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Get the most recent info and news about Apriorit on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

Apriorit provides advanced outsourcing software development services to the technology companies worldwide especially focusing on C/C++ development, driver development and reverse engineering projects

Apriorit

An Essay on the Foundations of Geometry, by Bertrand Russell is part of . The Table of Links for this book can be found . The Foundations of Geometry: Introduction HackerNoon’s Book Blog Post series here PREFACE. The present work is based on a dissertation submitted at the Fellowship Examination of Trinity College, Cambridge, in the year 1895. Section B of the third chapter is in the main a reprint, with some serious alterations, of an article in Mind (New Series, No. 17). The substance of the book has been given in the form of lectures at the Johns Hopkins University, Baltimore, and at Bryn Mawr College, Pennsylvania. My chief obligation is to Professor Klein. Throughout the first chapter, I have found his "Lectures on non-Euclidean Geometry" an invaluable guide; I have accepted from him the division of Metageometry into three periods, and have found my historical work much lightened by his references to previous writers. In Logic, I have learnt most from Mr Bradley, and next to him, from Sigwart and Dr Bosanquet. On several important points, I have derived useful suggestions from Professor James's "Principles of Psychology." My thanks are due to Mr G. F. Stout and Mr A. N. Whitehead for kindly reading my proofs, and helping me by many useful criticisms. To Mr Whitehead I owe, also, the inestimable assistance of constant criticism and suggestion throughout the course of construction, especially as regards the philosophical importance of projective Geometry. Haslemere. May, 1897. INTRODUCTION. OUR PROBLEM DEFINED BY ITS RELATIONS TO LOGIC, PSYCHOLOGY AND MATHEMATICS. Geometry, throughout the 17th and 18th centuries, remained, in the war against empiricism, an impregnable fortress of the idealists. Those who held—as was generally held on the Continent—that certain knowledge, independent of experience, was possible about the real world, had only to point to Geometry: none but a madman, they said, would throw doubt on its validity, and none but a fool would deny its objective reference. The English Empiricists, in this matter, had, therefore, a somewhat difficult task; either they had to ignore the problem, or if, like Hume and Mill, they ventured on the assault, they were driven into the apparently paradoxical assertion that Geometry, at bottom, had no certainty of a different kind from that of Mechanics—only the perpetual presence of spatial impressions, they said, made our experience of the truth of the axioms so wide as to seem absolute certainty. Here, however, as in many other instances, merciless logic drove these philosophers, whether they would or no, into glaring opposition to the common sense of their day. It was only through Kant, the creator of modern Epistemology, that the geometrical problem received a modern form. He reduced the question to the following hypotheticals: If Geometry has apodeictic certainty, its matter, i.e. space, must be à priori, and as such must be purely subjective; and conversely, if space is purely subjective, Geometry must have apodeictic certainty. The latter hypothetical has more weight with Kant, indeed it is ineradicably bound up with his whole Epistemology; nevertheless it has, I think, much less force than the former. Let us[2] accept, however, for the moment, the Kantian formulation, and endeavour to give precision to the terms à priori and subjective. One of the great difficulties, throughout this controversy, is the extremely variable use to which these words, as well as the word empirical, are put by different authors. To Kant, who was nothing of a psychologist, à priori and subjective were almost interchangeable terms[1]; in modern usage there is, on the whole, a tendency to confine the word subjective to Psychology, leaving à priori to do duty for Epistemology. If we accept this differentiation, we may set up, corresponding to the problems of these two sciences, the following provisional definitions: à priori applies to any piece of knowledge which, though perhaps elicited by experience, is logically presupposed in experience: subjective applies to any mental state whose immediate cause lies, not in the external world, but within the limits of the subject. The latter definition, of course, is framed exclusively for Psychology: from the point of view of physical Science all mental states are subjective. But for a Science whose matter, strictly speaking, is only mental states, we require, if we are to use the word to any purpose, some differentia among mental states, as a mark of a more special subjectivity on the part of those to which this term is applied. Now the only mental states whose immediate causes lie in the external world are sensations. A pure sensation is, of course, an impossible abstraction—we are never wholly passive under the action of an external stimulus—but for the purposes of Psychology the abstraction is a useful one. Whatever, then, is not sensation, we shall, in Psychology, call subjective. It is in sensation alone that we are directly affected by the external world, and only here does it give us direct information about itself. Let us now consider the epistemological question, as to the sort of knowledge which can be called à priori. Here we have nothing to do—in the first instance, at any rate—with the cause or genesis of a piece of knowledge; we accept knowledge as a datum to be analysed and classified. Such analysis will reveal a formal and a material element in[3] knowledge. The formal element will consist of postulates which are required to make knowledge possible at all, and of all that can be deduced from these postulates; the material element, on the other hand, will consist of all that comes to fill in the form given by the formal postulates—all that is contingent or dependent on experience, all that might have been otherwise without rendering knowledge impossible. We shall then call the formal element à priori, the material element empirical. Now what is the connection between the subjective and the à priori? It is a connection, obviously—if it exists at all—from the outside, i.e. not deducible directly from the nature of either, but provable—if it can be proved—only by a general view of the conditions of both. The question, what knowledge is à priori, must, on the above definition, depend on a logical analysis of knowledge, by which the conditions of possible experience may be revealed; but the question, what elements of a cognitive state are subjective, is to be investigated by pure Psychology, which has to determine what, in our perceptions, belongs to sensation, and what is the work of thought or of association. Since, then, these two questions belong to different sciences, and can be settled independently, will it not be wise to conduct the two investigations separately? To decree that the à priori shall always be subjective, seems dangerous, when we reflect that such a view places our results, as to the à priori, at the mercy of empirical psychology. How serious this danger is, the controversy as to Kant's pure intuition sufficiently shows. I shall, therefore, throughout the present Essay, use the word à priori without any psychological implication. My test of apriority will be purely logical: Would experience be impossible, if a certain axiom or postulate were denied? Or, in a more restricted sense, which gives apriority only within a particular science: Would experience as to the subject-matter of that science be impossible, without a certain axiom or postulate? My results also, therefore, will be purely logical. If Psychology declares that some things, which I have declared à priori, are not subjective, then, failing an error of detail in my proofs, the connection of the à priori and the subjective,[4] so far as those things are concerned, must be given up. There will be no discussion, accordingly, throughout this Essay, of the relation of the à priori to the subjective—a relation which cannot determine what pieces of knowledge are à priori, but rather depends on that determination, and belongs, in any case, rather to Metaphysics than to Epistemology. As I have ventured to use the word à priori in a slightly unconventional sense, I will give a few elucidatory remarks of a general nature.
