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
Apr 26 2021
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 & Mentions: (Neuralink Update) (The Nobel Prize: Kazuo Ishiguro) Resources: The Atlantic: (The Radiant Inner Life of a Robot) Wired: (The Future of Work: ‘Remembrance,’ by Lexi Pandell) CNN International: (Kazuo Ishiguro asks what it is to be human) Waterstones: (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 onboard...
Ishiguro’s Klara and the Sun Reveals Three Rights and Two Wrongs About the Future
Apr 26 2021
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 & Mentions: (Neuralink Update) (The Nobel Prize: Kazuo Ishiguro) Resources: The Atlantic: (The Radiant Inner Life of a Robot) Wired: (The Future of Work: ‘Remembrance,’ by Lexi Pandell) CNN International: (Kazuo Ishiguro asks what it is to be human) Waterstones: (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 onboard...
New ‘Liquid’ AI Has Neuroplasticity Like the Human Brain
Apr 19 2021
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 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 & Mentions (A Simple Explanation of AI) (AlphaFold & The Protein Folding Problem) (What is DALL·E?) Resources: SingularityHub: (New ‘Liquid’ AI Learns Continuously from Its Experience of the World) Analytics 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 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 created out of...
A Simple Explanation of AI
Apr 12 2021
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 & Mentions: (AlphaFold & The Protein Folding Problem) (What is Machine Learning?) Resources: SAS: (Neural Networks: What they are & why they matter) ExplainThatStuff: (Neural networks) Quanta 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 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...
Microscopic Robots Are Real and May Be Flowing Through Your Bloodstream Soon
Apr 5 2021
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 & Mentions (Super Sad True Love Story by Gary Shteyngart) (The Singularity is Near) (March of the Microscopic Robots) (The Future of Work: ‘Remembrance,’ by Lexi Pandell) Resources:  Singularity Hub: (An Army of Microscopic Robots Is Ready to Patrol Your Body) Interesting 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 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 be
A World Without Work - Daniel Susskind Says It's a Real Possibility
Mar 29 2021
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 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 & Mentions (A World Without Work) (The 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 Susskind) TED: (3 myths about the future of work (and why they're not true) | Daniel Susskind) The 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 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...
The Future of Work: Misplaced Anxiety?
Mar 22 2021
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 & 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 Work) Council on Foreign Relations: (Discussion of HBO VICE Special Report: The Future of Work) Daniel 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 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...
AlphaFold & The Protein Folding Problem
Mar 15 2021
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 do and what it cannot do. But there’s an astronomical number of ways a protein can fold into its final 3D structure. It’s called Levinthal’s paradox. Cyrus Levinthal, a molecular biologist, published a paper in 1969 called “How to Fold Graciously.” He found there are so many degrees of freedom in an unfolded chain of amino acids, the molecule has an astronomical number of possible configurations. There’re an estimated 200 million known proteins with 30 million new ones discovered every year. Each one has a unique 3D shape which determines how it works and what it does. For the last 50 years, biologists discovered the...
OpenAI: For-Profit for Good?
Mar 8 2021
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 mission How human-level AI or AGI differs from Narrow AI How far we are from using AGI in everyday life The 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 Intelligence) Technology Review: (The messy, secretive reality behind OpenAI’s bid to save the world) Wired: (To Compete With Google, OpenAI Seeks Investors---and Profits) Wired: (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 defense is to empower as many people as possible to have AI. He doesn’t want any one person or a small group of people to have AI superpower. OpenAI has a 400-word mission statement, which prioritizes AI for all, over its own self-interest. And it’s an environment where its employees treat AI research not as a job but as an identity. The most succinct summary of its mission has been phrased “… an ideal that we want AGI to go well” Two specific parts to its mission are to avoid building human-level AI that harms humanity or unduly concentrates...
What is DALL·E?
Mar 1 2021
What is DALL·E?
