Machsync

Artificial intelligence is a system or machine that can imitate human intelligence and perform tasks. It is a field of computer science. It can learn based on collected information, also solving, and identifying problems. In the past, artificial intelligence often appear in many science fiction movies which are also our imagination of the future, and now AI is no longer a fictional machine or system in fiction but is truly regarded as the reality of advanced computer science.

Professor Pedro Domingos is a researcher in this field. He proposed that there are five major sects of machine learning in AI, including symbolism (logic and philosophy), connectionism (branch of neuroscience), evolutionism (evolutionary biology), Bayesianism (statistics and probability), and analogism (psychology), and these techniques can be divided into "supervised" and "unsupervised" learning techniques, the former is training data with expected results, and the latter is not. The more data is provided for AI, the smarter it will become and the faster it will learn. Therefore, the appearance of IoT and sensors has increased the amount of data, including sources, places, and events that have not been touched in the past which assists the development of artificial intelligence.

Artificial intelligence is a highly technical and professional field. Each branch is very in-depth and covers a wide range. It can be divided into four parts:

• Expert System: Act as an expert to deal with the situation under review and bring in the desired effect with the information.
• Heuristic problem solution: Evaluate a specific range of possible solutions and find the best one given the guesswork that may be involved.
• Natural language processing: Realize the communication between humans and machines in a language.
• Computer Vision: Automatically generate the ability to recognize the shape and function.

The point of artificial intelligence is to build the capabilities of reasoning, knowledge, planning, learning, communication, and perception that are similar to human beings. Although there have been preliminary results in some aspects, continuous exploration and research are needed to achieve true artificial intelligence.

Knowledge engineering is a major part of artificial intelligence research in the past. The first step in the development of artificial intelligence is to allow machines to read a large amount of data, and to allow computers to have the ability to judge objects, and classify and compare the correlation between data. This development will allow computers to acquire specialized knowledge, however, it will be harder for computers to own common sense, reason and think, and the ability to solve problems.

Machine learning is another part of the development of modern artificial intelligence. It mainly solves problems by inductive reasoning after processing and learning huge data. When new data is obtained, machine learning will learn by itself and update its understanding and changing the original cognition of the world.

For example, if there is a person who has no concept of the aesthetics of clothes, we should start to instill in him, or her which clothes are beautiful, and which are ugly. When receiving this information, they will begin to have their own ideas about the aesthetics of clothes. In fact, the key is that the amount of this information must be large enough and of good quality, so that machine learning can have a model that can better judge the answer to the question.

Deep learning is part of a broader family of machine learning methods based on multi-level artificial neural networks, and the two main types are Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).

CNN is more suitable for pictures or film-type data. It can identify images through the characteristics of different classes, such as the characteristics of nose, eyes, mouth, and the relationship between each other, and become a face finally. The development is very important for autonomous driving because it needs to quickly recognize the surrounding environment to ensure safety. RNN is more suitable for speech or text. Unlike other neural networks, all inputs in this technology are the same, and all processed information will be remembered during learning, so it is very suitable for processing natural language.

Although the technology of neural networks has been developed in the past, it cannot be realized in a commercial environment due to the difficulty of data collection, the calculation speed, and other costs. However, the current calculation speed, cost, and algorithm are different from those before which makes these technologies have also begun to be applied in various fields.

• Speech recognition: Humans have a variety of accents and speech styles. Traditional computing or computer science methods would be difficult to complete the recognition task, however, deep learning algorithms can easily determine the content and intent.

• Natural language understanding: Natural language processing is to teach machines to understand human language, tone, and content. Among all of them, emotion or irony is very difficult to identify, but many companies hope to provide automated services through voice or text robots, this application is still under development.

• Recommendation engine: The Internet often recommends user products, movies, articles, etc. based on personal habits or preferences. As the technology of big data and deep learning becomes more and more mature, it is possible to check the products purchased or browsed in the past through algorithms and compare them with other products for a better web experience.

• Classification of videos and photos: It is now possible to add tags to photos or videos through algorithms, and even identify objects in the images, which is a very important advancement for many web services or searches.

