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Haskell, Genetic Algorithm, Graph Theory, Programming.
the Haskell community, but I’ll take my chances that no one reads this far down. Here’s the rest of the boilerplate. Much of it is copied without understanding from examples distributed with moo. Perhaps there’s some hyper-parameter tuning that would improve things, but I’m happy with what I got!
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main :: IO () main = do void $ runIO initialize $ loopIO [DoEvery 10 logStats, TimeLimit (12 * 3600)] (Generations maxBound) nextGen initialize = return (replicate popsize (encodeTiles originalTiles)) logStats n pop = do let best = decodeTiles $ head $ map takeGenome $ bestFirst Maximizing pop
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putStrLn $ show n ++ ":" mapM_ print best g <- newStdGen print $ scoreTiles g 500 best nextGen = nextGeneration Maximizing objective select elitesize (onePointCrossover 0.5) (pointMutate 0.5) select = tournamentSelect Maximizing 2 (popsize - elitesize) popsize = 20 elitesize = 5 objective :: Genome
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Bool -> Double objective gen = unsafePerformIO $ do g <- newStdGen return $ sum $ scoreTiles g 500 $ decodeTiles gen The Final Result Using moo, I was able to maintain or improve on all components of the scoring. For the tiles I drew in attempt #2: 13 bridges were needed across the 40 tile designs.
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The duplicate score (sum of squares of the count of each unique design) was 106, indicating several designs that were duplicated four and five times. There was an average of 9.1 extra edges creating cycles in the maze. Probabilities of a path to key locations was about 93%. For the newly optimized
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tiles: There were only 11 bridges needed, saving two bridges over the original design. The duplicate score was 72, indicating many fewer copies of the same design, and never more than three copies of a design. There was an average of 8.4 extra edges creating cycles in the maze, nearly one less than
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in the original. Probabilities of a path to key locations was about 92%, which is essentially the same as the original. For the most part, though, I think I learned that the tiles I’d previously devised were pretty well chosen, but I did get an incremental improvement in making the maze more
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challenging without compromising much on the possibility of success. The art for the new tile set is not yet as pretty as the old one, but here’s a maze laid out with the new tiles. A maze laid out with genetically optimized tiles So what about the rest of the game? It’s been a lot of work! The
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maze generation was actually a pretty small part. There are a bunch of details around character-building (each player chooses one of six characters with a unique personality, story, and abilities), other mechanics (there’s a monster who moves randomly around the maze, quests that take characters to
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various locations to collect items, a magical fog that hides and changes parts of the maze as you play, etc.), and artwork. Unfortunately, I return to my full-time job tomorrow and may not have the free time to pursue frivolous goals. But who knows… perhaps I’ll find the time, hit Kickstarter, and
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period. If we look at the difference between births and deaths, we see a sustained decline over the last decade, including large declines over the past two years. In short, births are down and deaths are up, but it’s an open question to which how much of that is due to the pandemic, and how much is
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simply the continuation of pre-pandemic trends. From a Canada-wide perspective, the other way the population can go (or shrink) is due to international migration. International Migration First, we’ll consider net non-permanent residents. In the piece Ontario’s boom in non-permanent residents starts
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Grand Unified Theory, Theory Of Everything, Timothian Model, Theoretical Physics, Magnetism.
