Midjourney, ChatGPT, neural networks in biometrics
Neural networks have already been well-established in our lives. A robot vacuum rescues you from dust, an app recommends music, a voice assistant makes restaurant reservations, and the art created by Midjourney can easily be confused with the works of professional designers or artists. These are only several obvious marks of the presence of artificial intelligence (AI) in our lives, which we can definitely notice and analyze. But behind the scenes, neural networks target ads in social media, marketplaces select products individually for each customer, video hosting sites offer personal recommendations for a movie night. The excitement around the latest developments in the field of artificial intelligence gave rise to discussions in society: can artificial neurons replace our human ones? Is their work as flawless as we expect? And does this mean that the labor market will change significantly in the near future, so people might lose their jobs? Let’s try to figure it out together and, importantly, rely on facts, not on emotions caused by a hype wave.
Contents:
- What artificial intelligence is and what it has to do with neurons
- Artificial intelligence in 2023: the future is now
- Nobody is perfect. Even artificial intelligence
- Security specialists, designers, programmers: is it time to look for a new job?
- RecFaces products: how we use neural networks
What artificial intelligence is and what it has to do with neurons
The potential for AI is enormous. People invest millions in it, expecting that it will pay for itself almost immediately. OpenAI is planning to make $1 billion from ChatGPT by 2024. This hype is easier to explain if you remember one great advantage of AI-based products over other software — they are inspired by how the human brain works and based on the neuron theory. For a more complete understanding, let’s look at the principles of inner workings of neural networks.
Artificial intelligence is a technology or, more precisely, an algorithm that works similar to a human brain. It is taught to perform a certain action. A specially selected data set is used to train AI to recognize a specific pattern. In other words, we can say that such training with constant repetition helps to form something similar to a human reflex. The more repetitions are made, the stronger the reflex is. The larger data set is used to train the neural network, the stronger connections appear in its “electronic brain”. Then, the neural network is trained to find the previously learned patterns in systems by itself. Mathematical algorithms in neural networks, unlike those in other intelligent products, are able to learn while working. So, the more they work and learn, the “smarter” and “stronger” they become. This is the reason why they gain considerable attention from the public, investors and users.
Artificial intelligence in 2023: the future is now
More and more fields of neural network application appear every day. The most popular ones now are image recognition (biometric technologies), prediction of the next step (neural networks in trading, where their capabilities are highly applicable in the stock market), collection and classification of data by parameters (programs for accepting or declining loan applications), creating texts and images (advertising).
The implementation of artificial intelligence tools is increasing year by year. Neural networks have rapidly gained popularity in marketing and design. One such example is the ChatGPT chatbot — a new type of a neural network language model developed by OpenAI, an American artificial intelligence research laboratory. ChatGPT was released in November last year. It took a couple of months for users to get acquainted with its features, and since the beginning of this year, everyone has started to talk about this innovation. And there is really something to discuss. This neural network is based on the powerful multiple language model and is capable of self-learning. The chatbot responds to questions using “common sense”, that is, it does not just give an answer, but asks clarifying questions. ChatGPT even has some creativity skills: it writes poetry and essays (fragments of fiction were also used in its training). ChatGPT is now being used to generate articles, theses and research papers, manage social media, and even flirt on dating apps.
Neural networks are widely used in programs that generate text. For example, Verse by Verse composes poems in a given style, Articoolo creates and rewrites articles on any topic, Anyword helps in writing short posts.
Some of the most popular neural networks are AI art generators. DALL·E 2, which, by the way, was also developed by OpenAI, creates illustrations based on textual description. Famous Midjourney works on the same principle. Also, there are digital assistants that can remove background from a picture or blur it, create logos and portraits, or enhance image quality.
Another important area for using artificial intelligence is facial recognition (biometric) technology. It is successfully implemented in banking, information security and air transportation. Artificial intelligence in biometrics helps security personnel make accurate decisions based on AI analytics. For this purpose, digital neurons are first taught to detect facial features, analyze them, and then compare them and draw a conclusion on the rate at which the potential employee matches the description earlier made by the system. On the basis of information obtained from AI, a specialist decides whether to give a person access to a secured facility or information.
