The Australian government has said it will buy a new drone and upgrade existing patrol and response aircraft in a 1.5 billion Australian dollar ($964.88m) The military will buy a fourth MQ-4C Triton drone and upgrade the air force's existing fleet of 14 P-8A Poseidon maritime patrol aircraft, Pat Conroy, minister for defence industry, said in a statement on Tuesday. The Triton will be delivered in 2024 and be based in northern Australia. The aircraft upgrades will provide enhancements to anti-submarine warfare, maritime strike and intelligence collection capabilities, the statement said. The first Poseidon will enter the upgrade programme in 2026, with the final aircraft to be completed in 2030.

Interactive dynamic influence diagrams(I-DIDs) are a well recognized decision model that explicitly considers how multiagent interaction affects individual decision making. To predict behavior of other agents, I-DIDs require models of the other agents to be known ahead of time and manually encoded. This becomes a barrier to I-DID applications in a human-agent interaction setting, such as development of intelligent non-player characters(NPCs) in real-time strategy(RTS) games, where models of other agents or human players are often inaccessible to domain experts. In this paper, we use automatic techniques for learning behavior of other agents from replay data in RTS games. We propose a learning algorithm with improvement over existing work by building a full profile of agent behavior. This is the first time that data-driven learning techniques are embedded into the I-DID decision making framework. We evaluate the performance of our approach on two test cases.

At the American Association of Physicists in Medicine (AAPM) 2019 meeting, new artificial intelligence (AI) software to assist with radiotherapy treatment planning systems was highlighted. The goal of the AI-based systems is to save staff time, while still allowing clinicians to do the final patient review. RaySearch demonstrated a new U.S. Food and Drug Administration (FDA)-cleared machine learning treatment planning system. The RaySearch RayStation machine learning algorithm is being used clinically by University Health Network, Princess Margaret Cancer Center, Toronto, Canada, where it was rolled out over several months in late-2019. Medical physicist Leigh Conroy, Ph.D., was involved in this rollout and helped conduct a study, showing the automated plans and traditionally made plans to radiation oncologists to get valuable feedback.

At the American Association of Physicists in Medicine (AAPM) 2019 meeting, new artificial intelligence (AI) software to assist with radiotherapy treatment planning systems was highlighted. The goal of the AI-based systems is to save staff time, while still allowing clinicians to do the final patient review. RaySearch demonstrated a new U.S. Food and Drug Administration (FDA)-cleared machine learning treatment planning system. The RaySearch RayStation machine learning algorithm is being used clinically by University Health Network, Princess Margaret Cancer Center, Toronto, Canada, where it was rolled out over several months in late-2019. Medical physicist Leigh Conroy, Ph.D., was involved in this rollout and helped conduct a study, showing the automated plans and traditionally made plans to radiation oncologists to get valuable feedback.

Amid the COVID-19 pandemic, federal agencies are broadening and expediting robotic process automation efforts to deal with disruptions and drive productivity. During a virtual ATARC panel Tuesday, officials from three agencies offered a glimpse into automaton use cases that are boosting operations, detailed how they're gaining buy-in from personnel along the way and considered how the present pandemic-induced reality could transform the future of work. The officials were joined by experts with insights into industry and state governments. "None of our job descriptions said'you will cut and paste, cut and paste, cut and paste.' So that's what RPA is letting us give up," Stacy Dawn, senior adviser for cybersecurity and privacy at the Housing and Urban Development Department, said.

JPMorgan Chase has hired a senior executive from Google as its new head of artificial intelligence (AI) and machine learning services. According to a memo obtained exclusively by CNBC, Apoorv Saxena, Google's head of product management for cloud-based artificial intelligence, will be joining JPMorgan on Aug. 31. In addition to his role as head of AI, Saxena will also serve as head of asset and wealth management artificial intelligence technology. He will be based in Palo Alto, California. As companies in various markets are racing to create the next generation of AI-powered services, a small group of proven AI experts are finding themselves in high demand, with some being offered salaries in the millions to join firms.

A majority of North American financial institutions plan to invest in machine learning analytics to help combat fraud, according to a new report by global research and advisory firm Aite Group and authentication and fraud prevention technology provider Iovation. It's clear that the threat environment continues to escalate, the report said, and effective fraud prevention is an increasingly competitive issue for financial institutions. The study was compiled from interviews with 28 senior fraud and data analytics executives at 20 North American financial institutions. The interviews were conducted by Aite between August and September 2017. "What this study highlighted is that those who are early adopters of advanced machine learning analytics will be able to greatly reduce fraud while also improving the customer experience, giving those [financial institutions] a decided edge over their competitors who lag in these advancements," said Julie Conroy, research director for Aite Group's Retail Banking & Payments practice.