Short & Sweet AI

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

Dr. Peper — Mon, 26 Apr 2021 02:00:00 -0500

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:

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 databases that would have quickly given Klara correct information about how humans die from illnesses different than robots. This part of the plot did not seem to fit.

But even more frustrating and inaccurate, I think, is how the author depicts immortality in the future. The longevity culture thrives today so we know it will be prominent in the decades ahead. In the novel, Ishiguro has immortality being carried out through robots trained to learn and replicate everything about a person they’re going to replace. Klara has the ability to exactly replicate the way a human speaks, walks, and sees the world. As a robot, she can capture a human’s personality, but Ishiguro makes the point that a robot is still unable to capture ‘the human heart”.

Established futurists, even today, have said that in the future we will achieve immortality in a different way. We won’t rely on a robot to use their deep learning algorithms to learn and mimic everything about a person so a human can live on as a robot after death like in this book.

No, our immortality will come by uploading our consciousness to the cloud. It’s already begun. Brain–computer interfaces exist today as I’ve discussed in my episode on Neuralink. Highly complicated fields such as synthetic biology, microscopic robots, nanomaterials, and quantum computing, along with others, are merging. It’s inevitable that we’ll have the capability to connect our neocortex and the totality of who we are to a dedicated computer. We can thus choose if we want immortality in a synthetic cloud and choose when to be downloaded at some future time.

As a final point, I think what Ishiguro addresses so eloquently in the novel by what happens to Klara in the end is one of the most important parts of the book. He makes us feel connected to Klara so that her end kindles in us ethical questions. Ethical questions about how to treat, care for and dispose of robots. When a robot truly feels, rather than simulates emotion, then doesn’t a robot have rights?

Thanks for listening. Be curious and if you like this episode, leave a comment, or a thumbs up to let me know you like the content. From short & Sweet AI, I’m Dr. Peper.

New ‘Liquid’ AI Has Neuroplasticity Like the Human Brain

Dr. Peper — Mon, 19 Apr 2021 02:00:00 -0500

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

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 computer software but shows a kind of built-in neuroplasticity like a human brain.

When the algorithm was tested on the task of keeping an autonomous vehicle in its lane, it was just as accurate and efficient as more advanced and complex machine learning algorithms. The worm-brain model also adapts new pathways. In one example the researchers found the algorithm could change its underlying mathematical equations when it had new information like, there was rain on the autonomous vehicle’s windshield. This “neuroplasticity” means the neural net is less likely to break when it’s given data it hasn’t been trained on.

Liquid AI Uses Less Parameters, Less GPUs

Also, with this new approach, the researchers reduced the neural net’s size. It has only 75,000 trainable parameters instead of the million or billion parameters in some machine learning algorithms. This decreases the GPUs or computing power needed to run the algorithm. You can appreciate the excitement this has generated. Liquid AI is an adaptable machine learning algorithm that consumes less power, uses a smaller neural net, while being as accurate as the larger machine learning systems.

New Liquid AI is More Transparent

But I saved the best for last. For many years AI ethicists and researchers have been deeply troubled by machine learning systems being “black boxes” meaning how they work and arrive at their results is largely impenetrable. No one can determine exactly what’s going on within the neural nets that lead to the successful results. This can be a big problem when unsupervised machine learning models are trained on the unfiltered internet because there’s no way of knowing or controlling what they learn.

But this AI system was designed differently. It’s a new type of AI architecture. This liquid neural net is more open to observation and study. Researchers are able to analyze the neural net’s decision making and diagnose how it arrived at the answers. It’s more transparent. It’s adaptable, efficient, smaller, accurate, and transparent. Liquid AI, now that’s a good thing.

Thanks for listening. I hope you found this helpful. Be curious and if you like this episode, click the thumbs up button and leave a comment. From Short and Sweet AI, I’m Dr. Peper.

A Simple Explanation of AI

Dr. Peper — Mon, 12 Apr 2021 02:00:00 -0500

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:

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 of neural networks, each processing and passing the information to another layer, it’s called a deep neural network. When machines learn from deep neural networks, it’s called deep learning.

Present day AI has Limitations

All of the software and computer calculations used in machine learning, especially deep learning, require absurd amounts of data and computer power. The neural nets can be hundreds of layers deep with billions of parameters like AlphaFold or GPT-3 which I talked about in previous episodes. These are gargantuan machine learning algorithms and require very powerful supercomputers to run them. This limits who can use machine learning to only large tech companies and corporations.

