Short & Sweet AI

Dr. Peper

What is Artificial Intelligence? It's a big part of our daily lives and you want to know. You need to know. But the explanations are so long and boring. Let me give you something short and sweet. Join me, Dr. Peper, for 5 minute, pleasing, and easy to understand flash talks about everything artificial intelligence. Short and Sweet AI.

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    Ishiguro's Klara and the Sun Reveals Three Rights and Two Wrongs About the Future

    We all have thoughts of the future. Some of us will only think of it in passing, but others will spend months or even years contemplating the endless possibilities. Kazuo Ishiguro’s vision for the future, beautifully presented in his latest book, ‘Klara and the Sun,’ shows an excellent level of thought and research. The British novelist presents an emotionally nuanced concept of what it means to be human or non-human. In this episode of Short and Sweet AI, I discuss Ishiguro’s latest book and its depiction of robots and artificial intelligence. I also delve into what immortality could look like for humans – will it be robots in our future or something different? In this episode, find out: What Ishiguro got right and wrong about the future of robots and AIHow Ishiguro depicts robots and the future of workThe debate about immortality – robots vs. the cloudThe ethical considerations of human-like robots Important Links & Mentions: Neuralink UpdateThe Nobel Prize: Kazuo Ishiguro Resources: The Atlantic: The Radiant Inner Life of a RobotWired: The Future of Work: ‘Remembrance,’ by Lexi PandellCNN International: Kazuo Ishiguro asks what it is to be humanWaterstones: Kazuo Ishiguro on Klara and the Sun Episode Transcript: Hello to you who are curious about AI, I’m Dr. Peper. We all have thoughts about the future, some of us in passing and some spend months and years thinking about it. Kazuo Ishiguro’s vision, beautifully presented in his latest book, Klara and the Sun, shows much thought and research. This British novelist presents emotionally nuanced concepts about what it means to be human and not human. I’m not an artificial intelligence expert nor a Nobel prizing winning author like Ishiguro. But I am someone who’s fascinated by artificial intelligence and want people to understand what AI means for our future. From that perspective, I’ve identified three things Ishiguro got right, and two things I think he got wrong, in his new book Klara and the Sun.  First, his depiction of Klara, an artificial friend, or robot, meshes with my understanding of what robots will be like in the future. They will have the ability to understand and integrate information and read and understand human emotions. This ability will surpass the ability of the humans around them at times. With exposure to more human situations and more human observations, robots will increase and refine their emotional abilities. They’ll have true feelings, not simulate them. The second thing Ishiguro gets right is the future of work. There will be substitutions of humans with machines as machines do more and more of the work. Humans will be displaced and just as in the novel, people will struggle to redefine their role in society and find new meaning. And the third thing that Ishiguro accurately writes about is the inequality created by those who choose and can afford to have gene-edited children, described as the lifted kids compared to the non-lifted kids, and those whose parents can’t afford or choose not to have their children’s genes edited before birth. I think this will be a real possibility in the near future. There will also be major inequalities in wealth,...

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    New ‘Liquid’ AI Has Neuroplasticity Like the Human Brain

