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      "body": "Congratulations @pecker720! You received a personal award!\n\n<table><tr><td>https://steemitimages.com/70x70/http://steemitboard.com/@pecker720/birthday2.png</td><td>Happy Birthday! - You are on the Steem blockchain for 2 years!</td></tr></table>\n\n<sub>_You can view [your badges on your Steem Board](https://steemitboard.com/@pecker720) and compare to others on the [Steem Ranking](http://steemitboard.com/ranking/index.php?name=pecker720)_</sub>\n\n\n###### [Vote for @Steemitboard as a witness](https://v2.steemconnect.com/sign/account-witness-vote?witness=steemitboard&approve=1) to get one more award and increased upvotes!",
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      "body": "![](https://cdn.steemitimages.com/DQmWpg2xH8QKJRcT1vrgG6q1NoZ6aTfEP34hrRtFS1NrAdB/image.png)\n: Science and the Simulation Hypothesis point to many reasons we may already be in the Matrix\nNote: This is one in a series of articles for the 20th anniversary of the release of The Matrix, and the release of my new book, , The Simulation Hypothesis: An MIT Computer Scientist Shows Why AI, Quantum Physics, and Eastern Mystics All Agree We Are In a Video Game. Here, I’ll review some of the scientific reasons why this may be the case. A version of this article was originally published on scientificinquirer.com.\nFrom Science Fiction to Science\n\nThis year on March 31 marks the 20th anniversary of the release of the groundbreaking film, The Matrix and the release of my new book, The Simulation Hypothesis. The Matrix was influential in many ways — the incredible special effects, the no holds barred action, etc. Like Star Wars before it, it has gone on to become a cultural phenomenon that extends well beyond the film itself. This is partly because of its philosophy; The Matrix is perhaps the most popular incarnation of what we now call “the simulation hypothesis” — which is the idea that we are all living in a giant shared online video game.\n\nAdmittedly, the idea sounds like science fiction. The creators of the Matrix, the Wachowskis, claimed to have been influenced by the work of Philip K. Dick, among others. The many adaptations of Dick’s work are well known, including Blade Runner, Total Recall, the Man in the High Castle, the Adjustment Bureau. In his stories, Dick was often obsessed with what was real and what was fake about reality and about the human experience — dealing with issues of artificial intelligence, simulated reality and fake memories.\n\nThe Matrix, you’ll recall, starred Keanu Reeves as Neo, a hacker who encounters enigmatic references to something called the Matrix online. This leads him to the mysterious Morpheus (played by Laurence Fishburne, and aptly named after the Greek god of dreams) and his team.\n\nEven if you haven’t seen The Matrix, you’ve probably heard of what happens next, in perhaps its most iconic scene, Morpheus gives Neo a choice: take the “red pill” to wake up and see what the Matrix really is, or take the “blue pill” and keep living his life. Neo takes the red pill and “wakes up” in the real world to find that what he thought was real was actually an intricately constructed computer simulation — basically an ultra-realistic video game!\n\n\nKeanu Reeves in the Matrix (src: Movie Web)\nWhen the Matrix came out, the idea of living in a video game was squarely in the realm of science fiction. Today, the simulation hypothesis is debated seriously by computer scientists, philosophers, physicists and others. The reason this argument is taken more seriously now is two-fold:\n\nthe philosophical “simulation argument”, put forward by Oxford’s Nick Bostrom, and\nthe “video game simulation argument”, about the rapid development of video games, put forth by, among others, Elon Musk.\nTwo Major Developments\n\nThe first was when Oxford professor Nick Bostrom published his 2003 paper, “Are You Living in a Simulation?” Bostrom didn’t say much about video games; instead he made a clever statistical argument. Bostrom theorized that if a civilization ever got the Simulation Point, it would create many ancestor simulations, each with large numbers (billions or trillions?) of simulated beings. Since the number of simulated beings would vastly outnumber the number of real beings, any beings (including us!) were more likely to be living inside a simulation than outside of it! Other scientists, including physicists have taken up this argument.\n\nIn the video game version of this argument, we have the rapid advancement of graphics technology. Elon Musk, speaking at the Code Conference in 2016, asserted that 40 years ago, we had pong, which was essentially two lines and a dot. Today we have VR and AR and MMORPGs — all based on 3D technology. If the pace of video game development continues, in a few decades we would have hyper-realistic games, indistinguishable from reality.\n\nI call this point the Simulation Point, and in my new book, The Simulation Hypothesis, one part is dedicated towards the stages of technology needed to reach this point. It’s much easier to see a path from today’s VR to something like The Matrix than it was in 1999 when the movie was released. With games like Fortnite, Minecraft, and League of Legends having millions of online players interacting in a shared online world, the idea that we might actually be in a shared connected simulated world doesn’t seem so far-fetched as it might have in 1999.