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/>


In 2023, it is challenging to envision a news feed without the concept of "machine learning." It is anticipated that the market for this technology will reach 225 billion dollars by 2030. For comparison, the forecast for 2023 predicted [**growth to 26 billion dollars**](https://www.fortunebusinessinsights.com/machine-learning-market-102226).


In other words, machine learning is here to stay for the long term. The technology will continue to be integrated into various aspects of human life: predicting, classifying, generating objects. Therefore, it is essential not to fall behind and understand what machine learning is, how ML emerged, and what methods and techniques it employs. We will cover these topics in our article.


## Essence of machine learning


Machine learning is a branch of [**artificial intelligence**](https://altcraft.com/blog/neural-networks-in-business-marketing-and-life) that simulates human thinking processes. Unlike traditional software that follows a rigid sequence of actions, machine learning involves continuous "thinking," mimicking the way the human brain operates.


Machine learning entails making predictions based on vast amounts of data, where algorithms discover patterns. The concept is closely related to neural networks, which are part of one of the types of machine learning and operate through deep learning.


**Machine learning algorithms are used to create services that:**


- **Recommend products, services, and content,** including based on user actions. For example, online cinemas suggest movies and series based on viewing history. <p>
</p>
- **Forecast the future:** possible trends, sales volumes, patient illnesses in medical clinics based on their medical history, events, and more. Machine learning models determine credit ratings in banks and predict the behavior of customers who may struggle to repay a loan. <p>
</p>
- **Recognize objects in images and videos, understand speech, and analyze text.** This speeds up routine processes, simplifies human work, and even enhances security. For instance, recognizing vehicle license plates through surveillance cameras adds an extra layer of control.

## How machine learning works

1. **Defining criteria for selection and data collection** involves gathering vast amounts of information. <p>
</p>
2. **Data preparation** involves labeling the data, which is crucial for ML algorithms to recognize the desired elements. Currently, labeling is mostly done by specialists and is less frequently automated, making this process time-consuming. <p>
</p>
3. **Data verification and pattern identification** identifies errors in the data to correct them and make the next stage more precise by searching for patterns. <p>
</p>
4. **Model selection and training commencement.** The algorithm processes the data and produces results. <p>
</p>
5. **Obtaining and evaluating algorithm performance.** At this stage, errors are corrected, and algorithms may be adjusted for further operations.

<Img
img="/uploads/functioning_of_machine_learning_773a210191.png"
alt="Functioning of machine learning"
/>


## History of machine learning

### 20th Century

The first instances of machine learning date back to the mid-20th century. Scientists Walter Pitts and Warren McCulloch successfully mimicked a neuron in 1943. There is also the notable secret project by the U.S. Army in 1946, aimed at creating tables to improve shooting accuracy through software.

The flourishing era of [**machine learning**](https://altcraft.com/blog/customer-data-for-machine-learning) began in the 1950s, marked by the appearance of Checkers-playing, a program by Joseph Weizenbaum, Frank Rosenblatt, and Arthur Samuel that could play checkers.

During the same period, the Mark I Perceptron, a neural network model simulating human brain function, was introduced by Rosenblatt. In the late 1950s, the SNARC, a neural network capable of handling complex tasks, was developed by American scientist Marvin Minsky.

The term "machine learning" was officially coined in 1959 at a conference at Dartmouth College (USA).

In the 1960s, the prototype of the first virtual assistant was launched – ELIZA, a system that simulated a conversation with a psychotherapist. In the same decade, an algorithm capable of recognizing and classifying data was invented. Towards the end of the 1960s, Bernard Widrow and Sebastian Trun developed the backpropagation algorithm, a significant advancement in improving neural network performance.

In the 1980s, scientists once again employed technologies for gaming, this time in chess. Young researchers from Carnegie Mellon University devised the ChipTest system. Based on this development, the supercomputer Deep Blue emerged in the late 1990s, defeating the renowned chess player Garry Kasparov. This showdown between man and machine showcased the power of machine learning in predicting robot actions.


### 21st Century

In our time, the computing power has increased, and data volumes have grown, propelling machine learning (ML) into a new stage of development. In the 2000s, the concept of "deep learning" emerged. The early 2010s marked an era of discovering new projects related to neural networks. Notably, Google entered the race, and by 2012, a Google X Lab algorithm had learned to recognize cats in images and videos. Google also introduced the Google Prediction API, a service for analytics and machine learning.

Amazon, Microsoft, Facebook, and other giants were not left behind; they developed their platforms where machine learning methods were applied. Mark Zuckerberg's company, in particular, introduced the DeepFace technology, which achieved high-precision facial recognition.

In the 2020s, the role of machine learning continues to grow. Technologies are already operational in the fields of finance, healthcare, industry, and transportation, gradually integrating into people's daily lives.


## Machine learning techniques

<Table>
| Method | Description |
| ------ | ------ |
| Supervised learning | The model is trained on already prepared data (labeled) and ready answers. The algorithm has to choose the answer to the hypothesis: right or wrong, and the human has to control the result. This is a relatively simple way of ML that is suitable for data classification. It is learning with a teacher that helps to distinguish between objects. |
| Unsupervised learning | For training, information without markup is taken. The algorithm itself must understand the patterns and features that distinguish objects. Suitable for prediction and for automated cleaning of input data, recognizing and understanding human language. |
| Semi-supervised learning | Here marked and unmarked data are used. The rest of the partitioning is performed by the algorithm itself according to the specified parameters. Such training is useful for processing long files (e.g., longreads). |
| Reinforcement learning | The method is based on a point system that the model receives depending on the actions. It is similar to a reward system in a game, where the player gets a bonus for a correct action. |
</Table>


## Machine learning methods

### Naive Bayes classifier

The method is named after Bayes' theorem. Its foundation lies in determining the class of an object based on its features. Considered one of the simplest methods, it is used for categorization, object recognition in images, and identifying [**spam emails**](https://altcraft.com/blog/how-to-send-millions-of-emails-and-avoid-spam-filters).

<Img
img="/uploads/naive_bayes_classifier_in_ml_3fba99f3e8.png"
alt="Naive Bayes classifier in ML"
/>

### Decision Trees

In this method, the model identifies the relationship between events and objects, as well as the consequences of their interactions. Visually, this scheme can be depicted as "branching trees," representing different development scenarios depending on the choices made. It is similar to algorithmic tests with "yes" or "no" answers, leading to a result. The method's advantage lies in its systematic approach, but it is not applicable everywhere.

<Img
img="/uploads/decision_trees_in_ml_f32278a671.png"
alt="Decision Trees in ML"
/>


### Logistic regression

A statistical method that has found application in machine learning. The logistic function determines the relationship between one or more variables. Objects are categorized into two classes based on specific values in the range of 0-1.

