Toward Super-Creativity

What is super creativity? From the simple creation of a meal to the most sophisticated artificial intelligence system, the human brain is capable of responding to the most diverse challenges and problems in increasingly creative and innovative ways. This book is an attempt to define super creativity by examining creativity in humans, machines, and human-machine interactions. Organized into three sections, the volume covers such topics as increasing personal creativity, the impact of artificial intelligence and digital devices, and the interaction of humans and machines in fields such as healthcare and economics

Abrupt and spontaneous strategy switches emerge in simple regularised neural networks

Humans sometimes have an insight that leads to a sudden and drastic performance improvement on the task they are working on. Sudden strategy adaptations are often linked to insights, considered to be a unique aspect of human cognition tied to complex processes such as creativity or meta-cognitive reasoning. Here, we take a learning perspective and ask whether insight-like behaviour can occur in simple artificial neural networks, even when the models only learn to form input-output associations through gradual gradient descent. We compared learning dynamics in humans and regularised neural networks in a perceptual decision task that included a hidden regularity to solve the task more efficiently. Our results show that only some humans discover this regularity, whose behaviour was marked by a sudden and abrupt strategy switch that reflects an aha-moment. Notably, we find that simple neural networks with a gradual learning rule and a constant learning rate closely mimicked behavioural characteristics of human insight-like switches, exhibiting delay of insight, suddenness and selective occurrence in only some networks. Analyses of network architectures and learning dynamics revealed that insight-like behaviour crucially depended on a regularised gating mechanism and noise added to gradient updates, which allowed the networks to accumulate "silent knowledge" that is initially suppressed by regularised (attentional) gating. This suggests that insight-like behaviour can arise naturally from gradual learning in simple neural networks, where it reflects the combined influences of noise, gating and regularisation.Comment: 17 pages, 5 figure

Intentions and Creative Insights: a Reinforcement Learning Study of Creative Exploration in Problem-Solving

Insight is perhaps the cognitive phenomenon most closely associated with creativity. People engaged in problem-solving sometimes experience a sudden transformation: they see the problem in a radically different manner, and simultaneously feel with great certainty that they have found the right solution. The change of problem representation is called "restructuring", and the affective changes associated with sudden progress are called the "Aha!" experience. Together, restructuring and the "Aha!" experience characterize insight. Reinforcement Learning is both a theory of biological learning and a subfield of machine learning. In its psychological and neuroscientific guise, it is used to model habit formation, and, increasingly, executive function. In its artificial intelligence guise, it is currently the favored paradigm for modeling agents interacting with an environment. Reinforcement learning, I argue, can serve as a model of insight: its foundation in learning coincides with the role of experience in insight problem-solving; its use of an explicit "value" provides the basis for the "Aha!" experience; and finally, in a hierarchical form, it can achieve a sudden change of representation resembling restructuring. An experiment helps confirm some parallels between reinforcement learning and insight. It shows how transfer from prior tasks results in considerably accelerated learning, and how the value function increase resembles the sense of progress corresponding to the "Aha!"-moment. However, a model of insight on the basis of hierarchical reinforcement learning did not display the expected "insightful" behavior. A second model of insight is presented, in which temporal abstraction is based on self-prediction: by predicting its own future decisions, an agent adjusts its course of action on the basis of unexpected events. This kind of temporal abstraction, I argue, corresponds to what we call "intentions", and offers a promising model for biological insight. It explains the "Aha!" experience as resulting from a temporal difference error, whereas restructuring results from an adjustment of the agent's internal state on the basis of either new information or a stochastic interpretation of stimuli. The model is called the actor-critic-intention (ACI) architecture. Finally, the relationship between intentions, insight, and creativity is extensively discussed in light of these models: other works in the philosophical and scientific literature are related to, and sometimes illuminated by the ACI architecture

Computational creativity: an interdisciplinary approach to sequential learning and creative generations

Creativity seems mysterious; when we experience a creative spark, it is difficult to explain how we got that idea, and we often recall notions like ``inspiration" and ``intuition" when we try to explain the phenomenon. The fact that we are clueless about how a creative idea manifests itself does not necessarily imply that a scientific explanation cannot exist. We are unaware of how we perform certain tasks, such as biking or language understanding, but we have more and more computational techniques that can replicate and hopefully explain such activities. We should understand that every creative act is a fruit of experience, society, and culture. Nothing comes from nothing. Novel ideas are never utterly new; they stem from representations that are already in mind. Creativity involves establishing new relations between pieces of information we had already: then, the greater the knowledge, the greater the possibility of finding uncommon connections, and the more the potential to be creative. In this vein, a beneficial approach to a better understanding of creativity must include computational or mechanistic accounts of such inner procedures and the formation of the knowledge that enables such connections. That is the aim of Computational Creativity: to develop computational systems for emulating and studying creativity. Hence, this dissertation focuses on these two related research areas: discussing computational mechanisms to generate creative artifacts and describing some implicit cognitive processes that can form the basis for creative thoughts

