The world of artificial intelligence is changing, adapting, and developing new technologies every day. Technology advancements that were once thought to be the stuff of science fiction are now commonplace; this sort of rapid technology development will only continue to grow as teams of scientists, technicians, and others working in the artificial intelligence fieldwork on new and exciting projects. The wearable AI market is predicted to rise from USD 35 billion in 2018 to around USD 180 billion by 2025, according to a 2019 Global Market Insights, Inc. report. One of the more exciting and daunting possibilities in the relatively near future is the idea that wearable artificial intelligence may someday change our lives–and our intelligence–forever. Let's take a closer look at the possibilities behind wearable AI and how it will change the world as we know it.

Artificially intelligent systems are slowly taking over tasks previously done by humans, and many processes involving repetitive, simple movements have already been fully automated. In the meantime, humans continue to be superior when it comes to abstract and creative tasks. However, it seems like even when it comes to creativity, we're now being challenged by our own creations. In the last few years, we've seen the emergence of hundreds of "AI artists." These complex algorithms are creating unique (and sometimes eerie) works of art.

We are living in interesting times, where digital assistants schedule meetings, chatbots work alongside humans as teaching assistants, and your suitcase can now become self driving luggage as showcased at CES, 2018. The implications are just starting to be felt in the workplace. In 2017, I wrote about how The Employee Experience is the Future of Work. Now, as we enter 2018, the next journey for HR leaders will be to leverage artificial intelligence combined with human intelligence and create a more personalized employee experience. As we increase our personal usage of chatbots (defined as software which provides an automated, yet personalized, conversation between itself and human users), employees will soon interact with them in the workplace as well.

Alan Turing is considered the father of artificial intelligence, and rightfully so. Marrying mathematical study with computer science, Turing was the first to contend that computers could think like humans, and he pioneered the concept of machines that could perform tasks on par with human experts – a bedrock concept of modern AI computer science to this day. Given the intense interest in AI of recent years, Turing is more famous now than he was at the time of his death, 15 days shy of his 42nd birthday in 1954. I'm constantly amazed by Turing's prescience in laying the theoretical groundwork for what he called thinking computers, those that exhibit intelligent behavior equal to or indistinguishable from that of a human. However, Turing's work occurred more than 65 years ago, and -- give the guy a break -- while several of his predictions are uncannily on the mark, he wasn't able to foresee all the advances that are shaping life in 2019.

Artificial intelligence is spreading through our lives. As it moves from new feeds to productivity tools, the boundary between what is human-made and machine-made is becoming almost invisible, resulting in vast shifts in how we perceive and interact with the world. AI has surprised us with how it can move, see and hear, but also, with what it can create. Once thought of as a solely human trait, creativity is now a debated topic. Or is a thought process constrained by what us humans define it as? In 2016, Google DeepMind's AlphaGo system trounced the world's reigning champion, Lee Sedol at Go – an ancient Chinese game – by using its creative intelligence to devise a winning strategy.

SEOUL, South Korea – North Korea has labeled Joe Biden a "fool of low IQ" and an "imbecile bereft of elementary quality as a human being" after the Democratic presidential hopeful during a recent speech called North Korean leader Kim Jong Un a tyrant. Pyongyang's official Korean Central News Agency on Wednesday accused Biden of insulting the country's supreme leadership and committing an "intolerable and serious politically-motivated provocation" against the North. Biden during a campaign launch in Philadelphia on Saturday accused President Donald Trump of cozying up to "dictators and tyrants" like Kim and Russian President Vladimir Putin.

Abstract: This paper demonstrates emergence of computational creativity in the field of music. Different aspects of creativity such as producer, process, product and press are studied and formulated. Different notions of computational creativity such as novelty, quality and typicality of compositions as products are studied and evaluated. We formulate an algorithmic perception on human creativity and propose a prototype that is capable of demonstrating human-level creativity. We then validate the proposed prototype by applying various creativity benchmarks with the results obtained and compare the proposed prototype with the other existing computational creative systems. I. INTRODUCTION Computational creativity is the modeling or replicating human creativity computationally. Traditionally computational creativity has focused more on creative systems' products or processes, though this focus has widened recently. Research on creativity offers four Ps of creativity (Rhodes, 1961; MacKinnon, 1970; Jordanous, 2016). These four P's are: 1. Person/Producer: a creative agent 2. Process: an activity done by the creative agent 3. Product: the product of the creative process 4. Press/Environment: the overall environment of creativity 110 The proposed methodology addresses all the four P's of creativity unlike most of recent works, which focus on these individually (Saunders, 2012; Gervas & Leon, 2014; Misztal & Indurkhya, 2014; Sosa & Gero, 2015; Besold & Plaza, 2015; Harmon, 2015). Figure 1 gives a simplified view of proposed computational creative system in the context of four P's of creativity.

The game of Go played between a DeepMind computer program and a human champion created an existential crisis of sorts for Marcus du Sautoy, a mathematician and professor at Oxford University. "I've always compared doing mathematics to playing the game of Go," he says, and Go is not supposed to be a game that a computer can easily play because it requires intuition and creativity. So when du Sautoy saw DeepMind's AlphaGo beat Lee Sedol, he thought that there had been a sea change in artificial intelligence that would impact other creative realms. He set out to investigate the role that AI can play in helping us understand creativity, and ended up writing The Creativity Code: Art and Innovation in the Age of AI (Harvard University Press). The Verge spoke to du Sautoy about different types of creativity, AI helping humans become more creative (instead of replacing them), and the creative fields where artificial intelligence struggles most.

Video understanding is emerging as a new paradigm for studying human-like AI. Question-and-Answering (Q&A) is used as a general benchmark to measure the level of intelligence for video understanding. While several previous studies have suggested datasets for video Q&A tasks, they did not really incorporate story-level understanding, resulting in highly-biased and lack of variance in degree of question difficulty. In this paper, we propose a hierarchical method for building Q&A datasets, i.e. hierarchical difficulty levels. We introduce three criteria for video story understanding, i.e. memory capacity, logical complexity, and DIKW (Data-Information-Knowledge-Wisdom) pyramid. We discuss how three-dimensional map constructed from these criteria can be used as a metric for evaluating the levels of intelligence relating to video story understanding.

Machine learning has been applied to a number of creative, design-oriented tasks. However, it remains unclear how to best empower human users with these machine learning approaches, particularly those users without technical expertise. In this paper we propose a general framework for turn-based interaction between human users and AI agents designed to support human creativity, called {co-creative systems}. The framework can be used to better understand the space of possible designs of co-creative systems and reveal future research directions. We demonstrate how to apply this framework in conjunction with a pair of recent human subject studies, comparing between the four human-AI systems employed in these studies and generating hypotheses towards future studies.