If you’ve ever played with magnets, you know how peculiar the attraction and repulsion between them can feel! Pointing like poles (North to North or South to South) toward each other feels like aiming two pressurized air hoses at one another, as if they’re ‘blowing’ each other apart. Likewise,
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pointing opposite poles toward each other, N to S, feels like pointing two vacuum hoses at each other, as if they’re ‘sucking’ each other together. In the Timothian Model, that is exactly what is happening! The central premise of the model is that all fundamental forces can be explained by the
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mechanics of subatomic (smaller than atoms) physical chunks of matter (link to Companion Article) Gravity, magnetism, and atomic forces each gain clarity through understanding the processes of these chunks. By the end of this article, you might start seeing magnetism not as a fundamental force, but
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as a result of pressures and flows. Overview Full Process of a Permanent Magnet Unlike conventional descriptions of magnetism, which state that magnetic field lines are static, the Timothian Model interprets these lines as literal flows and counterflows of subatomic matter. Physical chunks that
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have mass, take up space, and interact with other matter according to Newtonian laws are flowing out of the magnet. How does this work? Source Energy These chunks come in different sizes and densities, all smaller than atoms. They are literally everywhere an atom isn’t taking up! The secret to
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their ability to generate magnetism is in their varying sizes. Every atom in motion within the universe bumps into these subatomic chunks, imparting kinetic energy to them. They are moved by the spin of the Sun, the motions of the planets, and the oscillations from light and other electromagnetic
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waves. As they bump against each other and the much larger atoms, their movements are pretty random overall. Asymmetry through Rectification A permanent magnet can pass larger chunks from its south to north pole, while blocking them in the opposite direction. This is called “rectification” and
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operates like a valve that allows movement one way but blocks movement the other way. You might be able to think of this like a turnstile at an amusement park that lets you enter but not leave the same way you came. Amusement Park Turnstile Larger Chunks Can Only Flow One Way in a Magnet At the
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same time, smaller chunks can slip through the magnet either way. They aren’t affected by the magnet. They simply move to wherever gaps are created by the moving larger chunks. You can think of this like the air that flows behind you as you walk through the turnstile. You are too big to go both
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ways, but the air easily flows around you to fill the space you just left. This can be visualized as one-way gates in the magnetic material, only flexing enough for the larger chunks to go from south to north, and blocking any reverse travel. The actual mechanism is a bit more complex, based on
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material science and plastic deformation, but this image effectively illustrates the effect. A Permanent Magnet Rectifying Ambient Chunk Movements This is how a magnet converts available kinetic movements into structured flows. The larger chunks are constantly and randomly bumping against both
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sides of the magnet, but can only move from south to north. This creates a “flow” within the magnet. The smaller chunks backfill the larger ones. This creates a “counterflow”. In this way, a magnet rectifies the random movements of larger chunks to draw them into the south pole and eject them out
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of the north pole. At the same time, the smaller chunks naturally get knocked out of the way and fill the gaps, creating a counterflow. Forced Flows Drive Pressure Differentials External Pressure Differentials Drive Flux Flows Magnetic fields involve two distinct flows: one of larger chunks moving
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in one direction, and a counterflow of smaller chunks moving in the opposite direction. Once the larger chunks are outside the north end of the magnet, they encounter higher resistance outside the magnet than inside. Larger chunks pool outside, creating high pressure. They can’t go back through the
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one-way magnet, so they flow around the magnet to the low-pressure intake side. This creates the first half of magnetic flux lines, shown in red above. The smaller chunks are constantly being knocked out of the way and filling the gaps created by departing larger ones. This means there are a lot of
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smaller chunks just outside the south pole, and far fewer outside the north pole. They too flow from high-pressure to low-pressure, filling the gaps when their larger siblings move, which in this case is the opposite direction. This creates the second half of magnetic flux lines. Interim Recap So
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let’s recap what we’ve discussed so far, and add some more technical details: Magnetic fields get their inexhaustible source of energy from the random movements of subatomic chunks jostled about by every atom and wave they contact. Our Sun is our primary source of energy driving these movements in
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this part of space, but even the Earth’s rotation, TV and radio station broadcast waves, and weather moving the air around all contribute! Permanent magnets “rectify” these chunk movements, causing larger and smaller chunks to build up on opposite sides of the magnet. Flows leaving the
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low-resistance magnet material and entering the higher resistance air create opposing pressures outside the magnet. Pressures cannot equalize back through the rectifying magnet. Given a path to do so, pressures will naturally flow towards equalization through the path of least resistance. In this
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case, that path is around the magnet. Outside the magnet, each type of chunk flows from its own high-pressure side to the opposite low-pressure side seeking pressure equilibrium. These flows are what we call magnetic flux lines, visible when scattering iron filings around a magnet. Iron filings
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“align” with magnetic flux lines because the chunk flows stay in the low-resistance iron as long as possible. They have inertia and momentum, and take the path of least resistance. Countless subatomic chunks moving through the iron filings shift them into alignment. Whenever one iron filing touches
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another, it creates an overall path of least resistance, a self-reinforcing process. Magnetic Attraction and Repulsion Explained With all of this in mind, let’s return to how we started this journey: attraction and repulsion. An explanation of magnetism must clarify the development of flux lines