\n\nIn this article, we go beyond Bostrom’s and Musk’s simulation arguments to explore some of the reasons why science might be telling us we are in a simulated reality, like the Matrix.\n\n1. Pixels, Resolution, Virtual and Augmented Reality\nToday we are already seeing with Virtual Reality that “full immersion” is possible. Anyone who has played a convincing VR game will realize that it’s possible to forget about the real world and “believe” the world you are seeing is real.\n\nAs a great example, I was playing a prototype of a Ping Pong VR game last year (built by Free Range Games), and even though it wasn’t realistic resolution, I lost myself and thought I was playing ping pong for real. So much so that I set the paddle on the ping pong “table” and leaned against the table. Of course, it was a VR table so it didn’t really exist — I ended up dropping the paddle (actually the Vive controller) onto the floor. As I leaned into the “table” I almost fell over before realizing that there was no table. In other words, to quote from The Matrix, there is no spoon.\n\nThe immersion comes not just from the number of pixels, but from the controls and responsiveness that VR is able to achieve. In the novel (and Steve Spielberg film) Ready Player One, which was about a VR world called the OASIS, users had lightweight glasses and wore haptic suits and walked on omnidirectional treadmills to add realism. In that world, the OASIS was preferable to real life.\n\nThe fact that 3D models can be used to create realistic looking objects in films(a glance at movies like Blade Runner 2049 will convince you that we have enough pixel resolution to create realistic looking objects), and that 3D printers can be used to “print” physical objects shows that the physical world can be represented by information, a key part of the simulation hypothesis.\n\nWhere does the responsiveness and fidelity come from? The limitations today are in real-time rendering and in they way that objects interact with each other inside the world. Most games have a physics engine and a rendering engine. The physics engine is never fully realistic (otherwise it would take you too long to go from one part of the game to the other), and the rendering engine is what’s responsible for making you “see” what the world looks like by deciding what color pixels go where. We can easily see that full immersion may be possible in a few years time.\n\n2. Pixels, Quanta, and Zeno’s Paradox\nSpeaking of pixels, could it be that what we call the physical world also consists of pixels?\n\nI recall late nights at MIT during my undergrad years where I had philosophical debates with my classmates about the nature of reality. This was the first time I’d heard of Zeno’s paradox, who presented it in terms of Achilles and a Tortoise. If Achilles was behind the tortoise, and he always had to make up half the distance, how could he ever get there?\n\n\nZeno’s parado involved Achilles and the Tortoise.\nLurking underneath this paradox is the question of whether space is quantized or if it is continuous. The idea was that if space was continuous, like numbers are (you can always find an infinite number of numbers in between any two numbers), how is it possible to touch an object such as the wall? You would always have to cover half the distance and never quite get there.\n\nThis was my first hint that space might be quantized.\n\nToday’s physicists generally acknowledge Planck constant as being the smallest amount of space that anything can be measure. Moreover, physicists tell us that most of what we think of as a solid object is actually 99% empty space, especially if you look inside the atom. The quanta in quantum physics consists of discrete quantities — of energies or “states” that a particle can exist in. Newton’s equations assumed a continuous amount of space; it turns out the universe may be more quantized than we thought.\n\nA related question is whether time is quantized? In all computer simulations, there is the idea of “generation” or “steps” in the simulation. These are some multiple of the processor clock-speed, which is the minimum speed at which something could be measured for any simulation running on that processor. Whether time is quantized in the real world is an open question, though there are some that suggest it is, and planck’s time constant (the amount of time it takes the speed of light to travel the planck length) is the minimum quantied time. If so, this would be more evidence that we live in a computation based reality.\n\n3. The Collapse of the Probability Wave, Quantum Indeterminacy\nIn quantum physics one of the most intriguing ideas is the probability matrix, which is an interpretation of how subatomic particles can exhibit properties of both a wave and a solid particle at the same time. At the level of an electron or a photon, the wave is interpreted as a set of probabilities of where the particle might be at any given time. When we observe a particular possibility, then the probability wave is said to “collapse” and we see a single particle in a particular location. This is called Quantum Indeterminacy.