51 episodes

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.

Short & Sweet AI Dr. Peper

    • Technology

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.

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

    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 AI
    How Ishiguro depicts robots and the future of work
    The debate about immortality – robots vs. the cloud
    The ethical considerations of human-like robots
    Important Links and Mentions:
    https://drpepermd.com/podcast-2/ep-neuralink-update/ (Neuralink Update)
    https://www.nobelprize.org/prizes/literature/2017/ishiguro/facts/ (The Nobel Prize: Kazuo Ishiguro)
    Resources:
    The Atlantic: https://www.theatlantic.com/magazine/archive/2021/04/kazuo-ishiguro-klara-and-the-sun/618083/ (The Radiant Inner Life of a Robot)
    Wired: https://www.wired.com/story/future-of-work-remembrance-lexi-pandell/ (The Future of Work: ‘Remembrance,’ by Lexi Pandell)
    CNN International: https://www.youtube.com/watch?v=vybotERG0SU (Kazuo Ishiguro asks what it is to be human)
    Waterstones: https://www.youtube.com/watch?v=6GJ7mrqo9nQ (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, employment, and opportunity as depicted in the novel.
    But one thing that doesn’t make sense is that Klara is able to learn and understand her surroundings so exceedingly well and yet make a very major wrong conclusion. In the book, Klara reasons that people, like robots, need the sun to sustain, nourish and heal them after she misinterprets one example. In the future, robots will have onbo

    • 6 min
    New ‘Liquid’ AI Has Neuroplasticity Like the Human Brain

    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 https://drpepermd.com/2021/04/12/a-simple-explanation-of-ai/ (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 AI
    How researchers created Liquid AI
    How Liquid AI differs from other types
    How Liquid AI solves the limitations of computing power with smaller neural nets
    Why Liquid AI is more transparent and easier to analyze
    Important Links and Mentions
    https://drpepermd.com/2021/04/12/a-simple-explanation-of-ai/ (A Simple Explanation of AI)
    https://drpepermd.com/podcast-2/ep-alphafold-the-protein-folding-problem/ (AlphaFold and The Protein Folding Problem)
    https://drpepermd.com/podcast-2/ep-what-is-dall-e/ (What is DALL·E?)
    Resources:
    SingularityHub: https://singularityhub.com/2021/01/31/new-liquid-ai-learns-as-it-experiences-the-world-in-real-time/ (New ‘Liquid’ AI Learns Continuously from Its Experience of the World)
    Analytics Insight: https://www.analyticsinsight.net/why-is-liquid-neural-network-from-mit-a-revolutionary-innovation/ (Why is a ‘Liquid’ Neural Network from MIT a Revolutionary Innovation?)
    TechCrunch: https://techcrunch.com/2021/01/28/mit-researchers-develop-a-new-liquid-neural-network-thats-better-at-adapting-to-new-info/ (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 AI.
    Computer Software with Neuroplasticity
    The worm-brain algorithm is much simpler than the huge neural nets and yet accomplishes similar tasks. In current AI architecture, the neural net’s parameters are locked into the system after training. With liquid AI based on the mathematical models of the worm’s neurons, the parameters are able to change with time and with experience. This is a fluid neural net. As it encounters new information, it adapts. It’s an artificial brain

    • 6 min
    A Simple Explanation of AI

    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 works
    What machine learning and neural networks are
    How deep learning works
    The limitations of AI
    How AI neuroplasticity could solve the limitations of AI
    Important Links and Mentions:
    https://drpepermd.com/podcast-2/ep-alphafold-the-protein-folding-problem/ (AlphaFold and The Protein Folding Problem)
    https://pub.towardsai.net/what-is-machine-learning-ml-b58162f97ec7 (What is Machine Learning?)
    Resources:
    SAS: https://www.sas.com/en_us/insights/analytics/neural-networks.html (Neural Networks: What they are and why they matter)
    ExplainThatStuff: https://www.explainthatstuff.com/introduction-to-neural-networks.html (Neural networks)
    Quanta Magazine: https://www.quantamagazine.org/artificial-neural-nets-finally-yield-clues-to-how-brains-learn-20210218/ (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 Machine Learning
    Before computers can give us the answers, they have to be trained on large amounts of data. As the computer processes more and more information, it learns from the data. This is called training the machine. Then when you give the computer completely new data, the machine knows what to do with it and can give you a correct answer to your specific question.
    If you have many, many layers of...