Determining credit potential, predicting sales success, advertising campaigns, and other probabilistic events are identified through the logistic regression method.

<Img
img="/uploads/logistic_regression_in_ml_b1142fb219.png"
alt="Logistic regression in ML"
/>

### Support vector machines

Incorporating several algorithms, support vector machines classify objects in a hyperplane separated by vectors. The system's task is to find the most appropriate positioning of the line in the plane for optimal classification.

This method solves complex problems and can determine factors like whether there is a man or woman in a photo, what ads to show a user on a website, and so on.

<Img
img="/uploads/support_vector_machines_in_ml_618826ad84.png"
alt="Support vector machines in ML"
/>

### Linear regression

The essence of this method is to relate one variable (N) to another variable or several variables. Conceptually, on a plane, there are data points for which one needs to find the most suitable line that connects them.

<Img
img="/uploads/linear_regression_in_ml_8fe2d66c32.png"
alt="Linear regression in ML"
/>

This algorithm easily predicts trends and determines what information is missing in simple linear sequences. For example, what needs to be added to the sequence 10, 20, 30, 40, 50…

### Assembly methods

An ensemble of methods where various classifiers are generated and data is divided based on averaging or voting principles. This approach reduces the probability of errors. Assemblies include:

- **Bagging:** Basic models are trained in parallel with the collection of more complex classifiers.

<Img
img="/uploads/bagging_in_ml_e74a5f22b8.png"
alt="Bagging in ML"
/>

- **Boosting:** Strong models are created using weak ones. Training occurs on previous classifiers to improve and correct errors for subsequent ones.

<Img
img="/uploads/boosting_in_machine_learning_367667ed4d.png"
alt="Boosting in machine learning"
/>

### K-Means clustering

One of the clustering methods in machine learning. The algorithm utilizes a random number of clusters K. Each has its center (point), from which the distance to each data point in the groups is calculated. Then the center of each cluster is listed, vectors again partition data points into clusters. This process continues until no further changes occur.

Clustering is useful for biological research, IT technologies, and sociology.

<Img
img="/uploads/k_means_clustering_a983f099a5.png"
alt="K-Means clustering"
/>

### Generative adversarial learning

This method is based on the operation of neural networks that, in a way, compete with each other (hence the name). The algorithm involves discriminator and generator networks. The discriminator attempts to classify incoming data and correlate it with categories, while the generator, conversely, aims to choose images that fit the categories. The generator creates objects, and the discriminator checks their authenticity. This algorithm is capable of generating images of non-existent people that resemble real ones.

<Img
img="/uploads/generative_adversarial_machine_learning_3bf8f109d1.png"
alt="Generative adversarial machine learning"
/>

## Summary

Machine Learning is a form of artificial intelligence that simulates the activity of the human brain. The initial machine learning algorithms emerged in the 1940s and 1950s, but significant growth was achieved in the 21st century, and they are now utilized in various aspects of human life.

ML methods are classified based on human involvement. There is supervised, unsupervised, semi-supervised, and reinforcement machine learning.

Additionally, methods are categorized by the algorithm's operation. This includes Bayesian classifier, decision trees, logistic regression, assembly methods (bagging, boosting), K-means clustering, generative adversarial learning, among others.

**Product of the company Meta, prohibited on the territory of the Russian Federation.*


ML methods are categorized based on human involvement, comprising supervised, unsupervised, semi-supervised, and reinforcement learning.

machine-learning.png

About what machine learning is, how ML appeared, what methods and methods it uses.

Machine Learning: Essence, History, Methods & Techniques

Methods and Techniques of Machine Learning

<Img
img="/uploads/etika_nejrosetej_c5ddc95460.png"
alt="Ethical Challenges with Neural Networks"
/>

Discussions about neural networks are ongoing, with new AI-based programs and applications constantly being released. For marketing professionals, keeping up with these trends is challenging, and it's even harder to choose among the multitude of applications to find those truly worth using.

Certainly, neural networks are not a panacea. They cannot accomplish anything without human input. The key issue here is ethical considerations. Neural networks are almost incapable of generating content that could be immediately applied across any industry without concerns. There should always be symbiotic relations between human activity and technology: AI suggests ideas, and humans edit and refine them.

To train a neural network, an enormous dataset is required. The more data, the more sophisticated the AI model will be. However, access to information can be hindered by issues related to confidentiality and intellectual property rights. Ethical questions stand out distinctly. They will be explored within the scope of this article.

## Human or Machine?

Today, virtually no online business operates without content generated by neural networks. This technology significantly eases the work of specialists: they can enjoy the creative process rather than deal with routine tasks. However, every time [**we use AI**](https://altcraft.com/blog/neural-networks-in-business-marketing-and-life), the following questions come to mind.


### 1. Who owns the content or code created by a neural network?

If you use AI to write code or blog posts, who owns the copyrights? Is it you? Or the provider of the neural network? Legislation currently lacks a clear answer regarding the ownership of intellectual property results obtained through neural networks.


### 2. Do you want to assist your competitors?

If you create content using a neural network, the data you upload will be used to train other AI models. This pertains to situations where competitors might be interested in your company's data, such as closed-source code under NDA or a marketing strategy that surpasses theirs.


### 3. How to deal with the risk of legal action for using content created by AI?

There have been cases where people were sued for using content generated by neural networks. Even companies like Google have faced similar issues.


### 4. What if there's no longer a need for manually created content in the future?

Neural networks can generate content optimized for search engines. Will this lead to a decrease in the value of 'human' content? Or will generated and manually created content organically complement each other?


### 5. Isn't there a risk in giving confidential and corporate information to neural networks?

Artificial intelligence tools need data for training and operation. What data is needed? How will they be used? What are the confidentiality issues when transferring data to artificial intelligence?


### 6. Who is responsible for incorrect information?

AI tools and [**chatbots**](https://altcraft.com/blog/what-distinguishes-chatgpt-bard-and-bing-in-the-world-of-marketing) are not flawless. They make mistakes. What happens when neural networks generate inaccurate content? Who is responsible for this?

Artificial intelligence tools largely depend on the data they are trained on to create the final text. What goes into the input data will reflect in the output. Sometimes, AI can learn biases and stereotypical thinking.

<Img
img="/uploads/stereotypical_AI_thinking_performed_by_chat_GPT_e4a1b0136a.jpg"
alt="Stereotypical AI thinking performed by ChatGPT"
src="Stereotypes performed by Artificial Intelligence"
/>

### 7. Can AI replace humans in the workplace?

Will neural networks replace marketers when they can create 10 times more content than humans using only 10% of the resources? How will the decision-making process change for humans? What will happen to human rights?


### 8. How will the implementation of AI affect the cost of content?

If AI can create content indistinguishable from human-generated content, will the cost of content decrease? People might not want to pay for material if they can get it for free from AI.

