Machine Learning Inspirational Quotes

Warren Buffett is one of the best learning machines on this earth. The turtles which outrun the hares are learning machines. If you stop learning in this world, the world rushes right by you.

Remember this, for it is as true as true gets: Your body is not a lemon. You are not a machine. The Creator is not a careless mechanic. Human female bodies have the same potential to give birth well as aardvarks, lions, rhinoceri, elephants, moose, and water buffalo. Even if it has not been your habit throughout your life so far, I recommend that you learn to think positively about your body.

And who wouldn't wish that? Certainly everyone here- dressed up as aliens, and wizards, and zombies, and superheroes- wants desperately to be inside a story, to be part of something more logical and meaningful than real life seems to be. Because even worlds with dragons and time machines seem to be more ordered than our own. When you live for stories, when you spend so much of your time immersed in careful constructs of three and five acts, it sometimes feels like you're just stumbling through the rest of life, trying to divine meaningful narrative threads from the chaos. Which, as I learned the hard way this weekend, can be painfully fruitless. Fiction is there when real life fails you. But it's not a substitute.

I think that it’s extraordinarily important that we in computer science keep fun in computing. When it started out it was an awful lot of fun. Of course the paying customers got shafted every now and then and after a while we began to take their complaints seriously. We began to feel as if we really were responsible for the successful error-free perfect use of these machines. I don’t think we are. I think we’re responsible for stretching them setting them off in new directions and keeping fun in the house. I hope the field of computer science never loses its sense of fun. Above all I hope we don’t become missionaries. Don’t feel as if you’re Bible sales-men. The world has too many of those already. What you know about computing other people will learn. Don’t feel as if the key to successful computing is only in your hands. What’s in your hands I think and hope is intelligence: the ability to see the machine as more than when you were first led up to it that you can make it more.

Human-level AI is defined by systems that are continuously improving and can not only do complex automatic tasks but also deal with complex life situations like caring, nourishing, inspiring, guiding, motivating, negotiating, keeping good relationships, and controlling diseases at a level similar to that of humans.

Artificial Intelligence is highly Interdisciplinary. Therefore, let’s approach it in a Multidisciplinary & Holistic way

Artificial intelligence fires the imagination of many people. Unfortunately, also that of the foolish.

Don’t be scared of racist people. Be frightened of ‘racist’ algorithms because they have no conscience and are much more effective.

Artificial Intelligence is not a new wave of technology. It is much more like a Tsunami that threatens to flood us if we are not mindful.

A neural network, also known as an artificial neural network, is a type of machine learning algorithm that is inspired by the biological brain.

I think that it's extraordinarily important that we in computer science keep fun in computing. When it started out, it was an awful lot of fun. Of course, the paying customers got shafted every now and then, and after a while we began to take their complaints seriously. We began to feel as if we really were responsible for the successful, error-free perfect use of these machines. I don't think we are. I think we're responsible for stretching them, setting them off in new directions, and keeping fun in the house. I hope the field of computer science never loses its sense of fun. Above all, I hope we don't become missionaries. Don't feel as if you're Bible salesmen. The world has too many of those already. What you know about computing other people will learn. Don't feel as if the key to successful computing is only in your hands. What's in your hands, I think and hope, is intelligence: the ability to see the machine as more than when you were first led up to it, that you can make it more.

...Not yet dry behind the ears, not old enough to buy a beer, but old enough to die for his country. He can recite to you the nomenclature of a machine gun or grenade launcher and use either one effectively if he must. He digs foxholes and latrines and can apply first aid like a professional. He can march until he is told to stop, or stop until he is told to march. He obeys orders instantly and without hesitation, but he is not without spirit or individual dignity. He is self-sufficient. ...He sometimes forgets to brush his teeth, but never to clean his rifle. He can cook his own meals, mend his own clothes, and fix his own hurts. If you're thirsty, he'll share his water with you; if you are hungry, food. He'll even split his ammunition with you in the midst of battle when you run low. He has learned to use his hands like weapons and weapons like they were his hands. He can save your life-or take it, because that is his job. He will often do twice the work of a civilian, draw half the pay, and still find ironic humor in it all. He has seen more suffering and death than he should have in his short lifetime. He has wept in public and in private, for friends who have fallen in combat and is unashamed. He feels every note of the National Anthem vibrate through his body while at rigid attention, while tempering the burning desire to "square-away" those around him who haven't bothered to stand, remove their hat, or even stop talking. ...Just as did his father, grandfather, and great-grandfather, he is paying the price for our freedom. Beardless or not, he is not a boy. He is the American Fighting Man that has kept this country free for over two hundred years. He has asked nothing in return, except our friendship and understanding. Remember him, always, for he has earned our respect and admiration with his blood. And now we have women over there in danger, doing their part in this tradition of going to war when our nation calls us to do so. As you go to bed tonight, remember this. A short lull, a little shade, and a picture of loved ones in their helmets.

