Many algorithms, whether supervised or unsupervised, make use of distance measures. These measures, such as euclidean distance or cosine similarity, can often be found in algorithms such as k-NN, UMAP, HDBSCAN, etc. Understanding the field of distance measures is more important than you might realize. Take k-NN for example, a technique often used for supervised learning. As a default, it often uses euclidean distance. However, what if your data is highly dimensional?

While the problem of spray drift is not new, it has been a significant problem in the agriculture industry for some time. Robots and machine learning technologies, which form part of the broader field of AI, have the capability to facilitate new, more sustainable agricultural methods that will take farming practices to new heights by conserving resources, minimising the use of pesticides and shortening the time to market.

The National Institute of Standards and Technology (NIST), the U.S. agency responsible for developing technical metrics to promote "innovation and industrial competitiveness," this week published a document outlining feedback and recommendations for mitigating the risk of bias in AI. The paper, about which NIST is accepting comments until August, proposes an approach for identifying and managing "pernicious" biases that can damage public trust in AI. As NIST scientist Reva Schwartz, who coauthored the paper, points out, AI is transformative in its ability to make sense of data more quickly than humans. But as AI pervades the world, it's becoming clear that its predictions can be affected by algorithmic and data biases. Making matters worse, some AI systems are built to model complex concepts that can't be directly measured by data in the first place.

Stochastic nested optimization, including stochastic compositional, min-max and bilevel optimization, is gaining popularity in many machine learning applications. While the three problems share the nested structure, existing works often treat them separately, and thus develop problem-specific algorithms and their analyses. Among various exciting developments, simple SGD-type updates (potentially on multiple variables) are still prevalent in solving this class of nested problems, but they are believed to have slower convergence rate compared to that of the non-nested problems. This paper unifies several SGD-type updates for stochastic nested problems into a single SGD approach that we term ALternating Stochastic gradient dEscenT (ALSET) method. By leveraging the hidden smoothness of the problem, this paper presents a tighter analysis of ALSET for stochastic nested problems. Under the new analysis, to achieve an $\epsilon$-stationary point of the nested problem, it requires ${\cal O}(\epsilon^{-2})$ samples. Under certain regularity conditions, applying our results to stochastic compositional, min-max and reinforcement learning problems either improves or matches the best-known sample complexity in the respective cases. Our results explain why simple SGD-type algorithms in stochastic nested problems all work very well in practice without the need for further modifications.

The large-scale data stream problem refers to high-speed information flow which cannot be processed in scalable manner under a traditional computing platform. This problem also imposes expensive labelling cost making the deployment of fully supervised algorithms unfeasible. On the other hand, the problem of semi-supervised large-scale data streams is little explored in the literature because most works are designed in the traditional single-node computing environments while also being fully supervised approaches. This paper offers Weakly Supervised Scalable Teacher Forcing Network (WeScatterNet) to cope with the scarcity of labelled samples and the large-scale data streams simultaneously. WeScatterNet is crafted under distributed computing platform of Apache Spark with a data-free model fusion strategy for model compression after parallel computing stage. It features an open network structure to address the global and local drift problems while integrating a data augmentation, annotation and auto-correction ($DA^3$) method for handling partially labelled data streams. The performance of WeScatterNet is numerically evaluated in the six large-scale data stream problems with only $25\%$ label proportions. It shows highly competitive performance even if compared with fully supervised learners with $100\%$ label proportions.

We propose a method to estimate the uncertainty of the outcome of an image classifier on a given input datum. Deep neural networks commonly used for image classification are deterministic maps from an input image to an output class. As such, their outcome on a given datum involves no uncertainty, so we must specify what variability we are referring to when defining, measuring and interpreting "confidence." To this end, we introduce the Wellington Posterior, which is the distribution of outcomes that would have been obtained in response to data that could have been generated by the same scene that produced the given image. Since there are infinitely many scenes that could have generated the given image, the Wellington Posterior requires induction from scenes other than the one portrayed. We explore alternate methods using data augmentation, ensembling, and model linearization. Additional alternatives include generative adversarial networks, conditional prior networks, and supervised single-view reconstruction. We test these alternatives against the empirical posterior obtained by inferring the class of temporally adjacent frames in a video. These developments are only a small step towards assessing the reliability of deep network classifiers in a manner that is compatible with safety-critical applications.

New AI software developed by researchers at Flinders University shows promise for enabling timely support ahead of relapse in patients with severe mental illness. The AI2 (Actionable Intime Insights) software, developed by a team of digital health researchers at Flinders University, has undergone an eight-month trial with psychiatric patients from the Inner North Community Health Service, located in Gawler, South Australia. The digital tool is tipped to revolutionise consumer-centric timely mental health treatment provision outside hospital, with researchers labelling it as readily available and scalable. In the trial of 304 patients, the AI2 software found that 10% of them were at increased risk of not adhering to treatment plans by failing to take medication or disengaging with health services. This led to interventions which clinicians believe could have prevented the patient from relapsing and experiencing a deterioration of their mental health.

Newly appointed Minister for Industry, Science and Technology, Christian Porter. The federal government has unveiled its first action plan dedicated to establishing Australia as a global leader in developing and adopting responsible artificial intelligence (AI). Industry, Science and Technology Minister Christian Porter said the benefits of AI include protecting the environment, improving health outcomes, promoting smart cities, and boosting the economy. "AI could contribute more than $20 trillion to the global economy by 2030, and the AI Action Plan will help us leverage opportunities for AI to further strengthen the economy and improve the quality of life of all Australians, while ensuring that the development and adoption of AI is guided by appropriate safeguards, privacy and ethical considerations," he said. The government allocated $124.1 million in funding through the May budget to deliver some of the plan's key measures.

Outgoing Secretary-General of the Organisation for Economic Co-operation and Development (OECD) ... [ ] Angel Gurria applauds as new Secretary-General of the Organisation for Economic Cooperation and Development (OECD) Mathias Cormann, of Australia, takes over at the OECD headquarters in Paris, Tuesday, June, 1 2021. A recent study from the Pew Research Center showed that 53% of people in 20 countries feel that artificial intelligence has been a good thing for society. While over half the world's population has a positive view of AI, this means that one in every three people in these countries are concerned about the impacts AI can have on society. How do we ensure that AI is trustworthy and its benefits are shared by all? As the statistics show, while there is incremental improvement, there is still a level of hesitancy and suspicion towards AI among the citizens around the world.

Farming robots that can move autonomously in an open field or greenhouse promise a cleaner, safer agricultural future. But there are also potential downsides, from the loss of much-needed jobs to the safety of those working alongside the robots. To ensure that the use of autonomous robots on farms creates more benefits than losses, a process of responsible development is required. Society as a whole needs to be involved in setting the trajectories for future farming. We are part of a project called Robot Highways, which is currently demonstrating multiple uses for autonomous robots made by Saga Robotics on a fruit farm in south-east England.