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      "body": "<html>\n<p>While developing <a href=\"https://ethereum.stackexchange.com/questions/2464/what-does-it-mean-that-ethereum-is-turing-complete/2465\">Turing-complete smart contracts</a> it is always hard to assure the absence of significant bugs and errors. Decades of software development in modern computer industry have shown that software code executed on Turing-complete machines always has some bugs that get discovered only with time. This is not strange: being a <a href=\"http://mathworld.wolfram.com/DynamicalSystem.html\">dynamical system</a> Turing-complete code will show chaotic behavior. As with any <a href=\"http://mathworld.wolfram.com/Chaos.html\">chaotic system</a>, minimal difference in input parameters will lead to exponentially-different result with time, making it practically impossible to predict all possible system behaviors, since all possible input data can not be enumerated and tested beforehand.</p>\n<p><img src=\"https://cdn-images-1.medium.com/max/600/1*6Q3pho6gT__pt-4F2wlrCA.jpeg\" width=\"376\" height=\"250\"/></p>\n<p><em>Gödel looks at Turing-complete system as if it’s incomplete (</em><a href=\"https://twitter.com/macx0r/status/889215308954820608\"><em>source</em></a><em>)</em></p>\n<p>Arguably we can also take another example from Gödel theorems and later developments, showing that within any complex system there exist such true axioms that can not be proven to be true using the apparatus of the system itself. Thus, within Turing-complete code there could be true (working) solutions or patterns of behavior (exploits) which can not be detected with Turing-complete machine (another computer). <em>[Not all mathematicians would strictly agree with this statement, though].</em> Also, we can not test all possible inputs to see all possible outcomes of computations due to the reason mentioned in the previous paragraph (the nature of dynamic systems). Thus bugs are inevitable and there is no way to detect all of them beforehand with any type of code audit or tests.When we work with common computers, whether it’s a desktop, laptop, mobile or enterprise mainframe, we have very strict address and code separation spaces at the level of the processing unit (CPU, GPU etc). Bugs appearing in the code can create damage only within this space, i.e. the damage is limited by (a) the cost of the computer itself, (b) information kept on it and (c) collateral damage related to functions performed by this computer in the real world (for instance if this computer operates some other system, bug in the code may destroy that system as well). So the damage is located, measurable — and thus can be insured. That’s why computers and software can be used in business: risks created by them can be controlled.As with Ethereum, or many other smart-contracts blockchains making ICOs these days, we have global execution and address space: one smart contracts in EVM can call method of any other smart contract or library and invoke operations across them. It is like if there were no threads within CPUs, no protected kernel/user modes for software in the OS, and all the world code was executed on a single processor! What would happen? The damage of a software bug would be unlimited: it would be able to affect all value of the entire computing network! That is the situation we have today with Ethereum.We can draw a very important conclusion from this:</p>\n<blockquote><em><strong>Ethereum decentralizes only computing resource, but centralizes computations themselves, creating unmeasurable and uncontrollable risks</strong></em></blockquote>\n<p>Let’s look at the recent past:</p>\n<ul>\n  <li><a href=\"https://www.coindesk.com/understanding-dao-hack-journalists/\">The DAO hack</a>;</li>\n  <li><a href=\"https://blog.ethcore.io/the-multi-sig-hack-a-postmortem/\">Parity multisig wallet hack</a> happened in a library code, which spread damage across <a href=\"https://news.bitcoin.com/ethereums-parity-client-users-lose-millions-multi-sig-hack/\">many businesses</a>, <a href=\"https://blog.aeternity.com/parity-multisig-wallet-hack-47cc507d964d\">startups</a> and even <a href=\"https://steemit.com/ethereum/@hipster/updates-satoshi-pie-ethereum-multisig-has-been-hacked\">investment funds</a>;</li>\n  <li>Multiple ICOs and Ethereum-based project hacks: <a href=\"https://medium.com/@Digix/security-vulnerability-discovered-digixdao-fdb358c6128c\">one</a>, <a href=\"http://fortune.com/2017/07/18/ethereum-coindash-ico-hack/\">two</a>, <a href=\"https://www.hackread.com/veritaseum-hack-another-ethereum-ico-hacked-loses-84m/\">three</a> etc</li>\n  <li><a href=\"https://medium.com/@AugurProject/serpent-compiler-vulnerability-rep-solidity-migration-5d91e4ae90dd\">Serpent compiler vulnerability causing Augur project rewrite</a></li>\n  <li>and so on…</li>\n</ul>\n<blockquote><em><strong>So, within Ethereum (and possible future-release Turing-complete smart contracts) we can not measure possible damage, and thus, insure possible risks. This basically renders the system to be unusable for business, adding a lot of deep sense to the previous statements from Ethereum team that it is an early-stage highly experimental technology that should not be used by mission-critical business and production environments.