The à priori, since Kant at any rate, has generally stood for the necessary or apodeictic element in knowledge. But modern logic has shown that necessary propositions are always, in one aspect at least, hypothetical. There may be, and usually is, an implication that the connection, of which necessity is predicated, has some existence, but still, necessity always points beyond itself to a ground of necessity, and asserts this ground rather than the actual connection. As Bradley points out, "arsenic poisons" remains true, even if it is poisoning no one. If, therefore, the à priori in knowledge be primarily the necessary, it must be the necessary on some hypothesis, and the ground of necessity must be included as à priori. But the ground of necessity is, so far as the necessary connection in question can show, a mere fact, a merely categorical judgment. Hence necessity alone is an insufficient criterion of apriority. To supplement this criterion, we must supply the hypothesis or ground, on which alone the necessity holds, and this ground will vary from one science to another, and even, with the progress of knowledge, in the same science at different times. For as knowledge becomes more developed and articulate, more and more necessary connections are perceived, and the merely categorical truths, though they remain the foundation of apodeictic judgments, diminish in relative number. Nevertheless, in a fairly advanced science such as Geometry, we can, I think, pretty completely supply the appropriate ground, and establish, within the limits of the isolated science, the distinction between the necessary and the merely assertorical. There are two grounds, I think, on which necessity may be sought within any science. These may be (very roughly) distinguished as the ground which Kant seeks in the[5] Prolegomena, and that which he seeks in the Pure Reason. We may start from the existence of our science as a fact, and analyse the reasoning employed with a view to discovering the fundamental postulate on which its logical possibility depends; in this case, the postulate, and all which follows from it alone, will be à priori. Or we may accept the existence of the subject-matter of our science as our basis of fact, and deduce dogmatically whatever principles we can from the essential nature of this subject-matter. In this latter case, however, it is not the whole empirical nature of the subject-matter, as revealed by the subsequent researches of our science, which forms our ground; for if it were, the whole science would, of course, be à priori. Rather it is that element, in the subject-matter, which makes possible the branch of experience dealt with by the science in question[2]. The importance of this distinction will appear more clearly as we proceed[3]. These two grounds of necessity, in ultimate analysis, fall together. The methods of investigation in the two cases differ widely, but the results cannot differ. For in the first case, by analysis of the science, we discover the postulate on which alone its reasonings are possible. Now if reasoning in the science is impossible without some postulate, this postulate must be essential to experience of the subject-matter of the science, and thus we get the second ground. Nevertheless, the two methods are useful as supplementing one another, and the first, as starting from the actual science, is the safest and easiest method of investigation, though the second seems the more convincing for exposition. The course of my argument, therefore, will be as follows: In the first chapter, as a preliminary to the logical analysis of Geometry, I shall give a brief history of the rise and development of non-Euclidean systems. The second chapter will prepare the ground for a constructive theory of Geometry, by a criticism of some previous philosophical views; in this chapter, I shall[6] endeavour to exhibit such views as partly true, partly false, and so to establish, by preliminary polemics, the truth of such parts of my own theory as are to be found in former writers. A large part of this theory, however, cannot be so introduced, since the whole field of projective Geometry, so far as I am aware, has been hitherto unknown to philosophers. Passing, in the third chapter, from criticism to construction, I shall deal first with projective Geometry. This, I shall maintain, is necessarily true of any form of externality, and is, since some such form is necessary to experience, completely à priori. In metrical Geometry, however, which I shall next consider, the axioms will fall into two classes: (1) Those common to Euclidean and non-Euclidean spaces. These will be found, on the one hand, essential to the possibility of measurement in any continuum, and on the other hand, necessary properties of any form of externality with more than one dimension. They will, therefore, be declared à priori. (2) Those axioms which distinguish Euclidean from non-Euclidean spaces. These will be regarded as wholly empirical. The axiom that the number of dimensions is three, however, though empirical, will be declared, since small errors are here impossible, exactly and certainly true of our actual world; while the two remaining axioms, which determine the value of the space-constant, will be regarded as only approximately known, and certain only within the errors of observation[4]. The fourth chapter, finally, will endeavour to prove, what was assumed in Chapter III., that some form of externality is necessary to experience, and will conclude by exhibiting the logical impossibility, if knowledge of such a form is to be freed from contradictions, of wholly abstracting this knowledge from all reference to the matter contained in the form. I shall hope to have touched, with this discussion, on all the main points relating to the Foundations of Geometry. FOOTNOTES: [1] Cf. Erdmann, Axiome der Geometrie, p. 111: "Für Kant sind Apriorität und ausschliessliche Subjectivität allerdings Wechselbegriffe." [2] I use "experience" here in the widest possible sense, the sense in which the word is used by Bradley. [3] Where the branch of experience in question is essential to all experience, the resulting apriority may be regarded as absolute; where it is necessary only to some special science, as relative to that science. [4] I have given no account of these empirical proofs, as they seem to be constituted by the whole body of physical science. Everything in physical science, from the law of gravitation to the building of bridges, from the spectroscope to the art of navigation, would be profoundly modified by any considerable inaccuracy in the hypothesis that our actual space is Euclidean. The observed truth of physical science, therefore, constitutes overwhelming empirical evidence that this hypothesis is very approximately correct, even if not rigidly true. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Bertrand Russell. (1897). AN ESSAY ON THE FOUNDATIONS OF GEOMETRY. Urbana, Illinois: Project Gutenberg. Retrieved DATE, from https://www.gutenberg.org/cache/epub/52091/pg52091-images.html. This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever.  You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

