💡 enliteAI is a technology provider for Artificial Intelligence specialized in Reinforcement Learning and Computer Vision/geoAI. We are the makes of 📍 Detekt, a modern geospatial data platform for object detection in mobile mapping data supporting the entire asset management life cycle. ⚡️best-in-class powergrid optimization with Reinforcement Learning. l2rpn challenge 2022 winner & neurips 2022 participant ⭐️ enliteAI is an active member of the pan-European research cooperation ELLIS together with, among others, the LIT AI Laboratory and Oxford University. 👩💻 enliteAI is always looking for motivated employees who actively support us on our way.

EnliteAI

enliteai

The internet has ushered in an era where individuals can invest like never before. A vast amount of information and trading tools are readily available online, empowering them to make informed decisions. Additionally, individual investor communities like r/WallStreetBets have utilized social media to create significant buzz around certain stocks, thereby influencing their prices. In short, the internet has radically empowered individual investors. Now, a puzzling question arises. Despite the enthusiastic adoption and utilization of the internet so far, why aren't people embracing Web3 yet? Ironically, Web3 represents the next iteration of the internet, defined as "the Internet of Money." The Web3 Adoption Gap: Users vs. Investors Despite 580 million people holding cryptocurrency by the end of 2023, the daily average of Unique Active Wallets (UAW) interacting with Web3 or dApps remained modest at 4.2 million. Since the emergence of Web3, active users have comprised less than 1% of the global population. Two primary factors typically impede the adoption of new technologies: lack of product-market fit and ineffective marketing and communication strategies. Given the sizable number of investors, it can be inferred that they are aware of the potential of Web3. Therefore, we may need to revisit the current Web3 marketing approaches. Airdrop marketing, where projects distribute free tokens for specific actions, is often associated with crypto marketing. This method has the remarkable ability to transform a project from obscurity to achieving significant Key Performance Indicators. By immediately enlivening a project, airdrops attract users, generate transactions, create liquidity, and boost brand awareness. Recipients view airdrops as potential windfalls requiring no initial investment, making them highly desirable. While both projects and users favor airdrops for the excitement they generate around Web3, it's crucial to evaluate their actual effectiveness in driving meaningful adoption and engagement. Web2 Marketing: Leveraging Rewards for Network Effects Offering rewards to attract customers is nothing new, with origins dating back to the early 1700s. Initially used to foster customer loyalty, businesses provided discounts, points, coupons, and cashbacks to encourage repeat purchases. This approach evolved to directly incentivize customer acquisition with the rise of the Internet, particularly during the Web2 era characterized by the concept of ‘the web as a platform’. PayPal's phenomenal marketing strategy, which attracted over 100,000 users within a month, set a benchmark for many startups. Elon Musk explained it in an interview, stating: “We started off first by offering people $20 if they opened an account. And $20 if they referred anyone. And then we dropped it to $10. And we dropped it to $5. As the network got bigger and bigger, the value of the network itself exceeded any sort of carrot that we could offer.”  The more users a platform has, the more valuable it becomes - this network effect has been the hallmark of web2. Traditional marketing focused on maximizing revenue and profit by targeting specific customer segments who could afford their products. However, the internet era shifted the focus to user acquisition. In particular, startups aiming for rapid scale-up coined the term 'growth hacking' for marketing strategies solely focused on recruiting the maximum number of users in the shortest time. This made the role of a growth hacker the most sought-after position in marketing. Online vs. On-chain: Calibrating Marketing for Divergent Users Based on indicators alone, it appears that a record-breaking successful growth hacking is occurring in Web3. Last February, Starknet announced the STRK token distribution worth $2 billion, the largest airdrop in crypto history, to 1.3 million eligible wallets. Blast has not launched its mainnet yet, but secured $1.1 billion worth of liquidity by promising an airdrop in May 2024. This substantial amount of deposits was collected on a network that is not even operational. As a result, there is a growing perception that the hallmark of Web3 is a pyramid scheme. Observations suggest that user engagement and liquidity sharply decline immediately following the airdrop events. In contrast, PayPal reportedly invested around $60 million in rewards marketing and ceased the program upon reaching 100 million users. Yet it has sustained its growth, currently serving over 400 million users. This raises the question: what is amiss with Web3's marketing that prevents it from fostering sustainable growth? An important distinction to make is that Web2 online platforms and Web3 on-chain networks differ fundamentally in their nature. As a result, the audiences they target are bound to diverge as well. 1. Platform vs.  Protocol: Network Effects in Infrastructure Web2 platform companies can only sustain growth by locking users into their platforms and monopolizing the market. However, Web3 represents a ‘protocol network’ characterized by ‘interoperability’ and ‘composability.’ For instance, you may have heard of Airbnb's famous growth hacking story, where Airbnb reverse engineered Craigslist's high-traffic website to acquire early users. In contrast, Web3 projects operate within a decentralized system owned by their communities and based on open source principles. These projects can seamlessly integrate with others, either by building their dApps on existing platforms or interacting with other dApps. They do not need permission from other projects to collaborate or access their users. From the perspective of integrated projects, as more projects combine with its protocol, the ecosystem expands and the token value increases, nurturing a virtuous cycle that attracts even more users and projects to join. This phenomenon, particularly prominent in DeFi, is referred to as ‘money legos,’ where each DeFi dApp serves as a building block, continuously delivering new and diverse products. In the Web2 era, the 'network effect' was synonymous with acquiring a large number of users. However, in the Web3 era, the power dynamics have shifted. A large user base no longer holds the same influence, as users are more interchangeable. Instead, the network effect at the infrastructure stage is more crucial. Marketing strategies that solely distribute airdrops to secure early users may overlook the essence of Web3. 2. Platform vs. Community: Users vs. Owners The most common definition of Web3 is the "internet of value," allowing value to be exchanged peer-to-peer without intermediaries. From a marketer's perspective, this definition only explains the features of blockchain technology and is not enough to explain user benefits. The true innovation lies in (a) that anyone can issue their own currency (b) that activities on the network establish economies, and (c) The token price can increase through the joint efforts of the community, so the community members collaborate with the common goal of growing the project. As a result, while the Web2 network is owned by the company, the Web3 network serves as a public infrastructure where users collectively own the protocol and have a vested interest in its success. To sum it up in one word, it is a community, more precisely, an economic community. While traditional marketing viewed customers as ‘consumers’ focused on purchasing products, the Web2 era redefined them as ‘users’ expected to interact with the network through actions like following, liking, and sharing. In Web3, however, customers take on a new role as ‘community members’ who actively contribute by investing in project tokens, participating in governance, and proposing and promoting initiatives. This represents a shift from the Web2 approach, where marketing efforts aimed at increasing passive user interactions. Web3 marketing should focus on engaging users to foster a sense of belonging and loyalty, encouraging their active participation in the project's development and adoption. Although the Web3 network operates as an open public infrastructure, it is not a product for everyone. To cultivate a robust community, targeting individuals with the financial means to invest, the time to dedicate, intelligence, interest in technology and the economy, and a passion for economic empowerment provided by Web3 is essential. Therefore, marketing that focuses on accelerated scale-up in the early stages does not align with Web3. Conclusion If the network effect was the driving force behind Web2 growth, Web3 is defined by the powerful community effect. In this new era, the path to growth lies not in mere user acquisition but in curating a dedicated community of quality members. Marketing efforts must be tailored to identify and invite individuals who can actively engage and contribute their expertise, ideas, and passion. Empowering these invested members with avenues for continuous collaboration and value creation is the key to unlocking the true potential of Web3 projects and propelling them toward long-term sustainability and impact. The internet has ushered in an era where individuals can invest like never before. A vast amount of information and trading tools are readily available online, empowering them to make informed decisions. Additionally, individual investor communities like r/WallStreetBets have utilized social media to create significant buzz around certain stocks, thereby influencing their prices. In short, the internet has radically empowered individual investors. Now, a puzzling question arises. Despite the enthusiastic adoption and utilization of the internet so far, why aren't people embracing Web3 yet? Ironically, Web3 represents the next iteration of the internet, defined as "the Internet of Money." The Web3 Adoption Gap: Users vs. Investors Despite 580 million people holding cryptocurrency by the end of 2023, the daily average of Unique Active Wallets (UAW) interacting with Web3 or dApps remained modest at 4.2 million. Since the emergence of Web3, active users have comprised less than 1% of the global population. Two primary factors typically impede the adoption of new technologies: lack of product-market fit and ineffective marketing and communication strategies. Given the sizable number of investors, it can be inferred that they are aware of the potential of Web3. Therefore, we may need to revisit the current Web3 marketing approaches. Airdrop marketing, where projects distribute free tokens for specific actions, is often associated with crypto marketing. This method has the remarkable ability to transform a project from obscurity to achieving significant Key Performance Indicators. By immediately enlivening a project, airdrops attract users, generate transactions, create liquidity, and boost brand awareness. Recipients view airdrops as potential windfalls requiring no initial investment, making them highly desirable. While both projects and users favor airdrops for the excitement they generate around Web3, it's crucial to evaluate their actual effectiveness in driving meaningful adoption and engagement. Web2 Marketing: Leveraging Rewards for Network Effects Offering rewards to attract customers is nothing new, with origins dating back to the early 1700s. Initially used to foster customer loyalty, businesses provided discounts, points, coupons, and cashbacks to encourage repeat purchases. This approach evolved to directly incentivize customer acquisition with the rise of the Internet, particularly during the Web2 era characterized by the concept of ‘the web as a platform’. PayPal's phenomenal marketing strategy, which attracted over 100,000 users within a month, set a benchmark for many startups. Elon Musk explained it in an interview, stating: “We started off first by offering people $20 if they opened an account. And $20 if they referred anyone. And then we dropped it to $10. And we dropped it to $5. As the network got bigger and bigger, the value of the network itself exceeded any sort of carrot that we could offer.”  The more users a platform has, the more valuable it becomes - this network effect has been the hallmark of web2. Traditional marketing focused on maximizing revenue and profit by targeting specific customer segments who could afford their products. However, the internet era shifted the focus to user acquisition. In particular, startups aiming for rapid scale-up coined the term 'growth hacking' for marketing strategies solely focused on recruiting the maximum number of users in the shortest time. This made the role of a growth hacker the most sought-after position in marketing. Online vs. On-chain: Calibrating Marketing for Divergent Users Based on indicators alone, it appears that a record-breaking successful growth hacking is occurring in Web3. Last February, Starknet announced the STRK token distribution worth $2 billion, the largest airdrop in crypto history, to 1.3 million eligible wallets. Blast has not launched its mainnet yet, but secured $1.1 billion worth of liquidity by promising an airdrop in May 2024. This substantial amount of deposits was collected on a network that is not even operational. As a result, there is a growing perception that the hallmark of Web3 is a pyramid scheme. Observations suggest that user engagement and liquidity sharply decline immediately following the airdrop events. In contrast, PayPal reportedly invested around $60 million in rewards marketing and ceased the program upon reaching 100 million users. Yet it has sustained its growth, currently serving over 400 million users. This raises the question: what is amiss with Web3's marketing that prevents it from fostering sustainable growth? An important distinction to make is that Web2 online platforms and Web3 on-chain networks differ fundamentally in their nature. As a result, the audiences they target are bound to diverge as well. Web2 online platforms and Web3 on-chain networks differ fundamentally in their nature. 1. Platform vs.  Protocol: Network Effects in Infrastructure Web2 platform companies can only sustain growth by locking users into their platforms and monopolizing the market. However, Web3 represents a ‘protocol network’ characterized by ‘interoperability’ and ‘composability.’ For instance, you may have heard of Airbnb's famous growth hacking story, where Airbnb reverse engineered Craigslist's high-traffic website to acquire early users. In contrast, Web3 projects operate within a decentralized system owned by their communities and based on open source principles. These projects can seamlessly integrate with others, either by building their dApps on existing platforms or interacting with other dApps. They do not need permission from other projects to collaborate or access their users. From the perspective of integrated projects, as more projects combine with its protocol, the ecosystem expands and the token value increases, nurturing a virtuous cycle that attracts even more users and projects to join. This phenomenon, particularly prominent in DeFi, is referred to as ‘money legos,’ where each DeFi dApp serves as a building block, continuously delivering new and diverse products. In the Web2 era, the 'network effect' was synonymous with acquiring a large number of users. However, in the Web3 era, the power dynamics have shifted. A large user base no longer holds the same influence, as users are more interchangeable. Instead, the network effect at the infrastructure stage is more crucial. Marketing strategies that solely distribute airdrops to secure early users may overlook the essence of Web3. A large user base no longer holds the same influence, as users are more interchangeable. Instead, the network effect at the infrastructure stage is more crucial. 2. Platform vs. Community: Users vs. Owners The most common definition of Web3 is the "internet of value," allowing value to be exchanged peer-to-peer without intermediaries. From a marketer's perspective, this definition only explains the features of blockchain technology and is not enough to explain user benefits. The true innovation lies in (a) that anyone can issue their own currency (b) that activities on the network establish economies, and (c) The token price can increase through the joint efforts of the community, so the community members collaborate with the common goal of growing the project. As a result, while the Web2 network is owned by the company, the Web3 network serves as a public infrastructure where users collectively own the protocol and have a vested interest in its success. To sum it up in one word, it is a community, more precisely, an economic community. While traditional marketing viewed customers as ‘consumers’ focused on purchasing products, the Web2 era redefined them as ‘users’ expected to interact with the network through actions like following, liking, and sharing. In Web3, however, customers take on a new role as ‘community members’ who actively contribute by investing in project tokens, participating in governance, and proposing and promoting initiatives. This represents a shift from the Web2 approach, where marketing efforts aimed at increasing passive user interactions. Web3 marketing should focus on engaging users to foster a sense of belonging and loyalty, encouraging their active participation in the project's development and adoption. Although the Web3 network operates as an open public infrastructure, it is not a product for everyone. To cultivate a robust community, targeting individuals with the financial means to invest, the time to dedicate, intelligence, interest in technology and the economy, and a passion for economic empowerment provided by Web3 is essential. Therefore, marketing that focuses on accelerated scale-up in the early stages does not align with Web3. individuals with the financial means to invest, the time to dedicate, intelligence, interest in technology and the economy, and a passion for economic empowerment provided by Web3 Conclusion If the network effect was the driving force behind Web2 growth, Web3 is defined by the powerful community effect. In this new era, the path to growth lies not in mere user acquisition but in curating a dedicated community of quality members. Marketing efforts must be tailored to identify and invite individuals who can actively engage and contribute their expertise, ideas, and passion. Empowering these invested members with avenues for continuous collaboration and value creation is the key to unlocking the true potential of Web3 projects and propelling them toward long-term sustainability and impact. If the network effect was the driving force behind Web2 growth, Web3 is defined by the powerful community effect.

