The cognitiveAI Platform is a cloud-based AI software platform for deriving new levels of Enterprise value from multiple data sources. The AI platform is designed to be cost-effective, easily deployed and used to simplify complexity while unlocking the power of Semantic Computing.

Vice President and Chief Evangelist of SAP, Shailendra Kumar joined us on the show to talk about Industry 4.0. We had a conversation about how prepared the world is for Industry 4.0 and how businesses of the future will leverage AI and automation. Shailendra or Shaily is the Vice President and Chief Evangelist of SAP and is on the Advisory Board of Aegis School of Business, Data Science, Cyber Security, and Telecommunication. A keynote speaker across Asia and Oceania around Emerging Technologies which are also showcased through #TheShailyShow. With over a quarter of a century's experience in the field of Emerging Technologies like Artificial Intelligence, Machine Learning, Advanced Analytics and Data Science, Shailendra has built upon extensive knowledge of data-driven analytics strategies for revenue growth, cost reduction, marketing and customer behaviour management to drive business outcomes.

Varjo, the Finnish startup that has developed a virtual and mixed reality headset capable of "human-eye resolution" for use in various enterprise applications, has closed a $51.7 million in Series C funding. Existing investors including Lifeline Ventures, Atomico, EQT Ventures and Volvo Cars Tech Fund have also followed on. It brings total raised by Varjo to around $100 million to date. The company is also announcing the appointment of Timo Toikkanen, who was previously president and COO of Varjo, as its new CEO. Co-founder and previous CEO, Niko Eiden, becomes CXO where he'll be tasked with continuing to drive the company's technology innovations and, notably, remains a board member.

Come join us on September 8th, 2020 for an AI-mazing virtual summit on Artificial Intelligence each hour of the day! ​We kick off in Australia and Asia then move on to Europe and the Americas. What will you gain and experience? ​ 50 keynotes, panels,...

In this paper, we introduce a non-linear Lanchester model of NCW-type and investigate an optimization problem for this model, where only the Red force is supplied by several supply agents. Optimal fire allocation of the Blue force is sought in the form of a piece-wise constant function of time. A threatening rate is computed for the Red force and each of its supply agents at the beginning of each stage of the combat. These rates can be used to derive the optimal decision for the Blue force to focus its firepower to the Red force itself or one of its supply agents. This optimal fire allocation is derived and proved by considering an optimization problem of number of Blue force troops. Numerical experiments are included to demonstrate the theoretical results.

In the context of multi-agent systems, the rational verification problem is concerned with checking which temporal logic properties will hold in a system when its constituent agents are assumed to behave rationally and strategically in pursuit of individual objectives. Typically, those objectives are expressed as temporal logic formulae which the relevant agent desires to see satisfied. Unfortunately, rational verification is computationally complex, and requires specialised techniques in order to obtain practically useable implementations. In this paper, we present such a technique. This technique relies on a reduction of the rational verification problem to the solution of a collection of parity games. Our approach has been implemented in the Equilibrium Verification Environment (EVE) system. The EVE system takes as input a model of a concurrent/multi-agent system represented using the Simple Reactive Modules Language (SRML), where agent goals are represented as Linear Temporal Logic (LTL) formulae, together with a claim about the equilibrium behaviour of the system, also expressed as an LTL formula. EVE can then check whether the LTL claim holds on some (or every) computation of the system that could arise through agents choosing Nash equilibrium strategies; it can also check whether a system has a Nash equilibrium, and synthesise individual strategies for players in the multi-player game. After presenting our basic framework, we describe our new technique and prove its correctness. We then describe our implementation in the EVE system, and present experimental results which show that EVE performs favourably in comparison to other existing tools that support rational verification.

Modeling user preferences (long-term history) and user dynamics (short-term history) is of greatest importance to build efficient sequential recommender systems. The challenge lies in the successful combination of the whole user's history and his recent actions (sequential dynamics) to provide personalized recommendations. Existing methods capture the sequential dynamics of a user using fixed-order Markov chains (usually first order chains) regardless of the user, which limits both the impact of the past of the user on the recommendation and the ability to adapt its length to the user profile. In this article, we propose to use frequent sequences to identify the most relevant part of the user history for the recommendation. The most salient items are then used in a unified metric model that embeds items based on user preferences and sequential dynamics. Extensive experiments demonstrate that our method outperforms state-of-the-art, especially on sparse datasets. We show that considering sequences of varying lengths improves the recommendations and we also emphasize that these sequences provide explanations on the recommendation.

For the third straight year, Deloitte surveyed executives about their companies' sentiments and practices regarding AI technologies. We were particularly interested in understanding what it will take to stay ahead of the pack as AI adoption grows--and we wanted to learn how adopters are managing risk around the technologies as AI governance, trust, and ethics become more of a boardroom issue. Get the Deloitte Insights app. Adopters continue to have confidence in AI technologies' ability to drive value and advantage. We see increasing levels of AI technology implementation and financial investment. Adopters say they are realizing competitive advantage and expect AI-powered transformation to happen for both their organization and industry. Early-mover advantage may fade soon. As adoption becomes ubiquitous, AI-powered organizations may have to work harder to maintain an edge over their industry peers.

The pandemic is a severe stress test for the business continuity plans of global corporations. The operators of call centres are playing an important role in meeting that challenge, and it has not been easy. In normal times, if an earthquake hits Bangalore, you can switch capacity to your call centre in Manila. But what do you do when all the call centres around the world that serve your customers are hit – all at the same time? The big outsourcing call centre companies which serve corporate giants have hundreds of thousands of employees, and many of these people are working from home now.

How long will it be until artificial intelligence surpasses that of our own? UQ graduate Matthew Dahlitz explores the issue in his new documentary, featuring scientists from the Queensland Brain Institute. While we may feel bombarded by doomsday predictions about creating robots with human-like intelligence, UQ graduate, neuropsychotherapist and filmmaker Matthew Dahlitz believes there's no need to panic. Dahlitz (Bachelor of Arts (Psychological Science) '94, Master of Counselling '14) has combined his knowledge of the human mind with his passion for the arts to release his first feature-length documentary with son Jachin, through their independent film production and media house, Perfekt Studios. Titled Toward Singularity, the documentary explores how brain science is being used to inform the development of super intelligent computers and features interviews with a number of scientists from UQ's Queensland Brain Institute (QBI).