WHY AI WILL NEVER REPLACE HUMANS

 When most people think about AI, the picture is sci-fi, dystopian future with man versus machine. Terminator, Skynet, Black Mirror, Blade Runner, Westworld. But is someone who is working on the most ambitious AI projects in the world everyday. I can tell you that is far from reality. To me, it's the contrary of that. AI enhances an even superchargers humanity. Let me explain why there are many.

 Reasons why AI will never replace humans. AI always

WHY AI WILL NEVER REPLACE HUMANS 

has and always will rely on humans. That's one of the reasons that I was actually inspired to start an AI company that and my background have had a huge impact on me and why I started scale. My. parents were brilliant scientists of Los Alamos who accomplished a lot in advancing their field. That inspired me to use science and technology to have a real impact on the world. My dad was a physicist and my mom was an astrophysicist, both at the top of their field who made meaningful contributions to plasma fluid dynamics and the beginnings of the universe. Their scientific work will have meaningful impacts on how we understand and perceive our world. And I wanted to work on something as impact. or even more impactful than that. That's why I decided to become a programmer so I can make a difference in the world growing up as a programmer despite how powerful computers are, you quickly realize how limited they are. In particular, lack judgment and intelligence Programming is the art giving clear,

 robotic instructions to computers to accomplish simple objectives. It's all black and white, and there's no gray area. As I learned about AI was clearly transformational. It changed the game. It equipped computers with intelligence, and I knew I wanted to be deeply involved. You don't need to play pond, Are you studying AI out of my team? And slowly became more and more excited about all the potential applications of AI for solving more nuanced problems. For example, there was one class project where I worked on applying AI to human emotions. The goal was to take pictures of human expressions and ultimately identify and understand the emotion through very subtle signals and facial expressions. Using AI, we built an algorithm that was able to detect intent. with 80% accuracy and efficacy. We were extremely proud of that. It was the start of using AI to do entirely new things using computers That's when I realized the implications of AI and how we could tackle all the gray areas that involve judgment or intelligence. You see AI needs humans to teach it individual values, nudge it to find thoughtful outcomes and ensure that human intentions and values are aligned with the A. A. I. It was a revelation before. coding was like a black and white film and walked versus watching technicolor. What's more, what's more, 

AI is the potential to take away the repetition in our lives, meaning that new and fresh ideas will matter more and ultimately enable us to be more human. But to power AI you need powerful data, which is especially hard to come by at that time in 2016. While I was at MITI realized that nobody was building anything with AI outside of school. It's unusual for MIT students to not be building something. Mechanical engineering majors are building catapults in the lawn. Electrical engineering majors are building robots and computer science majors are building apps for their friends to use, but nobody was building anything using AI. That's when I discovered would have bottleneck data can be to building meaningful and powerful AI systems. You can't treat data as an afterthought. Bad data or lack of data results in bad ai. I even realized this in my personal life I put a camera on my fridge to gather data to tell me when to refill my groceries and what I needed to buy. That's when I realized just how much data I needed to actually be able to successfully predict what to purchase. There's no way I could create enough data to be successful with the algorithms on my own, but it. did help me discover that my roommate was dealing with food. at that point I realized that this was going to be a pivotal problem for AI building large scale high quality datasets to power every single application. This was the impetus behind starting scale. Quality data to create reliable a. I outcomes requires human insight and guidance. If you think about the core setup of AI, the algorithms need data and data needs humans to ensure data is accurate and expert human is often required. Only humans can understand the context and nuance to properly annotate the data to be fed to algorithms. Humans. are the one who teach the algorithms what to do. They're the ones making the decisions. They guide them if something happens. Here's what you should do, and the AI learns from that and replicates it. We are teaching they are individual values and nudging the AI to find thoughtful outcomes. Machines make mistakes. We have to teach them and incentivize them to tell the truth. This is why teaching the AI human intentions and values is so important. It's through this process that we will ensure that AI will have fair ethical outcomes in line with human values. It's this alignment that we must solve for the constant alignment of AI to human intentions will always require humans and human ideas and creativity can actually matter much more with the power of AI behind them.