Web3 Marketing is not about the Network Effect

I awoke, and my first thought was, Adrian and Clara are dead.

I discovered the total revolution that the disasters of Raymond

Common Sense Applied to Religion; Or, The Bible and the People by Catharine Esther Beecher, is part of the HackerNoon Books Series. You can jump to any chapter in this book . WRONG ACTION OF MIND, AND ITS RESULTS IN A FUTURE STATE. here CHAPTER XXVII. WRONG ACTION OF MIND, AND ITS RESULTS IN A FUTURE STATE. We are now to inquire as to the results of the wrong action of mind in a future state, so far as reason and experience can furnish data for any anticipations. The following are the principles of mind from which we reason on this subject. It appears that its constitution is such that the repetition of one particular mode of securing happiness induces a habit; and that the longer a habit continues, the more powerful is its force. An early habit of selfishness is always formed in the human mind, and the penalties following from self-indulgence and selfishness are not sufficient to prevent the continued increase of this habit. Though men, from the very beginning of existence, feel that they are happier in obeying the dictates of conscience, and that increase of guilt is increase of sorrow, yet this does not save them, in numberless cases, from increasing evil habits. It is also established by experience that, when a strong habit is formed, the mere decisions of the will are not sufficient for an immediate remedy. In this life, it requires a period of long and painful efforts of the will to rectify an established habit. Every human being is conscious how difficult it is to force the mental and bodily faculties to obey its decisions when contrary to the stream of a long-indulged habit. There are few who have not either experienced or witnessed the anguish of spirit that has followed the violations of solemn resolutions, those firmest decisions of the will, in the contest between habit and conscience. Another principle of mind is this, that when selfishness and crime have been long indulged, the natural constitution of mind seems changed, so that inflicting evil on others is sought as an enjoyment. In illustration of this, it is related of Antiochus Epiphanes that, in his wars with the Jews, after all opposition had ceased, and all danger and cause of fear was removed, he destroyed thousands for the mere pleasure of seeing them butchered. An anecdote is related of him, too horrible to record in all its particulars, where he sat and feasted his eyes on the sufferings of a mother and her seven sons, when the parent was doomed to witness the infliction of the most excruciating and protracted tortures on each of her seven children, and then was tortured to death herself. It is recorded of Mustapha, one of the Turkish sultans, that by honorable capitulations he gained the person of a brave Venetian commander called Bragadino, who was defending his country from the cruelty of invaders. After having promised him honorable protection, he ordered him, bound hand and foot, to behold the massacre of his soldiers, then caused his person to be cut and mutilated in the most horrible manner, and then taunted him as a worshiper of Christ, who could not save his servants. When recovered of his wounds, he obliged him to carry loaded buckets of earth before the army, and kiss the ground whenever he passed his barbarous tormentor. He then had him hung in a cage, to be tormented by his own soldiers, who were chained as galley-slaves, that they might be agonized by the indignities and sufferings of their venerated commander. After the most protracted sufferings and indignities in the public place, at the sound of music he was flayed alive. The history of some of the Roman emperors, even of some who, in early childhood and youth, were gentle, amiable, and kind, presents the same horrible picture. Nero set fire to Rome, and dressed the Christians in garments of flaming pitch, to run about his garden for his amusement. Tiberius tormented his subjects, and murdered them in cruel pangs, to gratify his love of suffering, while Caligula butchered his people for amusement with his own hand. The mind turns with horror from such revolting scenes, and asks if it is possible human nature can be so perverted and debased. But this is the humiliating record of some of the , even of our own countrymen, that have occurred in some parts of this refined and Christian nation. "Many of the interludes are filled up with a , which becomes memorable by feats of . When two boxers are wearied with fighting and bruising each other, they come to close quarters, each endeavoring to twist his forefinger into the earlocks of his antagonist. When they are thus fast clenched, the thumbs are extended, and are turned out of their sockets. The victor is hailed with shouts of applause from the sporting throng, while his poor antagonist, thus blinded for life, for his misfortune." now amusements boxing match gouging both the eyes is laughed at One very striking fact bearing on this subject has been established by experience, and that is, that , either mental or bodily, tends to awaken the desire to inflict evil upon other minds. This is probably one mode of accounting for the increased cruelty of the Roman emperors. As the powers of enjoyment diminished by abuse, and the horrors of guilt harassed their spirits, this dreadful desire to torment others was awakened. extreme suffering There are many undisputed facts to establish the principle that extreme suffering is the cause of terrible malignity. The following is from a statement of Mr. Byron, who was shipwrecked on the coast of South America: "So terrible was the scene of foaming breakers, that one of the bravest men could not help expressing his dismay, saying it was too shocking to bear. In this dreadful situation malignant passions began to appear. The crew grew extremely riotous, and fell to beating every thing in their way, and broke open chests and cabins for plunder that could be of no use. So earnest were they in this wantonness of theft, that in the morning a strangled corpse was found of one who had contested the spoil." A still more terrible picture is given in an account of the loss of the Medusa frigate on the coast of Africa. In the midst of dreadful suffering from cold, danger, and famine, it is recorded that "a spirit of sedition arose and manifested itself by furious shouts. The soldiers and sailors began to cut the ropes, and declared their intention of murdering the officers. About midnight, they rushed on the officers like desperate men, each having a knife or sabre, and such was their fury that they tore their clothes and their flesh . The next morning the raft was strewed with dead bodies. The succeeding night was passed in similar horrors, and the morning sun saw twelve more lifeless bodies. The next night of suffering was attended with a horrid massacre, and thus it continued till only fifteen remained of the whole one hundred and fifty!" with their teeth Another principle of mind having a bearing on this subject is the fact that those qualities of mind which are the causes of enjoyment in others around may be viewed with only pain and dislike by a selfish person. Thus intellectual superiority, in itself considered, is a delightful object of contemplation; but if it becomes the means of degradation or of contemptuous comparison to a selfish mind, it is viewed with unmingled pain. Benevolence and truth are objects of delightful contemplation to all minds when disconnected with obligations or painful comparisons, but if they are viewed as causes of evil to a selfish mind, it will view them with unmingled dislike and hatred. Now we find that there are two classes of minds in this world: those who are more or less benevolent, and find their happiness in living to promote the general interests of their fellow-beings, and those who are selfish, and are living to promote their own enjoyment irrespective of the general happiness. If, then, we reason from the known laws of mind and from past experience, we must suppose that the habits of mind which are existing in this life will continue to increase, and if the mind is immortal, a time must come when one class will become perfectly benevolent and the other perfectly selfish. A community of perfectly benevolent beings, it has been shown, would, from the very nature and constitution of mind, be a perfectly happy community. Every source of enjoyment of which mind is capable would be secured by every individual. It can be seen, also, that there must, in the nature of the case, be an entire separation between two such opposite classes; for it is as painful for minds suffering from conscious guilt, shame, and malignity, to look upon purity, benevolence, and happiness, as it is for the virtuous to associate with the selfish, the debased, and the abandoned. This separation, therefore, would be a voluntary one on both sides, even did we suppose no interference of Deity. But if the Creator continues his present constitution of things, we may infer that his power would be exerted to prevent the intrusion of malignity into a perfect and well-ordered community; for he has so constituted things , that those who are incorrigible pests to society are confined from interfering with its interests. here From the laws of mind, then, and from past experience as to the tendencies of things, we can establish the position that, at some future period, if the mind of man is immortal, the human race will be permanently divided into two classes, the perfectly selfish and the perfectly benevolent. Should it be objected to this conclusion that when the mind passes into another world more effectual motives may be brought to operate, it may be replied that it is not the office of reason to meet of , but to show what the are by deductions from principles already known. A thousand possibilities may be asserted, such as the annihilation of mind or the alteration of its powers, but these are mere suppositions, and have nothing to do with the conclusions of reason. suppositions possibilities probabilities If mind is immortal and continues its present nature, habits will continue to strengthen; and in regard to motives, we know already that the will not save from continuance in crime. How often has a man who has yielded to habits of guilt been seen writhing in the agonies of remorse, longing to free himself from the terrible evils he has drawn around him, acknowledging the misery of his course and his ability to return to virtue, and yet, with bitter anguish, yielding to the force of inveterate habits and despairing of any remedy. fear of evil consequences We know, also, that it is a principle established by long experience, that punishment does not tend to soften and reform. Where is the hardened culprit that was ever brought to repentance and reformation by lashes or the infliction of degradation? Such means serve only to harden and brutify. Experience forbids the hope that punishment will ever restore a selfish and guilty mind to virtue and peace. Reason and experience, then, both lead to the conclusion that the two classes of minds into which mankind are here divided will, on leaving this world, eventually become two permanently distinct communities—one perfectly selfish, and the other perfectly benevolent. What, then, would reason and experience teach us as to the probable situation of a community of minds constituted like those of the human race, who, in the progress of future ages, shall establish habits of ? perfect selfishness and crime In regard to the Creator, what may we suppose will be the feelings of such minds? If he is a benevolent, pure, and perfectly happy being, and his power is exerted to confine them from inflicting evil on the good, he will be the object of unmingled and tormenting envy, hatred, and spite; for when a selfish mind beholds a being with characteristics which exhibit its own vileness in painful contrast, and using his power to oppose its desires, what might in other circumstances give pleasure will only be cause of pain. If they behold, also, the purity and happiness of that community of benevolent beings from which they will be withdrawn, the same baleful passions will be awakened in view of their excellence and enjoyment. There is no suffering of the mind more dreaded and avoided than that of . It is probable a guilty creature never writhes under keener burnings of spirit than when all his course of meanness, baseness, ingratitude, and guilt is unveiled in the presence of dignified virtue, honor, and purity, and the withering glance of pity, contempt, and abhorrence is encountered. This feeling must be experienced, to its full extent, by every member of such a wretched community. Each must feel himself an object of loathing and contempt to every pure and benevolent mind, as well as to all those who are equally debased. shame Another cause of suffering is ungratified desire. In this world, perfect misery and full happiness is seldom contrasted. But in such circumstances, if we suppose that the happiness of blessed minds will be known, the keenest pangs of ungratified desire must torment. Every mind will know what is the pure delight of yielded and reciprocated affection, of sympathy in the happiness of others, of the sweet peace of conscious rectitude, and of the delightful consciousness of conferring bliss on others, while the ceaseless cravings of hopeless desire will agonize the spirit. Another cause of suffering is found in the . In such a degraded and selfish community, all ties of country, kindred, friendship, and love must cease. Yet all will know what the endearments of home, the mild soothings of maternal love, the ties of fraternal sympathy, and all the trust and tenderness of friendship and love. What vanished blessing of earth would not rise up, with all the sweetness and freshness that agonizing memory can bring, to aggravate the ! loss of enjoyment were loss of all But the mind is so made that, however wicked itself, guilt and selfishness in others is hated and despised. Such a company, then, might be described as those who were "hateful and hating one another." It has been shown that both suffering and selfishness awaken the desire to torment others. This, then, will be the detested purpose of every malignant mind. Every action that could irritate, mortify, and enrage, would be deliberately practiced, while disappointed hopes, and blasted desires, and agonizing misery would alone awaken the smile of horrible delight. And if we suppose such minds in a future state reclothed in a body, with all the present susceptibilities of suffering, and surrounded by material elements that may be ministers of hate, what mind can conceive the terror and chaos of a world where every one is actuated by a desire to torment? Suppose these beings had arrived at only such a degree of selfishness as has been witnessed in this world; such, for example, as Genghis Khan, who caused unoffending prisoners to be pounded to death with bricks in a mortar; or Nero, who dressed the harmless Christians in flaming pitch for his amusement; or Antiochus Epiphanes and Mustapha, who spent their time in devising and executing the most excruciating tortures on those who could do them no injury. What malignity and baleful passions would actuate such minds, when themselves tormented by others around, bereft of all hope, and with nothing to interest them but plans of torment and revenge! What refined systems of cruelty would be devised in such a world! what terrific combinations of the elements to terrify and distress! If such objects as "the lake which burneth with fire and brimstone, and the worm that never dies," could be found, no Almighty hand would need to interfere, while the "smoke of their torment" would arise from flames of their own kindling. To fearful sufferings thus inflicted would be added the pangs of agitating ; for where all around were plotting misery, what relief, by day or by night, from its withering terrors? Then surely "fear would come upon them like desolation, and destruction as a whirlwind." fear Another cause of suffering is inactivity of body and mind. It has been seen that the desire of good is what gives activity to the intellectual and moral powers. In such a world, no good could be hoped or sought, but the gratification of inflicting ill. And even a malignant mind must often weary in this pursuit, and sink under all the weight and misery of that awful , when, in torpid inactivity, it has nothing to love, nothing to hope, nothing to desire! death of the soul Another cause of misery is the consciousness of guilt; and such, even in this life, have been the agonies of remorse, that tearing the hair, bruising the body, and even gnawing the flesh have been resorted to as a temporary relief from its pangs. What, then, would be its agonizing throes in bosoms that live but to torment and to destroy all good to themselves and to other minds? In this life, where we can allow the mind to be engrossed by other pursuits, and where we can thus form a habit of suppressing and avoiding emotions of guilt, the conscience may be seared. But it could not be thus when all engaging and cheerful pursuits were ended forever. Then the mind would view its folly, and shame, and guilt in all their length and breadth, and find no escape from the soul-harrowing gaze. To these miseries must be added despair—the loss of all hope. Here hope comes to all; but, in such a community, that fearful susceptibility of the soul—that terrific power of —would bind in chains which would be felt to be stronger than brass and heavier than iron. If the spirit is conscious that its powers are immortal, with this consciousness would come the despairing certainty of increasing and never-ending woe! habit This terrifying and heart-rending picture, it must be remembered, is the , and who can point out its fallacy? Is not habit appalling in its power, and ofttimes, even in this life, inveterate in its hold? Are not habits increased by perpetual repetition? Is not the mind of man immortal? Do not the tendencies of this life indicate a period when a total separation of selfish and benevolent minds will be their own voluntary choice? If all the comforts, gentle endearments, and the enlivening hopes of this life; if all the restraints of self-interest, family, country, and laws; if in Christian lands the offers of heaven, and the fearful predictions of eternal woe; if the mercy and pardon, and all the love and pity of our Creator and Redeemer, neither by fear, nor by gratitude, nor by love, can turn a selfish mind, what hope of its recovery when it goes a into a world of spirits, to sojourn in that society which, according to its moral habits, it must voluntarily seek? And if there exists a community of such selfish beings, can language portray, with any adequacy, the appalling results that must necessarily ensue? deduction of reason stranger 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. Catharine Esther Beecher (2017). Common Sense Applied to Religion; Or, The Bible and the People. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/55531/pg55531-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

WRONG ACTION OF MIND, AND ITS RESULTS IN A FUTURE STATE.

