The small, unadorned church has long ranked as the oldest in the Swiss city of Lucerne. But Peter's chapel has become synonymous with all that is new after it installed an artificial intelligence-powered Jesus capable of dialoguing in 100 different languages. "It was really an experiment," said Marco Schmid, a theologian with the church. "We wanted to see and understand how people react to an AI Jesus. What would they talk with him about? Would there be interest in talking to him? The installation, known as Deus in Machina, was launched in August as the latest initiative in a years-long collaboration with a local university research lab on immersive reality. After projects that had experimented with virtual and augmented reality, the church decided that the next step was to install an avatar. Schmid said: "We had a discussion about what kind of avatar it would be – a theologian, a person or a saint?

As we look to the next generation of adaptive optics systems, now is the time to develop and explore the technologies that will allow us to image rocky Earth-like planets; wavefront control algorithms are not only a crucial component of these systems, but can benefit our adaptive optics systems without requiring increased detector speed and sensitivity or more effective and efficient deformable mirrors. To date, most observatories run the workhorse of their wavefront control as a classic integral controller, which estimates a correction from wavefront sensor residuals, and attempts to apply that correction as fast as possible in closed-loop. An integrator of this nature fails to address temporal lag errors that evolve over scales faster than the correction time, as well as vibrations or dynamic errors within the system that are not encapsulated in the wavefront sensor residuals; these errors impact high contrast imaging systems with complex coronagraphs. With the rise in popularity of machine learning, many are investigating applying modern machine learning methods to wavefront control. Furthermore, many linear implementations of machine learning methods (under varying aliases) have been in development for wavefront control for the last 30-odd years. With this work we define machine learning in its simplest terms, explore the most common machine learning methods applied in the context of this problem, and present a review of the literature concerning novel machine learning approaches to wavefront control.

The project brochure and poster provide the most important information about the project's partners, activities, and goals. For more details, please take a look at the first digital presentation. During the second semester of the project, the MACHINA partners collected evidence on workplace requirements regarding ML skills. The project partners then defined six learning units based on analyzing the collected evidence and identifying each unit's knowledge, skills, and competencies. For more details, please download the second digital presentation.

Bengaluru, NFAPost: C3 AI (NYSE: AI), one of the leading Enterprise AI software provider, today announced the general availability of C3 AI Ex Machina, a next-generation predictive analytics application that empowers anyone to develop, scale, and produce AI-based insights without writing code. Analysts, operators, and subject matter experts across all industries and business functions are increasingly required to develop predictive and prescriptive insights compiled from vast and disparate datasets. While there are many no-code tools available that lower the barrier for users to build ML models and perform data analysis, none provide end-to-end capabilities that enable them to capture and process the volume and variety of data required, automatically generate interpretable AI models, and productise, deploy, and scale the results across their company. Current predictive analytics tools are typically complicated to use and limit the ability of their insights to drive real business outcomes. Con Edison's data analysts use C3 AI Ex Machina to identify malfunctioning meters in near-real-time, realizing significant business value.

This piece was a finalist for the inaugural Gradient Prize. Machine Learning is a powerful technique to automatically learn models from data that have recently been the driving force behind several impressive technological leaps such as self-driving cars, robust speech recognition, and, arguably, better-than-human image recognition. We rely on these machine learning models daily; they influence our lives in ways we did not expect, and they are only going to become even more ubiquitous. Consider a couple of example machine learning models: 1) Detecting cats in images 2) Deciding which ads to show you online 3) Predicting which areas will suffer crime, and 4) Predicting how likely a criminal is to re-offend. The first two seem harmless enough.

The Ancient Greek playwrights knew how to tell a good story, but occasionally found themselves searching for a way to solve whatever conflict they had concocted. So they invented the "deus ex machina"--literally, god from the machine--in which an actor playing a god was brought on stage via a mechanical device to miraculously resolve the problem as only a god can do. Where we go, AI follows--for better or for worse. These days, artificial intelligence (AI) is becoming our version of the deus ex machina, promising to swoop in and solve our most pressing business problems. But, like the Greek gods, AI can be fickle and fallible.

Dyad X Machina is a research partnership that combines affective neuroscience and deep learning. In this episode, Laurence meets with the co-founder, Haohan Wang, who explains Dyad's mission as bringing emotion into machine learning. Haohan and her partner Christian created a course -- Applied Deep Learning with TensorFlow and Google Cloud AI -- that is the synthesis of their learning. It covers everything from building your first deep learning model to taking it all the way to deployment. Watch to learn more about the intersection of deep learning and affective computing and Haohan's four P's of learning.

GRANT: Trade finance is very paper-oriented business and has been for a very long time. I would describe the pace of technology driven change around trade finance as rather modest, certainly over the past couple of decades. "Machine learning is really just a term for something that's been around for years - coding a computer to take certain actions in certain scenarios." However the emergence of blockchain and machine-learning technology has already revolutionised the way we look at trade finance globally, providing us with a unique opportunity to shift gears. Certainly, at ANZ we have meaningful programs of work that leverage those two technology capabilities which impact a material component of our trade-finance business.

What Dr. Machina has done in the pages of the book you are holding is take us once again to the future…even if it looks a lot like the past. Scissorwalk combines the idea of MST3K and memes into a (somewhat) coherent tale of Chet, who looks like the 1950s idea of what a real American should look like. And while that doesn't all sound that futuristic you have to understand that it was made, in part, by a robot!

Rapid progress is being made on the development of machine learning models that are capable of producing original images, text, and audio. This prompts the questions: what changes when architecture comes from the machine? Is it possible to leverage this technology to design buildings, cities, and structures? Architecture ex Machina (AexM) is a working group and knowledge share that is focused on exploring issues around the incorporation of machine learning into the architecture, engineering, and construction (AEC) industry. Artificial intelligence and machine learning have the potential to drive dramatic change and disruption.