Nobody is perfect. Even artificial intelligence
As it often happens, ideas may be good in theory but not in practice, revealing weaknesses of a technology. Artificial intelligence is not at all perfect. As an idea, a neural network is free of human factors, it has no emotions or passions, and it remains objective. But in fact, artificial intelligence cannot replace experienced professionals, at least in the near future. It only reproduces patterns that it has been taught. Information used for its training may be extensive but it can’t be full: there will never be enough time and capacity to analyze everything.
For example, in algorithms used in machine translation and chatbots, it is important how frequently one word is used along with another one. AI is trained on massive text collections and consequently learns all the possible stereotypes from them. For example, the word “architect” is more associated with masculine than feminine pronouns, whereas the word “teacher” has the opposite association. It turns out that the algorithm adopts the worldview where some professions are mostly done by men and others by women. Algorithms do not only hold prejudice but even add to it. They may increase stereotypes and biases formed in society. Classification systems are used for AI training. AI creates representations of the initial data, but none of them can be full, as the information is reduced and misrepresented. Data sets for neural network training are prepared by people. And their subjectivity in creating such data sets cannot lead to an unbiased result.
Speaking of inaccuracy of neural networks, let’s focus on another aspect. AI often makes decisions preventively based on probability of a certain action rather than on its outcome. Neural networks can predict how certain parameters of reality might change in the future, so governments and corporations use them to make socially responsible decisions. For example, in Australian immigration detention centers, it is the algorithms that evaluate whether the detainee poses a threat or not. The model makes a decision based on the set of parameters: age, health, behavior before and during detention. The conditions of detention directly depend on the “opinion” of the algorithm, in particular, whether the detainee will be handcuffed.
Security specialists, designers, programmers: is it time to look for a new job?
Discussions about the potential of using AI in business are pushing top management of different companies to try neural networks for their work tasks. Analysts evaluated the results. The ResumeBuilder.com company conducted a survey among 1,000 executives that are using or planning to use ChatGPT.
The researchers have obtained the following data:
- 49% of companies are now using ChatGPT;
- 48% of companies that use ChatGPT replace their employees with it;
- 25% of companies using ChatGPT have already saved more than $75,000;
- 93% of ChatGPT current users claim they are planning to expand the scenarios of using ChatGPT;
- 90% of business leaders consider the experience of using ChatGPT a useful skill for job candidates.
The research outcomes reveal a great interest of businesses in using neural networks. However, according to specialists, development of such tools as ChatGPT represents a significant technological stage rather than a revolution. Their rapid growth and popularity in 2023 are caused by the development of the Internet itself — any information becomes public very quickly, and the audience does not hesitate to try a new technology by themselves.
Chatbots and similar programs are created to assist people, so it is still far from the day when AI might take their place. Neural networks are already trained to perform routine tasks, which helps specialists save time for more important work. Designers, copywriters, PR specialists, and journalists are concerned about the future of their professions. But their fears are premature. Experiments have shown that tasks that involve creativity, strategic planning, flexible thinking, and deep understanding are not within the capabilities of AI. Even OpenAI CEO Sam Altman admitted that ChatGPT is a “horrible product” and that “it’s a mistake to be relying on it for anything important”, and it is only a matter of time until the quality of content produced by neural networks decreases. The mass implementation of neural network assistants will help specialists free up time for creativity and testing new ideas, as routine tasks can be delegated to AI. Experts believe that this does not mean a complete replacement of humans, because the parameters for the operation of a neural network are set by the human brain.
More than a third of people are afraid of artificial intelligence. Skepticism about the implementation of neural networks is also common among security specialists. The human brain often struggles to adjust to the changing circumstances and slowly adapts to innovations. The roots of this phenomenon lie in the theory of evolution: for centuries it was helping people to conserve energy. And now, when science is changing at a rapid pace, people still find it difficult to adjust to new knowledge, as it requires changing their entire ways of thinking, which is not so easy.