Off on a Comet! a Journey through Planetary Space by Jules Verne, is part of the HackerNoon Books Series. You can jump to any chapter in this book . DREARY MONTHS here CHAPTER XIII. DREARY MONTHS Henceforth, then, with a velocity ever increasing, Gallia would re-approach the sun. Except the thirteen Englishmen who had been left at Gibraltar, every living creature had taken refuge in the dark abyss of the volcano’s crater. And with those Englishmen, how had it fared? “Far better than with ourselves,” was the sentiment that would have been universally accepted in Nina’s Hive. And there was every reason to conjecture that so it was. The party at Gibraltar, they all agreed, would not, like themselves, have been compelled to have recourse to a stream of lava for their supply of heat; they, no doubt, had had abundance of fuel as well as food; and in their solid casemate, with its substantial walls, they would find ample shelter from the rigor of the cold. The time would have been passed at least in comfort, and perhaps in contentment; and Colonel Murphy and Major Oliphant would have had leisure more than sufficient for solving the most abstruse problems of the chess-board. All of them, too, would be happy in the confidence that when the time should come, England would have full meed of praise to award to the gallant soldiers who had adhered so well and so manfully to their post. It did, indeed, more than once occur to the minds both of Servadac and his friends that, if their condition should become one of extreme emergency, they might, as a last resource, betake themselves to Gibraltar, and there seek a refuge; but their former reception had not been of the kindest, and they were little disposed to renew an acquaintanceship that was marked by so little cordiality. Not in the least that they would expect to meet with any inhospitable rebuff. Far from that; they knew well enough that Englishmen, whatever their faults, would be the last to abandon their fellow-creatures in the hour of distress. Nevertheless, except the necessity became far more urgent than it had hitherto proved, they resolved to endeavor to remain in their present quarters. Up till this time no casualties had diminished their original number, but to undertake so long a journey across that unsheltered expanse of ice could scarcely fail to result in the loss of some of their party. However great was the desire to find a retreat for every living thing in the deep hollow of the crater, it was found necessary to slaughter almost all the domestic animals before the removal of the community from Nina’s Hive. To have stabled them all in the cavern below would have been quite impossible, whilst to have left them in the upper galleries would only have been to abandon them to a cruel death; and since meat could be preserved for an indefinite time in the original store-places, now colder than ever, the expedient of killing the animals seemed to recommend itself as equally prudent and humane. Naturally the captain and Ben Zoof were most anxious that their favorite horses should be saved, and accordingly, by dint of the greatest care, all difficulties in the way were overcome, and Zephyr and Galette were conducted down the crater, where they were installed in a large hole and provided with forage, which was still abundant. Birds, subsisting only on scraps thrown out to them did not cease to follow the population in its migration, and so numerous did they become that multitudes of them had repeatedly to be destroyed. The general re-arrangement of the new residence was no easy business, and occupied so much time that the end of January arrived before they could be said to be fairly settled. And then began a life of dreary monotony. Then seemed to creep over everyone a kind of moral torpor as well as physical lassitude, which Servadac, the count, and the lieutenant did their best not only to combat in themselves, but to counteract in the general community. They provided a variety of intellectual pursuits; they instituted debates in which everybody was encouraged to take part; they read aloud, and explained extracts from the elementary manuals of science, or from the books of adventurous travel which their library supplied; and Russians and Spaniards, day after day, might be seen gathered round the large table, giving their best attention to instruction which should send them back to Mother Earth less ignorant than they had left her. Selfish and morose, Hakkabut could never be induced to be present at these social gatherings. He was far too much occupied in his own appropriated corner, either in conning his accounts, or in counting his money. Altogether, with what he had before, he now possessed the round sum of 150,000 francs, half of which was in sterling gold; but nothing could give him any satisfaction while he knew that the days were passing, and that he was denied the opportunity of putting out his capital in advantageous investments, or securing a proper interest. Neither did Palmyrin Rosette find leisure to take any share in the mutual intercourse. His occupation was far too absorbing for him to suffer it to be interrupted, and to him, living as he did perpetually in a world of figures, the winter days seemed neither long nor wearisome. Having ascertained every possible particular about his comet, he was now devoting himself with equal ardor to the analysis of all the properties of the satellite Nerina, to which he appeared to assert the same claim of proprietorship. In order to investigate Nerina it was indispensable that he should make several actual observations at various points of the orbit; and for this purpose he repeatedly made his way up to the grotto above, where, in spite of the extreme severity of the cold, he would persevere in the use of his telescope till he was all but paralyzed. But what he felt more than anything was the want of some retired apartment, where he could pursue his studies without hindrance or intrusion. It was about the beginning of February, when the professor brought his complaint to Captain Servadac, and begged him to assign him a chamber, no matter how small, in which he should be free to carry on his task in silence and without molestation. So readily did Servadac promise to do everything in his power to provide him with the accommodation for which he asked, that the professor was put into such a manifest good temper that the captain ventured to speak upon the matter that was ever uppermost in his mind. “I do not mean,” he began timidly, “to cast the least imputation of inaccuracy upon any of your calculations, but would you allow me, my dear professor, to suggest that you should revise your estimate of the duration of Gallia’s period of revolution. It is so important, you know, so all important; the difference of one half minute, you know, would so certainly mar the expectation of reunion with the earth—” And seeing a cloud gathering on Rosette’s face, he added: “I am sure Lieutenant Procope would be only too happy to render you any assistance in the revision.” “Sir,” said the professor, bridling up, “I want no assistant; my calculations want no revision. I never make an error. I have made my reckoning as far as Gallia is concerned. I am now making a like estimate of the elements of Nerina.” Conscious how impolitic it would be to press this matter further, the captain casually remarked that he should have supposed that all the elements of Nerina had been calculated long since by astronomers on the earth. It was about as unlucky a speech as he could possibly have made. The professor glared at him fiercely. “Astounding, sir!” he exclaimed. “Yes! Nerina was a planet then; everything that appertained to the planet was determined; but Nerina is a moon now. And do you not think, sir, that we have a right to know as much about our moon as those ”—and he curled his lip as he spoke with a contemptuous emphasis—“know of theirs?” terrestrials “I beg pardon,” said the corrected captain. “Well then, never mind,” replied the professor, quickly appeased; “only will you have the goodness to get me a proper place for study?” “I will, as I promised, do all I can,” answered Servadac. “Very good,” said the professor. “No immediate hurry; an hour hence will do.” But in spite of this condescension on the part of the man of science, some hours had to elapse before any place of retreat could be discovered likely to suit his requirements; but at length a little nook was found in the side of the cavern just large enough to hold an armchair and a table, and in this the astronomer was soon ensconced to his entire satisfaction. Buried thus, nearly 900 feet below ground, the Gallians ought to have had unbounded mental energy to furnish an adequate reaction to the depressing monotony of their existence; but many days would often elapse without any one of them ascending to the surface of the soil, and had it not been for the necessity of obtaining fresh water, it seemed almost probable that there would never have been an effort made to leave the cavern at all. A few excursions, it is true, were made in the downward direction. The three leaders, with Ben Zoof, made their way to the lower depths of the crater, not with the design of making any further examination as to the nature of the rock—for although it might be true enough that it contained thirty per cent. of gold, it was as valueless to them as granite—but with the intention of ascertaining whether the subterranean fire still retained its activity. Satisfied upon this point, they came to the conclusion that the eruption which had so suddenly ceased in one spot had certainly broken out in another. February, March, April, May, passed wearily by; but day succeeded to day with such gloomy sameness that it was little wonder that no notice was taken of the lapse of time. The people seemed rather to vegetate than to live, and their want of vigor became at times almost alarming. The readings around the long table ceased to be attractive, and the debates, sustained by few, became utterly wanting in animation. The Spaniards could hardly be roused to quit their beds, and seemed to have scarcely energy enough to eat. The Russians, constitutionally of more enduring temperament, did not give way to the same extent, but the long and drear confinement was beginning to tell upon them all. Servadac, the count, and the lieutenant all knew well enough that it was the want of air and exercise that was the cause of much of this mental depression; but what could they do? The most serious remonstrances on their part were entirely in vain. In fact, they themselves occasionally fell a prey to the same lassitude both of body and mind. Long fits of drowsiness, combined with an utter aversion to food, would come over them. It almost seemed as if their entire nature had become degenerate, and that, like tortoises, they could sleep and fast till the return of summer. Strange to say, little Nina bore her hardships more bravely than any of them. Flitting about, coaxing one to eat, another to drink, rousing Pablo as often as he seemed yielding to the common languor, the child became the life of the party. Her merry prattle enlivened the gloom of the grim cavern like the sweet notes of a bird; her gay Italian songs broke the monotony of the depressing silence; and almost unconscious as the half-dormant population of Gallia were of her influence, they still would have missed her bright presence sorely. The months still glided on; how, it seemed impossible for the inhabitants of the living tomb to say. There was a dead level of dullness. At the beginning of June the general torpor appeared slightly to relax its hold upon its victims. This partial revival was probably due to the somewhat increased influence of the sun, still far, far away. During the first half of the Gallian year, Lieutenant Procope had taken careful note of Rosette’s monthly announcements of the comet’s progress, and he was able now, without reference to the professor, to calculate the rate of advance on its way back towards the sun. He found that Gallia had re-crossed the orbit of Jupiter, but was still at the enormous distance of 197,000,000 leagues from the sun, and he reckoned that in about four months it would have entered the zone of the telescopic planets. Gradually, but uninterruptedly, life and spirits continued to revive, and by the end of the month Servadac and his little colony had regained most of their ordinary physical and mental energies. Ben Zoof, in particular, roused himself with redoubled vigor, like a giant refreshed from his slumbers. The visits, consequently, to the long-neglected galleries of Nina’s Hive became more and more frequent. One day an excursion was made to the shore. It was still bitterly cold, but the atmosphere had lost nothing of its former stillness, and not a cloud was visible from horizon to zenith. The old footmarks were all as distinct as on the day in which they had been imprinted, and the only portion of the shore where any change was apparent was in the little creek. Here the elevation of the ice had gone on increasing, until the schooner and the tartan had been uplifted to a height of 150 feet, not only rendering them quite inaccessible, but exposing them to all but certain destruction in the event of a thaw. Isaac Hakkabut, immovable from the personal oversight of his property in the cavern, had not accompanied the party, and consequently was in blissful ignorance of the fate that threatened his vessel. “A good thing the old fellow wasn’t there to see,” observed Ben Zoof; “he would have screamed like a peacock. What a misfortune it is,” he added, speaking to himself, “to have a peacock’s voice, without its plumage!” During the months of July and August, Gallia advanced 164,000,000 leagues along her orbit. At night the cold was still intense, but in the daytime the sun, here full upon the equator, caused an appreciable difference of 20 degrees in the temperature. Like birds, the population spent whole days exposed to its grateful warmth, rarely returning till nightfall to the shade of their gloomy home. This spring-time, if such it may be called, had a most enlivening influence upon all. Hope and courage revived as day by day the sun’s disc expanded in the heavens, and every evening the earth assumed a greater magnitude amongst the fixed stars. It was distant yet, but the goal was cheeringly in view. “I can’t believe that yonder little speck of light contains my mountain of Montmartre,” said Ben Zoof, one night, after he had been gazing long and steadily at the far-off world. “You will, I hope, some day find out that it does,” answered his master. “I hope so,” said the orderly, without moving his eye from the distant sphere. After meditating a while, he spoke again. “I suppose Professor Rosette couldn’t make his comet go straight back, could he?” “Hush!” cried Servadac. Ben Zoof understood the correction. “No,” continued the captain; “it is not for man to disturb the order of the universe. That belongs to a Higher Power than ours!” 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. Jules Verne (1998). Off on a Comet! a Journey through Planetary Space. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/1353/pg1353-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

