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Artificial Intelligence (AI) has become one of the most talked-about topics, even among those who are not usually interested in tech news. AI is a broad term used to describe the use of technology to create machines and computers that can perform human-like cognitive tasks, such as seeing, understanding language, responding to it, analyzing data, making recommendations, and much more.
Although many people believe artificial intelligence to be a standalone system, it is, in fact, a combination of technologies that work together to enable a system to reason, learn, and act to solve complex problems. One of the most significant features of AI is its ability to learn and adapt, making it valuable in many industries. Let's look at some of the most popular AI applications today and examine the lack of AI regulation and the potential risks associated with it.
Image processing has been one of the most popular AI applications thanks to the users of many social media platforms who created different art with it or used it to change their own photos. To improve digitized images' quality, they must be analyzed and manipulated.
AI-powered apps or platforms can process images and enhance their quality based on the algorithm's depth of knowledge or experience. For instance, if you want to improve the quality of a picture of your dog, an app needs an idea of what a dog looks like. As the computer receives more dog samples, its machine learning improves, and the resulting processed image becomes more accurate.
The same principles apply to the creation of new images. AI apps do not create new images; they combine many existing photos and pictures, mixing details like used colors, forms, composition, and brush strokes. Since such image-generating apps need time and experience to generate new art, the results are often imperfect, resulting in users receiving funny or bizarre images.
Computers’ ability to understand human speech is often interchangeably called speech or voice recognition. However, those are two distinct functions. Voice recognition involves identifying the speaker’s voice, while speech recognition involves identifying and transcribing spoken words.
Voice recognition technology enables machines to comprehend and respond to user commands. Speech recognition, on the other hand, converts spoken words into text, allowing for automatic transcriptions and translations. Voice recognition is commonly used for security purposes like voice biometrics, while speech recognition serves as an accessibility tool for individuals who are unable to type. This technology allows them to accomplish important everyday tasks like writing emails, searching for information, and completing school assignments.
The latest developments in deep learning and big data have resulted in remarkable breakthroughs in speech recognition technology. Sophisticated speech recognition software utilizes machine learning and AI to understand and interpret human speech, including its grammar, syntax, structure, and voice signals. Ideally, it should continuously learn and improve with each interaction with humans. However, various challenges may prevent that.
One such challenge is the lack of speech standards, as each person speaks differently based on their native language, region, age, gender, etc. Different accents and word pronunciations can also affect speech recognition technology, leading to difficulty understanding what is being said and, consequently, reduced accuracy rates for some users. Moreover, as the machine also relies on rules, it may face difficulties when working with spontaneous speech. Another challenge is the different contexts and environments. For example, speech recognition accuracy can be affected by wind or other overwhelming noises.
Machine learning is a branch of artificial intelligence that enables machines or systems to learn and improve themselves by gaining experience. Machine learning algorithms scrutinize large volumes of data, extract insights, and arrive at informed decisions. As the algorithms are trained with more data, they become more proficient over time.
Machine learning can be classified into two types: supervised and unsupervised. The critical difference between these two types is the need for labeled training data. Supervised machine learning employs labeled input and output data to train the model. It helps experts to refine the model until it can accurately predict the outcomes of new data. However, supervised learning can be resource-intensive since experts must label data beforehand and work with it during the learning process.
Unsupervised machine learning involves training computers using raw data that is not labeled, learning to recognize and distinguish patterns and relationships in the dataset. This type of learning is often used to identify underlying trends in the given data. However, achieving adequate levels of explainability can be challenging in unsupervised machine learning due to the absence of human oversight.
Data science is another field that heavily relies on machine learning. It enables algorithms to make predictions and identify key insights during data mining. These insights can then be used to inform decision-making in any area of life. Without machine learning, analyzing vast amounts of data would be more time-consuming and less focused, and the results might not be as specific.
Automated decision-making refers to the process of using algorithms and computer systems to make decisions without human intervention. It can be valuable in various contexts and industries, including traditional finance and DeFi.
One of the benefits of automated decision-making is its speed. Because computer systems can process vast amounts of data much faster than humans, they can decide in a matter of minutes where a human would need hours. Such quality can be essential in industries where time is of the essence.
Another advantage of automated decision-making is its objectivity. Since algorithms are based on data and rules, they can make decisions without being influenced by human emotions or biases (although this is only possible if the algorithms are designed to compensate for existing biases). Therefore, this technology can reduce the risk of errors or inconsistencies in decision-making.
While some countries have established guidelines for developing and deploying AI systems (or at least started conversations about it on a government level), many have yet to do so. By leaving this field unregulated, they increase the risk that some companies can use AI in a way that is unethical or otherwise harmful.
One of the concerns that prevails in the media the most is the potential impact of AI on the job market. As AI systems become more sophisticated and capable of performing tasks previously done by humans, there is a risk that many jobs may become unavailable for humans, leading to significant unemployment and economic turmoil.
Another pressing concern is that AI might maintain or even increase existing biases and social inequalities. It may happen if the data used to train AI systems turns out to be biased or incomplete or if the algorithms used to make decisions will not consider existing biases. For instance, AI systems used in hiring or loan approval processes may start discriminating against certain groups of people.
The next concern is the potential malicious use of AI. For instance, criminals can use AI to develop autonomous weapons systems, carry out cyber attacks, or other forms of digital sabotage. Additionally, there are concerns about the potential use of AI for unlawful surveillance and monitoring purposes.
Finally, there is the risk that AI systems may become too powerful or autonomous, potentially leading to unimaginable consequences. If an AI system is given too much control over critical infrastructure or decision-making processes, it could inadvertently make harmful or even catastrophic decisions.
Artificial Intelligence has the potential to offer numerous benefits to society. However, it is crucial to carefully consider and address the potential risks associated with its development and use. Governments should ensure that AI systems are transparent and their users are liable by developing ethical frameworks.
The Kinetex team is developing AI-related technologies for automated market maker solutions. It includes designing smart bots that can analyze the market and adapt to quickly changing trends. With the help of self-learning bots, Kinetex resolvers will be able to perform transactions automatically, providing users with the fastest execution times and most profitable rates. You can learn more about the use of AI in the crypto industry here.
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