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New research shows that 68 percent of chief technical officers (CTOs) have implemented machine learning at their company.

What's more the study, from software development company STX Next, reveals that 55 percent of businesses now employ at least one team member dedicated to AI/ML solutions, although only 15 percent have their own separate AI division.

The findings come from STX Next's 2021 Global CTO Survey, which gathers insights from 500 global CTOs about their organisation's tech stack and what they're looking to add to it in the future. It shows that 72 percent of respondents identify machine learning as the most likely technology to come to prominence in the next two to four years, with 57 percent predicting the same for cloud computing.

In addition 25 percent of CTOs report that they've implemented natural language processing, with 22 percent implementing pattern recognition and 21 percent applying deep learning technologies. 87 percent of businesses employ up to five people in a dedicated AI, machine learning or data science capacity.

ukasz Grzybowski, head of machine learning and data engineering at STX Next, says:

The implementation of AI and its subsets in many companies is still in its early stages, as evidenced by the prevalence of small AI teams.

It's unsurprising to see machine learning as a definite leader when it comes to future technologies as its applications are becoming more widespread every day. What's less obvious is the skills that people will need to take full advantage of its growth and face the challenges that will arise alongside it. It's important that CTOs and other leaders are wise to these challenges, and are willing to take the steps to increase their AI expertise in order to maintain their innovative edge.

Deep learning is a good example of where there is plenty of room for progress to be made. It is one of the fastest developing areas of AI, in particular when it comes to its application in natural language processing, natural language understanding, chatbots, and computer vision. Many innovative companies are trying to use deep learning to process unstructured data such as images, sounds, and text.

However, AI is still most commonly used to process structured data, which is evidenced by the high popularity of classical machine learning methods such as linear or logistic regression and decision trees.

The full report is available from the STX Next site.

Image credit:Jirsak/depositphotos.com

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Machine learning implemented by 68 percent of organizations - BetaNews

Machine learning isstreamlining and optimizing processesranging from credit decisions to quantitative trading and financial risk management.

This exciting technology has the potential to transform financial services business models and markets for trading, credit and blockchain-based finance, reduce friction and enhance product offerings.

Machine learningis a subset of artificial intelligence that utilizes advanced statistical techniques to enable computing systems toimprove at tasks with experience over time. Chatbots like Amazons Alexa and Apples Siri improve every year thanks to constant use by consumers coupled with the machine learning that takes place in the background.

Machine learning has grown substantially within the finance industry, enabled by the abundance of available data and the increase in the affordability of computing capacity.

The technology is increasingly deployed by financial services organizations in the following areas:

Machine learning in finance is creating a huge impact; lets take a look how.

Gone are the days when financial services only meant saving money in the bank or taking a loan from it. Machine learning expands the gamut of financial services by means of what are called as consumer financial services. Consumer financial services keep the consumers and their unique demands at the core of their highly optimized offerings. Machine learning makes it possible to provide consumers with a personal financial concierge that automatically lets you decide a suitable style of spending, saving, and investing that are based on your personal habits and goals. With machine learning in finance, its possible to create intelligent products that can learn from your financial data and determine whats working for you and whats not, and help you track your financial activities better.

This is something we all must have experienced and would, therefore, agree with. Machine learning in finance has automated processes and drastically reduced the cost of serving customers. While machine learning has, on one hand, reduced the cost of financial services, on the other, it has made financing extremely convenient to avail. Through various digital servicing channels, Machine learning is proving effective in attracting that large section of the population to financial services, which previously found them cumbersome, expensive, and time-consuming.

Machine learning in finance is opening up new avenues for banking and insurance leaders to seek advice. No more are financial experts limited to human opinions in order to make forecasts or recommendations in the field of finance. Withmachine learning in finance, these leaders can now ask machines questions that are pertinent to their business and these machines can, in turn, analyze data and help them take data-driven management decisions. As far as consumers are concerned, they can have their financial portfolio managed at essentially no management fee and with high efficiency, as opposed to availing the services of a traditional advisor who may charge around 1% of your investments.

With machine learning, it is possible to simulate umpteen situations where a fraud or cyber crime may occur. Machine learning in finance, therefore, follows a proactive approach to making the financial services environment safe and breach-proof. Unlike before, designers of a financial service system do not need to wait for an incidence of fraud to be detected and then secure a system. Machine learning is helping the field of finance innovate freely by securing its products and services through a continuous understanding of human psychology. Besides,machine learning in finance also helps keep a strict regulatory oversight. Machine learning ensures that all policies, regulations, and security measures are being sincerely followed while designing and delivering any financial service.

Critical decisions in fields like finance cannot afford to be marred by the inaccuracy involved in human decisions. Machine learning in finance implies thorough research, understanding, and learning over long periods of time and vast volumes of data. Machine learning introduces automation in areas that require high degrees of incisiveness thereby, safeguarding the trust of consumers.

Machine learning is all about continuous learning and re-learning of patterns, data, and developments in the financial world.