Is DALL·E the latest breakthrough in artificial intelligence? It seems there’s no end to the fascinating innovations coming out in the world of AI. DALL·E, the most recent tool developed by OpenAI, was announced just months after unveiling its groundbreaking GPT-3 technology. DALL·E is another exciting breakthrough that demonstrates the ability to turn words into images. As a natural extension of GPT-3, DALL·E takes pieces of text and generates images rather than words in response. In this episode of Short and Sweet AI, I discuss DALL·E in more detail, how it differs from GPT-3, and how it was developed. In this episode, find out: What DALL·E is How DALL·E can generate images from words What unintended yet useful behaviors DALL·E can produce The human-like creativity of DALL·E. Important Links and Mentions: (DALL·E: Creating Images from Text) (This avocado armchair could be the future of AI) Resources: The Next Web: (Here’s how OpenAI’s magical DALL-E image generator works) Venture Beat: (OpenAI debuts DALL-E for generating images from text) CNBC: (Why everyone is talking about an image generator released by an Elon Musk-backed A.I. lab) Episode Transcript: Hello to you who are curious about AI. I’m Dr. Peper and today I’m talking about DALL·E. In a previous episode, I highlighted a new type of AI tool called GPT-3. GPT-3 is a machine learning language model trained on a trillion words that generates poetry, stories, even computer code. Within months of announcing GPT-3, OpenAI released DALL·E. DALL·E is not just another breathtaking breakthrough in AI technology. It represents the ability, by a machine, to manipulate visual concepts through language. DALL·E is a combination of the surrealist artist Salvador Dali and the animated robot Wall-E. What it does is simple but also revolutionary. It’s a natural extension of GPT-3. The AI system was trained with a combination of the 13 billion features of GPT-3 added to a dataset of 12 billion images. DALL·E takes text prompts and responds not with words but images. If you give the system the text prompt, “an armchair in the shape of an avocado” it generates an image to match it. It’s a text-to-image technology that’s very powerful. It gives you the ability to create an image of what you want to see with language because DALL·E isn’t recognizing images, it draws them. And by the way, I would buy one of those avocado chairs if they existed. You can visit OpenAI’s website and play with images generated by this astounding technology: a radish in a tutu walking a dog, a robot giraffe, a spaghetti knight. The images are from the real world or are things that don’t exist, like a cube of clouds. How does It Work? Text-to-image algorithms aren’t new but have been limited to things such as birds and flowers or other unsophisticated images. DALL·E is significantly different from others that have come before because it uses the GPT-3 neural network to train on text plus images. DALL·E uses the language and understanding provided by GPT-3 and its own underlying structure to create an image prompted by a text. Each time it generates a large set of images. Then another machine learning algorithm called CLIP ranks the images and determines which pictures best match the text. As a result, the illustrations are much more coherent and reflect a blend of more complex concepts. This is what makes DALLE the most realistic text-to-image system ever produced. Unintended But Useful Behaviors DALL·E also demonstrates...
What is GPT-3?
Feb 22 2021
What is GPT-3?
Some have called it the most important and useful advance in AI in years. Others call it crazy accurate AI. GPT-3 is a new tool from the AI research lab OpenAI. This tool was designed to generate natural language by analyzing thousands of books, Wikipedia entries, social media posts, blogs, and anything in between on the internet. It’s the largest artificial neural network ever created. In this episode of Short and Sweet AI, I talk in more detail about how GPT-3 works and what it’s used for. In this episode, find out: What GPT-3 is How GPT-3 can generate sentences independently What supervised vs. unsupervised learning is How GPT-3 shocked developers by creating computer code Where GPT-3 falls short. Important Links and Mentions: (Meet GPT-3. It Has Learned to Code (and Blog and Argue)) (GPT-3 Creative Fiction) (Did a Person Write This Headline, or a Machine?) Resources: Disruption Theory - (GPT-3 Demo: New AI Algorithm Changes How We Interact with Technology) Forbes - (What Is GPT-3 And Why Is It Revolutionizing Artificial Intelligence?) Episode Transcript: Today I’m talking about a breathtaking breakthrough in AI which you need to know about. Some have called it the most important and useful advance in AI in years. Others call it crazy, accurate AI. It’s called GPT-3. GPT-3 stands for Generative Pre-trained Transformers 3, meaning it’s the third version to be released. One developer said, “Playing with GPT-3 feels like seeing the future”. Another Mind-Blowing Tool from OpenAI GPT-3 is a new AI tool from an artificial intelligence research lab called OpenAI. This neural network has learned to generate natural language by analyzing thousands of digital books, Wikipedia in its entirety, and