## The problems with AI are becoming evident

Generative neural networks are not always accurate. But the main issue arises when sensitive information about a company is uploaded to AI; the AI developer gains access to that information and can freely utilize it.

Therefore, confidential data should not be handed over to neural networks without the owner's permission. When training AI models, it's crucial to ensure that the information used for training was obtained legally and doesn't violate any laws or regulations. Most publicly available generative AI services (such as ChatGPT) do not disclose the datasets they were trained on. As a result, it's unclear who actually owns the data.


<Img
img="/uploads/problems_with_AI_generated_content_2e4c4a5759.jpg"
alt="Problems with AI-generated content"
/>


The situation is exacerbated by the inability to verify the objectivity of the results, and AI bias is a real problem. Robots 'pick up' stereotypes from training data. They can convincingly lie. Therefore, it's unwise to trust content generated by AI as the sole source of information, especially when it comes to scientific research.

Identifying issues with AI is not difficult. Content created solely by neural networks stands out significantly:

- Long, complex paragraphs with repetitive content <p>
</p>
- Clumsy clichés, awkward structure, and syntactically long sentences <p>
</p>
- Abundant use of adjectives <p>
</p>
- Facts based on subjective opinion.

<Gallery>
<GalleryItem
original="/uploads/ethical_problems_with_neural_networks_504eeff9b9.png"
thumbnail="/uploads/ethical_problems_with_neural_networks_504eeff9b9.png"
/>
<GalleryItem
original="/uploads/what_problems_of_ehtics_with_AI_50e97b3737.png"
thumbnail="/uploads/what_problems_of_ehtics_with_AI_50e97b3737.png"
/>
</Gallery>

## Ethical rules for using artificial intelligence

Using generative AI is recommended with a license from the company. This is important in terms of responsibility and ownership rights. However, it's crucial not to directly copy the content or code of a neural network. Always edit, modify, and supplement material to make it unique.

- [**Data protection**](https://altcraft.com/blog/how-to-respond-to-data-breaches) is the most important aspect. The accuracy of information generated by neural networks is questionable, and it should not be blindly trusted, especially in the case of rapidly changing topics. 
- The output of a neural network should not be copied verbatim. Generating content is good, but always remember the need to refine the output for truly fresh ideas. 
- Artificial intelligence is excellent for surface-level exploration and idea testing. It should not be used to prove one's ideas. 
- Most information is considered 'sensitive' content. Any information entered into a neural network may be accessible to competitors, current and potential clients. 
- Do not share personal data with AI tools like ChatGPT. The same applies to customers' personal data (names, email addresses, phone numbers, and other personally identifiable information).

## Using neural networks: pros and cons

You decide which ethical principles you want to adhere to and what standards to apply in your company. However, relying solely on a neural network to create content isn’t the solution.

Gather as much information as possible on the topics you're interested in. Review videos, documents, blogs, drafts, etc., and compile them to create a substantial library of information. Then, write a portion of the content yourself and let artificial intelligence refine the rest. Maintain a consistent writing style and incorporate relevant examples and keywords.

### Examples of misuse of neural network tools

- Generating content for marketing emails using personal licenses. <p>
</p>
- Inputting a company's financial data into an AI algorithm. <p>
</p>
- Generating code using neural networks and using it without making any modifications.

### Examples of effective use of AI tools

- Using corporate licenses to generate content for email campaigns; testing and meticulously editing content before publication. <p>
</p>
- Engaging neural networks to improve algorithms without handing over the code to AI directly. <p>
</p>
- Generating ideas for new marketing campaigns and analyzing how they could benefit the business.

## Everyone knows that your content is written by a neural network

Let's set aside ethical issues related to AI, confidentiality, and cybersecurity. One of the biggest problems arising from the improper use of AI is that text generated by neural networks is relatively easy to identify.

### Why is it a problem?

Let's say you use AI to automate all your "cold" emails. Outlook by Microsoft and Gmail by Google are the two largest email service providers. [**To protect their clients from spam**](https://altcraft.com/blog/how-to-avoid-spam-folder-in-gmail), they flag emails generated by neural networks as spam. The capability to identify such messages has improved. So even if your crafted message looks impressive, it will be rendered useless if it doesn't land in the potential client's mailbox.

While it might seem like just an email, phishing attacks based on artificial intelligence are increasingly common. To safeguard their clients, major tech companies are blocking content entirely created by neural networks.

People might lose interest in your email if they suspect its content was entirely crafted by a neural network. Expanding the volume of text at the expense of quality will likely cost both you and your company.

However, using AI and machine learning algorithms isn’t a crime. Not all content needs to be generated by artificial intelligence. It’s more practical to use AI for inspiration, planning, and headline creation, but it shouldn’t entirely replace human effort.

Ongoing neural network advancements pose challenges in marketing. Ethical considerations and human-AI collaboration remain vital.

ethics-neural-networks.png

Today we suggest to consider aspects of ethical problems in working with neural networks.

Important Ethical Aspects of Neural Networks Today

Ethical Challenges in Working with Neural Networks

<Img
img="/uploads/neural_networks_8668b76426.png"
alt="Neural Network Hallucination, Types, Causes, Mitigation"
/>

Almost every day, there are news reports about the achievements of neural networks. ChatGPT, Midjourney, and others are highly popular and frequently searched on Google Trends. It may seem like neural networks can already do everything, and humans should prepare for unemployment in the near future.

Machines indeed solve many problems, but their "brains" are not perfect yet. Creativity is entirely beyond the reach of robots. Frequent hallucinations in artificial intelligence make it an unreliable replacement for humans, especially in fields where human safety and health are at stake.

Let's explore why and how neural networks make mistakes and how to reduce their hallucinations at the user level.

## How neural networks make mistakes and why

### Opacity of results

It's impossible to explain the underlying principles by which a neural network generates a specific result. Although we understand [**the principles of its operation**](https://altcraft.com/blog/neural-networks-in-business-marketing-and-life), the "thought process" of a robot remains a black box. For example, you show an image of a car to a neural network, and it insists it's a flower. Asking why it arrived at that result is futile.

While such errors might be amusing for an average user, the unpredictability can have unpleasant consequences for business processes.

This lack of transparency can pose a problem for any business that aims to communicate openly with customers, explaining reasons for rejections and other company actions. Social media and various platforms that rely solely on automated moderation are particularly at risk.

For example, in 2021, Facebook's* artificial intelligence (using neural networks as its data processing method) removed user posts featuring historical photos of a Soviet soldier with the Victory Banner over the Reichstag. The neural network deemed it a violation of community rules. The company later apologized and explained that, due to the pandemic, many content moderators were not working, leading to the use of automated checks that resulted in such errors.