Yet as mighty as these neural nets appear, at a core level they are very narrow. And that’s another limitation. They do exactly the one thing they’re trained to do such as recognize an image, steer a car to the left or right, or translate something from one language to another. When you ask a neural net to do something that deviates from its training, it acts brittle and breaks.

New AI Neuroplasticity

But there’s something new in artificial intelligence. AI has reached a point where it’s less artificial and more biological. Like the human brain, AI has developed “neuroplasticity.” Now that you understand basic artificial intelligence, next time let’s discuss something called “liquid” AI which is so cool. It solves a lot of these limitations with a type of artificial neuroplasticity.

I hope you found this helpful. Be curious and if you like this episode, please follow my channel. Or you can leave a comment and click the thumbs up button, which lets me know you like the content. From Short and Sweet AI, I’m Dr. Peper.

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

Dr. Peper — Mon, 05 Apr 2021 02:00:00 -0500

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

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 equipped with sensors to report on conditions in whatever environment it’s in. These are truly miniaturized machines capable of being injected through a syringe and still maintain their structure and function. And since they use the same well understood manufacturing process as computer chips, they are easy and cheap to produce. Millions of tiny robots can be made at the same time. The end result is electronically integrated, mass manufactured, microscopic robots.

Like in the novel Super Sad True Love Story, we could have smart blood with nanobots injected into our bloodstream. The nanobots could be used to deliver cancer drugs in humans right where they’re needed and avoid harmful side effects to other tissues. They could be used to reduce plaque which has built up in arteries, or for treating hard to reach areas of the human body with microsurgery.

And by the way, the author of that book, Gary Shteyngart, has credited his ideas to Ray Kurzweil, whom you’ve heard me speak of many times. Kurzweil is convinced that nanotechnology is the way we can someday merge humans and technology. As he explained just several years ago, “These robots will go into the brain and provide virtual and augmented reality from within the nervous system rather than from devices attached to the outside of our bodies. The most important application … is that we will connect the top layers of our neocortex to the synthetic cortex in the cloud.”

Leave a comment and let me know if you could access all its power and knowledge, would you connect your brain to the cloud? What if it meant you could store your consciousness in the cloud after you die? I’ve come across a short story called Remembrance which chillingly depicts this in the future. These may seem like crazy ideas, but microrobots are real and soon may be flowing through our bloodstream.

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

Dr. Peper — Mon, 29 Mar 2021 02:00:00 -0500

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

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 makes, in my opinion, is that we think machines can’t perform some human tasks because they can’t perform them the same way humans do. Many doctors use gut instinct and vast hands-on experience when treating patients. Machines won’t be able to diagnose this same way, the way doctors do. Machines will be able to accurately perform the task but in a different way.

So, the three capabilities that humans use to earn a living, manual skills, cognitive skills, and emotional intelligence, are all being replaced by machines. Susskind doesn’t know exactly when this will happen, but he thinks it will be sooner than most people realize, within just decades, and certainly during the 21^st century because during the next 80 years, machines will become a trillion times more powerful.

In a future world without work, Susskind asks how we will earn enough to live on and how will we all find meaning in our life. His assessment is that government, or the big state as he calls it, will be needed to redistribute income and wealth. And even more importantly, governments will need to introduce programs to nudge us into behaviors that will give us fulfillment rather than yielding to Netflix, boredom and despair. Instead of labor market policies, governments will need to form leisure policies that shape the way people use their spare time because the future of work will be the future of leisure.

At different points in time throughout history large groups from the Greeks to the English have lead life with meaning but without work. For instance, in Victorian England, the upper classes were far from depressed by their idleness. Indeed, they created some of the greatest poetry, literature, and science the world has known.

According to many AI experts, as well as Susskind, AI advances will continue at an exponential rate. It’s inevitable. Machines will ultimately do most of what humans do. In the science fiction series, The Expanse, in the future there won’t be work for the majority of people on Earth. People exist on a type of universal basic income. But some families feel strongly that they want their children to live a life with meaning that comes from work. Their only option is to move to Mars which exists to defend Earth, and where everyone has a job, working for the military.

I highly recommend reading A World Without Work. David Susskind goes into much more detail with entertaining examples and comprehensive discussions of universal basic income, the age of labor, the limits of education, and much more.

But what do you think? Could you cultivate a meaningful life without work?

Thanks for listening. I hope you found this helpful. If you liked this episode, please leave a review and subscribe. From Short and Sweet AI, I’m Dr. Peper.

The Future of Work: Misplaced Anxiety?

Dr. Peper — Mon, 22 Mar 2021 02:00:00 -0500

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:

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 4^th 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 in the foreseeable future. But when you read the report in more detail, some red flags emerge.

