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The Lost Continent by Edgar Rice Burroughs, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . Chapter CHAPTER III. The report of a gun blasted the silence of a dead Devonport with startling abruptness. It came from the direction of the launch, and in an instant we three were running for the boat as fast as our legs would carry us. As we came in sight of it we saw Delcarte a hundred yards inland from the launch, leaning over something which lay upon the ground. As we called to him he waved his cap, and stooping, lifted a small deer for our inspection. I was about to congratulate him on his trophy when we were startled by a horrid, half-human, half-bestial scream a little ahead and to the right of us. It seemed to come from a clump of rank and tangled bush not far from where Delcarte stood. It was a horrid, fearsome sound, the like of which never had fallen upon my ears before. We looked in the direction from which it came. The smile had died from Delcarte’s lips. Even at the distance we were from him I saw his face go suddenly white, and he quickly threw his rifle to his shoulder. At the same moment the thing that had given tongue to the cry moved from the concealing brushwood far enough for us, too, to see it. Both Taylor and Snider gave little gasps of astonishment and dismay. “What is it, sir?” asked the latter. The creature stood about the height of a tall manl’s waist, and was long and gaunt and sinuous, with a tawny coat striped with black, and with white throat and belly. In conformation it was similar to a cat—a huge cat, exaggerated colossal cat, with fiendish eyes and the most devilish cast of countenance, as it wrinkled its bristling snout and bared its great yellow fangs. It was pacing, or rather, slinking, straight for Delcarte, who had now leveled his rifle upon it. “What is it, sir?” mumbled Snider again, and then a half-forgotten picture from an old natural history sprang to my mind, and I recognized in the frightful beast the Felis tigris of ancient Asia, specimens of which had, in former centuries, been exhibited in the Western Hemisphere. Snider and Taylor were armed with rifles and revolvers, while I carried only a revolver. Seizing Sniderl’s rifle from his trembling hands, I called to Taylor to follow me, and together we ran forward, shouting, to attract the beastl’s attention from Delcarte until we should all be quite close enough to attack with the greatest assurance of success. I cried to Delcarte not to fire until we reached his side, for I was fearful lest our small caliber, steel-jacketed bullets should, far from killing the beast, tend merely to enrage it still further. But he misunderstood me, thinking that I had ordered him to fire. With the report of his rifle the tiger stopped short in apparent surprise, then turned and bit savagely at its shoulder for an instant, after which it wheeled again toward Delcarte, issuing the most terrific roars and screams, and launched itself, with incredible speed, toward the brave fellow, who now stood his ground pumping bullets from his automatic rifle as rapidly as the weapon would fire. Taylor and I also opened up on the creature, and as it was broadside to us it offered a splendid target, though for all the impression we appeared to make upon the great cat we might as well have been launching soap bubbles at it. Straight as a torpedo it rushed for Delcarte, and, as Taylor and I stumbled on through the tall grass toward our unfortunate comrade, we saw the tiger rear upon him and crush him to the earth. Not a backward step had the noble Delcarte taken. Two hundred years of peace had not sapped the red blood from his courageous line. He went down beneath that avalanche of bestial savagery still working his gun and with his face toward his antagonist. Even in the instant that I thought him dead I could not help but feel a thrill of pride that he was one of my men, one of my class, a Pan-American gentleman of birth. And that he had demonstrated one of the principal contentions of the army-and-navy adherents—that military training was necessary for the salvation of personal courage in the Pan-American race which for generations had had to face no dangers more grave than those incident to ordinary life in a highly civilized community, safeguarded by every means at the disposal of a perfectly organized and all-powerful government utilizing the best that advanced science could suggest. As we ran toward Delcarte, both Taylor and I were struck by the fact that the beast upon him appeared not to be mauling him, but lay quiet and motionless upon its prey, and when we were quite close, and the muzzles of our guns were at the animal’s head, I saw the explanation of this sudden cessation of hostilities—Felis tigris was dead. One of our bullets, or one of the last that Delcarte fired, had penetrated the heart, and the beast had died even as it sprawled forward crushing Delcarte to the ground. A moment later, with our assistance, the man had scrambled from beneath the carcass of his would-be slayer, without a scratch to indicate how close to death he had been. Delcarte’s buoyance was entirely unruffled. He came from under the tiger with a broad grin on his handsome face, nor could I perceive that a muscle trembled or that his voice showed the least indication of nervousness or excitement. With the termination of the adventure, we began to speculate upon the explanation of the presence of this savage brute at large so great a distance from its native habitat. My readings had taught me that it was practically unknown outside of Asia, and that, so late as the twentieth century, at least, there had been no savage beasts outside captivity in England. As we talked, Snider joined us, and I returned his rifle to him. Taylor and Delcarte picked up the slain deer, and we all started down toward the launch, walking slowly. Delcarte wanted to fetch the tigerl’s skin, but I had to deny him permission, since we had no means to properly cure it. Upon the beach, we skinned the deer and cut away as much meat as we thought we could dispose of, and as we were again embarking to continue up the river for fresh water and fuel, we were startled by a series of screams from the bushes a short distance away. “Another Felis tigris,” said Taylor. “Or a dozen of them,” supplemented Delcarte, and, even as he spoke, there leaped into sight, one after another, eight of the beasts, full grown—magnificent specimens. At the sight of us, they came charging down like infuriated demons. I saw that three rifles would be no match for them, and so I gave the word to put out from shore, hoping that the “tiger,” as the ancients called him, could not swim. Sure enough, they all halted at the beach, pacing back and forth, uttering fiendish cries, and glaring at