Evolutionaries and connectionists have something important in common: they both design learning algorithms inspired by nature. But then they part ways. Evolutionaries focus on learning structure; to them, fine-tuning an evolved structure by optimizing parameters is of secondary importance. In contrast, connectionists prefer to take a simple, hand-coded structure with lots of connections and let weight learning do all the work. This is machine learning’s version of the nature versus nurture controversy, and there are good arguments on both sides.

Spurred on by both the science and science fiction of our time, my generation of researchers and engineers grew up to ask what if? and what’s next? We went on to pursue new disciplines like computer vision, artificial intelligence, real-time speech translation, machine learning, and quantum computing.

Assessment centers on demonstrated competencies, not memorized content. Standardized tests are used thoughtfully to identify and assist students lagging in “learning how to learn” skills. Students teach and learn from each other. They learn to make the most of online resources and machine intelligence and draw on adults for guidance.

Not all of us are born with fingers that move like fucking Ferraris, homie,” he rants in good humour. ​“Some of us are just fuck-ups who look normal and wear shitty clothes because we can’t afford good ones, and we’re angry and we just wanna take out our angst and shit with a guitar. I’m not inspired by how good you are, it’s almost like the opposite. I wanna feel you. "...the kids that I went to school with fucking hated me, and I’d worn the same clothes for five days, and I was tall, skinny and didn’t fit in. I was a basement; where the fuck was I going to learn how to play like Steve Vai? I couldn’t! I was broke. No-one gave a fuck about me. Give me three chords, though, and tell me to show you how I feel, and I bet you I will.

How do we learn? Is there a better way? What can we predict? Can we trust what we’ve learned? Rival schools of thought within machine learning have very different answers to these questions. The main ones are five in number, and we’ll devote a chapter to each. Symbolists view learning as the inverse of deduction and take ideas from philosophy, psychology, and logic. Connectionists reverse engineer the brain and are inspired by neuroscience and physics. Evolutionaries simulate evolution on the computer and draw on genetics and evolutionary biology. Bayesians believe learning is a form of probabilistic inference and have their roots in statistics. Analogizers learn by extrapolating from similarity judgments and are influenced by psychology and mathematical optimization.

What shapes the best in us dies when the best education dies! The best in us shall always be undermined when they that are responsible for shaping the best in us are always undermined! I stand for a different education: a different education where students will not just learn books but life! I stand for a different education: a different education where students will not just learn moral principles, but they shall be living examples of moral principles. I stand for a different education: a different education where students don’t just understand what they learn, but practice what they learn with understanding! I stand for a different education: a different education where students will not just learn about people of different beliefs, culture and backgrounds, but how to live with people who don’t share common perspective with them and know how to show their emotions of bitterness and misunderstanding rightly! I stand for a different education: a different education where students will be perfect ambassadors’ of God on earth and live their daily lives with all due diligence! I stand for a different education: a different education where students will understand why we all breathe the same air, sleep and wake up each day in the same manner to continue the journey of life! I stand for a different education: a different education where students will learn with inspiration even in their desperations! I stand for a different education: a different education where teachers are seen as true epitome of education! I stand for a different education: a different education in which the value of the teacher is well understood and the teacher is well valued as a treasure! I stand for a different education: a different education where students will not just learn, but they will reproduce great and noble things with what they learn! I stand for a different education: a different education where students will understand the real meaning of integrity and responsibility and with true courage and humility be that as such! I stand for a different education: a different education where education means creativity! Education is the spine of every nation! The better the education, the better the nation! The mediocre the education, the mediocre the nation! A good nation is good because of how education has shaped the perspective and understanding of the populace! A nation that does not know where it is heading towards must ask the machine that produces the populace who drive the nation: education! Until we fix our education, we shall always have a wrong education and we shall always see a wrong nation!

