Whatever the future of video game technology holds, Doom will always be a part of it.

90s Video Game ‘Doom’ Is Famous For Being Able To Run On Any System, So Researchers Put It To The Test And Ran it On A Neural Network

While they look cool, diffraction spikes aren't necessarily desirable, since they can blot out other objects. For now, scientists will have to live with them. But future telescopes, like the planned Giant Magellan Telescope in Chile, could have no spikes at all due to its advanced configuration ...

Once You Notice This Weird Thing About James Webb Space Telescope Images, You Won't Be Able to Unsee It

But to get cars to fully drive themselves — a future version of the company's infamous "Full Self-Driving" (FSD) software that won't need to be supervised, according to Musk — customers may need a whole new hardware upgrade.

Elon Musk Suddenly Realizes That Teslas With "Self-Driving" Computers May Never Be Able to Actually Self-Drive

Able

able

The massive, catastrophic flash flood that occurred in Spain on October 29 was so horrific and devastating that it left the world aghast at its destructive nature. Over 200 people died, dozens went missing, and whole towns were covered in mud and debris, especially in the eastern Valencia region. In the United States alone, in 2023, 25 weather or climate disasters [caused at least $1 billion in losses and the [deaths](https://www.ncei.noaa.gov/access/billions/#:~:text=In%202023%20(as%20of%20December,and%201%20winter%20storm%20event) of 482 people, according to the National Centers for Environmental Information (NCEI). According to the US Government Accountability Office (GAO), “Severe storms, hurricanes, floods, and wildfires kill hundreds of people and cause billions of dollars in damage in the U.S. each year.” In fact, [according](https://www.ncei.noaa.gov/access/billions/#:~:text=In%202023%20(as%20of%20December,and%201%20winter%20storm%20event) to the National Oceanic and Atmospheric Administration (NOAA), “the U.S. has sustained 400 weather and climate disasters since 1980 where overall damages/costs reached or exceeded $1 billion (including CPI adjustment to 2024). The total cost of these 400 events exceeds $2.785 trillion.” India suffers from a similar problem. The frequency of flooding is getting worse due to the rapid growth of cities and climate change. Numerous other countries, especially in Africa, have seen similar catastrophes caused by unprecedented flooding. Widespread flooding in West and Central Africa, according to the World Health Organization, “has affected more than 4 million people, caused extensive damage to homes, schools, health facilities and swaths of farmland, heightening the risk of water-borne diseases, food insecurity and malnutrition.” Human-induced climate change “is likely to have significantly worsened the situation”, according to scientists at the Red Cross Climate Centre. Many experts now agree that with the advancement of artificial intelligence, this technology can and should play a bigger role in predicting those events and saving lives. And Northeastern University faculty experts predict it will. In an interview last year, Auroop R. Ganguly, professor of civil and environmental engineering and director of AI4CaS (AI for Climate and Sustainability) said, “In the next 12 months, we are going to see more and more efforts where data-driven systems and artificial intelligence come together.” One example is the Earth-2 platform, which is able to simulate global weather patterns in great detail. Taiwan is already using a similar tool to precisely predict the path and impact of typhoons. AI4CaS serves as a focus area within Northeastern University’s Institute for Experiential AI. The GAO found that AI weather forecasting models can help predict these catastrophic weather events. Machine learning specifically “can help improve model accuracy, increase warning times, and reduce uncertainty” and while some of these types of models are currently in use, “there are challenges to developing and adopting them more widely.” Google recently released an AI model that it claims can generate accurate weather forecasts at scale — while being cheaper than conventional physics-based forecasting. The "Scalable Ensemble Envelope Diffusion Sampler" (SEEDS) model is designed similarly to popular large language models (LLMs) like ChatGPT. SEEDS generates many ensembles — or multiple weather scenarios — much quicker and cheaper than traditional predicting models can. The team described its findings in a paper published March 29 in the journal Science Advances. In yet another effort to get ahead of life-threatening weather patterns, the University of Illinois and IBM have joined forces to work together on an AI model capable of anticipating heavy rainfall and flash floods in the Appalachian Mountains. Such models can triple or quadruple the warning time currently available to meteorologists and scientists. In August, NVIDIA Research, announced that it has developed a new AI model for predicting extreme weather events, called StormCast, part of a larger effort by NVIDIA to improve climate research through AI. These new AI models are financially beneficial since they can process information far quicker than older models. As extreme weather events, like flooding, become more frequent and intense, it is becoming increasingly important to be able to predict, understand, and prepare for these events. AI models will assist in predicting weather catastrophes and will help reduce not just major damage, but will also save thousands of lives. Such AI technologies will help scientists make faster and more accurate predictions, which can ultimately save lives and reduce the cost of disasters. In September, Smithsonian Magazine interviewed computer scientist and meteorologist Amy McGovern. She said that while AI will not replace human meteorologists in the near future, “We’re developing things to give them more options so that they can focus on turning the forecasts into actionable information for people to make better decisions.” “Humans are a lot better at that than AI is right now,” she said. The massive, catastrophic flash flood that occurred in Spain on October 29 was so horrific and devastating that it left the world aghast at its destructive nature. Over 200 people died, dozens went missing, and whole towns were covered in mud and debris, especially in the eastern Valencia region. In the United States alone, in 2023, 25 weather or climate disasters [caused at least $1 billion in losses and the [deaths](https://www.ncei.noaa.gov/access/billions/#:~:text=In%202023%20(as%20of%20December,and%201%20winter%20storm%20event) of 482 people, according to the National Centers for Environmental Information (NCEI). According to the US Government Accountability Office (GAO), “Severe storms, hurricanes, floods, and wildfires kill hundreds of people and cause billions of dollars in damage in the U.S. each year.” kill hundreds of people In fact, [according](https://www.ncei.noaa.gov/access/billions/#:~:text=In%202023%20(as%20of%20December,and%201%20winter%20storm%20event) to the National Oceanic and Atmospheric Administration (NOAA), “the U.S. has sustained 400 weather and climate disasters since 1980 where overall damages/costs reached or exceeded $1 billion (including CPI adjustment to 2024). The total cost of these 400 events exceeds $2.785 trillion .” The total cost of these 400 events exceeds $2.785 trillion India suffers from a similar problem. The frequency of flooding is getting worse due to the rapid growth of cities and climate change. The frequency of flooding is getting worse Numerous other countries, especially in Africa, have seen similar catastrophes caused by unprecedented flooding. Widespread flooding in West and Central Africa , according to the World Health Organization, “has affected more than 4 million people, caused extensive damage to homes, schools, health facilities and swaths of farmland, heightening the risk of water-borne diseases, food insecurity and malnutrition.” Widespread flooding in West and Central Africa Human-induced climate change “is likely to have significantly worsened the situation”, according to scientists at the Red Cross Climate Centre . Red Cross Climate Centre Many experts now agree that with the advancement of artificial intelligence, this technology can and should play a bigger role in predicting those events and saving lives. And Northeastern University faculty experts predict it will. In an interview last year, Auroop R. Ganguly, professor of civil and environmental engineering and director of AI4CaS (AI for Climate and Sustainability) said, “In the next 12 months, we are going to see more and more efforts where data-driven systems and artificial intelligence come together.” One example is the Earth-2 platform , which is able to simulate global weather patterns in great detail. Earth-2 platform Taiwan is already using a similar tool to precisely predict the path and impact of typhoons. AI4CaS serves as a focus area within Northeastern University’s Institute for Experiential AI. The GAO found that AI weather forecasting models can help predict these catastrophic weather events. Machine learning specifically “can help improve model accuracy, increase warning times, and reduce uncertainty” and while some of these types of models are currently in use, “there are challenges to developing and adopting them more widely.” Google recently released an AI model that it claims can generate accurate weather forecasts at scale — while being cheaper than conventional physics-based forecasting. The " Scalable Ensemble Envelope Diffusion Sampler " (SEEDS) model is designed similarly to popular large language models (LLMs) like ChatGPT. Scalable Ensemble Envelope Diffusion Sampler SEEDS generates many ensembles — or multiple weather scenarios — much quicker and cheaper than traditional predicting models can. The team described its findings in a paper published March 29 in the journal Science Advances . Science Advances In yet another effort to get ahead of life-threatening weather patterns, the University of Illinois and IBM have joined forces to work together on an AI model capable of anticipating heavy rainfall and flash floods in the Appalachian Mountains. joined forces Such models can triple or quadruple the warning time currently available to meteorologists and scientists. In August, NVIDIA Research, announced that it has developed a new AI model for predicting extreme weather events, called StormCast, part of a larger effort by NVIDIA to improve climate research through AI. announced These new AI models are financially beneficial since they can process information far quicker than older models. As extreme weather events, like flooding, become more frequent and intense, it is becoming increasingly important to be able to predict, understand, and prepare for these events. AI models will assist in predicting weather catastrophes and will help reduce not just major damage, but will also save thousands of lives. Such AI technologies will help scientists make faster and more accurate predictions, which can ultimately save lives and reduce the cost of disasters. In September, Smithsonian Magazine interviewed computer scientist and meteorologist Amy McGovern. She said that while AI will not replace human meteorologists in the near future, “We’re developing things to give them more options so that they can focus on turning the forecasts into actionable information for people to make better decisions.” Smithsonian Magazine interviewed “Humans are a lot better at that than AI is right now,” she said.