    What is Liquid AI, and could it prove more effective than other types of AI? New research into neural nets and algorithms has revealed what some call “Liquid AI,” a more fluid and adaptable version of artificial intelligence. In my previous episode, I discussed the basics of AI and the limitations that hold it back. It looks like Liquid AI could provide the very solutions that the AI community has been searching for. In this episode of Short and Sweet AI, I explore the new research behind Liquid AI, how it works, and what it does better than other types of AI. In this episode find out: The limitations of traditional neural networks in AIHow researchers created Liquid AIHow Liquid AI differs from other typesHow Liquid AI solves the limitations of computing power with smaller neural netsWhy Liquid AI is more transparent and easier to analyze Important Links & Mentions A Simple Explanation of AIAlphaFold & The Protein Folding ProblemWhat is DALL·E? Resources: SingularityHub: New ‘Liquid’ AI Learns Continuously from Its Experience of the WorldAnalytics Insight: Why is a ‘Liquid’ Neural Network from MIT a Revolutionary Innovation?TechCrunch: MIT researchers develop a new ‘liquid’ neural network that’s better at adapting to new info Episode Transcript: Hello to you who are curious about AI, I’m Dr. Peper. Machine learning algorithms are getting an overhaul from a very unlikely source. It’s a fascinating story. Neural Nets have Traditional Limitations Neural nets are the powerhouse of machine learning. They have the ability to translate whole books within seconds with Google Translate, change written text into images with DALLE, and discover the 3D structure of a protein in hours with AlphaFold. But researchers have struggled with neural networks because of their limitations. Neural nets cannot do anything other than what they’re trained for. They’re programed with parameters set to give the most accurate results. But that makes them brittle which means they can break when given new information they weren’t trained on. Today the deep learning neural nets used in autonomous driving have millions of parameters. And the newest neural nets are so complex, with hundreds of layers and billions of parameters, they require very powerful supercomputers to run the algorithms. A Neuroplastic Neural Net based on a Nematode Now researchers from MIT and Austria’s Science Institute have created a new, adaptive neural network they’re describing as “liquid” AI. The algorithm’s based on the nervous system of a simple worm, C. elegans. And elegant it truly is. This worm has only three hundred and two neurons but it’s very responsive with a variety of behaviors. The teams were able to mathematically model the worm’s neurons and build them into a neural network. I’ve explained neural networks in my previous episode called A Simple Explanation of...

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    A Simple Explanation of AI

    What is AI really, and how does it work? If you are interested in AI, you’ll undoubtedly know that many of the concepts are a bit overwhelming. There are plenty of terminologies to understand, such as machine learning, deep learning, neural networks, algorithms, and much more. With the world of AI continually evolving, it’s good to go over some of the basic concepts to better understand how it’s changing. In this episode of Short and Sweet AI, I address some of the questions that I get asked a lot: what is AI? How does AI work? I also delve into some of the limitations of AI and their possible solutions. In this episode find out: How AI worksWhat machine learning and neural networks areHow deep learning worksThe limitations of AIHow AI neuroplasticity could solve the limitations of AI Important Links & Mentions: AlphaFold & The Protein Folding ProblemWhat is Machine Learning? Resources: SAS: Neural Networks: What they are & why they matterExplainThatStuff: Neural networksQuanta Magazine: Artificial Neural Nets Finally Yield Clues to How Brains Learn Episode Transcript: Hello to you who are curious about AI. I’m Dr. Peper. If you’re listening to this, you probably think AI’s interesting and important like me. But sometimes I find the concepts are a little overwhelming. I want to go over something I get asked a lot. People ask me, what is AI really, how does it work? Actually, there’re new things going on with how AI works. So, it’s good to go over some of the basic concepts in order to understand the way AI is changing. How does AI work? Artificial Intelligence happens with computers. They’re programed using algorithms. Algorithms are step by step instructions telling the computer what to do to solve a problem. Just like a recipe has specific steps you follow in sequence, to bake a cake, or cook something. Computer scientist write algorithms using a programming language the computer understands. These computer languages have strange names like Python or C plus, plus. The computers also perform math calculations or computations to analyze the information and give an answer. This is known as computational analysis. Basically, the programing language and math calculations are computer software. Using this software, the algorithms come up with an answer from data sets fed into the computer. Machine Learning is a type of AI The major AI being used today is called machine learning. Machine learning is carried out by artificial neural networks, or nets for short. Neural nets underpin the most advanced artificial intelligence being used today. They’re called neural networks because they’re based in part on the way neurons in the brain function. In the brain the neuron receives inputs or information, processes the information, and then gives a result or output. Artificial intelligence uses digital models of brain neurons. These are artificial neurons, based on the computer binary code of ones and zeros. The digital neurons process information and then pass it along to other higher layers of processing. Higher, meaning the results become more specific, just like in the brain. Deep Learning is a type of...

