Macro-operators (macros) are a well-known technique for enhancing performance of planning engines by providing "short-cuts" in the state space. Existing macro learning systems usually generate macros by considering most frequent action sequences in training plans. Unfortunately, frequent action sequences might not capture meaningful activities as a whole, leading to a limited beneficial impact for the planning process. In this paper, inspired by resource locking in critical sections in parallel computing, we propose a technique that generates macros able to capture whole activities in which limited resources (e.g., a robotic hand, or a truck) are used. Specifically, such a Critical Section macro starts by locking the resource (e.g., grabbing an object), continues by using the resource (e.g., manipulating the object) and finishes by releasing the resource (e.g., dropping the object). Hence, such a macro bridges states in which the resource is locked and cannot be used. We also introduce versions of Critical Section macros dealing with multiple resources and phased locks. Usefulness of macros is evaluated using a range of state-of-the-art planners, and a large number of benchmarks from the deterministic and learning tracks of recent editions of the International Planning Competition.

MineExcellence, which is a leading provider of digital technologies for mining, with a special focus on drill and blast operations, in the controlled use of explosives to break rock for excavation, has established a collaboration with A2I2 to enhance the efficiency and safety of drill and blast processes, using AI and machine learning (ML). The mining industry is increasingly using AI and ML as tools to optimise processes, enhance decision-making, derive value from data, and improve safety. A memorandum of understanding between the two organisations was recently signed by MineExcellence MD Amit Bhandari and Deakin Research Innovations senior manager of commercialisation Greg Pullen. A2I2 head of Translational Research Professor Rajesh Vasa highlighted the importance of this collaboration and the core aim of the research project. "The collaboration between Mine Excellence and A2I2 includes joint research, industry connections and PhD placements. "Our research in this area aims to pioneer new methods and approaches that deliver high impact results to drilling and blasting, making the process safer and more efficient." Initial work has already started through a co-funded PhD student based at A 2I2 and working in collaboration with the industry. MineExcellence will provide its drill and blast domain knowledge as well as its digital platform. AI models will be developed in collaboration with A2I2, with support from drill and blast expert Professor Sushil Bhandari as external supervisor. Vasa said A2I2 has a solid capacity to apply novel AI techniques to the mining sector. "A2I2 has a proven track record of positively impacting society, spanning health, education, and defence technologies.

A trial of Maptek DomainMCF at an underground metals mine has concluded that machine learning will most likely become the preferred modelling …

The AI system is replacing human creativity. DALL•E links users with AI tools to create and share AI architecture. Machine Learning tools ready to use

Autonomous ships just took a small but important step forward. Hyundai's Avikus subsidiary says it has completed the world's first autonomous navigation of a large ship across the ocean. The Prism Courage (pictured) left Freeport in the Gulf of Mexico on May 1st, and used Avikus' AI-powered HiNAS 2.0 system to steer the vessel for half of its roughly 12,427-mile journey to the Boryeong LNG Terminal in South Korea's western Chungcheong Province. The Level 2 self-steering tech was good enough to account for other ships, the weather and differing wave heights. The autonomy spared the crew some work, of course, but it may also have helped the planet. Avikus claims HiNAS' optimal route planning improved the Prism Courage's fuel efficiency by about seven percent, and reduced emissions by five percent.

If business wants to use AI at scale, adhering to the technical guidelines for responsible AI development isn't enough. It must obtain society's explicit approval to deploy the technology. Six years ago, in March 2016, Microsoft Corporation launched an experimental AI-based chatbot, TayTweets, whose Twitter handle was @TayandYou. Tay, an acronym for "thinking about you," mimicked a 19-year-old American girl online, so the digital giant could showcase the speed at which AI can learn when it interacts with human beings. Living up to its description as "AI with zero chill," Tay started off replying cheekily to Twitter users and turning photographs into memes. Some topics were off limits, though; Microsoft had trained Tay not to comment on societal issues such as Black Lives Matter. Soon enough, a group of Twitter users targeted Tay with a barrage of tweets about controversial issues such as the Holocaust and Gamergate. They goaded the chatbot into replying with racist and sexually charged responses, exploiting its repeat-after-me capability. Realizing that Tay was reacting like IBM's Watson, which started using profanity after perusing the online Urban Dictionary, Microsoft was quick to delete the first inflammatory tweets. Less than 16 hours and more than 100,000 tweets later, the digital giant shut down Tay.

To mark its its centennial, Japan's Asahi Kasei has unveiled the AKXY2 concept pod vehicle. The vehicle has been designed to reimagine values of sustainability, satisfaction and society and how these will influence the needs of future mobility on the road to automation and electrification. When looked at closely, the concept AKXY2 can be seen featuring a split body with a streamlined lower section and an upper glass canopy. The latter can be lifted up vertically, while a door folds down to provide access to the cabin. The exterior of the vehicle features slender lighting units and aerodynamic wheel covers with transparent inserts.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Like innovation before it, co-innovation is quickly becoming both corporate buzzword and gospel. By partnering with tech companies to harness the power of emerging technologies like artificial intelligence and big data, co-innovation has been heralded as essential to future success – especially for businesses that exist outside of the digital realm. But before companies rush to embrace this growing trend in corporate America, they would be well served to keep a few key principles in mind when it comes to co-innovation, lest they fall prey to shiny, new capabilities that look great on paper but can't be implemented or scaled in reality. As the chief strategy and transformation officer at PepsiCo, I oversee our digitalization strategy so I recognize the power of new technologies – we're already using machine learning and data analytics to improve existing systems, processes and products.

The proportion of packaging companies hiring for machine learning related positions rose significantly in April 2022 compared with the equivalent month last year, with 25% of the companies included in our analysis recruiting for at least one such position. This latest figure was higher than the 10.7% of companies who were hiring for machine learning related jobs a year ago and an increase compared to the figure of 17.9% in March 2022. When it came to the rate of all job openings that were linked to machine learning, related job postings rose in April 2022, with 0.5% of newly posted job advertisements being linked to the topic. This latest figure was an increase compared to the 0.2% of newly advertised jobs that were linked to machine learning in the equivalent month a year ago. Machine learning is one of the topics that GlobalData, from whom our data for this article is taken, have identified as being a key disruptive force facing companies in the coming years.

Litmus, the Edge Data Platform for Industry 4.0, today announced CHIMEI, a leading Taiwan-based performance materials manufacturer, has deployed Litmus Edge to power artificial intelligence at the edge. CHIMEI will deploy Litmus Edge to many of their plants to expand factory data collection and run AI models at the edge to improve quality in production processes. Previously CHIMEI was collecting data and running AI models for each of their production processes separately using different edge devices, which was not efficient and maintenance costs were high. Complex integration with multiple AI model servers prompted them to look for a new solution that would allow them to collect data from more machines and provide AI model runtime functionality. CHIMEI chose Litmus Edge to consolidate production processes and run most AI models from the same edge device.