DREARY MONTHS

THE COST OF LIVING

The Art Of Writing & Speaking The English Language Word-Study, by Sherwin Cody is part of HackerNoon Books Series. You can jump to any chapter in this book here . CHAPTER VIII. CRITICISM: Matthew Arnold and Ruskin. The term “criticism” may appropriately be used to designate all writing in which logic predominates over emotion. The style of criticism is the style of argument, exposition, and debate, as well as of literary analysis; and it is the appropriate style to be used in mathematical discussions and all scientific essays. Of course the strictly critical style may be united with almost any other. We are presenting pure types; but very seldom does it happen that any composition ordinarily produced belongs to any one pure type. Criticism would be dull without the enlivening effects of some appeal to the emotions. We shall illustrate this point in a quotation from Ruskin. The critical style has just one secret: It depends on a very close definition of work in ordinary use, words do not have a sufficiently definite meaning for scientific purposes. Therefore in scientific writing it is necessary to define them exactly, and so change common words into technical terms. To these may be added the great body of words used in no other way than as technical terms. Of course our first preparation for criticism is to master the technical terms and technical uses of words peculiar to the subject we are treating. Then we must make it clear to the reader that we are using words in their technical senses so that he will know how to interpret them. But beyond that we must make technical terms as we go along, by defining common words very strictly. This is nicely illustrated by Matthew Arnold, one of the most accomplished of pure critics. The opening paragraphs of the first chapter of “Culture and Anarchy”―the chapter entitled “Sweetness and Light”―will serve for illustration, and the student is referred to the complete work for material for further study and imitation. From “Sweetness and Light.” The disparagers of culture, [says Mr. Arnold], make its motive curiosity; sometimes, indeed, they make its motive mere exclusiveness and vanity. The culture which is supposed to plume itself on a smattering of Greek and Latin is a culture which is begotten by nothing so intellectual as curiosity; it is valued either out of sheer vanity and ignorance, or else as an engine of social and class distinction, separating its holder, like a badge or title, from other people who have not got it. No serious man would call this culture, or attach any value to it, as culture, at all. To find the real ground for the very different estimate which serious people will set upon culture, we must find some motive for culture in the terms of which may lie a real ambiguity; and such a motive the word curiosity gives us. I have before now pointed out that we English do not, like the foreigners, use this word in a good sense as well as in a bad sense. A liberal and intelligent eagerness about the things of the mind may be meant by a foreigner when he speaks of curiosity, but with us the word always conveys a certain notion of frivolous and unedifying activity. In the Quarterly Review, some little time ago, was an estimate of the celebrated French critic, M. Sainte-Beuve, and a very inadequate estimate it in my judgment was. And its inadequacy consisted chiefly in this: that in our English way it left out of sight the double sense really involved in the word curiosity, thinking enough was said to stamp M. Sainte-Beuve with blame if it was said that he was impelled in his operations as a critic by curiosity, and omitting either to perceive that M. Sainte-Beuve himself, and many other people with him, would consider that this was praiseworthy and not blameworthy, or to point out why it ought really to be accounted worthy of blame and not of praise. For as there is a curiosity about intellectual matters which is futile, and merely a disease, so there is certainly a curiosity,―a desire after the things of the mind simply for their own sakes and for the pleasure of seeing them as they are,―which is, in an intelligent being, natural and laudable. Nay, and the very desire to see things as they are implies a balance and regulation of mind which is not often attained without fruitful effort, and which is the very opposite of the blind and diseased impulse of mind which is what we mean to blame when we blame curiosity. Montesquieu says: ‘The first motive which ought to impel us to study is the desire to augment the excellence of our nature, and to render an intelligent being yet more intelligent.’ This is the true ground to assign for the genuine scientific passion, however manifested, and for culture, viewed simply as a fruit of this passion; and it is a worthy ground, even though we let the term curiosity stand to describe it. Starting with exact definitions of words, it is easy to pass to exact definitions of ideas, which is the thing we should be aiming at all the time. The logical accuracy of our language, however, is apparent throughout. Matthew Arnold does not embellish his criticism, nor does he make any special appeal to the feelings or emotions of his readers. Not so Ruskin. He discovers intellectual emotions, and makes pleasant appeals to those emotions. Consequently his criticism has been more popular than Matthew Arnold's. As an example of this freer, more varied critical style, let us cite the opening paragraphs of the lecture “Of Queens' Gardens”——in “Sesame and Lilies”: From “Sesame and Lilies.” It will be well … that I should shortly state to you my general intention… The questions specially proposed to you in my former lecture, namely How and What to Read, rose out of a far deeper one, which it was my endeavor to make you propose earnestly to yourselves, namely, Why to Read I want you to feel, with me, that whatever advantage we possess in the present day in the diffusion of education and of literature, can only be rightly used by any of us when we have apprehended clearly what education is to lead to, and literature to teach. I wish you to see that both well directed moral training and well chosen reading lead to the possession of a power over the ill-guided and illiterate, which is, according to the measure of it, in the truest sense kingly;* conferring indeed the purest kingship that can exist among men. Too many other kingships (however distinguished by visible insignia or material power) being either spectral, or tyrannous; spectral―that is to say, aspects and shadows only of royalty, hollow as death, and which only the “likeness of a kingly crown have on;” or else tyrannous―that is to say, substituting their own will for the law of justice and love by which all true kings rule. *The preceding lecture was entitled “Of Kings's Treasures.” There is then, I repeat (and as I want to leave this idea with you, I begin with it, and shall end with it) only one pure kind of kingship, ―an inevitable or eternal kind, crowned or not,―the kingship, namely, which consists in a stronger moral state and truer thoughtful state than that of others, enabling you, therefore, to guide or to raise them. Observe that word “state” we have got into a loose way of using it. It means literally the standing and stability of a thing; and you have the full force of it in the derived word “statue”―“the immovable thing.” A king's majesty or “state,” then, and the right of his kingdom to be called a State, depends on the movelessness of both,―without tremor, without quiver of balance, established and enthroned upon a foundation of eternal law which nothing can alter or overthrow. Believing that all literature and all education are only useful so far as they tend to confirm this calm, beneficent, and therefore kingly, power,―first over ourselves, and, through ourselves, over all around us,―I am now going to ask you to consider with me further, what special portion or kind of this royal authority, arising out of noble education, may rightly be possessed by women; and how far they also are called to a true queenly power,―not in their households merely, but over all within their sphere. And in what sense, if they rightly understood and exercised this royal or gracious influence, the order and beauty induced by such benignant power would justify us in speaking of the territories over which each of them reigned as ‘Queens' Gardens.’ Here still is the true critical style, with exact definitions; but the whole argument is a metaphor, and the object of the criticism is to rouse feelings that will lead to action. It will be observed that words which by definition are to be taken in some sort of technical sense are distinguished to the eye in some way. Matthew Arnold used italics. Ruskin first places “state” within quotation marks, and then, when he uses the word in a still different sense, he writes it with a capital letter―State. Capitalization is perhaps the most common way for designating common words when used in a special sense which is defined by the writer―or defined by implication. This is the explanation of the capital letters with which the writings of Carlyle are filled. He constantly endeavors to make words mean more than, or something different from, the meaning they usually have. The peculiar embellishments of the critical writer are epigram, paradox, and satire. An epigram is a very short phrase or sentence which is so full of implied meaning or suggestion that it catches the attention at once, and remains in the memory easily. The paradox is something of the same sort on a larger scale. It is a statement that we can hardly believe to be true, since it seems at first sight to be self-contradictory, or to contradict well known truths or laws; but on examination we find that in a peculiar sense it is strictly true. Satire is a variation of humor peculiarly adapted to criticism, since it is intended to make the common idea ridiculous when compared with the ideas which the critic is trying to bring out: it is a sort of argument by force of stinging points. We may find an example of satire in its perfection in Swift, especially in his “Gulliver's Travels”―since these are satires the point of which we can appreciate to-day. Oscar Wilde was peculiarly given to epigram, and in his plays especially we may find epigram carried to the same excess that the balanced structure is carried by Macaulay. More moderate epigram may be found in Emerson and Carlyle. Paradox is something that we should use only on special occasion. 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. Cody, Sherwin, 2007. The Art Of Writing & Speaking The English Language Word-Study. Urbana, Illinois: Project Gutenberg. Retrieved April 2022 from https://www.gutenberg.org/cache/epub/19719/pg19719-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 . Photo by on Waldemar Brandt Unsplash

The Art Of Writing & Speaking The English Language : Chapter VIII - Criticism

The Essays of Adam Smith: THE IMITATIVE ARTS - Part II

The Essays of Adam Smith, by Adam Smith is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here . Part I, SEC. I, Chapter II: Of the Pleasure of mutual Sympathy Chapter II. Of the Pleasure of mutual Sympathy BUT whatever may be the cause of sympathy, or however it may be excited, nothing pleases us more than to observe in other men a fellow-feeling with all the emotions of our own breast; nor are we ever so much shocked as by the appearance of the contrary. Those who are fond of deducing all our sentiments from certain refinements of self-love, think themselves at no loss to account, according to their own principles, both for this pleasure and this pain. Man, say they, conscious of his own weakness, and of the need which he has for the assistance of others, rejoices whenever he observes that they adopt his own passions, because he is then assured of that assistance; and 14 grieves whenever he observes the contrary, because he is then assured of their opposition. But both the pleasure and the pain are always felt so instantaneously, and often upon such frivolous occasions, that it seems evident that neither of them can be derived from any such self-interested consideration. A man is mortified when, after having endeavoured to divert the company, he looks round and sees that nobody laughs at his jests but himself. On the contrary, the mirth of the company is highly agreeable to him, and he regards this correspondence of their sentiments with his own as the greatest applause. Neither does his pleasure seem to arise altogether from the additional vivacity which his mirth may receive from sympathy with theirs, nor his pain from the disappointment he meets with when he misses this pleasure; though both the one and the other, no doubt, do in some measure. When we have read a book or poem so often that we can no longer find any amusement in reading it by ourselves, we can still take pleasure in reading it to a companion. To him it has all the graces of novelty; we enter into the surprise and admiration which it naturally excites in him, but which it is no longer capable of exciting in us; we consider all the ideas which it presents rather in the light in which they appear to him, than in that in which they appear to ourselves, and we are amused by sympathy with his amusement which thus enlivens our own. On the contrary, we should be vexed if he did not seem to be entertained with it, and we could no longer take any pleasure in reading it to him. It is the same case here. The mirth of the company, no doubt, enlivens our own mirth, and their silence, no doubt, disappoints us. But though this may contribute both to the pleasure which we derive from the one, and to the pain which we feel from the other, it is by no means the sole cause of either; and this correspondence of the sentiments of others with our own appears to be a cause of pleasure, and the want of it a cause of pain, which cannot be accounted for in this manner. The sympathy, which my friends express with my joy, might, indeed, give me pleasure by enlivening that joy: but that which they express with my grief could give me none, if it served only to enliven that grief. Sympathy, however, enlivens joy and alleviates grief. It enlivens joy by presenting another source of satisfaction; and it alleviates grief by insinuating into the heart almost the only agreeable sensation which it is at that time capable of receiving. It is to be observed accordingly, that we are still more anxious to communicate to our friends our disagreeable than our agreeable passions, that we derive still more satisfaction from their sympathy with the former than from that with the latter, and that we are still more shocked by the want of it. How are the unfortunate relieved when they have found out a person to whom they can communicate the cause of their sorrow? Upon his sympathy they seem to disburthen themselves of a part of their 15 distress: he is not improperly said to share it with them. He not only feels a sorrow of the same kind with that which they feel, but, as if he had derived a part of it to himself, what he feels seems to alleviate the weight of what they feel. Yet by relating their misfortunes they in some measure renew their grief. They awaken in their memory the remembrance of those circumstances which occasion their affliction. Their tears accordingly flow faster than before, and they are apt to abandon themselves to all the weakness of sorrow. They take pleasure, however, in all this, and, it is evident, are sensibly relieved by it; because the sweetness of his sympathy more than compensates the bitterness of that sorrow, which, in order to excite this sympathy, they had thus enlivened and renewed. The cruellest insult, on the contrary, which can be offered to the unfortunate, is to appear to make light of their calamities. To seem not to be affected with the joy of our companions is but want of politeness; but not to wear a serious countenance when they tell us their afflictions, is real and gross inhumanity. Love is an agreeable, resentment a disagreeable, passion; and accordingly we are not half so anxious that our friends should adopt our friendships, as that they should enter into our resentments. We can forgive them though they seem to be little affected with the favours which we may have received, but lose all patience if they seem indifferent about the injuries which may have been done to us: nor are we half so angry with them for not entering into our gratitude, as for not sympathizing with our resentment. They can easily avoid being friends to our friends, but can hardly avoid being enemies to those with whom we are at variance. We seldom resent their being at enmity with the first, though upon that account we may sometimes affect to make an awkward quarrel with them; but we quarrel with them in good earnest if they live in friendship with the last. The agreeable passions of love and joy can satisfy and support the heart without any auxiliary pleasure. The bitter and painful emotions of grief and resentment more strongly require the healing consolation of sympathy. As the person who is principally interested in any event is pleased with our sympathy, and hurt by the want of it, so we, too, seem to be pleased when we are able to sympathize with him, and to be hurt when we are unable to do so. We run not only to congratulate the successful, but to condole with the afflicted; and the pleasure which we find in the conversation of one whom in all the passions of his heart we can entirely sympathize with, seems to do more than compensate the painfulness of that sorrow with which the view of his situation affects us. On the contrary, it is always disagreeable to feel that we cannot sympathize with him, and instead of being pleased with this exemption from sympathetic pain, it hurts us to find that we cannot share his uneasiness. If we hear a person loudly lamenting his misfortunes, which however, upon bringing the case home to ourselves, we feel, can produce 16 no such violent effect upon us, we are shocked at his grief; and, because we cannot enter into it, call it pusillanimity and weakness. It gives us the spleen, on the other hand, to see another too happy or too much elevated, as we call it, with any little piece of good fortune. We are disobliged even with his joy; and, because we cannot go along with it, call it levity and folly. We are even put out of humour if our companion laughs louder or longer at a joke than we think it deserves; that is, than we feel that we ourselves could laugh at it. 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. Smith, Adam. 2018. The Essays of Adam Smith. Urbana, Illinois: Project Gutenberg. Retrieved May 2022 from https://www.gutenberg.org/files/58559/58559-h/58559-h.htm#page13 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 Essays of Adam Smith: Part I, SEC. I, Chapter II - Of the Pleasure of mutual Sympathy

