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5 Artificial Intelligence Trends In 2019

Sep 28, 2020 12:49:39 PM / by MindBehind Team

Artificial intelligence phenomen on has recently been a popular topic and this topic has an important role to shape the future. Since important technology producing companies around the world are aware of this fact, artificial intelligence is one of the most important agenda.

In 2018, we have experiences of highly efficient artificial intelligence work. Numerous existing applications have been supported by artificial intelligence and achieved better performances. Advanced microscope applications developed by using artificial intelligence in health fields and developments to diagnose eye diseases are some of the most prominent examples. Similarly, science-based successful artificial intelligence such as climate modeling, ocean health, and animal protection projects can be listed as important examples.

Successful steps in artificial intelligence are rapidly advancing in 2019 and many businesses are planning for the following years. These work include natural language production, speech recognition, virtual agents, machine learning platforms, AI optimized hardware, decision making methods, deep learning platforms, biometry, robotic process automation, text analysis and NLP, digital twin-AI modelling, cyber security, compliance analysis, content creating, one to one networks, emotion recognition, image recognition and marketing automation.

Intensive Learning

Intensive learning is a type of machine learning based on the behaviorist principle that learns what it has to learn to reach a conclusion. In intensive learning, the machine will react to problems they face and receive reward points for these reactions. In the intensive learning process, the machine tries to maximize received reward points. At this stage, it is expected that the machine will use prior states and repeat the situations that gave rewards. As a result, the machine will learn with trial-and-error and find the route for the best results in a gradual way. Intensive learning interacts with other engineering fields as well as neuroscience and psychology. Development work on intensive learning which is based on 4 fundamental elements called policy, rewarding, value/state value and environmental model are in line with artificial intelligence studies.

Artificial Intelligence and Ethics

Although using artificial intelligence will make a difference in various fields, it is certain that some fields are open for discussion in an ethical sense. One of the examples of such situations is that artificial intelligence might decide to affect the lives of humans. For example, popular social media applications or search engines show certain results with their algorithms. These results might not always be the desired results. Similarly, in financial terms, artificial intelligence applications might have an influence on your decisions. Decision algorithms obtained by analyzing similar situations mainly access to certain data ratio with a pluralist approach. Decision structures that do not have human-centric values will lead to discussing artificial intelligence in an ethical sense.

Quantum Computing

Quantum computing is calculating me using quantum-mechanic events. The idea of quantum computing emerged based on the idea that new generation quantum computers with quantum calculation skills perform better than traditional computers. In traditional computers, information is represented with structures called bits. But quantum computers represent information as quantum bits called qubits. While traditional computers operate with a single-state approach represented with zero and one, quantum computers are based on quantum theory that accepts two states can simultaneously be valid for a particle. Artificial intelligence studies have gained a new dimension with quantum computers that will be ground-breaking in computer technology and it is predicted that these computers will have tangible effects on human life.

Biased Data

As artificial intelligence has become wide spread, decision making structures have been automatized. However, decisions these types of decision-making structures take, might not always be correct. Actually, decision making structures created based on certain criteria contains the risk to reach biased results. These results created with biased data might misguide the business process and end up with incorrect evaluations. To prevent this, models should be constantly tested and results should be analyzed with current data with the interactive approach. Only then, it is possible to take positive steps to obtain unbiased data.

Neural Networks

Neural networks attract attention as an important artificial intelligence application.Basically, these networks imitate how the human brain works. The main purpose is to analyze data with imitation and to obtain new learning algorithms. Neural networks are characterized by non-linear, parallel, learning, generalizing, operation with missing data and adaptability. These networks are used in various fields including making economic insights, financial predictions, voice and audio recognition, operation modeling and management as well as quality control operations.

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MindBehind Team

Written by MindBehind Team