The concept of artificial intelligence (ai) was formally introduced in 1956, with the aim of developing human-like intelligence. Although technology has made great strides since then, it is still impossible to predict how AI will develop and what steps still need to be taken. What is certain is that the corona pandemic has accelerated digitization, thereby further stimulating investment in AI.
The Netherlands is third in the EU ranking of digital economies, and compared to other European citizens, the Dutch have above-average digital skills. In addition, research into AI has grown significantly in recent years, especially with regard to planning and decision-making. In the EU, the strategy aims to increase investment to €20 billion per year by 2030. Global technology and automation companies are even investing in developing their own AI algorithms.
That said, AI is still an evolving technology and there are tradeoffs that companies need to consider when integrating AI into their processes and decision-making.
What can the history of AI teach us about the future? And what are the pitfalls of AI that we need to be aware of to ensure a more productive future?
“In 1956 Newel, Simon and Shaw introduced the first AI-based program known as the Logic Theorist”
In 1950, the pioneer Alan Turing tried to prove that a computer would be able to convince people of its intelligence. This was the beginning of the imitation game, also known as the Turing test, which is known worldwide today and is used in many case studies on AI. Later, in 1956, Allen Newel, Herbert Simon, and Cliff Shaw introduced the first AI-based program known as the Logic Theorist. This program was able to explore a search tree based on logic, by applying ad hoc rules (later called heuristics) and using a programming language called Information Processing Language to process lists.
Today, artificial intelligence is used in all kinds of situations: from personal environments, such as smart IoT devices in the home, to companies that want to automate financial processes or the use of robots in warehouses. We use AI to augment our own intelligence – to find complex solutions, deal with uncertainties, learn from large data sets, and much more. The opportunities offered by this technology continue to evolve, but so do the challenges.
AI technology has changed the way we interact with each other and the world. See the facial recognition on our phones or the systems we work with. In doing so, it has also changed the way we do business. Process automation, the use of robots for warehouse optimization, real-time insights and data-driven decisions are some examples of AI that we encounter every day.
Globally, companies are using AI to expand their capabilities and optimize their performance and operations. Another benefit of AI is the automation of repetitive tasks, leaving employees more time for more complex tasks. But AI can also present challenges and more complexity for companies. Think of the integration with existing systems, the high costs of the technology, and the limited number of people with the right expertise in the current labor market.
Those who are skeptical of AI’s advancement point to aspects such as bias, ethics, modeling, and security. Data and software are programmed by humans, and that means AI is too. How do we prevent human bias from becoming part of the way artificial intelligence analyzes data? There is no simple answer, but by doing the programming together, we can ensure diverse perspectives and less bias.
“As fast as technology moves, that’s how fast we have to move”
However, AI is about more than just data sets and should be seen as a continuously evolving technology tool that helps automate repetitive, tedious tasks. In AI labs, developers apply AI and machine learning to discover new patterns in large amounts of data. Models can then be developed that provide insight into this data, so that companies can do their work better.
An example is expense management. Using AI, information is automatically read from payment receipts, card transactions or supplier invoices and correctly linked to the corresponding PO numbers and files. This is a task that used to have to be done manually. By automating this, companies can save time and money, while reducing the chance of fraud or errors.
Since there is no international standard for how information should be organized on receipts, the software is constantly evolving to understand how to read these documents and process information. As fast as technology moves, we must move with it.
With everything we’ve learned about AI over the past 75 years, and considering the current limitations of technology, the future of AI looks bright. She has not only helped companies recover from global crises by reducing costs and increasing efficiency, but also paved the way for future opportunities that can positively impact the economy and society as a whole.