One can find a significant difference by comparing classical software and neural networks. Neural networks require teaching them to execute numerous tasks, including voice recognition, painting creating, etc. It is a modern sector that automates multiple processes in business, health care, marketing, production, and other industries. Let’s explore the prime neutral network trends for 2023, their purpose, and how they function.
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What are neural networks for, and where to employ them?
Neural networks are helpful for numerous sectors of life, especially if it requires achieving human functionality. Its concerns lack a straightforward script algorithm for robots. Input information can be any type; hence, a neural network can process any option.
The neural network teaching process
Currently, primarily significant companies, health care institutions, and holding employ neural networks. Developing a decent technology that will function under complex conditions requires powerful equipment and massive data volumes. Therefore, not all companies can implement and use neural networks.
Neural networks will fit any industry; prime technology tasks are:
The employment of neural networks increases early. According to the Allied Analytics report, in 2023, the market volume of neural networks will reach 39 billion dollars, about six-fold more than in 2016.
- Classification. The network receives an object and defines its particular class. For instance, it can group company clients, sort audiences according to their interests, and filter emails and ads. All examples are straightforward yet help to understand the purpose of classification.
- Recognition. The neural network's task is to recognize a specific object among countless others, e.g., a face on an image. Photo filters employ this principle. Also, identifying includes searching data on photos, and images, reading text files, etc. A similar task is beneficial for people with limited accessibility. Health care enjoys the technology in the diagnosing sector.
- Forecasting. This is another method of getting information to analyze it and make forecasts. Usually, the marketing and financial sector use it. Software that can complete a text or painting also do forecasting; search engines function similarly.
- Generation. The networks can independently create content, and the software develops each year and becomes more competent. Now, machines can independently create paintings, music, and other complex tasks.
The neural network functioning principle
Human neural networks are similar to connections that allow us to analyze information and make decisions. Neural network technology helps solve tasks like other machine learning models. The prime difference is the ability to teach the program.
The difficulty of global employment of this technology comes from the cost and teaching procedure since the software requires massive volumes of data. This allows neural networks to analyze and solve necessary tasks without former issues. The integration process would be faster if the neural network developers managed to boost the teaching process.
The algorithm for reaching a neural network involves several steps:
Neural networks are basically for analyzing, forecasting, and recognizing objects. The main issue is that one can fool a neural network. The scientists use it to verify the network’s resilience to non-typical situations.
- Providing necessary data for executing tasks and giving feedback to received data. The general rule is that there must be 10-fold more information than the volume of neurons. While teaching, the machines receive data and explanations of what that is. The learning goes through formulas and numbers.
- The next step of working with a neural network is converting. The network processes the received data and sends it further with the help of math and formulas. It is similar to an eye perceiving an image; there is an impulse that signals the brain, and when it processes the picture, we see it. Working with machines is likewise, yet employs mathematical coefficients.
- Next, it processes the information and gives feedback.
The features of neural networks
The developers and scientists highlight several neural network features according to their architecture and functioning methods:
Considering possible issues of innovative machines, the scientist emphasizes forgetfulness, overlearning, and unpredictability. This is typical of humans as well, so you can use adjustment methods to solve problems.
Numerous software can’t react to too many situations; hence forgetfulness occurs. If the conditions constantly change, the artificial neural network tries to adjust, decreasing accuracy.
- Neural networks are not open; it’s hard to say how the machine defines what or who is on the image, whether the text is poetry, etc. These are automatic processes; the most significant thing is that the creator properly explains the structure and formulas. Likewise, in humans, no one can see what’s happening in the brain. Anyone can see a cat and say it is a cat, regardless of the breed, fur, tail, or color. It is an automatic process that generates the answer by analyzing specific parameters. The neural networks function the same.
- Neurons are independent and not interconnected with the functioning of others. They exchange data, yet within the network, neurons are separate. If one neuron fails, the others will function without spoiling the general process. Biological neural networks operate the same. The Prime disadvantage of this independence is that all solutions are complex and sometimes chaotic, thus hardly possible to predict or impact.
- The neurons are independent; hence, the neural network is flexible, making it more efficient than other machine learning solutions. The architecture employs the main biological neural network qualities: self-learning, adjusting to new information, and ignoring useless details. The flexibility provides vast opportunities to utilize neural networks and adapt them to any circumstances.