</strong></em></blockquote>\n<p>However, this is not an unresolvable problem. We will talk on the possible scenarios in our further publications. One of them — to separate computation space much like it is <a href=\"https://en.wikipedia.org/wiki/Protected_mode\">done in modern OSes and CPUs</a>. Something like <a href=\"https://en.wikipedia.org/wiki/Compartment_%28ship%29\">ships that are split inside into watertight compartments</a> to reduce possible damage from water and limit risks. Another way — to introduce completely new contexts by taking analogy from natural systems and implementing robustness via cross-disciplinary samples from the living organisms: DNA and cellular expression system is a sort of Turing-complete machine, and special evolutionary protection has being developed to limit and reduce risks of “code hacks” within DNA itself. We will write more on this topic in our next publication about the proposed “Double-helix blockchain”, which will be used in our <a href=\"https://medium.com/pandoraboxchain/world-decentralized-ai-on-blockchain-with-cognitive-mining-and-open-markets-for-data-and-algorithms-4e192554024a\">Pandora Boxchain</a> technology. Stay tuned!</p>\n</html>",
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      "body": "<html>\n<p>The brain is the only known source of intelligence. At least for now… Have you ever wondered in which way existing computers differ from the brain in their architecture? With humanity trying to understand how to build strong artificial intelligence, shall we take more samples from nature and from the only existing intelligence known to us? Here at <a href=\"http://bicalabs.org/\">BICA Labs</a>, we strongly believe that we shall. BICA stands for “biologically-inspired cognitive architectures”, and it is our passion to take samples from nature and find how they can be applied for building artificial cognition. So let’s start our journey: with this publication, we open a series of articles on how brain differs from computers.</p>\n<p><img src=\"https://cdn-images-1.medium.com/max/2000/0*GC4vXMOgAPVdMEp4.jpeg\" width=\"100%\"/></p>\n<p>The brain is composed of neurons. Most of the modern successful artificial intelligence models use the concept of “artificial neuron” to build their own “neural networks”. This naming is not a coincidence: real progress with AI algorithms has happened only after McCulloch &amp; Pitts <a href=\"https://link.springer.com/article/10.1007/BF02478259\">introduced the concept of artificial neuron back in 1943</a>, and they had drawn their inspiration from biological neurons.</p>\n<iframe src=\"https://www.youtube.com/embed/osa3zIEJjgw\" width=\"640\" height=\"360\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"></iframe>\n<p>These artificial neurons are still being used in many successful deep learning developments, including the AlphaGo system which won in Go game. However, McCulloch neurons are extremely simplified samples of biological neuron — and artificial neural networks have little in common with complexity of the brain itself. There is a high probability that the gap between specialized AI and strong, human-level AI can be bridged only if we go deeper into replicating real brain and neuronal properties into our computing machines, both on the software and on the hardware level. So let’s dive into how the brain is different from a modern computer.</p>\n<h1>Part 1. Analog vs Digital and why its important for emotions</h1>\n<h2>Digital age shall pass</h2>\n<p style=\"float: left\"><img src=\"https://cdn-images-1.medium.com/max/1200/0*TsoMekRdQd2HPYis.png\" width=\"33%\"/></p>\n<p>First, and foremost: a brain is an analog machine. “Oh, so outdated! We moved to the digital age more than a decade ago!” — might say some digital nerds :) But our analog technologies of the past century were much, much less advanced than the least analog part of our brain. Under such circumstances digitalization allowed unprecedented increase in quality, reliability and speed of computer communications. If so, why we need to go back into the analog age? No, not back, but forward: progress has the form of spiral, and its new coil shall add on top of digital quality, reliability and speed… the ability to use emotions! Yep, the thing that distinguishes humans from machines to the most extent: our emotions. A growing body of <a href=\"https://www.google.com/url?sa=t&amp;rct=j&amp;q=&amp;esrc=s&amp;source=web&amp;cd=1&amp;ved=0ahUKEwiWxv7J67rSAhUFEJoKHRIuA1YQFggbMAA&amp;url=https%3A%2F%2Fwww.thinkmind.org%2Fdownload.php%3Farticleid%3Dcognitive_2016_3_20_40101&amp;usg=AFQjCNE-HrsbkMhzgscO55PhTbQXYUN9TA&amp;sig2=l57RyOcVZGAfq5jyZfS9sA\">evidence</a> suggests that the source for emotions lies in … chaos, noise in neural networks. And not only for emotions, but probably for intuition, eurekas and insights – thus, for talent and genius. So, with precise digital machines, we are leaving all these things out.