It gives financial organizations more flexibility to build upon their current systems, products and services.

Successful banking-related chatbot interactions will grow 3,1505% between 2019-2023.

826 million hours will be saved by banks through chatbot interactions in2023.

79% of successful chatbot interactions will be through mobile banking apps in 2023.

Read more here:
How Machine Learning is Impacting the Finance Industry - BBN Times

Email security is a secure email communication technique to transfer and access sensitive information against unauthorized loss and compromise. Increase in digitization and adoption of cloud email services in different industries to reorganize the email security architecture of companies is expected to fuel the adoption of email security solutions in companies.

Adoption of email security solutions in enterprises eliminates the need for expensive security solutions providers, which further improves phishing detection and provides good customer experience. Adoption of email security solutions in enterprises is increasing consistently to reduce the workload of the IT department and to minimize manual management of email security in enterprises in order to block threats. This factor is expected to drive theemail security marketduring the forecast period.

Increase in investment in R&D activities and high rate of adoption of cloud-based technology to store and secure the high amount of data generated by governments and various industries is projected to drive the market during the forecast period. Rise in adoption ofartificial intelligence(AI) andmachine learning(ML) by large enterprises and SMEs to provide better security experience to customers is estimated to boost the demand for email security during the forecast period

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High implementation cost of email security solutions restrains the market. Lack of awareness about email security solutions among enterprises further hinders the email security market. Increase in adoption of email protection solutions to fulfil the requirements of managed security providers (MSP) creates significant opportunities for the email security market

Impact of COVID-19 on the Global Email Security Market

North America to Hold Major Share of Global Email Security Market

Global Email Security Market: Competition Landscape

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Email Security Market : Rise in adoption of artificial intelligence and machine learning by large enterprises is estimated to drive market - Digital...

To make fusion energy a viable resource for the worlds energy grid, researchers need to understand the turbulent motion of plasmas: a mix of ions and electrons swirling around in reactor vessels. The plasma particles, following magnetic field lines in toroidal chambers known as tokamaks, must be confined long enough for fusion devices to produce significant gains in net energy, a challenge when the hot edge of the plasma (over 1 million degrees Celsius) is just centimeters away from the much cooler solid walls of the vessel.

Abhilash Mathews, a PhD candidate in the Department of Nuclear Science and Engineering working at MITs Plasma Science and Fusion Center (PSFC), believes this plasma edge to be a particularly rich source of unanswered questions. A turbulent boundary, it is central to understanding plasma confinement, fueling, and the potentially damaging heat fluxes that can strike material surfaces factors that impact fusion reactor designs.

To better understand edge conditions, scientistsfocus on modeling turbulence at this boundary using numerical simulations that will help predict the plasma's behavior. However, first principles simulations of this region are among the most challenging and time-consuming computations in fusion research. Progress could be accelerated if researchers could develop reduced computer models that run much faster, but with quantified levels of accuracy.

For decades, tokamak physicists have regularly used a reduced two-fluid theory rather than higher-fidelity models to simulate boundary plasmas in experiment, despite uncertainty about accuracy. In a pair of recent publications, Mathews begins directly testing the accuracy of this reduced plasma turbulence model in a new way: he combines physics with machine learning.

A successful theory is supposed to predict what you're going to observe, explains Mathews, for example, the temperature, the density, the electric potential, the flows. And its the relationships between these variables that fundamentally define a turbulence theory. What our work essentially examines is the dynamic relationship between two of these variables: the turbulent electric field and the electron pressure.

In the first paper, published in Physical Review E, Mathews employs a novel deep-learning technique that uses artificial neural networks to build representations of the equations governing the reduced fluid theory. With this framework, he demonstrates a way to compute the turbulent electric field from an electron pressure fluctuation in the plasma consistent with the reduced fluid theory. Models commonly used to relate the electric field to pressure break down when applied to turbulent plasmas, but this one is robust even to noisy pressure measurements.

In the second paper, published in Physics of Plasmas, Mathews further investigates this connection, contrasting it against higher-fidelity turbulence simulations. This first-of-its-kind comparison of turbulence across models has previously been difficult if not impossible to evaluate precisely. Mathews finds that in plasmas relevant to existing fusion devices, the reduced fluid model's predicted turbulent fields are consistent with high-fidelity calculations. In this sense, the reduced turbulence theory works. But to fully validate it, one should check every connection between every variable, says Mathews.

Mathews advisor, Principal Research Scientist Jerry Hughes, notes that plasma turbulence is notoriously difficult to simulate, more so than the familiar turbulence seen in air and water. This work shows that, under the right set of conditions, physics-informed machine-learning techniques can paint a very full picture of the rapidly fluctuating edge plasma, beginning from a limited set of observations. Im excited to see how we can apply this to new experiments, in which we essentially never observe every quantity we want.

