Artificial Intelligence (AI) is one of the most promising technologies for growth today. According to recent data released by the consulting firm Gartner organizations that have implemented AI grew from 4 to 14% between 2018 and 2019.
In fact, the same consultancy firm includes Artificial Intelligence in its technology trends for the year 2020. Specifically, AI focused on improving IT security.
AI is a key technology in Industry 4.0 because of all the advantages it brings to companies and all those who want to start a digital transformation process would have to adopt it in their processes.
What is artificial intelligence?
The concept of Artificial Intelligence has been around for a long time. In fact, John McCarthy created the term Artificial Intelligence in 1950 and Alan Turing already started talking about this reality that same year in an article entitled “Computing Machinery and Intelligence”.
Since then this discipline of computer science has evolved a lot.
For Massachusetts Institute of Technology professor Patrick H. Winston, IA are “constraint enabled algorithms exposed by representations that support looping models that link thought, perception and action. ”
Other authors, such as DataRobot CEO Jeremy Achin, define artificial intelligence as a computer system that is used for machines to perform work that requires human intelligence.
For the head of Tech Target’s technological encyclopedia, Margaret Rose, it is a system that simulates different human processes such as learning, reasoning and self-correction.
As we can see, the three definitions of AI refer to machines or computer systems that think. They emit reasoning emulating human intelligence to perform tasks that only people can do.
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However, other sources go further and define AI as a computer system used to solve complex problems that are beyond the capacity of the human brain.
In this sense, AI harnesses the power of machines to solve complex problems that the human mind cannot reach.
The president of the Future Life Institute, Max Tegmark, shoots in this direction and states that “since everything we like about our civilization is a product of our intelligence, amplifying our human intelligence with artificial intelligence has the potential to help civilization flourish like never before”.
Regarding this issue, Google Deep Mind, and Oxford University conducted research whose conclusions indicate that AI is capable of deciphering damaged and illegible Ancient Greek texts. While the error rate of historians and epigraphers is 57.3%, the error rate of the algorithm responsible for this feat is 30.1%.
These examples show us how AI goes beyond the human capacity to solve complex problems. But how does AI work?
How does AI work?
AI works through algorithms that act from programming rules and its subset Machine Learning (ML) and the different ML techniques such as Deep Learning (DL).
Machine Learning (ML)
It is a branch of Artificial Intelligence and one of the most common that is responsible for developing techniques for the algorithms that have been developed to learn and improve over time. It involves a large amount of code and complex mathematical formulas to enable machines to find the solution to a given problem.
This aspect of AI is one of the most developed for commercial or business purposes at present, as it is used to process large amounts of data quickly and deposit them in a manner that is understandable to humans.
A clear example of this is data from production plants where the connected elements feed a constant flow of data on machine status, production, functionality, temperature, etc. to a central core.
This enormous amount of data derived from the production process must be analyzed in order to achieve continuous improvement and appropriate decision making, however the volume of this data means that humans must spend a great deal of time (days) on analysis and traceability.
This is when Machine Learning comes into play, allowing data to be analyzed as it is incorporated into the production process and identifying patterns or anomalies in operation more quickly and accurately. In this way, warnings or alerts can be triggered for decision making.
However, the ML is a relatively broad category. The development of these artificial intelligence nodes has given rise to what is now known as Deep Learning (DL).
Deep Learning (DL)
It is an even more specific version of Machine Learning (ML) that refers to a set of algorithms (or neural networks) that are designed for machine learning and participate in non-linear reasoning.
In this technique the algorithms are grouped into artificial neural networks that are intended to act like the human neural networks present in the brain. It is a technique that allows you to learn in a deep way without a specific code for it.
Deep Learning is fundamental to perform much more advanced functions allowing the analysis of a wide range of factors at the same time.
For example, Deep Learing is used to contextualize the information received by the sensors used in autonomous cars: the distance of objects, the speed at which they move, predictions based on the movement they are making, etc. They use this information to decide how and when to change lanes, among other things.
We are still at a stage where the DL is still in a very early stage of development of its full potential. We see that it is increasingly used in business by converting data into much more detailed and scalable sets.
AI in business environment
AI is already used in many commercial and production applications, including automation, language processing and production data analysis.
This allows that at a general level, companies are optimizing both their manufacturing processes, operations and improving their internal efficiency.
AI works through different computer programming rules that allow a machine to behave like a human and solve problems.
The interest of companies in implementing AI techniques in their processes lies in the advantages it brings.
Benefits of AI
Different voices in the technology sector defend the benefits of Artificial Intelligence (AI).
Infinia ML’s Product Manager, Andy Chan, at a TED Talks with over 40,000 visits on Youtube, breaks down the various benefits of AI at work.
Kai-Fu Lee, founder of the venture capital fund Sinovation Ventures and a leading figure in the field of technology, also describes the main benefits of AI in a TED Talks video with over 600,000 plays.
Taking into account these two experts, these would be the main advantages of AI applied to a business sector:
- 1. Automates the processes.Artificial Intelligence allows robots to develop repetitive, routine and process optimization tasks automatically and without human intervention.
- 2. Enhance creative tasks. AI frees people from routine and repetitive tasks and allows them to spend more time on creative functions.
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- 3. Provides precision.The application of AI is capable of providing greater precision than humans, for example in industrial environments, machines can make decisions that were previously made manually or monitored without AI.
- 4. Reduces human error. AI reduces failures caused by human limitations. In some production lines, AI is used to detect, by means of infrared sensors, small cracks or defects in parts that are undetectable by the human eye.
- 5. Reduces time spent on data analysis. It allows the analysis and exploitation of the data derived from production to be carried out in real time.
- 6. Predictive maintenance. It allows to carry out a maintenance of the industrial equipment based on the times and conditions of operation of the same, allowing to increase its performance and life cycle.
- 7. Improvement in decision making at both production and business levels. By having more information in a structured way, it allows each of the people in charge to make decisions in a faster and more efficient way.
- 8. Control and optimization of productive processes and production lines Through AI, more efficient, error-free processes are achieved, obtaining greater control over production lines in the company.
- 9. Increase in productivity and quality in production. AI not only increases productivity at the machine level, it also makes workers more productive and increases the quality of the work they do. Having more information allows them to have a more focused view of their work and make better decisions.
Risks and barriers of AI
Some voices believe that Artificial Intelligence (AI) has risks. Especially if the potential of AI is explored and not just limited to reproducing human tasks. Authors such as Stephen Hawking or Bill Gates and different researchers have expressed their concern about AI.
With regard to barriers to entry, these would be some of the most common that can occur in the business environment:
- Data availability. Often,data is presented in isolation across companies or is inconsistent and of low quality, presenting a significant challenge for companies seeking to create value from AI at scale. In order to overcome this barrier, it will be vital to draw up a clear strategy from the outset so thatAI data can be extracted in an organized and consistent manner.
- Lack of qualified professionals. Another obstacle that often occurs at the business level for the adoption of AI is the scarcity of profiles with skills and experience in this type of implementations. It is crucial in these cases to have professionals who have already worked on projects of the same magnitude.
- The cost and implementation time of AI projects. The cost of implementation, both at the time and the economic level, is a very important factor in choosing to execute this type of project. Companies that lack internal skills or are not familiar with AI systems, must value the outsourcing of both implementation and maintenance in order to obtain successful results in their project.
In short, AI has become a very important resource for companies as it allows them to be much more competitive and obtain greater benefits, especially in manufacturing and production environments.
It is for all these reasons that these types of professional profiles are increasingly in demand in the industrial sector, making it essential to have groups of experts in the field to develop efficient strategies for digital transformation.
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