- No AI model will surpass a human since the human brain can’t be replicated; an actual brain has 86 billion neurons. Currently, no network will approximate this number; therefore, neural networks make mistakes. The most advanced networks have about 10 billion neurons.
Prime trends for neural networks in 2023
Machines employ neural networks to analyze the input information that helps to eliminate issues like human factors. According to experts, these technologies will make life easier by sparing them complex, monotonous tasks, yet it is too fast to talk of mass employment of these innovations. In 2023, there will be several trends that can show positive dynamics in the next five years.
Natural Language Processing
The most advanced NLP neural network is GPT-3. It can answer questions, communicate, and it is expected to make logical conclusions. However, the most advanced models with a vast set of information can’t understand the meaning of phrases and words they produce. Their teaching requires massive data and computing, which leaves a carbon footprint. The next issue is the imperfection of data since the information in the network is often manipulative and distorted.
A promising sector in 2023 is advancing the recognition function, namely:
Scientists say that AI lacks emotions and feelings to be closer to humans. People can perceive, offer solutions, consider the context of various factors, and adjust to a changing environment. AlphaGo algorithm by DeepMind can win the chess tournament from world champions, yet the strategy will not go beyond the playing desk. Even the most innovative technologies, including GPT-3, have to develop. The scientists’ task is to build a multimodal system that allows connecting sensory perception and text recognition to work with data and sourcing solutions.
OpenAI released a Codex update for GPT-3. Such a model can do text editing and pasting rather than continuing. As a result, the machine is suitable for speeding up the work of editors.
The trend in 2023 is implementing knowledge about the environment into language neural networks with the help of Wiki and similar sources. This will make it possible to apply not only information from the training sample but also directly from a factual basis during the design of the answer. The RETRO model by DeepMind is a striking example of how this works.
- Voices and sound.
Multimodal neural network
These models became famous in 2021 and will maintain the trend in 2023. They deal with text and images. In 2022, the OpenAI company introduced the DaLL-E-2 network, creating realistic and fantasy images. The image quality is maximum and generated via a brief text description. After OpenAI, Google introduced its Imagen model.
An example of a deep multimodal learning
The designers and digital artists can benefit from this trend since they can easily find inspiration and boost their work on unique pieces.
Modern neural network for speech synthesis is hard to differentiate from natural speech. In addition, models include intonation and emotions. Such a trend makes it possible to remove the barrier of implementing voice assistants in everyday life. Programs are actively implemented in mobile applications, "smart" technologies, and cars.
The B2B sphere allows full automation of call centers; there is an opportunity to implement Text-To-Speech in media to create audio recordings based on text.
A model of a neural network that helps to identify faces, objects, image generation, and other objects. Face recognition has been used for many years, especially in video surveillance; industries widely use neural networks to identify objects, allowing them to control particular things. This also includes improving the picture while taking pictures with the phone.
In 2023 and the next 5-10 years, there will be a lot of interest in metaverses and virtual reality. Neural networks are also needed here because they can generate 3D characters using computer vision, detect movements, facial expressions, etc.
Computer sight: from face recognition to exploring space
Drones industry is one of the primary computer sight utilizers. Many car manufacturers are ready to replace drivers; Tesla and Chrysler are excellent examples. Succeeding in face recognition can replace actual sellers. For instance, Amazon Go scans the basket content via a neural network and automatically charges funds when the individual leaves the shop.
Health care also benefits since it can analyze MRI scans, X-Rays, search for cancer, etc. In the field of cosmetology, the model is used to monitor the skin's condition; as a solution, the neural network offers options to combat aging.
The trend of the development and application of computer vision on the construction site is a hot topic for 2023. This is all because of the high mortality rate of construction workers on their jobs. According to statistics, the number of deaths is five times higher in construction than in other occupations. It can be a blow, a fall, electrocution, and other causes. Neural networks in this area and machine learning techniques will allow the use of "smart" cameras, working for the safety of people. Mounting such devices at the construction site allows a continuous stream of video broadcasting to separate servers. All clips are divided into frames, after which a neural network begins to analyze. Such technology makes it possible:
The market already offers several systems that can recognize employees and notify them about dangers or violations via mics. The innovation can help to automatize numerous processes related to staff security.
- Find fire quickly.
- Recognize employees who do not wear protective equipment.