In 2003, Meryl Streep won a career achievement César Award, the French equivalent of an Oscar. Streep’s words (my translation) acknowledged the enduring interest of French audiences in women’s lives and women’s stories: "I have always wanted to present stories of women who are rather difficult. Difficult to love, difficult to understand, difficult to look at sometimes. I am very cognizant that the French public is receptive to these complex and contradictory women. As an actress I have understood for a long time that lies are simple, seductive and often easy to pass off. But the truth—the truth is always very very very complicated, often unpleasant, nuanced or difficult to accept." In France, an actress can work steadily from her teens through old age—she can start out in stories of youthful rebellion and end up, fifty years later, a screen matriarch. And in the process, her career will end up telling the story of a life—her own life, in a sense, with the films serving, as Valeria Bruni Tedeschi puts it, as a “journal intime,” or diary, of one woman’s emotions and growth. No wonder so many French actresses are beautiful. They’re radiant with living in a cinematic culture that values them, and values them as women. And they are radiant with living in a culture—albeit one with flaws of its own—in which women are half of who decides what gets valued in the first place. Their films transcend national and language barriers and are the best vehicles for conveying the depth and range of women’s experience in our era. The gift they give us, so absent in our own movies, is a vision of life that values emotional truth, personal freedom and dignity above all and that favors complexity over simplicity, the human over the machine, maturity over callowness, true mysteries over false explanations and an awareness of mortality over a life lived in denial. In the luminous humanity of their faces and in the illuminated humanity of their characters, we discover in these actresses something much more inspiring than the blank perfection and perfect blankness of the Hollywood starlet. We discover the beauty of the real.

There was much more she would have liked to tell her brother. But within a few months, she would be able to tell him in person. When he learned of the attack on the airship, nothing would stop Archimedes and his wife from coming. But at least they would fly to the Red City instead of Krakentown, where he might be recognized as the smuggler Wolfram Gunther-Baptiste. One day, she might write a story inspired by that part of his career. She would call it The Idiot Smuggler Who Destroyed the Horde Rebellion’s War Machines and Changed His Name to Avoid the Rebel Assassins. Zenobia would take pity on the idiot’s sister and leave her out of the tale. She

If Bezos took one leadership principle most to heart—which would also come to define the next half decade at Amazon—it was principal #8, “think big”: Thinking small is a self-fulfilling prophecy. Leaders create and communicate a bold direction that inspires results. They think differently and look around corners for ways to serve customers. In 2010, Amazon was a successful online retailer, a nascent cloud provider, and a pioneer in digital reading. But Bezos envisioned it as much more. His shareholder letter that year was a paean to the esoteric computer science disciplines of artificial intelligence and machine learning that Amazon was just beginning to explore. It opened by citing a list of impossibly obscure terms such as “naïve Bayesian estimators,” “gossip protocols,” and “data sharding.” Bezos wrote: “Invention is in our DNA and technology is the fundamental tool we wield to evolve and improve every aspect of the experience we provide our customers.

Almost 20 years ago, Margaret J. Wheatley and Myron Kellner-Rogers began A Simpler Way, a prophetic book about what organizations could be, with these words: There is a simpler way to organize human endeavor. It requires a new way of being in the world. It requires being in the world without fear. Being in the world with play and creativity. Seeking after what’s possible. Being willing to learn and be surprised. The simpler way to organize human endeavor requires a belief that the world is inherently orderly. The world seeks organization. It does not need us humans to organize it. This simpler way summons forth what is best about us. It asks us to understand human nature differently, more optimistically. It identifies us as creative. It acknowledges that we seek after meaning. It asks us to be less serious, yet more purposeful, about our work and our lives. It does not separate play from the nature of being. … The world we had been taught to see was alien to our humanness. We were taught to see the world as a great machine. But then we could find nothing human in it. Our thinking grew even stranger—we turned this world-image back on ourselves and believed that we too were machines. Because we could not find ourselves in the machine world we had created in thought, we experienced the world as foreign and fearsome. … Fear led to control. We wanted to harness and control everything. We tried, but it did not stop the fear. Mistakes threatened us; failed plans ruined us; relentless mechanistic forces demanded absolute submission. There was little room for human concerns. But the world is not a machine. It is alive, filled with life and the history of life. … Life cannot be eradicated from the world, even though our metaphors have tried. … If we can be in the world in the fullness of our humanity, what are we capable of? If we are free to play, to experiment and discover, if we are free to fail, what might we create? What could we accomplish if we stopped trying to structure the world into existence? What could we accomplish if we worked with life’s natural tendency to organize? Who could we be if we found a simpler way?143

They don’t want people who are smart enough to sit around the kitchen table and figure out how badly they’re getting fucked by a system that threw them overboard thirty years ago. They want people who are just smart enough to run the machines and do the paperwork, and just dumb enough to passively accept all the increasingly shitty jobs with the less pay, reduced benefits, the end of overtime—and the vanishing pension that disappears the minute you come to collect it. And now they’re coming for your Social Security. They want your retirement money. They want it back so they can give it to their criminal Wall Street friends. And you know what? They’ll get it! They’ll get it all. They count on the fact that Americans will remain willfully ignorant.” The prophetic Mr. George Carlin “It’s just a ride. We can change it any time we want. It’s just a choice. No effort, no work, no job, no savings of money—a choice, right now, between fear and love. The eyes of fear want you to put bigger locks on your door, buy guns, close yourself off. The eyes of love instead see all of us as one. Here’s what we can do to make this world a better ride. Take all the money we spend on weapons every year and use it to feed and clothe the poor of the world. There will be enough to help every person in the world, not one left out—and we can explore space, both inner and outer, together, in peace.” Bill Hicks “Try to learn to breathe deeply, really taste food when you eat, and when you sleep to really sleep. Try as much as possible to be wholly alive with all your might, and when you laugh, laugh like hell. And when you get angry, get good and angry. Try to be alive. You will be dead soon enough.” William Saroyan