Surveys show 43% of businesses are set to reduce their workforce due to technology, 50% of all employees will need reskilling in the next 5 years, and job creation is slowing while job destruction accelerates.

Many articles on the world economic forum website also dampen the prevailing optimism for the future of work. One example is the profession of psychologists.

Previous projections assumed the work of a psychologist requires extensive empathic and intuitive skills. Initially, it was thought very unlikely to be replicated by a machine during our lifetime. But experts have found artificial intelligence has become woven into the fabric of our daily lives at an accelerating pace. With the pandemic, the use of meditation and mindfulness apps such as Headspace and Calm has soared, as well as other technology-mediated forms of therapy. The most recent report concluded it’s almost certain the work of psychologists will be replaced in large part by artificial intelligence.

So what’s going on here? Is anxiety about technological unemployment misplaced or will machines be able to perform most human tasks, and how soon?

Well, I’ve uncovered another penetrating viewpoint on the future of work in a book by Daniel Susskind. The book’s been described as “required reading for any presidential candidate.” His premise is the future of work is A World Without Work, which is the title of his book. And I had a glimpse of what a world without work looks like in the science fiction series, The Expanse. I’ll be talking about both in my next episode.

What do you think? Do you feel anxious that your job will be replaced by a machine during your lifetime?

Please leave me your thoughts in the comments, and a review if you like this episode. From Short and Sweet AI, I’m Dr. Peper.

AlphaFold & The Protein Folding Problem

Dr. Peper — Mon, 15 Mar 2021 02:00:00 -0500

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:

Resources:

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 exact 3D structure of only a tiny fraction of known proteins.

The protein folding problem led to a global competition called CASP, which stands for Critical Assessment of Structure Protein. Scientists measure and compare their research efforts using computer-based predictions. The competition started in 1994 to improve computational methods for accurately predicting a protein’s 3D shape.

DeepMind, an AI research lab owned by Google, has made headlines for creating deep learning neural networks AlphaGo and AlphaZero, which beat the world’s leading chess and Go champions. I’ve talked about them in previous episodes. Protein folding has been called the challenge of a lifetime, and the researchers at DeepMind wanted to use AI, not for only game playing but to make a real-world impact. So, DeepMind went to work creating AlphaFold, a deep learning computer system, to solve the protein folding problem.

In 2018 AlphaFold entered the CASP competition for the first time. It achieved the highest score for accurately predicting various protein structures, scoring 60 out of a possible 100 points. But the AlphaFold researchers thought it could improve the accuracy and developed the deep learning neural network even further.

In addition to using a data set with 170,000 protein structures, DeepMind supercharged the algorithm. They added data about physics, geometry, and evolutionary history into their training model. The algorithm analyzed any buried relationships or patterns and was able to determine highly accurate structures in a matter of days, even hours. It could predict a protein’s shape down to the width of an atom.

The turning point came in the CASP competition in November 2020. AlphaFold, as well as teams from Microsoft and the Chinese tech company Tencent, competed to predict protein structures considered to be moderately difficult. The best performance of the other teams was 75 points on a 100-point scale. AlphaFold performed so unbelievably well, it was called a revolution in biology. AlphaFold scored 90 out of 100.

One researcher had been looking for the structure of a protein for 10 years, an absolute decade. AlphaFold’s predictions gave him the protein’s 3D structure in half an hour. You can’t make this stuff up. His exuberance is understandable when he says: “This will change medicine. It will change research. It will change bioengineering. It will change everything.”

Here are a few more comments made by experts that convey why Alphafold is not just a big story but rivals the discovery of DNA.

One researcher said, “I nearly fell off my chair when I saw these results.” Another proclaimed, “It’s a breakthrough of the first order, certainly one of the most significant results of my lifetime.” Another commented, “…a stunning advance…It’s occurred decades before many people in the field would have predicted.”

And John Moult, a professor who helped to create the CASP competition, describes it as a dream come true. He said “I always hoped I would live to see this day. But it wasn’t always obvious I was going to make it.”

Thanks for listening. I hope you found this helpful. If you like this episode, please leave a review and subscribe, because then you’ll receive my episodes weekly. From Short and Sweet AI, I’m Dr. Peper.

OpenAI: For-Profit for Good?

Dr. Peper — Mon, 08 Mar 2021 02:00:00 -0500

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:

Resources:

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 power in the hands of a few.

