Age of AI) Ep. 2 Healed through AI

Unlocking Teen Potential in the Age of AI

In the latest ‘Age of AI’ episode, I encountered a revelation. It wasn’t just about the wonders of Machine Learning, Voice Synthesis, or Image Recognition. It was about a future where these technologies heal, diagnose, and even prevent the once-impossible. This vision stirred something in me, igniting a passion to delve into programming and artificial intelligence. Why? Because I see its power to transform lives, especially those of teenagers.

Picture this: a classroom, vibrant and buzzing with potential. Yet, there’s always a few who seem unreachable, disconnected from the methods and motivations we traditionally employ. This is where AI steps in, like a bridge over a chasm we’ve long deemed uncrossable. Imagine a software, fueled by vast data, crafting tailored motivation and curriculum for each unique mind. This isn’t just a tool; it’s a revolution in education.

The key here is belief — a ‘growth mindset’ that we, as educators and mentors, must nurture. But how do you believe in someone who’s lost faith in themselves? AI offers an answer. With its predictive capabilities and insightful diagnostics, we can provide concrete, personalized support. This isn’t about replacing the human touch; it’s about enhancing it, giving us the means to reach every heart and mind.

As I embark on this journey of learning and discovery, I’m fueled by a vision: a future where technology doesn’t just advance our capabilities but also deepens our connections. Where every teenager, regardless of their background or challenges, is given the chance to flourish. This, I believe, is the true promise of the Age of AI.”

Summary of Ep. 2 Healed through AI

Fixing Death

• 100 years ago, life expectancy was only 45
• Then by the 1950s, it was up to 65, and today, it’s almost 80.
• We’ve eradicated epidemics that used to kill millions, but life is fragile.
• What if we could improve diagnosis? Innovate to predict illness instead of just react to it?
• In this episode, we’ll see how machine learning is combating one of the leading causes of blindness, and enabling a son with a neurological disease to communicate with his family.
• AI is changing the way we think about mind and body, life and death, and what we value most, our human experience.

Tim’s Story

• It feels like everything has changed. I think the hardest part for me is that I’m in this in between where I can still talk, but not everything I say is understandable.
• In 2012, my body started to do things it hadn’t done before. My muscles were twitching, I was stumbling, or I was not making a play I would have always made. I just wasn’t the same athlete, I wasn’t the same football player that I’d always been.
• The three letters that had defined Tim’s life up to that point were not the three letters that the doctor told him that day. A-L-S, which stands for “amyotrophic lateral sclerosis,” is also known as Lou Gehrig’s Disease. It causes the death of neurons controlling voluntary muscles.
• His phone didn’t recognize the word “Dad.” So, he had said to me… “Dad, I’ve changed your name. I’m calling… I now call you “Yo-yo.” So he would say into his phone, “Call Yo-yo.”

Communication Development

• Language, the ability to communicate with one another. It’s something that makes us uniquely human, making communication an impactful application for AI.
• Communications — it starts with understanding someone, and then being understood, and for a lot of people, their voice is like their identity.
• In the US alone, roughly one in ten people suffer acquired speech impairments, which can be caused by anything from ALS, to strokes, to Parkinson’s, to brain injuries. Solving it is a big challenge
• AI-powered Live Transcribe can help people communicate.

1. First, the sound of our voice is converted into a waveform, which is really just a picture of the sound.
2. Waveforms are then matched to transcriptions, or “labels” for each word. These maps exist for most words in the English language.
3. This is where machine learning takes over. Using millions of voice samples, a deep learning model is trained to map input sounds to output words.
4. Then the algorithm uses rules, such as grammar and syntax, to predict each word in a sentence. This is how AI can tell the difference between “there,” “their,” and “they’re.”

Project Euphonia — Julie Cattiau

• Improved Speech Recognition who has variety of medical conditions
• Give people their voice back. Recreating their voice to sound as they used to before they were diagnosed.
• Voice is their Identity
• ALS Therapy Development Institute is an organization that’s dedicated to finding treatments and cures for ALS.
• We are life-focused. How can we use technologies we have to help these people right away?
• However, Making a new unique model with unique date of every new and unique patient is very slow and inefficient. → ALS Ice bucket challenge. “Let’s collect voice models of ALS patients as fast and as many as we can!”
• Machine Learning: Using millions of voice samples
• Deep Learning Model: trained to map input sounds to output words.
• Algorithm uses rules such grammar and syntax to predict each word in a sentence.

Machine Learning

• The goal, right, for Tim, is to get it so that it works outside of the things that he recorded. The problem is that we have no idea how big of a set that this will work on.
• Dimitri had recorded upwards of 15,000 sentences, which is just an incredible amount of data. We couldn’t possibly expect anyone else to record so many sentences, so we know that we have to be able to do this with much less recordings from a person. So it’s not clear it will work. How can we improve by collecting less data and predicting more accurately?
• When it doesn’t recognize, we jiggle around the parameters of the speech recognizer, then we give it another sentence, and the idea is that you’ll get it to understand.

