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Key applications of artificial intelligence (AI) in banking and finance

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Artificial intelligence (AI) technology has become a critical disruptor in almost every industry and banking is no exception. The introduction of AI in banking apps and services has made the sector more customer-centric and technologically relevant. AI-based systems can help banks reduce costs by increasing productivity and making decisions based on information unfathomable to a human agent. Also, intelligent algorithms are able to spot anomalies and fraudulent information in a matter of seconds. A report by Business Insider suggests that nearly 80% of banks are aware of the potential benefits that AI presents to their sector. Another report suggests that by 2023, banks are projected to save $447 billion by using AI apps.


AI expands capabilities of surveillance and public safety tech

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Video management platforms, equipped with technologies such as artificial intelligence (AI) and machine learning, are vastly expanding capabilities in the area of urban surveillance and public safety, according to research. The Covid-19 pandemic has spurred the use of technologies, such as crowd monitoring, which ABI Research believes are here to stay. It adds that other developments in urban surveillance from live video feeds to bodycams, will be assisted by the introduction of 5G. In its report, Urban Surveillance Technologies and Public Safety Strategies, global technology intelligence firm, ABI Research, forecasts a compound annual growth rate of 11.6 per cent with 1.4 billion closed-circuit television (CCTV) surveillance cameras in urban areas worldwide in 2030. "Currently, the main use of CCTV in public safety is to aid authorities to solve crimes retroactively," said Lindsey Vest, smart cities and smart spaces research analyst at ABI Research.


The chemist's gambit

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Next moves: learn chemistry, synthesise molecules, take over world? Elegant retrosynthesis is an artform, it is strategic: you need to plan ahead, but also be able to adapt, if reactions do not work out. When the lockdown in 2020 started, I came upon those very same patterns in completely different field – chess. With everyone stuck at home, online chess suddenly became highly popular, especially after the release of the series The Queen's Gambit. My friends and I were among those who gave it a shot.


Natural Language Processing First Steps: How Algorithms Understand Text

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This article will discuss how to prepare text through vectorization, hashing, tokenization, and other techniques, to be compatible with machine learning (ML) and other numerical algorithms. I'll explain and demonstrate the process. Natural Language Processing (NLP) applies Machine Learning (ML) and other techniques to language. However, machine learning and other techniques typically work on the numerical arrays called vectors representing each instance (sometimes called an observation, entity, instance, or row) in the data set. We call the collection of all these arrays a matrix; each row in the matrix represents an instance.


Dynam.AI unveils Vizlab, a next-generation AI platform

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Dynam.AI, an artificial intelligence (AI) software development firm best known for full stack AI innovation, today announced the early commercial release of Vizlab, an AI/Machine Learning (ML) platform designed to address the complex needs of enterprise data scientists and solve the key problems with AI/ML applications in the market today. This customizable, intuitive, end-to-end AI/ML development solution enables ML data scientists to design, build, improve and deploy AI engines at scale. Vizlab empowers data scientists with necessary, in-demand tools to deploy explainable AI solutions with highly accurate analytic insights. Vizlab was initially built as an internal tool by the Dynam.AI team as a platform to support Dynam's consultative services, to automate and standardize AI pro-workflow and advance Dynam's core AI capabilities for customers with complex business use cases. Customers without internal data science teams seek out Dynam's end-to-end AI development services to learn more from their proprietary business and customer data to automate and improve processes and target more of their best customers.


Update on Artificial Intelligence as a Patent Inventor

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Our previous blog posts, Artificial Intelligence as the Inventor of Life Sciences Patents? and Update on Artificial Intelligence: Court Rules that AI Cannot Qualify As "Inventor," discuss recent inventorship issues surrounding AI and its implications for life sciences innovations. Continuing our series, we now look at the appeal recently filed by Stephen Thaler ("Thaler") in his quest to obtain a patent for an invention created by AI in the absence of a traditional human inventor. As we previously reported, on September 3, 2021, the U.S. District Court for the Eastern District of Virginia ruled that an AI machine cannot qualify as an "inventor" under the Patent Act, in a case that Thaler filed seeking, among other things, an order compelling the USPTO to reinstate his patent applications. Those patent applications name an AI system called "Device for Autonomous Bootstrapping of Unified Sentience" aka "DABUS," as the sole inventor. Thaler, who developed DABUS, remains the owner of any patent rights stemming from these applications.


How ConvNets found a way to survive the Transformers invasion in computer vision

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Traditionally, Convolutional Neural Networks (CNN) have been the preferred choice for computer vision tasks. CNNs, composed of layers of artificial neurons, calculate the weighted sum of the inputs to give output in the form of activation values. In the case of computer vision applications, CNNs accept pixel values to output various visual features. Indubitably, the invention of AlexNet was the apogee of the CNN movement. AlexNet has become the leading CNN-based architecture for object detection tasks in the computer vision field.


How AI can automate SEO tasks at scale

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Artificial intelligence, machine learning and neural networks are major buzzwords in the SEO community today. Marketers have highlighted these technologies' ability to automate time-consuming tasks at scale, which can lead to more successful campaigns. Yet many professionals often have trouble distinguishing between these concepts. "Artificial intelligence is essentially the term that defines the whole space," said Eric Enge, president of Pilot Holding and former principal at Perficient, in his presentation at SMX Next. "Machine learning is a subset of that [AI] set around specific algorithms."


How artificial intelligence can help small businesses to hire faster and smarter

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Automation has been a part of the hiring process for some time now, but it's only in recent years that artificial intelligence has made such a significant impact. By leveraging AI to automate various aspects of the hiring process and improve candidate experience, HR professionals have more time to focus on coreresponsibilities. Yes, artificial intelligence and machine learning will eventually take over many tasks currently performed by humans. However, no amount of AI will be able to replace the human judgement necessary in hiring choices. Artificial intelligence, on the other hand, will greatly assist human intelligence in improving recruiting and job search procedures, as well as making the entire process exceedingly fast and efficient. This has resulted in a notable paradigm change in terms of rapid technology stacks and cutting-edge problem resolution across a wide range of industries.


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