...He can recite to you the nomenclature of a machine gun or grenade launcher and use either one effectively is he must. He digs foxholes and latrines and can apply first aid like a professional. He can march until he is told to stop, or stop until he is told to march. He obeys orders instantly and without hesitation, but he is not without spirit or individual dignity. He is self-sufficient. ...He sometimes forgets to brush his teeth, but never to clean his rifle. He can cool his own meals, mend his own clothes, and fix his own hurts. ...He'll even split his ammunition with you in the midst of battle when you run low. He has learned to use his hands like weapons and weapons like they were his hands. He can save your life- or take it, because that is his job. He will often do twice the work of a civilian, draw half the pay, and still find ironic humor in it all. He has seen more suffering and death than he should have in his short lifetime. He has wept in public and in private, for friends who have fallen in combat and is unashamed. He feels every note of the National Anthem vibrate through his body while at rigid attention, while tempering the burning desire to "square-away" those around him who haven't bothered to stand, remove their hat, or even stop talking. ...Just as did his father, grandfather, and great-grandfather, he is paying the price for our freedom. Beardless or not, he is not a boy. He is the American Fighting Man that has kept this country free for over two hundred years. He has asked nothing in return, except our friendship and understanding. Remember him, always, for he has earned our respect and admiration with his blood. And now we even have women over there in danger, doing their part in this tradition of going to war when our nation calls us to do so. As you go to bed tonight, remember this. A short lull, a little shade, and a picture of loved ones in their helmets.

Gradually I came to learn what every great philosophy has been up to now, namely, the self-confession of its originator and a form of unintentional and unrecorded memoir, and also that the moral (or immoral) intentions in every philosophy made up the essential living seed from which on every occasion the entire plant has grown. In fact, when we explain how the most remote metaphysical claims in a philosophy really arose, it's good (and shrewd) for us always to ask first: What moral is it (is he -) aiming at? Consequently, I don't believe that a "drive to knowledge" is the father of philosophy but that knowledge (and misunderstanding) have functioned only as a tool for another drive, here as elsewhere. But whoever explores the basic drives of human beings, in order to see in this very place how far they may have carried their game as inspiring geniuses (or demons and goblins), will find that all drives have already practised philosophy at some time or another - and that every single one of them has all too gladly liked to present itself as the ultimate purpose of existence and the legitimate master of all the other drives. For every drive seeks mastery and, as such, tries to practise philosophy. Of course, with scholars, men of real scientific knowledge, things may be different -"better" if you will - where there may really be something like a drive for knowledge, some small independent clock mechanism or other which, when well wound up, bravely goes on working, without all the other drives of the scholar playing any essential role. The essential "interests" of scholars thus commonly lie entirely elsewhere, for example, in the family or in earning a living or in politics. Indeed, it is almost a matter of indifference whether his small machine is placed on this or on that point in science and whether the "promising" young worker makes a good philologist or expert in fungus or chemist - whether he becomes this or that does not define who he is.5 By contrast, with a philosopher nothing is at all impersonal. And his morality, in particular, bears a decisive and crucial witness to who he is - that is, to the rank ordering in which the innermost drives of his nature are placed relative to each other.

When I launched my AI career in 1983, I did so by waxing philosophic in my application to the Ph.D. program at Carnegie Mellon. I described AI as “the quantification of the human thinking process, the explication of human behavior,” and our “final step” to understanding ourselves. It was a succinct distillation of the romantic notions in the field at that time and one that inspired me as I pushed the bounds of AI capabilities and human knowledge. Today, thirty-five years older and hopefully a bit wiser, I see things differently. The AI programs that we’ve created have proven capable of mimicking and surpassing human brains at many tasks. As a researcher and scientist, I’m proud of these accomplishments. But if the original goal was to truly understand myself and other human beings, then these decades of “progress” got me nowhere. In effect, I got my sense of anatomy mixed up. Instead of seeking to outperform the human brain, I should have sought to understand the human heart. It’s a lesson that it took me far too long to learn. I have spent much of my adult life obsessively working to optimize my impact, to turn my brain into a finely tuned algorithm for maximizing my own influence. I bounced between countries and worked across time zones for that purpose, never realizing that something far more meaningful and far more human lay in the hearts of the family members, friends, and loved ones who surrounded me. It took a cancer diagnosis and the unselfish love of my family for me to finally connect all these dots into a clearer picture of what separates us from the machines we build. That process changed my life, and in a roundabout way has led me back to my original goal of using AI to reveal our nature as human beings. If AI ever allows us to truly understand ourselves, it will not be because these algorithms captured the mechanical essence of the human mind. It will be because they liberated us to forget about optimizations and to instead focus on what truly makes us human: loving and being loved. Reaching that point will require hard work and conscious choices by all of us. Luckily, as human beings, we possess the free will to choose our own goals that AI still lacks. We can choose to come together, working across class boundaries and national borders to write our own ending to the AI story. Let us choose to let machines be machines, and let humans be humans. Let us choose to simply use our machines, and more importantly, to love one another.