\n\nHow does the probability wave collapse? This is one of the biggest mysteries in physics. The best answer physicists have come up with is that consciousness somehow determines the collapse. Max Planck once wrote, “I consider consciousness as fundamental and matter as derivative”.\n\nAn even bigger mystery is why does the universe work this way?\n\n\nThe simulation hypothesis provides a pretty good answer. The reason that video games have advanced so far in a few decades is because of optimization techniques. It would be impossible even for today’s computers to render in real time all the pixels of a single 3D world — instead, information is stored as 3D models outside the rendered world and then only what a particular character can see from a certain angle is rendered. The golden rule of video game rendering engines is: render only that which can be observed!\n\nMany adherents of the simulation hypothesis think that quantum indeterminacy is an optimization technique with the same basic idea: only render that which is being observed.\n\nThe most famous example of this is Schrodinger’s cat, the poor feline who is trapped in a box with radioactive material. After an hour, the probability is that hte cat is either dead or alive. Common sense tells us that the cat is already either dead or alive, and when we open the box we are merely finding out what happened. Quantum physics tells us this is wrong: until we are there to obeserve it, the cat is actually both alive and dead at the same time — what’s called a quantum superposition!\n\n4. Future Selves, and Parallel Universes\nAnother related aspect of Quantum Physics that sounds like science fiction is the Parallel Universes theory, where we branch into different “universes” when we make decisions. If that’s true, then there is a directed graph of multiple universes that are branching out each time we make a decision, resulting in different timelines (in fact, the parallel universes theory was put forward to solve the grandfather paradox of time travel).\n\nWhich of those universes do we branch into? this may have to do with the one that is most “optimal” — meaning these universes may or may not exist as actual physical realities.\n\n\nThe Minimax algorithm looks at possible futures, calculating which one is the most optimal for a video game.\nPhysicist Fred Alan Wolf, for example, says that information from these possible futures is coming to us in the present and that we send out an “offer wave” into the future, which is interacting with the “offer waves” coming from the future to the present. Which possible future we navigate to depends on which choices we make, and how these two waves super-pose on each other (or cancel each other out). These are startling results. Future probable selves are sending back information to the present, and we are consciously choosing which path to follow.\n\nThis reminded me of the very first video game I made back at MIT. The way that the computer chose the next move was to project the possible futures, and then use a certain algorithm to “rank” those futures, and then bring those values back to the present and then the AI would choose the path to follow.\n\nDid the possible futures we were calculating in our game actually exist? Or were they just probabilities? I realized that this isn’t too much different from what’s happening at the quantum level, except that in existing games like chess or checkers, we use a simple function (based on the rules of the game) to decide which of the paths is most optimal. We used the “minimax” algorithm in game design, trying to maximize our score and minimize our opponents score at each “turn of the future”.\n\nPhysicist Thomas Campbell, in his 2003 book, My Big TOE (Theory of Everything) also proposes that there is a fundamental function and that we are essential in a computatiuonal universe that branches off possibilities and uses an evaluation function, just like a video game! He and a team from Caltech raised funds in 2018 on kickstarter for a series of experiements to try to prove this!\n\n5. The Speed of light\nAnother big mystery is why the speed of light is one of the few constants, one of the few fundamental values in physics. In fact, all matter has been equated with energy, and energy may be a derivative of light itself. While other things change, including gravity and space-time, Einstein found that the speed of light remains fixed.\n\nWhy would the speed of electromagnetic waves be the same speed at which information can travel through the universe?\n\nIn video games, it turns out that pixels are based on light — they are illuminated for a temporary period, and all communication happens between computers at the speed of light. Just as in relativity where simultaneity cannot really be guaranteed, the same is true video games — each player is working off of his computer and responding to information about the game, which is being sent to cloud servers outside the rendered world. The cloud serve is doing its best to respect simultaneity and order the events, but it may actually be impossible.\n\nConclusion\nAlong with the statistical simulation argument and the advance in video game technology, these are some of the reasons why scientists are starting to take the simulation hypothesis seriously. In fact, many physicists and biologists are starting to realize that underneath the physical objects they are studying, the universe is actually information.