This is today's reality: Artificial intelligence has already made a lot of buzz in the mobile app development industry. More cheap and available screens, easy real-time access to the data robust analysis tools have become even more powerful - all of this is already a normal part of our daily routine in society. We're approaching the stage of smart everything The implementation of Artificial Intelligence is highly varied, depending on specific industries. Chatbots, spam filters, cloud APIs, natural language processing, face recognition robotics and more are expected to be used for many goals. For example, installing chatbots into retail screens and POSs, inserting built-in intelligence to popular business applications, and integrating with an environment-aware smart detector. And, what is most important, AI will become such a normal thing in everyday life that it's already used in small shops and many industrial factories. AI will become more affordable Apple is taking a shot at a processor planned exclusively to perform AI-related errands, and it will, in the long run, be remembered for huge numbers of its smart gadgets, including iPhones and iPads. Curiously, this is one case where Apple isn't driving the way - partly because it's playing catch up for the lost time. Qualcomm, as of late, discharged the Snapdragon 835 portable processor, which has a module for AI and taking care of man-made brainpower assignments. Snapdragon processors are utilized in numerous Android telephones. Google revealed an AI-related processor back to the Tensor Processor Unit in 2016, in spite of the fact that, until further notice, the chip is utilized in Google server farms for picture acknowledgment and to convey quicker, better query items. Don't be astounded if Google builds up an AI-centered portable processor too. Simultaneously, the cost of sensors has come down, making them affordable for the cheapest vehicles and devices, yet it can be used for anything – from modern machines to shoes, to drink bottles. Increasingly reasonable sensors, implanted in everything from apparel to hardware, will add data to a regularly developing system of associated gadgets. For instance, Wearable X reported $299 yoga pants that will use implanted sensors and a versatile application to help yogis of all capacities to improve their flexibility, etc.. Indeed, even the most costly classes of sensors are seeing costs fall. In April, Velodyne reported another, minimal effort strong state LiDAR framework called Velarray, which could make self-driving autos a modern reality. The sensor is equipped for perceiving objects with low reflectivity up to 200 meters away, flaunts improved vertical and flat fields of view than forerunners, and expects to sell for only two or three hundred bucks when delivered in mass volumes. We'll likely see sensors transmitting information on everything from street conditions, to office use and modern hardware. Undertakings, which are utilizing more mobile phones and wearables than any time in recent history, should transmit, gather and investigate this information to settle on always making smart choices – and they'll be needing to do it progressively. Keen hardware representatives with smartphones on the shop should be fixed, sensors cautioning field administration workers with smartphones that they have to make a quick help call.  This is only the tip of the iceberg. This new equipment is clear proof that AI is genuinely turning into an ordinary reality. Be that as it may, while the equipment improvements are basic, the other piece of the story is the product – or the applications. When we gather the information from every one of these sensors, AI will help examine and instruct representatives on the next steps, yet the venture work environment has changed. Representatives have moved away from work area PCs and paper-and-clipboards and are accomplishing increasingly more business on their smartphones. Conventional business application designers must think of AI as far as versatile applications. What separates mobile apps out from web applications on PCs is their capacity to know about their location and the external world utilizing these sensors. Combining AI with these abilities makes applications significantly more valuable. Here are several cases of AI and machine learning contributions from the sensors that make proposals on what you ought to do alongside with the intensity of a portable application: AI implementation across the industries Buoy Health is an AI-based symptom and medicine controller that applies computer algorithms to identify and cure sicknesses. The principle: a chatbot receives a person’s symptoms and information from their medical companies.  The AI then leads that person to the right protection in accordance with its analysis. Harvard Medical School already uses Buoy’s AI to diagnose and immediately and more smoothly handle many cases. AI implementation in Healthcare creates deep learning medical applications to modernize radiology examinations. The AI platform interprets disorganized therapeutic info (radiology pictures, blood analyses, EKGs, patient therapeutic records) to provide doctors with more useful penetration into a patient’s interactive requirements. Enlitic Komatsu offers a solution, SmartConstruction, that associates work data concerning gear and individuals. SmartConstruction enables clients to review their workplace data by means of automaton or 3D scanning to deliver a 3D model of current conditions, bringing the information into KomConnect to consequently recreate the making of development plans, and afterward transmitting the information into mechanized, driverless, earth-moving vehicles to computerize exhuming. Komatsu's shrewd machine control can wipe out human mistakes and forestall security perils. Simultaneously, Komatsu's versatile application enables clients to see machine data from the Komtrax armada observing framework on their Apple or Android cell phones or tablet gadgets. AI implementation in Development Kasisto has built an AI platform, KAI, which allows banks to create their own personal chatbots and virtual assistants. It’s entrenched in AI logic and human-language recognition and creation, which would imply that it can deal with complex issues about finance and banking service. AI implementation in Banking Here is another example of using AI in financial applications: Simudyne is a tech vendor that applies AI and machine learning to reach thousands of business situations. Simudyne's program enables financial companies to manage resistance testing examinations and manage the risks for market contamination on huge ranges. The CEO of the corporation announced that the simulation supports investment managers to avoid risks and improve critical debt provisioning for the purpose of making better-informed judgments. How to apply AI in current mobile applications Even a few years ago, many revolutionary and innovative technologies needed years to be implemented to business, however, today you can begin easily and reach your goals much more quickly. Business developers all across the globe are already supporting businesses to enhance independence and modernize enterprise processes with restricted resources, by using AI with modern UI design in current applications. How can developers begin to use artificial intelligence for their applications to enhance UX and reduce the chances of failure: Traditionally, signals from mobile apps are set on a timer, but AI provides alerts or warnings set on customers' circumstances or interactive actions. Real-time and Immediate Alerts Applications can make smart, personalized notes relied on various operations and actions that are obtained and then interpreted. Custom Notification Apps can read the shopping habits and routines of customers and are able to provide owners with valuable information to aid them in their business operations. Self-Reading Applications that use AI can do even more than “enough” to create a comfortable and safe environment. For example, such an app can turn off an air conditioner or electric outlet even when a person is not in the house. Smart Actions When AI is integrated into a search engine it is able to make more habitual and less painful processes for mobile users to fasten a search, suggest searching options and grammar improvements, and amplify voice search.. To sum up, applying Artificial Intelligence (AI) to a mobile app can constantly help to streamline the searching process for mobile users. Streamline the searching process For the largest app development corporations, fingertips and face recognition are the most common AI technologies. Artificial intelligence reads eye position, nose size, a form of the lips, etc. Those people who are using the latest iPhones are already familiar with facial identification technology. Google goes further with FaceNet. On the broadly applied Labeled Faces in the Wild (LFW) dataset, they reached a new high certainty of 99.63%. Using an AI and a smart algorithm has managed to associate a face to its owner with excellent accuracy. Face recognition and authentication As reported by Gartner in the , the amount of organizations realizing AI technologies has increased 2.7 times from 2015. 2019 CIO Survey Artificial intelligence (AI) is the most discussed technology and one-ups the data and analytics as a ground-breaking technology. In terms of realization, when told about the company’s projections about coming digital technologies and tendencies, 37% answered that they already extended AI. Actually, AI appears in second, behind cybersecurity (88 %). The Future of Artificial Intelligence in Mobile App Development Based on the Statista report, by the year 2025, the growth of the artificial intelligence (AI) software market worldwide will reach $89 billion. However, the embedding of AI in the mobile application is in the initial phases. There is a lot that can still be changed and modified. Your complete UX can be formed by applying AI and then handled in various mobile apps. We can see a remarkable increase in AI use in 2020 already. AI has already started to integrate into 5G smartphones which means that the customers can get more privileges of ML and AI to gather, maintain, and handle real-time information. With the purpose of getting the new impressive functions in a mobile app, you should think about choosing mobile developers for your company. Here at KitRUM , we're always happy to hear from you and help you to get access to the top 5% of most talented application developers. Previously published at https://kitrum.com/blog/artificial-intelligence-in-mobile-app-development-upcoming-trends-for-2020/