Similar issues in AI moderation were reported on YouTube. During the pandemic, neural networks became overly zealous, removing twice as many videos for inappropriate content as usual (eleven million). Of these, 320 thousand were appealed and reinstated. Thus, the digital world still relies on human intervention to address these challenges.

### Discrimination by neural networks

Sometimes, artificial neural networks exhibit unfair behavior towards individuals of a certain nationality, gender, or race, especially in the fields of business and law enforcement.

In 2014, [**Amazon's AI**](https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G) assessed responses to job descriptions and consistently assigned lower scores to female candidates. As a result, female applicants were more frequently denied employment, despite the absence of objective reasons for such rejections.

In the United States, facial recognition systems have been known [**to incorrectly identify**](https://gizmodo.com/detroit-police-chief-admits-face-recognition-doesnt-wor-1844209113) a significant percentage of African Americans, leading to unjust arrests.

### Need for large volumes of data

The need for large volumes of data is a significant challenge for neural networks. They are data-hungry and require vast amounts of information for training, sometimes even personal data. Collecting and storing such data is neither straightforward nor cost-effective. When neural networks lack sufficient data for training, they tend to make errors.

For example, in 2022, drivers of right-hand drive vehicles [**started receiving fines**](https://lenta.ru/news/2022/05/23/shtrafy/) due to AI. The neural network couldn't distinguish between a person sitting in the seat and an empty seat, leading to fines for not wearing seat belts. Developers attributed this problem to data insufficiency. They explained that the AI needed a substantial amount of right-hand drive vehicle data to avoid errors. However, this doesn't guarantee accuracy, as AI can be confused by glares and reflections on cameras.

Sometimes, these cases can be comical. For instance, Tesla's autopilot system [**failed to recognize**](https://korzik.net/video/133351-tesla-mozhet-ponjat.html) a rare form of transportation – a horse-drawn carriage on the road. On other occasions, neural network glitches can be quite alarming. There have been instances where [**Tesla's system detected**](https://motor.ru/news/tesla-fail-13-01-2021.htm) a person in an empty cemetery, causing panic among drivers.

### Lack of definitive answers

Neural networks can sometimes struggle with tasks that a five-year-old child can easily handle, like distinguishing between a circle and a square. A child would accurately point out where the square is and where the circle is. In contrast, a neural network might say it's a circle with 95% confidence and 5% confidence that it's a square.

### Risk of hacking and deception of neural networks

Neural networks are susceptible to hacking just like any other systems. Hacking a network is almost like a scene from science fiction where the villain forces robots to go haywire. Indeed, knowing the weaknesses of a neural network allows manipulation of its operations. For instance, a car's autopilot might start running red lights instead of stopping for green.

In an experiment, researchers from Israel and Japan attempted [**to deceive a facial recognition neural network**](https://arxiv.org/abs/2109.06467) using minimal makeup, significantly reducing its accuracy. Prior to this, the network would malfunction in the presence of camouflage, glasses, or bright elements in the frame, but such disguises were highly noticeable. Therefore, this artificial hallucination was crucial to test whether simple makeup could confuse an AI, which was also developed using a neural network. However, poorly thought-out makeup did not induce any significant recognition errors.

In another demonstration, researchers from Toronto illustrated [**how a neural network could be hacked**](https://arxiv.org/abs/2106.09898) using simple symbols that are imperceptible to the user.

[**Since 2013, a vulnerability in neural networks has been identified**](https://arxiv.org/abs/1312.6199) – the adversarial attack, where a tiny amount of noise is added to an image. This causes the network to misidentify objects in the image, such as labeling a table as a car or a tree as a supermarket trolley. Even after a decade, there is no absolute solution to this problem.

### The complexity of neural networks

Like any complex system, neural networks are imperfect. They learn quickly and can process vast amounts of data, but they lack a complete analogue to human critical thinking. Although a neural network can challenge information and understand its mistakes, sometimes it simply "believes" in incorrect things and confuses facts, leading to the creation of its own fabrications. This tendency is especially pronounced when the initial information is derived from unreliable sources.

The complexity of neural networks and their aspiration to emulate human intelligence raise concerns in society. In March 2023, Elon Musk, the founder of Tesla and SpaceX, [**published a letter**](https://futureoflife.org/open-letter/pause-giant-ai-experiments/) requesting a six-month halt to the training of neural systems more advanced than the GPT-4 version. Over 1,000 specialists, including Evan Sharp, the founder of Pinterest, and Steve Wozniak, the co-founder of Apple, endorsed this statement. The number of signatures has now exceeded 3,000.

### Superficial assessment of information

When it comes to generating text, a neural network doesn't think metaphorically like a human; it merely uses statistics based on word usage and phrases. The system, in a way, predicts the continuation. It doesn't have the capability to conduct a deep analysis and find causal relationships in the text.

### Lack of uniqueness in results

Although a neural network is designed to generate new content based on a multitude of examples, there are instances where the robot might simply [**produce a slightly modified image**](https://arxiv.org/pdf/2301.13188.pdf) from its training data. In such hallucination scenarios, personal user data could inadvertently be included in images accessible to outsiders.

A probable cause for this error is attributed to the repetition of certain images in the training data.

### Neural networks do not understand human anatomy well

Often, images generated by various neural networks show extra or distorted fingers, additional hands, legs, or missing body parts, strange shapes for teeth, tongues, eyes, and more.

Neural networks particularly struggle with fingers, which may resemble animal paws more than human hands, and they often make errors in counting them. This is because the network processes images of people from various angles but lacks a deep understanding of how to anatomically position body parts like fingers, eyes, teeth, and so on, resulting in these kinds of images.

<Img
img="/uploads/Neuralnetworkhallucination_6e202446c3.jpg"
alt="Neural network hallucination problems with counting fingers"
src="When hallucinating, a neural network can generate a person with three fingers on their hand"
/>

<Img
img="/uploads/Neuralnetworkhallucination2_ab230210cb.jpg"
alt="Neural network hallucination produce pictures with strange eyes"
src="Generated image from several user photos that produced strange eyes"
/>

<Img
img="/uploads/Neuralnetworkhallucination3_0e192733c6.jpg"
alt="Neural network hallucination produce pictures with strange hands"
src="This photo is fine at a glance, but look at the hands"
/>

<Img
img="/uploads/anatomy_c566e22a74.jpg"
alt="Neural network hallucination produce pictures of knights with strange anatomy"
src="Here the neural network drew on-demand knights that turned out to have no eyes, not enough hands, and some don't even have feet"
/>

<Img
img="/uploads/creepycreation_b1c21b44b1.jpg"
alt="Neural network hallucination produce pictures with strange anatomy"
src="Sometimes the pictures generated by the neural network look kind of creepy. Like, for example, the kind of gnarled teeth that the monsters have."
/>

### Neural networks simply go mad


American researchers have found that [**certain neural networks**](https://altcraft.com/blog/what-distinguishes-chatgpt-bard-and-bing-in-the-world-of-marketing), such as ChatGPT and Midjourney, [**start hallucinating significantly**](https://petapixel.com/2023/08/11/ai-trained-on-ai-images-produces-terrible-results-study-finds/) after undergoing five training cycles. There's even a term for this phenomenon called Model Autophagy Disorder (MAD), where the results deteriorate with each new cycle of multiple training sessions.