Sometimes we think we are not capable of doing certain things. I hear comments from my students such as, “My brain isn’t wired to do math,” or “I am not good at math.” It is true that there are people who are better at math than you, but that does not mean you can’t do it. This just means you need to put in more effort than others do. Focusing on our weaknesses may hinder our progress. We may think that we must be born with certain skills and abilities; they must be in our genes. This is not the case. Do you think Nephi could build a ship? Could the brother of Jared have caused light to come into dark barges? Do you think Noah could have built an ark that would hold two of every animal species on the earth? Do you think Moses had the power to part a sea? Actually, no. None of these men had the power to do any of these things. However, they all had something in common. They all knew how to tap into the power of someone who could—the Savior’s power. It is so important that we learn how to tap into that power. The Atonement literally means “at-one-ment,” or becoming one with God. The Savior gave us the power to become gods. He enabled us so we would be able to perform miracles through Him. But we must understand that this kind of power is not free. There is only one thing that the Savior, through His Atonement, gave us for free and that is the power to overcome death. Everything else that He offers must come “after all we can do.” [2] For example, Jesus Christ promises us eternal life, but only after we have faith in Him, obey His commandments, and endure to the end. Similarly, He gives us power to move mountains, but only after doing all we can and having trust in Him. The power to change our lives, change the world, and perform miracles is within each of us. However, we need to have enough humility to realize that, in the end, we are not the ones performing the miracles—He is. Occasionally, I have a student who does not do their homework, rarely comes to class, and then comes at the end of the semester and asks, “Sister Qumsiyeh, is there anything I can do to pass? Do you offer any extra credit?” I know some of you are smiling right now because you know you have done this to your teachers. This is what I wish I could say to the student who asks that question: “You need to invent a time machine and go back and do what you should have done this semester. You failed because you did not try your best. It is too late.” Do we all really hope to stand before the Savior at the Judgement Day and expect Him to save us without us doing our part? Do we really expect Him to allow us into the celestial kingdom and to just save us? No, that is not how the Atonement works. It does not work without us having tried our best. Of course, our best may not be enough. In fact, it hardly ever is. But if we do our best and have faith in Him, He magnifies our efforts. The brother of Jared could not make the 16 stones shine, but he spent hours preparing them and then humbly took them to the Lord and basically said, “Here is my small effort; magnify it.” This the Lord did. [3] Elder David A. Bednar said, “The power of the Atonement makes repentance possible and quells the despair caused by sin; it also strengthens us to see, do, and become good in ways that we could never recognize or accomplish with our limited mortal capacity.

The business would do good to understand that success or failure is not final in Data Science. For this reason, the business should develop a persistent spirit.

We are manipulating machine intelligence, and in the process, we have forgotten to effectively utilize the true potential of our own mind, let alone focus on improving the mind. We are developing artificial intelligence and have forgotten to develop our own psyche.

DeepMind soon published their method and shared their code, explaining that it used a very simple yet powerful idea called deep reinforcement learning.2 Basic reinforcement learning is a classic machine learning technique inspired by behaviorist psychology, where getting a positive reward increases your tendency to do something again and vice versa. Just like a dog learns to do tricks when this increases the likelihood of its getting encouragement or a snack from its owner soon, DeepMind’s AI learned to move the paddle to catch the ball because this increased the likelihood of its getting more points soon. DeepMind combined this idea with deep learning: they trained a deep neural net, as in the previous chapter, to predict how many points would on average be gained by pressing each of the allowed keys on the keyboard, and then the AI selected whatever key the neural net rated as most promising given the current state of the game.