But there’s a big controversy. OpenAI recently reorganized to form a separate section that’s for profit. It never released the software for GPT-3 as open code for programmers to use and build on. Instead, it licensed GPT-3 exclusively to Microsoft for a billion dollars. OpenAI realized staying a non-profit was financially untenable. It defends its decision explaining it needs billions of dollars to build AGI and fulfill their mission. Personally, I see the necessity for this. I’ve said elsewhere, “If you’re dedicated to your mission, you first have to find consistent funding. We don’t always need more ideas about how to make the world better. We need more ways to consistently fund the ideas we have.” It’s a huge challenge when you realize DeepMind, Open AI’s main competitor, spent 442 million dollars on research the same year OpenAI spent only 11 million.

But there’s been an outcry from critics who say switching to a for profit model is inconsistent with OpenAI’s mission to democratize AI for all. I’d be interested to know what you think about OpenAI’s decision. Do you think its non-profit mission justifies it becoming for profit? Let me know your thoughts and leave a comment.

Thanks for listening, I hope you found this helpful. Be curious and if you like this episode, please leave a review and subscribe because then you’ll receive my episodes weekly. From Short and Sweet AI, I’m Dr. Peper.

What is DALL·E?

Dr. Peper — Mon, 01 Mar 2021 02:00:00 -0500

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:

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 another example of “zero-shot visual reasoning.” Zero-shot learning or ZSL, is the ability of models to perform tasks that they weren’t specifically trained to do. They’re unintended but useful behaviors.

In the case of GPT-3, it can write computer code even though it wasn’t trained to do coding. DALL·E “learned” to generate images from captions or if given the right text prompt it can transform images into sketches. Another task it wasn’t specifically trained to do was to design custom text on street signs. Essentially DALL·E can behave as a Photoshop filter.

It also shows an understanding of visual concepts. It can, in a sense, answer questions visually. When given hidden patterns and prompted to solve an uncompleted grid with images to match, DALL·E was able to fill in the grid with matching patterns without being given any prompts.

Creativity is a Measure of Intelligence

Experts agree language grounded in visual understanding like DALL·E makes AI smarter. This machine learning system has the ability to take two unrelated concepts such as an armchair and an avocado and put them together in a coherent, new way. This is stunning because the ability to coherently blend concepts and use them in a new way is key to creativity. In essence, the machine stores information about our world to use and generalize in a very human-like way. And in the AI world, creativity is one measure of intelligence. So, is this how machine intelligence becomes human-like intelligence?

Thanks for listening, I hope you found this helpful. Be curious and if you like this episode, please leave a review and subscribe because then you’ll receive these episodes weekly. From Short and Sweet AI, I’m Dr. Peper.

What is GPT-3?

Dr. Peper — Mon, 22 Feb 2021 02:00:00 -0500

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:

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 generate a Harry Potter parody in the style of Ernest Hemingway. Take a listen: "It was a cold day on Privet Drive. A child cried. Harry felt nothing. He was dryer than dust. He had been silent too long. He had not felt love. He had scarcely felt hate. Yet the Dementor’s Kiss killed nothing.”

I think that sounds pretty good!

And there’s a twitter feed called gptwisdom which generates quotes using GPT-3. Here are a few examples:

“Dull as a twice-told tale.” Or: "The point at which a theory ceases to be a theory is called its limit.” Or this thoughtful gpt3 generated quote: “The truthfulness of your simplicity can only grow, as you improve your character.”

Things to Know About This Technology

In essence, GPT-3 is a universal language model. The model learned to identify more than 175 billion different distinguishing features of language. These features are mathematical representations of patterns. The patterns are a map of human language. Using this map, GPT-3 learned to perform all sorts of tasks it was not even built to do.

Unintended Abilities

One unexpected ability is GPT-3 can write computer code. Makes sense, because computer code is a type of language. But this behavior was entirely new. It even surprised the designers of GPT-3. They didn’t build GPT-3 to generate computer code, they trained it to do just one thing. Predict the next word in a sequence of words.

All in all, people discovered it can do many tasks that it wasn’t originally trained to do. They found it could build an app by giving it a description of what they wanted the app to do. It can generate charts and graphs from plain English. It can identify paintings from written descriptions. It can generate quizzes for practice on any topic and explain the answers in detail.

The Best But Flawed

GPT-3’s ability to generate text is the best that has ever been seen in AI. Yet it’s far from flawless. It can spew offensive and biased language and struggles with questions that involve reasoning by analogy. It isn’t guided by any coherent understanding of reality because it doesn’t have an internal model of the world. Sometimes it produces nonsense because it’s essentially word-stringing. Other AI researchers say it’s like a black box and it’s hard to figure out what this thing is doing.

