Scientists have made artificial nerve cells, paving the way for new ways to repair the human body. The tiny "brain chips" behave like the real thing and could one day be used to treat diseases such as Alzheimer's. A team from the University of Bath used a combination of maths, computation and chip design to come up with a way to replicate in circuit form what nerve cells (neurons) do naturally. Neurons carry signals to and from the brain and the rest of the body. Scientists are interested in replicating them, because of the potential that offers in treating diseases such as Alzheimer's, where neurons degenerate or die.

Today Microsoft announced an update to the Seeing AI app that will include new language output options, including Dutch, French, German, Japanese, and Spanish. The iOS exclusive app was first released in 2017 as a free tool to help people with visual impairments navigate day-to-day life. It's built around a series of different channels, which users can select depending on their particular needs or circumstances. For the first time Microsoft's Seeing AI app will speak in languages other than English. Today's update enables audio output in Japanese, German, Spanish, Dutch, and French In one channel, the app will read out the text of any document the iPhone's or iPad's front facing camera is pointed at.

We introduce a novel keyword-aware influential community query KICQ that finds the most influential communities from an attributed graph, where an influential community is defined as a closely connected group of vertices having some dominance over other groups of vertices with the expertise (a set of keywords) matching with the query terms (words or phrases). We first design the KICQ that facilitates users to issue an influential CS query intuitively by using a set of query terms, and predicates (AND or OR). In this context, we propose a novel word-embedding based similarity model that enables semantic community search, which substantially alleviates the limitations of exact keyword based community search. Next, we propose a new influence measure for a community that considers both the cohesiveness and influence of the community and eliminates the need for specifying values of internal parameters of a network. Finally, we propose two efficient algorithms for searching influential communities in large attributed graphs. We present detailed experiments and a case study to demonstrate the effectiveness and efficiency of the proposed approaches.

Multi-language speech datasets are scarce and often have small sample sizes in the medical domain. Robust transfer of linguistic features across languages could improve rates of early diagnosis and therapy for speakers of low-resource languages when detecting health conditions from speech. We utilize out-of-domain, unpaired, single-speaker, healthy speech data for training multiple Optimal Transport (OT) domain adaptation systems. We learn mappings from other languages to English and detect aphasia from linguistic characteristics of speech, and show that OT domain adaptation improves aphasia detection over unilingual baselines for French (6% increased F1) and Mandarin (5% increased F1). Further, we show that adding aphasic data to the domain adaptation system significantly increases performance for both French and Mandarin, increasing the F1 scores further (10% and 8% increase in F1 scores for French and Mandarin, respectively, over unilingual baselines).

Methods based on vector embeddings of knowledge graphs have been actively pursued as a promising approach to knowledge graph completion.However, embedding models generate storage-inefficient representations, particularly when the number of entities and relations, and the dimensionality of the real-valued embedding vectors are large. We present a binarized CANDECOMP/PARAFAC(CP) decomposition algorithm, which we refer to as B-CP, where real-valued parameters are replaced by binary values to reduce model size. Moreover, we show that a fast score computation technique can be developed with bitwise operations. We prove that B-CP is fully expressive by deriving a bound on the size of its embeddings. Experimental results on several benchmark datasets demonstrate that the proposed method successfully reduces model size by more than an order of magnitude while maintaining task performance at the same level as the real-valued CP model.

Achieving fusion of deep learning with combinatorial algorithms promises transformative changes to artificial intelligence. One possible approach is to introduce combinatorial building blocks into neural networks. Such end-to-end architectures have the potential to tackle combinatorial problems on raw input data such as ensuring global consistency in multi-object tracking or route planning on maps in robotics. In this work, we present a method that implements an efficient backward pass through blackbox implementations of combinatorial solvers with linear objective functions. We provide both theoretical and experimental backing. In particular, we incorporate the Gurobi MIP solver, Blossom V algorithm, and Dijkstra's algorithm into architectures that extract suitable features from raw inputs for the traveling salesman problem, the min-cost perfect matching problem and the shortest path problem.

Machine learning techniques are currently used extensively for automating various cybersecurity tasks. Most of these techniques utilize supervised learning algorithms that rely on training the algorithm to classify incoming data into different categories, using data encountered in the relevant domain. A critical vulnerability of these algorithms is that they are susceptible to adversarial attacks where a malicious entity called an adversary deliberately alters the training data to misguide the learning algorithm into making classification errors. Adversarial attacks could render the learning algorithm unsuitable to use and leave critical systems vulnerable to cybersecurity attacks. Our paper provides a detailed survey of the state-of-the-art techniques that are used to make a machine learning algorithm robust against adversarial attacks using the computational framework of game theory. We also discuss open problems and challenges and possible directions for further research that would make deep machine learning-based systems more robust and reliable for cybersecurity tasks.

New South Wales in Australia has rolled out high-definition cameras to catch people using their cellphones while driving, according to media reports. The technology is intended to target illegal use of cellphones through fixed and mobile trailer-mounted cameras, New South Wales Minister for Roads Andrew Constance was quoted as saying by the CNN. The cameras use artificial intelligence to scan images and zero in on the offenders. The identified images will be verified by authorised personnel, and the images will be securely stored and managed, authorities said. As many as 45 portable cameras will be set up across the Australian state at unknown locations and without warning signs in the next three years, CNN affiliate Sky News Australia reported.

Artificial intelligence is intended to mimic the behavior of humans. However, AI technology can be applied in reverse – it can be used to change human behavior. There are a number of reasons AI can be great for social engineering, especially in the field of marketing. Julita Vassileva, a Professor at the University of Saskatchewan in Canada wrote a great paper on AI for Human Learning and Behavior. This paper was published in Frontiers in Artificial Intelligence Journal.

The fast-changing, uncertain and ambiguous environments that organisations operate in today, require organisations to re-think all their internal business processes and customer touchpoints. Besides, due to the availability of Emerging Information Technologies such as big data analytics, blockchain and artificial intelligence, it has become easier for startups to compete with existing organisations. Often these startups are more flexible and agile than Fortune 1000 companies, and they can become a significant threat if not paid attention to. Therefore, focusing purely on the day-to-day operation is simply not enough and organisations have to become innovative and adaptive to change if they wish to remain competitive. When big data, blockchain and AI are combined, it will change collaboration among individuals, organisations and things, moving from pure human-to-human collaboration to increasingly human-to-machine collaboration and machine-to-machine collaboration. It will turn every organisation into a data organisation.