New AI tools are springing up like mushrooms. This is not surprising when you consider that Artificial Intelligence (AI) and Machine Learning (ML) are among the most revolutionary technologies of the last 10 years. AI and ML are the answer to growing data volumes, changing customer needs, compliance requirements, and competitive pressure.
Find out what’s behind the hype, how these tools are empowering both tech and business teams, and what not to expect.
AI vs. ML vs. Deep Learning: What's the Difference?
Artificial Intelligence refers to the ability of machines to perform human-like tasks. AI-based tech can process complex information, recognize patterns, draw conclusions, and make data-driven decisions.
Machine Learning is a subset of AI. It involves the development of algorithms and models that enable computers to learn from data and experience and to improve their performance at specific tasks.
What Are the Key Benefits of AI & ML?
Learning how to use ML and AI strategically can be your competitive advantage. The top 3 benefits of Artificial Intelligence are:
What Are the Technical Capabilities of AI and ML?
Technical Benefits of Artificial Intelligence and Machine Learning
AI and ML create new design possibilities unlike any other technology.
Technical Applications of AI and ML
Your developers can automate repeatable tasks and identify bottlenecks using intelligent data analysis and predictive models. Routine processes, such as data processing or information sorting, can be performed more efficiently and accurately.
Audio, image, and video processing
Computer vision can detect and process objects in digital images and video. Use it to automatically recognize gestures, identify faces, classify images, or improve video surveillance systems and visual analytics.
Data analysis and pattern recognition
Algorithms identify patterns, trends, and correlations even in large volumes of data. If you want to optimize specific business processes and identify trends early on, let algorithms do the work.
Speech and text analysis
Natural Language Processing (NLP) and Large Language Models (LLMs) such as ChatGPT enable AI systems to understand, process, and generate human speech. This puts automated translation, text generation, and dialog systems right at your fingertips.
What business areas are suitable for AI and ML?
Whether you want to speed up production, increase safety, or improve the customer experience – with machine learning models you’ll achieve better results in almost any area.
Want to see some examples?
Use image and video analytics to automatically detect and monitor security threats based on anomalies or potential errors, such as during routine inspections.
Customer service and personalized customer experience
Recommendation systems use ML algorithms to generate personalized product suggestions based on your customers' preferences and behavior.
Product development and innovation
AI and ML make it easier to optimize production processes. In generative design, for example, algorithms generate multiple design options at once that meet your specified parameters and constraints.
Human recognition and re-identification
Digression: Machine Learning in digital health, wellness & sports
In digital health, AI and ML have been booming for years because they make it easier for medical professionals to diagnose and treat patients. In medical imaging, algorithms help detect anomalies and diseases at an early stage. With AI-based personalized medicine, healthcare professionals can tailor therapies and treatments to individual patients. In patient monitoring, the systems enable seamless monitoring of vital signs in home nursing and early detection of equipment defects, anomalies, or disease.
Wellness and sports
Algorithms are also transforming wellness and sports applications. In mood and stress management, they support personalized recommendations and psychological interventions. In sports, they help users improve their performance, prevent injuries, or protect themselves from smog by tracking and analyzing various data. AI even revolutionizes games by accurately assessing human movement and tracking ball trajectories.
In these areas, ML is often combined with embedded systems. By using ML algorithms in embedded systems, devices such as IoT devices, sensors, and wearables become data-driven and intelligent. This means they can analyze data from sensors, detect obstacles or perform driving maneuvers. What’s more: They allow you to perform speech recognition or image recognition locally, without the need for an external connection or cloud-based systems.
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What are the challenges and limitations of AI?
As many benefits as Artificial Intelligence and Machine Learning offer, they are not the Holy Grail. We continue to see companies face the following challenges:
An innovator for (almost) any use case
Whether it's process automation, video processing or data analytics: ML offers a wide range of technical capabilities that can make your business more innovative and competitive. You’ll increase productivity, save resources, and improve the customer experience. That is, if you have good data, realistic expectations, and access to the skills you need, from AIOps to data protection and agile project management.