us in the most malevolent manner. As we motored away, we presently heard the calls of similar animals far inland. They seemed to be answering the cries of their fellows at the waterl’s edge, and from the wide distribution and great volume of the sound we came to the conclusion that enormous numbers of these beasts must roam the adjacent country. “They have eaten up the inhabitants,” murmured Snider, shuddering. “I imagine you are right,” I agreed, “for their extreme boldness and fearlessness in the presence of man would suggest either that man is entirely unknown to them, or that they are extremely familiar with him as their natural and most easily procured prey.” “But where did they come from?” asked Delcarte. “Could they have traveled here from Asia?” I shook my head. The thing was a puzzle to me. I knew that it was practically beyond reason to imagine that tigers had crossed the mountain ranges and rivers and all the great continent of Europe to travel this far from their native lairs, and entirely impossible that they should have crossed the English Channel at all. Yet here they were, and in great numbers. We continued up the Tamar several miles, filled our casks, and then landed to cook some of our deer steak, and have the first square meal that had fallen to our lot since the Coldwater deserted us. But scarce had we built our fire and prepared the meat for cooking than Snider, whose eyes had been constantly roving about the landscape from the moment that we left the launch, touched me on the arm and pointed to a clump of bushes which grew a couple of hundred yards away. Half concealed behind their screening foliage I saw the yellow and black of a big tiger, and, as I looked, the beast stalked majestically toward us. A moment later, he was followed by another and another, and it is needless to state that we beat a hasty retreat to the launch. The country was apparently infested by these huge Carnivora, for after three other attempts to land and cook our food we were forced to abandon the idea entirely, as each time we were driven off by hunting tigers. It was also equally impossible to obtain the necessary ingredients for our chemical fuel, and, as we had very little left aboard, we determined to step our folding mast and proceed under sail, hoarding our fuel supply for use in emergencies. I may say that it was with no regret that we bid adieu to Tigerland, as we rechristened the ancient Devon, and, beating out into the Channel, turned the launchl’s nose southeast, to round Bolt Head and continue up the coast toward the Strait of Dover and the North Sea. I was determined to reach London as soon as possible, that we might obtain fresh clothing, meet with cultured people, and learn from the lips of Englishmen the secrets of the two centuries since the East had been divorced from the West. Our first stopping place was the Isle of Wight. We entered the Solent about ten o’clock one morning, and I must confess that my heart sank as we came close to shore. No lighthouse was visible, though one was plainly indicated upon my map. Upon neither shore was sign of human habitation. We skirted the northern shore of the island in fruitless search for man, and then at last landed upon an eastern point, where Newport should have stood, but where only weeds and great trees and tangled wild wood rioted, and not a single manmade thing was visible to the eye. Before landing, I had the men substitute soft bullets for the steel-jacketed projectiles with which their belts and magazines were filled. Thus equipped, we felt upon more even terms with the tigers, but there was no sign of the tigers, and I decided that they must be confined to the mainland. After eating, we set out in search of fuel, leaving Taylor to guard the launch. For some reason I could not trust Snider alone. I knew that he looked with disapproval upon my plan to visit England, and I did not know but what at his first opportunity, he might desert us, taking the launch with him, and attempt to return to Pan-America. That he would be fool enough to venture it, I did not doubt. We had gone inland for a mile or more, and were passing through a park-like wood, when we came suddenly upon the first human beings we had seen since we sighted the English coast. There were a score of men in the party. Hairy, half-naked men they were, resting in the shade of a great tree. At the first sight of us they sprang to their feet with wild yells, seizing long spears that had lain beside them as they rested. For a matter of fifty yards they ran from us as rapidly as they could, and then they turned and surveyed us for a moment. Evidently emboldened by the scarcity of our numbers, they commenced to advance upon us, brandishing their spears and shouting horribly. They were short and muscular of build, with long hair and beards tangled and matted with filth. Their heads, however, were shapely, and their eyes, though fierce and warlike, were intelligent. Appreciation of these physical attributes came later, of course, when I had better opportunity to study the men at close range and under circumstances less fraught with danger and excitement. At the moment I saw, and with unmixed wonder, only a score of wild savages charging down upon us, where I had expected to find a community of civilized and enlightened people. Each of us was armed with rifle, revolver, and cutlass, but as we stood shoulder to shoulder facing the wild men I was loath to give the command to fire upon them, inflicting death or suffering upon strangers with whom we had no quarrel, and so I attempted to restrain them for the moment that we might parley with them. To this end I raised my left hand above my head with the palm toward them as the most natural gesture indicative of peaceful intentions which occurred to me. At the same time I called aloud to them that we were friends, though, from their appearance, there was nothing to indicate that they might understand Pan-American, or ancient English, which are of course practically identical. At my gesture and words they ceased their shouting and came to a halt a few paces from us. Then, in deep tones, one who was in advance of the others and whom I took to be the chief or leader of the party replied in a tongue which while intelligible to us, was so distorted from the English language from which it evidently had sprung, that it was with difficulty that we interpreted it. “Who are you,” he asked, “and from what country?” I told him that we were from Pan-America, but he only shook his head and asked where that was. He had never heard of it, or of the Atlantic Ocean which I told him separated his country from mine. “It has been two hundred years,” I told him, “since a Pan-American visited England.” “England?” he asked. “What is England?” “Why this is a part of England!” I exclaimed. “This