Introduction To Geometry

With the growing number of zero-day attacks of various kinds that traditional antivirus software has not been able to detect effectively, a whole new market segment has emerged. Zero-Day Exploits that are developed, distributed, and executed by Zero Day Attacks, are becoming increasingly common and widespread in the world of cybersecurity and security research and development. What Is A Zero-Day Exploit and Attack? A zero-day exploit is an undisclosed security or device loophole that an attacker at risk may use malicious code to address. A zero-day attack is for the attacker to develop a malicious program that targets the zero-day vulnerability. The attack can be carried out via email, social media, and other means such as using a social engineering technique. Once the exploit has been developed, the next step is to deliver the malware to the target system to perform the zero-day attack. The malware is detected by the Threat Emulation Engine so that the hacker can use evasive techniques that try to bypass the sandbox. Defense Against Zero-Day Attacks The best defense against zero-day attacks is one that focuses on detection and response, as prevention efforts typically fail on unknown vulnerabilities and exploits. Because attackers can exploit zero-day exploits, most organizations are slow to respond to newly discovered vulnerabilities. Traditional protection tools depend on binary malware signatures or external URLs and server reputations. These protections only recognize known, confirmed threats by design. Code-morphing and obfuscation strategies produce new malware variants more quickly than conventional defense companies would create new signatures. And spam filters do not stop attacks by spear-phishing, which are limited in number. Operating system-level protection including Address Space Layout Randomization (ASLR) and Data Execution Prevention (DEP) is also being decreased. Simultaneously, operating system level protection is being less effective. Innovative solutions that combine advanced heuristics to protect infections from targeted attacks, such as those used in zero-day attacks of various types, and advanced detection methods. Existing approaches can be divided into three categories: The statistical approach to real-time detection for zero-day vulnerabilities focuses on historical evidence dependent on attack profiles. This strategy generally does not fit in well with shifts in zero-day exploit trends. Any changes in a zero-day exploits pattern would require a new profile to be learned by the system. Statistical-based approach: In , a maintained archive of all signature files is the primary prerequisite of the method. The exactness is based entirely on the system's signature database. The new malware cannot be detected using the signature-based detection technique when no new virus information is available in the database. Although this method of intrusion detection is quick and reliable since there is a very small risk for false alarms. Overall, the ability to generate accurate signatures that match real malware is key to determining whether a signature-based approach actually works to detect zero-day exploits. One of the only ways to use signature recognition as protection against zero-day attacks is to use machine learning and similar algorithms to generate real-time signatures that may match and thus recognize currently unknown malware. signature-based detection tracks the processes of any abnormal behavior on a host computer. If suspicious behavior is detected, a warning alert may be raised that the malware might be present. The machine uses the heuristics it collects to classifies an operation as natural or malicious in this detection technique. While the likelihood of false alarms in this system is comparatively greater, it is more accurate since it can also detect new viruses. An anomaly-based detection system There is no guarantee that these methods detect malicious activities, so antivirus solutions that use heuristic analysis can be a great weapon against zero-day malware. Products that can use heuristic techniques to detect new malware by recognizing its similarities to known malware and other features. The Heuristic Method Unfortunately, zero-day exploits cannot be detected by antivirus signatures, and existing solutions are unable to detect the possibilities that lead to zero-day attacks on networks. In the future, however, we could detect zero-days from behaviors - tracking algorithms that detect suspicious malicious behavior. By monitoring behavior instead of signatures, antivirus software can detect undetected malware and effectively fight zero-day attacks. A good antivirus also uses a technique called heuristics analysis Heuristic detection can scan files for suspicious characters and detect new malware without signature recognition. It checks files for features that the system finds questionable rather than requiring an accurate file