Artificial Intelligence in Mobile App Development in 2020

Part 20 of where I interview my heroes. The series Index to “Interviews with ML Heroes” Today, I’m super excited to be interviewing one of the domain experts in Practice: A Radiologist, a great member of the fast.ai community and a kaggle expert: . Medical Dr. Alexandre Cadrin-Chenevert Alexandre is an MD, Radiologist and a Computer Engineer. He is also a Deep Practitioner, Kaggle Competition Expert (Ranked #72). He is actively working in the application of Deep Learning in the Medical Domain. Learning About the Series: I have very recently started making some progress with my . But to be honest, it wouldn’t be possible at all without the amazing community online and the great people that have helped me. Self-Taught Machine Learning Journey In this Series of Blog Posts, I talk with People that have really inspired me and whom I look up to as my role-models. The motivation behind doing this is, you might see some patterns and hopefully you’d be able to learn from the amazing people that I have had the chance of learning from. **Sanyam Bhutani:**​ Hello Alexandre, Thank you so much for taking the time to do this interview. Hi Sanyam! Thank you very much for your kind invitation. Dr. Alexandre Cadrin-Chenevert: **Sanyam Bhutani:**​ Today you’re working as a radiologist and are actively working on ML Research focused on your domain. Can you tell us when did Deep Learning first come into the picture, What got you interested in Deep Learning at first? Back in 2016, after 7 years of clinical practice in radiology, I read some information about a startup company based in San Francisco named Enlitic. They were using deep learning algorithms to improve the quality and accessibility of medical imaging services. I got even more curious when I realized that the founder Jeremy Howard was working with Rachel Thomas on the first iteration of a free online deep learning course named fast.ai. Dr. Alexandre Cadrin-Chenevert: During my early clinical years in a public and well-funded healthcare system in Canada, I observed a significant divergence between the growing needs and allowed resources for healthcare services in the aging population. That was particularly true in radiology. In this context, the opportunity offered by deep learning applied to computer vision was absolutely compelling in my field. With a worldwide perspective, this opportunity was, even more promising to balance the accessibility and universality of healthcare services. Almost everything in my life was reasonably suggesting me to stay with a high-level perspective about deep learning. But, instead of approaching this subject in the surface, I decided to wake up the young software developer that I was almost 20 years ago and I dove deeply in the field, mostly on the computer vision side. Online courses (fast.ai, Coursera) and open science (arxiv, Kaggle, open access articles) were definitely mandatory to make this self-learning possible after work hours. You’re working on an amazing intersection of Radiology, Healthcare, and Deep Learning. Sanyam Bhutani: Could you tell us more about your Research? The research community around deep learning in radiology is mainly formed by the union of 3 different research communities: clinical radiology, medical imaging analysis, and deep learning communities. Learning algorithms force all these groups to collaborate together to develop useful and meaningful solutions to clinical problems. Dr. Alexandre Cadrin-Chenevert: The clinical radiology community is currently discovering tons of supervised learning classification applications, mostly in subspeciality silos. The medical imaging analysis has a strong background with segmentation tasks and is massively moving to fully convolutional encoder-decoder networks with U-Net as a very popular and effective instance. The deep learning community in computer vision is definitely more focused on unsupervised learning algorithms. Consequently, my main research interests are at the intersection of these research communities. First, I have a strong interest in creating publicly available and strongly labeled datasets. Radiologists can lead this path by defining useful clinical tasks and curating large datasets available to the research community. For example, I have total respect for Ron Summers and his team at NIH who created the public dataset with more than 112000 labeled images. Stanford is also paving the way with different large labeled datasets (Bone age, ). There are many more initiatives in this way including the recent RSNA Pneumonia detection dataset. Of course, ethical issues, like strong deidentification and data security, are challenging issues to overcome. ChestXray upper extremity radiographs I also have a special interest in object detection tasks. Convolutional neural networks are basically mapping spatial information into semantic information. I consider the main computer vision tasks, classification, detection, and segmentation as different operating points in this spatial-to-semantic continuum. In my opinion, for supervised learning algorithms in medical imaging, the object detection task is the clinically useful sweet spot between spatial and semantic information. Classifications tasks, even if very useful, lack some objective spatial interpretability needed by radiologists. There are many known solutions to the interpretability issue with classification networks, but most of them are subjective. Object detection with a bounding box offers implicit and objective measurable interpretability which is a great benefit. On the other side, segmentation task offers the best of spatial interpretability but with a lot more resources needed to label images and with a tradeoff on semantic information. Finally, I also have an interest in unsupervised learning algorithms in medical imaging. Normal anatomy in humans has a relatively low statistical variance. On the other side, pathology frequently has high variance with very different visual representations. Cost of strong labeling in medical imaging is still relatively high, which is a limiting factor to implement some supervised learning algorithms. Consequently, there is definitely a significant place for anomaly detection with unsupervised learning algorithms. As a Domain Expert, what are your thoughts of using an ML Algorithm in an application where a Human Life is under consideration? Sanyam Bhutani: How can we ensure that we’re building the right tools or what steps should we take to ensure the Technology works in a positive manner? For the available resources, most societies want the best health outcome for their population. Dr. Alexandre Cadrin-Chenevert: When accessibility is not a huge problem, you basically want to improve the quality of health services to improve the outcome. There is an incredible potential to improve the quality of health services by combining the work of a medical expert with the output of an ML algorithm. For example, in radiology just lowering the variability of human interpretation can be a significant beneficial step. When accessibility is the main bottleneck, then ML algorithms can potentially help to triage the population into different levels of health priority. This ultimately can improve accessibility and outcome of your population with the same amount of human and financial resources. Of course, there is a significant statistical trap with machine learning algorithms. And this trap is even wider with deep learning algorithms. Data scientists usually present performance metrics of a trained algorithm based on an unseen test dataset. This is the usual proof of performance generalizability of an algorithm. But in healthcare, in clinical practice, there are tons of non-controlled and hidden variables in the data that can be very different compared to the test data distribution. These variables are related to genetic, gender, age, risk factors, symptoms, types of acquisition machines and many more. This is inducing a bias between the test and the clinical data. And this bias is frequently not directly measurable. But this bias in clinical data can generate very large variations of performance of the ML algorithm. So, I really recommend to anyone who considers using an ML/DL algorithm in clinical practice to strongly validate the reported performance on its own data. But this validation process can be very time consuming and difficult because most of the clinical institutions are not organized to do this kind of validation. But, I am definitely convinced about this need; I would even recommend explicit local validation even if an algorithm is already cleared by national regulators. Congratulations on your recent Kaggle win in the . Sanyam Bhutani: RSNA Pneumonia challenge Could you tell us about your kaggle journey? What are your thoughts about using kaggle as a testbed to enhance your DL skillset? Thanks Sanyam. My teammate and I learned so much during this intense Kaggle challenge. Dr. Alexandre Cadrin-Chenevert: Ian Pan Participating in Kaggle competitions was of paramount importance in my learning curve of deep learning. Maturation of almost any learning process is based on the transformation of knowledge to skills, or concept to actions. Each competition is an opportunity to evolve in this direction. But, naturally, the tradeoff is the time you invest in these competitions. So my own perspective was to participate in a limited number of computer vision competitions selected to catch efficiently most of the potential benefit. It is an ideal scenario to apply the needed iterative process of trying experiments by yourself to solve the problem, and then coming back to the forums to learn from the kernels and the forums posts. If you discover an interesting kernel then try to deeply understand what it is doing and why it was written that way. Some of these kernels are hidden gems and you need to meticulously unpack them to discover the value. I’m also honored to be a fellow student of yours in the fast.ai community. Sanyam Bhutani: Could you share your fast.ai experience? What are your thoughts about using the new library (v3) in Medical research? It is fascinating to see how the fast.ai students are tightly bound together. We are like a big international family. The democratization of deep learning is an important social mission and we are all part of it. Dr. Alexandre Cadrin-Chenevert: Since 2016, the different versions of the fast.ai courses changed significantly and the fast.ai library was written from scratch. In my opinion, that blog from Jeremy was historical in the transformation. post But the fundamental concept was always to allow learning with a top-down approach which is a specific characteristic of the course and the library. It allows usage of very powerful deep learning tools and techniques with a minimum amount of code. Consequently, beyond the teaching benefit, the fast.ai library is an amazing fast-prototyping library to rapidly train deep learning models with a reasonable amount of computing resources. This high-level concept of fast.ai over pytorch is similar to Keras over Tensorflow. In medical research, the number of applications of deep learning is huge. But for all these potential applications, it is not always possible to bring together an experienced team of data scientists with an experienced medical research team. The fast.ai course and library can definitely help to lower the bar to implement medical research. That is why I recommend the course to all the tech-savvy radiologists and physicians that are interested to learn more about deep learning. I hear many histories of radiologists who are learning python to follow fast.ai and bring these concepts to actions. And if someone has an interesting idea of medical application but without any coding experience, I still recommend to follow the course with a friend who is coding. Just a pair formed by a computer scientist and a physician is enough to start medical research with the fast.ai course and library as a communication catalyzer. What developments are you most excited about in the ML and Medical Research intersection? Sanyam Bhutani: Beyond the predictive tasks of machine learning, I’m really excited by new statistical correlations not directly visible by the human eye that are found by machine learning algorithms. These new correlations in the data will push us to find new causal links to better understand and explain the observations. Dr. Alexandre Cadrin-Chenevert: Merging genetic and imaging databases is particularly promising and will most likely allow us in a not so distant future to find tons of new anatomical, physiological and pathological correlations to better understand the evolution of the human body. What is your best advice for a non-domain expert looking to apply ML to medical studies? Sanyam Bhutani: Don’t work in silos. Applying machine learning to a medical problem is a multi-disciplinary challenge. Communicate and share your ideas with clinical experts. They will add invaluable insights and will save you precious time. Dr. Alexandre Cadrin-Chenevert: How do you stay up to date with the cutting edge? Sanyam Bhutani: Quantity of research in deep learning and reinforcement learning is growing exponentially. So it is not humanly possible to follow everything in all sub-areas of research. So choosing a specific interest is the first step to keep some control. Dr. Alexandre Cadrin-Chenevert: Then, you need to find some structured sources to be informed of the most important advances related to your interest. In my case, I’m mostly using , twitter, Kaggle, best conference paper awards, fast.ai forums, and medical journals to keep the pace. Eventually, your own filters will become sharper to divide the important information from the background noise. arxiv-sanity Before we conclude, any advice for the beginners who even though are excited about the field, feel overwhelmed to even get started with Deep Learning? Sanyam Bhutani: To conclude, I’ll cite this guy, named Steve Jobs, who is a lot better than myself to give advices: Dr. Alexandre Cadrin-Chenevert: “You’ve got to find what you love. And that is as true for your work as it is for your lovers. Your work is going to fill a large part of your life, and the only way to be truly satisfied is to do what you believe is great work. And the only way to do great work is to love what you do. If you haven’t found it yet, keep looking. Don’t settle. As with all matters of the heart, you’ll know when you find it. And, like any great relationship, it just gets better and better as the years roll on. So keep looking until you find it. Don’t settle.” TL;DR Don’t settle. Thank you so much for doing this interview. Sanyam Bhutani: If you found this interesting and would like to be a part of My Learning Path , you can find me on Twitter here . If you’re interested in reading about Deep Learning and Computer Vision news, you can checkout my newsletter here .

Interview with Radiologist, fast.ai fellow and Kaggle expert: Dr. Alexandre Cadrin-Chenevert

What I Would Have Missed in December 2017

What I Would Have Missed in November 2017

Will the computer see you now ? What we learnt from the ChexNet paper for pneumonia diagnosis … Written by Judy Gichoya & Stephen Borstelmann MD In December 2017 , we (radiologists both in training, staff radiologists and AI practitioners) discussed our role as knowledge experts in world of AI, summarized here . For the month of January, we addressed the performance of algorithms for disease diagnosis , specifically focusing on the paper by the stanford group — We continue to generate a large interest in the journal club , with 347 people registered , 150 of whom signed on January 24th 2018 to participate in the discussion. https://becominghuman.ai/radiologists-as-knowledge-experts-in-a-world-of-artificial-intelligence-summary-of-radiology-ec63a7002329 deep learning CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. The paper has had 3 revisions and is available here . Like many deep learning papers that claim super human performance , the paper was widely circulated in the news media, several blog posts , on and twitter. https://arxiv.org/abs/1711.05225 reddit Please note that the findings of superhuman performance are increasingly being reported in medical AI papers. For example, denotes that “Medical AI May Be Better at Spotting Eye Disease Than Real Doctors” this article To help critique the ChexNet paper , we constituted a panel composed of the team (most of the authors listed on the paper were kind enough to be in attendance — thank you!), Dr. Luke( ) and Dr. Paras ( ) who had critiqued the used and Jeremy Howard (past president and chief scientist of , a data analytics competition site, Ex-CEO of a healthcare imaging company, and the Current CEO of , a deep learning educational site) to provide insight to deep learning . author blog blog data Kaggle Enlitic, Fast.ai methodology In this blog we summarise the methodology of reviewing medical AI papers. Radiology 101 The ChexNet paper reviews performance of AI versus 4 trained radiologists in diagnosing pneumonia. Pneumonia is a clinical diagnosis — a patient will present with fever and cough , and can get a chest Xray(CXR) to identify complications of pneumonia. Patients will usually get blood cultures to supplement diagnosis. Pneumonia on a CXR is not easily distinguishable from other findings that fill the alevolar spaces — specifically pus , blood , fluid or collapsed lung called atelectasis. The radiologists interpreting these studies can therefore use terms like infiltrates , consolidation and atelectasis interchangeably. Show me the data The data used for this study is the ChestX-ray14 dataset which is the largest publicly available imaging data set that consists of 112,120 frontal chext xray radiographs of 30,805 unique patients and expands the ChestX-Ray 8, described . Each radiograph is labeled with one or more of 14 different pathology labels, or a ‘no finding’ label. by Wang, et. al Labeling of the radiographs was performed using Natural Language Processing (NLP) by mining the text in the reports. Individual case labels were not assigned by humans. radiology : Labeling medical data remains a big challenge especially because the radiology report is a tool for communicating to ordering doctors and not a description of the images. For example , in an ICU film with a central line, tracheostomy tube and chest tube may be reported as “stable lines and tubes” without detailed description of the every individual finding on the CXR. This can be missclassified by NLP as a study without findings. This image-report disconcordance occurs at a high rate on this dataset. Critique Moreover reportable findings could be ignored by the NLP technique and/or labeling schema, either through error or pathology outside of one of the 14 labels. The paper’s claims of 90%+ NLP mining accuracy do not appear to be accurate. (SMB,LOR,JH). One of the panelists — Luke reviewed several hundred examples and found the NLP labeling about 50% accurate overall compared to the image, with the pneumonia labeling worse — 30–40%. Jeremy Howard notes that the use of an old NLP tool contributes to the inaccuracy due to the preponderance of ‘No Findings’ cases in the dataset skewing the data — he doesn’t think that the precision of normal findings in this dataset is likely improved over random. Looking at the pneumonia label, it is only 60% accurate. A lot of the discrepancy can be drawn back to the core NLP method, which he characterized as “massively out of date and known to be inaccurate”. He feels a re-characterization of the labels with a more up-to-date NLP system is appropriate. Chest Xray showing a tracheostomy tube , right internal jugular dialysis line and diffuse infiltrates likely pulmonary edema. The lines and tubes for an ICU patient are easily reported as “Stable” The stanford group tackled the labeling challenge by having 4 radiologists (one specializing in thoracic imaging and 3 non thoracic radiologists) assign labels to a subset of the data for training created through a stratified random sampling, for a minimum of 50 positive cases of each label, with a final N=420. : The ChestXRay14 contains many patients with only one radiograph but those who had multiple studies tended to have many. While the text-mined reports may match clinical information, any mismatch between the assigned label and radiographic appearance hurts the predictive power of the dataset. Critique Moreover , what do the labels actually mean? Dr. Oakden-Rayner questions what the labels mean — do they mean a radiologic pneumonia or a clinical pneumonia? In an immunocompromised patient, radiography of a pneumonia might be negative, largely because the patient cannot mount an immune response to the pathogen. This does not mean that the clinical diagnosis of pneumonia is inaccurate. The imaging appearance and clinical appearance/diagnosis therefore would not match. The closeness of four of the labels: Pneumonia, Consolidation, Infiltration, and Atelectasis introduces a new level of complexity. Pneumonia is a subset of consolidation and infiltration is a superset of consolidation. While the dataset labels these as 4 separate entities, to the radiologic practitioner they may not be separate at all. It is important to have experts look at images when doing an image classification task. See a great summary of the data problems on this posting from Luke who was one of the panelists . blog here Model The CheXNet algorithm is a 121-layer deep 2D Convolutional Neural Network; a after . The Densenet’s multiple residual connections reduce parameters and training time, allowing a deeper, more powerful model. The model accepts a vectorized two-dimensional image of size 224 pixels by 224 pixels. Densenet Huang & Liu Densenet connection To improve trust in CheXNet’s output, a Class Activation Mapping (GRAD-CAM) heatmap was utilized after . This allows the human user to “see” what areas of the radiograph provide the strongest activation of the Densenet for the highest probability label. Zhou et al : Jeremy notes that image preprocessing of resizing to 224x224 pixel size images and adding random horizontal flips is fairly standard, but leaves room for potential improvement, as effective data augmentation is one of the best ways to improve a model. Image downsizing to 224x224 is a known issue — both from research and practical experience at Enlitic, larger images perform better in medical imaging (SMB: Multiple top 5 winners of the 2017 RSNA Bone age challenge had image sizes near 512x512). Mr. Howard feels there is no reason to leave trained models this size any longer. Regarding the model choice, the Densenet model is adequate, but in the last 12 months have shown significant improvement (50%) over older models. Critique Imagenet NasNets Pre-trained Imagenet weights were used, which is fine & a standard approach; but Jeremy felt it would be nice if we had a medical imagenet for some semi-supervised training of an encoder or a to patients — leaving room for improvement. Consider that Imagenet consists of color images of dogs, cats, planes and trains — and we are getting great results on X-rays? While better than nothing, pretrained network trained on medical images in any modality would probably perform superiorly. AutoML siamese network cross validate ANY The Stanford team’s was to train on multiple labels at the same time — it is best to build a single model that predicts multiple classes — counterintuitive, but bears out in deep learning models, and likely responsible for their model yielding better results than prior studies. . best idea The more classes you train the model on properly, the better results you can expect Results were used to evaluate both CheXNet model and the Stanford Radiologists. F1 scores Calculating F1 score Each Radiologists’ F1 score was calculated by considering the other three radiologists as “ground truth.” ChexNet’s F1 score, was calculated vs. all 4 radiologists. A was added to yield 95% confidence intervals. bootstrap calculation CheXnet’s results are as follows: Results From the results, ChexNet outperforms human radiologists. The varying F1 scores can be interpreted to imply that for each study , 4 radiologists do not seem to agree with each other on findings. However there is an outlier (rad 4 — with an F score of 0.442) who is the thoracic trained radiologists who performs better than the ChexNet. Moreover CheXNet has State of the Art (SOTA) performance on all 14 pathologies compared to prior publications. In my search , the Machine Intelligence Lab, Institute of Computer Science & Technology, Peking University, directed by Prof. Yadong Mu reports superior performance than the Stanford group. The code is open source and available here — https://github.com/arnoweng/CheXNet Results from various implementations of ChexNet — Various studies that assess cognitive fit show that human performance can be affected by lack of clinical information or prior comparisons that may affect their performance. Moreover, before the most recent version of the paper, human performance was unfairly scored against the machine. Critique Clinical significance With the majority of labelled CXRs with pneumothorax having chest tubes present, the question must be raised: “are we training the Densenet to recognize pneumothoraces or chest tubes?” Peer review Luke Oakden-Rayner MD, a radiologist in Australia with expertise in AI & deep learning who was on our panel independently , and . He praises the , and their willingness to modify the paper to correct a methodologic flaw which biased against evaluating radiologists. evaluated the ChestXRay-14 dataset CheXNet Stanford team for their openness and patience in discussing the paper’s methodology Summary For the second AI journal club we analysed the pipeline of AI papers in medicine. You must make sure you are asking the right clinical question to be answered and not doing algorithms for the sake of doing something. Thereafter understand whether your data will help you answer the question you have, looking into details on how the data was collected and labeled. To determine human level or super human performance, ensure the baseline metrics are adequate and not biased against one group. Pipeline for AI in medicine The model appears to give at-human performance for experts, or better than human performance for less-trained practitioners. This is in line with research findings and Enlitic’s experience. We should not be surprised by that; the research in Convolutional Neural Networks has consistently reported near-human or super-human performance consistently. Take Aways There is exists a critical gap in the labeling of medical data. Do not forget the clinical significance of your results. Embrace peer review especially in medicine and AI These were the best tweets regarding the problem of labeling medical data — aka do not get discouraged to attempt deep learning for medicine. Labeling challenge The journal club was a success, so if you are a doctor or an AI scientist , join us at to continue with the conversations on AI and medicine. You can listen to the recording of this journal club here : . Our next guest is who worked on US demographic household prediction using Google Street view images on 22nd February 2018. She will be talking on ). https://tribe.radai.club https://youtu.be/xoUpKjxbeC0 Timnit Gebru Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States ( http://www.pnas.org/content/114/50/13108 Coming soon For the journal club we developed a human versus AI competition for interepreting the CXRs in the dataset hosted at . We will be publishing the outcome of our crowdsourced labels soon, with a detailed analysis to check whether the model performance improves. https://radai.club Say thanks This I would like to thank the panelists including , , , and the . Thanks to the ACR RFS AI advisory council members including . Jeremy Howard Paras Lakhani Luke Oakden-Rayner Stanford ML team Kevin Seals Article corrections made This article referred to Jeremy Howard (Ex-CEO of ) — updated to “president and chief scientist of Kaggle” Kaggle Article stated _NLP performance on that dataset is not likely improved over random._Jeremy clarified that the was what was not likely improved over random precision of the normal finding _the human side of medical care in this country._healthcareinamerica.us Healthcare in America

