Am I a loser?

Am I a loser?

I think we all are at some point in our lives. You have to lose to win. You have to fail to win. You learn more from your failures than you do your wins.

The difference between the super successful people and most people is they take a lot of risks and they fail all the time. Because of the frequency of their risk taking, eventually some of them may pay off. ‘You can fail over and over again, but all you need is one win’ as Marc Cuban has said. You just need to experience one success to see how your failures were all worth it.

Look at what Elon’s doing now with SpaceX. He put up billions of dollars of his own money to watch rocket after rocket crash. Yet he successfully did a test run recently. He’d been working on that problem for decades and experienced tons of failures. But that never stopped him from persisting. He kept putting more and more money into it because of his beliefs. Humans are an interplanetary species and it’s his duty to build something that can explore other planets.

He might be the tipping point in world history. Think about all the kids that are going to be inspired by the upcoming space race. The quest to get to space commercially will hopefully inspire a generation of thinkers who push the boundaries of what we thought were possible.

Evolution.

Knowledge is the ultimate evolutionary concept because we have no clue how far human intelligence will go.

With AI in the mix now, it’s becoming possible to build digital ‘humans.’ Robots that exude emotion and tend to your exact needs. They’re learning how to do it through Alexa and Google home, so don’t be surprised if Amazon or Google build some kind of human robot in the future because of all the voice data they’ve collected.

What’s possible now with the amount of voice data collected is vocal biomarkers. This could be a game changing addition to how clinical trials are run. Biomarkers are basically objective measurements of if your drug worked. To tell if a drug works, you often test these objective measurements because it’s a way to get an objective view of what’s happening in the body. It removes patient bias.

Right now, researchers around the world are analyzing that data and looking for signs/signals that you could have depression, or Alzheimer’s, or multiple sclerosis, or schizophrenia. We are now using machine learning to analyze vocal biomarkers and match them with people’s medical history to predict if a person may be at risk of depression.

This is a scary experiment to think about, but also a cool one. If a machine can predict if you’re more likely to be depressed, then does that mean they know you better than you know you? The machines you talk to everyday understand who you are better than you?

Yes. In some ways.

They’re using objective data across a large number of people to match to see if the way you speak is similar to the way people who are depressed speak. How? By analyzing numerous characteristics of your voice like tone, speed, volume, grammar, word choice, pauses, content, etc. and matching them to people’s medical records in a dataset that ‘learns over time.’

This means that the bigger the data sets, the better the algorithm/AI is able to predict an outcome. Eventually we’ll get to a point where the AI will understand and analyze data better than we even thought possible. Look at Alpha fold.

What Google did with Alpha fold in predicting the folding of proteins is incredible. But it’s also because the machine is continuing to learn. The more people working on the problem, the more data inputted into the system, the more the machine learns, the better it gets at predicting things.

This assumes your data is clean. What if it’s dirty? One of the biggest issues with certain prediction AI algorithms is the composition of the data. Considering most of the health data we have, and clinical trial data is from white people, we don’t know if certain things work in people of other ethnicities. That’s dangerous because that means the algorithm is prejudiced to people of colour.

The information that the AI has learned from is not a complete data set. It’s a specific data set. The problem is right now these algorithms are being used in public. The more the AI is used, the better it is. But in the meantime, it’s prejudiced because the data it’s used to learn is not complete.

This is all flawed though because prediction algorithms are never going to be 100% accurate, just like humans. What the algorithms and machines lack is emotion. They don’t understand judgement. They just make decisions and conclusions based on what the data tells them, but humans are more complicated than that.

Emotion is hard to comprehend. You can’t build that in an AI. You can try using tons and tons of data, but you can’t get it to think like a human. We’ve scratched the surface of consciousness and neuroscience. How can we build an AI that thinks for itself if we can’t even understand how the human brain works?

So AI taking over and becoming human robots is not going to happen near term. But could it happen in the next 500 years? Yea. Then what? Is it just a world full of robots with a smaller population that exists now moving across planets? Who knows.

That’s the thing about knowledge though. It compounds. It evolves with the times. The AI revolution is upon us. The more data they accumulate, the better they get and data is being generated now more than ever. Their knowledge compounds. This will completely change the world.

This is why the next 500 years is going to be vastly different from the last 500. I listened to a podcast the other from Matt Ridley talking about how people from year 1000 to 1500 AD probably lived a pretty similar life. If you dropped a person from year 1000 in year 1500, the world probably looked like a similar place.

But if you dropped a guy from 1500 in 2000 with a phone, the Internet, big buildings, planes, cities, skyscrapers, etc. he’d be in a completely different world.

Now imagine 500 years from now?

It’s going to be completely different than the last 500 years. We’re still in the early stages of the Internet revolution. We’ve only been using it over the last 70 years and most of the world isn’t even online yet. Now they’re coming.

Citizens of India, Africa, China, Asia, etc. all have smartphones now. Hundreds of millions of people that were never online before and didn’t have access to the Internet are now coming onto the Internet. It’s incredible to think what can be achieved by humanity in the next 100 years having all these great minds learning about the world. Even if you have a small subset commit to learning and making the world a better place, that’s a massive difference. That’s potentially millions of people who are dedicated to learning and making a world a better place.

People want to help.

But they’re going to fail a lot along the way. Are they ready for that? I hope this new generation is because you need struggle in order to win.

You have to lose a lot before you can win. Without the failure, you won’t have the lessons learned. The wisdom and knowledge gained through experiencing failure.

You’re not a loser, just a winner-in-waiting.