Neural networks can sometimes be unpredictable. For example, in response to the query "Who was the sole survivor of the Titanic?" the neural network confidently provided the name of the supposed "sole" survivor and even shared some details about their life.


<Img
img="/uploads/wronganswerfromneural_84622eff6c.png"
alt="ChatGPT hallucinating"
/>


It's interesting to note that the neural network, when asked for sources in the subsequent query, promptly apologized for any misinformation and provided the correct information: that there were 705 survivors out of a total of 2224 passengers on the Titanic.


<Img
img="/uploads/correction_9404fb277d.png"
alt="ChatGPT corrects itself after hallucinating"
/>


Autonomous behavior was noticed in the MY AI chatbot from Snapchat, which once [**posted**](https://www.independent.co.uk/tech/snapchat-twitter-b2394029.html) an unclear photo in its stories. Users were alarmed by such behavior, and some even began to uninstall the app. Interestingly, initially, MY AI did not have the capability to create stories as part of its functionality. Hence, a small machine uprising occurred.

## Avoiding neural network hallucinations

Neural network developers are aware of these issues and are exploring solutions. For instance, [**OpenAI**](https://www.ixbt.com/news/2023/06/02/v-openai-pridumali-sredstvo-ot-galljucinacij-u-nejrosetej.html) has proposed a new method to combat hallucination. The idea is to reward the AI specifically for correct answers, not just any final outcome, encouraging the network to better verify facts. However, the problem is likely to remain relevant for a long time, as research is ongoing.

Despite these challenges, people continue to use neural networks and encounter difficulties. While the average user may not delve into the code, certain actions can improve AI outcomes.

**How to use neural networks to minimize hallucinations:**

**1. Be specific in your queries:** Avoid abstract tasks and provide clear, specific instructions. For example, instead of saying, "Write a text about fashion in 2023," say, "Write a text about fashion trends for the spring-fall season of 2023."

Paul McCartney reportedly used a method involving AI to collaborate on a song with John Lennon, who had long passed away. The same algorithm [**was applied**](https://www.cnet.com/tech/the-new-beatles-video-and-how-ai-is-helping-and-hindering-the-music-industry/) earlier in creating the documentary series "Get Back" about the legendary Beatles. Using neural networks with precise commands, they managed to separate Lennon's voice from the instrumental sounds.

**2. Provide more information:** Neural networks have vast knowledge from their training data, but understanding your request without context can be challenging. Explain your query in detail. For instance, if you expect a text about the Joker character in a Batman movie, specify that you mean "The Dark Knight" from 2008.

**3. Fact-check:** Verify the information provided by the neural network and point out errors if any.

For instance, in earlier versions of ChatGPT, it could be easily confused by questions containing deliberately unreal information:

<Img
img="/uploads/question_397bef3789.png"
alt="Neural network hallucinates, real example"
/>

The neural network confidently provides an answer even without understanding that a person cannot walk on water.

## Summary


Despite the high expectations for neural networks, they are far from perfect. Robots often make mistakes, providing incorrect information, generating strange images, misidentifying objects, and more. AI is also sometimes accused of discrimination and generating content that is not entirely original but slightly altered. In other words, they cannot operate without human supervision and corrections.

Hallucinations in neural networks occur due to the complexity of the system, the large volumes of data that need to be processed (and their insufficiency). The reasons behind decision-making are unclear, definitive answers are not always provided, and responses are often probabilistic. Additionally, neural networks are vulnerable to hacking, and they can sometimes go haywire due to frequent retraining.

To mitigate these issues, it is essential to provide clear and context-rich input to the neural network and always verify the results for accuracy.

**Facebook and other products by Meta are considered to be extremist and are forbidden in the Russian Federation.*


Neural networks may hallucinate, producing convincing but false outputs, raising concerns about the reliability of AI-generated information.

neural networks.png

Neural networks may hallucinate and produce convincing but false outputs. This leads to concerns about the reliability of AI-generated content.

Neural Network Hallucinations: Types, Causes, Mitigation

Neural Network Hallucinations: Types, Causes, and Mitigation

<Img
img="/uploads/3_nejroseti_konkurenta_7e54f78322.png"
alt="What Distinguishes ChatGPT, Bard, and Bing in Marketing"
/> 

The first AI-powered conversational service, ChatGPT by OpenAI, was introduced in November 2022 and immediately gained popularity. Within just 5 days, it had attracted over a million users to the platform. Similar figures [**were achieved by Facebook* in 10 months**](https://www.britannica.com/topic/Facebook).

At that time, other AI-based projects were also under development. The success of ChatGPT demonstrated the market demand for such a product and inspired other players.

Three months later, [**Google introduced Bard to the world**](https://blog.google/technology/ai/bard-google-ai-search-updates/), followed by Microsoft releasing [**its counterpart, Bing**](https://blogs.microsoft.com/blog/2023/02/07/reinventing-search-with-a-new-ai-powered-microsoft-bing-and-edge-your-copilot-for-the-web/), exactly one day later.

The interest of ordinary users in AI-based technologies is quite understandable. Chatbots, in addition to their functional purposes, also serve an entertaining function, for example, they can tell jokes.

<Img
img="/uploads/1_073326c7be.png"
alt="ChatGPT tells a joke"
/>

The content generated by AI technologies is often catchy and appealing. Each of the three projects undoubtedly deserves attention. In this article, we will explore the features, advantages, and disadvantages of ChatGPT, Bing, and Bard.

## ChatGPT by OpenAI: advantages and disadvantages

The paid version, ChatGPT Plus, was released on February 1st, while the public free version was available earlier.

The chatbot is capable of recognizing and understanding natural language questions and finding relevant answers on the internet. However, it's not simply copying information; ChatGPT processes the retrieved information and generates it as unique content.

The tool excels not only in search tasks but also in handling simple requests, such as "Write a message to my manager informing them that I won't be coming to work on Friday due to family circumstances."

A significant drawback of the system is that the bot doesn't verify the accuracy and correctness of the data. The creators of ChatGPT acknowledge this issue: "The output generated by the model needs to be checked, as it may be plausible but incorrect." The question of data relevance arises because the chatbot is limited to searching information up to the year 2021.

