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Within the ever-evolving technological environment,
Artificial intelligence involves various disciplines. It doesn’t just rely on computer science, statistics, etc. Depending on where it is applied, it also requires other fields, such as psychology (cognitive modeling) and philosophy (philosophy of mind).
Examples of artificial intelligence and its applications
Artificial intelligence continues to grow rapidly. The areas where it is applied have expanded, ranging from retail, banking, and health care to automotive. The logistics, entertainment, and manufacturing sectors have also started implementing it.
Today, AI has become a significant driving force for technological transformation. Many companies adopt it to empower decision-making, drive efficiency, and improve customer experience.
An early development example is a chess-playing computer system. In fact, it can beat human players. An example is Deep Blue, which won against world chess champion Garry Kasparov on May 11, 1997.
Expert system
Another example is an expert system. This is a further development. This system utilizes computers to codify experts in a particular field. The system stores, organizes, and retrieves information and stores knowledge and experience.
So, when we ask questions to the system, it will give answers like these experts. In other words, it will work as if it were an expert.
Expert systems have many applications. They perform various tasks, such as analyzing company results, reviewing loan applications, buying stocks, making medical diagnoses, and identifying poisons.
Apart from codifying experts, the system can also create new knowledge by extrapolating data and patterns. The Google search engine is an example. Google can recognize our online habits and interpret queries in search engines. Then, the system sends a personalized ad tailored to what we were looking for earlier.
Another example is Google Translate. The first time it was launched, it gave relatively clumsy translation results.
Google Translate allows us to provide feedback on the correct translation. Then, Google’s engine stores and builds algorithms with it, along with other sources. Now, Google Translate results are better than ever.
Robotics
Developers build robots and program them to react to sensory stimuli and respond accordingly. They act as if they were human. Therefore, robotics techniques are often applied to specific fields to replace humans in carrying out tasks.
For example, car manufacturers use robots on their assembly lines. Robots replace human hands to perform tasks.
Unlike humans, robots can work tirelessly without rest, making them more productive than humans. In addition, their precision is measured, resulting in consistent quality.
Weak AI vs. Strong AI
There are two categories of AI: Weak and Strong AI. Weak AI is designed for a specific task. Apple’s Siri and other virtual personal assistants are examples.
Meanwhile, Strong AI has general human cognitive abilities. Strong AI systems can find solutions when presented with specific tasks without human intervention.
Types of artificial intelligence
Arend Hintze categorizes AI into 4 types. They are:
- Reactive machines
- Limited memory
- Theory of mind
- Self-awareness
Reactive machines. Deep Blue is an example. The reactive engine identifies pieces on the chessboard, makes predictions, and analyzes the opponent’s possible moves. It also identifies and takes the most strategic steps.
But, it has no memory and stores information. Thus, the machine cannot use past experience to tell the next step.
It has limited memory. It uses past experiences to inform future decisions, but the information is not stored permanently.
Its application in self-driving cars is an example. Some of its functions use a limited memory system.
Theory of mind. This AI system is more complex than the previous two types. It does not only represent actual conditions but also has its own beliefs, desires, and intentions to influence its decisions.
Self-awareness. It is designed to have a sense of self and awareness. It works like a human. For example, the system understands and uses the current situation to infer how others feel.
Advantages and disadvantages of artificial intelligence
Artificial intelligence (AI) is revolutionizing industries, and its impact on the business world is undeniable. Here’s a closer look at the advantages and disadvantages of AI adoption, crucial considerations for potential investors.
Advantages of artificial intelligence
- Enhanced efficiency and productivity:
Artificial intelligence automates repetitive tasks, freeing up human workers to focus on more strategic initiatives. This translates to significant cost savings and increased production output. - Precision and accuracy: AI algorithms can analyze vast amounts of data with unmatched precision, minimizing errors and ensuring consistent high-quality results. Imagine AI-powered robots on a manufacturing line, flawlessly assembling complex components.
- Hyper-personalization:
Artificial intelligence excels at understanding customer behavior and preferences. Businesses can leverage this to personalize product recommendations, marketing campaigns, and overall customer experiences, leading to higher satisfaction and loyalty. - Real-time decision-making: AI can process information and identify patterns in real-time, enabling businesses to make data-driven decisions faster and more effectively. For instance, AI can analyze customer sentiment on social media and alert companies to potential issues before they escalate.
- 24/7 customer service: Chatbots powered by AI can provide customer support around the clock, answering basic inquiries and resolving simple issues. This frees up human customer service representatives for more complex situations.
Disadvantages of artificial intelligence
- Implementation costs: Developing and implementing AI solutions can be expensive, requiring significant investments in infrastructure, software, and specialized talent. This can be a barrier for smaller companies.
- Talent shortage: The pool of skilled AI professionals is limited, driving up recruitment costs. investors should consider companies with the resources and expertise to attract and retain top AI talent.
- Task-specific limitations: Current AI systems are often designed for specific tasks and may struggle to adapt to new situations or generalize their knowledge. Investors should look for companies that are actively developing AI with broader learning capabilities.
- Emotional disconnect: AI lacks the human touch that can be crucial in customer interactions. Building trust and rapport with customers may still require a human element, especially in service-oriented industries.
- Security risks: As AI becomes more sophisticated, so do potential security threats. Companies integrating AI need robust cybersecurity measures to protect sensitive data and prevent malicious attacks.
Artificial intelligence controversy
Rapidly evolving technology and knowledge are important driving factors for artificial intelligence. In the past, we may have considered a basic-function computing engine, a graphics simulator, and a text recognition engine
However, this view has changed over time. Such functions are now embedded in the latest computers and are commonly used.
Artificial intelligence is under scrutiny from scientists and society. It was criticized by various parties for several reasons.
First, machines will become very advanced, and humans will be unable to keep up. This view is a common reason to criticize AI. AI systems will take off, redesigning themselves exponentially and threatening human existence.
Second, privacy threats and cyber crimes are increasing. Criminals exploit it to hack into people’s privacy and even weaponize it.
Third, other arguments argue about ethics. Even though it has a mind and works like a human, AI may not or, until now, does not have emotions and feelings like humans. So, should we treat intelligent systems like robots with the same rights as humans?