is Grubitten,” he assured me. “I know nothing about England, and I have lived here all my life.” It was not until long after that the derivation of Grubitten occurred to me. Unquestionably it is a corruption of Great Britain, a name formerly given to the large island comprising England, Scotland and Wales. Subsequently we heard it pronounced Grabrittin and Grubritten. I then asked the fellow if he could direct us to Ryde or Newport; but again he shook his head, and said that he never had heard of such countries. And when I asked him if there were any cities in this country he did not know what I meant, never having heard the word cities. I explained my meaning as best I could by stating that by city I referred to a place where many people lived together in houses. “Oh,” he exclaimed, “you mean a camp! Yes, there are two great camps here, East Camp and West Camp. We are from East Camp.” The use of the word camp to describe a collection of habitations naturally suggested war to me, and my next question was as to whether the war was over, and who had been victorious. “No,” he replied to this question. “The war is not yet over. But it soon will be, and it will end, as it always does, with the Westenders running away. We, the Eastenders, are always victorious.” “No,” I said, seeing that he referred to the petty tribal wars of his little island, “I mean the Great War, the war with Germany. Is it ended—and who was victorious?” He shook his head impatiently. “I never heard,” he said, “of any of these strange countries of which you speak.” It seemed incredible, and yet it was true. These people living at the very seat of the Great War knew nothing of it, though but two centuries had passed since, to our knowledge, it had been running in the height of its titanic frightfulness all about them, and to us upon the far side of the Atlantic still was a subject of keen interest. Here was a lifelong inhabitant of the Isle of Wight who never had heard of either Germany or England! I turned to him quite suddenly with a new question. “What people live upon the mainland?” I asked, and pointed in the direction of the Hants coast. “No one lives there,” he replied. “Long ago, it is said, my people dwelt across the waters upon that other land; but the wild beasts devoured them in such numbers that finally they were driven here, paddling across upon logs and driftwood, nor has any dared return since, because of the frightful creatures which dwell in that horrid country.” “Do no other peoples ever come to your country in ships?” I asked. He never heard the word ship before, and did not know its meaning. But he assured me that until we came he had thought that there were no other peoples in the world other than the Grubittens, who consist of the Eastenders and the Westenders of the ancient Isle of Wight. Assured that we were inclined to friendliness, our new acquaintances led us to their village, or, as they call it, camp. There we found a thousand people, perhaps, dwelling in rude shelters, and living upon the fruits of the chase and such sea food as is obtainable close to shore, for they had no boats, nor any knowledge of such things. Their weapons were most primitive, consisting of rude spears tipped with pieces of metal pounded roughly into shape. They had no literature, no religion, and recognized no law other than the law of might. They produced fire by striking a bit of flint and steel together, but for the most part they ate their food raw. Marriage is unknown among them, and while they have the word, mother, they did not know what I meant by “father.” The males fight for the favor of the females. They practice infanticide, and kill the aged and physically unfit. The family consists of the mother and the children, the men dwelling sometimes in one hut and sometimes in another. Owing to their bloody duels, they are always numerically inferior to the women, so there is shelter for them all. We spent several hours in the village, where we were objects of the greatest curiosity. The inhabitants examined our clothing and all our belongings, and asked innumerable questions concerning the strange country from which we had come and the manner of our coming. I questioned many of them concerning past historical events, but they knew nothing beyond the narrow limits of their island and the savage, primitive life they led there. London they had never heard of, and they assured me that I would find no human beings upon the mainland. Much saddened by what I had seen, I took my departure from them, and the three of us made our way back to the launch, accompanied by about five hundred men, women, girls, and boys. As we sailed away, after procuring the necessary ingredients of our chemical fuel, the Grubittens lined the shore in silent wonder at the strange sight of our dainty craft dancing over the sparkling waters, and watched us until we were lost to their sight. 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. Edgar Rice Burroughs (1994). The Lost Continent. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/149/pg149-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 www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

The report of a gun blasted the silence

I can bet that everyone who just started their trading journey faces difficulties with the interface, wording, and logic behind the order management process. I lost about 500 USD by miss-clicking, creating orders in the wrong direction, and so on. If I had a chance to use a demo account, I would not lose money. I found “Mock trading“ on Binance futures during the development of the trading app btw, our app supports this functionality too. Tiger.Trade I decided to share that knowledge with a broader audience so people can be prepared for real trading and not repeat my stupid mistakes 😀 It’s also called “ “ when an exchange or a broker gives you an opportunity to make trades without having real money. What is “Mock trading“? paper trading If you are a newbie in trading, Binance has an easy-to-use solution for you. Everything you need is just an email and password to create an account where you can perform trades! So, let's see what you have to do. (int’s an official site from Binance) Go to https://testnet.binancefuture.com/ . If you already have an account on Binance, I’d recommend using the same email and password for this account. The important thing, those two accounts are linked but don't have anything in common. Your finances are securely stored in your main account! Register account You have to receive an email from Binance, to confirm your email address. Please follow the instructions in the received email. Login by using previously used credentials. Start trading! I hope, this article helps you to become a more mature trader and get a profit in the future from each trade!