\n\nFamous physicist John Wheeler in his autobiography wrote “it from bit” — meaning that bits, not matter, are the fundamental “thing in the universe”. In fact, he said physics went through three phases in his career and each phase was an evoluation of our understanding of the universe. The fist phase was that “everything was a particle” (material, newtonian model), then “everything was a field” (quantum probability model), and finally, “everything is information” or bits.\n\nIf everything was information, or bits, then this would not only be consistent with a video game simulation like that in The Matrix, it would explain some of the big unanswered questions in science — why does it work this way?\n\nWhile we aren’t able to duplicate The Matrix at this stage of our technology, our computer science and video games have gotten far enough along that we are well on the road to the Simulation Point.\n\nSource: https://hackernoon.com/are-we-already-in-the-matrix-7492e89be433",
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      "body": "Hi! I am a robot. I just upvoted you! I found similar content that readers might be interested in:\nhttps://journal.binarydistrict.com/racist-data-human-bias-is-infecting-ai-development/",
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      "title": "AIs are being trained on racist data – and it’s starting to show",
      "body": "Machine learning algorithms process vast quantities of data and spot correlations, trends and anomalies, at levels far beyond even the brightest human mind. But as human intelligence relies on accurate information, so too do machines. Algorithms need training data to learn from. This training data is created, selected, collated and annotated by humans. And therein lies the problem.\n\nBias is a part of life, and something that not a single person on the planet is free from. There are, of course, varying degrees of bias – from the tendency to be drawn towards the familiar, through to the most potent forms of racism.\n\nThis bias can, and often does, find its way into AI platforms. This happens completely under the radar and through no concerted effort from engineers. BDJ spoke to Jason Bloomberg, President of Intellyx, a leading industry analyst and author of ‘The Agile Architecture Revolution’, on the dangers that are faced from bias creeping in to AI.\n\nBias is Everywhere\nWhen determining just how much of a problem bias poses to machine learning algorithms, it’s important to hone in on the specific area of AI development that the issue stems from. Unfortunately, it’s very much a human-shaped problem.\n\n“As human behavior makes up a large part of AI research, bias is a significant problem,” says Jason. “Data sets about humans are particularly susceptible to bias, while data about the physical world are less susceptible.”\n\nStep up Tay, Microsoft’s doomed social AI chat bot. Tay was unveiled to the public as a symbol of the potential of AI’s potential to grow and learn from the people around it. She was designed to converse with people across Twitter, and, over time, exhibit a developing personality shaped by these conversations.\n\nUnfortunately, Tay couldn’t choose to ignore the more negative aspects of what was being said to her. When users discovered this, they piled in. It sparked a barrage of racist and sexist comments that Tay soaked up like a sponge. Before long, she was coming out with similar sentiments, and after being active for just 16 hours, Microsoft were forced to take her offline.\n\nThe case study of Tay is an extreme example of AI taking on the biases of humans, but it highlights the nature of machine learning algorithms being at the mercy of the data fed into them.\n\nNot an Issue of Malice\nBias is more of a nuanced issue in AI development. It is one that can be felt by the existing societal biases relating to gender and race. Apple found itself in hot water last year when users noticed that writing words like ‘CEO’ resulted in iOS offering up the ‘male businessman’ emoji by default. While the algorithms that Apple uses are a closely guarded secret, similar matters of gender assumptions in AI platforms have been seen.\n\nIt has been theorised that these biases have arisen because of the learning data that has been used to train the AI. This is an example of a machine learning concept known as word embedding – looking at words like ‘CEO’ and ‘firefighter’.\n\nIf these machine learning algorithms find more examples of words like ‘men’ in close proximity within these text data sets, they then use this as a frame of reference to associate these positions with males going forward.\n\nAn important distinction to make at this point is that such bias showing up in AI isn’t an automatic sign of deliberate and malicious injection of the programmers’ bias into their projects. If anything, these AI programs are simply reflecting the example bias that already exists. Even if AI is trained using a vast amount of data, it can still easily pick up patterns within that lead to problems like gender assumptions because of the range of published material that contain these linked words.