Recently IT Brief had the opportunity to get in touch with Adrian Jones, Automation Anywhere APJ EVP, to discuss AI and its impact on the modern workforce. Can you tell me a bit more about Automation Anywhere? Automation Anywhere is a global leader in Robotic Process Automation (RPA) software and AI technology for enterprises looking to deploy intelligent digital workforces. Our technology uses software bots that work alongside the human workforce to take on repetitive, mundane work, allowing people to do more meaningful work. Beyond automating tasks, Automation Anywhere's platform also helps improve them on the back-end by enhancing efficiency, minimising error and reducing operational costs, while helping enterprises manage and scale business processes faster.

New research suggests dolphins are even smarter than first thought and can coordinate their behaviour with one another with'extreme precision'. In a new experiment, bottlenose dolphins had to press an underwater button at the same time as their partner. Scientists found they could synchronise their actions almost perfectly. The marine mammals were working so closely they pressed the button within an average of 370 milliseconds of their partner doing the same. Pictured is a triple synchronous dive by a trio of male bottlenose dolphins.

A World Food Programme convoy carries humanitarian aid to Aleppo, Syria. Getting food into conflict zones is a major hurdle -- and a topic of discussion at the WFP's Innovation Accelerator. A World Food Programme convoy carries humanitarian aid to Aleppo, Syria. Getting food into conflict zones is a major hurdle -- and a topic of discussion at the WFP's Innovation Accelerator. Let's figure out how to end hunger forever.

We introduce a dynamic artificial neural network-based (ANN) adaptive inference process, which learns temporal predictive models of dynamical systems. We term the process REPRISE, a REtrospective and PRospective Inference SchEme. REPRISE infers the unobservable contextual state that best explains its recently encountered sensorimotor experiences as well as accompanying, context-dependent temporal predictive models retrospectively. Meanwhile, it executes prospective inference, optimizing upcoming motor activities in a goal-directed manner. In a first implementation, a recurrent neural network (RNN) is trained to learn a temporal forward model, which predicts the sensorimotor contingencies of different simulated dynamic vehicles. The RNN is augmented with contextual neurons, which enable the compact encoding of distinct, but related sensorimotor dynamics. We show that REPRISE is able to concurrently learn to separate and approximate the encountered sensorimotor dynamics. Moreover, we show that REPRISE can exploit the learned model to induce goal-directed, model-predictive control, that is, approximate active inference: Given a goal state, the system imagines a motor command sequence optimizing it with the prospective objective to minimize the distance to a given goal. Meanwhile, the system evaluates the encountered sensorimotor contingencies retrospectively, adapting its neural hidden states for maintaining model coherence. The RNN activities thus continuously imagine the upcoming future and reflect on the recent past, optimizing both, hidden state and motor activities. In conclusion, the combination of temporal predictive structures with modulatory, generative encodings offers a way to develop compact event codes, which selectively activate particular types of sensorimotor event-specific dynamics.

Abstract--The ability to estimate joint, conditional and marginal probability distributions over some set of variables is of great utility for many common machine learning tasks. However, estimating these distributions can be challenging, particularly in the case of data containing a mix of discrete and continuous variables. This paper presents a nonparametric method for estimating these distributions directly from a dataset. The data are first represented as a graph consisting of object nodes and attribute value nodes. Depending on the distribution to be estimated, an appropriate eigenvector equation is then constructed. This equation is then solved to find the corresponding stationary distribution of the graph, from which the required distributions can then be estimated and sampled from. The paper demonstrates how the method can be applied to many common machine learning tasks including classification, regression, missing value imputation, outlier detection, random vector generation, and clustering. Being able to estimate joint, conditional and marginal probabilities from some dataset allows a broad range of useful tasks to be performed. For example, classification and regression involve predicting the value of some target variable conditional on the values of the other variables. If we can sample values from the estimated distributions, we could perform random vector generation by generating full random vectors that display the same correlations as the vectors (i.e., data points) in the original data [4], [5]. If we can estimate the joint distribution for the full dataset, then we should also be able to do this for subsets of data, leading to the use of Expectation-Maximization [6] to cluster the data [7]. Taken together, these activities form a large chunk of the tasks commonly used in machine learning. All of this depends, of course, on being able to estimate the various probabilities, and this is particularly challenging on datasets containing a complex mix of continuous and discrete variables.

A Paralympic athlete has been accused of exaggerating symptoms, a BBC investigation has found. Amanda Reid (formerly Fowler) won a silver medal in cycling for Australia at the Rio Games in 2016. Her former coach and other athletes who spoke to the BBC's File on 4 said they were highly suspicious about the changes in her condition. Reid and her mother did not respond to detailed BBC questions about the allegations. The Australian Paralympic Committee strongly denied any knowledge of misconduct relating to classification.

A new study finds that freshmen from 19 colleges in eight countries report symptoms consistent with a diagnosable psychological disorder. "While effective care is important, the number of students who need treatment for these disorders far exceeds the resources of most counseling centers, resulting in a substantial unmet need for mental health treatment among college students," said lead author Randy P. Auerbach, Ph.D., of Columbia University. "Considering that students are a key population for determining the economic success of a country, colleges must take a greater urgency in addressing this issue." For the study, Auerbach and his research team analyzed data from the World Health Organization's World Mental Health International College Student Initiative.

Electric drones booked through smartphones pick people up from office rooftops, shortening travel time by hours, reducing the need for parking and clearing smog from the air. This vision of the future is driving the government's "flying car" project. Major carrier All Nippon Airways, electronics company NEC Corp. and more than a dozen other companies and academic experts hope to have a road map for the plan ready by the year's end. "This is such a totally new sector Japan has a good chance for not falling behind," said Fumiaki Ebihara, the government official in charge of the project. For now, nobody believes people are going to be zipping around in flying cars any time soon.

ARTIFICIAL INTELLIGENCE (AI) is crucial in transforming businesses from followers to leaders. It promises to improve business operations and bring better customer experiences. There's just one catch – it's data hungry. For a majority of businesses in the Asia Pacific, this becomes the largest hurdle in adopting AI. More than half of them are struggling to gather and integrate big data into their operations.

Say you wake up tomorrow morning and there's a robo-truck just sitting in your driveway. Today, no one really has a self-driving truck yet--though plenty are working on it. Even the US Army is in on the act. Their advances--and testing operations in states like Nevada, California, Florida, Arizona, and Georgia--are impressive, but not there yet. Still, the tech should arrive one day, which is why that thought experiment is helpful.