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that cause attraction and repulsion. Check. It must also explain all of the interactions between north and south poles of magnets. Likes Repel Let’s consider when the same poles are pointed at each other first, N to N or S to S. As shown in the two images below, in each of these cases the facing
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poles are ejecting the same types of chunks. This is like two air hoses pointing at each other, literal streams of the same type of matter pointed at each other. This creates a turbulent flow and high-pressure zone. The opposing flows must come all the way around from the outside and squeeze
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between the facing poles, wedging them apart. In both “like” pole cases, one flow is directly blowing into its twin, and the opposing flows are wedging between the magnets to reenter. Both flows contribute to… repulsion! Flows Cause Facing North Poles to Repel Flows Cause Facing South Poles to
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Repel Opposites Attract Now let’s consider when opposite poles are pointed at each other. In this case, the north pole of the first magnet is ejecting larger chunks the second magnet needs at its south pole. At the same time, the first magnet is drawing in larger chunks the second magnet is
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ejecting. Both magnets provide what the other is drawing in, creating a combined low-pressure between the facing magnets. This creates a laminar flow and low pressure zone. At the opposite extremes, both larger and smaller chunks flow outside of the entire system to their respective low-pressure
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zones. Together, the low-pressures between the magnets and high-pressures outside of the magnets drive them together. Attraction! Flows Cause Facing North and South Poles to Attract Wrap-Up The secret ingredients for this recipe comes can be summed up in two new concepts. First, grasping the
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properties and processes of the chunk medium itself, summarized in a sibling article. Second, understanding how a permanent magnet’s rectification of ambient chunk movements creates both a flow and a counterflow around a permanent magnet. To review, the Timothian Model is successful at explaining
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every aspect of basic magnetism mechanistically. An active ambient chunk medium provides the source energy for the ‘inexhaustible’ fields generated by a permanent magnet. A magnet’s material properties allow it to be altered into a discretionary chunk rectifier, channeling the random kinetic
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motions into structured flows and counterflows inside the magnet. Outside the magnet, the created pressure differentials flow naturally toward equalization, visualized through iron shavings or Gauss meters as familiar flux paths. Distinct species between flows and counterflows interact by either
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turbulence or laminar flows between facing magnet poles, explaining all combinations interactions, North to North, South to South, and North to South. Related Topics As we shift our thinking from magnetic flux lines being static — to being actual flows of chunks having volume, density, inertia, and
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momentum, we can begin to understand magnetic interactions with other materials and fields more clearly. I will be creating additional extracted articles related to magnetic interactions with strongly attracted metals (ferromagnetic) and weakly repelled materials (diamagnetic), as well as
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electromagnetic interactions. Hint: Everything can be understood in terms of mechanistic resistance, pressures, and flows! For example, magnetic induction into wires takes on a whole new perspective when a constant chunk flow across a wire creates a constant pressure within that wire… Closing I
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hope this article has ‘attracted’ your interest! In the Timothian Model, magnetism is not a fundamental force, but rather an emergent property involving the rectification of random ambient chunk movements into pressures and flows. These flows interact with each other and everyday materials in
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predictable ways. As you observe magnets in your daily life, notice how the Timothian Model offers a concrete, intuitive way to understand their behavior and interactions with each other, materials, and conductors. Remember, a Grand Unified Theory (GUT) must challenge conventional thinking to
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reconcile gravity, magnetism, and atomic forces. Understanding magnetism as pressure-driven chunk flows is a key step in seeing the big picture and a precursor to understanding electromagnetic waves, as well as interactions between magnetic flows, gravity, and light. I encourage you to read the
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other overviews and if you are so inclined, my more detailed work in “GUT Check — The Timothian Model Unifying Gravity, Magnetism, and Nuclear Forces.” I’m excited to hear your constructive feedback! You can find more overview articles on https://medium.com/@timothy.arthur.jones and react to my
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decentralized digital economies, accurate and secure code is crucial to prevent major losses. It examines the code thoroughly, using a vast amount of industry data to spot risks and bug exploits that might cause problems later. It’s easy to use and offers several benefits: it enhances security from
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patterns like Double Top/Bottom, Head and Shoulders, and others and provides key stats like price, volumes, and market cap. Based on different data sources, it also signals whether the market might go up (bullish) or down (bearish) and offers detailed market indicators about who’s making money, how
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mentorship and selective focus on highly talented, high-potential teams. Such a meticulous approach has propelled only the most promising startups to success in the competitive Web3 space. The platform’s effectiveness is highlighted by its recognition from major Web3 authorities, like CryptoRank,
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Figure out what tools can detect AI (if any) This is a comprehensive review of the best AI detectors on the internet. I’ve tested every tool and here are the insights, experiments, and my valuation. In these reviews, I assess these detectors' quality, features, accuracy, and more. If you want to
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read a quicker 5-minute recap of this post, make sure to check this post. Top Picks Here are the top 3 picks for your convenience: Originality.AI Content at Scale Crossplag AI Detector Disclaimer: This post has affiliate links at no cost to you. Also, I don’t believe in AI detection. But this is a
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list that I could put together and has the tools that worked the best when I tried. :) Learn my realistic, no-BS blogging strategy with my free course. Click here to join! Access Free Blogging Masterclass — Bloggersgoto Free Blogging Masterclass Build a Successful Blogging Business in 12 Months.
