What happens when robots not only learn to write well, but the tech becomes easily accessible and cheap? As Hal Crawford explains, it'll likely be teachers who feel the effects first. There are two schools of thought when it comes to artificial intelligence: there are the people who have heard of the GPT-3 language model, and then there are those who have heard about it, gone to the OpenAI site, created a guest login and tried it out for themselves. The first group contains people who are wondering what the big deal is. The second group does not. I haven't heard of anyone who's actually used GPT-3 and doesn't think AI is going to change the world profoundly. Education in particular is going to feel its influence immediately.

The threat of wildfires has never been greater than it is today. In recent years, countries around the world – from the US, Argentina and Brazil to Italy, Greece and Australia – have been gravely affected by wildfires. This has resulted in many human and animal deaths, as well as the loss of millions of hectares of forests. And wildfire risks continue to grow – a recent UN Environment Programme report warns that the number of wildfires will rise by 50% by 2100 and governments are not prepared.

The UK's data protection watchdog has confirmed a penalty for the controversial facial recognition company, Clearview AI -- announcing a fine of just over £7.5 million today for a string of breaches of local privacy laws. The watchdog has also issued an enforcement notice, ordering Clearview to stop obtaining and using the personal data of UK residents that is publicly available on the internet; and telling it to delete the information of UK residents from its systems. The US company has amassed a database of 20 billion facial images by scraping data off the public internet, such as from social media services, to create an online database that it uses to power an AI-based identity-matching service which it sells to entities such as law enforcement. The problem is Clearview has never asked individuals whether it can use their selfies for that. And in many countries it has been found in breach of privacy laws.

Clearview AI has been fined £7.55 million ($9.5 million) by the UK's privacy watchdog for illegally scraping the facial images of UK residents from social media and the web. It was also ordered to stop obtaining the data of UK residents and to delete any it has already collected. "The company not only enables identification of those people, but effectively monitors their behavior and offers it as a commercial service. That is unacceptable," said UK information commissioner John Edwards in a statement. The UK's Information Commissioner's Office (ICO) opened a joint investigation with Australia into Clearview AI back in 2020, and issued a preliminary fine of £17 million ($21.4 million) against the company late last year.

A doctor can't tell if somebody is Black, Asian, or white, just by looking at their X-rays. The study found that an artificial intelligence program trained to read X-rays and CT scans could predict a person's race with 90 percent accuracy. But the scientists who conducted the study say they have no idea how the computer figures it out. "When my graduate students showed me some of the results that were in this paper, I actually thought it must be a mistake," said Marzyeh Ghassemi, an MIT assistant professor of electrical engineering and computer science, and coauthor of the paper, which was published Wednesday in the medical journal The Lancet Digital Health. "I honestly thought my students were crazy when they told me."

The University of Western Australia (UWA) and Rio Tinto Iron Ore (RTIO) have entered into a four-year, $6.1 million research partnership to develop innovative data science solutions (artificial intelligence) for automated geological logging to improve mining practice. The partnership, which follows more than 10 years of collaboration between UWA's data science team and RTIO, will employ five full-time researchers and provide training opportunities for a number of industry-driven PhD programmes. Dr Daniel Wedge, from (CDG) in UWA's School of Geosciences, said UWA's expertise will be resorted to help RTIO's mine geology team tackle the challenge of objective well geological materials. "Until recently, geologist's specialists had to manually interpret and document material found in core samples, a process that was time-consuming and challenging," Dr Wedge said. "Our project can use artificial intelligence: machine learning, pc vision, spacial modelling and improvement techniques to integrate disparate borehole information, together with analysis, imagery, geochemical and natural science informationalong side chemical analysis, imagery, geochemical and earth science info, to."RTIO head Dr. Angus McFarlane said the past partnership between UWA and RTIO has led to the commercialisation of UWA's automated downhole image analysis software and three joint patent applications for RTIO-driven machine learning-based geological modelling.

Since 2002, Quantium have combined the best of human and artificial intelligence to power possibilities for individuals, organisations and society. Whether it be building forecasting engines that are driving down food wastage or creating mapping tools to support targeted measures in combatting human trafficking, Quantium believes in better goods, services, experiences, and championing the benefits of data for a brighter future. Q-Telco is the new joint venture between Quantium and Telstra to unlock the full potential of data and AI for Telstra and its customers. We'll do this by combining our market leading data science and AI capabilities with Telstra's customer, product and network data assets. This new partnership will not only provide personalised and data-enabled products and offers for Telstra's customers, but it will also embed proactive and predictive AI and machine learning across Telstra's core business.

Communications' Inside Risks columns have long stressed the importance of total-system awareness of riskful situations, some of which may be very difficult to identify in advance. Specifically, the desired properties of the total system should be specified as requirements. Those desired properties are called emergent properties, because they often cannot be derived solely from lower-layer component properties, and appear only with respect to the total system. Unfortunately, additional behavior of the total system may arise--which either defeats the ability to satisfy the desired properties, or demonstrates that the set of required properties was improperly specified. In this column, I consider some cases in which total-system analysis is of vital importance, but generally very difficult to achieve with adequate assurance.

This is my last editorial as Editor-in-Chief of Communications,a so it is a moment to share learnings and, of course, to reflect on accomplishments. First, we launched the Regional Special Sections (RSS) in November 2018 with a spotlight on computing in the China Region. With 40 pages of articles, spanning tech idols to gaming to computing culture to fintech and "superAI," the first RSS created an excitement that inspired and challenged co-hosts of the Europe, India, East Asia and Oceania, Latin America, and Arabia Regions. In just three years, we have circumnavigated the globe,b and with the second Europe Region Section (April 2022) and India Region Section (November 2022), a new circuit is well under way! The RSS are an exciting read for the ACM community (great job by the co-hosts and authors), delivering news insights and perspectives into how computing is shaping and being shaped around the world.

New software developed by Peter Mac and collaborators is helping patients diagnosed with acute lymphoblastic leukemia (ALL) to determine what subtype they have. ALL is the most common childhood cancer in the world, and also affects adults. "Thirty to forty percent of all childhood cancers are ALL, it's a major pediatric cancer problem," says Associate Professor Paul Ekert from Peter Mac and the Children's Cancer Institute, who was involved in this work. More than 300 people are diagnosed with the disease in Australia each year, and more than half of those are young children under the age of 15. Determining what subtype of ALL a patient has provides valuable information about their prognosis, and how they should best be treated.