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The Beautiful and Damned: Book I, Chapter II - Portrait of a Siren 

An inmate at a New York correctional facility, Glenn Rodriguez, was due for parole soon. The man has been on his best behavior and was looking forward to being released and starting a new life. To Glenn’s horror, he was denied parole. An AI algorithm the parole board used gave this inmate a poor score, and as it wasn’t explainable, no one knew that something was going terribly wrong. Mr. Rodriguez fought his case and eventually was released after spending another unnecessary year in prison. Unfortunately, this type of mistake can occur whenever AI is deployed. If we don’t see the reasoning behind algorithms’ decisions, we can’t spot the problem. You can prevent this issue in your organization by following the explainable AI principles while . developing your artificial intelligence solution So, what is explainable artificial intelligence (XAI)? How to decide on the right level of explainability for your sector? And which challenges to expect on the way? What is explainable AI, and why should you care? When speaking of AI, many people think of black-box algorithms that take millions of input data points, work their magic, and deliver unexplainable results that users are supposed to trust. This kind of model is created directly from data, and not even its engineers can explain its outcome. Black-box models, such as neural networks, possess superior skills when it comes to challenging prediction tasks. They produce results of remarkable accuracy, but no one can understand how algorithms arrived at their predictions. In contrast, users can understand the rationale behind its decisions with explainable white box AI, making it increasingly popular in business settings. These models are not as technically impressive as black-box algorithms. Still, their transparency is a tradeoff as it offers a higher level of reliability and is preferable in highly regulated industries. What is explainable AI? Explainable AI (XAI) refers to a set of techniques, design principles, and processes that help developers/organizations add a layer of transparency to AI algorithms so that they can justify their predictions. XAI can describe AI models, their expected impact, and potential biases. Human experts can understand the resulting predictions and build trust and confidence in the results with this technology. When speaking of explainability, it all boils down to what you want to explain. There are two possibilities: Explaining AI model’s pedigree: how the model was trained, which data was used, which types of bias are possible, and how they can be mitigated. Explaining the overall model: this is also called “model interpretability.” There are two approaches to this technique: : a more understandable model, such as a decision tree, is used as an approximation of a more cumbersome AI model. Even though this technique gives a simple overview of what to expect, it remains an approximation and can differ from real-life results. Proxy modeling : designing AI models in a way that forces simple easy-to-explain behavior. This technique can result in less powerful models as it eliminates some complicated tools from the developer’s toolkit. Design for interpretability Source Explainable AI principles The US National Institute of Standards and Technology (NIST) developed : four explainable AI principles The system should be able to explain its output and provide supporting evidence (at least). There are several explanation types: ○ Explanations that benefit the end-user ○ Explanations that are designed to gain trust in the system ○ Explanations that are expected to meet regulatory requirements ○ Explanations that can help with algorithm development and maintenance ○ Explanations that benefit the model’s owner, such as movie recommendation engines The given explanation has to be meaningful, enabling users to complete their tasks. If there is a range of users with diverse skill sets, the system needs to provide several explanations catering to the available user groups This explanation needs to be clear and accurate, which is different from output accuracy The system has to operate within its designed knowledge limits to ensure a reasonable outcome Explainable AI example XAI can provide a detailed model-level explanation of why a particular decision was made. This explanation comes in a set of understandable rules. Following a simplified loan application example below, when applicants are denied a loan, they will receive a straightforward justification: everyone over 40 years old saves less than $433 per month and applies for credit with a payback period of over 38 years will be denied a loan. The same goes for younger applicants who save less than $657 per month. Why is explainable AI important? In some industries, an explanation is necessary for AI algorithms to be accepted. This can be either due to regulations and/or human factors. Think about brain tumor classification. No doctor will be comfortable preparing for a surgery solely based on “the algorithm said so.” And what about loan granting? Clients who got their application denied would want to understand why. Yes, there are more tolerant use cases where an explanation is not essential. For instance, predictive maintenance applications are not a matter of life or death, but even then, employees would feel more confident knowing why particular equipment might need preemptive repair. Senior management often understands the value of AI applications, but they also have their concerns. According to Gaurav Deshpande, VP of Marketing at TigerGraph, there is always a “but” in executives’ reasoning: “…but if you can’t explain how you arrived at the answer, I can’t use it. This is because of the risk of bias in the black box AI system that can lead to lawsuits and significant liability and risk to the company brand as well as the balance sheet.” The ideal XAI solution is the one that is reasonably accurate and can explain its results to practitioners, executives, and end-users. Incorporating explainable AI principles into intelligent software: Brings relief to system users. They understand the reasoning and can get behind the decisions being made. For example, a loan officer will be more comfortable informing a customer their loan application was declined if he can understand how the decision was made. Ensures compliance. By verifying the provided explanation, users can see whether the algorithm’s rules are sound and in accordance with the law and ethics. Allows for system optimization. When designers and developers see the explanation, they can spot what is going wrong and fix it. Eliminates bias. When users view the explanation, they can spot any presence of biased judgment, override the system’s decision, and correct the algorithm to avoid similar scenarios in the future. Empowers employees to act upon the system’s output. For example, an XAI might predict that a particular corporate customer will not renew their software license. The first reaction of the manager can be to offer a discount. But what if the reason behind leaving was poor customer service? The system will tell this in its explanation. Empowers people to