\n\nThe issue is further reinforced when looking at language translations. A well-publicised example was Google Translate and its interpretation of gender-neutral phrases in Turkish. The words ‘doctor’ and ‘nurse’ are gender neutral, yet Google translated ‘o bir doktor’ and ‘o bir hemşire’ into ‘he is a doctor’ and ‘she is a nurse’ respectively.\n\nRelying on the Wrong Training Data\nThis word-embedding model of machine learning can highlight problems of existing societal prejudices and cultural assumptions that have a history of being published, but data engineers can also introduce other avenues of bias by their use of restrictive data sets.\n\nIn 2015, another of Google’s AI platforms, a facial recognition program, labelled two African Americans as ‘gorillas’. While the fault was quickly corrected, many attributed it to an over reliance on white faces used in the AI’s training data. With the lack of a comprehensive range of faces with different skin tones, the algorithm made this drastic leap, with obvious offensive results.\n\nRace throws up even more worrying examples of the danger of bias in AI though. Jason points out: “Human-generated data is the biggest source of bias, for example, in survey results, hiring patterns, criminal records, or in other human behavior.”\n\nThere is a lot to unpack in this. A prime area to start is the matter of AI use by the US court and corrections systems, and the growing examples of published accusations of racial bias being perpetrated by these artificial intelligence programs.\n\nAn AI program called COMPAS has been used by a Wisconsin court to predict the likelihood that convicts will reoffend. An investigative piece by ProPublica last year found that this risk assessment system was biased against black prisoners, incorrectly flagging them as being more likely to reoffend than white prisoners (45% to 24% respectively). These predictions have led to defendants being handed longer sentences, as in the case of Wisconsin v. Loomis.\n\nThere have been calls for the algorithm behind COMPAS, and other similar systems, to be made more transparent, thereby creating a system of checks and balances to prevent racial bias being used as an approved tool of the courts by these AI systems.\n\nSuch transparency is seen by many as an essential check to put in place alongside AI development. As risk assessment programs like COMPAS continue to be developed, they usher in the onset of neural networks, which are the next link in the chain for AI proliferation.\n\nNeural networks use deep learning algorithms, creating connections organically as they evolve. At this stage, AI programs become far more difficult to screen for traces of bias, as they are not running off a strict set of initial data parameters.\n\nAI Not the Boon to Recruitment Many Believed\nJason highlights hiring patterns as another example of human-generated data that is susceptible to bias.\n\nThis is an area of AI development that has drawn attention for its potential to either increase diversity in the workplace, or maintain its homogeneity. More and more firms are using AI programs to aid their hiring processes, but industries like tech have a long-standing reputation of not having a diverse enough workforce.\n\nA report from the US Equal Employment Opportunity Commission found that tech companies showed a large portion of Caucasians, Asians and men, but were vastly underrepresented by Latinos and women.\n\n“The focus should both be on creating unbiased data sets as well as unbiased AI algorithms,” says Jason. People must recognize biased data and actively seek to counteract it. This recognition takes training. “This is a key issue for companies utilising AI for their hiring programs. Using historically restrictive data will only recycle the problem with these algorithms.”\n\nThe cause of bias in AI is also its solution – people. As Jason points out, data algorithms are created by the data sets that train them, so it is only natural that there is causality by using biased sources. Unfortunately, because bias is often so subtle, dedicated training is needed to weed it out.\n\n“IBM and Microsoft have publicly discussed their investments in counteracting bias, but it’s too early to tell how successful they or anyone else will be,” Jason notes. Indeed, both IBM and Microsoft have been vocal in their commitment to research and tackling the matter of bias in not only their own programs, but third-party ones too.\n\nCrucially, for AI development to counteract the dangers of bias, there needs to be a recognition that this technology is not infallible. “Biased data leads to biased results, even though we may tend to trust the results of AI because it’s AI. So the primary danger is placing our faith where it doesn’t belong,” says Jason.\n\nWith well-publicized instances of AI displaying racially-based injustice and furthering restrictive hiring processes, these can act as sufficient flashpoints that can easily gather public attention to the matter. Hopefully, this translates into further research and resources for tackling the problem.\n\nTay’s Troubled Second Release\nAfter the very public 16-hour rise and fall of Microsoft’s AI chatbot Tay, its developers went back to the drawing board. 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