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But it doesn’t need to stay that way. Don’t get me wrong, I love Google Maps. Its a borderline miraculous service that has given me superb driving directions, public transport timetables, and walking routes. All over the world. For free. However, when I tried to get cycling directions to a store a short distance across town I encountered some issues. I plugged in my headphones, put away my phone, and was promptly directed to take a left onto a four lane highway. Never mind that Maps’ network of cycle lanes are a disjointed nightmare The root of the issues is that , rather than a distinct mode of transport. The key differences that Maps ignores are: Maps treats cyclists like cars that have a few extra roads Cyclists tend to be more discerning about which roads they take, sticking in their own personal comfort zone when it comes to traffic and speed. Giving the same routes to all riders ignores this. (For example, Maps shows the road with the as a “cycling friendly route” as it has a marked bike lane) most bike crashes in the city Unless you risk your phone on your handle bars, you can’t view the screen to see a route overview. This leaves the rider without a gauge of their progress in the overall route, leading many to pull over to ensure they are on the right track. The final kicker is that the handful of cyclists I talked to (and myself) already know how to get around their cities. If asked to ride in any direction they could safely do so using roads, bike paths, and shortcuts they have found, exploiting side streets, continuing past dead ends, crossing tram lines — all actions that Maps may never suggest or know are possible. They don’t need turn-by-turn directions at all. All they need Maps to tell them is which direction to ride in, and when to stop. The solution: Just give that information! A simple voice based interface could be used, providing the user is willing to ride with a headphone in, and an on screen interface could provide an arrow towards the destination, readable at a glance if the user had the phone within eyesight. Now I could cross my fingers that somebody from the Maps team reads this, agrees, and builds in these arguably very simple features. I don’t like the odds of this happening, and I also don’t want to shell out $200 for a when my phone already has all the requisite capabilities. hardware solution So I built this app. admittedly below average Live cycling demo As mentioned, once the user enters a destination, the app will begin to provide directions: Verbal instructions on how to alter their current course and how far they to ride are given, while on the screen an arrow points to the destination. The Details The app is written in Swift, using the built in location, geocoding, and text-to-speech services. At this stage directions given are and do not incorporate a knowledge of the road layout. This implicitly assumes the user is navigating through a network of largely uninterrupted streets, without large obstacles that would require them to take a indirect route. dumb Water hazards are not accounted for While the directions are dumb, the timing of verbal instructions is adjusted to balance providing a reassuring frequency of updates with not being super annoying. The frequency of instructions increases as the user approaches the destination, and when their course deviation has changed from the last instruction they have recieved. This aims to keep the instructions passive, allowing the user to ride their own route while the app provides assistance. If feedback is positive, there is potential to incorporate suggestions of roads that take the user in the desired general direction, using historical data from or other service. Strava If you want to give this navigation method a go, the code can be downloaded and installed using Xcode. here If you are reading this, cheers for making it to the end! I hope you have a great day! 👍

Google Maps sucks for cyclists

“Four lane urban busy traffic congestion in Bangkok” by on Connor Williams Unsplash As a data scientist who has worked on geospatial data for more than one year, has always been a great challenge for our team. We use a algorithm for traffic estimation and a navigation system based on our live traffic estimated data. We have built a simple traffic estimation prediction that is used to predict navigation travel time. The output of our services is surprisingly accurate. On top of that, our travel time prediction has shown brilliant results, except in anomalies like vacation days or unpredictable changes in traffic behaviors. These anomalies happen less than 20 percent of the time but our users need more accurate prediction especially in anomalies moments, so here I will talk about the approach we took to solve the problem. traffic prediction machine learning There are many kinds of solutions for traffic prediction like ARIMA, SARIMA, Kalman filter, particle filter, and theoretical traffic propagation methods. But in the real world, when you must respond to your users in less than half a second and you have a huge amount of dirty/missed/imbalanced data, employing these methods does not seem to be working. So, here is what we did to predict traffic: As an initial idea, I proposed to use a simple linear algebra-based method to find the most similar traffic state to live traffic. Suppose we found the most similar state to the current state. We assume that 20 minutes later from now, the traffic should be similar to 20 minutes after the state we found as similar. This method had a bunch of