Marine biologists have a new AI tool for monitoring and protecting coral reefs. The project--a collaboration between Google and Australia's Commonwealth Scientific and Industrial Research Organization (CSIRO)--employs computer vision detection models to pinpoint damaging outbreaks of crown-of-thorns starfish (COTS) through a live camera feed. Keeping a closer eye on reefs helps scientists address growing populations quickly, to protect the valuable Great Barrier Reef ecosystem. Despite covering less than 1% of the vast ocean floor, coral reefs support about 25% of sea species including fish, invertebrates, and marine mammals. When healthy, these productive marine environments provide commercial and subsistence fishing and income for tourism and recreational businesses.

Do you feel bad about the current cryptocurrency and NFT market? But there is a plan. This whole period can be turned into a very profitable activity. There is no need to call or persuade anyone to order something, then participate in the pyramid and end up disappointed. Creativity is the essential ingredient.

Today there are more incredible uses for robots than ever before. Innovators and inventors worldwide are applying countless types of robots to fascinating tasks. You may have heard of mechanized surgeons and AI cars, but have you heard of these six exciting uses? More eateries, especially fast-food businesses, are facing difficulties with staffing today. Developers have devised innovative solutions and unique new types of robotics.

Artificial Intelligence (AI) often reminds us of Hollywood with films such as The Terminator and The Matrix presenting dystopic futures where humans and robots face off in epic battles. However, AI and automated decision-making (ADM) are increasingly being adopted in organisations across Australia. It is estimated digital technologies, including AI, will be worth $A315b to the Australian economy by 2028. The Federal Government recently completed consultations regarding potentially regulating AI and ADM, with Governance Institute of Australia lodging a submission in response to the issues paper. This article discusses the technologies, the potential they offer and the challenges they pose.

With millions of boats across the world, it is difficult for regulators to stop fishers who exceed catching limits. Even those with no malicious intent can accidentally catch too much because of manual reporting practices. Enter OnDeck Fisheries AI, a Vancouver, B.C.-based startup developing software to automatically scan how many fish are brought onto a ship. The company's machine learning and computer vision technology tracks the precise biomass and type of fish without requiring human observers. The idea is to help regulators and fishers rely on an automated solution to ensure they are complying with fishing regulations.

Stuttering is a varied speech disorder that harms an individual's communication ability. Persons who stutter (PWS) often use speech therapy to cope with their condition. Improving speech recognition systems for people with such non-typical speech or tracking the effectiveness of speech therapy would require systems that can detect dysfluencies while at the same time being able to detect speech techniques acquired in therapy. This paper shows that fine-tuning wav2vec 2.0 [1] for the classification of stuttering on a sizeable English corpus containing stuttered speech, in conjunction with multi-task learning, boosts the effectiveness of the general-purpose wav2vec 2.0 features for detecting stuttering in speech; both within and across languages. We evaluate our method on FluencyBank , [2] and the German therapy-centric Kassel State of Fluency (KSoF) [3] dataset by training Support Vector Machine classifiers using features extracted from the finetuned models for six different stuttering-related event types: blocks, prolongations, sound repetitions, word repetitions, interjections, and - specific to therapy - speech modifications. Using embeddings from the fine-tuned models leads to relative classification performance gains up to 27% w.r.t. F1-score.

Defence ministers from NATO countries and other parts of the world will convene to discuss weapons deliveries to Ukraine on Wednesday. The meeting comes as Kyiv seeks a significant increase in arms to fight off Russian forces. Ahead of the talks, NATO Secretary General Jens Stoltenberg said allies would continue to deliver heavy weapons and long-range systems to Ukraine. Since the Russian invasion on February 24, Ukraine has received billions of dollars' worth of weapons and military equipment from at least 28 countries. Twenty-five of the 28 nations providing military assistance to Ukraine are NATO members, including the US and UK, which are supplying Kyiv with sophisticated weapons such as multiple rocket launch systems (MLRS). Despite its growing arsenal, Ukraine, which has an active military personnel of just 200,000, is significantly outgunned by Russian forces.

Have you ever seen a bridge collapse on TV? We only care about inspection and maintenance if it does not work. However, robotics are changing the inspection & maintenance landscape, and significant societal and political implications follow the magnitude of innovation. To pay due attention to these developments, the communication on innovation in robotics for inspection & maintenance has to play a role in informing and influencing the target groups for the successful adoption of robotics technologies. The question on what role communications play in forming the perception of innovative technology was discussed in the workshop "Communicating innovation: What can we do better?" on May 25th, 2022.

Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we provide a new introduction to the field. We introduce the necessary logical notation and the main learning settings; describe the building blocks of an ILP system; compare several systems on several dimensions; describe four systems (Aleph, TILDE, ASPAL, and Metagol); highlight key application areas; and, finally, summarise current limitations and directions for future research.

The Industrial Revolution was a time of great change. With the steam engine, industries shifted away from skilled human labour towards mechanisation and machinery. As a result, many specialised workers lost their jobs and were forced to adapt to their new reality. The Luddites, a radical organisation of textile workers who were made redundant by textile machines, retaliated by destroying these machines and assassinated business owners. The Luddites gained public sympathy as many were afraid that they, like the retrenched textile workers, would lose their jobs to automated machinery.