signature match. This can be performed statically or by emulation where the anti-virus uses a low clocking cycle to simulate the execution of the file. In this approach, the "suspicious" behavior is mostly a perception dependent on the software's risk thresholds. Since several features that have been observed together may give an alert, heuristic mechanisms to detect legitimate files are noted as malware. Antivirus heuristics that detect suspicious activity - looking for activity - can also block zero-day attacks. The fact that Anti-Virus Solutions protect against advanced zero-day attacks should underscore the need for effective runtime protection. An ideal security solution for the protection of the runtime should be able to detect a zero-day attack without generating false positive alarms at the same time. The heuristic engine used by an antimalware program includes rules for the following: a program that tries to copy itself into other programs (in other words, a classic computer virus) a program that tries to write directly to the disk a program that tries to remain resident in memory after it has finished executing a program that decrypts itself when running (a method often used by malware to avoid signature scanners) a program that binds to a TCP/IP port and listens for instructions over a network connection (this is pretty much what a bot—also sometimes called drones or zombies—do) a program that attempts to manipulate (copy, delete, modify, rename, replace, and so forth) files that are required by the operating system a program that is similar to programs already known to be malicious To be able to detect modern zero-day attacks, the solution must be able to monitor as many events as possible, including, but not limited to, monitoring of all system processes including hidden, existing hooks and floating-point vulnerabilities. Advanced heuristic protection algorithms to detect behavior that indicates malicious activity even before an attack attempt is made within zero days. Cloud technology now offers a complementary feature to file and device behavioral research. It is also one of some of the top anti-virus packages on the market. How cloud-based heuristics detection work? Data was collected on secured computers from endpoints – that is to say, customers with lightweight anti-virus system installations. This could provide relevant information about the file configuration and how it operates on the endpoint system. The recorded data is analyzed on the platform of the cloud service, which may include numerous computers and connections to online databases. Any unusual behavior found on client endpoint devices from non-malicious files is applied and integrated into subsequent review into the cloud service database. In a nutshell, traditional antivirus software is usually only effective in fending off known threats and is therefore often ineffective in protecting against zero-day exploits. Zero-day attacks do not give security analysts and developers enough time to overcome the threat. Detecting zero-day attacks should be faster and give hackers less time to develop exploit code. Although virus protection software manufacturers are aware of the dangers of zero-day exploits, not all software has been developed with this goal in mind when it comes to protecting themselves against them. The advantage of a heuristic analysis of code is it can detect not just variants (modified forms) of existing malicious programs but new, previously-unknown malicious programs, as well. Cited Sources https://www.hindawi.com/journals/scn/2017/4749085/ https://techtalk.gfi.com/defending-zeroday-threats/ https://www.bdo.com/digital/blog/security/prevent-zero-day-malware-email-office-365-advanced-threat-protection https://www.k2io.com/zero-day-attacks-are-on-the-rise/ https://digitalguardian.com/blog/what-zero-day-definition-zero-day-exploits-vulnerabilities https://www.howtogeek.com/140262/htg-explains-what-is-a-zero-day-exploit-and-how-to-protect-yourself/ https://www.cynet.com/advanced-threat-protection/zero-day-attack-prevention/ https://www.anomali.com/blog/weekly-threat-briefing-apt-groups-ransomware-vulnerabilities-zero-day-exploits-and-more https://guardiandigital.com/blog/zero-day-attack https://searchsecurity.techtarget.com/tip/The-case-for-using-anomaly-based-monitoring-in-zero-day-detection https://seniordba.wordpress.com/2020/02/17/10-steps-to-stopping-lateral-movement-attacks/ https://link.springer.com/article/10.1186/s13635-018-0074-y http://info300.net/alai2/Brief1.html https://trendmicro.optrics.com/network-security-ATP.aspx https://www.safetydetectives.com/blog/what-is-a-zero-day-exploit-and-how-to-defend-yourself/ https://depthsecurity.com/solutions/next-generation-threat-prevention https://www.imperva.com/learn/application-security/zero-day-exploit/ https://www.apriorit.com/dev-blog/450-zero-day-attack-detection https://www.pcmag.com/news/can-your-antivirus-handle-a-zero-day-malware-attack