take action. XAI enables the parties affected by certain decisions to challenge and potentially change the outcome (such as mortgage granting situations). Which industries need XAI the most? Healthcare Finances Automotive Manufacturing 1. Explainable AI in healthcare AI has many . Various AI-powered medical solutions can save doctors’ time on repetitive tasks, allowing them to primarily focus on patient-facing care. Additionally, algorithms are good at diagnosing various health conditions as they can be trained to spot minor details that escape the human eye. However, when doctors cannot explain the outcome, they are hesitant to use this technology and act on its recommendations. applications in healthcare One example comes from Duke University Hospital. A team of researchers installed a machine learning application called , which would send an alert when a patient was at risk of developing sepsis. The researchers discovered that doctors were skeptical of the algorithm and reluctant to act on its warnings because they did not understand it. Sepsis Watch This lack of trust is passed to patients who are hesitant to be examined by AI. Harvard Business Review published a study where participants were invited to take a free assessment of their stress level. registered for the test when they knew a human doctor would do the evaluation. Only 26% signed up when an algorithm was performing the diagnosis. 40% of the participants When it comes to diagnosing and treatments, the decisions made can be life-changing. No surprise that doctors are desperate for transparency. Luckily, with explainable AI, this becomes a reality. For example, Keith Collins, CIO of SAS, mentioned his company is already developing such a technology. Here is what he said: “We’re presently working on a case where physicians use AI analytics to help detect cancerous lesions more accurately. The technology acts as the physician’s ‘virtual assistant,’ and it explains how each variable in an MRI image, for example, contributes to the technology identifying suspicious areas as probable for cancer while other suspicious areas are not.” 2. XAI in finances Finance is another heavily regulated industry where decisions need to be explained. It is vital that AI-powered solutions are auditable; otherwise, they will struggle to enter the market. AI can help assign credit scores, assess insurance claims, and optimize investment portfolios, among other applications. However, if the algorithms provide biased output, it can result in reputational loss and even lawsuits. Not long ago, with its Apple Card product, which was inherently biased against women, lowering their credit limits. Apple’s co-founder, Steve Wozniak, confirmed this claim. He recalled that together with his wife, they have no separate bank accounts nor separate assets, and still when applying for Apple Card, his granted limit was ten times higher than his wife’s. As a result of this unfortunate event, the company was investigated by the New York State Department of Financial Services. Apple made headlines With explainable AI, one can avoid such scandalous situations by justifying the output. For example, loan granting is one use case that can benefit from XAI. The system would be able to justify its final recommendation and give clients a detailed explanation if their loan application was declined. This allows users to improve their credit profiles and reapply later. 3. Explainable AI in the automotive industry Autonomous vehicles operate on vast amounts of data, requiring AI to analyze and make sense of it all. However, the system’s decisions need to be transparent for drivers, technologists, authorities, and insurance companies in case of any incidents. Also, it is crucial to understand how vehicles will behave in case of an emergency. Here is how Paul Appleby, former CEO of a data management software company Kinetica, voiced his concern: “If a self-driving car finds itself in a position where an accident is inevitable, what measures should it take? Prioritize the protection of the driver and put pedestrians in grave danger? Avoid pedestrians while putting the passengers’ safety at risk?” These are tough questions to answer, and people would disagree on how to handle such situations. But it is important to set guidelines that the algorithm can follow in such cases. This will help passengers decide whether they are comfortable traveling in a car designed to make certain decisions. Additionally, after an incident, the provided explanation will help developers improve the algorithm in the future. 4. Explainable artificial intelligence in manufacturing AI has many applications in manufacturing, including predictive maintenance, inventory management, and logistics optimization. With its analytical capabilities, this technology can add to the “tribal knowledge” of human employees. But it is easier to adopt decisions when you understand the logic behind them. Heena Purohit, Senior Product Manager for IBM Watson IoT, explains how their AI-based maintenance product approaches explainable AI. The system offers human employees several options on how to repair a piece of equipment. Every option includes a percentage confidence interval. So, the user can still consult their “tribal knowledge” and expertise when making a choice. Also, each recommendation can project the knowledge graph output together with the input used in the training phase. Challenges on the way to explainable AI The need to compromise on the predictive power Black box algorithms, such as neural networks, have high predictive power but offer no output justification. As a result, users need to blindly trust the system, which can be challenging in certain circumstances. White box AI offers the much-needed explainability, but its algorithms need to remain simple, compromising on predictive power. For example, where algorithms produce remarkable results classifying brain tumors and spotting breast cancer faster than humans. However, when doctors decide on patients’ treatment, it can be a life and death situation, and they want to understand why the algorithm came up with this diagnosis. It can be daunting for doctors to rely on something they do not understand. AI has applications in radiology The concept of explainability There is no universal definition of explainability. It is often a subjective concept. Users might expect one type of explanation, while developers are willing to provide something else. Also, different audiences require tailored justifications, which results in one XAI system having to explain the same output in several different ways. Security and robustness-related issues With XAI, if clients gain access to the algorithm’s decision-making process, they might apply adversarial behaviors, meaning they will take deliberate actions to change their behavior to influence the output. One published a concern that someone with technical skills can recover parts of the dataset used for algorithm training after seeing the explanation, thereby violating privacy regulations. study How to get started with responsible and explainable AI When your company is preparing to deploy responsible XAI solutions, the first thing would be to determine what exactly you need to explain and to