Despite this limitation, ChatGPT remains a powerful marketing tool, a source of inspiration, and an invaluable assistant for many marketers. For example, if an employee needs to write a post about the strengths and weaknesses of using chatbots in business, they can assign the task to the system: "Write a post about the advantages and disadvantages of using chatbots in business." The generated result can then be used as a draft for their work.

Marketers should always verify the correctness of the data, edit the text, and optimize it according to the desired tone of voice.

<Table>
| Pros of ChatGPT | Cons of ChatGPT |
| ------ | ------ |
| It simplifies the work of marketers by generating draft emails, posts, and descriptions. | The information can be sourced from anywhere, including outdated and unreliable sources. |
| It provides starting material for blog authors who struggle to find inspiration. | It cannot ask clarifying questions to provide the most accurate response to user queries. |
| It curates a list of internet sources for conducting marketing research. | It relies on internet data up to 2021. All information generated by ChatGPT needs to be verified. Additionally, the results may not always meet expectations, as the robot may provide simplistic and cliché answers. |
</Table>

## Bard by Google: advantages and disadvantages

Bard is currently being presented as an AI-powered dialogue system experiment. It operates based on LaMDA and is currently available only in beta version, with no general access yet.

It represents a breakthrough in search engine technology. Like many other search engines, Bard searches for answers to queries by leveraging the vast resources of the internet. The difference lies in the quality of information processing and the resulting output.

When composing a response to a user's question, the chatbot provides a summary from various perspectives. This allows specialists, including marketers, to familiarize themselves with Bard's output instead of having to read through thousands of sources to delve into the issue.

The most significant distinguishing feature of Bard is its ability to engage in conversation with humans. Thanks to AI technology, the chatbot goes beyond simply copying and pasting text snippets from the internet and instead captures patterns in human speech. Google announces that future updates will enable Bard to respond to complex, ambiguous questions and those without a definitive answer.

Currently, if you were to ask the robot a question like, "Is the piano or the guitar easier to learn, and how much practice does each need?" Bard would provide a response along the lines of:

<Img
img="/uploads/2_34be1b70f7.png"
alt="Bard figures out about playing the instruments 1"
/>

In another variant, the response may look like this: "Some say the piano is easier to learn, as the finger and hand movements are more natural, and learning and memorizing notes can be easier. Others say that it’s easier to learn chords on the guitar and you could pick up a strumming pattern in a couple of hours."

<Img
img="/uploads/3_5f4add1040.png"
alt="Bard figures out about playing the instruments 2"
/>

Bard from Google has undoubtedly turned out to be more advanced than ChatGPT. However, it is important not to place too much trust in the chatbot. Once, it made a mistake in a product presentation, resulting in Google losing $100 billion in stocks.

<Table>
| Pros of Bard | Cons of Bard |
| ------ | ------ |
| Engages in a dialogue with the user and can ask clarifying questions. | Occasionally provides false, outdated, and biased information. |
| Takes into account different perspectives on the issue and offers the user to explore the problem from various angles. |   | 
| Utilizes more recent and up-to-date information compared to ChatGPT for answering questions. |   | 

</Table>

## Bing by Microsoft: advantages and disadvantages

Bing, developed by Microsoft, is not a dialogue service but a full-fledged search engine. It demonstrates high performance as it operates on AI technology. Therefore, Bing successfully provides answers to users' complex questions.

In response to search results, users can engage in a dialogue with the AI by asking follow-up questions. This allows for a comprehensive interaction between the user and the AI, and [**Microsoft proudly refers to this feature**](https://blogs.microsoft.com/blog/2023/02/07/reinventing-search-with-a-new-ai-powered-microsoft-bing-and-edge-your-copilot-for-the-web/) as "AI for the web."

Previously, the company invested significant resources in OpenAI. However, after the emergence of Bing, Microsoft stated that this new product is more powerful and intelligent than ChatGPT. In an interview, Microsoft expressed the following about Bing: "We’re excited to announce the new Bing is running on a new, next-generation OpenAI large language model that is more powerful than ChatGPT and customized specifically for search. It takes key learnings and advancements from ChatGPT and GPT-3.5 – and it is even faster, more accurate and more capable."

If you ask Bing, "I am planning a trip for our anniversary in September. What are some places we can go that are within a 3 hour flight from London Heathrow?" you will receive an answer similar to this:


<Gallery>
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original="/uploads/4_3bac2236f1.png"
thumbnail="/uploads/4_3bac2236f1.png"
/>
<GalleryItem
original="/uploads/5_438612d5d0.png"
thumbnail="/uploads/5_438612d5d0.png"
/>
</Gallery>



Did you evaluate the amount of information provided in response to the query? Bing even divided the data into categories. Here you will find activities for every taste. Below, the system offers you several phrases to choose from to continue or end the dialogue.

And that’s what ChatGPT responded to the same query:



<Img
img="/uploads/6_f998b6bbe7.png"
alt="ChatGPT gives a piece of advice"
/>

This is also a fairly comprehensive answer, but it lacks a human touch. Well, and you won't be able to continue the conversation with the robot.

<Table>
| Pros of Bing | Cons of Bing |
| ------ | ------ |
| Carefully selects and organizes information, providing deep answers to questions. | Works only with the Edge browser on MacOS and Windows. |
| Can communicate with users, allowing for query clarification through dialogue. | May provide false, unreliable, and outdated information. |
| Generates unique content, assisting marketers in saving significant time and effort in writing [**emails**](https://altcraft.com/ru/blog/tipy-rassylok-po-email), posting, and creating product descriptions. |   |
| Responds to unrealistic and dangerous queries. For example, Bing will indicate that rapid weight gain is harmful to health. |   |
</Table>

Microsoft warns users: "Always verify the information obtained from Bing. Sometimes the system distorts information. Its response may appear convincing and reliable, but in reality, it is not."

## Conclusion

Each of the three digital products has the potential to greatly enhance the efficiency of marketers and other professionals. It is essential to objectively evaluate the capabilities of the robot and diligently verify the data provided. Moreover, it is crucial to adapt the information to the intended tone of voice and refrain from utilizing it in its raw, unprocessed state.

**The products of the organization Meta are recognized as extremist in the territory of the Russian Federation.*


Each of AIs has its potential to greatly enhance the efficiency of marketers and other professionals if its capabilities are objectively evaluated.

3-nejroseti-konkurenta.png

Who wins the race between ChatGPT, Bard, and Bing when it comes to AI-involved marketing?

What Distinguishes ChatGPT, Bard, and Bing in Marketing

What Distinguishes ChatGPT, Bard, and Bing in the World of Marketing

<Img
img="/uploads/nejroseti_8b681ef75d.png"
alt="Neural Networks in Business, Marketing and Life"
/> 

The technology market is growing: if in 2020 [**it was worth $14 billion**](https://www.alliedmarketresearch.com/neural-network-market), then by 2030, according to [**forecasts**](https://www.alliedmarketresearch.com/neural-network-market), the volume will reach $152 billion. Therefore, we will hear more and more about neural networks in the news.

