How artificial intelligence complements humans
How artificial intelligence complements humans
25. July 2022
Reading time: 8 minutes
Artificial intelligence is firmly integrated into our everyday lives. It is evolving faster than expected and in some cases is already outpacing human decision-making. From talking devices to digital assistants, cooperative robots and autonomous vehicles to drones, they are everywhere, taking over routine tasks in the workplace. Increasingly, we find it harder to distinguish between bots and humans in digital media.
Progressive digitization is affecting all sectors of the economy and has its origins in Big Data and the US internet corporations. Machine learning on high-performance hardware and software platforms provide artificial intelligence with the tools to learn from large amounts of data with complex interrelationships without explicit programming. The process of deep learning from artificial neurons enables intelligent machines to speak to us in any language, to perceive and interpret our shared environment. In doing so, artificial intelligence is creating a new communication interface for our homes, cars and wearable devices in the real world, replacing keyboards and touchscreens.
Classification of artificial intelligence
Classification of artificial intelligence, machine learning and deep learning
Artificial intelligence
Artificial intelligence (AI) is an umbrella term for applications in which machines approximate key functions of the human brain, such as learning, judgment, and problem solving, and provide human-like intelligence capabilities. These include machine learning, natural language processing, and deep learning.Artificial intelligence systems operate in a constantly changing and increasingly complex environment. They perceive their situation and explain it with the help of their knowledge - be it experiences and facts or abstract models and rules - in order to select their next actions, statements or other contributions required to fulfill their tasks and achieve their goals. In this way, they learn continuously.
In general, systems are distinguished between:
Weak artificial intelligence refers to a machine that can reproduce the individual cognitive ability of humans and thus perform certain tasks. In doing so, they behave intelligently - i.e. they provide solutions to concrete application problems - but possess neither creativity nor explicit abilities to learn independently in the universal sense.
Strong artificial intelligence refers to a machine that generally possesses the same capabilities as humans and can thus perform any intellectual task. Research is currently underway to replicate these highly complex human thought processes. Compared to weak artificial intelligence, strong AI not only possesses intelligence but also consciousness. It always acts rationally and, unlike humans, does not make any emotional decisions. So far, we have only seen concrete examples of its application in science fiction.
Superintelligence refers to a machine that not only reaches but exceeds human capabilities and can thus perform tasks on an intellectual level better than humans. Compared to strong artificial intelligence, superintelligence is capable of learning and can acquire new knowledge completely independently.
Machine learning
Machine learning is a subfield of artificial intelligence and computer science that deals with the use of data and algorithms to mimic the way humans learn and incrementally improve accuracy. As such, it is an important component of the growing field of data science.Through the use of statistical methods, algorithms are trained to make classifications or predictions to gain important insights in data mining projects. These insights, in turn, then serve as the basis for decision-making in applications and businesses, ideally impacting key growth metrics. As Big Data continues to grow, the demand for data scientists to identify the important business questions and answer them using the available data will increase.
Deep learning
Deep learning is a special method of information processing and a subfield of machine learning, which uses neural networks to analyze large data sets. Inspired by the biological neural network, the way it works mimics the human brain: it perceives something, thinks about it, and draws a conclusion. However, artificial neural networks have the ability to examine much larger data sets much faster than would ever be possible for humans.Deep learning models are characterized by their ability to learn. This learning process occurs when the systems constantly combine what they have learned with new content and thus learn again. The analysis is left entirely to the machine, so that humans no longer intervene in the actual learning process, which is also the decisive difference to machine learning.
Use cases from different industries
Artificial intelligence is already indispensable in some areas and has established itself profitably in various sectors with its sub-areas such as machine learning, natural language processing, deep learning and data analytics. In others, however, it is lagging behind. In the following, I will show a concrete use case from various sectors to illustrate how AI is being used or can be used in the near future.Artificial intelligence in healthcare
Predictive analytics is an innovation for healthcare. Based on historical data, it predicts the future and can thus predict the need for surgery while taking risks into account. Artificial intelligence helps physicians assess whether surgery is actually necessary or whether there are alternatives with lower risks to reduce the burden on patients.Artificial intelligence in banking and finance
Insurance companies rely heavily on data. They check applicants' backgrounds before processing insurance applications. Insurance companies sort through huge amounts of data to identify high-risk cases and reduce risk. Using natural language processing (NLP), insurance companies can analyze large amounts of text and identify the most important factors for specific claims and actions.Artificial intelligence in the automotive sector
Companies are using AI-based robotics in combination with human workers to perform manufacturing and supply chain tasks. The use of artificial intelligence has been proven in the proper handling of materials, the performance of tests and the packaging of finished products. In automotive production, for example, AI shortens the manufacturing process because robots can use deep-learning programs to determine which parts to remove and how.Artificial intelligence in manufacturing and factories.