For the hardest problems—the ones we really want to solve but haven’t been able to, like curing cancer—pure nature-inspired approaches are probably too uninformed to succeed, even given massive amounts of data. We can in principle learn a complete model of a cell’s metabolic networks by a combination of structure search, with or without crossover, and parameter learning via backpropagation, but there are too many bad local optima to get stuck in. We need to reason with larger chunks, assembling and reassembling them as needed and using inverse deduction to fill in the gaps. And we need our learning to be guided by the goal of optimally diagnosing cancer and finding the best drugs to cure it. Optimal learning is the Bayesians’ central goal, and they are in no doubt that they’ve figured out how to reach it. This way, please …

This book is a compilation of interesting ideas that have strongly influenced my thoughts and I want to share them in a compressed form. That ideas can change your worldview and bring inspiration and the excitement of discovering something new. The emphasis is not on the technology because it is constantly changing. It is much more difficult to change the accompanying circumstances that affect the way technological solutions are realized. The chef did not invent salt, pepper and other spices. He just chooses good ingredients and uses them skilfully, so others can enjoy his art. If I’ve been successful, the book creates a new perspective for which the selection of ingredients is important, as well as the way they are smoothly and efficiently arranged together. In the first part of the book, we follow the natural flow needed to create the stimulating environment necessary for the survival of a modern company. It begins with challenges that corporations are facing, changes they are, more or less successfully, trying to make, and the culture they are trying to establish. After that, we discuss how to be creative, as well as what to look for in the innovation process. The book continues with a chapter that talks about importance of inclusion and purpose. This idea of inclusion – across ages, genders, geographies, cultures, sexual orientation, and all the other areas in which new ways of thinking can manifest – is essential for solving new problems as well as integral in finding new solutions to old problems. Purpose motivates people for reaching their full potential. This is The second and third parts of the book describes the areas that are important to support what is expressed in the first part. A flexible organization is based on IT alignment with business strategy. As a result of acceleration in the rate of innovation and technological changes, markets evolve rapidly, products’ life cycles get shorter and innovation becomes the main source of competitive advantage. Business Process Management (BPM) goes from task-based automation, to process-based automation, so automating a number of tasks in a process, and then to functional automation across multiple processes andeven moves towards automation at the business ecosystem level. Analytics brought us information and insight; AI turns that insight into superhuman knowledge and real-time action, unleashing new business models, new ways to build, dream, and experience the world, and new geniuses to advance humanity faster than ever before. Companies and industries are transforming our everyday experiences and the services we depend upon, from self-driving cars, to healthcare, to personal assistants. It is a central tenet for the disruptive changes of the 4th Industrial Revolution; a revolution that will likely challenge our ideas about what it means to be a human and just might be more transformative than any other industrial revolution we have seen yet. Another important disruptor is the blockchain - a distributed decentralized digital ledger of transactions with the promise of liberating information and making the economy more democratic. You no longer need to trust anyone but an algorithm. It brings reliability, transparency, and security to all manner of data exchanges: financial transactions, contractual and legal agreements, changes of ownership, and certifications. A quantum computer can simulate efficiently any physical process that occurs in Nature. Potential (long-term) applications include pharmaceuticals, solar power collection, efficient power transmission, catalysts for nitrogen fixation, carbon capture, etc. Perhaps we can build quantum algorithms for improving computational tasks within artificial intelligence, including sub-fields like machine learning. Perhaps a quantum deep learning network can be trained more efficiently, e.g. using a smaller training set. This is still in conceptual research domain.

That serves to illustrate there is this element of faith and this element of power. When you have people praying for you and you're praying yourself, coupled with the power of fasting, which makes you more humble and more teachable, you can learn things, even if it is given to you by revelation. ~~Russell M. Nelson: Father, Surgeon, Apostle by Spencer Condie

Sewing is an enjoyable hobby that allows you to be creative and make a variety of items for yourself and others. At Clothingus.com, we offer a range of resources to help you learn how to sew, including easy projects and information about different sewing tools and their uses. Here are some interesting facts about sewing and related materials that may inspire you to try this useful craft: Cotton fabric can last for up to 100 years with proper care. In fact, cotton fabric has been found in many archaeological sites, indicating its longevity. Women's buttons are typically sewn onto the left side of a garment due to historical reasons. In the past, buttons were expensive, and only wealthy women with domestic help could afford them. To make it easier for the help to button up the garments, they were placed on the left side. Zippers were invented in 1893 and were initially used only on shoes and boots to make them easier to put on. Over time, they gained popularity and were used on other garments as well. The term "calico" refers to a type of cotton print that originated in the city of Calcutta, India. These hand-woven printed fabrics were made in the late 18th century and were named after the city. Buttons on sleeves were introduced by Napoleon Bonaparte. He wanted to prevent his soldiers from wiping their noses on their sleeves, so he ordered buttons to be sewn onto the ends of the sleeves. Sewing is believed to be one of the first skills that Homo sapiens learned. Archaeologists have found evidence of people sewing together fur, hide, skin, and bark for clothing dating back to 25,000 years ago. Early sewing needles were made of bone and ivory, with metal needles being developed later in human history. By the 20th century, more than 4000 different types of sewing machines had been invented. However, only those that made sewing simple, fun, and easy survived over time. If you're interested in learning more about sewing, visit Clothingus.com for lessons and projects that can help you build a solid foundation in this skill. Whether you're a beginner or have some experience, we have something for you. Visit Clothingus.com now.

