Artificial intelligence (AI) is woven into the fabric of today’s business world.
However, the integration of the AI business model is in its infancy and small businesses often lack the resources to take advantage of AI.
Related: Deploy human safety sensors
Even so, AI is useful across a wide range of industries. There are already many models of human work augmented by AI. Understanding Established Patterns before AI integration is essential. For example, here are some common algorithm designs that use data sets to detect patterns and draw conclusions.
•Linear regression. Using supervised learning, this finds relationships between input and output variables; for example a person’s weight based on a known height.
•Logistic regression. It is a statistical model to predict the class of dependent variables from a set of given independent variables.
•Linear discriminant analysis. LDA is commonly used when two or more classes need to be differentiated; it is particularly useful in the field of medicine and computer vision.
•Deep neural networks. It refers to a artificial neural network having many layers between the input and output strata; often used in image and speech recognition.
•Naive Bayes. This is based on the Bayes’ theorem and is commonly used for test classification, including multi-class and binary classifications.
•Decision trees. Simple and efficient, DTs divide data into smaller portions. DTs are applicable to regression and classification problems.
• Support Vector Machines (SVM): SVMs are excellent for analyzing limited data. They are faster than many newer models and better used for text classification problems.
Many companies are using AI to automate a variety of processes. Companies that deploy AI to liberate their workers reap short-term productivity gains. While companies that have succeeded in developing a synergy between AI and human inputs experience a significant improvement in their performance.
Using collaborative intelligence, AI and humans can improve their strengths and build on their weaknesses in areas such as leadership, teamwork, social skills, speed, scalability, creativity, and skills. quantitative capabilities.
However, before that happens, it’s important to understand how humans can effectively augment AI and how to redesign business operations to support collaborations. Here are areas that can help develop this interaction:
• Human assistance machines. It is necessary for employees to train machines to perform tasks. Humans are needed to explain the outcome of tasks and maintain responsible use of machines. This will get the most out of the machines, even when the results are controversial or counter-intuitive.
•Machines assisting man. Conversely, machines should help humans develop their abilities. AI can help amplify human cognitive strengths and interactions with customers and employees, freeing humans from higher-level tasks. AI should embody human skills to help extend the physical capabilities of employees.
• Checks and balances. The World Economic Forum (WF) offers a holistic approach to implementing ethics in AI use. Three recommended approaches: the bottom-up approach, the top-down approach and the dogmatic approach.
From the bottom up, machines learn to make ethical decisions by observing human behavior. Top-down means that the ethical approach is programmed into the AI machines. Subsequently, the AI can react when situations require making ethical decisions. The dogmatic approach involves the programming of specific ethical schools of thought.
Knowing the above, you can begin integration in a few key areas that can transform your business operations. The first is customer experience. AI can help n enable highly personalized customer experiences – by ingesting and bringing together various customer data to drive customer behavior. Selling power reports that 50% of customers are likely to switch brands if you fail to anticipate their needs. AI helps avoid this by providing timely and relevant information and marketing prescriptions.
The second area is the cybersecurity talent shortage. AI digital assistants can help alleviate burnout among cybersecurity practitioners and positively impact skills shortage and retention issues. Digital Cybersecurity Analysts, a new conceptcan help practitioners improve their skills, improve their decision-making and automate processes.
In fact, the potential of AI to improve employee engagement and retention, at all levels, is quite enormous. Sentiment analysis systemsfor example, can help companies better understand employee engagement and better understand what drives their behavior.
In short, AI appears to be a secret weapon given its wide range of applications. However, it is necessary to apply the most suitable and efficient model for your business in order to get the best value. Reports have shown that augmenting human action with AI is the most beneficial use for businesses. It is advisable to create a beneficial synergy between employees and machines to achieve productivity gains.
About the essayist: Anurag Gurtu, CPO of Strike Ready, a California supplier of a cloud-based security management and operations platform that empowers and empowers cybersecurity teams with institutional insights and automation.
*** This is a syndicated blog from the Security Bloggers Network of The Last Watchdog written by bacohido. Read the original post at: https://www.lastwatchdog.com/guest-essay-a-primer-on-why-ai-could-be-your-companys-cybersecurity-secret-weapon-in-2022/