A Machine Like Us

And yet, the consensus is GPT-3 is shockingly good. But because it can generate convincing tweets, blog posts and computer code, people think of it as being like them. They are reading humanity into the GPT-3 system and, as such, run the risk of ignoring its limits. Sam Altman, one of the founders of OpenAI which developed GPT-3, has thanked everyone for their compliments. But he urges caution about the hype. He says, “AI is going to change the world but GPT-3 is just a very early glimpse. We still have a lot to figure out.”

What is AI Bias?

Dr. Peper — Mon, 15 Feb 2021 02:00:00 -0500

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:

Resources:

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 statements at Tay and Tay used those statements to train itself. As a result, Tay’s responses became racist and misogynistic and had to be shut down after 24 hours.

Amazon’s Recruiting Bias

Latent bias is when an algorithm may incorrectly identify something based on historical data or because of an existing stereotype. A well-known example of this occurred with Amazon’s recruiting algorithm. The company realized after several years their program for selecting and hiring software developers favored men. This was because Amazon’s computer systems were trained with a dataset containing resumes from mainly men.

Because of this, their algorithm penalized resumes that included the word “women’s” as in women’s chess champion. And it downgraded an applicant if they had graduated from an all womens’ college. Amazon ultimately abandoned the program because even with editing, they could not make the program gender neutral.

Selection Bias Ignores the Real Population

In selection bias a dataset overrepresents one certain group and underrepresents another. It doesn’t represent the real population. For example, some machine learning datasets come from scrapping the internet for information. But major search engines and the data in their systems are developed in the West. As a result, algorithms are more likely to recognize a bride and groom in a western style wedding but not in an African wedding.

Can Big Tech Really Self -Police

Researchers are just beginning to understand the effects of bias in the machine learning algorithms. And the big tech companies which create these systems have pledged to address the problem. But others question their ability to self-police. Google recently fired an expert, vocal, high profile employee who they hired to focus on ethical AI. She was concerned about problems in the language models they used. This raises the point that ethical AI has to mean something to the most powerful companies in the world, for it to mean anything at all

The Power of Diversity

So, what can we do about algorithms which judge us and make decisions about us at every stage of our life, without us ever knowing? Experts say we need to be aware of the problem. We need to ensure the datasets are unbiased. We should develop and use programs that can test algorithms to check for bias. And a recent study emphasized that if the people training the systems come from diverse backgrounds, there is less bias.

We know data scientists inject their bias into the algorithms they build. Having diversity means the algorithms are built for all types of people. We’ve come to learn we need AI ethics because as one headline put it, “We Teach AI Systems Everything Including Our Bias.”

Thanks for listening, I hope you found this helpful. Be curious and if you like this episode, please leave a review and subscribe because then you’ll receive my podcasts weekly. From Short and Sweet AI, I’m Dr. Peper.

AI + Covid-19 Vaccine

Dr. Peper — Mon, 08 Feb 2021 02:00:00 -0500

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

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 AI’s second big contribution to conquering Covid-19. It’s called computational analysis. It involves using AI to gather insights from huge sources of experimental, and well as real world data, on the virus.

At the outset of the pandemic The Allen Institute for AI started an online repository of research articles about Covid-19. Today it has over 30,000 academic articles. Researchers can use this data set for the machine learning algorithms to train on, so they better understand the virus.

For example, as early as April 2020, computational scientists harnessed neural networks to sort through medical records by the thousands. The machines were able to confirm the lack of smell and taste is one of the earliest symptoms of Covid infection. There existed isolated reports of anosmia, which is the medical term for loss of smell and taste, but computer data analysis validated the finding. The CDC then added these to their list of Covid symptoms which helped identify when a person had the infection.

In another instance, medical charts from 96 hospitals in several different countries were analyzed with machine learning. What emerged was insight that many Covid patients had really off the chart readings of blood clotting. This alerted doctors to use blood thinners in patients hospitalized with Covid.

As scientists explain, the human brain becomes pretty quickly overwhelmed by the endless combinations of things, but when you use AI, the machines can find and directly move in on important findings, very quickly and effectively. AI is routinely depicted as evil in fiction, social media, and by Hollywood, and yet, its revolutionized how vaccines are created. It’s also become a workhorse of this pandemic as a powerful technology for processing massive amounts of information. Maybe, the machines will save us.

What is the 4th Industrial Revolution?

Dr. Peper — Mon, 01 Feb 2021 02:00:00 -0500

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 4^th Industrial Revolution is
A brief overview of the previous industrial revolutions
Whether the 4^th 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

Resources:

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 4^th industrial revolution.