whom. Some of the questions addressed during the planning stage can be: Is it a particular decision or the overall model? Who is your audience? Are they technical users? Do you need to provide different explanations for every output, or does one explanation suffice for everyone involved? Next, your company needs to decide on the degree of explainability. Not all AI-based tools require the same degree of interpretability. six application criticality components that will help you determine your desired XAI level: PwC identifies Decision impact: how will the decision affect all the involved parties? This is not limited to revenue-related aspects. For example, an incorrect cancer diagnosis will have a dire impact, while recommending an irrelevant movie will not be a big issue. Control level: is it an autonomous AI system that makes decisions and acts upon them, or does the system’s output recommend actions to humans who can choose whether to follow or not? Risks: any potential harm that the algorithms can cause, such as impacting operations, sales, employees, any ethical, legal, environmental, or health-related considerations. Regulations: the legal framework of the industry and whether the decisions need to be explained. Reputation: how the system interacts with stakeholders — within the business and with the society in general. Rigor: if the application is accurate and able to generalize its conclusions to unseen data. Finally, even after explainable artificial intelligence is in place, it is best to take action to ensure your data usage is ethical. Perform timely audits of your algorithms. A strong explainability feature can reveal any bias sneaking into your software. However, if you settled on a limited explainability solution, bias could remain unnoticed. Also, you can subscribe to the . Or even develop your own set of ethical principles of data usage, as Microsoft did. They included fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. Partnership on AI consortium On a final note… Implementing traditional , let alone its explainable and responsible version. Despite the obstacles, it will bring relief to your employees, who will be more motivated to act upon the system’s recommendations when they understand the rationale behind it. Moreover, XAI will help you comply with your industry’s regulations and ethical considerations. AI is full of challenges If you have an idea of an explainable AI solution to build, or if you are still unsure of how explainable your software needs to be, . consult ITRex XAI experts

Explainable AI Principles: What Should You Know About XAI

What if I told you that cross-platform app development may not be a universally smart business decision, after all? And creating a carbon copy of a successful Windows app for Mac? Yeah, a bit myopic also, if you ask me. Mac traders deserve better, or more specifically — they deserve “different”. My name is , I am a Product Manager for – the makers of a popular “swiss multitool” trading platform for Windows.  Let me tell you the story of how we skipped on the obvious decision and built something our audience was actually excited about. Sergii Alekseev Tiger.Trade Confirming a gap in the market It all started a few months ago when our company reached a point where expansion started to look like the next logical step. At that point, we had a vague sense that there was a demand for a macOS-based trading terminal. There was even a portion of our users who ran the Tiger.Trade Windows terminal on their Macs, with varying degrees of success and overheating. But that fact alone did not convince us that we should simply reproduce what we already had for a different OS. We decided to take a closer look at the niche before we initiated development. Here’s what we found: There are no UI/UX-friendly terminals for macOS. None! Mac users are forced to combine multiple apps to trade Existing platforms have no professional “DOM” / “level 2” to trade futures as a scalper The conclusion? The current offering is scarce, bad-looking, and has limited functionality. Our research confirmed that there was a gap in the market we were capable of filling. But we’ve decided to go a little further with our market scaping. So, Mac users. What are they like? One in ten desktop/laptop users is a proud owner of a Mac. What do we know about this distinct audience? We know that Apple users are generally design-savvy. They are also more likely to have disposable income and to be early adopters of new trends and technologies. We can see it by the wildfire that Revolut and Robinhood were for the iOS and macOS crowd. And yet, when we took a closer look at the Fintech product lineup for Mac, we found neo-banks, investment platforms and stock trading apps, but one element was distinctly missing. Can you guess what it was? A Crypto trading terminal for Mac That’s right. In 2021 if you had a Macbook, disposable income and an acute interest in all things Blockchain, your options would be limited to pretty much a bunch of web-based exchanges. “But what’s wrong with web exchanges? Does everything need to be an app?” – you might ask. The answer is simple. A standalone application is a way more secure solution than a web page, which could be vulnerable to phishing attacks, malware and other types of fraud. Meanwhile, the cryptocurrency momentum was building. The explosion of cryptocurrency trading in the first half of the year led to +100% growth on all exchanges listed. Q1 2021 saw the highest levels of crypto trading ever. Our Windows terminal was already part of the movement, but it was by no means crypto-centric. Instead, the majority of our user base did and still does comprise pro-level traders, investors, and educators, chained to the stock exchanges. And as is the case with most pros, they don’t care about the bells and whistles of superior UX design. At least not to the extent Mac users do. Clearly, a carbon copy was completely out of the question at that point. Instead, we figured that our best bet for a market fit is a well-designed native cryptocurrency trading terminal for Mac. The result It has now been 4 months of hard work and we have a fantastic prototype of for macOS. It’s a 100% native application that visually stuns upon the first launch. Our design team did a wonderful job of making T.T look and feel totally in-line with other macOS applications. Tiger.Trade Everything was designed with our target audience in mind. It’s fresh, sleek and above all — intuitive and inviting. Our user can get to trading crypto immediately, no need for two hour-long webinars explaining the intricacies of this software. We are due to release in the late summer of 2022. If you want to get notified as soon as the terminal goes live, . And if you can’t wait that long for Tiger.Trade for macOS, there’s a way to get the app before the launch. You can sign up to become a beta tester! click here to sign up for updates If you’re interested in crypto trading and technical analysis, join our Tester Squad on , or . Facebook Telegram Discord I hope our experience of expanding our trading product line gave you some ideas about your own options. Do let me know if you have any questions or if you have a different experience of your own you’d like to share.