Are computers better than doctors?

I originally wrote this piece as an internal R&D direction document for my colleagues at . We’ve decided to share it publicly because we feel that it’s important that as many people as possible are thinking about these issues. doc.ai has throughout history been a artisanal vocation — that is, it has focused on the skill and experience of the individual doctor, rather than looking to build a standardized process for diagnosing and treating patients. In recent years this has started to change, as initiatives like Evidence Based Medicine and Precision Medicine have tried to inject additional rigor and data-driven practices into the field. However, the vast majority of medical care is provided through the traditional Hippocratic philosophy. Medicine This needs to change. The largest population centers on the planet have less than 1/10th of the doctors they need, and it will take hundreds of years to fill the gap. Misdiagnoses, late diagnoses, and over-diagnoses kill millions and cost tens of billions. The technology is now being developed to fix this problem — to give medical workers and patients a clear summary of the exact information they need, when they need it. Such technology can give a remote area community health worker access to a distillation of the world’s medical knowledge. It can make doctors in the developed world dramatically more productive and accurate , while giving patients and families more control over and insight into their medical care. AI, specifically , has already shown that it can be a powerful diagnostic tool, for instance, showing super-human performance in medical imaging work such as: deep learning Google’s diagnostic retinopathy system Stanford’s dermatology algorithm detection and malignancy classification Enlitic’s work in lung cancer The challenges Labeled historical data It is widely believed that deep learning algorithms require vast amounts of data to be effective. This is not necessarily true. For instance, Enlitic’s lung cancer algorithm had access to scans of just a thousand patients with cancer. It’s important to understand that although the dataset (from the National Lung Screening Trial) was relatively small, it had the key characteristics to allow effective modeling: It contained annual scans over 3 years for each patient; seeing the development of a disease over time is critical to creating diagnostic algorithms Radiologists had provided annotations showing roughly where nodules were located, allowing the algorithm to focus on the important information The dataset included information about each patient’s medical outcomes after the 3 years of the trial — labels showing things such as patient survival are necessary for creating diagnostic systems. It’s also useful to see what couldn’t be provided by this project: treatment recommendations. Because the dataset did not contain longitudinal data for the patients showing what interventions were provided and how they responded, the algorithm developed was only useful for diagnosis, not treatment planning. However, even this is extraordinarily powerful: currently the death rate for people diagnosed with lung cancer is nearly 90%, with nodules not found until they’re on average 40 mm in size. The system developed by Enlitic reliably found nodules of 5 mm or smaller. When found early, probability of survival is 10x higher! Now think about how unusual this kind of dataset is. How often can we access a unified medical record containing all information about tests, diagnoses, and interventions for a patient over a multi-year period? The information is spread over multiple institutions, and within an institution over multiple departments. Legal conservatism Even when the data is available in a central location, or can be cobbled together from multiple sources, it is frequently the case that the institutions that hold it are wary of sharing it with the data scientists that can build these powerful algorithms. Legal staff recognize that one failure of privacy could end their career and cost their institution millions, whereas the theoretical cost of the missed opportunity (from saying ‘no’ to a data request) is hard to pin down. Yet when patients are asked if they’re willing to share their medical data if it could help others in the future, most are very happy to grant permission to use their data — especially when that sharing could lead to better options for their own treatment in the future. The opportunity for patients Patient-controlled data This leads to a clear opportunity: let patients be in control of their own medical data, across all their visits to different institutions and departments, as well as their own collected information (such as data from wearables, and self-reported data). Give those patients the opportunity to opt-in to sharing that data with particular data scientists for particular projects, give them a secure data environment , and in return give them: Early access to the resulting medical breakthroughs Financial remuneration Information about the work that was completed thanks to their data, and how it’s helping other patients. This may be the only way we’re going to see the true potential of deep learning in medicine — at least in the US (some centrally managed countries may be able to create the needed datasets through government decree). There’s a very related opportunity for families caring for loved ones with rare or untreatable diseases: get together with other patients in the same situation and agree to pool patient data. The more other patients that can be brought into the pool, the more chance there is that the critical information will be available. The Blockchain Not all data is created equal. Data from people with rare diseases is critical to diagnosing and treating those diseases. Data over many years is more valuable than a short time period. At the other end, bad actors may even fake data in an attempt to fraudulently gain remuneration. By using a blockchain we can create a clear auditable record of medical data sources. Based on this record, data providers can be rewarded based on how useful their data is in practice. Therefore, the more complete and accurate and relevant the data they provide, the more the reward. It also provides very interesting opportunities for institutions, who (with their patient’s permission) can provide whole datasets to researchers, and in return get both financial returns and access to the technology that results from the data. I n the longer term, the patient can authorize their institutions to pass on their data to data scientists through the same blockchain-based approach. The opportunity for data scientists Most data scientists say they’d like to do something meaningful with their skills, but few get the opportunity. Much work for data scientists is in areas like ad-tech, hedge fund trading, and product recommendations. The major things stopping them from being able to do more meaningful work are access to data, knowing what problems need to be solved, and having a way for their solution to get noticed and implemented. In order to turn data into useful results, data scientists need to be able to complete the following steps (which in practice are repeated multiple times in various orders): Data cleaning Exploratory data analysis Creating a validation set Building a model Analyzing and validating the model To do these steps, data scientists need a rich analytical environment where they can use their choice of tools, libraries, and visualization solutions. Most data scientists doing this kind of work today use R (generally R Studio) or Python (generally Jupyter Notebook). By providing such an environment with the data pre-installed and the problem to solve clearly defined, the data scientist can quickly get to work on a meaningful problem. It may even be possible for multiple data scientists to independently work on the same problem, with rewards being shared based on the utility of their work. What we need to provide Data gathering We need to give each patient the ability to gather and maintain their personal medical data, including: Lab tests and imaging studies Diagnoses Medications prescribed Non prescription medications and supplements taken Other medical interventions Exercise and eating records Family history (ideally, automatically maintained by linking across family members) Self reported progress such as energy levels, happiness level, and so forth Genomics and other tests This means being able to download data from each patient’s medical providers, both a one-time download at setup, and on a regular basis after that, as well as using APIs for individual health tracking and wearable apps to import their data. Data sharing Each patient needs to be able to opt in or out of each request for their data. If the system is successful, there could be many requests and dealing with each individually could be burdensome — in this case we can give them the ability to set rules on which requests to automatically accept or reject, and which require manual intervention. Each piece of data needs to be tagged with its source in an auditable way. It need not necessarily be stored on the patient’s device; indeed some types of medical data can be too large for on-device storage. Once a patient gives a project access to some of their data, that data needs to be made available to the researcher. Each data scientist will need a rich analytical environment to be provided for their work. This will show them information about the problem they’ve been asked to solve, and show how to access the data for the project. The big opportunity Providing patients with the ability to have control over their medical data, and data scientists the ability to solve pressing medical problems, is a powerful idea. But it’s only the tip of the iceberg. The bigger opportunity is what happens when the models can be continually improved, and then all these models can be combined. Each data scientist’s feature engineering steps can be saved and made available to future researchers (and they would be compensated when their approaches are re-used), and their pre-trained model activations can be automatically brought into new models to see if they add predictive power. Allowing new data to continuously improve existing models requires that the meaning and format of all data sources is consistent. This is a complex topic, but experienced data product project managers should have past experience. Changes to data source formats or semantics need to be identified up front, and constant model testing is critical. Through re-using pre-trained models, we get all the benefits of combining data across all datasets, with none of the logistical or privacy challenges. This also means that rare diseases and pediatric diseases, where there is only a small amount of data, can be effectively tackled. Pre-trained models will be used to analyze the data in those situations, and very simple models with few parameters can be used to combine them. As we move forward, this approach to gathering and analyzing data will lead to new insights and will provide medical workers and patients a clear summary of the exact information they need, when they need it.