Will AI Ever Be Able to Predict Disasters and Save Lives?

Recently, someone asked me if one of the jobs I had shared ( ) was open to remote folks. While I work hard to ensure my weekly list of  jobs only includes remote-friendly opportunities, this particular role was listed as being in-office. something I do every week However, I offered the following advice based on my personal experience and what I’ve seen from other folks over the years: Even when a job is listed as being “in office,” it’s still possible to get the  company to consider a remote employee. Like so many things in business, a lot of it has to do with when you ask and how you make your case. Here’s the specific advice I offered to this person. I hope it helps you, too. If you have additional tricks, comments, questions, or  kudos, feel free to leave them in the comments below. Your best bet is to apply and see how far the conversation gets. I know that sounds both random and setting yourself up for failure, but hear me out: In the “before times” (pre-pandemic), I was a remote worker for over ten years. This was the strategy  I’d developed over the years  (given that almost nobody advertised for remote jobs). I would apply and  be very, VERY vague about location. “Everything is negotiable, but I want to find out more about the job and have you find out more about me,” was my standard answer. Once we got down to brass tacks, I would already have a sense of the company, how many remote folks there work, the job workflow, etc. I  could mount an argument for remote work based on that information. “How many times do you text or call someone who sits literally one cubicle away.” “During an outage, how many people go to a meeting room versus take the call from their desk so they can keep working on the problem?” “How much of the team is not in this location, so they’re effectively “remote” anyway?” “How often do I need to physically go into the data center to physically touch a machine?” …and so on. My point would be to highlight how much of the job is already remote, and so my being remote from an office location was functionally no different than being on-site. It worked 50% of the time. The combination of my proving I was the right person for the job AND the case I made about the remote nature of  the work gave the hiring manager the ammunition they needed to make the  argument. NOW… how many of those jobs admitted the job could be done remotely up-front? Zero. Absolutely NONE of them would have initially agreed to  consider a remote person. For companies that refuse to consider remote staff, the case I had to make five years ago is the same one we have to make today. And luckily  (for those of us who believe strongly in the power of remote work), the process is largely the same. : The one absolute deal-breaker for remote work status is tax status. If a company doesn’t  have a tax presence in the location where you live, it’s very, VERY hard to get them to consider doing so for you and you alone. CAVEAT (aka “death and taxes”) However, even this isn’t 100% impossible. I have two different companies that do it for me. Also published . here

Job Listed As In-Office? You Might Be Able To Go Remote

Mark Twain's Speeches by Mark Twain, is part of the HackerNoon Books Series. You can jump to any chapter in this book . LITERATURE here LITERATURE ADDRESS AT THE ROYAL LITERARY FUND BANQUET, LONDON, MAY 4, 1900

          Anthony Hope introduced Mr. Clemens to make the response to the
          toast “Literature.” MR. HOPE has been able to deal adequately with this toast without assistance from me. Still, I was born generous. If he had advanced any theories that needed refutation or correction I would have attended to them, and if he had made any statements stronger than those which he is in the habit of making I would have dealt with them. In fact, I was surprised at the mildness of his statements. I could not have made such statements if I had preferred to, because to exaggerate is the only way I can approximate to the truth. You cannot have a theory without principles. Principles is another name for prejudices. I have no prejudices in politics, religion, literature, or anything else. I am now on my way to my own country to run for the presidency because there are not yet enough candidates in the field, and those who have entered are too much hampered by their own principles, which are prejudices. I propose to go there to purify the political atmosphere. I am in favor of everything everybody is in favor of. What you should do is to satisfy the whole nation, not half of it, for then you would only be half a President. There could not be a broader platform than mine. I am in favor of anything and everything—of temperance and intemperance, morality and qualified immorality, gold standard and free silver. I have tried all sorts of things, and that is why I want to try the great position of ruler of a country. I have been in turn reporter, editor, publisher, author, lawyer, burglar. I have worked my way up, and wish to continue to do so. I read to-day in a magazine article that Christendom issued last year fifty-five thousand new books. Consider what that means! Fifty-five thousand new books meant fifty-four thousand new authors. We are going to have them all on our hands to take care of sooner or later. Therefore, double your subscriptions to the literary fund! 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. Mark Twain (2004). Mark Twain's Speeches. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/3188/pg3188-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