Creating a Crypto Terminal for macOS: Lessons Learned From Expanding Our Product Ecosystem

Meet the Winners of Startups in North and Central America

Thank you to the problem solvers who campaigned extra hard in the first-ever competition! HackerNoon Startups of the Year focuses on recognizing the passion and stamina of those willing to find and create effective tech solutions for their cities. HackerNoon’s Startups of the Year To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Awards, where cheaters were caught, are under review.) startups.hackernoon.com From all of us at , thank YOU for your excellent campaign perseverance🥺! We look forward to seeing you again in the 2022 campaign (stay tuned 😉)! HackerNoon Without further adieu, let us kick off by sharing the cities with top votes per region for 2021 🎉 . our series of Best Startups of the Year Stay tuned for the rest of the winners per region to be over the course of this week (Feb 14, 2022). announced North and Central America America is the concrete jungle where dreams are made of, the world’s capital of fashion, showbiz, business, major industry, etc., and surely, of Startups! Representing , we have: North and Central America (14,563 votes): Winner: Boulder Bury.com (10,521 votes) - Winner: New York City Hum Capital (7,987 votes) - Winner: San Francisco Zentist (7,713 votes) - Winner: Redwood City TigerGraph (469 votes) - Winner: Los Altos Stobox To see more awards in this region please click here. Europe Charming, sophisticated, cosmopolitan. There are many beautiful words used to describe this region. With its eccentric vibe, is also a place where new Startups thrive. Europe Representing Europe we have: (10, 010 votes) - Winner: Belfast Bob & Berts (5, 066 votes) - Winner: Ghent WeGroup (4, 712 votes) - Winner: Porto TAIKAI (3, 779 votes) - Winner: City of London Crypto Quantique To see more awards in this region please click here. Oceania The heat, the beaches, roaming kangaroos, or tarantulas? No. Startups! Representing the region of are: Oceania (1, 011 votes) - Winner: Sydney shotstack (430 votes) - Winner: Melbourne Doohly (118 votes) - Winner: Gold Coast Easil (76 votes) - Winner: Tauranga LawVu (40 votes) - Winner: Western Junction Today We Learned To see more awards in this region please click here. Asia Diverse in both ethnicity and culture, known for exotic sceneries as well as friendly people. Many Asian countries are on the verge of mass globalization and modernization. These fast-developing economies give way for many potential Startups. We present, for : Asia (12, 686 votes) - Winner: Singapore Cake DeFi (3,355 votes) - Winners: and (It’s a tie, we’re working on it!) Tel Aviv Applitools hyro (2, 402 votes) - Winner: Beirut Hovi Digital Lab (1.006 votes) - Winner: Hyderabad Quixy (756 votes) - Winner: Hanoi DeFi For You To see more city awards in this region please click here. Africa The second biggest continent in the world, home to many big animals, the longest river in the world AND the largest desert in the world. is known for many big things and sure enough, big Startups. Africa Representing Africa, we have: (884 votes) - Winner: Johannesburg MiBRAND (804 votes) - Winner: Lagos Goodtalent (526 votes) - Winner: Cape Town Revix (420 votes) - Winner: Lusaka ComGrow (224 votes) - Winner: Nairobi UTU To see more city awards in this region please click here. South America “South America is the land of the extreme.” - National Geographic Extremely beautiful people? Yes. Extremely worth-noticeable Startups? Hell yeah! We proudly present, Startups in : South America (217 votes ) - Winner: São Carlos Predify (193 votes) - Winner: Belo Horizonte CM Technologia (106 votes) - Winner: Porto Alegre Privacy Tools (82 votes) - Winner: Quito Agrodemanda (55 votes) -Winner: Rio De Janeiro Bybrand To see more city awards in this region please click here. Once again, and to all of the top Startups of each region this year! HUGE CONGRATULATIONS BIG PROPS The whole team at would like to thank you for your excellent campaign perseverance and we look forward to seeing you in the 2022 campaign. HackerNoon As part of honoring your participation, we will continue to host your company in this directory until further notice ( for legal reasons we hesitate to say ‘forever’..but pretty much forever).  As traffic is directed to the site, it will act as a promotional platform for your company. Have a great Valentine’s Day, HackerNoon loves y’all <3 More Information: If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network This post was written by Tuan Anh Vu. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

To the Winners of #Startups2021, With Love:

Goal: Migrate the UML-based engineering model of a spacecraft to TypeQL Why Do This Migration in the First Place? The lifecycle of a spacecraft is roughly divided into seven consecutive design phases. Part of the early design phases deals with the feasibility of the intended mission. Feasibility is identified by assessing each design aspect that is needed to accomplish the specific mission. This requires that engineers lay out all possible design options and iteratively go through them in relation to all the other engineering design options. Ultimately ending up with a sound system solution. This requires various experts across domains to be reporting into and checking against a central engineering model. This design process is called concurrent engineering. “A spacecraft is a complex puzzle of subsystems. Concurrent Engineering is a design process enhancing communication between experts and accelerating the convergence towards a sound design solution.” Each expert needs to report their design architecture into a central engineering model. This then becomes the blueprint for the spacecraft. Each design has a project tree comprised of all the components of a particular aspect of the space mission. This quickly becomes a lot of information to organize, maintain, and keep track of. The structure of this blueprint is defined by European space standards — ECSS-E-TM-10–25A — provided in a UML class diagram. Over time, more and more engineering models are created. However, these models are disparate, making it challenging for engineers to make use of them as a collective source of information to be used over time. To enhance the data linkage, reusability, and interpretability of stand-alone models, the need to merge the models into one database becomes clear. This then allows for the mining of information within the models, comparisons between them, and the ability to infer new knowledge from them. Why a Knowledge Graph and not a SQL database? SQL databases aren’t built to represent relationships in your data and, for the most part, don’t handle heterogeneous and unstructured data well. Since Audrey’s team’s goal is to mine and discover information within the models, a database that can model relations naturally and aid in inferring new knowledge is needed. To get the inference they were looking for, a schema that included the ability to define rules was an important