Causal Machine Learning Workshop SEW-HSG University of St.Gallen

#artificialintelligence

Program: Monday Session I Maximilian Kasy, "Adaptive treatment assignment in experiments for policy choice" Bezirgen Veliyev, "Functional Sequential Treatment Allocation" Keynote Uri Shalit about "Machine learning and causal inference: a two-way road": "This talk will have two parts. In the first we will discuss a framework we developed for learning individualized treatment recommendations from observational health data, merging ideas from machine learning and causal inference. We will see examples of our framework applied to two crucial health problems using data from tens of thousands of patients, and discuss some important causal-inference challenges that come to focus in this setting. In the second part we will show how we use ideas from the causal inference literature to address long standing problems in machine learning: off-policy evaluation in a partially observable Markov decision process (POMDP), and learning predictive models that are stable against distributional shifts." Heterogeneous effects of training programmes for unemployed in Belgium" Daniel Jacob, "Does Tenure make you love your Job?" Nicolaj Mühlbach, "Heterogeneous Treatment Effects of an Early Retirement Reform" Tuesday Session III Dmitry Arkhangelsky, "Double-Robust Identification for Causal Panel Data Models" Martin Spindler, "Uniform Inference in High-Dimensional Gaussian Graphical Models" Keynote Stefan Wager about "Designing Loss Functions for Causal Machine Learning": "Given advances in machine learning over the past decades, it is now possible to accurately solve difficult non-parametric prediction problems in a way that is routine and reproducible.


AI is not just another technology project

#artificialintelligence

AI, unlike any other initiative is a business transformation enabler and not another technology system implementation that business users need to be trained on. Traditionally, businesses choose either the classic waterfall approach of linear tasks, or the agile approach, where teams review and evaluate solutions as they are tested out. In contrast, implementing AI technology requires a different approach altogether. AI requires that you look at a problem and see if there's a way to solve it by reframing the business process itself. Instead of solving a problem with a 10-step strategy, is there a way to cut it down to six steps using data already available or by using new types of untapped internal or publicly available data and applying AI to it?


MIT CSAIL's radars map hidden features to help driverless cars navigate snowy terrain

#artificialintelligence

That's because precipitation covers cameras critical to the cars' self-awareness and tricks sensors into perceiving obstacles that aren't there. Plus, bad weather has a tendency to obscure road signage and structures that normally serve as navigational landmarks. Fortunately, researchers at MIT's Computer Science and Artificial Intelligence Laboratory and Lincoln Laboratory are on the case. In a paper that will be published in the journal IEEE Robotics and Automation Letters later this month and presented in May at the International Conference on Robotics and Automation (ICRA), they describe a system that uses ground-penetrating radar (GPR) to send very high frequency (VHF) electromagnetic pulses underground to measure an area's combination of pipes, roots, rocks, dirt, and other features. The GPR builds a basemap that an onboard computer correlates, contributing to a three-dimensional GPS-tagged subterranean database.


How AI Will Seduce You. w Stephen Fry

#artificialintelligence

When and how will AI come to life? How will we know it's conscious? Which part of you is consciously reading this? Could the internet already have some level of consciousness? And how anonymous google employees could save us.


UK government investigates AI bias in decision-making

#artificialintelligence

The UK government is launching an investigation to determine the levels of bias in algorithms that could affect people's lives. A browse through our'ethics' category here on AI News will highlight the serious problem of bias in today's algorithms. With AIs being increasingly used for decision-making, parts of society could be left behind. Conducted by the Centre for Data Ethics and Innovation (CDEI), the investigation will focus on areas where AI has tremendous potential – such as policing, recruitment, and financial services – but would have a serious negative impact on lives if not implemented correctly. "Technology is a force for good which has improved people's lives but we must make sure it is developed in a safe and secure way. Our Centre for Data Ethics and Innovation has been set up to help us achieve this aim and keep Britain at the forefront of technological development. I'm pleased its team of experts is undertaking an investigation into the potential for bias in algorithmic decision-making in areas including crime, justice and financial services. I look forward to seeing the Centre's recommendations to Government on any action we need to take to help make sure we maximise the benefits of these powerful technologies for society."


Artificial Intelligence Marketing is the AIM of Advertisers in 2020

#artificialintelligence

Artificial Intelligence Marketing (AIM) provides superior solutions to bridge the gap between analytics and execution. It is the process of going through massive piles of data to originate positive results. As per the courtesy of Forbes, retailers invested around 5.9 billion US dollars on AIM. North America, Europe, and Asia-Pacific are mainly using this type of digital marketing and advertising. Likewise, remote health monitoring, wearable AR, IoT kitchen appliances, and brain-sensing gadgets lie under the game-changing innovations.


Building Machine Learning Models by Integrating Python and SAS Viya

#artificialintelligence

SAS Scripting Wrapper for Analytics Transfer (SWAT), a powerful Python interface, enables you to integrate your Python code with SAS Cloud Analytic Services (CAS). Using SWAT, you can execute CAS analytic actions, including feature engineering, machine learning modeling, and model testing, and then analyze the results locally. This article demonstrates how you can predict the survival rates of Titanic passengers with a combination of both Python and CAS using SWAT. You can then see how well the models performed with some visual statistics. After you install and configure these resources, start a Jupyter Notebook session to get started!


First-ever Robot "supermicrosurgery" performed successfully

#artificialintelligence

Robotic technology has played an important part in the medical field in the last two decades. The best example in this regard is the Da Vinci system, which is considered the best-selling surgery robot on the market today. This robot can perform high-precision surgical procedures -- down to one millimeter. However, the system comes with a hefty price tag of $2 million, plus the expensive maintenance costs. For those of you who don't know supermicrosurgery refers to a precise reconstructive procedure that connects ultra-thin blood and lymph vessels ranging from 0.3 to 0.8 millimeters.


This AI Researcher Thinks We Have It All Wrong

#artificialintelligence

Luis Perez-Breva is a Massachusetts Institute of Technology (MIT) professor and the faculty director of innovation teams at the MIT School or Engineering. He is also an entrepreneur and part of The Martin Trust Center for MIT Entrepreneurship. Luis works to see how we can use technology to make our lives better and also on how we can work to get new technology out into the world. On an episode of the AI Today podcast, Professor Perez-Breva managed to get us to think deeply into our understanding of both artificial intelligence and machine learning. Are we too focused on data?


Can AI flag disease outbreaks faster than humans? Not quite

#artificialintelligence

John Brownstein is co-founder of HealthMap, a system using artificial intelligence to monitor global disease outbreaks. John Brownstein is co-founder of HealthMap, a system using artificial intelligence to monitor global disease outbreaks. John Brownstein is co-founder of HealthMap, a system using artificial intelligence to monitor global disease outbreaks. John Brownstein is co-founder of HealthMap, a system using artificial intelligence to monitor global disease outbreaks. BOSTON -- Did an artificial intelligence system beat human doctors in warning the world of a severe coronavirus outbreak in China?