Advanced Heuristics to Detect Zero-Day Attacks

If governments had been orange-pilled first instead of citizens, it would have been the end of finance as we know it. A Brief History before UN Bitcoin My idea of Bitcoin starting in the hands of multiple governments and not a handful of individuals is one I find too good to leave to only one blog article. Two days ago, I introduced you to this idea with the article ‘ ’. To recap, I mentioned how it would have been genius beyond Da Vinci, Gauss and Einstein combined, if the great Satoshi Nakamoto had, on that fateful day when he published the Bitcoin white paper online, taken a major step back and stopped his finger from pressing the send key. The Big Boy train that Bitcoin missed “If I do this, Bitcoin will likely become a global phenomenon. But governments will have been left behind so they will play to sabotage the technology. They won’t be glad to play catch up. But if I get them in the lead, they will gladly play lead while citizens gladly play catch up. Citizens have unlimited energy for playing catch up to such great power - the power to own money. Satoshi’s personal notes (Timeline - UN Bitcoin). Moreover, the power is now decentralized and decentralization is by definition the foray of the individual”- I lack the philosophical and other devices of knowledge to properly capture what this means. But I think we would have gotten something to rival the very power of the internet itself. Something to rival any emerging AI technology. and surpass Something powerful enough to finance continuous human space exploration. So that we would have had humans on the moon this year, and humans on Mars 2 years later. The thing is, this would be a financial revolution as momentous as the invention of money. Maybe more. When money was invented, the economic energy for trade was finally captured in a singular good, which dramatically improved the efficiency of our markets. For example, prices were cut from an exponential number that increases with the number of market goods to prices only as many as the number of goods – all priced in the good we now call ‘money’. However, the invention of money was challenged by the fact that the power to own money was still vested, apriori, in the hands of Lords, Chiefs, Kings and Presidents of our time. These political rulers could control the money because the supply was centralized. Some gold mine somewhere, some cowrie shells beach somewhere. They could impose exorbitant taxes, they could inflate the currency, they could loot it. To invent a currency immune to all 3 would be magic. Because it would mean the power to control money would now be something everybody and every organization have to earn. Even Kingdoms and Empires. There has to be proof-of-work. Wanna loot someone’s coins? you might be utterly disappointed, embarrassed, and jobless faster than the time it takes a rocket to get into the exosphere. After all, even a King is an individual. This is what UN Bitcoin would have given us. Bitcoin gave us a diluted version of this, so no wonder it is snailing to global victory. Wall Street in the era of UN Bitcoin As I mentioned before, our collective greed would likely push the UN Bitcoin mining hash rate to 500 zetta hashes / second. Let’s go with a modest 50 zetta hashes per second, 100 times the hash rate of Bitcoin in our timeline. The market cap would likely 100x as well to make UN Bitcoin worth 80 trillion US Dollars circa. 2023. Let that sink in. This is almost the current (our timeline) world’s money supply, which is only $83 trillion and almost , so the world’s GDP would have doubled in 14 years! To move from one to 80 trillion dollars in 14 years would have broken Wall Street’s speedometer, with an average yearly return of 400%, ~40 times the growth rate of the S&P 500. the GDP of all countries combined As I mentioned before, this is not wealth replacing our other assets like energy resources, gold, silver, housing, technology companies. It is wealth adding on and boosting them. From hitherto unimagined sources prior to the 2008 market crash. Unlike CDOs, UN Bitcoin would actually be adding valuable things to the world economy. It would also be the greatest story of money to ever tell. The first thing is dramatic expansion of the electricity energy grid. UN Bitcoin would be using stranded energy from nuclear power, volcanoes and renewables to inch us closer to a Type 1 civilization on the Kardashev scale, and away from carbon-pollutant fossil fuels. With such a big market cap, billions of dollars of UN Bitcoin would be changing hands every day around the world, a lot of it going towards carbon capture and storage technologies. This would make many people excited about the future while they earn very handsome salary packages. Me too. The next thing is Wall Street itself. The invention of lecherous instruments like CDOs and Ponzi ‘crypto tokens’ would be banned and canceled because there would be really few people gullible enough to invest in derivative assets that do not provide more value. The idea of ballooning the global debt would be put somewhere in the FED’s not-to-do list and quickly forgotten, with the idea of sustaining the hash rate for the US government put front and centre. The FED would get an honest day job. UN Bitcoin money flows would be huge (billions) and asymmetric. Spread out to all corners of this planet. While the world’s governments would be leading on the mining front, hence collecting those pretty fees before most private enterprises, innovations like the lightning network would be mostly in the private domain. In Africa, it would greatly help sustain livelihoods for the disadvantaged, while growing the middle class in developing countries on the speedy growth of UN Bitcoin. Good housing would be ubiquitous even in slums, using 3D printers of plastic materials. Cheap electricity would be abundant as Bitcoin’s growth takes the electric grid wherever it goes. Cheap electricity would bring piped utilities like fresh water closer to everybody, hence bring cleanliness and disease-free environs even in our slums. World politics, music, fashion, social media, everything would be transformed in wonderful ways. It cannot be, because it is only a fairy tale. But as Walt Disney has shown us, fairy tales should be told. To give us hope and an ideal to look up to. ******************* P.S. >>  Who wants a comic strip?