errors. For example, when you use a dot product, you can not avoid the effect of an accident in the north-west of a city on the traffic state of the south-east in predicting similar states. Then we used a method to do a local search in order to find the most similar traffic state in our traffic history. The whole idea is very simple. Cut the cities map into some pieces. For example, we used square cells with 200m * 200m dimensions. Then we defined a mesh which consists of 5*5 cells, so its area was 1Km². Each mesh had 9 collections of 3*3 cells as illustrated in the following figure. The basic idea of building mesh is borrowed from convolution neural networks. The traffic in this local area will be predicted by its own data. The whole amount of data is huge, on the other hand, we face totally missed and imbalanced data in smaller pieces. What we needed here was feature engineering on our data. I defined some features for all traffic data in each cell and did some matrix operations on each mesh and evaluated the live state of the mesh with its historical states. Finally, I found the best method to find the most similar state is to consider a 25-dimensional space so that each mesh is a 25-element vector. Now we can calculate the dot product between each historical states with live traffic state and find the most similar state very fast by the use of cosine similarity. After finding the similar state, in the next step, we tested our initial idea. We examined if we can consider 20 minutes after that state as a prediction of 20 minutes later traffic from now on. The answer is yes. The predictions are amazingly fast and accurate. Another advantage of this method is the fact that it can predict traffic in a database layer via SQL queries. How to get rid of the network`s issues that cause an error in traffic predictions? Here we go to the last part of our traffic prediction journey. When we are working on an open street map network graph, it always has issues like mistaken dead ends and some reverse directions. So when you are predicting traffic on this network the results may be incorrect. Next idea is to create a HOG from this images. HOG is a very simple representation that captures the basic structure of a face in a simple way. For more information on HOG please read . Assume each state of traffic as an image. We can take an image from each mesh by opening our lens for 10 minutes and then take a shot. this post The original image is turned into a HOG representation that captures the major features of the image regardless of image brightness. We have some traffic movements in our mesh. Each of them has a location and a velocity. This velocity has a direction and a magnitude. So we can define our HOG by using their location and normalizing the magnitude of the velocity for the length of that line on the image. Then we divide the length of each line into 2 equal parts and will use intense of white color to represent the vector direction. For instance, if we have a movement from north to south, the top north part of the line in the image will have 2 times more intense than the bottom south part. Now we converted traffic prediction into a near duplicate image search. If we want to find the most similar image very fast we can create a hash from the image of each state. As I have tested some methods, it seems Locality Sensitive Hashing has very good results in this case. For more information, I recommend reading about finding near-duplicate images very fast. this post This method can be applied in a variety of situations and can easily predict traffic on any graph network. Even if our information about the network has some errors or enough knowledge does not exist, we can use the last part to predict traffic. Applications of this method can help us to predict a website traffic in a black Friday by collecting social media data about the site. Furthermore, we can use this approach in case of fraud detection, finding anomaly behavior in network traffic, aerial traffic prediction, and maybe in many other situations which you may know. I would like to thank my teammates , and who helped me to solve this problem. Mostafa Jalambadani Davood Amini Mahdi Yeilaghi _View Mahdi R.'s profile on LinkedIn, the world's largest professional community. Mahdi has 3 jobs listed on their…_www.linkedin.com Mahdi R. - Data Scientist & Product Owner - Rajman Information Structures | LinkedIn

How to combine some machine learning methods for traffic prediction?

https://urbanphotomag.wordpress.com/category/empty-streets/ Question for you: when waiting to cross the path of a vehicle like the one at the intersection below, do you take Path A and walk in front, or Path B and walk behind? As both a pedestrian and a driver, it infuriates me to see people cross in front of vehicles. At stop signs, at driveways, at parking lots, even at traffic light intersections when you know the light is about to change. Whenever feasible, I cross behind the lead car. It’s not just because the lead car has a lot to focus on (me, signage, other vehicles, the path they want to take, etc), and because any car behind them only needs to focus on the front of their vehicle (where I am), and because the second vehicle’s space to accelerate is limited and predictable given where the lead vehicle is. It’s also because, all things being equal, there is less of the universe’s energy spent when I take a few extra steps around the car than when said car has to stop before their preferred point. In fact, I’d argue that all ped crosswalks should be behind the lead car. In theory. Cars often