Decision Point AI is a business unit of Veriluma Limited (ASX: VRI) one of Australia's leading Artificial Intelligence companies providing prescriptive analytics software solutions, serving Europe and the USA. The world is experiencing Data Overload and Insights are highly interpretive and time sensitive. Critical decisions cannot wait for manual analysis to create contextual meaning from insights, they need to be automated to deliver decision guidance and Augmented Outcomes for Companies, Executives and Shareholders. The platform can deal with what is known and can also consider what is not known. It extends the results of big data tools, descriptive and predictive analytics as well as business intelligence solutions to deliver assessments with actionable outcomes. Assessments highlight what has contributed to the likelihood, indicate potential risks, opportunities and conflicts.

From this academic year (2019-20), aspiring engineering candidates in Karnataka will have the opportunity to study the most in-demand courses - Artificial Intelligence (AI) and Machine Learning (ML). The Visvesvaraya Technological University (VTU) in its recent Executive Council meeting on May 30 resolved to introduce Bachelor of Engineering (BE) in Artificial Intelligence (AI) and Machine Learning with effect from the academic year 2019-20. The eligibility for admission to this course remains the same as BE and B.Tech programs in VTU. As of now, several colleges have modules in Machine Vision, Robot Programming and Artificial Intelligence in the third semester of the Instrumentation engineering course. But, offering it as degree course in itself is a first in VTU.

Lastly, any use of AI that might lead to a lethal result would require the greatest level of oversight of all DoD's AI systems. This top layer could include systems that are weaponized (even if the weapon itself is not initiated by AI) and systems that may have lethal outcomes (such as cyber tools that may result in lethal effects). One of the goals of a working group overseeing this top layer may be the creation of an oversight body that includes members from outside of the executive branch, including from the legislative branch and from nongovernmental organizations (such as civil liberties advocates and experts from academia). The working group could create policies to dictate how often programs are reviewed by this oversight body, which milestones trigger a review, and so on.

The insurance sector has fallen behind the curve of financial services innovation - and that's left hundreds of billions in potential cost savings on the table. The most valuable area in which insurers can innovate is the use of artificial intelligence (AI): It's estimated that AI can drive cost savings of $390 billion across insurers' front, middle, and back offices by 2030, according to a report by Autonomous NEXT seen by Business Insider Intelligence. The front office is the most lucrative area to target for AI-driven cost savings, with $168 billion up for grabs by 2030. In the AI in Insurance Report, Business Insider Intelligence will examine AI solutions across key areas of the front office - customer service, personalization, and claims management - to illustrate how the technology can significantly enhance the customer experience and cut costs along the value chain. We will look at companies that have accomplished these goals to illustrate what insurers should focus on when implementing AI, and offer recommendations on how to ensure successful AI adoption.

Slender marine structures such as deep-water marine risers are subjected to currents and will normally experience Vortex Induced Vibrations (VIV), which can cause fast accumulation of fatigue damage. The ocean current is often three-dimensional (3D), i.e., the direction and magnitude of the current vary throughout the water column. Today, semi-empirical tools are used by the industry to predict VIV induced fatigue on risers. The load model and hydrodynamic parameters in present VIV prediction tools are developed based on two-dimensional (2D) flow conditions, as it is challenging to consider the effect of 3D flow along the risers. Accordingly, the current profiles must be purposely made 2D during the design process, which leads to significant uncertainty in the prediction results. Further, due to the limitations in the laboratory, VIV model tests are mostly carried out under 2D flow conditions and thus little experimental data exist to document VIV response of riser subjected to varying directions of the current. However, a few experiments have been conducted with 3D current. We have used results from one of these experiments to investigate how well 1) traditional and 2) an alternative method based on a data driven prediction can describe VIV in 3D currents. Data driven modelling is particularly suited for complicated problems with many parameters and non-linear relationships. We have applied a data clustering algorithm to the experimental 3D flow data in order to identify measurable parameters that can influence responses. The riser responses are grouped based on their statistical characteristics, which relate to the direction of the flow. Furthermore we fit a random forest regression model to the measured VIV response and compare its performance with the predictions of existing VIV prediction tools (VIVANA-FD).

