The Palmerston North-based Health Hub Project in New Zealand is aiming to reduce health inequities and increase access to care with the help of artificial intelligence, machine learning and blockchain. Project co-founder David Hill is a GP at the Health Hub Project in Palmerston North, which runs four general practices with around 9000 patients. Hill says clinically trained people are a diminishing resource in healthcare and the system cannot rely on that to ensure its sustainability in the future, therefore technology needs to be used to "balance that inequity of supply and demand". "The whole point of what we are doing is trying to make sure that we use IT in a way that allows or permits greater equity of access to patients and starts to reduce the reliance on the ever-dwindling resource of healthcare workers," he says. "Also, to advance the value proposition that we give to patients."

Variational Bayes (VB) is a recent approximate method for Bayesian inference. It has the merit of being a fast and scalable alternative to Markov Chain Monte Carlo (MCMC) but its approximation error is often unknown. In this paper, we derive the approximation error of VB in terms of mean, mode, variance, predictive density and KL divergence for the linear Gaussian multi-equation regression. Our results indicate that VB approximates the posterior mean perfectly. Factors affecting the magnitude of underestimation in posterior variance and mode are revealed. Importantly, We demonstrate that VB estimates predictive densities accurately.

Non-linear source separation is a challenging open problem with many applications. We extend a recently proposed Adversarial Non-linear ICA (ANICA) model, and introduce Cramer-Wold ICA (CW-ICA). In contrast to ANICA we use a simple, closed--form optimization target instead of a discriminator--based independence measure. Our results show that CW-ICA achieves comparable results to ANICA, while foregoing the need for adversarial training.

Collective intelligence is manifested when multiple agents coherently work in observation, interaction, decision-making and action. In this paper, we define and quantify the intelligence level of heterogeneous agents group with the improved Anytime Universal Intelligence Test(AUIT), based on an extension of the existing evaluation of homogeneous agents group. The relationship of intelligence level with agents composition, group size, spatial complexity and testing time is analyzed. The intelligence level of heterogeneous agents groups is compared with the homogeneous ones to analyze the effects of heterogeneity on collective intelligence. Our work will help to understand the essence of collective intelligence more deeply and reveal the effect of various key factors on group intelligence level.

In multi-agent path finding (MAPF), it is usually assumed that planning is performed centrally and that the destinations of the agents are common knowledge. We will drop both assumptions and analyze under which conditions it can be guaranteed that the agents reach their respective destinations using implicitly coordinated plans without communication. Furthermore, we will analyze what the computational costs associated with such a coordination regime are. As it turns out, guarantees can be given assuming that the agents are of a certain type. However, the implied computational costs are quite severe. In the distributed setting, we either have to solve a sequence of NP-complete problems or have to tolerate exponentially longer executions. In the setting with destination uncertainty, bounded plan existence becomes PSPACE-complete. This clearly demonstrates the value of communicating about plans before execution starts.

Dynamic Controllability (DC) of a Simple Temporal Problem with Uncertainty (STPU) uses a dynamic decision strategy, rather than a fixed schedule, to tackle temporal uncertainty. We extend this concept to the Controllable Conditional Temporal Problem with Uncertainty (CCTPU), which extends the STPU by conditioning temporal constraints on the assignment of controllable discrete variables. We define dynamic controllability of a CCTPU as the existence of a strategy that decides on both the values of discrete choice variables and the scheduling of controllable time points dynamically. This contrasts with previous work, which made a static assignment of choice variables and dynamic decisions over time points only. We propose an algorithm to find such a fully dynamic strategy. The algorithm computes the "envelope" of outcomes of temporal uncertainty in which a particular assignment of discrete variables is feasible, and aggregates these over all choices. When an aggregated envelope covers all uncertain situations of the CCTPU, the problem is dynamically controllable. However, the algorithm is complete only under certain assumptions. Experiments on an existing set of CCTPU benchmarks show that there are cases in which making both discrete and temporal decisions dynamically it is feasible to satisfy the problem constraints while assigning the discrete variables statically it is not.

Artificial intelligence (AI) is one of the most impactful technological revolutions the world has witnessed. Customers today are increasingly exposed to advanced technologies such as AI-enabled chatbots and intelligent voice assistants like Apple Siri, Google Assistant, and Amazon Alexa, making personalization a high priority for incumbent banks. Today, AI enables financial institutions to solve many critical problems, thereby saving money and increasing the efficiency of the workforce. By deploying AI-based solutions, banks can improve the outcome in various dimensions such as customer service, risk management, cross-sales, etc. A MEDICI research study of 34 major banks across several geographies (US, EU, Singapore, Africa, Australia, and India) found that 27 out of these 34 banks have implemented AI in their front-office functions in the form of a chatbot, virtual assistant, and digital advisor.

Hong Kong (CNN)Boeing Australia on Wednesday announced plans to make a jet drone with artificial intelligence that can act as a "loyal wingman" for manned jet fighters.

A new initiative by the US Army suggests "another significant step towards lethal autonomous weapons," warns a leading artificial-intelligence researcher who has called for a ban on so-called "killer robots." The Army Contracting Command has called on potential vendors in industry and academia to submit ideas to help build its Advanced Targeting and Lethality Automated System (ATLAS), which a Defense Department solicitation says will use artificial intelligence and machine learning to give ground-combat vehicles autonomous targeting capabilities. This will allow weapons to "acquire, identify, and engage targets at least 3X faster than the current manual process," according to the notice. Stuart Russell, a professor of computer science at UC Berkeley and a highly regarded AI expert, tells Quartz he is deeply concerned about the idea of tanks and other land-based fighting vehicles eventually having the capability to fire on their own. "It looks very much as if we are heading into an arms race where the current ban on full lethal autonomy"--a section of US military law that mandates some level of human interaction when actually making the decision to fire--"will be dropped as soon as it's politically convenient to do so," says Russell. The Defense Department contracting officer overseeing the solicitation did not immediately respond to a request for further details on ATLAS.

We present a predictive analysis model for 2019 men's Cricket World Cup. We believe this predictive analysis strategy would be very useful for viewers, sponsors, and team strategists. This would also give insights to various cricket analysts and commentators about the features that play a crucial role in the statistical analysis. This model is developed based on the historical data collected for the 10 participating teams (Afghanistan, Australia, Bangladesh, England, India, New Zealand, Pakistan, South Africa, Sri Lanka, and West Indies). In addition, we test our model on 2015 world cup data and measure the accuracy of predictions.