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Enter your best email 👇 100% FREE🔒 Your…www.bloggersgoto.com 1. Originality AI Originality.ai is an AI writing software that helps you detect if your content: Is written by AI. Has plagiarism. These are both important aspects of using AI in your writing. Even though these days AI-written content
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looks human-written, some programs can still detect whether the content is written by AI or not. Originality.ai demonstrates this well. Also, even though AI mostly produces unique and original content, there’s always a chance of plagiarism. This is why it makes sense to run AI-written content
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through a plagiarism checker to be on the safe side. Let’s test Originality.ai to see how it detects AI-written and human-written content. Try Originality.ai Performance To get an idea of the performance of Originality.ai’s AI content detector, I fed the tool: 10 human-written text samples. 10
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AI-written text samples. And calculate the accuracy of the tool based on these inputs. Besides, I tried to trick the AI content detector with some easy tricks to make it think the AI-written content was human-written. 1. Human-Generated Content As the first test, let’s try to analyze human-written
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content with Originality.ai. To pull this off, I’ve taken 10 test samples from my blog posts. These pieces of content are 100% human-written. In an ideal world, the AI detector should score it all 100% original, and 0% AI. Example 1 Here’s the input: Output: 76% original — mission successful.
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Example 2 Here’s the input: And here’s the output from the AI detector: 4% original — mission failed. Example 3 Input: Output: 2% original — mission failed. Example 4 Input: Output: 83% original — mission successful. Example 5 Input: Output: 8% original — mission failed. Example 6 Input: Output:
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82% original — mission successful. Example 7 Input: Output: 23% original — mission failed. Example 8 Input: Output: 99% original — mission successful. Example 9 Input: Output: 0% original — mission failed. Example 10 Input: Output 2% original — mission failed. Based on these results, it’s clear to
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see that Originality.ai isn’t the best tool for recognizing human-written content. In the above tests, Originality.ai only recognized 4/10 human-written pieces of text right. But this is not a big problem. The tool is supposed to detect AI-written content. Most people are going to use this tool
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knowing that the content is AI-written and to make it sound less like AI. 2. AI-Written Content As another test, let’s feed originality.ai some AI-written text samples. I’ve generated these samples using ChatGPT. In an ideal situation, originality.ai would give a 0% original and 100% AI score. But
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detecting AI with 100% accuracy is hard. For our purposes, let’s count all successful missions where the AI score exceeds the original score. Example 1 Input: Output 100% AI written — mission successful. Example 2 Input: Output 99% AI written — mission successful. Example 3 Input: Output 61% AI
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written — mission successful. Example 4 Input: Output 66% AI written — mission successful. Example 5 Input: Output 1% AI written — mission failed. Example 6 Input: Output 98% AI written — mission successful. Example 7 Input: Output 100% AI written — mission successful. Example 8 Input: Output 100%
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AI written — mission successful. Example 9 Input: Output 70% AI written — mission successful. Example 10 Input: Output 59% AI written — mission successful. Pretty impressive! The AI detector was able to successfully identify 90% of the inputs as AI-written. Try Originality.ai Last but not least,
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let’s see if we can fool the AI detector by making a small change and get a big shift in the score. Can You Fool Originality AI Content Detector? I’ve tried a bunch of AI content detector tools. I’ve noticed that sometimes even the tiniest change in the input completely changes the score. So to
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make 100% AI-generated content look 100% human-generated, it might be enough to just remove a single letter or add one extra word in the mix. Let’s run some cheap tricks to see if this is also the case in Originality.ai. Test 1: Remove a Comma ➡️ TLDR; Adding a small grammatical error did not
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change the output of the Originality.ai AI detector significantly. As a first test, let’s introduce a small grammatical error in the Originiality.ai input. More specifically, I’ll remove the comma that follows the word “Additionally”: This didn’t change the originality score significantly. It seems
