Boost My Business shines a spotlight on a tech start-up whose app maps accessibility via its users and AI. Fiona Alston chatted to Matthew McCann CEO of Access Earth about how his app helps users find businesses which have the accessibility requirements for their particular needs. McCann has cerebral palsy and uses a rollator to get around, and it's his personal experience of trying to access businesses like restaurants and hotels which proved the need for the software. "Growing up it was difficult for me to find accessibility information whether it was going out to eat somewhere or going to the shops and for me that's a really important thing to know ahead of time," says McCann. "I realised then going into college I wanted to be able to do something about that - figuring out the world isn't accessible at the moment and I wanted to make that change," he says.

President Donald Trump said Saturday he has approved a deal in principle in which Oracle and Walmart will partner with the viral video-sharing app TikTok in the U.S., allowing the popular app to avoid a shutdown. "I have given the deal my blessing -- if they get it done that's great, if they don't that's okay too," Trump told reporters on the White House South Lawn before departing for North Carolina. "I approved the deal in concept." The U.S. Department of Commerce announced it would delay the prohibition of U.S. transactions with TikTok until next Sunday. Shortly after Trump's comments, Oracle announced it was chosen as TikTok's secure cloud provider and will become a minority investor with a 12.5% stake.

Victoria's Premier Daniel Andrews said the numbers were "cause for great optimism". His state, which has accounted for 75% of Australia's 26,900 cases and 90% of its 849 deaths, has been under lockdown since early July.

Machine learning becomes engaging when we face various challenges and thus finding suitable datasets relevant to the use case is essential. Flexibility refers to the number of tasks that it supports. For example, Microsoft's COCO( Common Objects in Context) is used for object classification, detection, and segmentation. Add a bunch of captions for the same, and we can use it as a dataset for an image caption generator as well. Well, when we are just starting, we shall be working with some of the small and standard machine learning datasets like the CIFAR-10, MNIS, Iris, etc.

Black-box Artificial Intelligence (AI) methods, e.g. deep neural networks, have been widely utilized to build predictive models that can extract complex relationships in a dataset and make predictions for new unseen data records. However, it is difficult to trust decisions made by such methods since their inner working and decision logic is hidden from the user. Explainable Artificial Intelligence (XAI) refers to systems that try to explain how a black-box AI model produces its outcomes. Post-hoc XAI methods approximate the behavior of a black-box by extracting relationships between feature values and the predictions. Perturbation-based and decision set methods are among commonly used post-hoc XAI systems. The former explanators rely on random perturbations of data records to build local or global linear models that explain individual predictions or the whole model. The latter explanators use those feature values that appear more frequently to construct a set of decision rules that produces the same outcomes as the target black-box. However, these two classes of XAI methods have some limitations. Random perturbations do not take into account the distribution of feature values in different subspaces, leading to misleading approximations. Decision sets only pay attention to frequent feature values and miss many important correlations between features and class labels that appear less frequently but accurately represent decision boundaries of the model. In this paper, we address the above challenges by proposing an explanation method named Confident Itemsets Explanation (CIE). We introduce confident itemsets, a set of feature values that are highly correlated to a specific class label. CIE utilizes confident itemsets to discretize the whole decision space of a model to smaller subspaces.

Temporal anomaly detection looks for irregularities over space-time. Unsupervised temporal models employed thus far typically work on sequences of feature vectors, and much less on temporal multiway data. We focus our investigation on two-way data, in which a data matrix is observed at each time step. Leveraging recent advances in matrix-native recurrent neural networks, we investigated strategies for data arrangement and unsupervised training for temporal multiway anomaly detection. These include compressing-decompressing, encoding-predicting, and temporal data differencing. We conducted a comprehensive suite of experiments to evaluate model behaviors under various settings on synthetic data, moving digits, and ECG recordings. We found interesting phenomena not previously reported. These include the capacity of the compact matrix LSTM to compress noisy data near perfectly, making the strategy of compressing-decompressing data ill-suited for anomaly detection under the noise. Also, long sequence of vectors can be addressed directly by matrix models that allow very long context and multiple step prediction. Overall, the encoding-predicting strategy works very well for the matrix LSTMs in the conducted experiments, thanks to its compactness and better fit to the data dynamics.

In a multi-turn knowledge-grounded dialog, the difference between the knowledge selected at different turns usually provides potential clues to knowledge selection, which has been largely neglected in previous research. In this paper, we propose a difference-aware knowledge selection method. It first computes the difference between the candidate knowledge sentences provided at the current turn and those chosen in the previous turns. Then, the differential information is fused with or disentangled from the contextual information to facilitate final knowledge selection. Automatic, human observational, and interactive evaluation shows that our method is able to select knowledge more accurately and generate more informative responses, significantly outperforming the state-of-the-art baselines. The codes are available at https://github.com/chujiezheng/DiffKS.

IEEE Intelligent Systems 24, 2 (2009) In the decade since then, the research community have done a lot with quantity, but quality has been left behind 16. http://lora-aroyo.org Data Quality is not only human error 20. Data Quality should consider context of use it is not easy to give Y/N answer for most of our AI tasks the answer typically depends on the context, on the task, on the usage, etc 21. http://lora-aroyo.org Data Quality should include real world diversity it is not easy to give Y/N answer for most of our AI tasks the answer typically depends on the context, on the task, on the usage, etc disagreement is signal for diversity and should be included in AI training 22. http://lora-aroyo.org Data Quality is difficult even with experts For prevention of malaria, use only in individuals traveling to malarious areas where CHLOROQUINE resistant P. falciparum MALARIA has not been reported.

In Neural Architecture Search (NAS), Differentiable ARchiTecture Search (DARTS) has recently attracted much attention due to its high efficiency. It defines an over-parameterized network with mixed edges each of which represents all operator candidates, and jointly optimizes the weights of the network and its architecture in an alternating way. However, this process prefers a model whose weights converge faster than the others, and such a model with fastest convergence often leads to overfitting. Accordingly the resulting model cannot always be well-generalized. To overcome this problem, we propose Minimum Stable Rank DARTS (MSR-DARTS), which aims to find a model with the best generalization error by replacing the architecture optimization with the selection process using the minimum stable rank criterion. Specifically, a convolution operator is represented by a matrix and our method chooses the one whose stable rank is the smallest. We evaluate MSR-DARTS on CIFAR-10 and ImageNet dataset. It achieves an error rate of 2.92% with only 1.7M parameters within 0.5 GPU-days on CIFAR-10, and a top-1 error rate of 24.0% on ImageNet. Our MSR-DARTS directly optimizes an ImageNet model with only 2.6 GPU days while it is often impractical for existing NAS methods to directly optimize a large model such as ImageNet models and hence a proxy dataset such as CIFAR-10 is often utilized.

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