need to nudge out, get better views, time their movements… if I make them stop for me well ahead of that optimal point of view, then I’m likely forcing their two-ton gas-burning vehicle to stop twice and wait longer. It’s unquestionably better for the environment if I use 3 extra calories and walk behind the vehicle. And there is the inconvenient truth: because pedestrians are small, free-roaming, can turn on a dime, and use less energy, it’s just a lot easier to design streets without them in mind and have pedestrians simply deal with it. That sounds okay in isolated situations. But when you take a broad-stroke approach to designing car-focused streets, you start optimizing only for throughput, which minimizes pedestrians’ freedom to choose their paths, which consequently decreases their engagement with the urban environment, which makes for a worse urban environment, which negates the value of that great throughput metric you nailed for cars. We see some innovations to address that engagement problem, though many of them still function primarily to protect pedestrians from cars rather than free up more path choices. You’ve got your unorthodox crosswalk: _We always say that streets are for people, but nobody works very hard to make them comfortable for people rather than…_www.treehugger.com Would A Curvy Crosswalk Reduce Accidents? Your idiot lights letting people know when they can walk: _949 points * 87 comments - This city started putting colored LED strips at crosswalks so people staring down at their…_iwastesomuchtime.com This city started putting colored LED strips at crosswalks so people staring down at their phone… And your “smart-whatever” streetlights to fancy up the crosswalk: _Our Smart Crosswalk solution combines LED street lighting, VMSs, road lane markers and Light Lines, with the latest…_www.roadtraffic-technology.com 5 - Verdict Traffic These are nice, but they don’t do much to improve pedestrian choice. The holy grail is to maximize both throughput choice, which is a contradiction to conventional traffic planning. If everyone gets to choose where they want to go and when, throughput plummets, right? It sure would be great if all those street users could somehow be in coordination — that’s what’s stopping any optimizations from happening. and Oh shit, that’s right — autonomous vehicles create coordination. Well then, if we can communicate with all vehicle traffic at once, then maybe we can up the ante on these pedestrian communication tools in turn? While my knee-jerk reaction to “smart city infrastructure” is a dramatic teenaged eye roll, this might actually be a useful opportunity. Here’s a static (albeit ) urban intersection, with an example of a traditional crosswalk. I added the roundabout feature as well. very well-designed What if, instead of forcing fixed 90-degree paths for our dynamos of direction, the pedestrians, we used tech to overlay temporary crosswalks based on pedestrian demand? You step up to (insert smart curb/light/whatever here) at the middle of the block, because you’re trying to get to a point Southeast from where you are. You point, or stand, or tap, or set your GPS to that direction, and eventually a crosswalk opens up for you. If more folks join in, it opens up faster, because you’re creating more demand for that path. All the vehicles know this is happening, and are routed to avoid that area — possibly by stopping, but also by changing their routes or circling the roundabout/block to maintain flow. Whatever optimizes throughput based on the current priorities. Note how this also educates the pedestrian to better understand efficient behavior, which is a potential benefit of AI . If you want to walk diagonally across an entire street, you’re gonna be sitting there for a long, long time waiting to be prioritized. The more perpendicular your crossing angle is, and the more you’re willing to follow where others are headed, the faster you’ll get prioritized. that I am rabidly supporting Got a mass of people exiting a busy transit terminal like Penn Station? A smart grid could open up the Northern half the street to let peds pile into a safe staging area, which then halves their Southbound crossing distance once the traffic eases up and the whole block can temporarily become the crosswalk. Remember too that with an all-autonomous traffic grid, vehicles don’t have to stick to one side of the road. The option below could still let a few Westbound vehicles squeeze through before the crosswalk fully expands by shifting Eastbound vehicles into one lane (effectively turning a four-lane, two-way block into a two-lane, two-way block.) Speaking of which, maybe the city wants to have a no-vehicle zone cover an entire block for lunch hour. Cool, that can easily be done. How about an insanely large scramble that doesn’t actually have to exist as a permanent epilepsy-inducing paintjob like the ones we find today? Also… sponsored crosswalks? I’d have to think long and hard about why we’d want a Mickey Mouse-shaped crosswalk, but sure. Now I want to be fair to the folks who would actually have to engineer these algorithms that manage dynamic paths across an entire city and decide how to prioritize people vs. cars: I am aware this is difficult. But it’s also an extension of where we’re going in general with a connected traffic grid. And as for feasibility, is it really so out of reach for the less-congested areas of the world? Suburban neighborhood streets? Perhaps roads like the ones they have in Tempe, Arizona? insanely Yes, it’s still probably extremely hard. Fine. Going back in my hole now.

