In this era of Big Data, supercomputing, advanced technology, extensive research, and seemingly non-ending pandemics like COVID-19, Health Informatics (HI) has the potential to minimize the data gap in public health between doctors, scientists, governments, and people. But the question is, "Are we making good use of these large untailored piles of data in the right way? Or, are the traditional computing tools and research procedures sufficient to analyze these data accurately?" These questions have only one answer: Artificial Intelligence (AI), an outstanding combination of computing power with human cognition capable of revolutionizing the healthcare industry[1]. HI is defined as an interdisciplinary study that uses Information Technology (IT) and Data Sciences (DS) in health science studies and practices[2]. But, in the real world, the applications of HI are just not limited to procurement, storage, and inspection of electronic health records (EHRs) only; it has more to offer.

Using Artificial Intelligence, corporations can see new patterns in their data and maintain a competitive edge. Blending these AI technologies into business strategy and operations is the subject of a newly published book. Using Artificial Intelligence, corporations can see new patterns in their data and maintain a competitive edge. Blending these AI technologies into business strategy and operations is the subject of a newly published book. Deploying an Artificial Intelligence (AI) in a corporate business can be a costly endeavour.

Generative neural network architectures such as GANs, may be used to generate synthetic instances to compensate for the lack of real data. However, they may be employed to create media that may cause social, political or economical upheaval. One emerging media is "Deepfake". Techniques that can discriminate between such media is indispensable. In this paper, we propose a modified multilinear (tensor) method, a combination of linear and multilinear regressions for representing fake and real data. We test our approach Figure 1: Deepfake technique replaces a person's appearance in by representing Deepfakes with our modified multilinear (tensor) an existing image or video with someone else's appearance [20].

In logic-based knowledge representation, query answering has essentially replaced mere satisfiability checking as the inferencing problem of primary interest. For knowledge bases in the basic description logic ALC, the computational complexity of conjunctive query (CQ) answering is well known to be ExpTime-complete and hence not harder than satisfiability. This does not change when the logic is extended by certain features (such as counting or role hierarchies), whereas adding others (inverses, nominals or transitivity together with role-hierarchies) turns CQ answering exponentially harder. We contribute to this line of results by showing the surprising fact that even extending ALC by just the Self operator - which proved innocuous in many other contexts - increases the complexity of CQ entailment to 2ExpTime. As common for this type of problem, our proof establishes a reduction from alternating Turing machines running in exponential space, but several novel ideas and encoding tricks are required to make the approach work in that specific, restricted setting.

It was reported that Venture Capital investments into AI related startups made a significant increase in 2018, jumping by 72% compared to 2017, with 466 startups funded from 533 in 2017. PWC moneytree report stated that that seed-stage deal activity in the US among AI-related companies rose to 28% in the fourth-quarter of 2018, compared to 24% in the three months prior, while expansion-stage deal activity jumped to 32%, from 23%. There will be an increasing international rivalry over the global leadership of AI. President Putin of Russia was quoted as saying that "the nation that leads in AI will be the ruler of the world". Billionaire Mark Cuban was reported in CNBC as stating that "the world's first trillionaire would be an AI entrepreneur".

The question whether an artificial intelligence ("AI") system can be named as an inventor in a patent application has obvious implications for the life science community, where AI's presence is now well established and growing. For example, AI is currently used to predict biological targets of prospective drug molecules, identify candidates for drug design, decode genetic material of viruses in the context of vaccine development, determine three-dimensional structures of proteins, including their folding form, and many more potential therapeutic applications. In a landmark decision issued on July 30, 2021, an Australian court declared that an AI system called DABUS can be legally recognized as an inventor on a patent application. It came just days after the Intellectual Property Commission of South Africa granted a patent recognizing DABUS as an inventor. These decisions, as well as at least one other pending case in the U.S. concerning similar issues, have generated excitement and debate in the life sciences community about AI-conceived inventions.

Lady Gaga sings'I'm on the edge of glory and I'm hanging on a moment of truth'. Until now, the longstanding crusade to allow inventions generated by the AI machine DABUS to be patentable under existing national patent laws across different jurisdictions had not had much success. Lawyers with the "Artificial Inventor Project" had filed patent applications around the world for DABUS' 'inventions' but received a steady stream of rejections from national IP offices and courts (for instance see our Lens posts on refusals by the UKIPO, UK High Court, EPO and USPTO). Surprisingly, DABUS has had better results in recent weeks in respect of its South African and Australian applications. Is this the edge of glory?

The world of technology, which often breaks down barriers, can significantly promote more integration of people with disabilities into social and work contexts. In particular, artificial intelligence solutions may allow the removal of accessibility barriers. For those who develop technology, it is essential not only to think about usability but increasingly about accessibility. Especially those who deal with AI have the opportunity to create systems and solutions that can really break down barriers for people with disabilities of various kinds. This opens up an important debate that must involve both the world of technology and all those involved in ethical issues.

Israeli startup Binah.ai says it has developed technology that turns smartphones into health monitoring devices that can check vital signs including heartrate, oxygen saturation and respiratory rate. The new technology comes as medical care worldwide has been stretched thin by the pandemic and other, longer-term trends, spurring demand for telemedicine and cheaper, more convenient health monitoring solutions. The user just needs to look into the camera to let the company's system measure their vital signs. Our skin is constantly undergoing rapid changes in color, too subtle for us to notice, that reflect our body's physical state and functioning. "Basically we're following around the tiny color changes that are happening to the skin and the tiny color changes indicate the blood flow that is happening below the skin surface," Maman said.

Robots in our daily surroundings are increasing day by day. Their usability and acceptability largely depend on their explicit and implicit interaction capability with fellow human beings. As a result, social behavior is one of the most sought-after qualities that a robot can possess. However, there is no specific aspect and/or feature that defines socially acceptable behavior and it largely depends on the situation, application, and society. In this article, we investigate one such social behavior for collocated robots. Imagine a group of people is interacting with each other and we want to join the group. We as human beings do it in a socially acceptable manner, i.e., within the group, we do position ourselves in such a way that we can participate in the group activity without disturbing/obstructing anybody. To possess such a quality, first, a robot needs to determine the formation of the group and then determine a position for itself, which we humans do implicitly. The theory of f-formation can be utilized for this purpose. As the types of formations can be very diverse, detecting the social groups is not a trivial task. In this article, we provide a comprehensive survey of the existing work on social interaction and group detection using f-formation for robotics and other applications. We also put forward a novel holistic survey framework combining all the possible concerns and modules relevant to this problem. We define taxonomies based on methods, camera views, datasets, detection capabilities and scale, evaluation approaches, and application areas. We discuss certain open challenges and limitations in current literature along with possible future research directions based on this framework. In particular, we discuss the existing methods/techniques and their relative merits and demerits, applications, and provide a set of unsolved but relevant problems in this domain.