MR. HOPE has been able to deal adequately

The Man in the Brown Suit by Agatha Christie, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . Chapter XXXIV CHAPTER XXXIV We were not able to return to Johannesburg that night. The shells were coming over pretty fast, and I gathered that we were now more or less cut off, owing to the rebels having obtained possession of a new part of the suburbs. Our place of refuge was a farm some twenty miles or so from Johannesburg—right out on the veld. I was dropping with fatigue. All the excitement and anxiety of the last two days had left me little better than a limp rag. I kept repeating to myself, without being able to believe it, that our troubles were really over. Harry and I were together and we should never be separated again. Yet all through I was conscious of some barrier between us—a constraint on his part, the reason of which I could not fathom. Sir Eustace had been driven off in an opposite direction accompanied by a strong guard. He waved his hand airily to us on departing. I came out on to the stoep early on the following morning and looked across the veld in the direction of Johannesburg. I could see the great dumps glistening in the pale morning sunshine, and I could hear the low rumbling mutter of the guns. The Revolution was not over yet. The farmer’s wife came out and called me in to breakfast. She was a kind, motherly soul, and I was already very fond of her. Harry had gone out at dawn and had not yet returned, so she informed me. Again I felt a stir of uneasiness pass over me. What was this shadow of which I was so conscious between us? After breakfast I sat out on the stoep, a book in my hand which I did not read. I was so lost in my own thoughts that I never saw Colonel Race ride up and dismount from his horse. It was not until he said “Good morning, Anne,” that I became aware of his presence. “Oh,” I said, with a flush, “it’s you.” “Yes. May I sit down?” He drew a chair up beside me. It was the first time we had been alone together since that day at the Matoppos. As always, I felt that curious mixture of fascination and fear that he never failed to inspire in me. “What is the news?” I asked. “Smuts will be in Johannesburg to-morrow. I give this outbreak three days more before it collapses utterly. In the meantime the fighting goes on.” “I wish,” I said, “that one could be sure that the right people were the ones to get killed. I mean the ones who wanted to fight—not just all the poor people who happen to live in the parts where the fighting is going on.” He nodded. “I know what you mean, Anne. That’s the unfairness of war. But I’ve other news for you.” “Yes?” “A confession of incompetency on my part. Pedler has managed to escape.” “What?” “Yes. No one knows how he managed it. He was securely locked up for the night—in an upper-story room of one of the farms roundabouts which the Military have taken over, but this morning the room was empty and the bird had flown.” Secretly I was rather pleased. Never, to this day, have I been able to rid myself of a sneaking fondness for Sir Eustace. I dare say it’s reprehensible, but there it is. I admired him. He was a thorough-going villain, I dare say—but he was a pleasant one. I’ve never met any one half so amusing since. I concealed my feelings, of course. Naturally Colonel Race would feel quite differently about it. He wanted Sir Eustace brought to justice. There was nothing very surprising in his escape when one came to think of it. All round Jo’burg he must have innumerable spies and agents. And, whatever Colonel Race might think, I was exceedingly doubtful that they would ever catch him. He probably had a well-planned line of retreat. Indeed, he had said as much to us. I expressed myself suitably, though in a rather lukewarm manner, and the conversation languished. Then Colonel Race asked suddenly for Harry. I told him that he had gone off at dawn and that I hadn’t seen him this morning. “You understand, don’t you, Anne, that apart from formalities, he is completely cleared? There are technicalities, of course, but Sir Eustace’s guilt is well assured. There is nothing now to keep you apart.” He said this without looking at me, in a slow, jerky voice. “I understand,” I said gratefully. “And there is no reason why he should not at once resume his real name.” “No, of course not.” “You know his real name?” The question surprised me. “Of course I do. Harry Lucas.” He did not answer, and something in the quality of his silence struck me as peculiar. “Anne, do you remember that, as we drove home from the Matoppos that day, I told you that I knew what I had to do?” “Of course I remember.” “I think that I may fairly say I have done it. The man you love is cleared of suspicion.” “Was that what you meant?” “Of course.” I hung my head, ashamed of the baseless suspicion I had entertained. He spoke again in a thoughtful voice: “When I was a mere youngster, I was in love with a girl who jilted me. After that I thought only of my work. My career meant everything to me. Then I met you, Anne—and all that seemed worth nothing. But youth’s call to youth. . . . I’ve still got my work.” I was silent. I suppose one can’t really love two men at once—but you can feel like it. The magnetism of this man was very great. I looked up at him suddenly. “I think that you’ll go very far,” I said dreamily. “I think that you’ve got a great career ahead of you. You’ll be one of the world’s big men.” I felt as though I was uttering a prophecy. “I shall be alone, though.” “All the people who do really big things are.” “You think so?” “I’m sure of it.” He took my hand and said in a low voice: “I’d rather have had—the other.” Then Harry came striding round the corner of the house. Colonel Race rose. “Good morning—Lucas,” he said. For some reason Harry flushed up to the roots of his hair. “Yes,” I said gaily, “you must be known by your real name now.” But Harry was still staring at Colonel Race. “So you know, sir,” he said at last. “I never forget a face. I saw you once as a boy.” “What’s all this about?” I asked, puzzled, looking from one to the other. It seemed a conflict of wills between them. Race won. Harry turned slightly away. “I suppose you’re right, sir. Tell her my real name.” “Anne, this isn’t Harry Lucas. Harry Lucas was killed in the War. This is John Harold Eardsley.” 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. Agatha Christie (2020). The Man in the Brown Suit. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/61168/pg61168-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.

