Making a decision is a complex task. Today’s guest Dongho Kim discusses how he and his team at Prowler has been building a platform that will be accessible by way of APIs and a set of pre-made scripts for autonomous decision making based on probabilistic modeling, reinforcement learning, and game theory. The aim is so that an AI system could make decisions just as good as humans can. At the moment Prowler is focusing on multi-agent systems for the video game industry, smart city applications and stock trading or portfolio optimization.
In many real world situations, a person/agent doesn’t necessarily know their own objectives or the mechanics of the world they’re interacting with. However, if the agent receives rewards which are correlated with the both their actions and the state of the world, then reinforcement learning can be used to discover behaviors that maximize the reward earned.
In this week’s episode, Kyle is joined by Risto Miikkulainen, a professor of computer science and neuroscience at the University of Texas at Austin. They talk about evolutionary computation, its applications in deep learning, and how it’s inspired by biology. They also discuss some of the things Sentient Technologies is working on in stock and finances, retail, e-commerce and web design, as well as the technology behind it– evolutionary algorithms.
Formally, an MDP is defined as the tuple containing states, actions, the transition function, and the reward function. This podcast examines each of these and presents them in the context of simple examples. Despite MDPs suffering from the curse of dimensionality, they’re a useful formalism and a basic concept we will expand on in future episodes.
Last week on Data Skeptic, we visited the Laboratory of Neuroimaging, or LONI, at USC and learned about their data-driven platform that enables scientists from all over the world to share, transform, store, manage and analyze their data to understand neurological diseases better. We talked about how neuroscientists measure the brain using data from MRI scans, and how that data is processed and analyzed to understand the brain. This week, we’ll continue the second half of our two-part episode on LONI.
Last year, Kyle had a chance to visit the Laboratory of Neuroimaging, or LONI, at USC, and learn about how some researchers are using data science to study the function of the brain. We’re going to be covering some of their work in two episodes on Data Skeptic. In this first part of our two-part episode, we’ll talk about the data collection and brain imaging and the LONI pipeline. We’ll then continue our coverage in the second episode, where we’ll talk more about how researchers can gain insights about the human brain and their current challenges. Next week, we’ll also talk more about what all that has to do with data science machine learning and artificial intelligence. Joining us in this week’s episode are members of the LONI lab, which include principal investigators, Dr. Arthur Toga and Dr. Meng Law, and researchers, Farshid Sepherband, PhD and Ryan Cabeen, PhD.
In artificial intelligence, the term ‘agent’ is used to mean an autonomous, thinking agent with the ability to interact with their environment. An agent could be a person or a piece of software. In either case, we can describe aspects of the agent in a standard framework.
This episode kicks off the next theme on Data Skeptic: artificial intelligence. Kyle discusses what’s to come for the show in 2018, why this topic is relevant, and how we intend to cover it.
We break format from our regular programming today and bring you an excerpt from Max Tegmark’s book “Life 3.0”. The first chapter is a short story titled “The Tale of the Omega Team”. Audio excerpted courtesy of Penguin Random House Audio from LIFE 3.0 by Max Tegmark, narrated by Rob Shapiro. You can find “Life 3.0” at your favorite bookstore and the audio edition via penguinrandomhouseaudio.com.
Kyle will be giving a talk at the Monterey County SkeptiCamp 2018.
This week, our host Kyle Polich is joined by guest Tim Henderson from Google to talk about the computational complexity foundations of modern cryptography and the complexity issues that underlie the field. A key question that arises during the discussion is whether we should trust the security of modern cryptography.