The 4^th 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 4^th 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 cities. The Second Industrial Revolution came in the late 1800s with inventions such as the internal combustion engine, the lightbulb, the telephone and major infrastructure such as railroads as well as the steel, oil and electricity industries. The Third Industrial Revolution began in the 1960s with the invention of the semiconductor, personal computers and ultimately, the internet.

Schwab rejects the idea these present-day developments are part of the third industrial revolution. Four IR is evolving superfast, at an exponential, not linear pace, like the previous IRs. For example, it took 75 years for 100 million people to have a traditional telephone, but it only took 2 years for 100 million people to sign up for Instagram and less than a month for 100 million people to use Pokémon Go. The 4^th industrial revolution involves many, many different technologies. Those technologies are combining and merging together and can transform entire systems, across companies and industries, and across cultures and countries.

What are the pro and cons of this new Industry 4.0? Advocates point out the increased productivity from technology and the improved quality of daily life where we can have almost anything we want on demand. There will be massive new markets created as more people come online. And more entrepreneurship exploding worldwide as barriers to new businesses are lowered.

But many thoughtful people are concerned about the cybersecurity risks as everything becomes so connected through the IOT. And disruption of core industries has already begun with Airbnb challenging hotels, Uber and Lyft dissolving the taxi industry, and Amazon threating any business that sells, well, anything. There are ethical concerns about access to data on individuals or groups being wide-spread and used for personal gain and manipulation.

But perhaps the greatest threat of the 4^th industrial revolution is the specter of massive inequality. Experts fear there will be a divide of high-skill/high-pay workers and low-skill/low-pay workers in a winner-take-all economy, as the middle-class dissolves. Even Schwab predicts that inequality will be the greatest concern affecting society in the 4^th Industrial Revolution.

Typically, early adopters of new technology gain the greatest financial benefits, allowing them to jump ahead, while the income gap widens. Sounds pretty dire and yet, no one knows. The French philosopher Voltaire said, “Doubt is an uncomfortable condition, but certainty is a ridiculous one.” This revolution is creating change at warp speed. And even those with knowledge and preparation may not be able to keep up with the ripple effects from the changes.

Personal Data as Private Property

Dr. Peper — Mon, 25 Jan 2021 02:00:00 -0500

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

Resources:

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 hackers could use software to figure out someone’s password from the timing of their keystrokes picked up by eavesdropping on a network. Professor Song and her students were also the first to demonstrate that computer vision can be fooled. She applied a few benign looking stickers to a stop sign. As a result a driverless vehicle identified the sign as a 40 mile per hour speed limit sign instead of recognizing it as a stop sign and continued through an intersection without stopping.

She began by showing that a lot of these machine learning algorithms have weaknesses and she became passionate about people having control over their personal data. Her expertise in machine learning, computer security, and blockchain gave birth to Oasis Labs. She describes Oasis as a privacy-first, cloud computing platform on blockchain. She is creating technology which empowers users to protect their personal information, to decide who can use it, and to get paid for their data.

Through a program with Stanford Medical School, patients can use the Oasis platform to decide who to share their medical data with and to get paid when it’s used. They agree to have scans of their retina and other medical data shared privately through a blockchain type application on the Oasis platform. And then researchers use this information to train computers to recognize eye diseases. Meanwhile Nebula, a genomics company, is jumping onboard and has integrated with Oasis to give users control of their personal genomic data.

Professors Song’s vision for the future is for people to have a revenue stream from their personal information. It may not be a lot on a monthly basis but could contribute to retirement savings as companies pay for using your data over your lifetime. As she says “Today, companies are taking users’ data and essentially using it as a product: they monetize it. The world can be very different if this is turned around and users maintain control of the data and get revenue from it.”

This is a really revolutionary idea. Professor Song has created an internet platform which uses blockchain technology to give us the ability to control our data and earn an income from it. Personal data as private property, I think it’s time.

If you like these flash talks, please leave a review and subscribe. From Short and Sweet AI, I’m Dr. Peper

Neuralink Update

Dr. Peper — Mon, 18 Jan 2021 02:00:00 -0500

In this exciting episode of Short and Sweet AI, I talk about the recent update that Elon Musk gave on his company Neuralink – including how and why his team implanted a coin-sized computer chip in a pig’s brain to create a brain-to-machine interface.

In this episode find out:

What Neuralink is
How the Neuralink chip device works
How Neuralink works when implanted in a pig’s brain
What the future holds for Neuralink and how it may be able to help cure serious health conditions

Important Links & Mentions:

Cyborgs Among Us

Resources:

The Godfather of AI

Dr. Peper — Mon, 11 Jan 2021 02:00:00 -0500

What does it take to be the godfather of AI? And, how does someone come to obtain such a legendary title?

