Goto

Collaborating Authors

U.S. Joins G7 Artificial Intelligence Group to Counter China

U.S. News

The partnership launched Thursday after a virtual meeting between national technology ministers. It was nearly two years after the leaders of Canada and France announced they were forming a group to guide the responsible adoption of AI based on shared principles of "human rights, inclusion, diversity, innovation and economic growth."


Robots learning to move like animals

AIHub

Whether it's a dog chasing after a ball, or a monkey swinging through the trees, animals can effortlessly perform an incredibly rich repertoire of agile locomotion skills. But designing controllers that enable legged robots to replicate these agile behaviors can be a very challenging task. The superior agility seen in animals, as compared to robots, might lead one to wonder: can we create more agile robotic controllers with less effort by directly imitating animals? In this work, we present a framework for learning robotic locomotion skills by imitating animals. Given a reference motion clip recorded from an animal (e.g. a dog), our framework uses reinforcement learning to train a control policy that enables a robot to imitate the motion in the real world.


Temel Selected as WEF Young Scientist

CMU School of Computer Science

Zeynep Temel, a robotics researcher who uses inspiration from nature to design novel means of motion and locomotion for tiny robots, has been named by the World Economic Forum to its Young Scientists Class of 2020. Temel, an assistant professor in the Robotics Institute, and Stephanie Sydlik, an assistant professor of chemistry, are the latest Carnegie Mellon University faculty members to join the WEF's Young Scientists community. The distinction recognizes scientific rising stars under the age of 40 who are pursuing high-impact research. "I am very excited to be a part of the WEF Young Scientists community and incredibly honored to be representing CMU," Temel said. "It will be a great adventure to learn from amazing scientists and develop projects that will improve the state of the world.


Optimizing loops in Pandas for Enhanced Performace Machine Learning Py

#artificialintelligence

In this tutorial, you will learn different ways of optimizing loops in pandas. Pandas is one of the most popular python libraries among data scientists. While performing data analysis and data manipulation tasks in pandas, sometimes, you may want to loop/iterate over DataFrame and do some operation on each row. While this can be a simple task if the size of the data is small, it is cumbersome and very much time consuming if you have a larger data-set. So, we need to find an efficient way to loop through the pandas DataFrame.


Google pledges to no longer build AIs for the fossil fuel industry

#artificialintelligence

Google has pledged to no longer build AIs for the fossil fuel industry as it further distances itself from controversial developments. A report from Greenpeace earlier this month exposed Google as being one of the top three developers of AI tools for the fossil fuel industry. Greenpeace found AI technologies boost production levels by as much as five percent. In an interview with CUBE's John Furrier, the leader of Google's CTO office, Will Grannis, said that Google will "no longer develop artificial intelligence (AI) software and tools for oil and gas drilling operations." The pledge from Google Cloud is welcome, but it must be taken in a wider context.


Top 9 languages for Data Science in 2020

#artificialintelligence

Data Science has been a big deal for quite some time now. In the rapidly expanding technological world of today, when humans tend to generate a lot of data, it is quintessential that we know how to analyze, process, and use that data for further knowledgable business insights. There has been enough said on Python vs R for Data Science but I am not talking about it here. We need both of them and that's about it. The languages made to the list on the basis of their popularity, number of Github mentions, the pros and the cons, and their relevancy to Data Science in 2020.


Vesta raises $125 million to fight payment fraud with AI

#artificialintelligence

Payments solutions provider Vesta today announced that it raised $125 million in capital, bringing its total raised to over $145 million. The company says it will use the financing to grow and accelerate the deployment of its fraud protection and ecommerce payment products. Payment fraud is pervasive -- in 2018, $24.26 billion was lost due to credit card fraud worldwide, reports Shift Processing. That same year, the rate of card fraud increased by nearly 20% as the U.S. took the lead in reported losses. Vesta says its AI-powered decisioning platform helps clients to assess the risk of this fraud and ultimately to prevent fraud from occurring, with connectors that tie into existing software from vendors including Magento, Shopify, WooCommerce, BigCommerce, and SAP Commerce Cloud.


Curriculum for Reinforcement Learning

#artificialintelligence

A curriculum is an efficient tool for humans to progressively learn from simple concepts to hard problems. It breaks down complex knowledge by providing a sequence of learning steps of increasing difficulty. In this post, we will examine how the idea of curriculum can help reinforcement learning models learn to solve complicated tasks. It sounds like an impossible task if we want to teach integral or derivative to a 3-year-old who does not even know basic arithmetics. That's why education is important, as it provides a systematic way to break down complex knowledge and a nice curriculum for teaching concepts from simple to hard. A curriculum makes learning difficult things easier and approachable for us humans.


A Fundamental Theorem for Epidemiology

#artificialintelligence

The work of an Italian mathematician in the 1930s may hold the key to epidemic modeling. That's because models that try to replicate reality in all its detail have proven hard to steer during this crisis, leading to poor predictions despite noble and urgent efforts to recalibrate them. On the other hand overly stylized compartmental models have run headlong into paradoxes such as Sweden's herd immunity. This approach is represented in the picture above. The important thing to note is that we are not attempting to find a model that is close to the truth, only close to the orbit. This will make a lot more sense after Section 1, I promise.


Google's federated analytics method could analyze end user data without invading privacy

#artificialintelligence

In a blog post today, Google laid out the concept of federated analytics, a practice of applying data science methods to the analysis of raw data that's stored locally on edge devices. As the tech giant explains, it works by running local computations over a device's data and making only the aggregated results -- not the data from the particular device -- available to authorized engineers. While federated analytics is closely related to federated learning, an AI technique that trains an algorithm across multiple devices holding local samples, it only supports basic data science needs. It's "federated learning lite" -- federated analytics enables companies to analyze user behaviors in a privacy-preserving and secure way, which could lead to better products. Google for its part uses federated techniques to power Gboard's word suggestions and Android Messages' Smart Reply feature.