An Inconvenient Truth About Pedestrians

MADAME MELMOTTE'S BALL

Anne's House of Dreams by L. M. Montgomery, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . CHRISTMAS AT FOUR WINDS CHAPTER 15. CHRISTMAS AT FOUR WINDS At first Anne and Gilbert talked of going home to Avonlea for Christmas; but eventually they decided to stay in Four Winds. "I want to spend the first Christmas of our life together in our own home," decreed Anne. So it fell out that Marilla and Mrs. Rachel Lynde and the twins came to Four Winds for Christmas. Marilla had the face of a woman who had circumnavigated the globe. She had never been sixty miles away from home before; and she had never eaten a Christmas dinner anywhere save at Green Gables. Mrs. Rachel had made and brought with her an enormous plum pudding. Nothing could have convinced Mrs. Rachel that a college graduate of the younger generation could make a Christmas plum pudding properly; but she bestowed approval on Anne's house. "Anne's a good housekeeper," she said to Marilla in the spare room the night of their arrival. "I've looked into her bread box and her scrap pail. I always judge a housekeeper by those, that's what. There's nothing in the pail that shouldn't have been thrown away, and no stale pieces in the bread box. Of course, she was trained up with you—but, then, she went to college afterwards. I notice she's got my tobacco stripe quilt on the bed here, and that big round braided mat of yours before her living-room fire. It makes me feel right at home." Anne's first Christmas in her own house was as delightful as she could have wished. The day was fine and bright; the first skim of snow had fallen on Christmas Eve and made the world beautiful; the harbor was still open and glittering. Captain Jim and Miss Cornelia came to dinner. Leslie and Dick had been invited, but Leslie made excuse; they always went to her Uncle Isaac West's for Christmas, she said. "She'd rather have it so," Miss Cornelia told Anne. "She can't bear taking Dick where there are strangers. Christmas is always a hard time for Leslie. She and her father used to make a lot of it." Miss Cornelia and Mrs. Rachel did not take a very violent fancy to each other. "Two suns hold not their courses in one sphere." But they did not clash at all, for Mrs. Rachel was in the kitchen helping Anne and Marilla with the dinner, and it fell to Gilbert to entertain Captain Jim and Miss Cornelia,—or rather to be entertained by them, for a dialogue between those two old friends and antagonists was assuredly never dull. "It's many a year since there was a Christmas dinner here, Mistress Blythe," said Captain Jim. "Miss Russell always went to her friends in town for Christmas. But I was here to the first Christmas dinner that was ever eaten in this house—and the schoolmaster's bride cooked it. That was sixty years ago today, Mistress Blythe—and a day very like this—just enough snow to make the hills white, and the harbor as blue as June. I was only a lad, and I'd never been invited out to dinner before, and I was too shy to eat enough. I've got all over THAT." "Most men do," said Miss Cornelia, sewing furiously. Miss Cornelia was not going to sit with idle hands, even on Christmas. Babies come without any consideration for holidays, and there was one expected in a poverty-stricken household at Glen St. Mary. Miss Cornelia had sent that household a substantial dinner for its little swarm, and so meant to eat her own with a comfortable conscience. "Well, you know, the way to a man's heart is through his stomach, Cornelia," explained Captain Jim. "I believe you—when he HAS a heart," retorted Miss Cornelia. "I suppose that's why so many women kill themselves cooking—just as poor Amelia Baxter did. She died last Christmas morning, and she said it was the first Christmas since she was married that she didn't have to cook a big, twenty-plate dinner. It must have been a real pleasant change for her. Well, she's been dead a year, so you'll soon hear of Horace Baxter taking notice." "I heard he was taking notice already," said Captain Jim, winking at Gilbert. "Wasn't he up to your place one Sunday lately, with his funeral blacks on, and a boiled collar?" "No, he wasn't. And he needn't come neither. I could have had him long ago when he was fresh. I don't want any second-hand goods, believe ME. As for Horace Baxter, he was in financial difficulties a year ago last summer, and he prayed to the Lord for help; and when his wife died and he got her life insurance he said he believed it was the answer to his prayer. Wasn't that like a man?" "Have you really proof that he said that, Cornelia?" "I have the Methodist minister's word for it—if you call THAT proof. Robert Baxter told me the same thing too, but I admit THAT isn't evidence. Robert Baxter isn't often known to tell the truth." "Come, come, Cornelia, I think he generally tells the truth, but he changes his opinion so often it sometimes sounds as if he didn't." "It sounds like it mighty often, believe ME. But trust one man to excuse another. I have no use for Robert Baxter. He turned Methodist just because the Presbyterian choir happened to be singing 'Behold the bridegroom cometh' for a collection piece when him and Margaret walked up the aisle the Sunday after they were married. Served him right for being late! He always insisted the choir did it on purpose to insult him, as if he was of that much importance. But that family always thought they were much bigger potatoes than they really were. His brother Eliphalet imagined the devil was always at his elbow—but I never believed the devil wasted that much time on him." "I—don't—know," said Captain Jim thoughtfully. "Eliphalet Baxter lived too much alone—hadn't even a cat or