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the detector doesn’t care about small changes and can see the big picture. Test 2: Make a Typo ➡️ TLDR; Adding a small typo to the content did not significantly change the AI detector output. Next, let’s try to misspell one of the words to see if such a small change would alter the AI detection
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score. I’m going to intentionally write “their” as “ther” without “i” and scan the content: Once again, this only slightly moved the score. As expected, such a small change shouldn’t alter the score by much. Test 3: Use an AI Paraphraser ➡️ TLDR; Rephrasing the AI-written content did not fool the
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Originality.ai detector. Based on a couple of tests, it seems small input changes aren’t able to change the score. Now, let’s try something more significant. These days, you can also use AI to paraphrase your content. One example of such a rewording tool is called QuillBot. Make sure to read my
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complete QuillBot review. Here I’ve reworded one of my AI-generated inputs. With QuillBot, making this change took only a second. Now, let’s try to input this AI-generated rephrased sample text into the Originality.ai detector to see what happens: Amazing! It still recognizes the content to be
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AI-generated. Some other tools failed this test miserably. Keep in mind that even though I ran quite some tests, the sample size is still quite small. I highly recommend you experiment with tests like this. Change words, change punctuation, add a sentence, or remove a sentence. Try Originality.ai
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Plagiarism Checker Notice that Originality.ai is not only an AI content detector. There’s a plagiarism checker too! For example, here I’ve copy-pasted a part of a blog post from my other blog. The originality.ai plagiarism score is correctly 100%. The sample I checked is indeed 100% copied from an
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existing blog post. It seems to be a powerful plagiarism checker based on the few examples of duplicates I ran through it. If you already tried Originality.ai, you probably noticed that the plagiarism checker is automatically enabled when you detect AI content. You can uncheck the plagiarism
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checker if you’re only interested in checking the content for AI. Pros Accurate. Originality.AI is an accurate tool when it comes to detecting AI-written content. In my case, it spotted 90% of AI-generated content, right? Detects plagiarism. There’s also a powerful plagiarism checker tool that you
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can use to ensure your writing is truly unique. Hard to fool. You can’t just change a word or punctuation to fool the AI detector to give a better score. This seems obvious but it’s not the case for many AI detector tools. Cons No free trial. Originality is a paid tool without a free trial. But
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luckily, the pricing is affordable at $1 for 10,000 scanned words. False positives. Originality.ai isn’t good at telling human-written content apart from AI-written text. Out of my 10 human-written samples, it claimed 6 to be AI-generated. Final Verdict I recommend experimenting with a tool like
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Originality.ai. It can help you write less AI-like content as well as be sure you’re not copying someone else’s work. Originality.ai works best when you already know that the input text is written by AI and you want to make it sound less like AI. But if you’re given a random piece of text, you
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can’t rely on Originality.ai to tell whether it’s AI-written or not. However, unfortunately, an AI detector like Originality.ai (or any other publicly available tool) isn’t enough to tell if Google still thinks AI writes your content. They most likely use a different approach. So if you are a
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blogger and get a 100% original score from Originality, it does not mean Google wouldn’t still be able to tell it’s written by AI. Try Originality.AI 2. Content at Scale Content at Scale is a complete AI writing tool with long-form content creation capabilities. One of the cool features of this
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tool is the free AI content detector. The idea of the AI detector is simple: Take a piece of text. Copy-paste it into the AI detector. See how likely your text was written by AI. Content at Scale AI detector gives you a human-written score between 0% and 100%. This score reveals how much the tool
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ChatGPT, AI, Writing, Blogging, Blogging Tips.
