Artificial intelligence in heavy industry
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Artificial intelligence, in modern terms, generally refers to computer systems that mimic human cognitive functions. It encompasses independent learning and problem-solving. While this type of general artificial intelligence has not been achieved yet, most contemporary artificial intelligence projects are currently better understood as types of machine-learning algorithms, that can be integrated with existing data to understand, categorize, and adapt sets of data without the need for explicit programming.
AI-driven systems can discover patterns and trends, discover inefficiencies, and predict future outcomes based on historical trends, which ultimately enables informed decision-making. As such, they are potentially beneficial for many industries, notably heavy industry.
While the application of artificial intelligence in heavy industry is still in its early stages, applications are likely to include optimization of asset management and operational performance, as well as identifying efficiencies and decreasing downtime.
Potential benefits[edit source | edit]
AI-driven machines ensure an easier manufacturing process, with additional benefits, at each new stage of advancement. Technology creates new potential for task automation, while increasing the intelligence of human and machine interaction. Some benefits of AI include directed automation, 24/7 production, safer operational environments, and reduced operating costs.
Directed automation[edit source | edit]
AI and robots can execute actions repeatedly without any error, and also design more competent production models by building automation solutions. In addition, they are also capable of eliminating human errors and delivering superior levels of quality assurance on their own.
24/7 production[edit source | edit]
While humans must work in shifts to accommodate sleep and mealtimes, robots can keep a production line running continuously. Businesses can expand their production capabilities and meet higher demands for products from global customers due to boosted production from this round-the-clock work performance.
Safer operational environment[edit source | edit]
More AI means fewer human laborers performing dangerous and strenuous work. Logically speaking, with fewer humans and more robots performing activities associated with risk, the number of workplace accidents should dramatically decrease. It also offers a great opportunity for exploration because companies do not have to risk human life.
Condensed operating costs[edit source | edit]
With AI taking over day-to-day activities, a business will have considerably lower operating costs. Rather than employing humans to work in shifts, they could simply invest in AI. The only cost incurred would be from maintenance after the machinery is purchased and commissioned.
Environmental impacts[edit source | edit]
Self-driving cars are potentially beneficial to the environment. They can be programmed to navigate the most efficient route and reduce idle time, which could result in less fossil fuel consumption and greenhouse gas (GHG) emissions. The same could be said for heavy machinery used in heavy industry. AI can accurately follow a sequence of procedures repeatedly, whereas humans are prone to occasional errors.
Additional benefits of AI[edit source | edit]
AI and industrial automation have advanced considerably over the years. There has been an evolution of many new techniques and innovations, such as advances in sensors and the increase of computing capabilities. AI helps machines gather and extract data, identify patterns, adapt to new trends through machine intelligence, learning, and speech recognition. It also helps to make quick data-driven decisions, advance process effectiveness, minimize operational costs, facilitate product development, and enable extensive scalability.
Potential negatives[edit source | edit]
High cost[edit source | edit]
Although the cost has been decreasing in the past few years, individual development expenditures can still be as high as $300,000 for basic AI. Small businesses with a low capital investment may have difficulty generating the funds necessary to leverage AI. For larger companies, the price of AI may be higher, depending on how much AI is involved in the process. Because of higher costs, the feasibility of leveraging AI becomes a challenge for many companies. Nevertheless, the cost of utilizing AI can be cheaper for companies with the advent of open-source artificial intelligence software.
Reduced employment opportunities[edit source | edit]
Job opportunities will grow with the advent of AI; however, some jobs might be lost because AI would replace them. Any job that involves repetitive tasks is at risk of being replaced. In 2017, Gartner predicted 500,000 jobs would be created because of AI, but also predicted that up to 900,000 jobs could be lost because of it. These figures stand true for jobs only within the United States.
AI decision-making[edit source | edit]
AI is only as intelligent as the individuals responsible for its initial programming. In 2014, an active shooter situation led to people calling Uber to escape the shooting and surrounding area. Instead of recognizing this as a dangerous situation, the algorithm Uber used saw a rise in demand and increased its prices. This type of situation can be dangerous in the heavy industry, where one mistake can cost lives or cause injury.
Environmental impacts[edit source | edit]
Only 20 percent of electronic waste was recycled in 2016, despite 67 nations having enacted e-waste legislation. Electronic waste is expected to reach 52.2 million tons in the year 2021. The manufacture of digital devices and other electronics goes hand-in-hand with AI development which is poised to damage the environment. In September 2015, the German car company Volkswagen witnessed an international scandal. The software in the cars falsely activated emission controls of nitrogen oxide gases (NOx gases) when they were undergoing a sample test. Once the cars were on the road, the emission controls deactivated and the NOx emissions increased up to 40 times. NOx gases are harmful because they cause significant health problems, including respiratory problems and asthma. Further studies have shown that additional emissions could cause over 1,200 premature deaths in Europe and result in $2.4 million worth of lost productivity.
AI trained to act on environmental variables might have erroneous algorithms, which can lead to potentially negative effects on the environment. Algorithms trained on biased data will produce biased results. The COMPAS judicial decision support system is one such example of biased data producing unfair outcomes. When machines develop learning and decision-making ability that is not coded by a programmer, the mistakes can be hard to trace and see. As such, the management and scrutiny of AI-based processes are essential.
Effects of AI in the manufacturing industry[edit source | edit]
Landing.ai, a startup formed by Andrew Ng, developed machine-vision tools that detect microscopic defects in products at resolutions well beyond the human vision. The machine-vision tools use a machine-learning algorithm tested on small volumes of sample images. The computer not only 'sees' the errors but processes the information and learns from what it observes.
In 2014, China, Japan, the United States, the Republic of Korea and Germany together contributed to 70 percent of the total sales volume of robots. In the automotive industry, a sector with a particularly high degree of automation, Japan had the highest density of industrial robots in the world at 1,414 per 10,000 employees.
Generative design is a new process born from artificial intelligence. Designers or engineers specify design goals (as well as material parameters, manufacturing methods, and cost constraints) into the generative design software. The software explores all potential permutations for a feasible solution and generates design alternatives. The software also uses machine learning to test and learn from each iteration to test which iterations work and which iterations fail. It is said to effectively rent 50,000 computers [in the cloud] for an hour.
References[edit source | edit]
- "How Artificial Intelligence Can Solve Industry Challenges | SAP Analytics Cloud | Resources". SAP. 2017-02-07. Retrieved 2019-04-03.
- "The Future of Artificial Intelligence in Manufacturing Industries". www.plantautomation-technology.com. 2018-04-19. Retrieved 2019-03-06.
- West, Jack Karsten and Darrell M. (2015-10-26). "How robots, artificial intelligence, and machine learning will affect employment and public policy". Brookings. Retrieved 2019-03-07.
- Welle (www.dw.com), Deutsche. "Electronic waste reaches record high of 45 million tons | DW | 13.12.2017". DW.COM. Retrieved 2019-04-26.
- Ayres, Crystal. "16 Artificial Intelligence Pros and Cons". Vittana. Retrieved 2019-04-18.
- "When Software Rules: Rule of Law in the Age of Artificial Intelligence | Environmental Law Institute". www.eli.org. 2018-02-15. Retrieved 2019-04-26.
- Insights Team. "How AI Builds A Better Manufacturing Process". Forbes. Retrieved 2019-04-17.
- Fitch, Robert; Butler, Zack (March 2008). "Million Module March: Scalable Locomotion for Large Self-Reconfiguring Robots". The International Journal of Robotics Research. 27 (3–4): 331–343. doi:10.1177/0278364907085097. ISSN 0278-3649.