We know machines and artificial intelligence (AI) can be many things, but can they ever really be creative? When I interviewed Professor Marcus du Sautoy, the author of The Creativity Code, he shared that the role of AI is a "kind of catalyst to push our human creativity." It's the machine and human collaboration that produces exciting results--novel approaches and combinations that likely wouldn't develop if either were working alone. Can Machines And Artificial Intelligence Be Creative? Instead of thinking about AI as replacing human creativity, it's beneficial to examine ways that AI can be used as a tool to augment human creativity.

Owing to small size, sensing capabilities and autonomous nature, the Unmanned Air Vehicles (UAVs) have enormous applications in various areas, e.g., remote sensing, navigation, archaeology, journalism, environmental science, and agriculture. However, the unmonitored deployment of UAVs called the amateur drones (AmDr) can lead to serious security threats and risk to human life and infrastructure. Therefore, timely detection of the AmDr is essential for the protection and security of sensitive organizations, human life and other vital infrastructure. AmDrs can be detected using different techniques based on sound, video, thermal, and radio frequencies. However, the performance of these techniques is limited in sever atmospheric conditions. In this paper, we propose an efficient unsupervise machine learning approach of independent component analysis (ICA) to detect various acoustic signals i.e., sounds of bird, airplanes, thunderstorm, rain, wind and the UAVs in practical scenario. After unmixing the signals, the features like Mel Frequency Cepstral Coefficients (MFCC), the power spectral density (PSD) and the Root Mean Square Value (RMS) of the PSD are extracted by using ICA. The PSD and the RMS of PSD signals are extracted by first passing the signals from octave band filter banks. Based on the above features the signals are classified using Support Vector Machines (SVM) and K Nearest Neighbor (KNN) to detect the presence or absence of AmDr. Unique feature of the proposed technique is the detection of a single or multiple AmDrs at a time in the presence of multiple acoustic interfering signals. The proposed technique is verified through extensive simulations and it is observed that the RMS values of PSD with KNN performs better than the MFCC with KNN and SVM.

Knowledge Graphs (KGs) are graph-structured knowledge bases storing factual information about real-world entities. Understanding the uniqueness of each entity is crucial to the analyzing, sharing, and reusing of KGs. Traditional profiling technologies encompass a vast array of methods to find distinctive features in various applications, which can help to differentiate entities in the process of human understanding of KGs. In this work, we present a novel profiling approach to identify distinctive entity features. The distinctiveness of features is carefully measured by a HAS model, which is a scalable representation learning model to produce a multi-pattern entity embedding. We fully evaluate the quality of entity profiles generated from real KGs. The results show that our approach facilitates human understanding of entities in KGs.

During a session that lasted two hours, European Film Market (EFM) Horizon attempted to determine whether artificial intelligence (AI) is film's new …

Recent surveys, studies, forecasts and other quantitative assessments of the progress of AI highlight the increasing presence of AI in the healthcare industry, the assistance AI may provide in the future to workers' cognitive tasks, and the continuing acceleration in data production and dissemination. Of the $27 billion raised, U.S. startups accounted for $17 billion, up from $13.3 billion the previous year. Chinese AI startups, on the other hand, raised only $2.9 billion in 2019, down from $4.7 billion in 2018. Big companies wouldn't be investing billions [in AI] if it wasn't producing for them"--Geoffrey Hinton "Artificial intelligence enables us to process the vast quantity of data across our businesses to generate new insights which can keep us ahead of the competition"--Yuri Sebregts, chief technology officer, Shell

Today we launched Neural, our new home for human-centric AI news and analysis. While we're celebrating the culmination of years of hard work from our behind-the-scenes staff, I'm taking the day to quietly contemplate the future of AI journalism. The Guardian's Oscar Schwartz wrote an article in 2018 titled "The discourse is unhinged: how the media gets AI alarmingly wrong." In it, he discusses the 2017 hype-explosion surrounding Facebook's AI research lab developing a pair of chat bots that created a short-hand language for negotiating. In reality, the chat bots' behavior was remarkable but not entirely unexpected.

Few months go by without another devastating earthquake somewhere in the world reminding us how we all remain at the mercy of major seismic events that strike without warning. But a new branch of geophysics powered by machine learning is uncovering fresh insights into the earth's slipping faults that often trigger these catastrophic earthquakes. Machine learning, which often goes by the catchier moniker of artificial intelligence, has captured the public's imagination with its promises of fully autonomous cars and the approaching "singularity" when machines out-think people. The current state of the art, however, shows little signs of true intelligence, such as the ability to abstract the principles behind a given phenomenon. In image recognition, AI systems learn from rote memorization to identify objects and are, therefore, often fooled.

The term "artificial intelligence" may sound intimidating to some, but it has been in use for decades and its applications are more common than you might imagine. AI assists in every area of our lives, whether we're trying to read our emails, get driving directions, or start a new business. In this article, we'll show you 17 ways that artificial intelligence is used in day-to-day activities such as: Google uses AI to ensure that nearly all of the email landing in your inbox is authentic. The program helps your emails get organized so you can find your way to important communications quicker. For example, Gmail sorts email into 4 different tabbed categories, and sends the spam mail to a separate folder.

So I've reading a lot about AI and the responsibility it involves. We hear a lot about the potential for AI to help people and society, but also a lot of potential for harm. I think it's because we're starting to see some of the ramifications of these systems that we're putting out in the real world. Hollywood movies and science fiction novels show AI as human-like robots that take over the world, the current evolution of AI technologies isn't that scary – or quite that smart. Instead, AI has evolved to provide many specific benefits in every industry.

To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as data sparsity and cold start. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the abovementioned issues for a more accurate recommendation, but also provide explanations for recommended items. In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field and summarize them from two perspectives. On the one hand, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. On the other hand, we introduce datasets used in these works. Finally, we propose several potential research directions in this field.