We were not able to return to Johannesburg that night

Since its release in 2020, OpenAI's GPT-3 and all other AI content generators based on it have shocked the tech world with their impressive capabilities at spinning out instant content with just a few prompts. https://www.youtube.com/watch?v=loRJkgeRgt4?embedable=true (Jaspar is one of the AI content generators built on GPT-3’s platform in action) By now, the vast majority of people who have anything to do with creating content for the web have seen demos of these tools in action. So they grasp all that it entails: its value proposition and benefit. Deep down, they know AI is changing the content game, but they are afraid of using it. They fear the repercussion that may later come down the line like the: Florida update in 2003, Penguin update in 2012 Panda update in 2014, Medic update in 2018, and Helpful Content update in 2022. But I think that fear is unfounded. Google will fail to crank down on AI-generated content. I didn't arrive at this conclusion on a whim. Neither did it appear to me in a dream. Nor was it some thought-up opinion. Instead, it's a result of experimenting with various AI tools available on the Market for months and seeing first-hand what they are capable of.' And that's what I will show you in this article. Google has performed beyond our wildest expectations Don't you get amazed by its search capability? From time to time, I do. You type in a bunch of keywords into that small little textbox. You hit the "Search button", and boom, in the blink of an eye, you see over 10 million results before you. Sometimes, you don't know exactly what you want. But if you have the faintest hint —put it into the search box, and Google will guide you with her smart autosuggestions. Isn't that impressive? At one point, the running joke on Twitter was that knowing things isn’t the most incredible skill in life, but one's ability to use Google to find them fast. But it wasn't always so Search in the late 90s was far different from what we have today. Back then, it was a completely frustrating experience — despite only a few thousand websites on the internet. In fact, Google wasn't the world's default Search engine. . So how did small Google, a baby Startup from Mountain View, beat big Yahoo to become the behemoth that it is today? It was $125B Yahoo A lot of people have taken a lot of stabs at that question over the years and missed Some say it's because of their minimalist design. Google's homepage was just a Search box and a Search button, While Yahoo's homepage was cluttered. Paul Graham, Y-combinator's Founder, dedicated . There, he says it's because Yahoo got excess unmerited revenue, and they were Media-centric instead of Tech-centric. a whole page on his site to this topic PG's answer reminds me of the warning I was giving about good Storytellers. It came from two different Nobel-Prize winners, Richard Feynman and Dr Daniel Kahneman, at two different times. Good storytellers can easily fool those of us who aren't experienced in scientific rigour or statistics. Now add PG's authority, and the claim begins to look solid. But in reality, it isn't. And the warning is: In an article on TechCrunch, Mohit Aron, a former Google Engineer now the CEO of Mohesity, says . He made reference to some scalability complexity kinds of stuff. it's because of the infrastructure But he's an Engineer, so I quite understand the sentiment Some Branding Expert cooked up an article I will not link to for some reason on Wharton's University of Pennsylvania website. Similar to the Engineer, they said it's because of the differences in their "brand approach". The article also made mention of the Brand's Purpose, Vision, and Mission statement. They even mentioned Logo, which overall, and to me, is laughable. When you see rhymes like "Yahoo Confusion VS Google's Clarity", there's probably bullshit in the air. Beware PS: And the (closest) answer? It came from another former Google Engineer, Robert Rossney. And it's in how Google easily presents the most relevant information to the user's search term. The algorithm behind this was called PageRank. PageRank rates the result using the number of high-quality backlinks a site has. In simple form: the more links from high-quality sites a page has, the more it is trusted and the better it ranks in the search engines. The invention's clear difference in producing the valuable result in 1996 tipped the Search war in Google's favour.  And ever since then, they've been Guardians of the web. Galaxy And whenever the algorithm got gamed, Google's subsequent over the years solidified its position and dominance. response and patches (updates) But they are not perfect Take Backlinks buying, for example. Bad actors use it to trick Google's algorithm into pushing their site to spots they don't deserve. Google knows. And has been fighting it as far back Oct 2007 ( ). PageRank update Yet, people are still selling it to date. In fact, it's a full-blown business. With so much capital in the market, it's one reason why the Search Engine result page has been shit in the past few years. So much so that . people now append the word "Reddit" to improve their search results This is one reason why I think they will fail to police AI-generated content. Like backlinks, it's entirely out of their watch. This is truer if you edit the output instead of pushing it directly into production. But that's just one reason; here are two more: AI is better than most Humans I'm not saying this to shame anyone; there are times I look at my piece and reconsider ever picking Content Marketing as a career. But the thing is, there's some really shitty content on the web that you'd think could only come from a bot, but it's, in fact, written by a human. It's as though English is not the writer's first language, and most times, that's true. Content mills in the west charge their clients thousands of dollars yet outsource them for pennies to writers living in the third world, like Pakistan, India, Thailand etc I have also used (iWriter et al) the quality is about the same. Ai-generated content has been written a lot better than some of the content writers I've used in the past. And because of the pay and the nature of the job, these Writers end up copying and rephrasing what already exists out there. No narrative, no storytelling whatsoever! Sometimes, their articles are full of what Ryan Law, Animalz VP of Marketing, calls "factual inaccuracies", resulting from the less-than-rigorous research that goes into them. This is the same weakness Experts accuse AI content of. Yet, these content rank, particularly when they are published on sites with high DR. We see them on the first result page all the time. So why should better content not rank because humans didn't write them? Is the Time-Compute worth it? In the online AI vs Google debates, besides asking if Google can detect AI content, I think the question not asked often is: How much will it cost them to do so for every page in their database? It's a lot and probably not worth it. For one, AI content is far different from "spun content". Ryan Law, whom I referenced earlier, describes Spun Content best: a fancily rephrased content whose underlying structure remains the same as the original. "It’s easy for Google to spot synonyms", he says. And countless Google updates after updates have shown this. But you can't say the same for AI-generated content. Sometimes, you wouldn't know if you weren't told! Not mere rephrasing. Cut and paste, find and replace. Now, if humans struggle to know, who will train the AI to learn to? I mean, people are using AI to win an art competition these days. How much more words! It gets even more interesting when you consider the pace of innovation for these tools. GPT-1 had 117 million parameters. GPT-2 had 1.5 billion. While GPT-3 has 175 billion, which is a whopping 116000% increase over GPT-2. Imagine what they have in store with GPT-4! And a quick trivial If Google is not sold on AI, then: Why are they rewriting Meta Descriptions and Title tags with AI? Why are they offering to help their AdWords customer rewrite their ads using AI? (and please don't @ me). Want to know a little secret? AI content is not new. It just became accessible to the public. Large media companies like the wall Street journal . have been using it for over five years now So also Google. Or, how do you think they create zero-click content? But I know Google. They will fight hard and won't relent And the only way I see them overcoming this challenge is by changing the incentives, I.e. Stop rewarding Quantity and start rewarding Quality. This will require a systematic change in how they review the content they index. I foresee them using a rating system for blog Authors. Your articles won't get indexed without registering. You are rewarded every time a reader finds your piece valuable. Break the rules, and it displays prominently on anything you write. While I've not thought through the idea for long, I believe this will raise the bar for written content and elevate the writer's value. Otherwise, Google will lose their search dominance to upcoming Search Engines or to Tiktok (which is already happening). I know some corporate defenders may want to throw the fact that they just recorded their all-time highest revenue at my face, so I'll remind you of the best time when having sex: just before it ends.

Google May Not Be Able to Crack Down on AI  Generated Words

Decentralized finance (DeFi) trading platform Mango Markets was recently exploited with hackers draining the platform of over worth of crypto. According to a tweet by Mango's official Twitter account, it seems that "oracle price manipulation" was the method used to attack the Solana-based DeFi platform. $110 million Another broke down how the attack went. The attackers started by depositing $5m worth of USDC to an account on the platform. The funds were then used to offer 483 million units of the platform's native Mango (MNGO) token in the form of perps (perpetual contracts). thread on Twitter Next, the attacker funded another account with to buy the 483 units of MNGO perpetual contracts. The attacker used this method to spike the price of MNGO from $0.03 to $0.91, making their MNGO perps worth . $5 million of USDC $423 million The money was then used to take out a loan of $116m using multiple tokens on the platform, including Bitcoin, Solana, and Serum. Unfortunately, the loan wiped out all of the liquidity in Mango Markets, causing the price of MNGO to crash to $0.02. Mango Market's development team confirmed that they are looking into what happened and have opened an investigation into it. The protocol announced via its various social media channels that it has blocked deposits while they investigate further. The team also told users not to deposit funds into the platform before they blocked deposits. Why Were Mango Markets exploited? Price manipulation was the method used to take out large perpetual contracts to push the price of MNGO over 45x in such as short time frame. This works by an attacker taking advantage of low liquidity to artificially boost a token's price. It is the same method used in pump and dump schemes; find a token with low liquidity, place large buy orders to drive up the price, and then use new investors as exit liquidity to cash out. However, this attack method is hard to pull off when there is a very high amount of liquidity since it would take a lot more capital to manipulate the price. This is why pump-and-dump schemes are more popular with new or fairly unknown tokens since they usually have very low liquidity. If they had sufficient liquidity, Mango Markets could have avoided this attack. One way that Mango Markets could have increased their liquidity is through the use of an automated market maker (AMM). Automated market makers are systems that gather liquidity from users and use algorithms to determine a token's price. AMMs work by large token holders known as liquidity providers (LPs), adding tokens pairs (i.e., MNGO/USDC) into pools in equal amounts. This enables decentralized exchanges to outsource their liquidity while compensating the LPs with a cut of the trading fees earned on the platform. According to Ben Roth, Co-founder and CIO of , having a sophisticated market maker can act as a clear deterrent for adverse trading behaviour during all market conditions. By working with a firm that is able to algorithmically provide deep, consistent liquidity, projects ensure that ‘bad actors’ are left with an increased level of uncertainty that their price manipulation strategy will be successful. Auros The Aftermath A day after the attack on Mango Markets, the attacker used the platform's decentralized autonomous organization (DAO) to make a proposal. that the Mango DAO use its $70 million community treasury to reimburse any bad debts. The attacker proposed If the proposal is approved, the Mango DAO team will utilize the cash from their treasury to compensate for any unpaid debts. Afterward, the hacker would transmit the stolen tokens to an address supplied by the team behind the Mango DAO. In another act of manipulation, the hacker seemed to support this proposition by voting with millions of tokens stolen via the attack. Nevertheless, the quorum requirements for passing the proposal have not been met, so the proposal may not be accepted. The attacker also requested that no criminal proceedings be pursued should the proposal go through. Conclusion Mango Markets was attacked using price manipulation to spike the price of its native token and then take out massive loans in various other tokens on the platform. The attacker then demanded that the Mango DAO reimburse any bad debts using their treasury funds in return for the stolen liquidity.