In this episode of Short and Sweet AI, I talk about Geoffrey Hinton, a neuroscientist, computer scientist, and the man Google hired to make AI a reality. In many ways, we have Geoffrey Hinton to thank for developing modern AI and deep learning. It is thanks to him that deep learning has become mainstream in the field of artificial intelligence.

So, how did Geoffrey Hinton rise to become the godfather of AI? Watch this video to find out!

In this episode find out:

How Geoffrey Hinton became the godfather of AI
Why Geoffrey Hinton believes machines need to think the way humans do
Understanding how deep neural networks replicate how the brain processes information
How deep learning became mainstream after 30 years in the wilderness
How deep learning became AI's "lunatic core"

Important Links & Mentions

Resources:

The End of Moore’s Law and Why It’s Important

Dr. Peper — Mon, 04 Jan 2021 02:00:00 -0500

Moore's Law is coming to an end, and many people don't know how to feel about it. In fairness, the end of Moore's Law is not something that crept up on us out of nowhere. Industry experts predicted the termination of Moore's Law years ago. They observed its gradual decline and forecasted a grim future for Moore's Law that has since proved to be an accurate calculation.

But the question remains… why is Moore's Law ending? And why should you care?

I'm kicking off the start of the year with a Short and Sweet AI podcast episode that focuses on endings. That is, the end of Moore's Law and why it matters. As always, I focus on AI in simple terms so that whether you're new to AI or a seasoned pro, you can follow along fully immersed!

In this episode find out:

What Moore’s Law is, who created it, and why it is so important
How Google's big "OMG" moment led to the end of Moore’s Law
What a Tensor Processing Unit (TPU) is
Why TPU is the “Helen of Troy” of AI
What could replace Moore’s Law in the future

Important Links & Mentions

Resources:

What is Quantum Computing? part 2

Dr. Peper — Tue, 05 May 2020 12:37:00 -0500

The world's most powerful supercomputer would take 10,000 years to solve a math problem a quantum computer solved in minutes. Welcome to quantum computing.

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What is Quantum Computing?

Dr. Peper — Tue, 28 Apr 2020 20:11:00 -0500

Quantum computing is an extraordinary technology based on quantum physics which uses quantum bits or qubits to solve problems in a magical way.

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A Physician during COVID

Dr. Peper — Wed, 22 Apr 2020 14:54:00 -0500

I've interrupted my podcasts to care for patients during the COVID surge in my area. Many death certificates, many flags at half-mast.

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How to Train Your Emotion AI

Dr. Peper — Tue, 24 Mar 2020 12:17:00 -0500

AI can help us best if machines can understand our emotions. Emotion metrics are becoming highly accurate but FATE flaws exist.

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What is Emotion AI?

Dr. Peper — Wed, 18 Mar 2020 01:56:00 -0500

Machines with emotional intelligence, emotion AI, can interact more naturally than ever before by reading emotional cues in voices and facial expressions.

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AI Audiobooks

Dr. Peper — Tue, 10 Mar 2020 12:30:00 -0500

AI can generate speech from text to record audiobooks with the exact emotional mix for each word and sentence. Listen for yourself.

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AI and Coronavirus

Dr. Peper — Tue, 03 Mar 2020 13:10:00 -0500

Is there an upside to the coronavirus? Nope. But the outbreak did show how AI can be used to predict and accurately track a pandemic.

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What is Edge AI or Edge Computing?

Dr. Peper — Fri, 28 Feb 2020 18:51:00 -0500

Data and AI are moving out of the cloud onto the edge of the network.

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5G: Fifth Generation Wireless. What is it?

Dr. Peper — Tue, 25 Feb 2020 13:15:00 -0500

What is 5G and how is 5G, IoT, and edge AI going to exponentially change our world?

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What is IoT and Why Does It Matter?

Dr. Peper — Tue, 18 Feb 2020 17:09:00 -0500

The IoT matters because combined with 5G and edge AI, your life is getting so much easier.

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What Are Mentats: Dune's Alternative to AI

Dr. Peper — Fri, 14 Feb 2020 14:33:00 -0500

Do mentats represent the future where 'Dune' intersects with 'The Singularity is Near'?

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Legendary Science Fiction Novel Dune Has No AI

Dr. Peper — Tue, 11 Feb 2020 13:01:00 -0500

The legendary science fiction novel Dune has futuristic technology but no artificial intellligence. What happened?

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Why is Bias in AI Important?

Dr. Peper — Tue, 04 Feb 2020 19:29:00 -0500

The less melodramatic but more real threat of AI is not the rise of the machines but the bias researchers inadvertantly feed into the algorithms.

