Over the past decade, we have seen exponential growth in online content fueled by social media platforms. Data generation of this scale comes with the caveat of insurmountable offensive content in it. The complexity of identifying offensive content is exacerbated by the usage of multiple modalities (image, language, etc.), code mixed language and more. Moreover, even if we carefully sample and annotate offensive content, there will always exist significant class imbalance in offensive vs non offensive content. In this paper, we introduce a novel Code-Mixing Index (CMI) based focal loss which circumvents two challenges (1) code mixing in languages (2) class imbalance problem for Dravidian language offense detection. We also replace the conventional dot product-based classifier with the cosine-based classifier which results in a boost in performance. Further, we use multilingual models that help transfer characteristics learnt across languages to work effectively with low resourced languages. It is also important to note that our model handles instances of mixed script (say usage of Latin and Dravidian - Tamil script) as well. Our model can handle offensive language detection in a low-resource, class imbalanced, multilingual and code mixed setting.

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AI systems being developed to diagnose skin cancer run the risk of being less accurate for people with dark skin, research suggests. The potential of AI has led to developments in healthcare, with some studies suggesting image recognition technology based on machine learning algorithms can classify skin cancers as successfully as human experts. NHS trusts have begun exploring AI to help dermatologists triage patients with skin lesions. But researchers say more needs to be done to ensure the technology benefits all patients, after finding that few freely available image databases that could be used to develop or "train" AI systems for skin cancer diagnosis contain information on ethnicity or skin type. Those that do have very few images of people with dark skin.

"What projects can I do with machine learning?" We often get asked this question a lot from beginners getting started with machine learning. ProjectPro industry experts recommend that you explore some exciting, cool, fun, and easy machine learning project ideas across diverse business domains to get hands-on experience on the machine learning skills you've learned.

Google is seeking volunteers for a new beta app called Project Relate, which aims to provide people with speech impairments with a voice assistant that can transcribe their speech in real time as well synthesize what they are saying. The app is part of Project Euphoria, which is a wider endeavor started in 2019 that's aimed at collecting data to be used for improving Google's AI algorithms when it comes to handling speech from people who "have difficulty being understood by others," such as those affected by neurological conditions. As for the Relate app, it has three key features. The Listen feature will transcribe a user's speech in real time, allowing them to copy and paste into other apps or show to other people. The Repeat feature will restate what the user is saying in a "clear synthesized voice," which Google hopes will aid face-to-face conversations and help when people with speech impairments want to speak a command to a smart home device.

What is AI bias and how can it affect human rights? In this blog post, we explore AI bias examples, challenges, and why the answer lies in ethical AI.

In the early days of the COVID-19 pandemic (June 2020), LinkedIn released a report showing that the demand for AI skills had cooled down--but by October 2020, demand had already come roaring back. This is not surprising: according to the 2020 RELX Emerging Tech Executive Report, AI adoption soared during the pandemic, and a staggering 68% of companies increased their AI investment during the year. Further, 81% of companies now report using AI technologies, up 33 percentage points since 2018. Companies are increasingly using AI technologies on mission-critical applications, which has led to an explosion in the need for data scientists and technologists to build and support these applications. Not surprisingly, 39% of companies now cite a lack of technology expertise as a leading stumbling block to AI usage and adoption.

A massive international research effort has led to development of a genetic model for the'ultimate' chickpea, with the potential to lift crop yields by up to 12 per cent. The research consortium genetically mapped thousands of chickpea varieties, and the UQ team then used this information to identify the most valuable gene combinations using artificial intelligence (AI). Professor Ben Hayes led the UQ component of the project with Professor Kai Voss-Fels and Associate Professor Lee Hickey, to develop a'haplotype' genomic prediction crop breeding strategy, for enhanced performance for seed weight. "Most crop species only have a few varieties sequenced, so it was a massive undertaking by the international team to analyse more than 3000 cultivated and wild varieties," Professor Hayes said. The landmark international study was led by Dr Rajeev Varshney from the International Crops Research Institute for the Semi-Arid Tropics in Hyderabad, India.

Lifelong learning is a long-standing aim for artificial agents that act in dynamic environments, in which an agent needs to accumulate knowledge incrementally without forgetting previously learned representations. We investigate methods for learning from data produced by event cameras and compare techniques to mitigate forgetting while learning incrementally. We propose a model that is composed of both, feature extraction and continuous learning. Furthermore, we introduce a habituation-based method to mitigate forgetting. Our experimental results show that the combination of different techniques can help to avoid catastrophic forgetting while learning incrementally from the features provided by the extraction module.

Complex Knowledge Base Question Answering is a popular area of research in the past decade. Recent public datasets have led to encouraging results in this field, but are mostly limited to English and only involve a small number of question types and relations, hindering research in more realistic settings and in languages other than English. In addition, few state-of-the-art KBQA models are trained on Wikidata, one of the most popular real-world knowledge bases. We propose CLC-QuAD, the first large scale complex Chinese semantic parsing dataset over Wikidata to address these challenges. Together with the dataset, we present a text-to-SPARQL baseline model, which can effectively answer multi-type complex questions, such as factual questions, dual intent questions, boolean questions, and counting questions, with Wikidata as the background knowledge. We finally analyze the performance of SOTA KBQA models on this dataset and identify the challenges facing Chinese KBQA.