Voice Synthesis

• Interpreting speech is one thing, but re-creating the way a real person sounds is an order of magnitude harder.
• Voice imitation is also known as voice synthesis, which is basically speech recognition in reverse.
• Converts text into waveforms → Waveforms are used to make sounds
• We have a speech recognition model that works for Tim Shaw, which is, you know, one person, and we’re really hoping that, you know, this technology can work for many people.
• Tim: It has been so long since I’ve sounded like that. I felt like a new person. I felt like a missing piece was put back in place.

Preventing Blindness

• Can AI predict blindness? Or even prevent it?
• Complications of diabetes include heart disease, kidney disease, but one of the really important complications is diabetic retinopathy. The reason it’s so important is that it’s one of the lead causes of blindness worldwide. This is particularly true in India.
• In the early stages, it’s symptomless, but that’s when it’s treatable, so you want to screen them early on, before they actually lose vision.
• In the early stages, if a doctor is examining the eye, or you take a picture of the back of the eye, you will see lots of those bleeding spots in the retina.
• Today, the doctors are not enough to do the screening. We are very limited ophthalmologists, so there should be other ways where you can screen the diabetic patients for diabetic complications.
• India only 11 doctors for every million people. 2000–2500 patients are seen every day by one doctor. This complication can be found before the symptoms show up.
• The interesting thing with diabetic retinopathy is there are ways to screen and get ahead of the problem. The challenge is that not enough patients undergo screening.
• Like Tim Shaw’s ALS speech recognizer, this problem is also about data, or lack of it.
• To prevent more people from experiencing vision loss, the team uses the same kind of machine learning that allows us to organize our photos or tag friends on social media, image recognition.

Retinopathy project

• First, models are trained using tagged images of things like cats or dogs. After looking at thousands of examples, the algorithm learns to identify new images without any human help.
• For the retinopathy project, over 100,000 eye scans were graded by eye doctors who rated each eye scan on a scale from one to five, from healthy to diseased. These images were then used to train a machine learning algorithm. Over time, the AI learned to predict which eyes showed signs of disease.
• What we try to do is think about big problems that are affecting many patients, and how can we bring the best tools and best technologies to get ahead of the problems.
• The technical pieces are so important, and so is the methodology. How do you capture the right image, and how does the algorithm work, and how do you deploy these tools not only here, but in rural conditions? If we can speed up this diagnosis process and augment the clinical care, then we can prevent blindness.
• The patient comes in. They get pictures of the back of the eye. One for the left eye, and right eye. The images are uploaded to this algorithm, and once the algorithm performs its analysis, it sends the results back to the system, along with a referral recommendation.
• Because the algorithm works in real time, you can get a real-time answer to a doctor, and that real-time answer comes back to the patient.
• In the past, Santhi’s condition could’ve taken months to diagnose, if diagnosed at all. By the time an eye doctor would’ve been able to see her, Santhi’s diabetes might have caused her to go blind.
• Now, with the help of new technology, it’s immediate, and she can take the hour-long bus ride to Dr. Kim’s clinic in Madurai for same-day treatment. Now thousands of patients who may have waited weeks or months to be seen can get the help they need before it’s too late.
• AI can step in and be that early detection system.
• (Pedro Domingos) I think one of the most important applications of AI is in places where doctors are scarce. In a way, what AI does is make intelligence cheap, and now imagine what you can do when you make intelligence cheap.
• People can go to doctors they couldn’t before. It may not be the impact that catches the most headlines, but in many ways it’ll be the most important impact.
• AI now is this next generation of tools that we can apply to clinically meaningful problems, so AI really starts to democratize healthcare.
• Even within these images, we’re starting to see some interesting signals that might tell us about someone’s risk factors for heart disease.
• Can you use AI or an algorithm to help patients and doctors get ahead of a given diagnosis? Take cancer as an example of how AI can help save lives. We could take a sample of somebody’s blood and look for the minuscule amounts of cancer DNA or tumor DNA in that blood. This is a great application for machine learning.
• Figuring out how cells work well enough that you can understand why a tumor grows and how to stop it without hurting the surrounding cells.
• And if cancer could be cured, maybe mental health disorders, like depression, or anxiety. There are facial and vocal biomarkers of these mental health disorders. People check their phones 15 times an hour. So that’s an opportunity to almost do, like, a well-being checkpoint. You can flag that to the individual, to a loved one, or in some cases even to a doctor.
• If you look at the overall field of medicine, how do you do a great job of diagnosing illness? Having artificial intelligence, the world’s greatest diagnostician, helps.

Just the Beginnings.

• Wouldn’t it be a wonderful opportunity to bring technology to problems that we’re solving in life science and healthcare, and in fact, it’s a missed opportunity if we don’t try to bring the best technologies to help people. This is really just the beginning.
• I think in the imaginable future for AI and healthcare is that there is no healthcare anymore, because nobody needs it. You could have an AI that is directly talking to your immune system, and is actually preemptively creating the antibodies for the epidemics that are coming your way, and will not be stopped. This will not happen tomorrow, but it’s the long-term goal that we can point towards.

The insights and information presented in these articles are based on the YouTube Originals Series “The Age of AI.” All script and content rights belong to the creators and producers of the series. This series served as a primary reference in the development of these articles.