thinks your content is human-written and how much AI-written. A score of 0% means the tool thinks your content is entirely written by AI. A score of 100% means the tool thinks you wrote it all yourself. Next, let’s put the Content at Scale AI content detector to the test. Performance I extensively
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ChatGPT, AI, Writing, Blogging, Blogging Tips.
tested the AI content detector by feeding it two types of data: 10 pieces of human-written text from my blog. 10 samples of AI-generated text (using ChatGPT). 1. Human-Generated Content Let’s start with human-generated inputs. These inputs are random samples of blog posts I’ve written myself. In
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other words, all these inputs should give me a close 100% score. Example 1 Falsely detected as AI-written — mission failed. Example 2 Falsely detected as AI-written — mission failed. Example 3 Correctly detected as human-written — mission successful. Example 4 Falsely detected as AI-written —
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mission failed. Example 5 Correctly detected as human-written — mission successful. Example 6 Correctly detected as human-written — mission successful. Example 7 Falsely detected as AI-written — mission failed. Example 8 Falsely detected as AI-written — mission failed. Example 9 Falsely detected as
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AI-written — mission failed. Example 10 Falsely detected as AI-written — mission failed. By tallying up the scores, the Content at Scale AI detector only detected 30% of the human-written samples to be human-written. So when it comes to detecting human-written content, Content at Scale did a really
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ChatGPT, AI, Writing, Blogging, Blogging Tips.
poor job. Next, let’s try the AI-detection tool by feeding it some AI-generated content. This is the main way content creators will use AI detectors anyways — to find out if their content looks natural. 2. AI-Written Content I generated 10 pieces of text using ChatGPT — the revolutionary AI writing
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model released by OpenAI. If the AI content detector works, it should give a score close to 0% for all the following pieces of text. Example 1 Correctly detected as AI-written — mission successful. Example 2 Correctly detected as AI-written — mission successful. Example 3 Falsely detected as
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human-written — mission failed. Example 4 Correctly detected as AI-written — mission successful. Example 5 Correctly detected as AI-written — mission successful. Example 6 Correctly detected as AI-written — mission successful. Example 7 Correctly detected as AI-written — mission successful. Example
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8 Correctly detected as AI-written — mission successful. Example 9 Correctly detected as AI-written — mission successful. Example 10 Correctly detected as AI-written — mission successful. Summing up the results, it appears that the Content at Scale AI detector is quite good at detecting AI content.
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In total, it correctly identified 90% of the AI-written articles as AI-written. However, the problem is that it didn’t quite identify human-written pieces as human-written. In total, the tool was successful 12/20 times which is only 60%. So if you don’t know in advance whether the piece of text is
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written by AI, there’s no way to detect it reliably with Content at Scale. Can You Trick the AI Content Detector? Now, let’s play some tricks with the Content at Scale AI detector to see if it’s easy to fool. I’ve reviewed other AI detectors and noticed that small changes in the input can have a
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big impact on the output. This means in the eyes of an AI detector, text with a 100% AI-written score becomes 100% human-written by a tiny change in the input — obviously not good! Small changes should only have little to no impact on the score. Let’s see what happens when I try to fool the Content
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ChatGPT, AI, Writing, Blogging, Blogging Tips.
at Scale AI detector. Test 1: Remove a Comma ➡️ TLDR; Removing a comma didn’t change the results significantly. As a first step, I tried to fool Content at Scale by tweaking the output by a tiny bit. More specifically, I removed a single comma that follows the word “Additionally”. This is a mistake
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ChatGPT, AI, Writing, Blogging, Blogging Tips.
AI these days would never make, so perhaps the tool now thinks I’ve written the content… But here’s what happened: The AI detector score changed a tiny bit. However, it was unphased by the fact that I removed a comma from the output. Test 2: Make a Typo ➡️ TLDR; Removing a single letter changed the
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ChatGPT, AI, Writing, Blogging, Blogging Tips.
AI detector score by quite a bit. I’m trying another similar “trick”. This time, I’ll put the comma back and instead introduce a typo by removing the letter “i” from “their”. Here’s what happened this time: Now the score changed quite a bit. The tool still says the content is written by AI but with
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