 The long tale of real world problems and the fact that there's always unknown unknowns means that humans will never be fully removed from the AI development life cycle. For example, I remember back in 2016 when chat bots were first starting to become a big thing. It was right when we were starting scale. It was right when we were starting scale we? were all thinking there's no way to build a fully automated system. There's so many different conversations that can have so many different pathways. It's hard to build AI systems that can properly handle all these possibilities for chat bots to work their humans behind it. Who made the decisions once and from there, the chat bots can replicate that over and over again. That's again why it's impossible for AI to improve or change without human input. Let's take you to the front lines of AI. The things that AI automates first are not what you might expect in unintuitive example is the weather. Humans have tried for many millennia to crack the code of how to predict the weather. It's especially hard for meteorologists because there's so many different small things that can cause massive impacts on the weather. It's the butterfly effect. Different elements react to one another in unexpected ways.

 There's. so many inputs that affect the weather, way more data than any person would be able to comprehend on their own. That's why we need AI to analyze the vast oceans of data and provide more accurate, nuanced and comprehensive analysis. At the moment, I can already provide extremely accurate short term predictions, including for critical storms and floods. So it's not what humans perceive to be the simplest task that AI will automate first, but rather where we have the most data. You. use cases, the brightest minds are focusing on are much more positive than what you might think, much more. so than Terminator or Westworld. That's again why you think AI will be a supercharger for humanity. Unlike the movies, AI developers aren't focusing focusing their attention on building replacements for humans. They're building tools to help free up our time and energy to focus on what humans can uniquely solve. A good example about how AI can be used in practice is health care. According to the Association of American Medical Colleges, the United States can see an estimated shortage of between 38,000 and 124,000 physicians by 2034. I. could save doctors time with road tasks and ultimately enable them to serve more patients and help more people. Healthcare is full of repetitive tasks which are right for AI when a patient is sick, they go through all kinds of tests which produce all sorts of data. Blood tests, imagery, lab results, X rays, etcetera. Doctors then analyze all this data to make decisions about a case. AI can analyze all this data proactively and go through a list of possibilities by cross referencing against all prior data in cases. It can identify when something isn't right long before physician can and flag it to a physician if it requires more attention. With AI doctors are still integral to the process, but it takes less time to get a diagnosis. You have to wait several weeks for your case to go from one doctor to another. They I will supercharge finding a diagnosis faster. So similarly in the field of drug discovery, it's all about using complex data, experiment data, patient data, protein simulations and far more to guide a more efficient process of solving disease through new drugs and compounds.

 Recent advancements in AI have dramatically speed up the scientific process by allowing us to process and make use of more data than ever before. Another. good example and potentially more concrete is the Russia Ukraine war All seen the images of Chang's lining up ready to enter Kiev. AI can help assess satellite imagery with superhuman speed and precision, so Ukrainian forces can respond faster at scale. We're using our platform to do damage assessment in key areas affected by the war. We've rapidly analyzed over 2000 square kilometers of Ukraine, identifying over 370,000 structures, including thousands not previously available via other data sets. We focused on Kiev, Kharkiv and Nepro and we should provide some data directly to government end users. We also made the data publicly available to the broader AI community via scale. We can also use this data to maximize allocations of resources, people or commodities. It's clear satellite data can be extremely useful in these types of situations. Thanks to satellite data, AI can analyze if planes or tanks have been moved from one place to another. This is called change detection. Algorithms can constantly be monitoring for this kind of data And if it notices a change or movement, it will lure human to further investigate. Otherwise known as predictive modeling AI can also help us understand the economic impacts of war. We can use AI to track farmland in Ukraine and measure the agricultural damage in real time. 

Ukraine is a major food supplier for much of the world. Understanding these impacts is absolutely critical. In conclusion, AI is not something to be feared, but it's a tool that can be used to better understand things to be better understood. And it's the potential to transform our lives for the better. AI enables us to make use and sense of massive amounts of data that has historically been beyond human capacity. It allows us to add intelligence and nuance to automated systems that will dramatically improve humanity. Areas like healthcare and agriculture. This then allows humans to do what they do best. Take this information put it into context with sensitivity to strategize and act in a timely manner. A. I. Is a supercharger for humanity. When AI is better than humans, it makes humans better. AI will automate repetitive tasks that don't require constant human judgment or creativity, which frees us up to explore and focus on fresher, newer ideas. AI will enable us to be even more creative and more idea-driven, which I personally find incredibly exciting. It allows us to embrace the generative aspects of human nature so we can run faster with ideas and build better and more powerful solutions to the world's biggest problems. That's why I believe that human led AI is the path forward, and I'm proud to usher all of us into a future with human led AI. Thank you.