- Detect a violation of the gate.
- Track the movement of specialized transport.
Artificial intelligence for scientists
Neural networks continue to be beneficial for science. AI solves genetic engineering, biology, quantum chemistry, and math tasks. AlphaFhold model by DeepMind predicted the protein structure. Currently, graph neural networks actively grow; they help receive information on nodes' connections and features.
Neural networks in diagnosis and medicine
According to IBM, 90% of the information in the healthcare industry contains images, and their number permanently grows if compared to other medical data volumes. As a result, the healthcare sector greatly benefits from neural networks in processing visual information. The trend of employing the technology gives many advantages:
After receiving an MRI scan, scan, or images from other examinations, the doctor should begin an analysis to determine abnormalities, pathologies, etc. Diagnosis of serious illnesses requires several imaging studies at once. Neural networks can quickly analyze images and report on abnormalities, such as tumors that doctors may not see due to human factors. Such a system identifies patterns, providing physicians with comprehensive information about abnormalities. The approach dramatically simplifies the work of physicians, saving them time.
In situations where patients have multiple images over different periods, AI can help see the dynamics of treatment or disease progression. Google tested and analyzed images. AI did better than certified radiologists. The machine saw 5% more cancerous tumors than humans, and false diagnoses were reduced by 11% with the help of a neural network.
- Saving time
- Saving funds for medical institutions
- Radiology industry benefits.
Examples of employing neural networks in health care
Neural networks for marketing
Marketers make the most active use of Big Data in business. Advertising is one of the primary uses of Big Data, and neural networks help buy ads and group audiences. This is enough for the market, but in 2023 and the next five years, the situation may change dramatically, and the demand for neural networks will increase several times. This factor in the future will begin to determine the success of advertising campaigns and marketing in the future.
Albert is one of the automatization platforms for various marketing promotions aspects.
Due to technological development, marketing is the most digitalized sphere. Machines and algorithms can make work much more accessible by taking over routine tasks, and people will have to learn how to work with neural systems.
The changes are:
The trends described may lead people in 2023 to the point where brands and businesses will need to create multiple creatives and messages for each advertising campaign to reach a narrow segment of the target audience. To be more effective, you still need to target messages to individuals or companies, which is where the neural network will help.
- Increasing amounts of data. Every year, new data grows by 30%, and a person looks at a hundred advertising messages daily. Winning a client is becoming more complex, and the essence of the marketer in such circumstances is to find a narrow segment of people, then deliver information with a message specifically for this category of customers to generate maximum interest.
- Personalization of communications. Today's users expect more personalized communication and connection. According to McKinsey, 80% of customers prefer working with companies with a customized approach, and 77% are willing to pay more for their services and products.
- Creating closed advertising verticals. Under the guise of customer anonymity and data protection, large corporations restrict the exchange of information between analysts and sites. These include Safari and Firefox, and in 2023, Chrome will start blocking third-party cookies. The main consequence of such actions is not the safety of people but the formation of a new type of advertising market where large companies will become monopolists. Any browser-based social media ecosystem can gather analytics only on its tools, without access to data on other sites. Such a method seeks to learn more about consumers outside its ecosystem.
Generating personal messages
Marketers have been working on grouping audiences into different advertising segments for several years. For example, running ads for the 18-55-year-old audience becomes ineffective, and this age criterion can be divided into 3-5 types. Still, marketers rarely do this due to a lack of information and opportunities to create content. Such a problem is relevant for 2023 because of trends in user segmentation, channel diversification, and content personalization.
Neural networks capable of creating a picture from text or a phrase will help in this process. For example, we can imagine Cosmopolitan magazine, the cover of which came from a DALL-E-2 machine.
Magazine cover created by DALL-E 2 neural network
Neural networks can not only create an image from the text but also perform analysis of the text component, providing options for which target audience, what age, such advertising will be relevant.
SMS or advertising images are more often created by people using personal experience and other factors. Neural networks can predict the CTR of such a message for a specific person or group of people. Knowing the possible conversion rate, a neural network can be taught to make recommendations, improve the text or image, and then write algorithms to generate creatives and advertising texts on their own. This simplifies the generation of hundreds of messages, especially when creating personalized offers. With the help of algorithms, robots will quickly adjust to a particular customer, which will be helpful not only in 2023 but also in the future.