it's not complexity we see if the system does not have features that are not shared and can be described by the features of its component parts.

Emergence vs. Complexity
Emergence and complexity are also different. "Complexity describes the behavior — it captures the available information, sensory capabilities, interaction dynamics and the range of possible actions a system can take," explained Adams. "Complexity captures these degrees of freedom and the information available, [while] emergent phenomena are the actual behaviors, the occurrence or the appearance of those behaviors."
So emergence seems to happen when the system has evolved to some critical point. "In self-organized systems, critical states act as a kind of attractor," said Adams. Once it reaches that critical state, the system seems to "flip a switch" and become resilient to future disruptions — the same disruptions that drove them to criticality in the first place. A collective then emerges, whose behavior as a whole is no longer correlated to the behavior of individual components. In this way, the system maintains its decentralized character, yet can act as a single entity. Thus, in tying the concepts back to computational systems, the expression of any algorithms of these individual components must necessarily be simple, distributed and scalable.

While we have many biological analogs of computational problems, Adams cautions that one cannot apply the solutions from biological systems to computers, or even more abstract problems like artificial intelligence, without a full understanding of the environmental pressures that prompted those biological solutions in the first place.

"There's also a problem of representation," notes Adams, as accurately specifying the relevant aspects and their components and how they interact in a model can be a huge challenge in itself.

So while any system may begin with a simple set of components, under the right conditions, it will nevertheless be enough to generate a diverse range of differently-scaled systems, whether in nature or computing. "And that's difficult, from an operational perspective, to handle," said Adams. "But simplicity and abstraction is something we should strive for in our software and our systems."


The Houw Liong

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Mario D Garrett Ph.D.

Complexity of Our Brain
Can we map over 125,000 trillion switches in the human brain?
Posted Feb 25, 2014

Our brain is the most complex machine that ever existed. With over 7.146 billion models it is also the most ubiquitous. Despite this, we are unsure of its complexity. We still do not yet understand how it works. By defining the functionality of certain areas of the brain, and by understanding some of the mechanics at the neural chemical level, we still remain ignorant of how the brain coordinates all of its activities and develops language, thought and a sense of self.

This three point three-pound wet mass—greyish on the outside, and whitish pink on the inside—controls every single thing you will ever do. Ever. Each one of us needs these complex structures because each one of us needs it to reflect the totality of the world we live in and how we function within it. Our brain constructs a representation of the world and how we function within it. Other animals do this as well, but what is important in their world is different from what our brain determines is important for us.

In the past we took a different attitude to studying the brain. Most of the scientific writing on the brain was focused on establishing the superiority of human intelligence. But there is not one single factor that we can apply to distinguish our brains from those of other animals. We cannot just use size, because some mammals (eg whales) have bigger brains. Perhaps it is the size of the brain in proportion to the body. When we try that by measuring the Encephalization Quotient (EQ) ratio, small birds beat us. Perhaps it is size, EQ and something else. The correct question is to ask what aspects of the world are we, as humans, trying to represent in our brain? And how complex is the brain really?

In 2009, the Brazilian scientist Suzana Herculano-Houzel performed a review of what we know about the physical structure of the brain. The adult human male brain has 86 billion neurons--more than any other primate. Each neuron has between 1,000 to 10,000 synapses that result in 125 trillion synapses in the cerebral cortex alone. That is at least 1,000 times the number of stars in our galaxy. Stephen Smith from Stanford University reported that one synapse might contain some 1,000 molecular-scale switches. That is over 125,000 trillion switches in a single human brain.

With such a lean mean machine then it is surprising to learn that the brain is obese. It is 60 percent fat, with over 25 percent of that being cholesterol. Cholesterol is in every cell in our body and becomes concentrated in our brain. Most of the cholesterol in the brain is produced in the hypothalamus itself, establishing cholesterol as an integral part of our brain. Cholesterol is used by a specific type of glial cells in the brain to form myelination—sheathing which enhances neuron speed and integrity of signal. Glial cells outnumber neurons ten times over with 860 billion cells. It was only in 2010 that glial cells were found to assist neurons in forming synaptic connections between each other. Once thought to be simply support cells, cleaning up and helping with myelination, they are now known to also promote dendrite growth, and to be as important as neurons in forming the neural network that make up cognitive activity. Glial cells can also reproduce—if neurons reproduce they do it slower—and similarly release transmitters and control neural activity just like neurons. All of this activity is monitored by microglia cells that not only clean up damaged cells but they also prune dendrites, forming part of the learning process.

Comparing mapping the brain to mapping the human genome is like comparing the artistry of the Mona Lisa to Sponge Bath Bob. The total length of the human genome is 3 billion base pairs, the brain has nearly 30 times more neurons. And whereas the genome base pairs has an on and off arrangement, each neuron might have a thousand switches. Mapping the brain will mean that if every switch in every synaptic end at every neuron is identified by a second of time then it will take 4,000,000,000 years to complete. The brain is that complex.

In the cortex alone, there are 100,000 miles of myelin-covered—insulted—nerve fibers. Each nerve leaves the base of the brain to the outer reaches of our skin, we have a neural network that is incomparable. We have millions of nerve endings in the outermost layer of our body that sense minute variations of light, sounds, vibrations, touch, smell, pressure, temperature; all extremely sensitive in most cases more sensitive than any computer on earth. The marvel of the brain is not just the capacity but the sensitivity to stimuli.The Human Protein Atlas identifies  some 318 proteins that are involved in all these activities.