Deployed machine learning models are confronted with the problem of changing data over time, a phenomenon also called concept drift. While existing approaches of concept drift detection already show convincing results, they require true labels as a prerequisite for successful drift detection. Especially in many real-world application scenarios--like the ones covered in this work--true labels are scarce, and their acquisition is expensive. Therefore, we introduce a new algorithm for drift detection, Uncertainty Drift Detection (UDD), which is able to detect drifts without access to true labels. Our approach is based on the uncertainty estimates provided by a deep neural network in combination with Monte Carlo Dropout. Structural changes over time are detected by applying the ADWIN technique on the uncertainty estimates, and detected drifts trigger a retraining of the prediction model. In contrast to input data-based drift detection, our approach considers the effects of the current input data on the properties of the prediction model rather than detecting change on the input data only (which can lead to unnecessary retrainings). We show that UDD outperforms other state-of-the-art strategies on two synthetic as well as ten real-world data sets for both regression and classification tasks.

Relational databases are the de facto standard for storing and querying structured data, and extracting insights from structured data requires advanced analytics. Deep neural networks (DNNs) have achieved super-human prediction performance in particular data types, e.g., images. However, existing DNNs may not produce meaningful results when applied to structured data. The reason is that there are correlations and dependencies across combinations of attribute values in a table, and these do not follow simple additive patterns that can be easily mimicked by a DNN. The number of possible such cross features is combinatorial, making them computationally prohibitive to model. Furthermore, the deployment of learning models in real-world applications has also highlighted the need for interpretability, especially for high-stakes applications, which remains another issue of concern to DNNs. In this paper, we present ARM-Net, an adaptive relation modeling network tailored for structured data, and a lightweight framework ARMOR based on ARM-Net for relational data analytics. The key idea is to model feature interactions with cross features selectively and dynamically, by first transforming the input features into exponential space, and then determining the interaction order and interaction weights adaptively for each cross feature. We propose a novel sparse attention mechanism to dynamically generate the interaction weights given the input tuple, so that we can explicitly model cross features of arbitrary orders with noisy features filtered selectively. Then during model inference, ARM-Net can specify the cross features being used for each prediction for higher accuracy and better interpretability. Our extensive experiments on real-world datasets demonstrate that ARM-Net consistently outperforms existing models and provides more interpretable predictions for data-driven decision making.

Researchers have demonstrated how to keep a network of nanowires in a state that's right on what's known as the edge of chaos – an achievement that could be used to produce artificial intelligence (AI) that acts much like the human brain does. The team used varying levels of electricity on a nanowire simulation, finding a balance when the electric signal was too low when the signal was too high. If the signal was too low, the network's outputs weren't complex enough to be useful; if the signal was too high, the outputs were a mess and also useless. "We found that if you push the signal too slowly the network just does the same thing over and over without learning and developing. If we pushed it too hard and fast, the network becomes erratic and unpredictable," says physicist Joel Hochstetter from the University of Sydney and the study's lead author.

Great problem solvers are made, not born. That's what we've found after decades of problem solving with leaders across business, nonprofit, and policy sectors. These leaders learn to adopt a particularly open and curious mindset, and adhere to a systematic process for cracking even the most inscrutable problems. And when conditions of uncertainty are at their peak, they're at their brilliant best. Six mutually reinforcing approaches underly their success: (1) being ever-curious about every element of a problem; (2) being imperfectionists, with a high tolerance for ambiguity; (3) having a "dragonfly eye" view of the world, to see through multiple lenses; (4) pursuing occurrent behavior and experimenting relentlessly; (5) tapping into the collective intelligence, acknowledging that the smartest people are not in the room; and (6) practicing "show and tell" because storytelling begets action (exhibit). Here's how they do it. As any parent knows, four-year-olds are unceasing askers.

Machine learning is a subset of artificial intelligence. The concept has evolved from computational learning and pattern recognition in artificial intelligence. It explores the construction and study of algorithms and carries out forecasts on data. Machine Learning Market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors.