How and Why Mango Markets Was Able to Be Exploited by Attackers

Hey Hackers! I’m Vijay and I’m an Entrepreneur, Designer & Engineer based in Auckland, New Zealand. First of all, a huge thank you to the community and staff for nominating me for a  few 2021 Noonies awards! HackerNoon I’ve been nominated in the following categories so please do check out these award pages and vote: HackerNoon Contributor of the Year - GO HackerNoon Contributor of the Year - GOLANG As someone in the tech industry, Learn more about my thoughts and opinions on open source and my journey in the tech industry via the interview below. I believe that the most exciting open-source technology of the present is Kubernetes because of its general-purpose nature and its ability to manage applications at planet scale. 1. What do you do and why do you do it? (tell us your story) In the year 2017, life events (workplace politics and corruption) led to what I believe was a turning point for me. The work I was doing was handed over to consulting firms and I was out of work. So I invested in learning something new. After a series of prototypes, I settled on Golang. It was easy to pick up, I had a lot of fun programming with it and I soon realised it was an amazing general-purpose programming language. It helped me solve every problem I threw at it. It naturally led me to the container movement and eventually to what I believe is the best open-source container orchestrator on the market - Kubernetes. Fast forward to the present day and I use Kubernetes and Golang on a daily basis. It’s amazing that tech so powerful is fully open source and free to use. 2. Tell us more about the things you create / write / manage / build! I currently spend most of my time working with teams designing and building containers that run on Kubernetes. Where possible, I contribute to open source projects and open source some of my work. This year I have started writing tech articles. I have written 3 so far. I published my first ever tech article on Medium and then re-published it here. I want people to be able to access content without too many restrictions and hence have moved to publish the remaining 2 articles exclusively on Hackernoon. 3. How did you end up on your current career path? Do you like it? I first saw a computer when I was about 12 years old. It was the game Prince of Persia running on MS-DOS. I was fascinated and after that was naturally attracted to computer-related courses at my school and university. I pursued a Computer Science degree, my first job was a software developer and I kept growing naturally in that career path. I love working with tech and feel that is the perfect fit for a mind like mine. 4. What tech are you most excited or passionate about right now and why? I like open-source general-purpose tech. Being open-source means that they don’t have restrictive or expensive licensing terms and being general-purpose means they can solve most problems you throw at it. Kubernetes and Golang fall into this category. RISC-V is another area that I am excited about. Like open-source software, RISC allows for open-source hardware solutions. A fully open-source hardware and software stack has the potential to disrupt the market. 5. What tech are you most worried about right now and why? Algorithms are designed to increase user addiction or spread misinformation. Software that is not open source and has ridiculous licensing terms, for example: need a different more expensive license if more nodes are added. I call such software “authorised ransomware”. Such software is far too popular in the corporate world. Tech that is advertised as Privacy focused but is not open source. Crypto mining and its impact on GPU prices has delayed my GPU upgrade. 6. If we gave you 10 million dollars to invest in something today, what would you invest in and why? I can’t think of a single consumer product that I can easily buy today that uses a fully open-source hardware and software stack. I would start a tech company that builds such products. I would probably start with a simple general-purpose use case and grow from there. 7. What are you currently learning? I am currently learning RISC-V. 8. What’s the best advice you’ve ever given someone? Go open source. 9. What’s the best advice you’ve ever received? Never stop learning. About HackerNoon’s 2021 Noonie Awards The annual Noonie Awards celebrate the best and brightest of the tech industry, bringing together all who are making the Internet and the world of tech what it is today. Please be sure to , nominate, and vote for the people and companies who you think are making the biggest impact on the tech industry today. check out our award categories The 2021 Noonies are sponsored by: , , and . Thank you so much to these sponsors who are helping us celebrate the accomplishments of all our nominees. bybit Dottech Domains Avast

"I want people to be able to access content without too many restrictions" #Noonies2021