dog to keep him human. When a man is alone he's mighty apt to be with the devil—if he ain't with God. He has to choose which company he'll keep, I reckon. If the devil always was at Life Baxter's elbow it must have been because Life liked to have him there." "Man-like," said Miss Cornelia, and subsided into silence over a complicated arrangement of tucks until Captain Jim deliberately stirred her up again by remarking in a casual way: "I was up to the Methodist church last Sunday morning." "You'd better have been home reading your Bible," was Miss Cornelia's retort. "Come, now, Cornelia, I can't see any harm in going to the Methodist church when there's no preaching in your own. I've been a Presbyterian for seventy-six years, and it isn't likely my theology will hoist anchor at this late day." "It's setting a bad example," said Miss Cornelia grimly. "Besides," continued wicked Captain Jim, "I wanted to hear some good singing. The Methodists have a good choir; and you can't deny, Cornelia, that the singing in our church is awful since the split in the choir." "What if the singing isn't good? They're doing their best, and God sees no difference between the voice of a crow and the voice of a nightingale." "Come, come, Cornelia," said Captain Jim mildly, "I've a better opinion of the Almighty's ear for music than THAT." "What caused the trouble in our choir?" asked Gilbert, who was suffering from suppressed laughter. "It dates back to the new church, three years ago," answered Captain Jim. "We had a fearful time over the building of that church—fell out over the question of a new site. The two sites wasn't more'n two hundred yards apart, but you'd have thought they was a thousand by the bitterness of that fight. We was split up into three factions—one wanted the east site and one the south, and one held to the old. It was fought out in bed and at board, and in church and at market. All the old scandals of three generations were dragged out of their graves and aired. Three matches was broken up by it. And the meetings we had to try to settle the question! Cornelia, will you ever forget the one when old Luther Burns got up and made a speech? HE stated his opinions forcibly." "Call a spade a spade, Captain. You mean he got red-mad and raked them all, fore and aft. They deserved it too—a pack of incapables. But what would you expect of a committee of men? That building committee held twenty-seven meetings, and at the end of the twenty-seventh weren't no nearer having a church than when they begun—not so near, for a fact, for in one fit of hurrying things along they'd gone to work and tore the old church down, so there we were, without a church, and no place but the hall to worship in." "The Methodists offered us their church, Cornelia." "The Glen St. Mary church wouldn't have been built to this day," went on Miss Cornelia, ignoring Captain Jim, "if we women hadn't just started in and took charge. We said WE meant to have a church, if the men meant to quarrel till doomsday, and we were tired of being a laughing-stock for the Methodists. We held ONE meeting and elected a committee and canvassed for subscriptions. We got them, too. When any of the men tried to sass us we told them they'd tried for two years to build a church and it was our turn now. We shut them up close, believe ME, and in six months we had our church. Of course, when the men saw we were determined they stopped fighting and went to work, man-like, as soon as they saw they had to, or quit bossing. Oh, women can't preach or be elders; but they can build churches and scare up the money for them." "The Methodists allow women to preach," said Captain Jim. Miss Cornelia glared at him. "I never said the Methodists hadn't common sense, Captain. What I say is, I doubt if they have much religion." "I suppose you are in favor of votes for women, Miss Cornelia," said Gilbert. "I'm not hankering after the vote, believe ME," said Miss Cornelia scornfully. "I know what it is to clean up after the men. But some of these days, when the men realize they've got the world into a mess they can't get it out of, they'll be glad to give us the vote, and shoulder their troubles over on us. That's THEIR scheme. Oh, it's well that women are patient, believe ME!" "What about Job?" suggested Captain Jim. "Job! It was such a rare thing to find a patient man that when one was really discovered they were determined he shouldn't be forgotten," retorted Miss Cornelia triumphantly. "Anyhow, the virtue doesn't go with the name. There never was such an impatient man born as old Job Taylor over harbor." "Well, you know, he had a good deal to try him, Cornelia. Even you can't defend his wife. I always remember what old William MacAllister said of her at her funeral, 'There's nae doot she was a Chreestian wumman, but she had the de'il's own temper.'" "I suppose she WAS trying," admitted Miss Cornelia reluctantly, "but that didn't justify what Job said when she died. He rode home from the graveyard the day of the funeral with my father. He never said a word till they got near home. Then he heaved a big sigh and said, 'You may not believe it, Stephen, but this is the happiest day of my life!' Wasn't that like a man?" "I s'pose poor old Mrs. Job did make life kinder uneasy for him," reflected Captain Jim. "Well, there's such a thing as decency, isn't there? Even if a man is rejoicing in his heart over his wife being dead, he needn't proclaim it to the four winds of heaven. And happy day or not, Job Taylor wasn't long in marrying again, you might notice. His second wife could manage him. She made him walk Spanish, believe me! The first thing she did was to make him hustle round and put up a tombstone to the first Mrs. Job—and she had a place