Now it's part of our everyday life. Neural networks draw, generate texts, calculate complex data needed for decision-making in business, marketing, and daily life.

In this article, we will explain what neural networks are, how they work, and what benefits they bring.

## What are neural networks

Neural networks are data processing systems (software code) that simulate the workings of the human brain: digital neurons are connected to each other by virtual synapses, through which information is transmitted. Neural networks belong to the field of machine learning and artificial intelligence.

Due to their similarity to the human brain, artificial neural networks are capable of analyzing, memorizing, and even reproducing data. The first such system appeared in 1958, thanks to neurophysiologist Frank Rosenblatt. At that time, a simple neural network (a mathematical model) could simulate the perception of machine information, similar to how the brain does it.

The future of neural networks turned out to be promising: today they are used in people's everyday lives. For example, users can generate unique avatars from their images for social networks in just a few minutes. To get such a photo from neural networks, it is enough to install an application.

## How neural networks work: basic principles

Neural network technologies mimic the workings of the human brain, where electronic impulses are transmitted from one neuron to another. In the artificial version, neurons are represented by software nodes that obey set algorithms and transmit signals from one to another through synapses.

### What is important for the system to work:

- Prepare the input data to train the neural network. Without information, nothing can be created or recognized. Therefore, the answer to the question of how to create a neural network is to first gather data. To train, you need many examples so that the system can understand patterns.


*For example, if the task of the neural network is to learn to distinguish between handwritten "A" and "B", you need to load hundreds or thousands of files with images of letters.*


- Training a neural network involves human participation. In one scenario, a specialist selects the necessary data and uploads it to the system, which then analyzes it on its own. In another scenario, a human sets algorithms and corrects the robot's mistakes.


*For example, having  analyzed handwritten "A" and "B", the system produced a numerical value as a result (the task was to find "B"). The higher the number, the more confident the neural network is that this option is correct. The humans know the answer, and if there is an error, they adjust the parameters in the system and give the command to recalculate everything.*


### What happens inside a neural network

A basic neural network consists of three layers:

- **Input layer** receives information from the external world. Here, the data is analyzed, distributed, and passed on to the next layer.
<p>

- **Hidden layer** (one or several) is responsible for processing the information from the first layer and other hidden layers. Specific features are extracted.
<p>

- **Output layer** gives the final result.

<Img
img="/uploads/image5_7765d2c4b9.png"
alt="Layers of NN"
/>

This is a simplified description, as the structure of neural networks is much more complex.

## Classification of neural networks

Types of neural networks are distinguished by their structure, tasks, and subject matter. There are many classifications, but the most common ones are as follows:

<Table>
| Classification | Description |
| ------ | ------ |
| Convolutional | Recognize objects in photos and videos, classify images, and determine languages. |
| Recurrent | Work with information that changes over time. Can make predictions. Such neural networks are used in bots that communicate with humans. |
| Generative | Create images and texts on their own based on data. |
| Perceptrons | Work with complex calculations. |
</Table>

## Why neural networks are required

The application of neural networks covers various spheres of human life.

**Forecasting** is used in finance, industry, and in the operation of human life support systems. *For example, a system can calculate the load on power grids during a certain period of time.*


**A neural network for marketing** works in recommendation lists: it analyzes online user behavior and provides offers that they will definitely like. Such [**personalization**](https://altcraft.com/glossary/personalization-what-it-is-and-why-personalized-marketing-matters) increases sales.

**Facial and image recognition** is important for finding the necessary information and ensuring security. *For example, if the system finds images that are not allowed for publication on an online resource, it immediately removes them.*

**In medicine**, a neural network analyzes images of similar diagnoses and quickly provides a result.

**The ability to compare and classify** is useful for quality control of products. Thus, a neural network for businesses eliminates human error and speeds up work.

**Recognition of spoken and written speech** improves communication channels. Neural networks work in voice assistants, transcription, and analyze comments on the internet.

Artificial neural networks excel in **creative fields** because they can create unique art. However, some artists do not appreciate robots behaving this way.

Drawings generated by neural networks have caused controversy and even legal precedents. *Several artists [**have sued**](https://www.polygon.com/23558946/ai-art-lawsuit-stability-stable-diffusion-deviantart-midjourney) image generation services Midjourney and Stable Diffusion, as well as the creative platform DeviantArt, which uses its own neural network called DreamUp. The plaintiffs argue that copyright is being violated because thousands of images from the internet were used to train these systems.*

*Artists on ArtStation platform [**have also protested**](https://kotaku.com/artstation-ai-art-generated-images-epic-games-protest-1849891085) against neural networks, uploading images with “AI crossed out” as a form of protest. The reason for their dissatisfaction is the appearance of robot-generated "art" on the site alongside authentic content.*

<Img
img="/uploads/2_c515319e96.jpg"
alt="AI crossed out on ArtStation platform"
/>

## Examples of using neural networks in marketing, business and everyday life

- One of the most popular neural networks [**DALL-E 2**](https://openai.com/product/dall-e-2) can create original and realistic images based on textual descriptions. This is how the generated cover of Cosmopolitan turned out.

<Img
img="/uploads/3_258175c31c.jpg"
alt="Cosmo by DALL-E"
/>

- *Heinz* collaborated with marketing agency [**Rethink Ideas**](https://rethinkideas.com/work/heinz-ketchup/2022/digital-social/heinz-a-i-ketchup/) to create "the first-ever ad campaign with visuals generated entirely by artificial intelligence." The agency used AI image generator DALL-E 2 to create ketchup-related prompts, and the results looked just like *Heinz* bottles. The campaign featured social media posts and print ads featuring the best AI-generated images.

<Img
img="/uploads/photo_2023_05_03_15_43_33_523c99b731.jpg"
alt="Heinz used AI in their ad campaign"
/>

- [**Everypixel's AI-powered filter**](https://www.everypixel.com) rates the aesthetic value and commercial potential of stock images, providing more accurate and efficient search results for designers and image editors.

<Gallery>
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original="/uploads/2_268292e91d.png"
thumbnail="/uploads/2_268292e91d.png"
/>
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original="/uploads/1_49de9202d5.png"
thumbnail="/uploads/1_49de9202d5.png"
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original="/uploads/3_687e2c9b21.png"
thumbnail="/uploads/3_687e2c9b21.png"
/>
</Gallery>

- *Perfect Corp.* has launched [**a new virtual try-on solution**](https://www.perfectcorp.com/business/products/virtual-hairstyles) that uses AI and AR technology to provide customers with hyper-realistic hairstyle simulations. Users can choose from 12 unique styles and view before and after simulations. The technology takes into account factors such as hair color and skin tone to create inclusive and impactful simulations.