Don't fear the rise of artificial intelligence, embrace the untapped potential of your own mind. To truly succeed, unlock the hidden depths of your brain and unleash your limitless capabilities. It's not about competing with machines, but about surpassing our own perceived limitations. Expand your horizons, embrace continuous learning, and let the boundless power of your mind inspire greatness that transcends any technological advancement.

The algorithm ponders its own existence

On a professional basis, when I’m asked what I want to be known for, my answer is simple: My work with children. I believe that every child is a leader and should be seen as such. When it comes to children, don’t define them by their behaviors. Visualize and affirm them as leaders. Leadership is affirming people’s worth and potential so clearly that they are inspired to see it in themselves. We can raise a generation of leaders by teaching the children their innate worth and goodness, by helping them see within themselves the great power and potential they have. I am so pleased to see that thousands of schools around the world are now teaching the 7 Habits to children, teaching them who they really are and what they are capable of. We’re teaching them integrity, resourcefulness, self-discipline, the win-win way of life. We’re teaching them to welcome instead of distrust people who are different from them. We’re teaching them how to “sharpen the saw,” to never stop growing and improving and learning. This is being done through our The Leader in Me program that is being implemented in thousands of schools around the world. In these schools they learn that everyone is a leader, not just a few popular ones. They learn the difference between primary success that comes from real, honest achievement and secondary success—worldly recognition—and they learn to value primary success. They learn that they have this marvelous gift of choice, that they don’t have to be discouraged victims or cogs in a machine. Imagine the future if children grow up deeply connected to these principles, banishing victimism and dependency, suspicion and defensiveness—as fully responsible citizens who take very seriously their obligations to others. That future is possible. That’s what I want to be remembered for.

You are a living, breathing, organic being. Of course you can feel pain, of course you can feel hurt, of course it feels overwhelming sometimes as the jagged, rough, and hard world outside bumps up against your soft skin. You are not a machine that rams through each experience, performing tasks with no emotions. You are alive. You are alive. You are alive. Be kind to this soft creature as it learns its way around a busy and loud world. Be easy with yourself because some days you’ll be the only one who is. But that’s okay because you’re the only one that makes a real difference.

But that's the way the world works. We have to move on and learn from our past.

Isaac Asimov’s short story “The Fun They Had” describes a school of the future that uses advanced technology to revolutionize the educational experience, enhancing individualized learning and providing students with personalized instruction and robot teachers. Such science fiction has gone on to inspire very real innovation. In a 1984 Newsweek interview, Apple’s co-founder Steve Jobs predicted computers were going to be a bicycle for our minds, extending our capabilities, knowledge, and creativity, much the way a ten-speed amplifies our physical abilities. For decades, we have been fascinated by the idea that we can use computers to help educate people. What connects these science fiction narratives is that they all imagined computers might eventually emulate what we view as intelligence. Real-life researchers have been working for more than sixty years to make this AI vision a reality. In 1962, the checkers master Robert Nealey played the game against an IBM 7094 computer, and the computer beat him. A few years prior, in 1957, the psychologist Frank Rosenblatt created Perceptron, the first artificial neural network, a computer simulation of a collection of neurons and synapses trained to perform certain tasks. In the decades following such innovations in early AI, we had the computation power to tackle systems only as complex as the brain of an earthworm or insect. We also had limited techniques and data to train these networks. The technology has come a long way in the ensuing decades, driving some of the most common products and apps today, from the recommendation engines on movie streaming services to voice-controlled personal assistants such as Siri and Alexa. AI has gotten so good at mimicking human behavior that oftentimes we cannot distinguish between human and machine responses. Meanwhile, not only has the computation power developed enough to tackle systems approaching the complexity of the human brain, but there have been significant breakthroughs in structuring and training these neural networks.

The human brain is, after all, the best example we have of an intelligent system. If we can learn its methods, we can use these biologically inspired paradigms to build more intelligent machines. This book is the earliest serious examination of the human brain from the perspective of a mathematician and computer pioneer. Prior to von Neumann, the fields of computer science and neuroscience were two islands with no bridge between them.

Wise men never grow up, because the spirit of youthfulness is a bed fellow of optimism that is the only reality in disruptive business models, machine learning, invasion of digitization or acute family turblance.

Wise men never grow up, because the spirit of youthfulness is a bed fellow of resilience, which is the only reality in disruptive business models, machine learning, invasion of digitization or acute family turblance.

