Coelho Mollo: Yeah exactly, like you know what we do is the benchmark for intelligence. If you know other animals and the AI systems don't, uh, you know, meet our capacities then they are not intelligent. And and if you think even about, you know the kinds of capacities that until recently lots of AI was interested in it was those kinds of things that we humans tend to think are markers of intelligence. Playing go, playing chess, you know, proving mathematical theorems and stuff like that, right? While all the things that we tend to think are, you know, not intelligence like just moving around and you know, uh, being able to look here, picking up things or turning open opening doors and so on so forth that we take not being intelligent.

Reminiscence therapy is mental health care based on the recollection of memories. However, the effectiveness of this method varies amongst individuals. To solve this problem, it is necessary to provide more personalized support; therefore, this study utilized a computational model of personal memory recollection based on a cognitive architecture adaptive control of thought-rational (ACT-R). An ACT-R memory model reflecting the state of users is expected to facilitate personal recollection. In this study, we proposed a method for estimating the internal states of users through repeated interactions with the memory model. The model, which contains the lifelog of the user, presents a memory item (stimulus) to the user, and receives the response of the user to the stimulus, based on which it adjusts the internal parameters of the model. Through the repetition of these processes, the parameters of the model will reflect the internal states of the user. To confirm the feasibility of the proposed method, we analyzed utterances of users when using a system that incorporates this model. The results confirmed the ability of the method to estimate the memory retrieval parameters of the model from the utterances of the user. In addition, the ability of the method to estimate changes in the mood of the user caused by using the system was confirmed. These results support the feasibility of the interactive method for estimating human internal states, which will eventually contribute to the ability to induce memory recall and emotions for our well-being.

Modulate, a leader in the fight against online toxicity, announced the completion of a $30 million Series A funding round led by Lakestar with participation from existing investors Everblue Management, Hyperplane Ventures, and others. In addition, Mika Salmi, Managing Partner of Lakestar, will join Modulate's Board of Directors. The company will use the funds to expand its team and continue scaling its groundbreaking proactive voice moderation platform, ToxMod. "ToxMod is changing the way game developers attack toxic behavior in their communities and this funding is a real validation of our mission to make online communities safer," said Mike Pappas, CEO of Modulate. "We're thrilled to welcome Mika and his vast store of experience to the Board as we grow our team and ramp up the development and deployment of ToxMod."

Cognitive Design for Artificial Minds explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition. It bridges the gap between the theoretical, experimental, and technological issues addressed in the context of AI of cognitive inspiration and computational cognitive science. Beginning with an overview of the historical, methodological, and technical issues in the field of cognitively inspired artificial intelligence, Lieto illustrates how the cognitive design approach has an important role to play in the development of intelligent AI technologies and plausible computational models of cognition. Introducing a unique perspective that draws upon Cybernetics and early AI principles, Lieto emphasizes the need for an equivalence between cognitive processes and implemented AI procedures, in order to realize biologically and cognitively inspired artificial minds. He also introduces the Minimal Cognitive Grid, a pragmatic method to rank the different degrees of biological and cognitive accuracy of artificial systems in order to project and predict their explanatory power with respect to the natural systems taken as a source of inspiration. Providing a comprehensive overview of cognitive design principles in constructing artificial minds, this text will be essential reading for students and researchers of artificial intelligence and cognitive science.

We present the COGnitive Neural GENerative system (CogNGen), a cognitive architecture that combines two neurobiologically-plausible, computational models: predictive processing and hyperdimensional/vector-symbolic models. We draw inspiration from architectures such as ACT-R and Spaun/Nengo. CogNGen is in broad agreement with these, providing a level of detail between ACT-R's high-level symbolic description of human cognition and Spaun's low-level neurobiological description, furthermore creating the groundwork for designing agents that learn continually from diverse tasks and model human performance at larger scales than what is possible with current systems. We test CogNGen on four maze-learning tasks, including those that test memory and planning, and find that CogNGen matches performance of deep reinforcement learning models and exceeds on a task designed to test memory.

Progress in artificial intelligence has enabled the creation of AIs that perform tasks previously thought only possible for humans, such as translating languages, driving cars, playing board games at world-champion level, and extracting the structure of proteins. However, each of these AIs has been designed and exhaustively trained for a single task and has the ability to learn only what's needed for that specific task. Recent AIs that produce fluent text, including in conversation with humans, and generate impressive and unique art can give the false impression of a mind at work. But even these are specialized systems that carry out narrowly defined tasks and require massive amounts of training. It still remains a daunting challenge to combine multiple AIs into one that can learn and perform many different tasks, much less pursue the full breadth of tasks performed by humans or leverage the range of experiences available to humans that reduce the amount of data otherwise required to learn how to perform these tasks. The best current AIs in this respect, such as AlphaZero and Gato, can handle a variety of tasks that fit a single mold, like game-playing.

Dubber Corporation Limited (Dubber) announced that it has signed a Foundation Partner agreement with Nuwave Communications, Inc. (NUWAVE). NUWAVE, based in Las Vegas, Nevada, is one of the fastest growing providers of Microsoft voice services in North America and a key player in the Microsoft Operator Connect calling program. Dubber Unified Conversational Recording (UCR) and voice data services are now integrated into iPILOTTM and available to all NUWAVE clients from August 1. Microsoft Teams has more than 270 million monthly active users, making it the world's fastest growing and most popular business communication suite. NUWAVE is a global communications and cloud platform as a service provider with a focus on simplification, automation, and innovation.

Long-term autonomy of robotic systems implicitly requires dependable platforms that are able to naturally handle hardware and software faults, problems in behaviors, or lack of knowledge. Model-based dependable platforms additionally require the application of rigorous methodologies during the system development, including the use of correct-by-construction techniques to implement robot behaviors. As the level of autonomy in robots increases, so do the cost of offering guarantees about the dependability of the system. Certifiable dependability of autonomous robots, we argue, can benefit from formal models of the integration of several cognitive functions, knowledge processing, reasoning, and meta-reasoning. Here we put forward the case for a generative model of cognitive architectures for autonomous robotic agents that subscribes to the principles of model-based engineering and certifiable dependability, autonomic computing, and knowledge-enabled robotics.

"AI" may be a hot buzzword – and a global market expected to grow to nearly $310 billion by 2026 – but what exactly does artificial intelligence mean? The definition of AI can be a willowy one to pin down because its application is so broad and ranging in degree of complexity, scope, algorithmic underpinnings and methodologies used. For these reasons, there is an increased call for a more advanced, specific definition of AI beyond "the simulation of human intelligence processes by machines." Some consider the AI next step – and ultimate evolution – to be cognitive computing. "It's such a vast amorphic term that AI has come to mean right now," said Stephen DeAngelis, founder and CEO of Enterra Solutions.

Artificial Intelligence and Cognitive Computing are often used as interchangeable terms, and as much as both refer to machines with human-like capabilities, there are some big and important differences. AI is talked about abundantly, and equally abundant are the definitions with which we want to describe this discipline. In synthetic terms, it could be said that with AI we try to make computers able to do things that the human mind can do. Some of these, such as more or less sophisticated forms of reasoning, are normally considered as belonging to the field of intelligence, while others, for example computer vision, are not in the strict sense of the word. In each case there is the involvement of psychological skills, such as perception, association, prediction, planning, motion control, which allow humans and animals to achieve their goals, whatever they are. Among the many possible definitions of Cognitive Computing, one is particularly emblematic, if only for its generality that brings little in the way of information content: the use of computer models to simulate human mental processes in complex situations where responses may be ambiguous or uncertain.