Podcast listening is hard to track, but that doesn’t mean advertisers aren’t trying By Adrianne Jeffries In these muddled times, at least one thing is clear: Nearly everyone’s tracking you, always. But there is one area that has been largely off-limits to digital data collectors for 20 years: podcast listening. Traditionally, the podcast ecosystem has been tracking-resistant, in part because podcasters release their shows through RSS, free technology dating back to 1999. Podcast players, also known as , like Apple Podcasts and Castro then aggregate those episodes in easy-to-use apps. Listener behavior is strewn across a plethora of apps, many of which don’t share data with anyone, including the podcast creators. podcatchers That’s all changing. Advertisers are on podcasts in 2020, and companies are devising ways to provide them with data that will persuade them to spend more. The most common tactics include using IP addresses to identify users, adding tracking URLs to ads, and abandoning RSS in favor of proprietary platforms that already track their users. projected to spend more than $800 million The change has provoked considerable debate, , within the podcasting industry. sometimes combative In September 2019, Basecamp its podcast hosting service, Art19, after discovering that the company was building the capability for personalized ads. In December 2019, Libsyn, one of the major podcast hosting services, banned Podsights, one of the major podcast data companies, , “[W]e feel it is necessary to protect listener privacy.” In February, podcasting giant PRX hosted a on “emerging threats” to privacy in order to encourage more productive discussion. Then in August, one of the major podcast apps, Overcast, a feature that lets users see which podcasts have enabled tracking. publicly dumped saying conference added “I see the storm clouds gathering,” said Michelle De Mooy, a privacy consultant who spoke at PRX’s conference. “There’s a danger that this is a moment that can change away from privacy.” Podcast listening habits, like browsing history, can potentially reveal a lot about someone’s interests. There are podcasts about mental illness, substance abuse, sexuality, debt, and other sensitive topics. So what do your podcasts know about you? Traditionally, Podcasts Couldn’t Track Much The main metric of success for most of podcasting history has been the one thing the RSS infrastructure allows: how many times the file was downloaded. It’s like the early days of the web, when websites proudly displayed a “hit counter” that tracked the raw number of visits. But the act of downloading a podcast still passes some data, and whoever hosts the file can log a download’s date and time, the podcatcher used, the IP address (which also reveals rough location), and typically, the type of device. I see the storm clouds gathering. - Michelle De Mooy, privacy consultant Advertisers, meanwhile, have tended to target listeners based on a podcast’s subject matter. If you’ve ever heard a host give out a promo code for listeners while reading an ad, it’s because that’s basically how advertisers would track which podcasts were actually driving customers. Ads were not personalized because there was no data to personalize them with, and no way to match an ad to a specific user. This could rapidly change if the podcasting industry were to ever shift away from RSS. And there are some signs of that happening. Spotify has been signing exclusive deals with megastars like Joe Rogan and Kim Kardashian West. Bloomberg that Apple is hiring an executive to lead the charge on original and exclusive podcasts, and Amazon has its first platform-exclusive podcast. reported announced Needless to say, these exclusives will likely not be available via RSS. “They are not podcasts. They’re audio shows,” said Andrew Kuklewicz, the chief technology officer of PRX. However, when the venture-capital-funded podcast network Luminary to be copying audio and rehosting it instead of using RSS, it triggered an exodus of podcasters. (Luminary clarified that it was not copying audio and was still using RSS, just rerouting traffic first.) appeared Meanwhile, as podcasts have grown and become more professional, the commercial industry around them has found ways to work around RSS’s limitations. How Are You Being Tracked Now? In 2014, the podcast “Serial” debuted, introducing the medium to mainstream listeners. (As of September 2018, “Serial” had across its first two seasons.) more than 340 million downloads That same year, Acast, a podcasting company based in Sweden, announced “dynamic ad insertion.” Previously, ads were “baked in” to a recording, but now podcasters could mark their files with ad breaks, enabling a hosting company to switch in a different ad based on the time or location the podcast was downloaded. It also paved the way for ads to be sold programmatically, based on a bidding system that automatically matches buyers and sellers without the need for salespeople. Much of the advertising on the web, including Google Ads, is bought and sold this way. Programmatic buying requires consumer data in order for advertisers to rapidly experiment with different ways to target users and optimize ads for the best results. “That’s probably when I certainly saw things start to change,” said Kuklewicz, the CTO of PRX. “Once you can actually dynamically inject ads, then that data that I’d be able to get from requests becomes actionable. And once that’s possible, then we start to look a lot like the rest of ad tech right now.” Our goal here is to make tools that will let this medium grow in general and bring more people into the space and create better content. - Andy Pellett, co-founder of Podsights Suddenly, companies on the hosting and ad tech side started to try to squeeze as much data as possible out of the limited interaction they had with listeners, using a couple of different methods: They could add an extra web address in the metadata of an ad, forcing a tiny download that allows the advertiser to register the listener’s IP address, the podcatcher they used, and usually the type of device. They could also use “prefixes” or redirects that take the user on a brief detour to another webpage so a third party can register them, similar to the way a URL shortening service like Bit.ly works. At this point, we’re still just talking about basic data. But advertisers want more granular data: Did someone listen to my ad on a podcast, and then visit my website or buy something? And what else can I learn about podcast listeners, so I can decide whom to target with my ads? Some companies, like Podsights, now collect basic data, such as IP address and device type, and combine it with information from third-party data collection companies. Podsights co-founder Andy Pellett said the company uses this data only to establish “attribution”—that the same person who listened to an ad also visited a brand’s website, for example—and does not share any data that could be used to identify listeners. “Our goal here is to make tools that will let this medium grow in general and bring more people into the space and create better content,” he said. Other companies at least advertise the ability to do more granular targeting, though it’s not clear how many podcasts actually use such services. Megaphone says that its Megaphone Targeted Marketplace, which allows automated buying and selling of ads, allows advertisers to target users by “ .” Megaphone did not respond to a request for comment. demographic and purchase intent Your Podcast Player Could Know More About You Podcatchers themselves have access to far more accurate data about users, such as the full list of podcasts a listener subscribes to and how far they listen in any given episode. Some of the smaller podcatchers, however, have emerged as bulwarks against anything potentially invasive. When Russell Ivanovic, head of product for the podcatcher Pocket Casts, heard that some analytics companies had figured out a way to place cookies on users’ devices when they downloaded a podcast, he immediately disabled the capability in Pocket Casts. “They might be doing innocent things with it, but we felt like that was like a bridge too far,” Ivanovic said. “That’s not something users are expecting.” They might be doing innocent things with it, but we felt like that was like a bridge too far. - Russell Ivanovic, Pocket Casts Companies like Spotify, Apple, Amazon, and Google, which all offer apps and devices that play podcasts, are in the best position to collect user data. These companies already have “first party” data, collected through their other services, that they can combine with someone’s podcast listening habits, as well as additional insights from the device you’ve installed them on. Spotify knows if your phone is , for example. in your hand or in your pocket Another way to collect more data would be if different layers of the ecosystem agreed to work together. National Public Radio attempted this with a project called , or RAD, essentially instructions for how podcasters and advertisers could finally measure if someone who downloaded a podcast actually listened to it, and for how long. NPR publishes lots of podcasts and also has its own podcast player, the NPR One app, giving it access to more data about how people listen. This data was used to reassure advertisers that their ads were actually getting heard, and also to tweak show formats. The creators of the show “Pop Culture Happy Hour,” for example, noticed that their listeners were dropping off in the middle of an episode. “The team was able to pivot and release episodes more frequently, but much shorter and more focused,” said Stacey Goers, senior product manager for podcasts at NPR. “And it really gives users what they wanted.” Remote Audio Data The ad server NPR uses, AdsWizz, also allows ads and stories to be swapped in and out of episodes. This enables NPR to experiment with its fund-raising campaigns as well as the content of the show. The first half of “Consider This,” for instance, is the same for everyone, but the second half comes from a station in the listener’s local area. The catch with RAD is that podcatchers also have to participate, and so far, they haven’t taken it up. “RAD has not seen a large amount of adoption,” Goers said. Part of that is because app developers would rather spend their limited resources on features that would attract new users, she said, and part of it is because of the lack of centralization. “Podcasting still doesn’t necessarily have a big governing body that’s putting its foot down and asking for it,” she said. The Changes Are Controversial—and It’s Not Clear How Widespread They Are “Ultimately if podcasts are going to grow as a media type, as they emerge, the advertisers are going to require the more granular level of tracking,” said Eric Picard, a consultant and former vice president of advertising product management at Pandora. But not everyone agrees that podcasting’s health relies on gathering more data. “That’s bullshit,” said Rob Walch, vice president of podcaster relations at Libsyn, a major hosting service that also sells ads for podcasters. “The podcasters that make the most money are the ones that are doing host-read ads that don’t do any of this. Joe Rogan makes more money podcasting with advertising than any of these people.” At least for now, podcasting is still a fairly private activity, in that it doesn’t produce much data on its own. “That’s one of the things, to be honest, that’s stunted podcast growth initially,” said Brad Smith, CEO of Simplecast, a hosting and analytics company. “Advertisers want to go where the deepest, richest data lives. And by default, you’re not going to get data out of podcasting.” Nothing has fundamentally changed, he said. “We gather the exact same information about a listening session today that we did five years ago.” Correction An earlier version of this story incorrectly identified the technology NPR uses for dynamic ad and content insertion. It is AdsWizz, not RAD. . Originally published as " When Zombie Data Costs You a Home " with the Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license

