As being widely used to measure human intelligence, Raven's Progressive Matrices (RPM) tests also pose a great challenge for AI systems. There is a long line of computational models for solving RPM, starting from 1960s, either to understand the involved cognitive processes or solely for problem-solving purposes. Due to the dramatic paradigm shifts in AI researches, especially the advent of deep learning models in the last decade, the computational studies on RPM have also changed a lot. Therefore, now is a good time to look back at this long line of research. As the title -- ``a comprehensive introduction'' -- indicates, this paper provides an all-in-one presentation of computational models for solving RPM, including the history of RPM, intelligence testing theories behind RPM, item design and automatic item generation of RPM-like tasks, a conceptual chronicle of computational models for solving RPM, which reveals the philosophy behind the technology evolution of these models, and suggestions for transferring human intelligence testing and AI testing.

According to Merriam-Webster, artificial intelligence is, "A branch of computer science dealing with the simulation of intelligent behavior in computers." Alternatively, the Encyclopedia Britannica deems AI to be, "The ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings." But these are very broad interpretations of a complex subject that are grossly insufficient to describe it. Alan Turing, one of the pioneers of the modern electronic computer, stated that in order for a machine to be considered capable of intelligent thought, it must convincingly imitate life. To this end he devised an elegant proof whereby a human interrogator blindly questions both a machine and a person to determine if their comparative responses betray the imitator.

Lyric-to-melody generation is an important task in songwriting, and is also quite challenging due to its unique characteristics: the generated melodies should not only follow good musical patterns, but also align with features in lyrics such as rhythms and structures. These characteristics cannot be well handled by neural generation models that learn lyric-to-melody mapping in an end-to-end way, due to several issues: (1) lack of aligned lyric-melody training data to sufficiently learn lyric-melody feature alignment; (2) lack of controllability in generation to better and explicitly align the lyric-melody features. In this paper, we propose Re-creation of Creations (ROC), a new paradigm for lyric-to-melody generation. ROC generates melodies according to given lyrics and also conditions on user-designated chord progression. It addresses the above issues through a generation-retrieval pipeline. Specifically, our paradigm has two stages: (1) creation stage, where a huge amount of music fragments generated by a neural melody language model are indexed in a database through several key features (e.g., chords, tonality, rhythm, and structural information); (2) re-creation stage, where melodies are re-created by retrieving music fragments from the database according to the key features from lyrics and concatenating best music fragments based on composition guidelines and melody language model scores. ROC has several advantages: (1) It only needs unpaired melody data to train melody language model, instead of paired lyric-melody data in previous models. (2) It achieves good lyric-melody feature alignment in lyric-to-melody generation. Tested by English and Chinese lyrics, ROC outperforms previous neural based lyric-to-melody generation models on both objective and subjective metrics.

As the buzz around ChatGPT continues, communication academics and experts say the AI-generated technology has uses for marketing and content creation but is far from replacing human creativity. This assessment was also found by researchers in Korea, who studied AI in fashion and textile design. The study compared human and AI-generated designs. And while they were similar, humans have an edge, the report found, while concluding that AI can help designers in the creative process. And it could open the door for nonprofessionals to create their own fashion designs. They discovered that AI has "a wide range of applications in fashion, from increasing efficiency of processes and reducing waste to improving the industry's overall functioning," authors of the report said in a statement, adding that while creative processes are not often automated, AI can help in the creative process of design.

Before becoming a devoted entrepreneur, I was a full-time actor appearing on TV and in film. From my experience, the marks of excellent performance, cinematography and entertainment were the ability to be absolutely convincing and creative. Creativity is the ability to find new solutions to problems or challenges. Creative people are innovative and able to see things differently from others, which helps them come up with new ideas or solutions. Creative thinking typically involves making connections between things that might not appear related at first glance.

Human intelligence and intuition are vital to training artificial intelligence (AI) and machine learning (ML) models to provide enterprises with hybrid cybersecurity at scale. Combining human intelligence and intuition with AI and ML models helps catch the nuances of attack patterns that elude numerical analysis alone. Experienced threat hunters, security analysts and data scientists help ensure that the data used to train AI and ML models enables a model to accurately identify threats and reduce false positives. Combining human expertise and AI and ML models with a real-time stream of telemetry data from enterprises' many systems and apps defines the future of hybrid cybersecurity. "Based on behaviors and insights, AI and ML allow us to predict [that] something will happen before it does," says Monique Shivanandan, CISO at HSBC, a global bank.

Check out all the on-demand sessions from the Intelligent Security Summit here. Human intelligence and intuition are vital to training artificial intelligence (AI) and machine learning (ML) models to provide enterprises with hybrid cybersecurity at scale. Combining human intelligence and intuition with AI and ML models helps catch the nuances of attack patterns that elude numerical analysis alone. Experienced threat hunters, security analysts and data scientists help ensure that the data used to train AI and ML models enables a model to accurately identify threats and reduce false positives. Combining human expertise and AI and ML models with a real-time stream of telemetry data from enterprises' many systems and apps defines the future of hybrid cybersecurity.

Why for Homo sapiens to become Homo deus, it must stop to be Homo stupidus? All my conscious life I have been studying intelligence, as "the power/ability/capacity to know and learn, understand and infer, decide and effectively interact with the world, at all its forms and levels". The latent variables describes unobservable aspects of reality, physical, mental, social or digital. They could relate to abstract entities, like real-world/ontological categories and classes, mentality or machine intelligence, or data structures. In human intelligence, the underlying causes of the observed variables is the g factor (general intelligence, general mental/cognitive ability or general intelligence factor), computed by the IQ.

This month, the internet was flooded with stunningly ethereal digital art portraits, thanks to the work of the latest artificial intelligence-assisted application to go viral: Lensa. Users uploaded their photographs to the app and then – for a small fee – it used AI to transform their profile pictures into, say, a magical elfin warrior princess version of themselves, in no time at all. This year has seen a breakthrough for AI-driven image generators, which are now better than ever in quality, speed and affordability. The AI models are "trained" on millions of pieces of image and text data scraped from publicly available content online, and as in the case of Microsoft-backed DALL-E, can turn short text prompts such as "Ronald McDonald performing open heart surgery" into unique images. Anyone can now produce professional-looking images tailored to their desires, without having any training in art or design themselves.

Imagine being able to generate endless ideas, simulate real-world behavior, and make smart decisions with the help of a computer program. AI is not only changing the way we design products, but it has the potential to change the world through the creation of transformative products. Embrace its power and let it help you design the future. Artificial intelligence (AI) is a type of computer technology that can simulate human intelligence, such as learning, problem-solving, and decision-making. This revolutionary technology is increasingly being used in product design to help companies create new and innovative products.