Traditional colonial power seeks unilateral power and domination over colonised people. It declares control of the social, economic, and political sphere by reordering and reinventing the social order in a manner that benefits it. In the age of algorithms, this control and domination occurs not through brute physical force but rather through invisible and nuanced mechanisms such as control of digital ecosystems and infrastructure. Common to both traditional and algorithmic colonialism is the desire to dominate, monitor, and influence the social, political, and cultural discourse through the control of core communication and infrastructure mediums. While traditional colonialism is often spearheaded by political and government forces, digital colonialism is driven by corporate tech monopolies--both of which are in search of wealth accumulation. The line between these forces is fuzzy as they intermesh and depend on one another. Political, economic, and ideological domination in the age of AI takes the form of "technological innovation", "state-of-the-art algorithms", and "AI solutions" to social problems. Algorithmic colonialism, driven by profit maximisation at any cost, assumes that the human soul, behaviour, and action is raw material free for the taking.

It's human nature to change nature. It seems part of our core program. We have the ability to imagine abstract things and then shape and mix the reality around us to create something new. As if we were gods, we have been intervening nature through history, cocreating our planet through arts, science, and the power of our fertile imagination. And when it comes to intervening our human nature itself, we don't think it twice.

We've all seen those videos of face-swapped individuals, often a parody of a popular film with a different celebrity's face superimposed over a another's via the power of artificial intelligence (AI). These videos and images have come to be known as deepfakes, and while many are indeed for harmless fun, the incredible realism of these videos leaves plenty of room for malicious behavior. Deep Trace Lab, a deepfake detection technology firm, found that the amount of detectable deepfake videos on the internet more than doubled to 49,081 in just the six months between January and June 2020. Not only that, but while deepfake prevalence was picking in the West last year, the first instances of convincing deepfakes here in Asia are starting to surface. The reported incident of face-swapping was reportedly in a Chinese TV series, when actress Liu Lu was blacklisted in the country, and her contract terminated.

Emergency response to incidents such as accidents, medical calls, and fires is one of the most pressing problems faced by communities across the globe. In the last fifty years, researchers have developed statistical, analytical, and algorithmic approaches for designing emergency response management (ERM) systems. In this survey, we present models for incident prediction, resource allocation, and dispatch for emergency incidents. We highlight the strengths and weaknesses of prior work in this domain and explore the similarities and differences between different modeling paradigms. Finally, we present future research directions. To the best of our knowledge, our work is the first comprehensive survey that explores the entirety of ERM systems.

The use of Deep Learning and Machine Learning is becoming pervasive day by day which is opening doors to new opportunities in every aspect of technology. Its application Ranges from Health-care to Self-driving Cars, Home Automation to Smart-agriculture, and Industry 4.0. Traditionally the majority of the processing for IoT applications is being done on a central cloud but that has its issues; which include latency, security, bandwidth, and privacy, etc. It is estimated that there will be around 20 Million IoT devices by 2020 which will increase problems with sending data to the cloud and doing the processing there. A new trend of processing the data on the edge of the network is emerging. The idea is to do processing as near the point of data production as possible. Doing processing on the nodes generating the data is called Edge Computing and doing processing on a layer between the cloud and the point of data production is called Fog computing. There are no standard definitions for any of these, hence they are usually used interchangeably. In this paper, we have reviewed the development boards available for running Artificial Intelligence algorithms on the Edge

We propose a method for combining probabilistic outputs of classifiers to make a single consensus class prediction when no further information about the individual classifiers is available, beyond that they have been trained for the same task. The lack of relevant prior information rules out typical applications of Bayesian or Dempster-Shafer methods, and the default approach here would be methods based on the principle of indifference, such as the sum or product rule, which essentially weight all classifiers equally. In contrast, our approach considers the diversity between the outputs of the various classifiers, iteratively updating predictions based on their correspondence with other predictions until the predictions converge to a consensus decision. The intuition behind this approach is that classifiers trained for the same task should typically exhibit regularities in their outputs on a new task; the predictions of classifiers which differ significantly from those of others are thus given less credence using our approach. The approach implicitly assumes a symmetric loss function, in that the relative cost of various prediction errors are not taken into account. Performance of the model is demonstrated on different benchmark datasets. Our proposed method works well in situations where accuracy is the performance metric; however, it does not output calibrated probabilities, so it is not suitable in situations where such probabilities are required for further processing.

Using personalized explanations to support recommendations has been shown to increase trust and perceived quality. However, to actually obtain better recommendations, there needs to be a means for users to modify the recommendation criteria by interacting with the explanation. We present a novel technique using aspect markers that learns to generate personalized explanations of recommendations from review texts, and we show that human users significantly prefer these explanations over those produced by state-of-the-art techniques. Our work's most important innovation is that it allows users to react to a recommendation by critiquing the textual explanation: removing (symmetrically adding) certain aspects they dislike or that are no longer relevant (symmetrically that are of interest). The system updates its user model and the resulting recommendations according to the critique. This is based on a novel unsupervised critiquing method for single- and multi-step critiquing with textual explanations. Experiments on two real-world datasets show that our system is the first to achieve good performance in adapting to the preferences expressed in multi-step critiquing.

It is going to be interesting to see how society deals with artificial intelligence, but it will definitely be cool. Artificial intelligence (AI) can be defined to mean the use of intelligent machines to replicate and augment the intelligence of human beings. The Turing test was propounded to show what factors determine whether a machine operates on artificial intelligence or not. AI applications are being used in various fields such as telecommunication, banking, agriculture, manufacturing, health care, and transportation. The implementation of AI in health care aims to enhance the lives of the patients and enable physicians, doctors, hospitals, and administrators to improve health care delivery in a cost-effective and time-efficient manner. The traditional drug industry is also experiencing a wave of change due to the implementation of AI-based processes in drug discovery and development. Substitution of AI technology-based solutions in place of the traditional methods for drug discovery is expected to reduce the time for drug development. Using AI in clinical trials has reduced the time required for drug trials from 4–6 months to three months. After the analysis of the genomic data from different patients, AI helps by selecting only those patients whose genetic profile suggests it will help them to undergo testing in the clinical trial.2 Machine learning technologies, deep learning algorithms, various neural networks (such as artificial neural networks or computational neural networks), and content screening are a few examples of AI that have brought radical changes to the process of drug discovery and development.

The graph represents a network of 1,962 Twitter users whose tweets in the requested range contained "#cloudcomputing", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 31 August 2020 at 11:18 UTC. The requested start date was Monday, 31 August 2020 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 9-day, 5-hour, 22-minute period from Friday, 21 August 2020 at 06:23 UTC to Sunday, 30 August 2020 at 11:46 UTC.

Not only has Adji Bousso Dieng, an AI researcher from Senegal, contributed to the field of generative modeling and about to become one of the first black female faculty in Computer Science in the Ivy League, she is also helping Africans in STEM tell their own success stories. Dieng, who is currently a researcher at Google and an incoming computer science faculty at Princeton, works in an area of Artificial Intelligence called generative modeling. "It allows you to learn from data without needing any supervision," she said, "Generative models have many real-world applications with regard to natural language processing, computer vision, healthcare, robotics, and in a range of sciences." In addition to this, Dieng started The Africa I Know (TAIK), a platform that showcases Africans who've had successful careers; highlight how Africans are leveraging technology to solve developmental problems –in agriculture, health and education– and narrate African history as told by Africans. "I founded TAIK to unearth the success stories of Africa and its people and to foster an economic and social consciousness in Africa," she said, adding that TAIK's volunteers are a group of eager and young Africans coming from every region of the continent and that the content is in both English and French.