Artificial Intelligence
Showing 7 out of 7 results
AI, ML & Data Science: What's the Difference?
There is a fine line between three disciplines that take the spotlight of computer science. What are the differences between machine learning, data science and artificial intelligence? Are we ready for truly creative computers, and what's the progress in this field? Feynman Liang, a Ph.D. student in statistics, and Phil Winder, CEO of Winder Research, try to answer all these questions while explaining what data science looks like in practice.
What is General Artificial Intelligence
Are you still wondering what Artificial Intelligence is and if it can actually be applied in real life? Join the discussion between Doug Lenat,CEO of Cycorp, and Danny Lange, SVP of AI at Unity, at GOTO Chicago 2019 to understand what is the status quo, what are the things for which AI is already being used, what are the struggles, and if we should fear it or not.
Improving Business Decision Making with Bayesian Artificial Intelligence
In a world where deep learning and other massively scalable perception machines are at our disposal, allowing us to build amazing applications, the time is now ripe to move beyond the concept of pure perception and into broader Artificial Intelligence (AI). The path towards AI goes through what's missing in many applications today; Inference. Only when we combine Inference machines and Perception machines can we truly talk about AI. The benefit will be a machine that knows what to expect before observing it's environment and that can take prior information into account. With ever more mature Probabilistic programming languages available, we can express this marriage of perception and inference. In this talk we will scrape the surface of how to build Bayesian predictive inference machines using Probabilistic programming. Resources: Chapter 5 MCMC Handbook: https://arxiv.org/abs/1206.1901 Statistical Rethinking: https://www.amazon.com/Statistical-Rethinking-Bayesian-Examples-Chapman/dp/1482253445 Bayesian Data Analysis: https://www.amazon.com/Bayesian-Analysis-Chapman-Statistical-Science/dp/1439840954/ Stan, a probabilistic programming language: http://mc-stan.org/
Machine Learning, Your First Steps
In this session we introduce the basics about Machine Learning, explain what it is and how it relates to terms like Big Data and Artificial Intelligence. We’ll show the various machine learning platforms that are used today like Watson, Tensorflow and Deepmind, and illustrate this with some live demo’s. In under one hour you’ll get an overview of what you can do with modern AI techniques. Put your seatbelts on!
Improving Business Decision Making with Bayesian Artificial Intelligence
In a world where deep learning and other massively scalable perception machines are at our disposal, allowing us to build amazing applications, the time is now ripe to move beyond the concept of pure perception and into broader Artificial Intelligence (AI). The path towards AI goes through what's missing in many applications today; Inference. Only when we combine Inference machines and Perception machines can we truly talk about AI. The benefit will be a machine that knows what to expect before observing it's environment and that can take prior information into account. With ever more mature Probabilistic programming languages available, we can express this marriage of perception and inference. In this talk we will scrape the surface of how to build Bayesian predictive inference machines using Probabilistic programming. **Resources**<br /> [Chapter 5 MCMC Handbook](https://arxiv.org/abs/1206.1901)<br /> [Statistical Rethinking](https://www.amazon.com/Statistical-Rethinking-Bayesian-Examples-Chapman/dp/1482253445)<br /> [Bayesian Data Analysis](https://www.amazon.com/Bayesian-Analysis-Chapman-Statistical-Science/dp/1439840954/)<br /> [Stan, a probabilistic programming language]( http://mc-stan.org/)
From Tic Tac Toe to AlphaGo: Playing games with AI
Google DeepMind's AlphaGo was an extraordinary breakthrough for Artificial Intelligence. The game of Go has 1.74×10^172 unique positions and is about a 'googol' times harder to calculate than chess. Experts thought it would take at least another decade before A.I. would be able to beat the best human players. So how did DeepMind tackle this problem? What algorithms did they use and how do they work? **What will the audience learn from this talk?**<br> During this talk we'll explore several algorithms that can be used to make a program play games, we'll start simple (Tic Tac Toe) and as the games get harder, the A.I.'s need to become smarter. **Does it feature code examples and/or live coding?**<br> The talk has some code (Java/pseudo code) and there is a small live demo explaning neural networks, not a lot of code. The focus is on exploring the algorithms. **Prerequisite attendee experience level:** <br> [Level 100](https://gotoams.nl/2019/pages/experience-level)
AI/ML, Quantum Computing and 5G – Opportunities, Challenges and the Impact on Society
Technical developments such as IoT, AI/ML, Quantum Computing, Robotic and 5G are going along with unprecedented opportunities while at the same time creating challenges and risks. Marco, who is a serial entrepreneur and professor for years advises governments and boards of large enterprises on digitalization will share some insides.