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    Microscopic Robots Are Real and May Be Flowing Through Your Bloodstream Soon

    Microscopic robots might sound like the plot of a futuristic novel, but they are very real. In fact, nanotechnology has been a point of great interest for scientists for decades. In the past few years, research and experimentation have seen nanotechnology's science develop in new and fascinating ways. In this episode of Short and Sweet AI, I delve into the topic of microscopic robots. The possibilities and capabilities of nanobots are something to keep a watchful eye on as research into nanotechnology starts to pick up speed. In this episode, find out: What microscopic robots areHow new research into nanotechnology has improved nanobot designWhy nanobots use similar technology to computer chipsThe possibilities of nanobots for healthcareHow nanotechnology could connect humans to technology and the Cloud Important Links & Mentions Super Sad True Love Story by Gary ShteyngartThe Singularity is NearMarch of the Microscopic RobotsThe Future of Work: ‘Remembrance,’ by Lexi Pandell Resources:  Singularity Hub: An Army of Microscopic Robots Is Ready to Patrol Your BodyInteresting Engineering: Nanobots Will Be Flowing Through Your Body by 2030 Episode Transcript: Today I’m talking about microscopic robots. In the book Super Sad True Love Story by Gary Shteyngart, set in the future, wealthy people pay for life extension treatments. These are called “dechronification” methods and include infusions of “smart blood” which contain swarms of microscopic robots. These tiny robots are about 100 nanometers long and rejuvenate cells and remodel major organs throughout the body via the bloodstream. In this way the wealthy live for over a century. That book was my first introduction to the idea of microscopic robots, also known as nanobots, more than a decade ago. Nanotechnology is more than a subplot in a futuristic novel. It’s an emerging field of designing and building robots which are only nanometers long. A nanometer is 1000 times smaller than a micrometer. Atoms and molecules are measured in nanometers. For example, a red blood cell is about 7000 nanometers while a DNA molecule is two and a half nanometers. The father of nanotechnology is considered to be Richard Feynman who won the Nobel prize in physics. He gave a talk in 1959 called “There’s Plenty of Room at the Bottom.” The bottom he’s referring to is size, specifically the size of atoms. He discussed a theoretical process for manipulating atoms and molecules which has become the core field of nanoscience. The microscopic robots are about the size of a cell and are based on the same basic technology as computer chips. But creating an exoskeleton for robotic arms and getting these tiny robots to move in a controllable manner has been a big hurdle. Then in last few years Marc Miskin, a professor of electrical and systems engineering, and his colleagues, used a fresh, new design concept. They paired 50 years of microelectronics and circuit boards to create limbs for the robots and used a power source in the form of tiny solar panels on its back. By shining lasers on the solar panels, they can control the robot’s...

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    A World Without Work - Daniel Susskind Says It's a Real Possibility

    Is a world without work a reality we need to prepare for?  In my last episode, I discussed whether the fear of machines taking over jobs was truly misplaced anxiety, as experts say. Experts believe that there’s no cause for alarm, but not everyone agrees. Some believe that a future where human workers become obsolete is a real possibility we need to prepare for. In this episode of Short and Sweet AI, I delve into the theory that our future will be a world without work. I discuss Daniel Susskind’s fascinating book, ‘A World Without Work,’ which explores the topic of technological unemployment in great detail. In this episode, find out: What Daniel Susskind believes about the future of workHow machines can replicate even cognitive skillsTheories on how society could adapt to a world without workHow we could live a meaningful life without work Important Links & Mentions A World Without WorkThe Future of Work: Misplaced Anxiety?How to Train Your Emotion AI Resources Oxford Martin School: "A world without work: technology, automation and how we should respond" with Daniel SusskindTED: 3 myths about the future of work (and why they're not true) | Daniel SusskindThe New York Times: Soon a Robot Will Be Writing This Headline Episode Transcript: Hello to you who are curious about AI. I’m Dr. Peper and today I’m talking about a world without work. In my last episode, I talked about the future of work. Economists, futurists, and AI thinkers generally agree that technological unemployment is not a real threat. Our anxiety about machines taking our jobs is misplaced. There have been three centuries of technological advances and each time, technology has created more jobs than it destroyed. So, no need for alarm.   But Daniel Susskind, an Oxford economist and advisor to the British government, thinks this time, with artificial intelligence, the threat really is very real. He wants us to start discussing the future of work because as he sees it, the future of work is A World Without Work, which is the title of his recent book. He explains why what’s been called a slow-motion crisis of losing jobs to machines and automation, needs to be discussed now because it really isn’t slow-motion anymore. Despite increased productivity and GDP from artificial intelligence, Susskind presents evidence technological unemployment is coming. As he says, we don’t need to solve the mysteries of how the brain and mind operate to build machines that can outperform human beings. Machines have been taking over jobs requiring manual abilities for decades. It’s happening now. Although the American manufacturing economy has grown over the past few decades, it hasn’t created more work. Manufacturing produces 70 percent more output than it did in 1986 but requires 30 percent fewer workers to produce it.  More importantly, machines are...