DeepMind soon published their method and shared their code, explaining that it used a very simple yet powerful idea called deep reinforcement learning.2 Basic reinforcement learning is a classic machine learning technique inspired by behaviorist psychology, where getting a positive reward increases your tendency to do something again and vice versa. Just like a dog learns to do tricks when this increases the likelihood of its getting encouragement or a snack from its owner soon, DeepMind’s AI learned to move the paddle to catch the ball because this increased the likelihood of its getting more points soon.

Educational technology integration is not about replacing teachers with machines; it's about empowering teachers with tools and machines that can enhance their ability to inspire, engage, and support student learning.

DeepMind soon published their method and shared their code, explaining that it used a very simple yet powerful idea called deep reinforcement learning.2 Basic reinforcement learning is a classic machine learning technique inspired by behaviorist psychology, where getting a positive reward increases your tendency to do something again and vice versa.

While neuroscience is an important source of inspiration, it need not be taken as a rigid guide.

To inspire trust, the AI models that encapsulate dynamic intelligence, should have a carefully configured ‘best before’ date.

One thing I think this is showing us is that focusing on the brain as the source of inspiration for machine learning is derived from a very specialized architecture. I’ve been suggesting that a true general purpose intelligence is much more likely to arise not from mimicking the structure of the core of the human cortex, or anything like that, but from actually taking seriously the computational principles that life has been applying since the very beginning. CHRISTINA: Paramecia? MICHAEL: Even before that. Bacteria biofilms. All that stuff has been solving problems in ways that we have yet to figure out. They’re able to generalize, they’re able to learn from experience with a small number of examples. They make self-models. It’s amazing what they can do. That should be the inspiration. I think the future of machine learning and AI technologies will not be based on brains, but on this much more ancient, general ability of life to solve problems in novel domains.

There are five ways technology can boost marketing practices: Make more informed decisions based on big data. The greatest side product of digitalization is big data. In the digital context, every customer touchpoint—transaction, call center inquiry, and email exchange—is recorded. Moreover, customers leave footprints every time they browse the Internet and post something on social media. Privacy concerns aside, those are mountains of insights to extract. With such a rich source of information, marketers can now profile the customers at a granular and individual level, allowing one-to-one marketing at scale. Predict outcomes of marketing strategies and tactics. No marketing investment is a sure bet. But the idea of calculating the return on every marketing action makes marketing more accountable. With artificial intelligence–powered analytics, it is now possible for marketers to predict the outcome before launching new products or releasing new campaigns. The predictive model aims to discover patterns from previous marketing endeavors and understand what works, and based on the learning, recommend the optimized design for future campaigns. It allows marketers to stay ahead of the curve without jeopardizing the brands from possible failures. Bring the contextual digital experience to the physical world. The tracking of Internet users enables digital marketers to provide highly contextual experiences, such as personalized landing pages, relevant ads, and custom-made content. It gives digital-native companies a significant advantage over their brick-and-mortar counterparts. Today, the connected devices and sensors—the Internet of Things—empowers businesses to bring contextual touchpoints to the physical space, leveling the playing field while facilitating seamless omnichannel experience. Sensors enable marketers to identify who is coming to the stores and provide personalized treatment. Augment frontline marketers’ capacity to deliver value. Instead of being drawn into the machine-versus-human debate, marketers can focus on building an optimized symbiosis between themselves and digital technologies. AI, along with NLP, can improve the productivity of customer-facing operations by taking over lower-value tasks and empowering frontline personnel to tailor their approach. Chatbots can handle simple, high-volume conversations with an instant response. AR and VR help companies deliver engaging products with minimum human involvement. Thus, frontline marketers can concentrate on delivering highly coveted social interactions only when they need to. Speed up marketing execution. The preferences of always-on customers constantly change, putting pressure on businesses to profit from a shorter window of opportunity. To cope with such a challenge, companies can draw inspiration from the agile practices of lean startups. These startups rely heavily on technology to perform rapid market experiments and real-time validation.

If you grind long enough you will quickly learn that generating reoccurring long-term wealth is not the problem or big headache you face rather the big headaches come from coming up with new places to put your money as the more money your money printing machine makes the more headaches you have each day to tend to

May we keep rage off of the freeways, and out of the workplace, and out of our homes, and direct it instead at racism, at poverty and at all the evils that we politely tolerate. May we learn in this new year that what really counts the most is not the years but the days, not the machines we have in our lives, but the people we have in our lives, not how much we can accumulate but how much we can share, and with whom.

Deep learning is an approach to machine learning. While machine learning is trying to put knowledge into computers by allowing computers to learn from examples, deep learning is doing it in a way that is inspired by the brain.