Australia Post is also using machine learning to predict mail volumes. "We got it to a point now off that data analytics that we can see plus or minus 5% …

In this post, Don Miner covers how to identify, evaluate, prioritize, and pick which data science problems to work on next. Don is a cofounder of Miner & Kasch, an artificial intelligence solutions and data science consulting firm. Don specializes in large-scale data analysis enterprise architecture and applying machine learning to real-world problems. He has architected and implemented dozens of mission-critical and large-scale data analysis systems within the U.S. Government and Fortune 500 companies. He is also the author of the O'Reilly book "MapReduce Design Patterns".

The goal of offline reinforcement learning is to learn a policy from a fixed dataset, without further interactions with the environment. This setting will be an increasingly more important paradigm for real-world applications of reinforcement learning such as robotics, in which data collection is slow and potentially dangerous. Existing off-policy algorithms have limited performance on static datasets due to extrapolation errors from out-of-distribution actions. This leads to the challenge of constraining the policy to select actions within the support of the dataset during training. We propose to simply learn the Policy in the Latent Action Space (PLAS) such that this requirement is naturally satisfied. We evaluate our method on continuous control benchmarks in simulation and a deformable object manipulation task with a physical robot. We demonstrate that our method provides competitive performance consistently across various continuous control tasks and different types of datasets, outperforming existing offline reinforcement learning methods with explicit constraints. Videos and code are available at https://sites.google.com/view/latent-policy.

This paper presents a focused analysis of human studies in explainable artificial intelligence (XAI) entailing qualitative investigation. We draw on the social science corpora of qualitative research to illustrate opportunities for making the human studies where XAI researchers used observations, interviews, focus groups, and/or questionnaires to capture qualitative data more rigorous. We contextualize the presentation of the XAI contributions included in our analysis according to the components of rigor described in the qualitative research literature: 1) underlying theories or frameworks, 2) methodological approaches, 3) data collection methods, and 4) data analysis processes. The results of our analysis support calls from others in the XAI community advocating for collaboration with experts from social disciplines to bolster rigor and effectiveness in human studies.

Sony has done some really interesting things with their controller and we're using that to try to improve the immersion of the feel of using a weapon,

PHILADELPHIA – Jefferson advances as a finalist in the nationwide challenge to improve health outcomes through artificial intelligence, sponsored by the U.S. Centers for Medicare & Medicaid Services. Each of the seven CMS finalists receives a $60,000 prize and the opportunity to win the grand prize of $1 million or $230,000 for the runner-up. A multidisciplinary team of data scientists, data engineers, and physicians combined their collective experience to create the submission for the CMS Artificial Intelligence Health Outcomes Challenge. The winner will show how AI and machine learning can more strongly predict unplanned admissions and adverse events at hospitals and skilled nursing facilities. These insights can help healthcare providers to identify risk, intervene early, and ultimately improve patient outcomes.

Just as it has been difficult for us to predict the course of this pandemic, so too have healthcare organizations been challenged to predict and evolve their operations to optimize patient care -- as well as revenue. In the initial wave, healthcare's technology needs shifted rapidly. Some organizations immediately shifted as clusters emerged, increasing bed capacity, converting non-clinical spaces to intensive care units and expanding telehealth programs. Meanwhile, others prepared for overflows that did not materialize, leading them to lose their predictable revenue streams from "regular" business. Radiology has become even more stretched thin, facing long- and short-term challenges and revealing just how unsustainable our current ways of working are. Healthcare leaders know the answer is to innovate.

GovCon Expert Chuck Brooks has published his latest article as a member of Executive Mosaic's GovCon Expert program on Wednesday. Brooks discussed the development and procurement of emerging technologies as they influence every sector of the federal marketplace, including the Department of Defense (DoD), the Department of Homeland Security (DHS), academia and the intelligence community. You can read Chuck Brooks' latest GovCon Expert article below: The development and procurement of emerging technologies is being institutionalized throughout government, particularly in national security areas. There are a variety of new initiatives and programs that have been created to ensure that the United States is prepared for a new era of technology leadership. If you are interested in transformative technologies, it is an exciting time to follow what is happening both in industry and in government.

Abstract: Rich functionalities of quantum and strongly correlated materials emerge from the interplay between the electronic, orbital, lattice, and spin degrees of freedom that often lead to complex structural and electronic phenomena spanning atomic to mesoscopic scales. In many cases, these phenomena are associated with translational symmetry breaking, local frozen disorder, or strongly correlated disorder. However, the relevant mechanisms and roles of individual subsystems often remain unknown. Over the last decade, Scanning Transmission Electron Microscopy has emerged as a powerful quantitative probe of materials structure on the atomic level, providing high veracity information on local chemical bonding, composition, and symmetry breaking distortions. We aim to harness the power of machine learning methods to build a comprehensive picture of the chemistry and physics of quantum materials from these observations.

Damian Ehrlicher is a Board Member of several emerging technology funds and companies and the CEO of Protected IT. In today's day and age of machine learning (ML) and artificial intelligence (AI), the number of organizations leveraging these technological advances to mitigate risk is growing quickly. As fast as the cybersecurity community can develop new solutions predicated on these technologies, malicious actors are developing tools leveraging these technologies as well. In days past, automation in the NOC/SOC was used to deploy new services or run a set of standard tests when a specific ticket came through the system. Now ticket enrichment is only part of the solution because software automation has made its way into the cybersecurity space.