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    The Future of Work: Misplaced Anxiety?

    Are you anxious that a machine will one day replace your job? It’s a common enough fear, especially with the rate technology is advancing. If you have watched any of my previous episodes, you will know that technology is accelerating exponentially! We have seen the equivalent of 20,000 years of technology in just one century. Naturally, people worry about what this means for the future of work. Will human workers become obsolete one day? In this episode of Short and Sweet AI, I explore “technological unemployment” in more detail and whether it’s something we should be concerned about. In this episode find out: Why some experts think the anxiety over technological unemployment is misplacedWhy economists and AI experts are optimistic about AI’s impact on jobsHow AI could contribute to job creation and lossThe surprising impact technology has on certain job roles Important Links & Mentions: What will the future of jobs be like?VICE Special Report: The Future of Work Resources: The Takeaway: What Happens Next: The Future of WorkCouncil on Foreign Relations: Discussion of HBO VICE Special Report: The Future of WorkDaniel Susskind’s book: A World Without Work Episode Transcript: Hello to you who are curious about AI. I’m Dr. Peper and today I’m talking about the future of work. For centuries there’ve been predictions that machines would put people out of work for good and give rise to technological unemployment. If you’ve been listening to my episodes you know that technology today is accelerating exponentially. We are living at a time when many different types of technology are all merging and accelerating together. This is creating enormous advances which some have said will lead to the equivalent of 20,000 years of technology in this one century. And experts are asking what does that mean for the future of work? Historians, economists, and futurists describe the anxiety about new machines replacing workers as a history of misplaced anxiety. Three hundred years of radical technological change have passed and there is still enough work for people to do. The experts say, yes, technology leads to the loss of jobs, but ultimately more new jobs are created in the process. Automation and the use of machines increases productivity which leads to creation of new jobs and increased GDP. A well-known example would be the rise in the use of ATM machines in the 1990s which led to many bank tellers losing their jobs. But at the same time, the ATMs enabled banks to increase their productivity and profits and led to more branches being opened and more bank tellers being hired. The bank tellers now spent their time carrying out more value-added, non-routine tasks. In the early industrial revolution, when mechanical looms were introduced, many highly skilled weavers lost their jobs, but even more jobs were created for less-skilled workers who operated the machines. People who study economics and AI are optimistic. They think machines can readily perform routine tasks in a job but would struggle with non-routine tasks. Humans will still be needed for...

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    AlphaFold & The Protein Folding Problem