These physics-informed deep-learning methods pave new ways in testing old theories and expanding what can be observed from new experiments. David Hatch, a research scientist at the Institute for Fusion Studies at the University of Texas at Austin, believes these applications are the start of a promising new technique.

Abhis work is a majorachievement with the potential for broad application, he says. For example, given limited diagnostic measurements of a specific plasma quantity, physics-informed machine learning could infer additional plasma quantities in a nearby domain, thereby augmenting the information provided by a given diagnostic. The technique also opens new strategies for model validation.

Mathews sees exciting research ahead.

Translating these techniques into fusion experiments for real edge plasmas is one goal we have in sight, and work is currently underway, he says. But this is just the beginning.

Mathews wassupported in this workby theManson Benedict Fellowship,Natural Sciences and Engineering Research Council of Canada,andU.S. Department of Energy Office of Science under the Fusion Energy Sciences program.

Excerpt from:
Seeing the plasma edge of fusion experiments in new ways with artificial intelligence - MIT News

Note: This special issue is a Thematic Track at IP&MC2022. For more information about IP&MC2022, please visit https://www.elsevier.com/events/conferences/information-processing-and-management-conference.

Human-Centered Artificial Intelligence (VSI: IPMC2022 HCAI)

Artificial Intelligence (AI) and machine learning, alongside the advances in decision making, prediction, knowledge extraction, and logic reasoning are widely implemented to address challenges in diverse areas, for example, chatbot, machine translation, fraud detection, content recommendation, clinical diagnosis, and autonomous devices. Effective and prevalent as AI is in real-world scenarios, AI-based systems also raise scholars and practitioners concerns about bias, discrimination, result interpretability, algorithmic transparency, and malicious use of AI. Indeed, todays most pressing questions in AI are Human-Centered, as pointed out by Dr. Perter Norvig in Stanford HAI (Lynch 2021). Such knowledge and concerns on HCAI motivated this Special Issue as an exploration of the matter in the era of Artificial Intelligence.

The appropriate AI adoption can facilitate human welfare, however, as a double-edged sword. Many people show little trust in AI owing to the unawareness of why and how decisions are made by AI systems. It is thus essential for AI to make the decision-making process transparent. By equipping AI systems with explanation capabilities, trust between users and AI is built. Though machines are categorized by their abilities to conduct massive computations, human beings outperform machines in terms of metacognition. For the information generated by machines, humans should infuse values to make reasoned judgments about the information quality. In such a way, humans involvement in the design, development, and evaluation of AI systems ensures practical insights, leading to more meaningful and relatable systems to users needs. Human-centered AI (HCAI) focuses on humanity benefits from AI via trustworthy and safe systems designed and developed by augmenting human intelligence with machine intelligence. HCAI comprises two categories; i) AI regarding the human condition, which emphasizes AI humanity by incorporating humans intention into AI systems and enabling AI to understand commonsense knowledge with respect to ethical and social implications, and ii) promotion of humans understanding of AI systems with various approaches for addressing and mitigating errors caused by AI and enhancing users confidence in AI decisions.

This Special Issue aims to advance knowledge and understanding of the design, development, deployment, application, and evaluation of human-centered AI and ML systems through in-depth dialogue between scholars and practitioners from diverse areas like human-computer interaction, ML, AI, law, cognitive science, complex systems, and humanities for the investigation and tackling of challenges derived during HCAIs development. We invite authors to submit their HCAI related research work (including full-length, original, and unpublished research papers based on theoretical or experimental contributions and review studies), especially in explainable AI, interpretable ML, human-centered design, and human-machine-systems.

Topics of interest include, but are not limited to:

Submit your manuscript to the Special Issue category (VSI: IPMC2022 HCAI) through the online submission system of Information Processing & Management. https://www.editorialmanager.com/ipm/

Authors will prepare the submission following the Guide for Authors on IP&M journal at (https://www.elsevier.com/journals/information-processing-and-management/0306-4573/guide-for-authors). All papers will be peer-reviewed following the IP&MC2022 reviewing procedures.

The authors of accepted papers will be obligated to participate in IP&MC2022 and present the paper to the community to receive feedback. The accepted papers will be invited for revision after receiving feedback on the IP&MC 2022 conference. The submissions will be given premium handling at IP&M following its peer-review procedure and, (if accepted), published in IP&M as full journal articles, with also an option for a short conference version at IP&MC2022.

Please see this infographic for the manuscript flow:https://www.elsevier.com/__data/assets/pdf_file/0003/1211934/IPMC2022Timeline10Oct2022.pdf

For more information about IP&MC2022, please visit https://www.elsevier.com/events/conferences/information-processing-and-management-conference.

Shana Lynch (2021) Peter Norvig: Todays Most Pressing Questions in AI Are Human-Centered. URL: https://hai.stanford.edu/news/peter-norvig-todays-most-pressing-questions-ai-are-human-centered, accessed on November 11 2021.

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Human-Centered Artificial Intelligence - Information Processing and Management Conference - Knovel