a trillion words found on social media, blogs, news articles, anything and everything on the internet. A trillion words. Essentially, it’s the largest artificial neural network ever created. And with language models, size really does matter. It’s a Language Predictor GPT-3 can answer questions, write essays, summarize long texts, translate languages, take memos, basically, it can create anything that has a language structure. How does it do this? Well it’s a language predictor. If you give it one piece of language, the algorithms are designed to transform and predict what the most useful piece of language should be to follow it. Machine learning neural networks study words and their meanings and how they differ depending on other words used in the text. The machine analyzes words to understand language. Then it generates sentences by taking words and sentences apart and rebuilding them itself. Supervised vs Unsupervised machine learning GPT-3 is a form of machine learning called unsupervised learning. It’s unsupervised because the training data is not labelled as a right or wrong response. It’s free from the limits imposed by using labelled data. This means unsupervised learning can detect all kinds of unknown patterns. The machine works on its own to discover information. In supervised machine learning, the machine doesn’t learn on its own. The machine is supervised during its training by using data labelled with the correct answer. This method isn’t flexible. It can’t capture more complex relationships or unknown patterns. Open AI first described GPT 3 in a research paper in May 2020. Then it allowed selected people and developers to use it and report their experiences online of what GPT 3 can do. There’s even an informative article about GPT 3 written entirely by GPT-3. Judge for Yourself One researcher used GPT-3 to...
What is AI Bias?
Feb 15 2021
What is AI Bias?
The ethics surrounding AI are complicated yet fascinating to discuss. One issue that sits front and center is AI bias, but what is it?  AI is based on algorithms, fed by data and experiences. The problem is when that data is incorrect, biased or based on stereotypes. Unfortunately, this means that machines, just like humans, are guided by potentially biased information.  This means that your daily threat from AI is not from the machines themselves, but their bias. In this episode of Short and Sweet AI, I talk about this further and discuss a very serious problem: artificial intelligence bias.  In this episode, find out:  What AI bias is? The effects of AI bias The three different types of bias and how they affect AI How AI contributes to selection bias Important Links & Mentions: (Amazon scraps secret AI recruiting tool that showed bias against women) (Google Hired Timnit Gebru to be an outspoken critic of unethical AI) (Biased Algorithms Learn from Biased Data: 3 Kinds Biases Found In AI Datasets) (Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics) Resources: (Venture Beat – Study finds diversity in data science teams is key in reducing algorithmic bias) (The New York Times - We Teach A.I. Systems Everything, Including Our Biases) Episode Transcript: Today I’m talking about a very serious problem: artificial intelligence bias. AI Ethics The ethics of AI are complicated. Every time I go to review this area, I’m dazed by all the issues. There are groups in the AI community who wrestle with robot ethics, the threat to human dignity, transparency ethics, self-driving car liability, AI accountability, the ethics of weaponizing AI, machine ethics, and even the existential risk from superintelligence. But of all these hidden terrors, one is front and center. Artificial intelligence bias. What is it? Machines Built with Bias AI is based on algorithms in the form of computer software. Algorithms power computers to make decisions through something called machine learning. Machine learning algorithms are all around us. They supply the Netflix suggestions we receive, the posts appearing at the top of our social media feeds, they drive the results of our google searches. Algorithms are fed on data. If you want to teach a machine to recognize a cat, you feed the algorithm thousands of cat images until it can recognize a cat better than you can. The problem is machine learning algorithms are used to make decisions in our daily lives that can have extreme consequences. A computer program may help police decide where to send resources, or who’s approved for a mortgage, who’s accepted to a university or who gets the job.   More and more experts in the field are sounding the alarm. Machines, just like humans, are guided by data and experience. If the data or experience is mistaken or based on stereotypes, a biased decision is made, whether it’s a machine or a human. Types of AI Bias  There are 3 main types of bias in artificial intelligence: interaction bias, latent bias, and selection bias. Microsoft’s Failed Chatbot Interaction bias arises from the users who are driving the interaction and their biases. A clear example was Microsoft’s Twitter based chatbot called Tay. Tay was designed to learn from its interactions with users. Unfortunately, the user community on Twitter repeatedly tweeted offensive...
