We propose solving the general Stochastic Shortest-Path Markov Decision Process (SSP MDP) as probabilistic inference. Furthermore, we discuss online and offline methods for planning under uncertainty. In an SSP MDP, the horizon is indefinite and unknown a priori. SSP MDPs generalize finite and infinite horizon MDPs and are widely used in the artificial intelligence community. Additionally, we highlight some of the differences between solving an MDP using dynamic programming approaches widely used in the artificial intelligence community and approaches used in the active inference community.

One of our customers is a German Automotive Company. We help the company steer towards a data-driven, AI-led company, by implementing and maintaining ML models on their platform. Their main objectives are data democratization and the culture of self service. We're talking about data from mileage, speed, consumption, RPM, engine faults, driver behavior, road driving patterns and car components that we need to process and carefully tailor for the machine learning models. To accomplish that, we're using S3, Glue, Lambda, Kinesis, Athena, Codepipeline, CLI, PySpark and Terraform together with AWS SageMaker for the machine learning solutions.

Walsh Middle School seventh grader Aariv Modi has always had a fascination for technology, especially his parent's Alexa device. "I loved the idea of just speaking to a device that it could allow you to play music, listen to the news, and it seemed futuristic to me," Aariv said. In April, Aariv was recognized as the Voice/AI Pioneer of the Year by Project Voice for his contributions to the conversational artificial intelligence industry. During the COVID-19 lockdown, he taught hundreds of kids how to make Alexa do things it isn't programmed to do through webinars, camps and posts on his blog that is available online. "In this generation, as kids are growing up, they are being exposed to technology and learning things in ways that we never thought was possible," said Bradley Metrock, CEO of Score Publishing, which organizes the Project Voice conference.

The goal of the Shared Visual Representations in Human and Machine Intelligence (SVRHM) workshop at NeurIPS 2019 is to discuss and disseminate relevant findings and parallels between the computational neuro/cognitive science and machine learning/artificial intelligence communities. In the past few years, machine learning tools -- especially deep neural networks -- have permeated the vision/cognitive/neuro science communities to become the leading computational models that describe many cognitive tasks. Huge strides are also being made on the machine learning/artificial intelligence community with biologically inspired algorithms providing large efficiency gains in both computational and learning capabilities. However, many mysteries remain with regards to the alignment of human and machine perception, and there are cases where we see divergent rather than convergent representations. To resolve such questions, this workshop aims to bring fruitful discussions between scientists and engineers with multi-disciplinary backgrounds to review the recent progress in shared visual representations in both humans and machines, and in doing so identifying road-blocks and areas of interest to further accelerate the growth of both fields.

As the number of people who work on a volunteer basis or try to contribute for good causes increase in Turkey, we look to the future with confidence. Furthermore, if these volunteers comprise of scientists, academicians and youths, supporting such formations mean paving the way of social developments. The Deep Learning Turkey community, which teaches and guides high school students and undergraduates, turning artificial intelligence into a social responsibility project, has reached out to thousands of youths although it was established only in August of last year. The community helps youths who are interested in artificial intelligence and want to have a career in this field as well as providing information sharing on an open platform for scientists. Deep Learning Turkey is the biggest and the most effective artificial intelligence community in Turkey.

Researchers used scanning electron microscope images of nanometers-thick mouse brain slices to reconstruct cells into a neocortex structure (center), whose various cell types appear in different colors. For decades, scientists have worked toward the'holy grail' of finding a cure for cancer. While significant progress has been made, their efforts have often been worked on as individual entities. Now, as organizations of all kinds seek to put the massive amounts of data they take in to good use, so, too, are the health care industry and the U.S. federal government. The National Cancer Institute (NCI) and the U.S. Department of Energy (DOE) are collaborating on three pilot projects that involve using more intense high-performance computing at the exascale level, which is the push toward making a billion billion calculations per second (or 50 times faster than today's supercomputers), also known as exaFLOPS (a quintillion, 1018, floating point operations per second).

A man arrives at an airport for a flight, and as he goes through security the agent asks some questions. Did anyone help him pack his suitcase? What is the purpose of his trip? During the conversation, the agent enters answers and facial reactions into a computer pre-programmed with millions of pieces of information relating to the behavior of suspicious passengers. Such man-and-machine collaborations, in this instance to detect terrorists, are not yet in place at airports.

Professor Barry O'Sullivan, Director of Insight at UCC, became the first Irish member of the EurAI board in 2014. Professor Barry O'Sullivan, Director of Insight at UCC, has been elected as Deputy President of the European Association for Artificial Intelligence (EurAI) at its General Assembly held in The Hague. Professor O'Sullivan, who became the first Irish member of the EurAI board after being elected in 2014, said: "It's a huge honour to be the first Irish person to serve on the board of the European Association for Artificial Intelligence, so to be elected as its deputy president is a major thrill." UCC professor named deputy president of world's largest AI body https://t.co/WNjjanZlfz EurAI, formerly ECCAI, was established in July 1982 as a representative body for the European artificial intelligence community.

Jody Williams received the Nobel Peace Prize in 1997 together with the International Campaign to Ban Landmines for their central role in establishing the 1997 Mine Ban Treaty. The US-based political activist is known across the world for her efforts to enhance understandings of security and related issues in the world today. She is also the chair of the Noble Women's Initiative that she founded in 2006 together with five other women Nobel Peace laureates. She, along with 20 of her fellow Nobel Peace laureates have called for a preemptive ban on Lethal Autonomous Weapons Systems (LAWS)--weapons that could operate without human supervision once activated even in matters of killing human beings. The UN's Convention on Certain Conventional Weapons (CCW) held their third informal government's meet in Geneva from 11-15 April.

Human core body temperature (Tcore) is an important measure of thermal state, e.g., hypo-or hyperthermia, but is difficult to measure using noninvasive wearable sensors. We estimated parameters for a discrete KF model from data collected during several Military training events and from distance runners (n 38). Model performance was evaluated in 25 physically-active subjects who participated in various laboratory and field studies involving exercise of 2-to-8 h duration at ambient temperatures of 20 to 40 C. Overall, the KF model's estimate of Tcore had a root mean square error of 0.30 0.13 ºC from the observed Tcore, and was within 0.5 ºC over 85% of the time. The benefit of the KF approach is that it requires only one input while current state of the art models typically require multiple inputs including individual anthropometrics, metabolic rate, clothing characteristics, and environmental conditions. This state estimation problem in computational physiology illustrates the potential for collaboration between the artificial intelligence and ambulatory physiological monitoring communities. Figure 1: U.S. National Guard Civil Support Team (CST) member engaged in a chemical biological training event.