The University of South Australia will lead a world-first study, using artificial intelligence, to map the risks of the most fatal reproductive cancer in women worldwide so it can be detected and treated earlier. Internationally-renowned nutritional epidemiologist Professor Elina Hypponen and a team from UniSA's Australian Centre for Precision Health have been awarded $1.2 million by the Federal Government to map the genetic and physical risks of ovarian cancer, based on the health records of 273,000 women from the UK Biobank database. A machine learning model, which automatically analyses the data to identify patterns of risk, is expected to accurately predict which women will develop ovarian cancer in the next 15 years. Ovarian cancer is usually diagnosed very late due to vague symptoms and few known causes, with a five-year survival rate of less than 30 per cent for women with late-stage cancer. Genes, diet and lifestyle come into play and the researchers say a computational approach will narrow down those most at risk.

The latest study released on the Global Artificial Intelligence in Accounting Market by AMA Research evaluates market size, trend, and forecast to 2026. The Artificial Intelligence in Accounting market study covers significant research data and proofs to be a handy resource document for managers, analysts, industry experts and other key people to have ready-to-access and self-analyzed study to help understand market trends, growth drivers, opportunities and upcoming challenges and about the competitors. Definition and Brief Information about Artificial Intelligence in Accounting: Rising application of AI in artificial intelligence will help to boost global AI in the accounting market. Artificial intelligence is being used by many accounting companies where it analyzes a large volume of data at high speed which would not be easy for humans. For example, Robo-advisor Wealthfront tracks account activity using AI capabilities to analyze and understand how account holders spend, invest, and make financial decisions, so they can customize the advice they give their customers.

People who think about the long-term existential risks of artificial intelligence sometimes discuss the notion of an "AI box." To prevent a superintelligent computer from starting a nuclear war or otherwise wreaking havoc, its minders would seal it off from direct interaction with the outside world by keeping it offline. The only output would be communication with its operators. But, people worry, it might still escape, not through hacking but through "social engineering"--manipulating someone into setting it free. Such a scenario dramatically played out in the 2014 sci-fi thriller Ex Machina, in which a wily imprisoned robot seduces a hapless human into helping it break out.

Intelligence necessitates memory. Without memory, humans fail to perform various nontrivial tasks such as reading novels, playing games or solving maths. As the ultimate goal of machine learning is to derive intelligent systems that learn and act automatically just like human, memory construction for machine is inevitable. Artificial neural networks model neurons and synapses in the brain by interconnecting computational units via weights, which is a typical class of machine learning algorithms that resembles memory structure. Their descendants with more complicated modeling techniques (a.k.a deep learning) have been successfully applied to many practical problems and demonstrated the importance of memory in the learning process of machinery systems. Recent progresses on modeling memory in deep learning have revolved around external memory constructions, which are highly inspired by computational Turing models and biological neuronal systems. Attention mechanisms are derived to support acquisition and retention operations on the external memory. Despite the lack of theoretical foundations, these approaches have shown promises to help machinery systems reach a higher level of intelligence. The aim of this thesis is to advance the understanding on memory and attention in deep learning. Its contributions include: (i) presenting a collection of taxonomies for memory, (ii) constructing new memory-augmented neural networks (MANNs) that support multiple control and memory units, (iii) introducing variability via memory in sequential generative models, (iv) searching for optimal writing operations to maximise the memorisation capacity in slot-based memory networks, and (v) simulating the Universal Turing Machine via Neural Stored-program Memory-a new kind of external memory for neural networks.

Continual learning has been a major problem in the deep learning community, where the main challenge is how to effectively learn a series of newly arriving tasks without forgetting the knowledge of previous tasks. Initiated by Learning without Forgetting (LwF), many of the existing works report that knowledge distillation is effective to preserve the previous knowledge, and hence they commonly use a soft label for the old task, namely a knowledge distillation (KD) loss, together with a class label for the new task, namely a cross entropy (CE) loss, to form a composite loss for a single neural network. However, this approach suffers from learning the knowledge by a CE loss as a KD loss often more strongly influences the objective function when they are in a competitive situation within a single network. This could be a critical problem particularly in a class incremental scenario, where the knowledge across tasks as well as within the new task, both of which can only be acquired by a CE loss, is essentially learned due to the existence of a unified classifier. In this paper, we propose a novel continual learning method, called Split-and-Bridge, which can successfully address the above problem by partially splitting a neural network into two partitions for training the new task separated from the old task and re-connecting them for learning the knowledge across tasks. In our thorough experimental analysis, our Split-and-Bridge method outperforms the state-of-the-art competitors in KD-based continual learning.