With the invention of neural networks, many specialists began to worry about losing their jobs, including those working in security services. After all, if there are “electronic eyes”, then human ones may no longer be so effective at the new stage of technology development. In fact, security officers are still needed, and they are not being replaced by robots or neural networks. The human resource remains in demand, and the final result depends on the competent management of AI capabilities.
For example, let’s have a look at biometric technology (of facial recognition). An accurate and calibrated algorithm could be trained on images of people of the same nationality, while a new employee may not fit this classification. The system quickly analyzes the data and finds similarities or deviations from the template. Whereas the algorithm most likely would not give access to a person in such a situation, a security officer in charge can check the data and quickly make the right and informed decision. Thus, various security systems in banks and other spheres are implemented in order to help employees optimize their work. The neural network works with a human and for a human, helps to instantly respond to incidents, and sometimes even prevent them.
RecFaces products: how we use neural networks
RecFaces develops ready-made biometric solutions that are popular on the market. Based on neural networks, they work with facial recognition technology and are able to analyze data on the similarity of human faces based on the uploaded information. To make the product convenient and efficient, a professional team is working on it: developers, analysts, QA engineers, designers. Only a competent specialist can not only assess the potential of using AI in their work, but also analyze how to implement it most effectively. RecFaces improves its products and releases updates that are important for users.
Let’s focus on how neural networks work in two main products of the company, which are Id-Guard and Id-Gate. They are particularly useful for security specialists.
The Id-Guard product from RecFaces is developed for biometric identification by a facial image in video stream. Its main purpose is to increase the level of security at the facility. It expands the functions of a classic video surveillance system using identification in video stream.
Id-Guard has the following advantages:
- does not require much time for installation (ready to work in 20 minutes);
- has ready-made integrations with products of world leading companies;
- offers a user all the needed possibilities for facial recognition in a real-time video monitoring system.
Id-Guard is an off-the-shelf solution, which is easy and convenient to use. Here are only some of its advantages. The program immediately notifies the operator about alarming events at their workplace or through a mobile application, helps to efficiently investigate incidents using parametric search in a long-term archive. Another useful feature is fast searching for people in an archive by photo, video or verbal description of facial features, hair, glasses, etc.
“Our company keeps moving forward, and we pay much attention to improving our products. Our aim is to make our biometric solutions as accurate and fast as possible and offer our customers all the features they need. With every new release we make our products even more effective and convenient for users,” — RecFaces experts point out.
Id-Gate is a software product for biometric identity verification in access control and management systems. This solution is developed to enrich the functions of existing or new access control systems with additional modern functions using facial recognition technology.
What benefits does the Id-Gate user get? The neural network ensures reliable access of employees and visitors to a facility using a face image, which is a unique and inalienable identifier. It is possible to configure virtual access control points in the solution, which makes it possible to detect if an employee or a visitor entered a highly secured area and send a notification to a security specialist.
RecFaces software solutions are developed to optimize the companies’ workflow, it is an additional tool to help in solving business tasks. Neural networks work for a human, and it is the human who configures the parameters of AI to get the desired results. And only the user decides whether to follow the algorithm advice or not.
While choosing a complex intelligent system for assistance, it is important to pay attention to training and adaptation programs arranged by a vendor. If you do not learn how to properly use the neural network potential, it is impossible to evaluate its benefits.
“An important part of successful implementation is informational and technical support of the customer. No matter how simple and user-friendly the system interface is, a vendor should be able to answer the client’s questions on time, help with configuring the system, and teach employees who will be responsible for the solution operation. RecFaces training center has already helped dozens of integrators and their clients in understanding the principles of biometric technology operation and learning how to work with it by the example of Id-Guard and Id-Gate,” — the company’s experts point out.
You can consult our specialists to evaluate the benefits of facial recognition systems and better understand how neural networks are applied in biometrics. During webinars, you may get all the needed information from a professional and get answers to any of your questions. It is a great opportunity for security service specialists to figure out whether neural networks have more benefits or threats for businesses, and whether there is any real reason to be concerned about their implementation.
To sign up for our webinars, send a request to our email: sales@recfaces.com.