    What is the protein folding problem that has left researchers stuck for nearly 50 years? Knowing the 3D shape of proteins is so important for our understanding of various diseases and vaccine development. However, these shapes are fantastically complex and difficult to predict. Researchers have spent years trying to determine the 3D structure of proteins. Thanks to AI systems like AlphaFold, it’s now much easier and faster to predict protein shapes. AlphaFold is currently leading the way in protein folding research and has been described as a “revolution in biology.” In this episode of Short and Sweet AI, I explore the protein folding problem in more detail and how AlphaFold is accelerating our understanding of protein structures. In this episode, find out: Why protein folding is so important Why it’s so difficult to predict protein structures How Google’s DeepMind created AlphaFold How successful AlphaFold has been in predicting protein structures Important Links and Mentions: AlphaFold: The making of a scientific breakthrough Protein folding explained Walloped by AlphaGo What is AlphaZero? AlphaFold: Using AI for scientific discovery Resources: Nature.com - ‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures SciTech Daily - Major Scientific Advance: DeepMind AI AlphaFold Solves 50-Year-Old Grand Challenge of Protein Structure Prediction Episode Transcript: Hello to you who are curious about AI. I’m Dr. Peper and today I’m talking about AlphaFold. One of Biology’s most difficult challenges, one that researchers have been stuck on for nearly 50 years is how to determine a protein’s 3D shape from its amino-acid sequence. It's known as “the protein folding problem”. When I first came across the subject, I thought, ok, that’s a biology problem and maybe AI will solve it but there’s no big story here. I was wrong. Some biologists spend months, years, or even decades performing experiments to determine the precise shape of a protein. Sometimes they never succeed. But they persist because having the ability to know how a protein folds up can accelerate our ability to understand diseases, develop new medicines and vaccines, and crack one of the greatest challenges in biology. Why is protein folding so important? Proteins structures contain as much, if not more information, than stored in DNA. Their 3D shapes are fantastically complex. Proteins are made up of strings of amino acids, called the building blocks of life. In order to function, the strings twist and fold into a precise, delicate shapes that turn or wrap around each other. These strings can even merge into bigger, megaplex structures. Only then can these proteins function in the way necessary to build and sustain life. A protein’s shape defines what the protein can

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    OpenAI: For-Profit for Good?

    One of the founding principles of OpenAI, the company behind technology such as GPT-3 and DALL•E, is that AI should be available to all, not just the few. Co-founded by Elon Musk and five others, OpenAI was partly created to counter the argument that AI could damage society. OpenAI was originally founded as a non-profit AI research lab. In just six short years, the company has paved the way for some of the biggest breakthroughs in AI. Recent controversy arose when OpenAI announced that a separate section of its company would become for-profit. In this episode of Short and Sweet AI, I discuss OpenAI’s mission to develop human-level AI that benefits all, not just a few. I also discuss the controversy around OpenAI’s decision to become for-profit. In this episode, find out: OpenAI’s missionHow human-level AI or AGI differs from Narrow AIHow far we are from using AGI in everyday lifeThe recent controversy around OpenAI’s decision to switch to a for-profit model. Important Links and Mentions: What is GPT-3?OpenAI’s mission statement Resources: Elon Musk on Artificial IntelligenceTechnology Review: The messy, secretive reality behind OpenAI’s bid to save the worldWired: To Compete With Google, OpenAI Seeks Investors---and ProfitsWired: OpenAI Wants to Make Ultrapowerful AI. But Not in a Bad Way Episode Transcript: Hello to you who are curious about AI. I’m Dr. Peper and today I’m talking about a truly innovative company called OpenAI. So what do we know about OpenAI, the company unleashing all these mind-blowing AI tools such as GPT-3 and DALL·E? Open AI was founded as a non-profit AI research lab just 6 short years ago by Elon Musk and 5 others who pledged a billion dollars. Musk has been openly critical that AI poses the greatest existential threat to humanity. He was motivated in part to create OpenAI by concerns that human-level AI could damage society if built or used incorrectly. Human-level AI is known as AGI or Artificial General Intelligence. The AI we have today is called Narrow AI, it’s good at doing one thing. General AI is great at any task. It’s created to learn how to do anything. Narrow AI is great at doing what it was designed for as compared to Artificial General Intelligence which is great at learning how to do what it needs to do. To be a bit more specific, General AI would be able to learn, plan, reason, communicate in natural language, and integrate all of these skills to apply to any task, just as humans do. It would be human-level AI. It’s the holy grail of the leading AI research groups around the world such as Google’s DeepMind or Elon’s OpenAI: to create artificial general intelligence. Because AI is accelerated at exponential speed, it’s hard to predict when human-level AI might come within reach. Musk wants computer scientists to build AI in a way that is safe and beneficial to humanity. He acknowledges that in trying to advance friendly AI, we may create the very thing we are concerned about. Yet he thinks the best

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What is Artificial Intelligence? It's a big part of our daily lives and you want to know. You need to know. But the explanations are so long and boring. Let me give you something short and sweet. Join me, Dr. Peper, for 5 minute, pleasing, and easy to understand flash talks about everything artificial intelligence. Short and Sweet AI.

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