Medicine and the need for AI

Unsupervised Investments (I): A Guide to AI Investors

A note on what follows: As I take what’s been a five-year night-job I’m putting pen to pixel in an attempt to articulate my lens on early-stage investing. I call this framework my own, though in truth it’s a synthesis of counsel from wise predecessors, observation of fantastic (and not-so-fantastic) entrepreneurs, industry literature (more history than blogs), personal experience — including more mis-steps than I would like to admit — and a splash of my own techno-optimism. full-time This is far from a formula. It is not meant to be an absolute calculus on predicting the future (I’ll leave that to or the ) but rather a lens through which to standardize the hundreds of pitches you see as an early-stage technology investor. Contrary to what many established VCs will have you believe, it’s not difficult to pick startups with prospects; investing is as much about avoiding the bad ones as identifying the good. While operational, financial, and technical knowledge are part and parcel to savvy early-stage investing, they have diminishing marginal returns. Hari Seldon TechnoCore The highest contributor to portfolio success (read “IRR”) is quantity*quality of deal flow, and ability to get into the best. VC is an access game, and in access games the motto is location, location, and likability. Criterion 1 of 2: Scored (100 of 120pts req’d for fiduciary-level conviction) i.) Founder + Founding Team (40pts) Ability * Background: I look for uncanny founder-market fit, industry-familiar smart creatives who intimately understand a vertical but approach it in a novel way with new technology–e.g. at , who’s an amputee building human augmentation software, or Jenna Brown at , who became frustrated with the software in dry bulk shipping while at a commodities house. Unusual familiarity with a market is an easy, early defensibility-driver. Jeff Huber Standard Cyborg Shipamax Determination (Grit) * Passion (Conviction): i.e. Are they truly with solving the problem at hand? Why? What would make them give up? The entrepreneur’s motivation (or lack thereof) is a major risk factor. All seems fine and dandy when you close your seed round, but you know, Murphy’s Law. obsessed Honesty + Character (gut read, ‘reference checks’): i.e. Can I trust them? At Seed the entrepreneur has total control — . Not to mention, culture stems from leadership, especially the CEO (a certain Lyft competitor comes to mind). It is my firm belief that Silicon Valley has without regard for empiricism largely deified the wrong entrepreneurs (and investors, for that matter). as they should, though that’s another post Vision * Pragmatism (user-centricity): i.e. Can they inspire others to see a common vision while building a realistic bridge? It’s often easier to see the long-term than the mid. Salespersonship * Connectedness: i.e. Can they sell, and who will read their emails? Note: I believe the Sales Ability-to-Success curve is non-linear. That is, past a certain point it is dangerous to be better at selling as it can disconnect expectation from reality. I’ve seen a lot of vaporware shortsightedly sold to both customers and investors. Your product should largely sell itself. Team history & Cohesiveness: How do they know one another, and for how long? By what mechanism do co-founders resolve disagreement and how well do they move on? There’s no room for resentment on their journey, or for ego. Co-founder disputes are one of a startup’s greatest risk factors. Capacity for speed. A good measure of this is how many code commits you have per week. Bonus points for women, minorities, ‘disabilities’. Venture capitalists systemically undervalue these founders. Notes: Brand and culture are more important (for growth and defensibility) than most entrepreneurs realize. However, both largely extend from the CEO and are incredibly nuanced. I will judge your e-book by its homepage. I look for CEOs who hate raising capital, because they love building businesses that make money. Funding should be a function of traction on positive gross margins, not vice versa. If you’re spending more time fundraising than running your business, you’ve got a problem. Drop VCs who drag their feet — they’re not investing. ii.) Technology * Defensibility / Competitive Landscape (35pts) Numerator: (15pts:) UNDERLYING TECHNOLOGY: If you just hired a freelance developer to build you an MVP then your company probably isn’t for me. If your problem wasn’t hard to solve then you’ll have quite a few competitors who are likely at least as smart as you are. . If you don’t have that — and no one has network effects from the start — then a great team with a head start will buy you a year, two (I saw this first-hand when Common Bond fast-followed SoFi). I’ll only bet on a ‘head start’ if there’s what I call “Competitive Osmosis” (see Note below). There are really only two ways to be long-term defensible: IP and Network Effects maybe : Only acceptable if you’re truly ‘deep-tech’, but by then your defensibility likely derives more from a world-class team than from the patents themselves. I care not a lick for business process patents (they’re circumventable) and steeply discount software patents by “1,000 ways to code a cat”. IP : When this term came into vogue a decade ago it referred to social network effects — i.e. each new user makes the network more valuable. This is why VCs like marketplaces so much: once the network is big enough then it’s difficult to build a viable competitor. You wouldn’t start an Airbnb competitor today and network effects are why. But I’m more interested in a new type of network effect: ML/DL. Machine learning models improve, definitionally, with more data. Network Effects very I believe that any software company not leveraging machine learning (or that’s built as a distributed application vis-a-vis blockchain) has a very good chance of not being around in 5–10 years. (13pts:) Why now? A solution can easily be too late or too early. What technological, societal, regulatory, or market development prevented this company from being started three years ago? Note: Technologies can be too late or early too… IMO, AR/VR and drones are a bit too early (and over-hyped), while mobile is (way) too late. (3pts:) Brand (this is often under-valued, esp. by 1st-time entrepreneurs) (2pts:) Do they have killer advisers? I don’t mean your buddy who’s worked in the industry a few years more than you, I mean people who can pick up the phone and get you your first five customers or coach your engineers through technical problems no one has ever solved before. (2pt:) Cash on hand (Note: Cash-to-Defensibility is non-linear; too much cash breeds profligacy and complacency.) Denominator: # of competitors (direct & possible; there’s always a competitor you don’t know about yet–maybe I’ve seen their deck) Agility of competitors Competitors’ cash on hand (over the “can catch up fast” threshold, discounted by solution’s prioritization on their product roadmap) As frustrating as it is, there are certain entrepreneurs and funds the lion’s share of VCs simply won’t bet against — e.g. Musk, Bezos, Sequoia. Notes on Verticals & Competitive Osmosis (more on this in an upcoming post): First of all, I don’t invest in consumer technology . I don’t pretend to know what consumers want. While the largest technology companies are predominantly B2C, the competition is more fierce, defensibility more difficult, and the risk profile such that it requires a large fund that’s able to make many bets and follow on big in the winners. That doesn’t work with a small fund like Cantos. Companies are much more predictable. They only want to do two things: Increase top-line and reduce cost. Venture capital is a game of statistics, and I like my odds in B2B much better. Now, this is where vertical really matters. I look for sectors that have big, slow movers, and where your average Millennial entrepreneur isn’t yet focused (i.e. NOT social networking, ad-tech, marketing-tech, CRM/sales-tech, on-demand, e-comm, cloud infrastructure, dev tools). This could mean manufacturing (e.g. Bigfinite , Standard Cyborg ), construction (e.g. BuildingConnected , ALICE ), finance (e.g. Qwil , Maxwell ), healthcare (e.g. Patch , GreenLight Medical ), logistics (e.g. Shipamax , EasyPost ), agriculture (e.g. Arable , AgriData ) and other niche sectors that require knowledge thereof. Generally speaking… YES: Finance/insurance (blockchain solutions only; pure software solutions have been played out), manufacturing (esp. +AI), logistics, agriculture (though sensitive to robotics), construction (though ALICE may be my only bet here), gov/law (where ‘humans are serving as middleware’, to borrow a quote from Manan Mehta at Unshackled), synthetic biology (non-clinical), infrastructure for a decentralized web NO: Social, on-demand, ad/marketing/sales-tech, e-commerce, hospitality-tech, hiring tech (too many of you), AR/VR (too soon), bio-pharma (takes a whole other skill set), real estate (played out) Because I don’t/can’t know all of these sectors intimately, be prepared for me to ferret out a SME and revert with more specific questions. iii.) Clarity of Business Model, Unit Economics (10pts) Numerator: Business model simplicity (do one thing, do it well). One of my mantras is: Sell one product to one demographic. Know your fixed and variable costs! Model in ~20-30% uncertainty. Complexity is the enemy of speed. Day 1 revenue or <6-month path to product-related revenue. I typically invest in companies with $5–30K monthly revenue, even at ‘pre-seed’ (pre-money valuation under $5M). Gross-margins > 60%. While I wouldn’t echo his hallmark condescension, a quote from Don Valentine comes to mind: “We need guys [/girls] to have 70% gross margins, because they abuse so much money. You need to be in a business that has huge gross margins, because the managements are not very experienced. You start with a 60% minimum margin as the ideal, because you know the management is going to spend 20% on marketing and 20% on research. We have guys who are 27 years old and have never managed anything.” LTV/CAC > 300%. Payback period also needs to be taken into account–can’t be longer than your ability to fundraise! Churn < 3%. Past that you eventually churn through the entire market. The revenue model must be aligned to incentives of the customers. Note: Its violation of this principle is my fundamental problem with companies driven by ad revenue. Denominator: Extreme bias toward low burn (sub-$50K/mo at pre-seed, $100K at seed). Use advisers for early sales, not FTEs, and don’t sign a lease before product-market fit! CEO will consider herself cash-poor until $5M+ raised. Until then use the currency you have: equity! Notes: Major preference toward enterprise — lower risk profile (granted, lower possible upside), lower churn, deeper pockets I will not invest in anything I feel is or could quickly become commoditized — UI-based products, storage, algorithms on publicly accessible datasets, cybersecurity, on-demand services (better called “labor arbitrage”), or anything that is ‘trending’. Advertising (eyeball-based) revenue is an unacceptable answer for my risk/return profile iv.) Exit Opportunities / Valuation (10pts) Numerator: Natural acquirers at multiple stages. I believe in optionality, not IPO-or-bust. Assume Google won’t buy you (though I do think their acq. activity will pick up; what else will they do with all that cash?) I want to know your market is large enough that a 10% share means at least $100M in revenue–$100M ARR roughly being the hurdle for a SaaS IPO Vertical has high M&A activity, incumbents have large cash reserves, industry is facing or will soon face sweeping changes at the hands of innovation Denominator: Look for “arbitrage” opportunities, i.e. underpriced deals. Note: these are in fact fairly priced deals, often found outside of the Bay Area, where we’ve accustomed ourselves to price inflation. Bias against seed round >$10M post-money valuation, as most acquisitions happen under $50M Berkeley, MIT, & other engineering-heavy universities are overlooked opportunities. Stanford is a fantastic school but over-emphasized. Note: Wary of scientist CEOs unless innate business acumen is present. Notes: If your total addressable market (TAM) — and this should be real revenue potential, NOT GMV! — is under $2B, you’re wasting your time speaking with most VCs, particularly $100M+ funds. What types of outcomes VCs will bet on are primarily a function of their AUM & # of investment professionals. Acquisitions have slowed in recent months but Google, Apple, Facebook, Oracle, and others (e.g. Berkshire Hathaway’s $60B war chest) are sitting on huge piles of cash that they need to put to work. These cash piles will be heightened should the current administration incentivize repatriation of foreign cash. I believe Google & Facebook will become the Berkshire Hathaway’s of tech. I like the types of acquirers that are more likely to be in $BRK’s portfolio than Benchmark’s — e.g. GE, GM, Marmon, Honeywell, Mastercard, Verisk, John Deere Train thoroughbreds, don’t hunt unicorns. Nine-figure exits are OK for a small fund like Cantos, though I do need conviction you’ll be able to raise Series A. Seed is less affected by bear markets due to lower burn and valuation. As NEA Co-Founder Dick Kramlich once said, “Good ideas don’t go on strike.” Companies seed funded in ‘08-’09 include Airbnb, Twilio, Dropbox, Slack, Credit Karma. v.) Financing Risk (5pts) Since I will write smaller checks, my co-investors must be able to carry the day. My $50–100K check will buy a seed-stage startup only 1–2 months… …Or the company must be riding a wave/in a market/run by a CEO that I’m confident up-stack VCs are likely to fund. Note: I know enough VCs to have a fairly good read on this ;) If the latter is the case, I want to be familiar with the metrics that will be required for a strong Series A and confident the CEO can hit them I want the round I’m participating in to buy the startup at least 12 months of runway. In this wary market, 18+ is preferable. Note: I always test companies against a few friendly VCs at larger funds before writing a check. I’m showing my hand here but if I introduce you to another investor I’m not being 100% altruistic. [Shout-out to some of my usual suspects… Arianna Simpson , Shivon Zilis , Zavain Dar , Vaughn Blake , John Komkov , Nan Li , Katherine Boyle, Ela Madej , Zal Bilimoria , Michael Berolzheimer , Trae Stephens , Anya Schiess , Mo.ElBibany , Howie Diamond , Alastair Trueger , Ryan Gembala , Seth Winterroth ] vi.) Additive Business (10pts) The company’s product or service is “additive” to the world, i.e. contributes to: Health (e.g. , , ) Patch Recursion Enlitic Financial Inclusion & Private Sector Accountability (e.g. Robinhood, , ) Qwil Ethic Democratization & Public Sector Accountability (e.g. OpenGov, ) Vendor Registry Environment, Waste Reduction, & Food Security (e.g. , , ) Opus 12 Tri Alpha Energy Arable Education (e.g. Duolingo, AltSchool, Treehouse) Quality of Worklife (e.g. Slack, WeWork, ) Ambition Notes: I acknowledge this is highly subjective but hey, it’s my fund! This may read as an ‘impact investing’ filter but is in fact a financial decision. IRR stems from value-creation, and I believe the most valuable companies are created by solving the largest pain points. This is also a meaningful differentiator in hiring talented Millennials… as sometimes even getting away with under-paying them (until you can afford market rates). vii.) Gut (5pts) The indescribable feeling that you should invest despite non-obvious reasons Note: I believe what we call “gut” is the brain’s own deep learning. It is a complex layering of so much information as to feel intuitive when it is in fact deeply logical. While it has an explicit 5% here, it affects all of the scores above. Criterion 2 of 2: Binary (e.g. technical risk, market risk, funding risk, competitive risk, hiring risk, retention risk, co-founder fallout risk) I typically frame this by asking, Then I attempt to give a % likelihood for each over 5- and 10-year timeframes. What risks am I taking, specifically , and am I willing to take them as a fiduciary? What assumptions must hold true for this company to succeed? Or, as one veteran VC put, “Ask yourself, ” What price glory?

My Seed Investing Source Code

“If you don’t know how to fly remember that you can only grow your wings on the way down.” The most powerful that somebody can have is to trust in themselves and in what they want. Trust implies that you are open to change, that you are allowing yourself to know what you want, but you are not holding on to that as a law carved in stone. attribute Trust is the opposite of having belief. Trust is the fundamental element to change. If you don’t trust yourself then you will get back to your former path, you will return to the place where you began. And you don’t want that, do you? Trusting yourself also means that you acknowledge that what you want is not influenced by the people around you. Again, . They created a certain image for yourself and in their view you should act accordingly. The good news is that you shouldn’t live up to anyone expectations of you. friends, family members and coworkers all expect something from you That’s right, you have to be the judge of your own life and understand that you will piss some people off with your “new and crazy ideas”. I mean, if you want to live a great life you will be misjudged and misunderstood. There is no other way around and your great battle is to not be influenced by it Whose life is it? Who will have great stories to tell their children and grandchildren? I mean would you put such an heavy mortgage on your life just because of ? “their opinion” Excuse my language but honestly… ! fuck their opinion Once a great man told me something that I will never forget. We were talking about this same subject and he said to me: “Who are you or anybody else to judge me? Seriously, who are they tojudge what you want? Do they have a Phd on your life? Did they received an award on how to live a great life? They have no fucking idea of what they are talking about, they are just entertaining themselves and you think that you should listen to those critics? Fuck them, seriously. . Period.” The only person who should judge your life is you Yeah, he was an intense kind of guy but he made his point clear. Most of the time we don’t as high as we could because we are too afraid, we want to dress in a certain way, behave in a certain way and be true to who we are but we throw all that to the side because of “their opinion”. fly You have nobody else to blame and it is not society faults. It is yours. Like Frank Zappa said: “If you end up with a boring, miserable life because you listened to your mom, your dad, your teacher, your priest, or some guy on television telling you how to do your shit, then you deserve it.” You can’t blame the man for speaking so clearly, can you? This lesson doesn’t have any action piece or mission, just do this the next time someone judges your path or questions your purpose: nod, tell them that they are right and move on doing what you were about to do anyway. No need for an argument, that only sucks up your energy and they won’t change their opinion anyway. Yeah sure, you are right. Shrug shoulders and keep moving forward. That’s the only way. PS. If you want to read more helpful lessons, download now the free guide “How To Find Your Purpose and Live a Good Life” . João Fernandes is a Portuguese writer, professional traveler and marketing strategist. He has been featured on Vice.com, ThoughtCatalog and EliteDaily. You can find out more about his writing and ineptitude to ride bikes on: www.adventurousartist.com

Stories, News & Mentions about ENLITEAI

Enliteai’s Stories on HackerNoon