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

    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 are
    How new research into nanotechnology has improved nanobot design
    Why nanobots use similar technology to computer chips
    The possibilities of nanobots for healthcare
    How nanotechnology could connect humans to technology and the Cloud
    Important Links and Mentions
    https://www.garyshteyngart.com/books/super-sad-true-love-story/ (Super Sad True Love Story by Gary Shteyngart)
    https://drpepermd.com/episode/13-the-singularity-is-near/ (The Singularity is Near)
    https://www.youtube.com/watch?v=2TjdGuBK9mI (March of the Microscopic Robots)
    https://www.wired.com/story/future-of-work-remembrance-lexi-pandell/ (The Future of Work: ‘Remembrance,’ by Lexi Pandell)
    Resources:
    Singularity Hub: https://singularityhub.com/2020/09/08/an-army-of-microscopic-robots-is-ready-to-patrol-your-body/ (An Army of Microscopic Robots Is Ready to Patrol Your Body)
    Interesting Engineering: https://interestingengineering.com/nanobots-will-be-flowing-through-your-body-by-2030 (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 movements. In fact, you can see a battalion of microscopic robots in a coordinated “march” on a video linked in the show notes.
    The genius of Miskin’s work is that the robot’s brain is based on computer chip technology. The same technology has powered our computers and phones for half a century. This means the tiny robots can be integrated with other circuits to respond to more complex commands.
    The nanobot can

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

    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 https://drpepermd.com/podcast-2/ep-the-future-of-work-misplaced-anxiety/ (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 work
    How machines can replicate even cognitive skills
    Theories on how society could adapt to a world without work
    How we could live a meaningful life without work
    Important Links and Mentions
    https://www.danielsusskind.com/a-world-without-work (A World Without Work)
    https://drpepermd.com/2021/03/22/the-future-of-work-misplaced-anxiety/ (The Future of Work: Misplaced Anxiety?)
    https://drpepermd.com/episode/how-to-train-your-emotion-ai/ (How to Train Your Emotion AI)
    Resources
    Oxford Martin School: https://www.youtube.com/watch?v=thZzDi5XRVs ("A world without work: technology, automation and how we should respond" with Daniel Susskind)
    TED: https://www.youtube.com/watch?v=2j00U6lUC-c (3 myths about the future of work (and why they're not true) | Daniel Susskind)
    The New York Times: https://www.nytimes.com/2020/01/14/books/review/a-world-without-work-daniel-susskind.html (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 increasingly being used in the cognitive skills areas, too. AI deep learning is used to read x-rays, compose music, review legal documents, detect eye diseases, and personalize online learning systems. And in the controversial area of synthetic media, AI systems can generate believable videos of events that never happened.
    Machines also have human skills such as empathy and the ability to determine how someone feels. Algorithms are making headway into effectively and accurately reading human emotion through facial recognition and language. I talked about this in my episode on Affective AI.
    The most significant point Susskind makes, in my opinion, is that we

    • 7 min
    The Future of Work: Misplaced Anxiety?

    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 misplaced
    Why economists and AI experts are optimistic about AI’s impact on jobs
    How AI could contribute to job creation and loss
    The surprising impact technology has on certain job roles
    Important Links and Mentions:
    https://www.weforum.org/videos/what-will-the-future-of-jobs-be-like (What will the future of jobs be like?)
    https://www.hbo.com/vice/special-reports/vice-special-report-the-future-of-work (VICE Special Report: The Future of Work)
    Resources:
    The Takeaway: https://www.wnycstudios.org/podcasts/takeaway/segments/what-happens-next-future-work (What Happens Next: The Future of Work)
    Council on Foreign Relations: https://www.cfr.org/event/discussion-hbo-vice-special-report-future-work (Discussion of HBO VICE Special Report: The Future of Work)
    Daniel Susskind’s book: https://www.danielsusskind.com/a-world-without-work (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 their cognitive, creative, and emotional skills that machines don’t have. In this way, workers will complement machines and will always be needed.
    The World Economic Forum, headed by Klaus Schwab who wrote the 4th Industrial Revolution, released a recent report on the Future of Work. They estimated by 2025, 85 million jobs will be lost through artificial intelligence, but 97 million new jobs created. This goes along with the mainstream thinking that technological unemployment is not something to worry about

    • 6 min

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