What is artificial intelligence?
The term artificial intelligence applies to a family of computing techniques that mimic human cognitive functions, giving machines the ability to perceive, reason, and learn. The terms “artificial intelligence” or “AI” are often used to refer to individual fields and subfields of AI, such as machine learning, neural networks, or natural language processing. However, “artificial intelligence” can also refer to broader systems capable of observing their environment, learning from experience, and successfully handling unforeseen events.
Artificial intelligence offers some key advantages over humans: It can quickly process immense volumes of information or spot patterns that evade human perception. Humans, however, have their own unique strengths, such as general intelligence and powerful intuition and creativity. The aim is to make artificial intelligence a collaborator that complements humans – not a replacement or a rival.
What are the advantages and risks of AI?
The use of artificial intelligence is a complex and controversial question. Understanding AI’s main benefits and risks provides a solid foundation for understanding the issues involved:
Key advantages of AI
Two areas where AI is indispensable are big data and intelligent automation. Digitalization has produced titanic amounts of data, and the need to utilize this data has driven digital transformation and the development of many new technologies. Artificial intelligence helps not only to collect and structure this data, but also to analyze it to create actionable insights. Decisions based on these insights can then be automated intelligently by AI systems – the result is smart technology. A simple example of this is Google Services, which process vast amounts of data to provide better search results or assist users in writing better emails. Another is smart manufacturing. Here, AI analyzes sensor data, synthesizes it with other data types, and generates decisions with the help of holistic analysis. It then routes automated commands based on these decisions to physical systems and evaluates the results to improve future processes.
In addition, artificial intelligence can provide powerful benefits in cybersecurity. It can automatically scan for threats and vulnerabilities, execute protective actions, and exploit big data to improve its capability and threat awareness. In the world of work, AI has great potential to free humans from repetitive tasks (auto-complete forms, automated workflows) or even dangerous ones (ATEX/IECEx Zone 1-certified robots for explosive environments). More optimistic arguments suggest AI could even liberate human beings for fun or creative work, or that the increased profits from AI labor could enable universal basic income to cover the essential costs of living for every citizen.
Key risks of AI
In some ways, the key risks of AI mirror its benefits. The powerful capabilities of artificial intelligence in the areas of data processing, automation, and computing present a range of challenges. The exploitive capitalization of consumer data is largely fueled by automated, intelligent data processing. The same can be said for the hordes of bots that make sales calls, send fraudulent emails, and troll social media channels. More troublesome is the potential for abuse with advanced tools such as deepfakes. This technology uses AI to create scandalous image or video data of real persons that is fake but indistinguishable from real content.
AI in the hands of cybercriminals can massively expand the number of cyberattacks while also outfitting them with new capabilities and intelligence. And while AI will create new jobs, it will also automate anything that can be automated – and at much lower costs. The sweeping effects of AI on the labor market must be addressed before the impact is felt.
The evolution and deployment of AI itself also pose unique risks. It is currently too easy for AIs to introduce bias into their decisions based on the data sets they are trained on. The high costs and environmental impact of training and running AI are also problematic. Furthermore, a deeper systemic risk remains: It is often impossible to say how AI has reached its decisions, how it will develop in the future, or how it will impact society. The need for transparency and consensus in the field of AI is acknowledged almost universally.
What is the difference between artificial intelligence and machine learning?
Machine learning (ML) is a subfield of artificial intelligence, a type of artificial intelligence, and a necessary functional component of most modern AI systems. Machine learning uses algorithms that automatically analyze data, work to achieve desired outcomes, and continuously improve performance instead of relying on preprogrammed instructions. Generally speaking, ML focuses on data science, smart automation, processing and analyzing data, generating insights, and improving business performance. Typically, machine learning algorithms train on vast data sets over millions of iterations before they are deployed.
Traditional forms of machine learning are becoming increasingly complemented by more advanced developments, such as deep learning and swarm intelligence. Because machine learning systems have access to large amounts of trend data and powerful techniques for making accurate predictions, one of the chief roles of ML is predictive analytics.
Because artificial intelligence and machine learning are both prominent, the topics are sometimes divided artificially. The question of whether artificial intelligence or machine learning should be used for a given application can be misleading. A better question to ask is: Would my application mostly benefit from the added value specific to machine learning, or does it need to involve more comprehensive AI systems?
Definition of Industrial AI, Machine Learning and Deep Learning
What are typical AI applications in the process industry?
AI is a core element of the Industrial Internet of Things (IIoT) and Industry 4.0. In I4.0, the human, digital, and physical spheres of industrial organizations are combined into an intelligent, integrated whole. Industrial AI systems need to be tailored to each customer and therefore require cutting-edge AI in combination with domain expertise.
Despite the specific nature of industrial AI, core applications can generally be divided into a few areas:
Optimizing operational efficiency: The number-one challenge faced in the process industry is unplanned downtime. By providing real-time monitoring, analytical, and preventive functions, AI programs are a huge step above human labor here. This is part of the central function of AI and ML: maximizing efficiency and reducing costs and waste. Artificial intelligence is used to optimize processes and resource management as well as many other tasks, including facility layout and labor allocation.
Predictive maintenance: Historically, industrial maintenance has been capped at the limitations of human operators and isolated physical systems. This has made maintenance limited, slow, and reactive. Paired with IIoT sensors and interlinked systems, AI provides reliable real-time data and automated control. AI and ML can also perform smart analysis on pooled data to generate insights, improve data models, and predict future situations. This makes industrial maintenance truly predictive, and one of the most important applications for industrial AI.
Optimizing infrastructure: AI systems can improve supply chain efficiency by monitoring and managing goods and materials across the supply chain. The AI-supported analysis of raw materials, machinery, and labor practices further helps companies understand how they impact overall quality. Another critical way that AI improves infrastructure is by merging IT and OT (operational technology) systems with enhanced security (IT/OT convergence).
Enabling smart analytics: AI and machine learning generate insights from the analysis of complex ongoing processes involving massive amounts of data, and can initiate appropriate actions when necessary. Machine learning is often used to analyze operating procedures and alarm behavior, whereas natural language processing can be applied to customer communications to identify pain points. Globally speaking, AI/ML allow for a unified data fabric and smart analytical layer across all company operations.
Artificial intelligence has already proven its value and will only continue to do so – exponentially. Today’s applications are quickly giving way to future-oriented fields, such as self-driving vehicles, personalized medicine, and adaptive entertainment. In industry, artificial intelligence is helping pave the way to the future of autonomous operations, where industrial organizations run autonomously with little or no human involvement. Today, a majority of companies acknowledge the importance of AI, but very few have made much progress when it comes to actual implementation. This leaves space for early adopters of artificial intelligence – and those who provide cutting-edge AI solutions – to enjoy a decisive market advantage.
Key Pillars for Industrial AI Enablement
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