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Artificial Intelligence in Retail Industry - Part 1

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  Retailing has always been called a combination of art and science but for much of its existence there’s usually been too much of the former and not enough of the latter. Traditional retailers' business models arc facing disruption by new entrants who can deliver greater value to customers more efficiently. In recent years, authors have argued that the traditional value chain drives inefficiencies and that the value chain is shortening as manufacturers, third parties and customers are increasingly engaging with customers directly. These inefficiencies combined with the inability to adapt to a changing competitive landscape leaves traditional retailers vulnerable to disruption from market entrants. To remain competitive and survive in an ever-changing and diversified customer market, retailers need to become leaner , more agile, and innovate their value chain by adopting new technologies. Of the new technologies that are impacting the retail industry, AI has been earmarked as

Artificial Intelligence in Law Enforcement - Part 2

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  In the first part of the article on AI use in Law Enforcement, we introduced how Machine Learning and Artificial Intelligence is being used by the law enforcing agencies around the world and what are the pros and cons of using this emerging technology. Now let us discuss different ways by which the police can use machine learning to achieve better results: 1) Pattern recognition One of the most powerful applications of machine learning in policing is in the field of pattern recognition. Crimes can be related and may either be carried out by the same person(s) or may use the same modus operandi. The police can benefit if they are able to spot patterns in crimes. The data that the police get from crimes is essentially unstructured data. This data needs to be organized and sifted through to find patterns. Machine learning tools can compare various crimes easily and generate a similarity score. These scores can then be used by the software to try and determine if there are common patter

Artificial Intelligence in Law Enforcement - Part 1

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  Fighting crime has historically been a field that drives technological innovation, and it can serve as an example of different governance styles in societies. Artificial Intelligence in policing is one of the recent innovations that covers technical trends such as machine learning, preventive crime fighting strategies, and actual policing in cities.   Broadly speaking, Artificial Intelligence uses data to teach computers to make decisions without explicitly instructing them how to do it. Machine learning (part of AI) is used successfully in many industries to create efficiency, prioritise risk and improve decision making. Although they are at a very early stage, the police in the western countries are exploring the benefits of using machine learning methods to prevent and detect crime, and to develop new insights to tackle problems of significant public concern.     The police can use machine learning effectively to resolve many challenges that are before them. The use of

Artificial Intelligence and its impact on society

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  For centuries, human beings have been working towards inventing new ways for making life better. Every once in a while, a revolutionary invention comes along with the power to advance humanity and change the course of history. From the wheel to the World Wide Web, there have been a lot of inventions that resulted in achieving efficiency in the work we do.   One such technological advancement is Artificial Intelligence, popularly known as AI. AI is changing the way we work and live and we see a lot of examples of how AI is affecting the society and impacting our lifestyle. But what really is AI?     Artificial Intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. A more vivid definition will be any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial

Artificial Intelligence in Coronavirus Drug and Vaccine Development - Part 2

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  In Part 1 of the article on AI in COVID Drug and Vaccine development, we discussed about how AI is used extensively in any drug and vaccine development. In this part, we will look deeper into how AI is specifically being used in COVID-19 drug and vaccine development. The recent applications of Artificial Intelligence for COVID-19 include the virtual screening of both repurposed drug candidates and new chemical entities. For repurposed drugs, the goal has been to rapidly predict and exploit interconnected biological pathways or the off-target biology of existing medicines that are proven safe and can thus be readily tested in new clinical trials. Scientists leveraged AI-derived knowledge graph, which integrates biomedical data from structured and unstructured sources. Similarly, other scientists published an application of their Deep Learning-based drug–target interaction model that predicted commercially available antiviral drugs that may target the COVID-19-related protease and hel

UNBOXING OF ASUS VIVOBOOK 14/ASUS LAPTOP

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Artificial Intelligence in Coronavirus Drug and Vaccine Development - Part 1

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  Coronavirus (COVID-19) has wreaked havoc across the world with more than 22 million infected cases and close to 800 thousand deaths as of 20th August 2020. The virus, coming from the family of Coronaviridae with its predecessors Severe Acute Respiratory Disease (SARS) and Middle Eastern Respiratory Syndrome (MERS), emerging in 2002 and 2013, respectively, has impacted human population across geographies with USA, Brazil and India emerging as the leading hotspots with more than 10 million confirmed cases each (USA has actually topped 27 million). Refer Bloomberg for current update on Mapping theCoronavirus Outbreak Across the World .     In recent years, machine learning has revolutionized many fields of science and engineering. It has largely transformed our daily lives, from speech and face recognition to customized targeted advertisements. The power of automatic abstract feature learning, combined with a massive volume of data, has immensely contributed to the successf