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Experts say that AI will never match human creativity. Artificial intelligence is more likely to help artists make new kinds of arts. AI lacks creativity, but AI human creativity, together can create wonders.

Artificial intelligence (AI) promises to transform health care as we know it, and experts are only beginning to grasp its full potential. Possible applications of AI in the field of medicine include algorithms that streamline approaches to medical research, and reinforced learning techniques that more accurately predict disease progression and improve patient care, to name a few. This forward-looking event highlights the important contributions that Canadian researchers are making in the field of AI and health. Université Laval professor and AI expert Audrey Durand will share her own research into the intersection of machine learning and medicine, which one day may help to improve treatment decisions for cancer patients. Participants will learn about the key challenges and opportunities involved in scaling AI solutions for medicine and shifting how health care is delivered in the future.

Apart from the potential for harmful misuse of a powerful Natural Language Processing tool like GPT 3, we also recognize the risk of stumping the growth of human creativity by making a creative process like writing much easier with the help of AI. How easy is too easy? And we recognize the small but potentially dangerous risk of loss of human jobs. We understand the risks that come with the incredibly powerful technology we use, but we also understand its limitations. At our core, we truly believe in human potential, and human potential for growth, creativity and ingenuity.

This paper presents an interdisciplinary project which aims at cross-fertilizing dance with artificial intelligence. It utilizes AI as an approach to explore and unveil new territories of possible dance movements. Statistical analyzes of recorded human dance movements provide the foundation for a system that learns poses from human dancers, extends them with novel variations and creates new movement sequences. The system provides dancers with a tool for exploring possible movements and finding inspiration from motion sequences generated automatically in real time in the form of an improvising avatar. Experiences of bringing the avatar to the studio as a virtual dance partner indicate the usefulness of the software as a tool for kinetic exploration. In addition to these artistic results, the experiments also raise questions about how AI generally relates to artistic agency and creativity. Is the improvising avatar truly creative, or is it merely some kind of extension of the dancer or the AI researcher? By analyzing the developed platform as a framework for exploration of conceptual movement spaces, and by considering the interaction between dancer, researcher and software, some possible interpretations of this particular kind of creative process can be offered.

People feared that one day, machines would overtake humans and seize control of everything in the past. However, irrespective of the fear, there has been ground-breaking research in Artificial General Intelligence (AGI), which makes artificial intelligence more human-like. The human brain comprises large sets of elements, and it self-organises the dynamical structures to respond with our bodies. Therefore, it has been a natural way to work on AGI to make it adaptive self-organisation. Another way that is used to build AGI models is computational neuroscience.

Technology plays a significant role in helping firms unleash creative innovation. Artificial intelligence is a perfect example. But AI can also help humans be more creative and therefore represents a great opportunity for enterprises to scale more creativity throughout their organization. In 2014, I explored the potential new creative business capabilities that developers can build by leveraging AI technology. Since then, AI has matured considerably and its adoption has increased tremendously.

Every human will interact with artificial intelligence (AI) in the visible future, directly or indirectly, some for creating products and services, some for research, some for government, some for education, and many for consumption. It is globally acknowledged that the vast majority of people are not sufficiently aware of AI technologies and their potential impacts, and are therefore unprepared for facing the emerging AI wave. This raises critical and urgent questions: Can AI affect human intelligence (HI) adversely? What are the immediate and long-term risks created by AI for HI? How can we develop human performance-supportive AI management policies? What is the role of AI education in preparing humans for interactions with AI? These are open research questions of immense importance and urgency. The critical difference between past scientific industrial revolutions and the current AI spearheaded transformation is that past revolutions replaced human muscle power, while AI has the potential to replace HI in many areas.Even as the information age augmented human information storage and processing capabilities, it created a massive need for higher human intelligence capabilities such as logic and problem-solving. Current artificial intelligences (‘AIs’, i.e., specific AI applications such as computer vision, adaptive systems, and natural language processing) can solve complex logical problems and perform better than human intelligences in many defined scenarios. A McKinsey ...

Figural analogy problems have long been a widely used format in human intelligence tests. In the past four decades, more and more research has investigated automatic item generation for figural analogy problems, i.e., algorithmic approaches for systematically and automatically creating such problems. In cognitive science and psychometrics, this research can deepen our understandings of human analogical ability and psychometric properties of figural analogies. With the recent development of data-driven AI models for reasoning about figural analogies, the territory of automatic item generation of figural analogies has further expanded. This expansion brings new challenges as well as opportunities, which demand reflection on previous item generation research and planning future studies. This paper reviews the important works of automatic item generation of figural analogies for both human intelligence tests and data-driven AI models. From an interdisciplinary perspective, the principles and technical details of these works are analyzed and compared, and desiderata for future research are suggested.

Many AI techniques are being used in organizations, and AI will continue to get sophisticated in the third wave of AI. In the Fourth Industrial Age, human creativity, AI at scale, and Emotion AI will be dominant forces that will shape the digital organizations of the future. What is the Third Wave of AI? In the first wave, artificial intelligence (AI) systems followed clear rules and were directed at individual applications of the algorithms to cover every eventuality. The second wave was the introduction of deep learning and reinforcement learning systems that mapped inputs to outputs to solve a certain type of problem.