left on it for her own name. She said there'd be nobody to make Job put up a monument to HER." "Speaking of Taylors, how is Mrs. Lewis Taylor up at the Glen, doctor?" asked Captain Jim. "She's getting better slowly—but she has to work too hard," replied Gilbert. "Her husband works hard too—raising prize pigs," said Miss Cornelia. "He's noted for his beautiful pigs. He's a heap prouder of his pigs than of his children. But then, to be sure, his pigs are the best pigs possible, while his children don't amount to much. He picked a poor mother for them, and starved her while she was bearing and rearing them. His pigs got the cream and his children got the skim milk. "There are times, Cornelia, when I have to agree with you, though it hurts me," said Captain Jim. "That's just exactly the truth about Lewis Taylor. When I see those poor, miserable children of his, robbed of all children ought to have, it p'isens my own bite and sup for days afterwards." Gilbert went out to the kitchen in response to Anne's beckoning. Anne shut the door and gave him a connubial lecture. "Gilbert, you and Captain Jim must stop baiting Miss Cornelia. Oh, I've been listening to you—and I just won't allow it." 'Anne, Miss Cornelia is enjoying herself hugely. You know she is.' "Well, never mind. You two needn't egg her on like that. Dinner is ready now, and, Gilbert, DON'T let Mrs. Rachel carve the geese. I know she means to offer to do it because she doesn't think you can do it properly. Show her you can." "I ought to be able to. I've been studying A-B-C-D diagrams of carving for the past month," said Gilbert. "Only don't talk to me while I'm doing it, Anne, for if you drive the letters out of my head I'll be in a worse predicament than you were in old geometry days when the teacher changed them." Gilbert carved the geese beautifully. Even Mrs. Rachel had to admit that. And everybody ate of them and enjoyed them. Anne's first Christmas dinner was a great success and she beamed with housewifely pride. Merry was the feast and long; and when it was over they gathered around the cheer of the red hearth flame and Captain Jim told them stories until the red sun swung low over Four Winds Harbor, and the long blue shadows of the Lombardies fell across the snow in the lane. "I must be getting back to the light," he said finally. "I'll jest have time to walk home before sundown. Thank you for a beautiful Christmas, Mistress Blythe. Bring Master Davy down to the light some night before he goes home. "I want to see those stone gods," said Davy with a relish. 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. L. M. Montgomery (1996). Anne's House of Dreams. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/544/pg544-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html. The lead image was generated using HackerNoon's Stable Disffusion Al Image Generator feature, via the prompt "ANNE'S HOUSE OF DREAMS".

CHRISTMAS AT FOUR WINDS

AN EVENING AT FOUR WINDS POINT

Many people are worried about artificial intelligence taking over the world. A recent study found that . Tesla founder Elon Musk says that AI is humanity’s “ ,” and that it poses a “ .” 60% of the people in the UK fear AI biggest existential threat fundamental risk to the existence of civilization Let’s look at four myths about AI in industry… The myths About AI Once AI advances to the point of human-level intelligence, robots will be able to read manuals, take classes, and learn to do all our jobs. Myth 1:  Robots will replace most human workers. Myth 2: AI-based viruses will decimate our IT infrastructures. They will no longer be needed once cars, trucks, and buses have full self-driving capabilities. Myth 3: Vehicle manufacturers will stop making steering wheels. Myth 4: Defense manufacturers will soon develop Terminator-like killer robots. The Reality Each of these myths about AI assumes researchers will figure out how to imbue computers that can think and reason like people. Given the recent progress in AI, it is natural to assume that human-level AI systems cannot be too far down the road. After all, we now have computers that can recognize our faces, translate languages, and beat the top Jeopardy! players in the world. Fortunately, despite the astounding progress in AI, computers and robots that can think and reason like people exist only in science fiction. Today’s AI systems can only learn to perform a single task and the AI technologies underlying today’s incredible AI systems cannot be extended to produce thinking and reasoning systems. See for a more in-depth explanation. this blog post Human Job Security Robots will not become smart enough to learn jobs by reading books and taking classes. AI technology will certainly be responsible for some job loss. However, conventional (non-AI) software has been responsible for far more job loss than AI software and this trend will likely continue. E-commerce is devastating brick-and-mortar stores, but uses conventional software, not AI. Conventional rideshare software like Uber and Lyft is displacing taxi drivers. The robots that take jobs in factories and warehouses use a sixty-year-old conventional software technique known as dynamic programming. Robert Gordon, professor of social sciences at Northwestern University, that, in the 250 years since the first industrial revolution, there has never been a technology that caused mass unemployment, and there is no reason to believe AI will change this pattern. said Cybersecurity We don’t have to worry about thinking viruses. Neither researchers nor hackers have a clue how to build such a thing. AI will likely have more of a positive impact on cybersecurity than a negative impact. The ability to use AI to defend against novel malware is