Bitcoin Is Snailing to Global Victory

বিটকয়েন বিশ্বব্যাপী বিজয়ের দিকে ধাবিত হচ্ছে

Bitcoin ist auf dem Weg zum globalen Sieg

Bitcoin está avanzando hacia la victoria global

Bitcoin se dirige vers la victoire mondiale

बिटकॉइन वैश्विक विजय की ओर बढ़ रहा है

ビットコインは世界的な勝利に向けて順調に進んでいる

비트코인은 글로벌 승리를 향해 달려가고 있습니다

Bitcoin está caminhando para a vitória global

Биткойн движется к глобальной победе

Bitcoin Küresel Zafere Sürülüyor

Bitcoin đang tiến tới chiến thắng toàn cầu

比特币正在走向全球胜利

Exploring Yonatan Sompolinsky's blockDAG Protocols 

The Power of Kaspa BlockDAGs: Go Beyond the Blockchain

Is your Brain a Data Scientist? Yes, according to the Bayesian Brain Hypothesis, your brain is a Bayesian statistician. Let me explain. Bayes’ Theorem and Fear of Birds Before we get to the nitty-gritty of Bayes’ Theorem, I want to tell you why I love the idea of the Bayesian Brain and how it help me to hack my brain. I’m afraid of birds. By saying afraid, I mean terrified. When I see pigeons or seagulls, I just … Gif by birdboxmovies I tend to think that they can murder me or cause a severe injury, at least. Thankfully, I can manage it now with a bit of help from the Bayes’ theorem. First, let’s revise the theorem. (If you have no idea what I’m talking about, please bear with me; it will make sense in a minute. I promise.) Now, let me show you how this equation helped me with my fear of being murdered by a pigeon or any other bird (we will work with pigeons here). The first thing I realized, thanks to this theorem, is that I only considered the probability of a flock of pigeons murdering me or any other humans. In my head, this probability is very high, around 90%. Instead, I should ask myself what is of pigeons murdering me . With this in mind, the equation would look like this: the probability given the evidence First, we need to fill in the probabilities and . I already mentioned that is around 90% for me, so = 0.90. I tried to find real-world evidence of pigeons murdering people, and I couldn’t find anything, so this must be very low. For ease of calculation, we will assume that = 0. P(Pigeon) P(Evidence) P(Pigeon) P(Pigeon) P(Evidence) Here comes my second realization: What happens when we divide by 0? Dividing by zero is , thus without going into much detail, the answer to my question is that the probability of pigeons murdering me is approaching zero. undefined My internal world model estimates that being murdered by a flock of birds is a highly likely event. However,  that's not the case. Bayes’ Theorem helped me recognize that real-world evidence is what matters. Bayesian Brain Hypothesis This theory assumes that our brain is a self-organizing system that computes Bayes's theorem constantly. There are two concepts to consider: : person's belief system or an internal world model Prior belief : the evidence from sensory experience Posterior perception In any circumstances, the Bayesian Brain wants to the difference between the prediction and the perception. Our brain is surprised by a discrepancy between our expectations and the evidence we receive through our sensory experience. This is captured in the form of an error analogous to the prediction error of a machine learning algorithm. Going back to my example, when I see a pigeon, my brain predicts an attack, but my perception tells me that there is no physical pain. minimize surprise Image adapted from research article. this How does the brain deal with such an error? As a self-organizing system, the brain has one priority - homeostasis. In other words, the brain wants to be stable, and it has two options - it can fulfill the prediction by or . action learning The action can be something that will lead to the expected input. In my example, when my internal model predicts pigeon attack, it increases my anxiety, which could be a substitute for the physical pain that didn't occur. Learning would be a better strategy. In the same way, algorithms can learn from data, the brain can update its prior beliefs about the world to align with reality. Unfortunately, my brain doesn't seem to be able to learn this. Hence, I need to hack my brain to manage the expectations about possible scenarios when I see a flock of pigeons flying by. But why can't the brain update its model? There is one more dimension I didn't mention - . Our internal model updates dynamically based on the prediction error, which is weighted by the precision ratio. uncertainty The precision ratio captures the uncertainty of both the prior belief and the sensory input. Learning is more likely when we are more certain about what we perceive than what we apriori believe. Back to the pigeons, if the brain is confident in the internal prediction of pain and unsure about the sensory input due to fear reaction, the update of the internal model doesn't occur. This is quite a complex topic, so this version of the hypothesis is simplified. If you are interested in learning more, I highly recommend this where the authors discuss the implication of Bayesian Brain for Autism Spectrum Disorder. This covers the academic debate about Bayesian Brains. In short, not everyone agrees that have Bayesian Brains. research paper article For more insight into the neurobiology of this process check . Machine Learning in the Brain For more about Bayes' theorem, see . Solving a Problem with Bayes’ Theorem and Decision Tree By the way, you a little bit afraid of birds too. should be

Bayesian Brain: Is Your Brain a Data Scientist?

