Autonomous business processes (ABPs), i.e., self-executing workflows leveraging AI/ML, have the potential to improve operational efficiency, reduce errors, lower costs, improve response times, and free human workers for more strategic and creative work. However, ABPs may raise specific concerns including decreased stakeholder trust, difficulties in debugging, hindered accountability, risk of bias, and issues with regulatory compliance. We argue for eXplainable ABPs (XABPs) to address these concerns by enabling systems to articulate their rationale. The paper outlines a systematic approach to XABPs, characterizing their forms, structuring explainability, and identifying key BPM research challenges towards XABPs.

If you spend any time on the internet, you're likely now familiar with the gray-and-teal screenshots of AI-generated text. At first they were meant to illustrate ChatGPT's surprising competence at generating human-sounding prose, and then to demonstrate the occasionally unsettling answers that emerged once the general public could bombard it with prompts. OpenAI, the organization that is developing the tool, describes one of its biggest problems this way: "ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers." In layman's terms, the chatbot makes stuff up. As similar services, such as Google's Bard, have rushed their tools into public testing, their screenshots have demonstrated the same capacity for fabricating people, historical events, research citations, and more, and for rendering those falsehoods in the same confident, tidy prose.

Fine as talcum powder, sharp as glass and seemingly everywhere, these super-fine particles coated the astronauts like soot and permeated their crew cabin, where it became more than a mere nuisance. Not only did it interfere with their equipment, it irritated their nostrils and eyes, giving some a mild allergic reaction. Nevertheless, between 1969 and 1972, Apollo astronauts brought back nearly 385 kilograms of lunar rock, pebbles and powder back to earth. Today, NASA deems interplanetary dust and dirt -- also known as regolith -- one of the greatest risks to long-term space settlements. On the Moon, it wears away at multi-million-dollar instruments and lodges into the slightest crevasses, weakening seals on pressure suits and causing hardware to malfunction.

Your organization may look like it is well on the way to a machine learning future. Your team is beyond the basics. They are now creating machine learning models that could impact real business problems. Yet they can only do that if those models are implemented. Once those models are created, many organizations seem to be experiencing a disconnect in getting them implemented.

A Navy scientist has re-engineered the software that allows long-endurance drones to powerlessly climb into the sky on bubbles of warm air. In a U.S. patent application published on May 2, Aaron Kahn, an engineer working on the Autonomous Locator of Thermals (ALOFT) project at the Naval Research Laboratory, reported that he has extensively tested the new software that detects and estimates the position of thermals, i.e., rising columns of warm air that birds use to stay aloft without flapping their wings. Unlike birds, soaring drones need the benefits of thermal detection and position estimation software as the warm air tends to drift relative to the ground due to winds. Prior systems relied on batch estimation processes that "require storing large arrays of data, which is not ideal for operation on small micro-controllers with limited memory resources." Kahn's new soaring software uses extended Kalman filtering, a kind of algorithm already used by the Navy for navigating submarines and cruise missiles. Now it can help orbit drones like the tiny CICADA glider or long-endurance solar-soaring UAVs that might also have photovoltaic or fuel cells feeding battery-powered propellers.

Editor's Note: Richard Metzger is a connoisseur of cannabis, and recently started growing his own. He's test-driving high-end rig good for small-scale grows from Cloudponics. This is not a sponsored post, Boing Boing is not getting anything from Cloudponics. Metzger's just really *that* enthusiastic about weed, and so far he likes the Cloudponics setup. In the first installment of This TARDIS Grows Weed with Artificial Intelligence, I explained how incredibly overwhelming it was for me to contemplate setting up a decent small grow situation as a rank novice.

Nasa has cancelled its only robotic vehicle under development to explore the surface of the moon. Members of the agency's Resource Prospector mission were told to close down the project by late May. While Nasa has not given details on why it cancelled the mission, one of the team's planetary scientists speculated it was likely budget related. The decision comes just months after US President Donald Trump ordered Nasa to return astronauts to the moon for the first time since 1972. Nasa has cancelled the only robotic vehicle under development to explore the surface of the moon.

Meagan Metzger is the founder of Dcode42, an accelerator program for companies with innovative technology products for which there is a current or potential future government need. Dcode42 recently partnered with Amazon Web Services to help speed the adoption of artificial intelligence and machine learning for problem solving in government. GCN spoke with Metzger about the role of AI in government and ways cloud-based AI can help government solve challenges. The interview has been edited for length and clarity. GCN: What government challenges do you see AI solving?

Santander customers will be able to make payments with their voice by talking to their smartphone app, in yet another sign of the technological revolution that is transforming the banking industry. Under a new pilot scheme, the company has revamped its voice recognition technology to allow customers to make transfers to existing payees by speaking to their iPhone SmartBank app. It is the first high street lender to offer the service and comes after the company launched its so-called "voice assistant banking" technology last year. Ed Metzger, Santander UK's head of innovation technology and operations, said: "The appetite for simple, intuitive banking solutions has grown significantly in recent years. "This pioneering technology has huge potential to become an integral part of the future banking experience, playing a transformational role in the industry and redefining how customers to choose to manage their money."

A data scientist is one of the most in-demand, high-profile careers in IT today, but Tom Walsh and Alex Krowitz have been working behind the scenes in the field for years. Walsh, a research engineer and Krowitz, a senior research engineer at cloud workforce management solutions company Kronos, sift through the influx of proprietary and customer data to identify patterns and gain insights based on that data. There are generally two kinds of projects we regularly handle; mining patterns within data to improve our own products is one and the other is taking on specific sets of customer data to gather and deliver insights from that," says Walsh. What companies are looking for is ultimately the capability to make predictions based on that data, says Krowitz. Companies use those predictions to help drive everything from marketing strategy to resource allocation, personnel levels and staffing, or to predict retail sales, he says. "We have products that use machine learning algorithms to help customers with these predictions.