The manufacturing sector is successfully using AI for advanced data analytics. Digital transformation has resulted in a wealth of real-time big data that is being used to provide detailed insights to predict current market trends. Combining data with advanced analytics provides tremendous help for risk management, data visualization, supply chain management, and fast and effective decision making.Artificial intelligence in education
AI tailors instruction and learning strategies to individual students through so-called personalized learning. Instead of the traditional one-size-fits-all approach, it serves learners with different abilities, considers individual knowledge gaps, and makes personalized learning recommendations to determine the most efficient learning pattern for each individual and respond to specific needs.Artificial intelligence in retail
Intelligent assistants can provide customers with automated responses in the form of physical robots with the function of a chatbot (as you already know from various websites today), give professional advice, and ideally even continue learning based on your information while you are talking to them.Artificial intelligence in telecommunications
One of the biggest contributions of artificial intelligence is the dialog-oriented AI. As a virtual assistant, it helps telecom companies manage massive customer support traffic, reduce wait times, maintain and fix problems, and also help with installation and setup of other projects. According to reports, virtual assistants can automate calls so efficiently that the telecommunications industry can save an estimated $8 billion annually by 2022.
"What AI-based machines lack is intuition, emotion and cultural sensitivity - yet these are precisely the human characteristics that make us so effective. When the symbiosis of the unique capabilities of humans and machines succeeds, it fosters the development of a new generation of products and services."
Riad BeqiriArtificial intelligence as a complement to humans
People often misunderstand the role of AI in the workplace and talk about machines replacing humans. In the following, I will show why they should rather be seen as supplements.That human workers will be replaced by artificial intelligence presupposes that humans and AI have the same characteristics and capabilities. In reality, this is far from the case. Faster, more accurate and consistently rational - that's how AI-based machines can be described. What they lack, however, is intuition, emotion, and cultural sensitivity, which are precisely the human qualities that make us so effective.
In general, modern computers are considered intelligent because they send out signals millions of times faster than human neurons and thus learn faster. With the ability to mimic human action, feeling, speech and decision-making, they recognize patterns of information and make their decisions on this basis. This type of intelligence appears to be extremely useful, especially in an organizational setting, as they recognize trends and optimize accordingly. Moreover, unlike the human body, AI does not fatigue. As long as the flow of data is guaranteed, it continues to work.
Compared to AI, which can only react to available data, humans have the ability to face changing situations, anticipate, react to, and assess the situation accordingly. This allows us to move from short-term to long-term considerations. In short, humans do not require a constant flow of externally provided data, which is the uniqueness of the human brain.
So humans represent the authentic intelligence – a different kind of AI, if you will. Open systems are needed for this kind of intelligence. Teams and organizations interact with an external environment and therefore have to cope with a wide variety of influences. Such a work environment requires the ability to respond to sudden changes, anticipate and deal with skewed information exchange. At the same time, creativity is required when it comes to developing a vision and a strategy for the future. This kind of interaction requires processes and systems to be constantly reshaped and thus to be truly intelligent.
Challenges in the use of AI systems
Looking at the above differences, it becomes clear that machines cannot completely replace humans, but that in combination a new form of efficiency can emerge. The challenges that arise in this context are manifold. Foremost among them are internal company issues surrounding the application and further development of AI methods. Also critical is social acceptance in dealing with the various technologies. In the following, I explain the most important pitfalls.A major danger in the use of AI is that humans forget how to make the right decisions in complex situations and no longer question the suggestions generated by the AI. This deskilling and loss of human expertise weakens companies in the long term and increases operational risks. AI should therefore only be used as a tool to enhance cognitive capabilities and not to completely replace the human workforce. This leads to the next difficulty, because the use of AI creates a black box through which decisions can no longer be interpreted or plausibilized. This task must be performed by a human with the appropriate expertise. AI should therefore be explanatory, so that the decisions are well-founded and justified and traceability is created.
Depending on the area of application, data protection, i.e. access to data and the right to use it, is often a major obstacle. Many current AI applications are based on machine learning and thus rely on the availability of high-quality, annotated data in sufficient numbers. A further challenge is the biased and complete data situation, the so-called bias. In addition, algorithms may exhibit systematic errors as a result of under- or overfitting. All this results in weak model predictions.
In the medical environment in particular, the question of additional regulations for the application of AI must be clarified at government level. Any regulations should be formulated with restraint and be balanced from an application perspective. It is important to base these guidelines on social values and interests, for example with regard to the explainability of AI decisions or the non-discriminatory application of the technology. At the same time, internationally aligned as well as industry-specific standards and guidelines will be needed to manage the compliance of the companies and the applied systems..
Outlook into the future
Artificial intelligence is a powerful tool. It is constantly evolving and offers many opportunities, but also poses risks. AI can optimize sales channels and maintenance techniques, increase production output and quality, improve customer service, and help save energy, to name just a few examples. It is already providing some support in many industries today and holds enormous potential for the future in particular.The greatest opportunity, but also the greatest risk, lies in the complementary use of AI and humans. AI is not a replacement for human labor but a complement. The full potential of AI can only be realized when insight is gained into this black box and human expertise is allowed to play a part. This requires that the AI be monitored more closely and that the justifiability of its decisions be developed to a greater extent..
In short, technology serves as a tool, while the company remains responsible. If this symbiosis of man and machine succeeds, it promotes the development of a new generation of products and services.
Author
Riad Beqiri
Riad's consulting focus is on ensuring uniform PM standards for efficient collaboration, optimizing project execution, and supporting project management. In addition, he accompanies software validations.
Riad Beqiri
Riad's consulting focus is on ensuring uniform PM standards for efficient collaboration, optimizing project execution, and supporting project management. In addition, he accompanies software validations.