Reinforcement learning is an area of machine learning. This method of uses the reward and punishment mechanism to train the algorithm model. Briefly speaking, when the algorithm does the behavior we expected, it will be rewarded for it, so that it can do as many as we expected behaviors, and vice versa. The effectiveness of each task to evaluate the algorithm is measured by the number of rewards. Reinforcement learning has a wide range of applications, including trajectory optimization, route planning, or motion planning for autonomous driving, and has also extended to the fields of marketing and sales.

Ensemble learning can reduce model bias, and variability, improve data accuracy and apply different machine learning algorithms at each stage to train the model's algorithm. It is very useful when the data is complex or there are many underlying assumptions because the model can be built based on different assumptions to define a more correct direction.

In the technology of artificial intelligence, whether it is the department of machine learning, ensemble learning, deep learning, or reinforcement learning, there is strong potential in various fields, and with the increasing need for transformation among various industries, the application of artificial intelligence is also increasing a lot, such as Industry 4.0 in manufacturing, smart cities, or smart homes.

No matter what field of application it is, we can divide the value of artificial intelligence into time series, image processing, audio processing, Neuro-Linguistic Programming (NLP), and image processing. All the technologies used in the industry are based on these values, such as Siri from IOS. Siri is a very mature system for processing audio and natural language.

• Enterprises’ artificial intelligence needs to build its own method: In fact, many enterprises have adopted artificial intelligence in combination with internal solutions. Internal development allows enterprises to customize according to different needs, they can also use off-the-shelf solutions to streamline processes or costs to solve more common business problems.

• Artificial intelligence can provide results immediately: The current technology of artificial intelligence is not perfect, and it will take time and careful planning to achieve the desired results after introduction.

• Enterprise artificial intelligence does not require human execution: The real meaning of AI is to increase people's capabilities, provide timely and correct information and analysis, also enable people to complete more strategies and tasks within a limited time.

• The more information the better: Artificial intelligence requires a lot of data, in order to obtain the best solutions or decision-making opinions, high-quality, up-to-date, and relevant data are needed, so that the benefits of artificial intelligence can truly be exerted.

• Artificial intelligence only needs data and models: Data, algorithms, and models are only preliminary, and more detailed solutions need to be expanded to meet ever-changing needs. Currently, most solutions are carried out by data analysts, and humans are required to set up and maintain them, and successful AI solutions need to be continuously expanded to meet new needs with the development.

AI has grown by leaps and bounds in recent years. In terms of the adoption rate and technology penetration rate of various industries, this technology has successfully brought a great impact on telecommunications, finance, software platforms, and manufacturing industries. There are indeed changes to human life, driven by artificial intelligence, there are six major trends that have become most evident in recent years.

1. The rapid growth of reinforcement learning: Since AlphaGo defeated South Korean chess player Lee Sedol in Go in 2015, reinforcement learning has been mentioned more and more in AI-related papers, and it has also begun to create value in various fields. Google even reduced energy consumption by more than 50% through reinforcement learning.
2. Change in business decisions: AI is involved in many business decisions, including operations, marketing, and sales, and even design, also creates the link between data and business decisions.
3. Process automation: This is the most common application of artificial intelligence. In past research on 152 AI applications, it was found that nearly half of them are based on process automation, and the development has been very mature in recent years. The application will be greatly improved, enabling various fields to complete tasks with high efficiency and almost zero errors in the future.
4. It is no longer necessary to rely only on big data: As long as artificial intelligence is given more data, it can continue to learn and become smarter and smarter, however, data in some fields (such as medical) is not very easy to obtain. Now, it can directly use existing data to simulate new data and allow these environments with only a small amount of data to build many models.
5. Ethics and credibility: There are still many controversies with the development of AI, such as simulating human voices and videos, monitoring systems, the future potential of AI, etc. People need to further explore how to enhance credibility from users and consumers. There are many policies and industrial norms that are gradually echoing this trend.
6. More diverse interaction ways: AI-driven interaction modes are often referred to as cognitive engagement, such as chatbots for 24-hour customer service, which can provide personalized services and experiences through mutual communication. In addition, AI will interact with users in more fields in the future which is worth looking forward to.

Although the development of AI has been applied in quite a few fields in recent years, there are many technologies and applications of artificial intelligence that need to be further explored and researched. In the future, we will continue to pay attention to its development, which is worth looking forward to.

Main image photo by Adobestock

References Wikipedia / OOSGA

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