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Trends in AI and Machine Learning for Digital Technology Specialists

Investment in artificial intelligence (AI) and machine learning (ML) grew from $10 billion in 2020 to $57 billion in 2023, according to global market research, which predicts it will increase to $117 billion in 2027 — a compound annual growth rate of 45% through the period.

“AI success is becoming the rule, not the exception,” PwC notes in a survey of business leaders. AI/ML systems increase productivity, improve decision-making and customer service, accelerate innovation, enrich employees’ job satisfaction and enable the development of data-driven business models.

As businesses use resources to keep up with accelerating innovations in AI/ML, professionals with advanced insights into emerging trends offered by an advanced degree in applied digital technologies are in high demand in industries ranging from healthcare and finance to retail, manufacturing and transportation. Graduates of the Arkansas State University (A-State) online Master of Science (M.S.) in Applied Digital Technology with a concentration in AI and ML program are well prepared to fill workforce demand for these professionals.

What Are Some Emerging Innovations in AI/ML?

AI and ML are distinct but related processes. As a subset of AI, ML uses mathematical algorithms and data to optimize computational performance without the need for explicit programming. AI, on the other hand, incorporates ML to mimic human thinking. Online chatbots, for instance, use natural language text inputs to simulate conversation.

Among the most disruptive innovations, AI-enabled design, also known as Generative AI, is improving its ability to replicate creativity and accelerate ideation, concept development, prototyping and evaluation.

Generative AI leverages algorithms and Natural Language Processing (another subset of AI that recognizes text and images as data) to generate suggestions for improvements in existing designs to identify user preferences and measure and reporting performance metrics.

One of the key benefits of AI-enabled design processes is the ability to rapidly generate and evaluate a large number of design concepts. Another is its ability to automate design processes to save time and increase efficiency, allowing designers to focus on creative and strategic aspects of, among other things:

  • Customer service and support: Using automated chatbots and virtual assistants that get “smarter” over time improves response and frees staff from routine tasks.
  • Sales and marketing: Analyzing customer behavior and preferences enable businesses to personalize product recommendations to increase sales and customer engagement.
  • Software and product development: Automating code generation, debugging, documentation and testing allows developers to focus on designing features rather than correcting errors.

Generative AI [represents] a paradigm shift in innovation, significantly impacting enterprises exploring AI applications,” according to CIO magazine. It adds that ongoing innovations will “generate output of astonishing sophistication.”

Another trend in AI/ML is automated machine learning (AutoML), a process that automates the development and deployment of algorithms and models to simplify data preprocessing and cleaning, feature selection and model selection and configuration for non-technical staff. As demand for ML deployment grows throughout the economy, organizations are adopting autoML to:

  • Accelerate ML development while reducing resource allocation to build and deploy ML models
  • Improve computational accuracy
  • Provide enterprise-wide access to statistical and analytic model development

“Companies with data science teams can create their own platforms, tweak algorithms and parameters, and cobble together autoML tools, while those with limited data science teams can use an autoML platform to still get the desired outcome,” according to TechTarget.

Businesses also are rapidly innovating and expanding the deployment of AI/ML systems in their cybersecurity protocols to identify, stop and remediate breaches even as cybercriminals exploit the same technologies to corrupt, steal and hold data and other digital assets for ransom.

AI/ML-based cybersecurity analyzes network traffic, system logs, user behavior and other data to detect patterns and trends that suggest anomalies that may indicate a security threat and act. It also reduces human error by automating repetitive activities patch management and vulnerability assessment.

How an Advanced Degree Can Help

The field of AI/ML is developing at the speed of data, driven by rapidly evolving technologies such as the Internet of Things (IoT), cognitive analytics, regulatory compliance and augmented intelligence. This growth is creating demand for project managers, process improvement managers, implementation analysts with expertise in deploying the digital technologies that are the future of business.

Graduates of A-State’s online M.S. in Applied Digital Technology with a Concentration in AI and ML program will gain foundational AI and ML knowledge to understand and apply developing strategies and resources in their careers.

Learn more about Arkansas State University’s online Master of Science in Applied Digital Technology with a Concentration in Artificial Intelligence and Machine Learning program.

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