Machine learning has become a diverse business tool to enhance the various elements of business operations. Also, it has a significant influence on the performance of the business. Machine learning algorithms are used widely to maintain competition with different industries. However, there is a different type of algorithms for goals and data sets. The selection of an algorithm depends on user role and the purpose. If you are using Linear regression, then you can quickly implement or train rather than other machine learning algorithms. But the drawback of this algorithm is that it is not applicable for complex predictions. So you should know about the different types of machine learning algorithms for getting better results. In this article, I am going to share five types of machine learning algorithms which enhance business success. These algorithms are user-friendly and support different goals. Besides, all of them are popular and used by thousands of enterprises. Linear Regression Linear regression is the pure form of algorithm which correlates between two variables in the data set. The input and output sets examined to show a relationship. It also shows how the change in one variable can affect the other variable. It is represented by plotting a line on the graph. The algorithm is popular because it is easy to explain, transparent, and requires no tuning. Companies use this algorithm to forecast sales and risk assessment to take long term business decisions. Decision Tree A decision tree consists of various branches which represent the outcome of many decisions. In this algorithm, the data is collected and graphed in multiple branches. It predicts response variables based on past decisions. This method communicated efficiently and visual for mapping out decisions and results. This algorithm work best for small data sets and low-stake decisions because of their long tail visuals. The popularity of this algorithm is due to the ability to show multiple outcomes and tests without any involvement of data scientists. If you need to check whether the decision traverse will affect the result or not, then this algorithm is best. Support vector Machine This algorithm is also known as SVM and internally analyzes the data set into classes. It is a helpful approach to future classifications. Then the main work of SVM is to find the line that separates training data into particular classes. Also, it maximizes the margins to enter future data into classes. The algorithm works best for training data, but nonlinear data can also be programmed into nonlinear SVMs. It is usually found in the financial sector because of its accuracy in current and future data sets. These algorithms are used widely for comparing gain in financial investment, values, and performance. Apriori Apriori algorithm is based on the principle of Apriori and is used in market analysis. This algorithm checks for the positive and negative correlation between products after analyzing the A and B in data sets. It is specially used by sales teams who keep an eye on the baskets of customers to find which products the customers will purchase with other products. For example, if most of the customers are buying A (bread) with B (butter), then this relation holds a high percentage. Also, it will conclude that Purchase of A will often lead to B. It will refer information in data sets and purchase ratios. Moreover, this algorithm informs marketing intentions as well as product placement strategies. The top-ranked companies, including Alibaba, Amazon, and Google, are using this algorithm to predict searches and product purchases. K-means clustering K- means clustering algorithms sorts of data sets through defined clusters. It is an iterative method which also put out similar groups with input data attached. For example, If you use K- means algorithm for sorting web results for word civil, then it will show the results in the form of groups. Accuracy is the main advantage of this algorithm. It has developed a reputation for providing the streamlined groupings in a short time as compared to other algorithms. It gives meaningful groups based on internal patterns. This algorithm helps marketers to identify target audience groups. Final Words Machine learning algorithms are available in different forms. Different users use these for distinct purposes so it is better to learn about different types of machine learning Algorithms. I hope this article has helped you in getting knowledge about ML algorithms.

