What AI doesn't know: we could be creating a global'knowledge collapse' As GenAI becomes the primary way to find information, local and traditional wisdom is being lost. And we are only beginning to realise what we're missing This article was originally published as'Holes in the web' on Aeon.co A few years back, my dad was diagnosed with a tumour on his tongue - which meant we had some choices to weigh up. My family has an interesting dynamic when it comes to medical decisions. While my older sister is a trained doctor in western allopathic medicine, my parents are big believers in traditional remedies. Having grown up in a small town in India, I am accustomed to rituals. My dad had a ritual, too. Every time we visited his home village in southern Tamil Nadu, he'd get a bottle of thick, pungent, herb-infused oil from a vaithiyar, a traditional doctor practising Siddha medicine. It was his way of maintaining his connection with the kind of medicine he had always known and trusted.

Warming temperatures mean shorter ice seasons in Sanikiluaq, Nunavut. As a result, the stretches of landfast ice formed from frozen seawater that Inuit use to travel and hunt on are increasingly unpredictable and unsafe. Polynyas, areas of open water and thin ice, occur where ocean currents or wind prevent pack ice from forming. They're typically found in the same locations each year enabling travellers to plan their routes safely. But climate change is affecting this predictability, causing smaller, unexpected polynyas that make travelling across the pack ice risky.

Most technological advances spark incremental progress. And then a few come along that are truly game-changing. AI is one of those truly transformative technologies. It's set to revolutionise our lives and workplaces at rapid speed in the coming decade. It will potentially reshape almost every job, industry and life.

Microsoft is blending Indigenous knowledge with AI to protect parts of Kakadu National Park. The tech behemoth partnered with the CSIRO and Indigenous rangers at the World Heritage-listed park to restore native wildlife using artificial intelligence (AI). Located in the Northern Territory, Kakadu is jointly managed by Parks Australia and traditional Indigenous owners. Its wetlands are home to protected Australian species such as magpie geese, which are considered by traditional custodians as a major indicator of'healthy country'. But the introduction of a weed called para grass has seen the reduction of native plants and has removed habitats for the magpie geese. Michael Douglas, leader of NESP Northern Australia Environmental Resources Hub, explained that para grass was planted in the area during the late 1960s before Kakadu was a national park, used as buffalo and cattle feed.

Abstract--Drought forecasting and prediction is a complicated process due to the complexity and scalability of the environmental parameters involved. Hence, it required a high level of expertise to predict. In this paper, we describe the research and development of a rule-based drought early warning expert systems (RB-DEWES) for forecasting drought using local indigenous knowledge obtained from domain experts. The system generates inference by using rule set and provides drought advisory information with attributed certainty factor (CF) based on the user's input. The system is believed to be the first expert system for drought forecasting to use local indigenous knowledge on drought. The architecture and components such as knowledge base, JESS inference engine and model base of the system and their functions are presented. The intricate complexity of drought has always been a stumbling block for drought forecasting and prediction systems [1]. This is mostly due to the web of environmental events (such as climate variability) that directly/indirectly triggers this environmental phenomenon. There are six broad categories of drought: meteorological, climatological, atmospheric, agricultural, hydrologic and water drought [1]. Nevertheless, irrespective of the category of drought, there is a consensus amongst scientist that drought is a disastrous condition of lack of moisture caused by a deficit in precipitation in a certain geographical region over some time period [2]. The effect of drought can be quantified based on the frequency, duration and intensity in the affected region subject to established timescales.

Data heterogeneity hampers the effort to integrate and infer knowledge from vast heterogeneous data sources. An application case study is described, in which the objective was to semantically represent and integrate structured data from sensor devices with unstructured data in the form of local indigenous knowledge. However, the semantic representation of these heterogeneous data sources for environmental monitoring systems is not well supported yet. To combat the incompatibility issues, a dedicated semantic middleware solution is required. In this paper, we describe and evaluate a cross-domain middleware architecture that semantically integrates and generate inference from heterogeneous data sources. These use of semantic technology for predicting and forecasting complex environmental phenomenon will increase the degree of accuracy of environmental monitoring systems.