How Much Exactly are Podcasts Able to Track us

Internet-connected technologies that gather, integrate, and analyze data about people's behavior in an area are referred to as IoB. Is the Internet of Behaviour Getting Better Everywhere? In the aftermath of the pandemic, how individuals interact with personal space has become incredibly important. When a lethal illness may spread with minimal touch, it's critical to keep public health under control and preserve social distance measures. That's where the Internet of Things (IoT) comes in. Environment IoB technologies are now installed in many workplaces welcoming staff back for the first time in over a year. RFID tags tracking hand-washing or sensors detecting mask-wearing, for example, might be combined with automated alarms if someone isn't following standards. These are a few of the more prevalent IoB applications right now, although they're far from the only ones. Data from many technologies (cameras, scanners, sensors) and multiple sources (private sector, public sector, social media) is combined to assist in the analysis of crowded locations. Understanding how and why people use spaces may help them become safer, cleaner, more convenient, and appealing – in other words, better in every sense. We can now know what we couldn't before thanks to the Internet of Things, which means we can improve and develop environments in ways that were before inconceivable. Our Way of Life There are many reasons to be excited about the Internet of Things — and to be concerned about a society that monitors our every move. However, both sides of the coin must be debated because IoB technologies appear to be on their way to being ubiquitous. Given how many diverse things IoB can perform, rapid growth appears to be a foregone conclusion. Consider the fact that there are already billions of connected devices collecting granular data on where people go and what they do. These gadgets can be found in offices, stores, and restaurants, as well as in our automobiles, phones, and wearable devices, all of which capture vast amounts of data on modern life. By 2025, there will be over 42 billion connected devices in operation, creating over 1 billion GB of data per day. Previously, we primarily used this data for certain objectives, such as tracking our health, and we made only a rudimentary effort to connect disparate data sources. At its foundation, the Internet of Things is about utilizing more of this data to gain a better understanding of how we live. Using IoB to Solve Complicated Issues The ramifications are enormous, but the applications are extremely narrow. For example, a business could utilize IoB to track when people join and leave Zoom meetings over time and use that information to create a more efficient schedule. Another application would be to use facial recognition technology during a meeting to watch attendees' facial cues and identify staff who may be overworked or under-engaged, allowing for early intervention rather than burnout. IoB Applications Aren't Just for the Office Anymore Data from traffic could be utilized to prevent traffic jams and accidents, as well as to plan future highways for maximum safety and efficiency. Crowd data might be used to prevent riots from erupting at protests or major celebrations, as well as to enable more people to pass through airport security in less time. Schools might potentially use data from the Internet of Things to assist, prevent, or stop school shootings and brawls. IoB might be used in any situation where people interact to discover and reduce pain points for everyone involved. Where Could IoB go Wrong? It is not an exaggeration to suggest that the Internet of Things (IoT) has the potential to solve some of humanity's most pressing, significant, and difficult issues. Unfortunately, none of these results are guaranteed. IoB is intriguing, but it's also troublesome for a variety of reasons. Because there are currently few regulations governing internet-connected gadgets, it is frequently up to whoever collects data to decide how to utilize it. To fully benefit from the Internet of Things, data must be shared early, regularly, and completely. However, sharing it will necessitate a deep commitment to ethics as well as a thorough awareness of how things can go wrong. The Battle Between Good and Evil Consider the following scenario, which demonstrates the delicate balance that IoB must strike: Suppose a monitoring firm has data detailing where someone travelled in the last month. The corporation may agree to share that information with a partner if the person's name is hidden, or it may agree to hand it over to a judge who issues a warrant. They could, however, elect to sell such information to the highest bidder or hand it over to government agents who will exploit it to suppress opposition. There are ethical and exploitative methods to use each piece of IoB data. Another disadvantage is that people's movements aren't always reliable indicators of their true motivations. For example, if a fitness tracker shows that someone goes to bed at 3 a.m. every night, are they staying up so late because they work late, party hard, or suffer from insomnia? That question can usually be answered with enough IoB data. When it can't, though, firms are left with a lot of data but few useful insights. How do we make the Internet of Things Work? Companies interested in the Internet of Things, whether designing or adopting the technology, must take legal and ethical considerations carefully. The question remains as to whether or not businesses will act ethically. Then there's the matter of the costs. Who pays for the upkeep of the internet's security? You? What about the government? What about the business? They must be open and honest about the data they acquire and why they collect it. More significantly, organizations and individuals must use IoB as a force for good, improving people's experiences rather than punishing, rejecting, or stalking them. In a nightclub, for example, IoB may be used to monitor audience sentiment and then modify the environment by playing various music. With this information, the nightclub may decide to admit more or fewer patrons, change drink specials, or change the lighting. This information could assist a club owner to fine-tune a room such that everyone feels like a VIP the moment they walk in. But, perhaps more importantly, the club owner will grow his business by catering to the specific requirements and desires of his clientele. IoB Possibilities are Endless Inside the house, where individuals have control over what technology observes and tracks, IoB might have huge implications without generating ethical concerns. When someone is running late for work, for example, the correct combination of technologies can detect this and immediately turn up the bedroom lights, start the coffee maker, and get the automobile warmed up. There's not much IoB can't change for the better if we get creative and stay mindful.

Are we Able to put Internet of Behaviour (IoB) to Good Use?

