As the British American computer scientist Andrew Ng has said, “It is difficult to think of a major industry that AI will not transform”. Indeed, as a vital element of the automation revolution, artificial intelligence technologies are already driving innovation, efficiency and transformative changes across a variety of sectors. We’ll explore the synergy between AI and automation in this blog, pointing the way to their deep integration going forward.
Understanding AI in Automation
AI refers to a collection of technologies which have human-like cognitive abilities. While the concept of AI dates back to antiquity, it emerged more obviously in the Industrial Revolution, with Charles Babbage and Ada Lovelace developing the first design for a programmable machine in 1837. Further developments were made in the 20th century, with notable inventions such as the Turing Test (1950), ELIZA natural language program (1965), and Edward Faigenbaum’s systems which mimicked human decision-making (1980s).
In the modern world people interact with AI systems on an everyday basis. AI is connected with the algorithms which generate search engine results and provide film recommendations based on our preferences. Such algorithms can learn, adapt and improve processes over time, which also makes them integral to automation.
AI subsets include:
- Machine learning - integrated with algorithms and massive datasets, machine learning programs can recognise patterns and deal with complex problems
- Deep learning - a subset of machine learning and AI, deep learning involves the human-like processing of information with reference to an artificial neural network
- Natural language processing - the ability of a computer program to correctly interpret human language in the spoken and written form.
Here are some real-world examples of AI-driven automation applications:
- Prediction of equipment failures, allowing for proactive maintenance
- Performance of repetitive factory tasks such as assembly and packaging
- Analysis of medical images (X-rays, MRIs, CT scans) for early diagnosis
- High-speed analysis of financial market trends and development of investment strategies
- Handling of routine customer enquiries and generation of instant responses.
AI-Powered Robotics and Automation
Although two distinct disciplines, it’s becoming increasingly common for robotic systems to be integrated with AI. Indeed, the combination of these technologies has allowed for the resolution of various business challenges. There’s great scope for the transformation of manufacturing through the integration of these systems in Industry 4.0. With sensors for the detection of everything from visual changes to vibrations, such integrated systems can perform a range of advanced tasks.
The range of AI-powered robots includes:
- Autonomous mobile robots - integrated with sensing, mapping and navigation sensors, these mobile robots can perform such tasks as the movement of factory inventory
- Articulated robots (robotic arms) - resembling human arms, these robots can also be integrated with vision sensors for the detection and classification of objects
- Cobots - short for collaborative robots, these systems are designed for applications which can be performed together with human workers.
The development of such robotic systems is allowing for the precise and efficient completion of complex tasks. As an example, the September/October 2023 edition of Machinery Update (page 28) focused on the combination of vision systems in an automatic unit for the accurate detection and picking of parcels. With the connection of the Karmin3 stereo vision camera to Nerian’s stereo vision sensor, the Esorter can be configured to suit almost any application.
The same edition of Machinery Update also showcased these examples of advanced robotic systems, as exhibited at the 2023 PPMA Show:
- MiR250 autonomous robot range, allowing for round-the-clock productivity (page 40)
- Robopac’s self-propelled stretch film packaging machine for the delivery of efficiency, reliability and ease of use to customers (page 67)
- Piab’s cobot palletising grippers, allowing the combined picking of variously sized boxes (page 72).
AI-Driven Process Optimisation
As highlighted by the search engine example above, AI algorithms can analyse vast datasets for the identification of patterns, optimisation of processes and prediction of outcomes. This can also be seen in the continued development of ChatGPT and other generative AI tools which allow for the rapid production of texts and images. Such systems can access huge libraries of information and come up with solutions that might not otherwise be considered.
The AI applications also extend to the optimisation of supply chain management, demand forecasting, inventory management and inventory planning. Complete with a range of sensors, such advanced systems allow for the automated monitoring of deliveries and in-depth analysis of logistical options. The integration of such systems makes for significant cost reductions, efficiency improvements, and better decisions.
AI in Customer Experience and Service
AI-powered chatbots, virtual assistants and sentiment analysis tools can make a big difference in the delivery of positive customer experiences. It’s becoming increasingly common for businesses to integrate these technologies on their websites and other digital channels. Able to analyse huge amounts of customer data, they allow for personalised services which boost customer satisfaction and loyalty. Continuous improvements can also be made as AI customer service agents are connected with machine learning algorithms.
Here are some examples of AI-powered customer service capabilities:
- AI chatbots handling of routine enquiries and generating instant responses
- Virtual assistants guiding customers through troubleshooting processes and identifying relevant information
- Machine learning systems analysing customer data for the identification of issues and ideal responses
- Automated ticketing systems routing customers based on the urgency and severity of issues.
There are numerous examples of companies using AI to improve customer satisfaction, loyalty and engagement through automation. For example, Krafton, a South Korean game development company, has used an integrated customer service platform for the optimisation of their ticketing system. A dynamic content feature has allowed for the translation of the company’s website text for customers of different nationalities. And Netflix uses machine learning to make film recommendations based on the viewers’ preferences.
Ethical Considerations and Challenges
With such a wide range of benefits, it’s little wonder that the pace of AI adoption is increasing. However, the developers and decision-makers need to consider ethical issues such as the potential bias of algorithms, data privacy challenges and worries over job displacement. There are also challenges when it comes to the integration of AI into existing systems and the technical complexities which have to be overcome by human workers. Given such challenges, it’s understandable that there’s some resistance to the adoption of these advanced technologies.
There are, however, some established strategies and best practices when it comes to ensuring the responsible use of AI in automation. Organisations are advised to create clear and definite guidelines, ensuring that such technologies are only adopted in line with standards and values. Such guidelines will ideally be developed in consultation with stakeholders, such as company employees and customers. Company motivations and expected benefits of AI technologies should also be clearly communicated to ensure buy-in rather than resistance. There’s also a need for human-level monitoring to ensure that AI has the best possible impact.
Future Possibilities and Innovations
Given the variety of concerns and risks, there’s a clear need to focus on AI ethics moving forward. Governments and businesses both have major roles to play, ensuring that the benefits are realised with respect to human rights and values. This can be seen in the European Union’s proposal to establish a legal framework for the trustworthy adoption of AI within its borders. There’s also a need for understanding and trust when it comes to the output produced by machine learning algorithms. This should extend to those without specialist AI knowledge.
Given the potential benefits in terms of growth and efficiency, we’re bound to see further investment in AI through 2024 and beyond. This is likely to involve the increased adoption of AI tools, with the potential to take on a huge range of physical and cognitive tasks. There are bound to be more opportunities for human-AI collaboration, with emerging technologies such as robotic process automation, computer vision, and augmented reality further revolutionising industries.
AI looks set to have an increasingly profound impact on automation. The symbiotic development and adoption of such technologies will likely have a transformative bearing on established industries. This is sure to be an exciting time in human history, involving the emergence of new job opportunities and breakthroughs. However, the risks and uncertainties point to the need for continued research, with a focus on ethics and collaboration in realising the combined benefits of AI and automation.
Embrace The Future With Automate UK
As the leading trade association for automation suppliers and technology end users, Automate UK is excited to be leading the way in this digitally-focused age. We encourage members and non-members alike to stay informed, innovate responsibly and embrace the transformative power of AI at the heart of automation.
We invite you to share your thoughts and perspectives on AI-driven automation through the variety of Automate UK platforms and channels. Go ahead and get involved in discussions about responsible AI. Further your knowledge with the variety of resources on AI technologies.
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