essential to defenders. This is balanced against the ability for attackers to create novel malware using AI and the larger attack surface created by AI applications. Self-driving cars and trucks AI enables self-driving vehicles to “see.” Today’s self-driving cars use individual machine-learning systems to detect pedestrians, to identify lane markers, and much more. However, the “brain” of today’s self-driving vehicles is still primarily conventional programming. People use commonsense reasoning to handle unexpected phenomena while driving: a deer darts onto the highway or a flood makes the road difficult or impossible to navigate. Self-driving vehicles have no commonsense reasoning capabilities and may never have them. It is therefore incumbent on self-driving vehicle manufacturers to identify all possible edge cases and develop software for each edge case. This is likely not possible for consumer vehicles. Because of edge cases, consumer vehicles will probably need steering wheels for the foreseeable future so that drivers can take over when an edge case presents itself. At the other end of the spectrum, we are starting to see slow-moving shuttles (lacking drivers and steering wheels) that simply shuttle people between two points on a corporate campus. However, edge cases will likely limit vehicles without steering wheels to a relatively small proportion on the road for the foreseeable future. Autonomous weapons The fearsome Terminator robot is not in our future because we have no clue how to build computers or robots that can think and reason. Unmanned aerial vehicles without AI have been used in warfare since the US began to deploy them after the 9/11 attacks. These non-AI UAVs include drones and missiles that are either controlled remotely by operators at consoles or that find targets using radar, geographic coordinates, or heat-seeking capabilities. AI technology enables slightly more UAV autonomy by also enabling visual identification of targets. Conclusions The sudden success of AI technology leads many people to assume that the intelligent robots of science fiction lore are right around the corner. They are not. However, AI is having a big impact on industry. To understand the impact, it is critical to first separate the fiction from the reality.  This article explains away four myths about AI. Please enter any comments below and visit AI Perspectives where you can find a free online AI 101 textbook with 15 chapters, 400 pages, 3000 references, and no advanced mathematics. Previously published at https://www.aiperspectives.com/myths-about-ai/

Addressing Four Myths About the AI Industry: Reasons Not to Fear Artificial Intelligence

Market Networks as a term today still has no clear definition or even a wiki page, yet it’s pretty easy to understand. Market Networks as a concept made a relatively big splash among VCs back in 2015 as evidenced by publications in TechCrunch and NFX (a venture fund): TechCrunch: From Social Networks To Market Networks NFX: The Next 10 Years Will Be About “Market Networks” It’s been four years, however, where’s the hype? In this article I am going to do a bit of explanation of what a market network is and offer a perspective. (Spoiler: if you follow me, you know it’s about blockchain). What is a market network? To make it really short, a market network is a blend of a marketplace and a social network with a SaaS solution to help the service providers — who are individuals in the market network as opposed to companies — work with customers and other service providers on longer-lasting and more complex projects instead of just quick transactions like on Uber or eBay. Although, as one user on Hacker News remarked, “the SaaS part seems just thrown in there, like ketchup on French food.” Marketplaces are primarily about transactions, even though there are definitely reviews and the overall score that potential customers base their purchasing decisions on. Most people check the driver score when ordering a ride on Uber, same with eBay and purchasing products, and so on. These, however, are marketplaces since buyers and sellers meet there for a one-off transaction. Marketplaces are aggregators of sellers. Social networks are — well, they are social networks. People communicate and build relationships on LinkedIn, Facebook and so on. Commerce is not the core objective of social networks. So, now you have two separate places for your online presence and commerce: a marketplace to sell stuff, and a social network to build relationships. What, however, if you provide a service that is more complex than a mere buy-sell transaction. And on top of that, what if you need collaboration with other professionals in your industry to form a team to provide an end-to-end service? Well, that’s where market networks come in. In the market network, you are a service provider, but also a person with a profile, with history, reviews, score, etc. You can team up with other people to provide a complex service to a customer and channel the workflow through a SaaS solution. Four years ago HoneyBook was lauded as the prime example of this new class of market network startups, and VCs competed to invest in it. HoneyBook was a market network for the events planner industry. A typical HoneyBook scenario would be: You sign up as an event planner — an individual willing to provide the service. You build your profile with HoneyBook, and it’s your home on the Web. You use the built-in SaaS solution to work with your customers, send proposals, digitally sign contracts and so on. You also team up with other professionals in the HoneyBook network, those that have profiles and reputation, to provide end-to-end service for an event. An example would be you doing all