How can artificial agents learn to solve many diverse tasks in complex visual environments without any supervision? We decompose this question into two challenges: discovering new goals and learning to reliably achieve them.

Model-based reinforcement learning (MBRL) has been a primary approach to ameliorating the sample efficiency issue as well as to make a generalist agent. However, there has not been much effort toward enhancing the strategy of dreaming itself. Therefore, it is a question whether and how an agent can "dream better" in a more structured and strategic way. In this paper, inspired by the observation from cognitive science suggesting that humans use a spatial divide-and-conquer strategy in planning, we propose a new MBRL agent, called Dr. Strategy, which is equipped with a novel Dreaming Strategy. The proposed agent realizes a version of divide-and-conquer-like strategy in dreaming. This is achieved by learning a set of latent landmarks and then utilizing these to learn a landmark-conditioned highway policy. With the highway policy, the agent can first learn in the dream to move to a landmark, and from there it tackles the exploration and achievement task in a more focused way. In experiments, we show that the proposed model outperforms prior pixel-based MBRL methods in various visually complex and partially observable navigation tasks.

The provision of robotic assistance during motor training has proven to be effective in enhancing motor learning in some healthy trainee groups as well as patients. Personalizing such robotic assistance can help further improve motor (re)learning outcomes and cater better to the trainee's needs and desires. However, the development of personalized haptic assistance is hindered by the lack of understanding of the link between the trainee's personality and the effects of haptic guidance during human-robot interaction. To address this gap, we ran an experiment with 42 healthy participants who trained with a robotic device to control a virtual pendulum to hit incoming targets either with or without haptic guidance. We found that certain personal traits affected how users adapt and interact with the guidance during training. In particular, those participants with an 'Achiever gaming style' performed better and applied lower interaction forces to the robotic device than the average participant as the training progressed. Conversely, participants with the 'Free spirit game style' increased the interaction force in the course of training. We also found an interaction between some personal characteristics and haptic guidance. Specifically, participants with a higher 'Transformation of challenge' trait exhibited poorer performance during training while receiving haptic guidance compared to an average participant receiving haptic guidance. Furthermore, individuals with an external Locus of Control tended to increase their interaction force with the device, deviating from the pattern observed in an average participant under the same guidance. These findings suggest that individual characteristics may play a crucial role in the effectiveness of haptic guidance training strategies.

The aim of this study is clustering students according to their gamification user types and learning styles with the purpose of providing instructors with a new perspective of grouping students in case of clustering which cannot be done by hand when there are multiple scales in data. The data used consists of 251 students who were enrolled at a Turkish state university. When grouping students, K-means algorithm has been utilized as clustering algorithm. As for determining the gamification user types and learning styles of students, Gamification User Type Hexad Scale and Grasha-Riechmann Student Learning Style Scale have been used respectively. Silhouette coefficient is utilized as clustering quality measure. After fitting the algorithm in several ways, highest Silhouette coefficient obtained was 0.12 meaning that results are neutral but not satisfactory. All the statistical operations and data visualizations were made using Python programming language.

Long thought of as a tool to increase efficiency and save costs, AI has now proven to be an innovation driver that enables business growth. According to our latest Accenture research on AI among 1,200 global companies, "AI Achievers"--those companies that are the most AI-mature--enjoy 50% greater revenue growth, clearly outpacing their competitors. Why, then, are most organizations (63%) stuck in the experimentation phase with AI? Because AI initiatives are often led with timidity and are hampered by "pilot-itis," a fixation on pilot projects at the expense of scale. Speaking to a select group of CEOs as part of the research revealed an opportunity--and the need--for companies' most senior leaders to increase their AI expertise.

AI maturity comes down to mastering critical capabilities in the right combinations--not only in data and AI but also in organizational strategy, talent, and culture. The AI transformation is occurring much faster than the digital transformation, because early successes have increased faith in AI as a value driver. There is a significant incentive to move rapidly. According to new research from Accenture, 'The art of AI Maturity', 63% of 1,200 companies were identified as "Experimenters," or companies stuck in the experimentation phase of their AI lives. They risk losing money since they haven't fully tapped into the technology's potential to innovate and revolutionize their industry. The companies with the highest advanced AI are already using this money.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Here's what businesses can learn from the small group of organizations that already use artificial (AI) to their competitive advantage. If the world's largest companies were people, most would be in their teenage years when it comes to using Artificial Intelligence (AI). According to new research from Accenture on AI maturity, 63% of 1,200 companies were identified as "Experimenters," or companies that are stuck in the experimentation phase of their AI lives.

The need for financial institutions to quickly operationalize their artificial intelligence capabilities has moved beyond important to imperative. More than supporting risk and fraud analysis, and increased productivity, a higher level of AI maturity at banks and credit unions will be a competitive differentiator, increasing business value across the organization. The banking industry must move out of the formative stages of AI deployment to help enhance human intelligence. This includes uncovering the drivers of key performance measures such as revenue and profit, and propelling innovation in products, services, processes and customer service. All financial institutions have the ability within reach to harness insights at scale – leveraging the right information, from the right people, at the right time.

The need for financial institutions to quickly operationalize their artificial intelligence capabilities has moved beyond important to imperative. More than supporting risk and fraud analysis, and increased productivity, a higher level of AI maturity at banks and credit unions will be a competitive differentiator, increasing business value across the organization. The banking industry must move out of the formative stages of AI deployment to help enhance human intelligence. This includes uncovering the drivers of key performance measures such as revenue and profit, and propelling innovation in products, services, processes and customer service. All financial institutions have the ability within reach to harness insights at scale – leveraging the right information, from the right people, at the right time.