AI + Covid-19 Vaccine
Feb 8 2021
AI + Covid-19 Vaccine
How fast can you develop a vaccine? Never has this challenge been put to the test quite so intensely as in 2020. In fact, Jason Moore, who heads Bioinformatics at UPenn thinks that if the virus had hit 20 years ago, the world might have been doomed. It’s only thanks to modern technology that we now have a safe vaccine. He said, “I think we have a fighting chance today because of AI and machine learning.” So, how did AI help to make the Covid-19 vaccine a reality? The short answer is a combination of computational analysis and the system of AlphaFold. I talk more about how researchers developed the vaccine so fast in this episode of Short and Sweet AI. In this episode find out:  How AI was used to learn more about Covid-19 through data analysis How AI helped researchers develop the vaccine so quickly Where we would be without AI and machine learning   Important Links & Mentions (Deep Mind, Gaming, + the Nobel Prize)  (AlphaFold: Using AI for Scientific Discovery) (Alpha Fold: the making of a scientific breakthrough)   Resources: IEEE Spectrum - (What AI Can–and Can’t–Do in the Race for a Coronavirus Vaccine) Wired.com - (AI Can Help Scientists Find a Covid-19 Vaccine) Washington Post - (Artificial Intelligence and Covid-19: Can the Machines Save Us?) Episode Transcript: Friends tease me because I’m so fascinated with artificial intelligence that I will claim AI is the reason we have a safe Covid-19 vaccine so quickly. And they’re right, it is one of the reasons. In fact, Jason Moore, who heads Bioinformatics at U Penn thinks if this virus had hit 20 years ago, the world might have been doomed. He said “I think we have a fighting chance today because of AI and machine learning.   How did AI help to make the Covid-19 vaccine a reality? The short answer is through computational analysis and Alpha Fold.  But first, a little background on vaccines. A vaccine provokes the body into producing defensive white blood cells and antibodies by imitating the infection. In order to imitate an infection, you need to find a target on the virus. Once you find the target you need to understand its 3D shape to make the vaccine against it. But it’s really hard to figure out all the possible shapes before you find the one, unique 3D shape of the target, unless…unless of course you use AI.    In the case of the Covid-19 vaccine, Google’s machine learning neural network called Alpha Fold saved the day. Alpha Fold predicted the 3D shape of the virus spike protein based on its genetic sequence. And did it really fast, as early as March 2020, three months after the pandemic started. Without AI, it would have taken months and months to come up with what the best possible target protein could be, and it might have been wrong. But with AI, researchers were able to race ahead to ultimately develop the mRNA vaccine.   It’s common knowledge that it can takes years or even decades to develop a vaccine. Before Covid-19, using other approaches, the quickest vaccine to be developed took 4 years. As of September 2020, there were 34 different Covid-19 vaccines being tested in humans. That’s an astonishing number in so short a time.  Neural networks excel at analyzing massive amounts of data to find patterns that humans might not spot. Computers use machine learning to sort and analyze incredible amounts of data to learn and train over time. And that’s been...
What is the 4th Industrial Revolution?
Feb 1 2021
What is the 4th Industrial Revolution?