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ImageNet

Dr. Peper — Tue, 28 Jan 2020 12:38:00 -0500

ImageNet is a 15 million image dataset created by computer rock start Fei Fei Li who revolutionized artificial intelligence and changed the world.

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Fake Radiology

Dr. Peper — Fri, 17 Jan 2020 18:02:00 -0500

Fake radiology is when artificially intelligent malware places fake tumors in patients' CT scans. With a hospital's permission, this threat became real.

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What is AlphaZero?

Dr. Peper — Tue, 14 Jan 2020 15:54:00 -0500

Alpha Zero is a computer program that trained itself to play chess, Go and Shogi at superhuman levels in 24 hours.

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Walloped by AlphaGo

Dr. Peper — Fri, 10 Jan 2020 13:14:00 -0500

In 1997 a chess playing computer built by IBM called Deep Blue beat the world chess champion Gary Kasparov. You […]

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Deepmind, Gaming and the Nobel Prize

Dr. Peper — Tue, 07 Jan 2020 12:58:00 -0500

DeepMind is the world’s largest and most prestigious company focused on artificial intelligence and really came into the public eye […]

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How AI Is Disrupting Medicine

Dr. Peper — Fri, 03 Jan 2020 13:10:00 -0500

AI and medicine…where to begin? There is so much going on in how AI is impacting healthcare. It’s a meta […]

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The Turing Test 2029

Dr. Peper — Tue, 31 Dec 2019 12:58:00 -0500

Tomorrow is January 1, 2020 and we do not just start a new year but a new decade. In the […]

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Self-Driving Cars: are we there yet?

Dr. Peper — Sat, 28 Dec 2019 20:04:00 -0500

Where are the promised self-driving cars? Right now they're at low speeds in defined routes.

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The Singularity Is Near

Dr. Peper — Tue, 24 Dec 2019 12:57:00 -0500

Gradually, and then suddenly From short and sweet AI, I’m Dr. Peper, and today I’m talking about The Singularity is […]

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Cyborgs Among Us

Dr. Peper — Fri, 20 Dec 2019 13:07:00 -0500

Cyborg is short for cybernetic organism and they'e already among us.

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AI Superpowers

Dr. Peper — Fri, 13 Dec 2019 12:18:00 -0500

Who will win the race to control Artificial Intelligence? China or the U.S? I’m Dr. Peper and today I’m discussing […]

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Facial Recognition

Dr. Peper — Wed, 11 Dec 2019 20:56:00 -0500

Facial Recognition Technology I’ve been hearing more and more about facial recognition technology and here’s what I’ve learned. Facial recognition […]

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Meta Trends

Dr. Peper — Fri, 06 Dec 2019 14:11:00 -0500

Meta Trends The world can be exciting but scary as the way we live changes and has changed in just […]

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Exponential Technology

Dr. Peper — Tue, 03 Dec 2019 12:13:00 -0500

Exponential Technology At Singularity University’s 2019 Global Summit, Peter Diamandis, one of the co founders, captivated the audience with his […]

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Brain Hacking

Dr. Peper — Tue, 19 Nov 2019 11:12:00 -0500

Elon Musk, best known for Tesla, Space X, and Open AI, also has a company called Neuralink which works on […]

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XR: What is Extended Reality?

Dr. Peper — Fri, 15 Nov 2019 18:04:00 -0500

#6 XR- What is Extended Reality? Have you heard of XR, extended reality? It’s something I came across just recently […]

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Is Creativity in AI Possible?

Dr. Peper — Fri, 08 Nov 2019 15:30:00 -0500

Creativity…. in this age of artificial intelligence it is thought to be the thing that distinguishes us from machines. #5 […]

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Are Machine Learning and Deep Learning the same as AI?

Dr. Peper — Wed, 30 Oct 2019 14:13:00 -0500

Algorithms, neural networks, data … it involves machines and it’s deep. #4 Are Machine learning and Deep Learning the same […]

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The Skinny on AI

Dr. Peper — Sun, 20 Oct 2019 11:24:00 -0500

Mainstream AI and all existing AI is called narrow artificial intelligence. Take 3 minutes and get the skinny on it. […]

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Voice First Computing

Dr. Peper — Thu, 10 Oct 2019 14:50:00 -0500

The hottest topic in AI this year is something called voice computing. In fact it’s been described as the latest […]

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Three Breakthroughs Unleashing AI

Dr. Peper — Tue, 01 Oct 2019 23:26:00 -0500

Present day artificial intelligence started back in the 1950s, but why is it everywhere now? #1 Three Breakthroughs Unleashing AI […]

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