Object-oriented representations in reinforcement learning have shown promise in transfer learning, with previous research introducing a propositional objectoriented framework that has provably efficient learning bounds with respect to sample complexity. However, this framework has limitations in terms of the classes of tasks it can efficiently learn. In this paper we introduce a novel deictic objectoriented framework that has provably efficient learning bounds and can solve a broader range of tasks. Additionally, we show that this framework is capable of zero-shot transfer of transition dynamics across tasks and demonstrate this empirically for the Taxi and Sokoban domains.

Multi-agent reinforcement learning has recently shown great promise as an approach to networked system control. Arguably, one of the most difficult and important tasks for which large scale networked system control is applicable is common-pool resource management. Crucial common-pool resources include arable land, fresh water, wetlands, wildlife, fish stock, forests and the atmosphere, of which proper management is related to some of society's greatest challenges such as food security, inequality and climate change. Here we take inspiration from a recent research program investigating the game-theoretic incentives of humans in social dilemma situations such as the well-known tragedy of the commons. However, instead of focusing on biologically evolved human-like agents, our concern is rather to better understand the learning and operating behaviour of engineered networked systems comprising general-purpose reinforcement learning agents, subject only to nonbiological constraints such as memory, computation and communication bandwidth. Harnessing tools from empirical game-theoretic analysis, we analyse the differences in resulting solution concepts that stem from employing different information structures in the design of networked multi-agent systems. These information structures pertain to the type of information shared between agents as well as the employed communication protocol and network topology. Our analysis contributes new insights into the consequences associated with certain design choices and provides an additional dimension of comparison between systems beyond efficiency, robustness, scalability and mean control performance.

User background We only use a single criterion for filtering users: Users must be native English speakers. We think this is required for our study (and any study that uses English) since the training, instructions, and ImageNet labels are in English. We used this filter to avoid the cases where users make arbitrary decisions without understanding some words. Prolific shows that our users are diverse, aging from 18-77 (median=31) and coming from a diverse set of countries (US, UK, Poland, India, Korea, Canada, Australia, South Africa, etc.). Please see Prolific for more description of their online userbase, which, according to a study, is more reliable than AMT Turkers [59]. Payment Our rate is higher than the Prolific recommended rate wage of $9.60/hr. In fact, during the study, we had increased our rate to attract more participants (up to $13.68/hr). As participants come from various countries in the world, we did not consider minimum wage per region because this recommended rate is suggested by Prolific and accepted by all participants.

A modification is described to the use of mean field approxima(cid:173) tions in the E step of EM algorithms for analysing data from latent structure models, as described by Ghahramani (1995), among oth(cid:173) ers. The modification involves second-order Taylor approximations to expectations computed in the E step. The potential benefits of the method are illustrated using very simple latent profile models.

One of the areas where technology law is likely to see development in South Africa is the regulation of data privacy. The Protection of Personal Information Act (PoPIA) protects personal information and regulates the processing of personal data. However, with the rise of big data and the increasing use of technology in various industries, the legal framework surrounding data privacy will likely evolve in the coming years. This may include changes to PoPIA itself, as well as new legislation and case law that addresses emerging issues in data protection. Another area where tech law will likely see development is regulating artificial intelligence (AI) and machine learning.

At our tech firm, we specialize in empowering businesses with the most advanced solutions on the market. We also provide invaluable insights on data analytics, application development, systems integration, process management, and penetration testing to ensure that our clients remain ahead of the curve in their respective fields. Trust us to optimize your operations and drive your success to new heights with our unparalleled expertise and innovative approach.

OUTsurance has been propelling the South African insurance industry forward for the last 25 years. As leaders in the field, we're always looking for innovative ways to create digitally-advanced solutions, without losing sight of our human values. Our continued success can be attributed to OUTstanding employees who set the bar high with their energy and expertise. If you're keen to grow your career in a vibrant environment with lots of friendly'gees', this could be the career opportunity you've been looking for. Do you live and breathe all things data related?

Our engineering team is split into organisations which we call Fleets. Each Fleet focuses on a core customer journey (onboarding, security, payments, support, new business, growth, and marketing, etc.). Each of these fleets contains multiple smaller teams called Pods, each of which focuses on a specific aspect of the product. Pods will include a product owner, product designer, back-end engineers, Android, iOS, and Web developers, who each bring a unique perspective to the problem you are all contributing towards. Luno offers a "Remote but Reachable" working approach.

Standard Bank Group is a leading Africa-focused financial services group, and an innovative player on the global stage, that offers a variety of career-enhancing opportunities – plus the chance to work alongside some of the sector's most talented, motivated professionals. Our clients range from individuals, to businesses of all sizes, high net worth families and large multinational corporates and institutions. Bringing true, meaningful value to our clients and the communities we serve and creating a real sense of purpose for you. To work with business stakeholders to identify and deliver on new AI initiatives. To apply deep domain expertise to shape/influence the AI-thinking in the organisation through thought leadership; enabling the successful adoption and acceleration of AI and ML across Standard Bank Group (SBG), ensuring the needs of stakeholders are correctly understood and addressed.

South Africa's Emoyamed Hospital has experienced a stunning 180% revenue growth within just three months of adopting cutting-edge AI and ChatGPT technology. The hospital's new Board of Directors and Management team have also leveraged the power of the 3-I's model – Integrity, Innovation, and Impact – to transform patient care and outcomes. Emoyamed Hospital in Bloemfontein, South Africa, has pivoted from old systems and embraced AI and ChatGPT to build better patient care and financial systems. These new technology innovations have allowed the hospital to serve a larger patient population and expand rapidly. In just two months, the hospital was authorized by the Free State Department to open 57% more beds, a remarkable feat that speaks to the effectiveness of the new AI decision-making systems for patient and system-management protocols. According to Professor Terrence Kommal, the Executive Chairman of Emoyamed, the new board and management team are "rooted in servant leadership and have a deep empathy for humanity."