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.

The robot possesses an infrared thermal imager and a visual light camera, thereby giving them the ability to replace 24-hour manual inspection. Artificial intelligence is about to trigger explosive changes in our lives, work, and leisure, but few understand what the technology can do beyond Amazon AMZN's Alexa or Apple AAPL's Siri. These are examples of virtual assistant or'weak AI' technology -- the most common example of AI application. But in the data-driven energy sector, sophisticated machine learning is paving the way for'strong AI' to improve efficiency, forecasting, trading, and user accessibility. Electricity is a commodity that can be bought, sold, and traded in open markets.

UK EDITION Ethics Guide to Artificial Intelligence in PR 2. The AIinPR panel and the authors are grateful for the endorsements and support from the following: In May 2020 the Wall Street Journal reported that 64 per cent of all signups to extremist groups on Facebook were due to Facebook's own recommendation algorithms. There could hardly be a simpler case study in the question of AI and ethics, the intersection of what is technically possible and what is morally desirable. CIPR members who find an automated/AI system used by their organisation perpetrating such online harms have a professional responsibility to try and prevent it. For all PR professionals, this is a fundamental requirement of the ability to practice ethically. The question is – if you worked at Facebook, what would you do? If you're not sure, this report guide will help you work out your answer. Alastair McCapra Chief Executive Officer CIPR Artificial Intelligence is quickly becoming an essential technology for ...

While there are many training programs in Nigeria related to developing AI talent, it is usually difficult to point out AI applications that are locally developed and are already available in the marketplace. This article will hopefully be the first of a series of articles; I will be uncovering several locally developed AI applications in Nigeria. Some you might know, some you won't. All in all, my goal in this series is to dispel beliefs that AI is an imported technology in Nigeria. Nigeria may not be pulling its weight compared to Ghana, South Africa, and Kenya in the African AI space, but there are notable strides that we should be aware of.

Rapid evolution in artificial intelligence (AI) applications, as well as improvements in computing power and the increasing availability of data, have led to significant growth in AI across most industries, write Leanne Mostert, a Partner and Wendy Tembedza, a Senior Associate at Webber Wentzel. The key developments in AI over the past few years have been driven by machine learning which, in turn, is fuelled by data. As more and more data is being gathered, so AI enables more sophisticated analysis of large data volumes. As the importance of data rises, so do the associated legal issues. In some cases businesses are free to use the data they hold for whatever purpose they want, including developing AI algorithms.