Technology breakthroughs are disrupting every industry at a rapid rate. In fact, advances in technology are massively transforming every industry exponentially faster than ever before in history. What do you call exponentially fast disruption and massive transformation in worldwide industries?   It’s called the 4th Industrial Revolution, which I talk about in more detail in this episode of Short and Sweet AI.    In this episode find out:  What the 4th Industrial Revolution is A brief overview of the previous industrial revolutions Whether the 4th Industrial Revolution should be considered a part of the Third Industrial Revolution Pros and cons of the new Industry 4.0 Why inequality may become the greatest threat of the 4th IR   Important Links & Mentions (What Is Edge AI or Edge Computing?) (5G: Fifth Generation Wireless, What Is It?) (What is IOT and Why Does it Matter?) (XR: What is Extended Reality?) Resources: (CNBC - Everything you need to know about the Fourth Industrial Revolution) (Salesforce - What Is the Fourth Industrial Revolution?) (World Economic Forum - The Fourth Industrial Revolution: what it means, how to respond) (What is the Fourth Industrial Revolution?) (What is the Fourth Industrial Revolution? | CNBC Explains) (The Fourth Industrial Revolution by Klaus Schwab) Episode Transcript: Welcome to those who are curious about AI. From Short and Sweet AI, I’m Dr. Peper. Right here, right now, technology breakthroughs are disrupting every industry and massively transforming every industry, exponentially faster than ever before in history. What do you call exponentially fast disruption and massive transformation in world-wide industries? It’s called the 4th industrial revolution.     The 4th industrial revolution is also known as 4 IR or Industry 4.0. But what does it mean? Klaus Schwab, founder of the World Economic Forum, coined the term and wrote a book of the same title. He details how we are now living during a 4th industrial revolution characterized by the fusion of AI, robotics, 3D printing, IOT, quantum computing, blockchain, autonomous vehicles, 5G, synthetic biology, virtual reality, and countless other technologies. He describes this as a “technological revolution… that is blurring the lines between the physical, digital and biological spheres”. Technology merges with humans as our smart watches monitor our hear rate, our temperature or how much we move. It embeds in our daily lives as facial recognition, voice activated assistants, or apps on our phone. This isn’t the future, this is happening now. It’s changing how we live and changing who we are. The three previous industrial revolutions also had new technology which fundamentally changed society. And yet, they were different. Let’s go back and look. The First Industrial Revolution occured in 1760 with the invention of the steam engine and led to factory manufacturing. Hand-made goods were replaced by mass produced products. And the agricultural society was replaced by a huge migration to the...
Personal Data as Private Property
Jan 25 2021
Personal Data as Private Property
Is it time we regained control of our data and found new and better ways to protect it? You and I know that the social media platforms and internet sites we visit collect data on us. In many ways, they monetize our data and use it as a product that can be purchased. In this episode of Short and Sweet AI, I talk about personal data as private property and whether there is a way for us to choose who gets to use our data. In this episode find out: The true value of data Whether we should get paid for our data Who Professor Song is How Professor Song and her company “Oasis Labs” are working on a system that could potentially help users protect their data and even get paid for it How you could potentially make your data your private property Professor Song’s vision for the future and why she believes that we should get revenue by sharing our data Important Links & Mentions (Oasis Labs) (Are Machine Learning and Deep learning the Same as AI?) Resources: (Oasis Labs' Dawn Song on a Safer Way to Protect Your Data) (Building a World Where Data Privacy Exists Online) (Get Paid for Your Data, Reap the Data Dividend) (Giving Users Control of their Genomic Data) (Oasis Labs' Dawn Song in Conversation with Tom Simonite) (deeplearning.ai's Heroes of Deep Learning: Dawn Song) (Computer Scientists Work To Fix Easily Fooled AI) Episode Transcript: From Short and Sweet AI, I’m Dr. Peper, and today I want to talk with you about personal data as private property. You and I know that social media platforms and internet sites we visit are collecting data on us. We know they’re selling our data to advertisers. I mean, that’s their business model. They provide a platform for us to connect with each other and we give them our personal data as payment. Data is valuable. Data is the new oil. It brings in billions of dollars of income for Google, Facebook, Instagram, Amazon, and countless other companies. When we’re online and we click on a pop-up that says “accept”, we’re essentially giving away our personal information to that company. And do we really have a choice? You either have to accept the terms or you’re not allowed to use that site. Well, what if we could be paid for our data, what if we could determine who gets data about what sites we visit, what apps we use on our phones, what physical locations we go to, what conversations we have, basically what if we could be paid for all the information companies are gathering on us now on a daily basis.  And what if we had a system that only provides our data to who we say with great privacy protection using the security of a block chain type technology. Enter Professor Dawn Song and her company Oasis and we are one step closer to that reality. Professor Song is considered to be one of the world’s expert on computer security. She is a Mac Arthur “genius’ recipient and a professor at UC Berkley. Much of her work is in the area of machine learning which I’ve talked about in a previous podcast and in adversarial AI. Adversarial AI is the study of how computer systems are hacked to transmit the wrong information. While still a graduate student at Berkeley, her research drew attention for showing machine learning algorithms can infer what someone is typing. She showed...