Advances of Italian Machine Design

This 2028 Special Issue presents recent developments and achievements in the field of Mechanism and Machine Science coming from the Italian community with international collaborations and ranging from theoretical contributions to experimental and practical applications. It contains selected contributions that were accepted for presentation at the Second International Conference of IFToMM Italy, IFIT2018, that has been held in Cassino on 29 and 30 November 2018. This IFIT conference is the second event of a series that was established in 2016 by IFToMM Italy in Vicenza. IFIT was established to bring together researchers, industry professionals and students, from the Italian and the international community in an intimate, collegial and stimulating environment

Sleep’s role in the reprocessing and restructuring of memory

Sleep disconnects us from our external environment and puts us in a vulnerable state, yet it is surprisingly universal. This thesis looks at the cognitive functions of sleep; specifically, the role of sleep in reprocessing and restructuring memory. It is now well-known that sleep actively consolidates memories, and even restructures them. This is likely achieved through the reactivation of memory representations. Previous research has shown that such reactivations can be triggered with a method called targeted memory reactivation (TMR). In Chapter 2, I used TMR during rapid eye movement (REM) and slow-wave sleep (SWS) to investigate the effect of cueing in these stages on electrophysiology and subsequent task behaviour in a two-handed serial reaction time task. TMR during SWS led to detectable memory reactivation, and significant behavioural improvements in the non-dominant but not the dominant hand. TMR during REM did not affect behaviour, although electrophysiological results indicated that cues were processed during this stage. Chapter 3 examined the effects of REM and SWS TMR on an associative memory task. We did not find any effect of SWS TMR. On the other hand, REM TMR improved remote associations between items which were not learned together but whose relationship could be inferred, indicating a role for REM sleep in memory restructuring. This was supported by a difference in event-related potentials in response to memory-related and control cues. However, two replications of the REM group showed that these results were not reliable. Chapter 4, finally, looked at the effects of wakefulness and sleep on two creative tasks. The more word-based task indeed benefitted from an interval containing sleep, but the more conceptual task showed improvements relating to wakefulness and time of day

Steps to an Ecology of Networked Knowledge and Innovation: Enabling new forms of collaboration among sciences, engineering, arts, and design

SEAD network White Papers ReportThe final White Papers (posted at http://seadnetwork.wordpress.com/white-paper- abstracts/final-white-papers/) represent a spectrum of interests in advocating for transdisciplinarity among arts, sciences, and technologies. All authors submitted plans of action and identified stakeholders they perceived as instrumental in carrying out such plans. The individual efforts led to an international scope. One of the important characteristics of this collection is that the papers do not represent a collective aim toward an explicit initiative. Rather, they offer a broad array of views on barriers faced and prospective solutions. In summary, the collected White Papers and associated Meta- analyses began as an effort to take the pulse of the SEAD community as broadly as possible. The ideas they generated provide a fruitful basis for gauging trends and challenges in facilitating the growth of the network and implementing future SEAD initiatives.National Science Foundation Grant No.1142510. Additional funding was provided by the ATEC program at the University of Texas at Dallas and the Institute for Applied Creativity at Texas A&M University

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 261)

This bibliography lists 281 reports, articles and other documents introduced into the NASA scientific and technical information system in July 1984

Legal Education: A New Growth Vision: Part II—The Groundwork: Building a Customer Satisfying Innovation Ecosystem

Financial sustainability awaits agile, future-focused legal education programs that deliver students with market-valued, cost-effective, and omnichannel knowledge and skills development solutions. Shifting from an atom-based, traditional law school mindset to a platform-based, human-artificial intelligence (AI) integrated education system requires vision, planning, and drive. Bold and determined leaders will invent the future of legal education. To do this, they will (1) edit the law school’s DNA to focus on delivering customer satisfactions, (2) build vibrant multidisciplinary ecosystems focused on cultivating modern education services, (3) embrace emerging digital technologies, and (4) seize new marketplace opportunities to diversify revenue streams—thereby enhancing program solvency and relevance. I. Introduction: Satisfied Customers Key to Sustainable Growth II. Assessing the Law School Landscape III. Getting Back to the Basics ... A. Customer-Focused Program Reinvention ... 1. What Is Your Business? ... 2. Who Are Your Customers? … 3. What Do Your Customers Want? ... 4. What Is Value and How Do You Add Value? ... B. Physical and Digital Convergence of Education ... C. Friction Audits and Resolving “Pain Points” ... 1. Friction Audit: Students ... 2. Friction Audit: Employers, Practitioners, and Community Professionals ... D. Modernizing Legal Education to Deliver Customer Satisfactions IV. Building an Innovation Ecosystem ... A. Ecosystems: An Explainer ... B. Theories of Innovation ... 1. Recombinant (Combinatorial) Innovation ... 2. Disruptive Innovation ... 3. Value Innovation ... 4. Open Innovation ... 5. Breakthrough/Revolutionary versus Incremental/Evolutionary Innovations ... C. Innovation in the Digital Age ... 1. Bits, Atoms, and Moore’s Law ... 2. Information Over Instinct ... 3. Agile and Lean Startup Methodologies ... 4. Basic Tools: Prototypes and Minimum Viable Products (MVPs) ... D. Resistance to Innovation ... E. Innovation Triumvirate: Visionary, Thinker-planner, and Driver V. Conclusion