requirement. “Without a schema to structure the database, there is no rule-based reasoning, thus no inference.” Once the team noted the requirements they were looking for in a database, they set out to find a suitable one. Researching the landscape, they noted quite a few options: Neo4J, Stardog, AllegroGraph, AnzoGraph, TigerGraph, and TypeDB. The parameters for making their decision were: OS portability: other evaluated databases were only available on Linux systems, and they needed to allow engineers to work with OS or Windows as well as Linux. Learning Curve: a s their team didn’t have a background in RDF, they felt it would be easier and faster to learn one language or tool vs. the multitude of Semantic Web Technologies. Scalability: they didn’t want to be limited by the database in the number of nodes and relations. Inference Engine: does the database allow for inference without requiring the development or integration of another tool? Visualization: is there a visualizer available? Support: what do the community and formal support look like for the database Licensing: a s their team is part of the University of Strathclyde, the availability of an open-source version was preferred. Ultimately, the team chooses Vaticle’s TypeDB for the simplicity of the language — making onboarding easier and faster for engineers — the integrated reasoning engine, the presence of a strong community that is active and supports each other, and the availability of an open-source version. How to Migrate UML to TypeQL Unified Modelling Language (UML): a standardized Object-Oriented (OO) modeling language for specifying, visualizing, constructing, and documenting software systems. A Brief Introduction to UML The building blocks of a class diagram in UML are: Class: describes a set of objects sharing the same features/constraints/semantics (mapped as an entity in TypeDB schema) Interface: a non-instantiable class declaring features to be implemented by another class (mapped as an abstract type in TypeDB schema) Property: a structural feature of a class representing an attribute or the end of a relationship (mapped as an attribute in TypeDB schema) Relationship: links one or more classes — there are multiple relation types as well: association, generalization, dependency, etc. (mapped as relations in TypeDB schema) Migrating Spacecraft Engineering UML to TypeDB There are two main steps to migrating an engineering model in UML to TypeDB: and . defining a schema in TypeQL inserting data into TypeDB TypeDB is a direct implementation of the Entity-Relation diagram and makes Entities, Attributes, and Relations first-class citizens of the database. Defining a Schema As noted above, we map a UML class to a TypeDB entity, a UML property to a TypeDB attribute, and a UML relation to a relation in TypeDB. The schema is also where we can define the rules for the reasoning engine. Starting with the mapping of a UML class to a TypeDB entity, the first thing to note is that TypeDB allows for type inheritance. UML Class to TypeDB Entity In the team’s UML model, they have a class called a Mixin, which is a class containing methods (attributes and roles) for use by other classes. These Mixin classes are not migrated, but instead, the attributes and role types are redefined for each of their “child” classes — . In TypeDB this means they are defined as attributes and role players owned or played by entities (classes). TypeDB’s type inference comes in handy here For the abstract classes, an abstract entity is defined. For concrete classes, a standard entity is defined. Each of these may have Mixin and non-Mixin classes, mapped as noted in the paragraph above. Looking at the example in the image below, we see that a Person is a subclass of four other classes. Three of these: , , and , are Mixin's. While Thing is a concrete class. We redefine the three Mixin's as attributes of the person entity, and using the TypeQL keyword sub; we define it as a subtype of thing. shortnamedthing namedthing deprecatablething UML Property to TypeDB Attribute A UML property can be an attribute (value type) — defined as an attribute in the schema — or the end of a relation (reference type) — defined as a role in the schema. Additionally, the UML property is either directly owned by the class or inherited from another class. In the case that a property is inherited from another class, it can inherit from a Mixin class or a non-Mixin class. When it is inherited from a non-Mixin class, we define the inheritance from the parent entity within the TypeDB schema. The example above shows all the attributes in the UML Diagram User Manual are defined as attributes owned by the person entity type. We also see an example of how we would define these using TypeQL (TypeDB's query language) in the console: define givenName sub attribute, value string; surname sub attribute, value string; organizationalUnit sub attribute, value string; isActive sub attribute, value boolean; The mapping of a UML relationship to a relation in a TypeDB schema is a bit more nuanced. As there are multiple types of relationships in UML, there are some decisions to make in how to map to a TypeDB schema. For Audrey’s team, they were working with just two types of UML relationships: association relationships and composition relationships. For these, their mapping decision was to use different naming conventions in their TypeDB schema: reference and containment relation types, respectively. UML Relationships to TypeDB Relations As we see in the example below, we need to define the relation type and the role type that is being played by the entity. Exporting the engineering models as JSON files, we are able to map this JSON file to the TypeDB schema and build an insert query to get it into TypeDB. This is done via migration pipeline templates that take each element found in the JSON file and look at the class type. Each class type has its own template, which then builds the TypeQL insert queries. We can see an example of one such template below. Inserting Data Where is This Method Currently Being Applied? The migration was applied to three Engineering Models, including two internal CubeSat models. This early implementation already demonstrated the power of using TypeDB as a knowledge graph for this use case. Tasks commonly performed by engineers such as mass budget computation could be automated, and TypeDB provided new insights into the data, enabling the quick recall of similar missions. Thank you and Some Helpful Links We appreciate Audrey and her team for their hard work and for taking the time to put together this presentation. The TypeDB community looks forward to having her back to share the progress and future work with the European Space Agency, the University of Strathclyde, RHEA Systems B.V. and Airbus within the Networking/Partnering Initiative (NPI). Watch the full presentation replay on YouTube Dig into TypeDB further via GitHub Reach out to Audrey and the Vaticle team on the TypeDB community Discord

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