In the age of the Internet, people's lives are increasingly dependent on today's network technology. Maintaining network integrity and protecting the legitimate interests of users is at the heart of network construction. Threat detection is an important part of a complete and effective defense system. How to effectively detect unknown threats is one of the concerns of network protection. Currently, network threat detection is usually based on rules and traditional machine learning methods, which create artificial rules or extract common spatiotemporal features, which cannot be applied to large-scale data applications, and the emergence of unknown risks causes the detection accuracy of the original model to decline. With this in mind, this paper uses deep learning for advanced threat detection to improve protective measures in the financial industry. Many network researchers have shifted their focus to exception-based intrusion detection techniques. The detection technology mainly uses statistical machine learning methods - collecting normal program and network behavior data, extracting multidimensional features, and training decision machine learning models on this basis (commonly used include naive Bayes, decision trees, support vector machines, random forests, etc.).

In this special guest feature, Zach Thomas, VP of Product Marketing, Conga, discusses the ways emerging technologies like AI and ML can expedite business operations to accelerate revenue. Zachary brings more than 20 years of experience in the technology sector to his role as Vice President of Product Marketing at Conga. Prior to Conga, he served as Senior Director, Head of Cloud Applications at Ellucian, a leading provider of software and services designed for higher education. His previous roles include leadership positions at Saba Software, Sage, Oracle, and Ultimate Software. Thomas holds a Bachelor of Arts degree in English and Political Science from Hamilton College in Clinton, New York. From predicting COVID-19 mortality to content personalization, artificial intelligence (AI) and machine learning (ML) are expanding the possibilities for organizations across the globe.

Protecting Consumer Privacy: AI can protect customer data through its ability to monitor network behavior and flag anomalies 24/7. Additionally, AI can accelerate the process of data identification to improve customer data privacy. Eliminating Biases: Because both conscious and unconscious biases are programmed into the data that an AI application is built upon, AI applications can become biased themselves. Fortunately, AI can actively mitigate the underlying biases of the models and systems deployed. Eliminating Mundane Tasks: Self-service AI-based HR portals enable employees to do for themselves what used to involve HR staff.

According to a recent Bloomberg Intelligence report, the metaverse is an $800 billion market. Still others argue about what the metaverse actually is, but with so much money and curiosity surrounding it, it has everyone talking. Undoubtedly, AI will play a huge role in the metaverse, especially as we communicate with others. While we'll be more connected than ever, AI untethered to any government, standard or ethical code can have diabolical implications. As former Google CEO Eric Schmidt asked recently, "Who gets to set the rules?"

The helpful person guiding you through your online purchase might not be a person at all. As artificial intelligence and natural language processing advance, we often don't know if we are talking to a person or an AI-powered chatbot, says Tom Kelleher, Ph.D., an advertising professor in the University of Florida's College of Journalism and Communications. What matters more than who (or what) is on the other side of the chat, Kelleher has found, is the perceived humanness of the interaction. With text-based bots becoming ubiquitous and AI-powered voice systems emerging, consumers of everything from shoes to insurance may find themselves talking to non-humans. Companies will have to decide when bots are appropriate and effective and when they're not.

The helpful person guiding you through your online purchase might not be a person at all. As artificial intelligence and natural language processing advance, we often don't know if we are talking to a person or an AI-powered chatbot, says Tom Kelleher, Ph.D., an advertising professor in the University of Florida's College of Journalism and Communications. What matters more than who (or what) is on the other side of the chat, Kelleher has found, is the perceived humanness of the interaction. With text-based bots becoming ubiquitous and AI-powered voice systems emerging, consumers of everything from shoes to insurance may find themselves talking to non-humans. Companies will have to decide when bots are appropriate and effective and when they're not.

Privacy is a complex thing. We all want to control our privacy and protect it from others: we may not want them to know or be able to share intimate things about our lives. However, the funny thing is that a lot of people do not mind sharing their data throughout their digital journey, even though this can be much more intimate and telling regarding who they are and what they like. In other words, many people do not mind companies knowing their intimate secrets or desires, but often mind other people knowing the same information. Globally, regulators have taken action or are planning to take action to protect people -- whether they care about their privacy or do not understand why their digital footprint needs protection.

You've likely heard that artificial intelligence (AI) is a powerful technology packed with all kinds of potential to enhance your marketing initiatives. You may also be a little anxious about starting to use it, as many marketers are. That's okay -- new technologies are often exciting, but they can also be a bit nerve-wracking. It can be tough not to worry about how to use AI when you're just starting. Is it easy to manage all the data?

Artificial Intelligence (AI) has gone mainstream when it comes to customer interactions, according to a new report from the Capgemini Research Institute. More than half of customers (54 percent) have daily AI-enabled interactions with organizations – a significant increase from the 21 percent reported in Capgemini's 2018 research on the subject. The report, 'The Art of Customer-Centric Artificial Intelligence: How organizations can unleash the full potential of AI in the customer experience', reveals the factors that have significantly contributed to AI adoption among customers, including increasing customer trust in AI; an increase in human-like AI interactions; increasing customer concerns arising from COVID-19; and organizations stepping up their AI deployments. COVID-19 has accelerated customer adoption of non-touch AI-based systems, such as voice assistants and facial recognition – a shot in the arm for AI adoption. Over three-quarters of customers (77 percent) expect to increase the use of touchless interfaces to avoid direct interactions with humans or touchscreens during COVID-19, and 62 percent will continue to do so post-COVID.

Artificial intelligence (AI) can suggest cancer treatments that doctors miss, rack up higher investment returns than human stockbrokers and perform various rote human tasks around the clock without ever tiring. In its many forms, it has advanced health discoveries, performed legal tasks and revolutionized personal technology. So, why are consumers still so wary of it? Despite its myriad applications, AI has limitations. During the technology's first big boom, many companies pushed those limitations to their brinks in consumer-facing products, from fault-prone early facial recognition technology to rogue chatbots to AI that made erroneous medical diagnoses.