This is a free sample chapter from my book, Meta-Learning: Powerful Mental Models for Deep Learning and Thriving in the Digital Age . First things first Before you can learn to do deep learning, you must become a developer. Being a developer is not only about programming. In the larger scheme of things, being able to write code is just a small part of what a developer can do. If I were to start all over, I would optimize this part of the journey for fun. I would want to get into the tinkering mindset and start feeling comfortable messing around on my computer. There is no better way to achieve this than by working on something you truly enjoy. I would take it easy. When it comes to learning, consistency beats intensity every time. When you are just starting something, there is no better way to stay consistent than to not exert yourself too much. I would still make quick progress, because this time around, I would know what to focus on. This is what the beginning of this chapter will cover. How to get started My first stop would be to complete one of the programming MOOCs. They don’t teach you a lot about being a developer, but they do teach you some things about writing code. I would probably go for Harvard CS50 on . And, as I want to have fun, I might choose the game programming track. edx Harvard CS50 has one great thing going for it: On top of teaching you how to program, it also teaches you about the computer and how it works! Two birds with one stone. Still, regardless of which MOOC or programming book you chose, it is hard to go wrong. Programming has been taught for many generations, and many good teachers are out there. The biggest mistake you can make at this stage is to do more than a single beginner programming course. Striving to learn any particular programming language really well would be a mistake. It doesn’t make sense to invest all of your time into learning calligraphy if you have nothing to write about! Likewise, it doesn’t make a lot of sense to work on building a table but focus all your effort on a single leg and then complain that the table will not stand! All we need at this stage is some familiarity with programming concepts — enough so that we can perform meaningful online searches. The programming skills we need are understanding the value of Stack Overflow, documentation, and how to reach both. This might sound tongue in cheek, but these skills are genuinely at the core of a programming career. Add knowing a few basic things about programming, and you have a very firm platform on which to build. How to move around in code is an essential skill The next leg of the table we are assembling is our ability to move effectively in code. This is not something that most programming courses cover, but it is an essential skill to have. This boils down to learning to use a code editor really well. Any major code editor will do. Some people swear by textmate, sublime text, atom, vscode, emacs, or vim. It doesn’t matter which one you choose. Focus the same attention on learning it as you would to writing code and you will be alright. If you are absolutely lost as to which one to pick, chose vscode. Do not worry about the choice too much — it is not one you must stick with forever. I do not know of a single developer who has not tried multiple editors in the course of their career. However, I also don’t know a single developer worth their salt who doesn’t believe that learning to use an editor is an important skill. Version control can give you superpowers To work on our next leg, we move to version control. Code is written in small iterations, often by more than one person. Additionally, code is so expensive to produce that we want to make sure we don’t lose our work and that we can explore new directions without destroying the work we have done up to now. All this is very hard to achieve without specialized tools. Thankfully, we have a software solution that addresses all the needs listed above and then some! The tool goes by the name of git. It is a small command line program that can change any directory into a git repository. In the process, it endows the directory with superpowers, such as an ability to travel back in time or create alternate timelines! Git comes with an online counterpart, called GitHub. While git excels at managing the source code on your computer, GitHub is great for code sharing and collaborating with others. Pushing your code to GitHub also provides you with a backup. If some accident should befall your PC, your work will be preserved at another location. Git is a marvel of software engineering but, unfortunately, the API it exposes is not very human-friendly. Thankfully, GitHub created its own command line tool, the . It has a much gentler learning curve than git. gh cli Being able to use the computer is where it's at The last leg of our four-legged table is probably the hardest to construct. The challenge lies solely in the lack of good learning resources. This part deals with learning how to use the computer in the context of writing code. For deep learning, this goes beyond your local setup. How do you spin up a cloud VM? How do you ssh into it? How do you move data around? How do you build your own home rig for experimentation? The good news is that you do not have to learn all of this at once. A great starting point might be to learn how to navigate the file system on the operating system of your choice. As you get more comfortable using the command line, you can gradually add more commands to your arsenal. The one resource that stands out for learning how to use a computer is by MIT. The Missing Semester of Your CS Education Maybe a day will come when a single course will cover all the basics of being a developer. For now, unfortunately, we must piece together our own curriculum. The good news is that it is enough to get just a taste of each of the four disciplines I outline above; there is no need to become an expert at any of them. How to become a machine learning expert All we have been doing thus far is preparing ourselves for taking the “Practical Deep Learning for Coders” course by . If you have a rudimentary understanding of programming and can move around on your computer, the course can take you from there. fast.ai And why a course and not something else? No other resource out there will teach you everything you need to know to do machine learning at a high level. Many good offerings focus on a particular set of tools or some aspect of the machine learning pipeline, but no other complete package exists. This is the only course I am aware of that will teach you the end-to-end process of arriving at a good solution to a machine learning problem. fast.ai In fact, for many, the course goes beyond showing one how to be a good deep learning practitioner. Through taking the fast.ai courses, I learned how to learn. I also started to share my work and became an active member of the deep learning community. The courses not only taught me nearly everything practical that I know about machine learning but also showed me how to thrive in the digital age. If you give the lectures a proper go and become an active member of the or , you will be alright. Nonetheless, the courses propose a way of learning that might be new to many. While the course explains the approach, a written summary of it, from my perspective, with the entire progression explained in one place, might still be beneficial. This is what we will be looking at in the remainder of this chapter. fast.ai forums fast.ai discord An effective way to learn It all begins with watching a lecture. The next step is to open the lecture notebook and figure out how all the pieces fit together. The idea is to run each line of code and look at the outputs. If you come across a function you have not previously encountered, this is a good time to read its documentation. How will the performance differ if you pass in different arguments or slightly tweak the hyperparameters? Once you understand the bigger picture, it’s time to reproduce the results. To do so, open a new notebook and try to recreate the training pipeline that the lecture demonstrated. It’s an open-book exercise — it’s okay if you need to look back at the lecture notebook, but the less you do so, the better. On the other hand, there are no limitations on reading the documentation or searching online. Both of these activities are encouraged and they closely resemble what programming feels like when it’s done completely on your own. Once everything is up and running, and you have obtained a result similar to that in the lecture, it is time to go a step further. This time, we search for a similar dataset and test drive the technique we just learned about. Your imagination is the only limit here. You could seek out a dataset online or you could put one together yourself. Alternatively, the fast.ai library provides access to many used for research. You can download them to your machine with a single command. datasets The idea is to systematically grow your skills. You start in a controlled setting of the lecture notebook, learning the ins and outs of the technique that the lecture discussed. With every new exercise, more of the training wheels come off, to the point where you’re doing everything on your own from start to finish. This approach to learning works like a charm. There are no hard and fast rules here and you are more than welcome to modify it as you go. Being creative and coming up with exercises that are challenging and interesting to you is a very valuable skill in its own right; acquiring it will take your learning to the next level. The result is always the same: to arrive at a place where you can comfortably apply the techniques you learn in the lectures to problems you haven’t previously seen. While practice is the only way to improve your ability to solve real-life machine learning problems, moving toward this way of learning does not require you to revolutionize your life. A couple of minutes a day, over multiple days, is all you need to get started. With every consecutive day of learning, you build momentum. Soon you start to notice the results, and the ability to do things you thought were out of reach not too long ago creates a lot of excitement. Very soon, you will find yourself losing track of time as you explore this or that technique. Where to go next From the broader picture, going through lecture materials is a starting point. It gives us a platform upon which we can build. Even before you finish the course, it is a good idea to start coming up with and working on mini-projects of your own. If you are low on inspiration, you can check out this with projects that fast.ai students worked on during one of the courses. There, you will find an assortment of ideas: blog posts, howtos, forum posts, GitHub repositories, explainer videos, and Kaggle competitions. amazing forum thread Summary The way of learning I describe above was completely new to me. However, after experiencing what it can do, I have never ceased being a fast.ai student. Any other approach feels like a waste of time. It wasn’t always like that. Initially, I thought that the fast.ai way of learning could not work. The exercises proposed in the lectures didn’t feel like learning to me. How could I make any progress spending so much time ? To me, learning was this process in which you sat down with a book for an hour or two and came out on the other end being able to discuss something highly complex and abstract. doing I was very lucky, as at the time I found fast.ai I was at the end of my rope. Nothing was working, so I was ready to try something completely new. Stumbling across fast.ai turned out to be the adventure of my life. Thank you very much for reading! If you enjoyed this and would like to learn more, you can connect with me on Twitter here .

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