the planning for a customer’s wedding, working with a person who does photography, and signing up a florist for the event. When the event is done, everyone gets paid. Makes sense, right? The world is ultimately moving towards the highest granularity level in how people work with each other — the level of an individual — including the provision of services. Market networks seem to be in tune with this, and back in 2015 it was proclaimed that the next decade was going to be about market networks, just like the previous one was about social media. It’s been four years, however, and the hype is just not there. Honeybook seems to have pivoted to a project management SaaS solution. What happened? I don’t have any insider knowledge, but I do have a perspective that I’d like to share. A quick stroll down the web evolution lane You might have heard the word Web 1.0, Web 2.0, and Web 3.0 ad nauseam, but I am going to use these terms since they are easiest to set the right context. Web 1.0 The very loose period for Web 1.0 is the 90s to early 2000s. This was when suddenly anyone was able to turn their connected computer into a web server and become an active part of the Internet. The ease of feeding your information to the anyone online was a great business and entrepreneurship opportunity, and it peaked roughly at the turn of the century. Web 2.0 Mid-2000s to roughly present days. While Web 1.0 was more of an online bulletin board where you could publish anything but in one direction and have no real feedback, Web 2.0 was more about collaboration and cooperation. The term that marked Web 2.0 was . The Internet was now all about platforms where you could communicate with other people online, exchange information, and create things together. Platformization became the new king. And it was great too. Instead of having a constellation of very different and wacky websites that you could check for information — as you would a billboard — you were able now to become an active part of a platform. Not only that, but you were able to earn too — for example, by renting out your room on Airbnb and building customer rapport through a review system. platform The thing with Web 2.0 is that it’s all about — from Reddit to Uber. And platforms are containerized versions of the network economy. They are isolated networks that can’t communicate with each other. And on top of that, they really don’t want to, because they always belong to someone. And that someone is never the community that brings value to the platform. platforms Web 3.0 There’s a lot of opinions on what Web 3.0 is or should be, with the key point being that its defining characteristic is true collaboration. True collaboration, however, is a pretty vague phrase. To make Web 3.0 more specific and in context with the two previous stages of the World Wide Web development, I’d propose the following two defining characteristics that are not vague: The isolated economy networks currently represented by and embodied in platforms cannot be isolated. The platforms must belong to the members that bring value to them and so they must be a network. That platforms cannot be isolated is illustrated by the history repeating itself — there was no World Wide Web in the 80s, it was all isolated networks. Just like you weren’t able to browse the Internet at the time and willy-nilly hop from one web server to a different one when viewing a document online, you can’t use, for example, your Reddit karma to run a shop on Amazon today. Which brings to the second point: platforms cannot be platforms. Currently, the level of granularity on the Internet is per platform, not per person. You can have an account on Kickstarter, but you can’t use it on GitHub. These are two completely isolated networks. The level of granularity must go one order lower — to the person. People — or more like our digital representations — a) must be able to operate independently from platforms, and b) the network they operate in and bring value to must belong to them. Where does that put market networks? See, I think that market networks, while in a way in tune with where the world is moving in terms of granularization to an individual, are stuck in the Web 2.0 mindset, while we are transitioning to Web 3.0. Ultimately, market network platforms are platforms and are seen as platforms. They are isolated and containerized. They further fragment the web. To be successful in operating a business in this vast digital space that only keeps growing in user numbers, you must maintain your profile in many networks — social or market — and you can’t effortlessly scale your reputation from one network to a different one. What if your digital selfhood on the Web always belonged to you — not to a service — and the reputation of your digital selfhood was maintained by the network. The network that also belongs to you? That would be the decentralized reputation that you’d be able to plug into any other centralized network, including market networks. It’s not that easy for a centralized market network to differentiate among service providing platforms and it takes a lot of effort to build a platform profile that you can use only in an isolated network. When your reputation is decentralized, the entirety of the digital space becomes your market network. Blockchain is the ultimate infrastructure tool that gives individuals true ownership of their digital selfhood and reputation. And that’s the biggest differentiator that matters in the transition to Web 3.0. Decentralized, pluggable, or portable — call it what you will — is the key component, not a SaaS solution in an isolated network. Oh, this is what we are doing with . U°Community

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