5 Types of Machine Learning Algorithms You Should Know

Last month, Oscar Schwartz wrote a byline for OneZero with a familiarly provocative headline: “ ”. The piece described the work of , a US company using machine learning to pick through the genetic data of embryos to establish the risk of health conditions. Given the title of the article, the upshot won’t surprise you. Prospective parents can now use this technology to expand their domain over the “design” of new offspring – and cognitive ability” is among the features up for selection. What If an Algorithm Could Predict Your Unborn Child’s Intelligence? Genomic Prediction Setting aside the contention over , the ethical debate around this sort of pre-screening is hardly new. Gender selection has been a live issue for years now. Way back in 2001, Oxford University bioethicist Julian Savulescu caused controversy by proposing a principle of “ ” stating that “couples (or single reproducers) should select the child, of the possible children they could have, who is expected to have the best life, or at least as good a life as the others, based on the relevant, available information.” (Opponents to procreative beneficence vociferously pointed out that  – regrettably – Savulescu’s principle would likely lead to populations dominated by tall, pale males…). whether intelligence is even inheritable Procreative Beneficence The topic continues to fascinate. Many instinctively reject the idea of “playing God” or stepping in to set a child’s fate apriori. But those who defend such techniques are usually careful to frame genetic selection as a choice rather than a moral obligation à la Savulescu. In Schwartz’s article, Genomic Prediction co-founder, Stephen Hsu, is quoted to this effect: “If I tell a couple that they are carriers of a disease and that there is a one in four chance that their kid is going to die a horrible death, and allow them to select the healthy embryo, is that eugenics? I guess it is. But if I give them the option to make to make a choice, and don’t coerce them, I don’t think there’s ethically anything wrong with it.” There’s a lot going on with this statement. But what strikes is the built-in assumption that granting a choice is uncontroversially okay. The expansion or contraction of human choice is often used as a barometer by which the ethical permissibility of new technologies is judged (indeed, my own work has looked at choice closely). The argument tends to go the same way: less choice equals bad, more choice equals good. But this isn’t something we should necessarily take as a given, and what Hsu’s comment doesn’t allow for is the fact that the broadening of choice can be a bad thing. For starters, when a new option is added we immediately lose the ability not to choose at all. This is particularly pertinent in the kind of situation described by Hsu, where parents might be forced to choose between a healthy embryo and one that carries the risk of a disability or a trait considered less desirable – like a lower intelligence score. The choice here creates a difficult and deeply troubling responsibility. It’s important to note at this juncture that Hsu is not genetically altering embryos. Nor is he facilitating the death of a child (by most people’s estimations). These parents-to-be are choosing which embryo(s) to bring into the world. The others will simply never be born. Yet, if they are procedurally encouraged to select “healthy” embryos – a choice that might strike many as a “no brainer” –  then they could be responsible for a broader harm to society. After all, whole groups of people would be edited out, and this winnowing could give way to a detrimental cultural shallowing. (Is it really “better” or preferable to live in a future world with no physically or mentally impaired people?) All of this is really to say that we cannot let technologists side-step issues of misuse and downstream harms by pointing to the alternative option their customers/users had – i.e. not to use their product or service at all. It is a deflection that functions as a flak jacket while granting makers permission not to reflect inwardly. The University of California philosopher, Professor Gerald Dworkin, wrote about the problems with choice in the paper “ ”, where he challenges lazy assumptions that having more options is always more desirable. Is More Choice Better than Less? Dworkin cites the work of philosophers like John Rawls, who go unchallenged in stating that any rational individual would prefer to have their choices widened as we do not suffer “for greater liberty.” Dworkin goes on to supply ample ammunition to prove that on occasions we do. For one, decisions often have costs associated with them that are not present when we only have a single option. Usually, to make a reasoned choice we must amass all the relevant information, which is a cost in itself. Dworkin says that the more serious the issue, the more difficult information can be to obtain which in turn forces costs higher. Moreover, there are also psychic costs to making a choice. We may second guess ourselves and agonize over whether the right decision was made based on the correct information. Choice also brings with it the pressure to conform, and adding new choices can be problematic for those who wish not to exercise them. For example, those who choose not to have a designer baby or – more trivially – purchase an Alexa or optimize facial recognition, may find themselves at a disadvantage in a society where increasing numbers choose differently. Feeling outcast or inconvenienced could obviously yield from this new and unwanted choice. Most importantly though, choice creates responsibility. By introducing a new option into the world it’s use or otherwise now becomes the responsibility of the user. As Dworkin says, “Once I am aware I have a choice, my failure to choose counts against me.” In future scenarios, perhaps we will see a decision to prevent a teenager using a as in some way negligent. The same when it comes to . conversational therapy bot surveillance technology in cars that can perceive emotions and alertness Responsibility may just manifest as a feeling, but of course there is also the possibility of being held responsible too. In a world where these new AI-driven systems are increasingly ubiquitous, non-adoption becomes equivalent to rejection. It ceases to be neutral. To be clear, I (via Dworkin) am not arguing that technological choice should be reduced or that the status quo is always (or even generally) preferable to the broadening of choice. Rather, this is an attempt to frame that idea that choice isn’t always preferable or non-controversial because it does create pressure points elsewhere. And commenting – as Hsu did – that a new technology simply provides an option that can be ignored willfully underestimates the ways in which such technologies disrupt, if only by creating that new binary. So, when considering the downstream ethical effects of new technologies, makers should be encouraged not only to consider the effect on direct and indirect users, but also how creating a new option might affect the choice environment more broadly, including the impact on those who “opt out.” They have a responsibility to these non-users because they have created this category in much the same way as they have with their consenting users. Admittedly, not all technologies will create the kinds of dilemma that embryo selection throws out, but there are many that create subtle changes to the status quo that should be acknowledged and thought through. Obviously the sphere of influence for a maker begins with their own product and users, so perhaps the starting point for considering responsibilities to non-users begins with imagining how life would change if their new tech option the only option – and then rolling back from there. were Could Genomic Prediction continue to justify their product if it were made mandatory? Or if they reached 70 per cent saturation, what would life be life for the remaining 30 per cent? Too often, proclaiming that users have “freely chosen a product” or are free “not to choose” a product seems like a bid to abdicate responsibility for any ill effects to the chooser (financial, moral, psychological, etc). In fact, it seems that creating a new choice where there was formerly a single option actually creates a broader responsibility for two categories – both users and non-users.

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Since 2002

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