</p>\n<h2>Chaos and noise feed the brain</h2>\n<p><a href=\"http://dx.doi.org/10.1016/j.bica.2015.04.009\">At BICA Labs we were collaborators on some research in this field</a>, and may testify that the real root for complexity and adaptability is hidden in chaotic behavior of dynamic systems. Dynamic systems are those that have recurrence: their state depends on their previous state. Recurrence is a basic quality of neural networks in the brain, as well as a tool for making artificial neural networks more efficient, grant them memory etc. A dynamic system can be characterized with strange attractor — phase space trajectory of its evolution. You have certainly heard about the Lorenz butterfly (pictured on the side): it is a classical sample of strange attractors.</p>\n<p style=\"float: left\"><img src=\"https://cdn-images-1.medium.com/max/1200/0*HO-OUUz_NyQYD9IJ.png\" width=\"33%\" /></p>\n<p>What makes this attractor so strange is that tiny changes in the initial state lead to a completely different result with time: unpredictable results. Thus, if our brain neural networks are subjected to randomness/noise, this can affect our decisions in unpredictable direction … and it certainly does with emotions!</p>\n<h2>Damn you, my loving one!</h2>\n<p>One might say that emotions are unnecessary, they are an atavism from animal’s ancestry of humankind. But wait, evolution has selected species of animals that are capable of strong emotional behavior and has created many, many brain structures and circuits especially for enabling emotions! Was it wrong?</p>\n<p>Probably not: first, emotions are evolutionary-created mechanism for making decisions under the lack of knowledge or information. Under conditions of insufficient information for rational behavior it’s still better to do something than to wait and do nothing — and here emotions are coming to help.</p>\n<p>Second, emotions enable very efficient feedback for memory formation and neural networks learning (we will touch that in more details when will discuss learning algorithms in the brain and artificial networks).</p>\n<p style=\"float: left\"><img src=\"https://cdn-images-1.medium.com/max/1200/0*u51veMWu4W21GaoU.jpeg\" width=\"33%\"/></p>\n<p>Third, they are tightly coupled with adaptation and natural selection, being involved in both positive selection and elimination. This works via distress and eustress mechanics, with eustress (positive emotions) enabling faster restoration and memory formation. Thus, machines will have to learn to make love, drink small amounts of alcohol and eat chocolate: these are main sources for brain eustress! — Joking, machines may not do all those things, but they certainly would need emotions to improve their ability to adapt, survive and learn. Even distress, frequently invoked by seizing negative emotional reactions and feelings of being incapable to reach goals/desires/comfort — even such distress is important, since evolutionary it allowed negative selection of those individuals who were unable to progress and exceed their own boundaries. One who knows a bit about machine learning can easily imagine how it could be important for genetic algorithms!</p>\n<p>Fourth, as you probably have guessed from the third, emotions boost our brain and organism under acute stress, helping to survive hard times. They burn and combust our psyche and body for short periods of time in order to get the maximum from it — and without them, we’ll be just a lazy slowly-thinking <em>invertebrata</em>.</p>\n<p>So, emotions are important, and they require injection of noise and randomness into the purity of digital computing, which will certainly lead to the invention of some new “analog computing 2.0” paradigm.</p>\n<p>nbsp;</p>\n<p><em>to be continued with other 7 parts…</em></p>\n<p>Meanwhile, why not visit our <a href=\"http://bicalabs.org/\">new labs website</a>, check <a href=\"http://bicalabs.org/research\">our research</a> or even <a href=\"http://bicalabs.org/contribute#donate\">support it with a funding</a>? :)</p>\n</html>",
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