Combining the concepts of individual and group fairness, we search for discrimination by matching individuals in different protected groups to each other, and comparing their classifier outcomes. Specifically, we formulate a GAN-based approximation of the optimal transport mapping, and use it to translate the distribution of one protected group to that of another, returning pairs of in-distribution samples that statistically correspond to one another. We then define the flipset: the set of individuals whose classifier output changes post-translation, which intuitively corresponds to the set of people who were harmed because of their protected group membership. To shed light on why the model treats a given subgroup differently, we introduce the transparency report: a ranking of features that are most associated with the model's behavior on the flipset. We show that this provides a computationally inexpensive way to identify subgroups that are harmed by model discrimination, including in cases where the model satisfies population-level group fairness criteria.

Artificial neural network training with stochastic gradient descent can be destabilized by "bad batches" with high losses. This is often problematic for training with small batch sizes, high order loss functions or unstably high learning rates. To stabilize learning, we have developed adaptive learning rate clipping (ALRC) to limit backpropagated losses to a number of standard deviations above their running means. ALRC is designed to complement existing learning algorithms: Our algorithm is computationally inexpensive, can be applied to any loss function or batch size, is robust to hyperparameter choices and does not affect backpropagated gradient distributions. Experiments with CIFAR-10 supersampling show that ALCR decreases errors for unstable mean quartic error training while stable mean squared error training is unaffected. We also show that ALRC decreases unstable mean squared errors for partial scanning transmission electron micrograph completion. Our source code is publicly available at https://github.com/Jeffrey-Ede/ALRC

As the capabilities and pervasiveness of machine learning (ML) and artificial intelligence (AI) increasingly affect society, there is increasing concern about the safety of such systems, i.e. the potential of accidental harm from implementation errors and unintended consequences in ML algorithms. As a result, there has been increasing interest in the nascent field of AI safety [1, 2, 3, 4, 5, 6], which seeks to understand and solve the technical challenges in developing and deploying AI that does what it is intended to do. The purpose of this chapter is to explore how the study of AI safety intersects with that of evolutionary computation (EC), to both highlight an exciting and important set of safety problems within EC, and to suggest that evolution and EC have important insights that could benefit the general study of AI safety. To frame the problem of AI safety, we adopt the framework of Amodei et al. [1], which defines AI safety as concerned with accidents in ML systems, and defines five problems within three broad categories of issues: (1) specifying the wrong objective function, (2) making safe and efficient use of a true but expensive objective (e.g.

In the last five years, private companies, research institutions as well as public sector organisations have issued principles and guidelines for ethical AI, yet there is debate about both what constitutes "ethical AI" and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analyzed the current corpus of principles and guidelines on ethical AI. Our results reveal a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted; why they are deemed important; what issue, domain or actors they pertain to; and how they should be implemented. Our findings highlight the importance of integrating guideline-development efforts with substantive ethical analysis and adequate implementation strategies.

Deep learning based data-driven approaches have been successfully applied in various image understanding applications ranging from object recognition, semantic segmentation to visual question answering. However, the lack of knowledge integration as well as higher-level reasoning capabilities with the methods still pose a hindrance. In this work, we present a brief survey of a few representative reasoning mechanisms, knowledge integration methods and their corresponding image understanding Figure 1: The diagram shows the information hierarchy for applications developed by various groups images and the knowledge associated with each level of information. of researchers, approaching the problem from a variety of angles. Furthermore, we discuss upon key efforts on integrating external knowledge with neural paper is to present a survey of recent works (including a few networks. Taking cues from these efforts, we of our works) in image understanding where knowledge and conclude by discussing potential pathways to improve reasoning plays an important role.