<Img
img="/uploads/AI_hairstyle_1efa35a469.gif"
alt="AI-provided hairstyle"
/>

- An AI-created sneaker collection inspired by iconic *Nike* designs [**has gone extremely viral**](https://hypebeast.com/2022/12/nike-sneakers-artificial-intelligence-ai-clothingdaily-viral-instagram-tiktok) in December 2022 after being shared on Instagram* by @ai_clothingdaily. The digitally created shoes feature gothic and romantic elements with lace and beads, drawing attention from sneaker enthusiasts who praised the unique design and dreamed about buying a real item. The collection's popularity on social media could inspire *Nike*'s future design choices.

<Gallery>
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original="/uploads/Krosy_5_b18c6af521.png"
thumbnail="/uploads/Krosy_5_b18c6af521.png"
/>
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original="/uploads/Krosy_4_d0aac616b2.png"
thumbnail="/uploads/Krosy_4_d0aac616b2.png"
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original="/uploads/Krosy_6_39281655c9.png"
thumbnail="/uploads/Krosy_6_39281655c9.png"
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original="/uploads/Krosy_7_82dbde5f6c.png"
thumbnail="/uploads/Krosy_7_82dbde5f6c.png"
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original="/uploads/Krosy_3_b49e079673.png"
thumbnail="/uploads/Krosy_3_b49e079673.png"
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original="/uploads/Krosy_2_9dc31687f5.png"
thumbnail="/uploads/Krosy_2_9dc31687f5.png"
/>
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original="/uploads/Krosy_1_e122be3598.png"
thumbnail="/uploads/Krosy_1_e122be3598.png"
/>
</Gallery>

- [**Kamil Banc**](https://www.amazon.com/stores/Kamil-Banc/author/B0BV79FGDR), an AI-assisted author, has published several books using AI image creator and prompts to speed up the writing and publishing process. His books, including an illustrated children's book “Bedtime Stories” and an adult coloring book, have received positive reviews despite only selling a few copies. It took Banc just four hours to publish an illustrated book. He credits AI for making the process simple and fast, saying he was surprised at how quickly he was able to go from concept to publishing.

<Img
img="/uploads/image14_51165340e1.png"
alt="Bedtime stories"
/>

- [**Looka**](https://looka.com) is a service that can generate logos. The system can create a logo for free within minutes, you just need to specify a few parameters. These are the options the neural network suggested for *Altcraft Platform*.

<Gallery>
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original="/uploads/1_kartinka_ce0af926a2.png"
thumbnail="/uploads/1_kartinka_ce0af926a2.png"
/>
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original="/uploads/2_kartinka_098d97e065.png"
thumbnail="/uploads/2_kartinka_098d97e065.png"
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</Gallery>

The use of neural networks for such purposes in business speeds up the process of creating logos and makes it cheaper.

- The service [**Deep Nostalgia**](https://www.myheritage.com/) brings old photos to life. The neural network recognizes faces and animates the images.
<p>

- [**Lensa**](https://prisma-ai.com/lensa) app generates unique portraits from the user's photos using 10-20 samples.

<Img
img="/uploads/photo_2023_05_03_15_32_02_d49bb97566.jpg"
alt="Portraits by Lensa"
/>

[**Balaboba**](https://yandex.com/lab/yalm-en) is a neural network that generates text based on inputs and styles them to fit specific formats. Here's an example of the text that was generated with the phrase "marketing automation service".

<Img
img="/uploads/image10_3c2eaee137.png"
alt="Balaboba"
/>

The service for removing the background from photos, [**Retoucher**](https://retoucher.online/), also works based on neural networks. This is a useful function for marketing, especially when a company uploads images to online stores.

<Img
img="/uploads/10_1d5fcf30e3.png"
alt="Retoucher"
/>

- [**Disney's neural network**](https://studios.disneyresearch.com/2022/11/30/production-ready-face-re-aging-for-visual-effects/) is capable of changing the age of actors in a frame. Now it is possible to avoid the use of make-up and searching for similar actors to portray characters at a different age.

<Img
img="/uploads/photo_2023_05_03_15_30_01_227a76514e.jpg"
alt="Disney’s Neural network"
/>

Automatic content creation using neural networks is a possible solution to the problem of copyright when creating videos for business and marketing purposes.

## Summary

A neural network is a data processing and analysis system that replicates the work of the human brain. The software code imitates neurons and connections between them (synapses) through which information is transmitted. The system learns from previous experience or from algorithms provided by humans.

Modern neural networks generate images, write texts, create music, recognize faces, predict events, and much more. Technology is becoming part of everyday life and business, simplifying and speeding up human labor.

**The product belongs to Meta, a company recognized as extremist in Russia.*


Neural networks draw, generate texts, calculate complex data needed for decision-making in business, marketing, and daily life.

image20.png

Figuring out how a neural network functions and whether to use neural networks in your business?

Neural Networks in Business, Marketing and Social Life

Neural Networks in Business, Marketing and Life

<Img 
img="/uploads/trendy_digital_marketinga_81bd643747.png"
alt="Digital marketing trends in 2023"
/>

New technologies open up more opportunities for business development, and the world of digital marketing also depends on them. Technology generates trends, but not all trends find their place in a specific industry. This is the choice of marketers, which trends to follow and which ones to ignore. The main thing is not to find yourself behind competitors one day.

In 2023, digital marketing will take a stronger position in the virtual world with VR and AR. Companies will also have to change the approach to data. NFT will remain popular, omnichannel and short video will continue to gain momentum.

In this post we will tell you what digital trends to pay attention to next year.

## Short videos

The greater the flow of information, the less time to view content. Therefore, short video formats (up to two minutes) will remain in trend. This type of content shows [**a higher ROI**](https://woosuite.com/stats/short-form-video/) than others. That’s why even YouTube, along with long videos, promotes [**Shorts**](https://www.youtube.com/intl/en_us/creators/shorts/). At the same time, TikTok remains one of the fastest growing media platforms in the world.

Short formats are cheap and quickly created. By the way, 72% of users choose video over text when learning a new product. This is a reason to create such content in 2023.

<Img 
img="/uploads/Screenshot_from_2022_12_01_17_22_44_7b9dd7322d.png"
alt=Short video as a trend in 2023""
/>

## Omnichannel marketing

It is not efficient to communicate with users in one channel. Customers buy both offline and online. Today they [**message your brand in apps**](messenger-marketing), tomorrow they call, and the day after tomorrow they check out in the online store. Everyone chooses a convenient way to communicate, so it is not surprising that omnichannel marketing is becoming a trend. And when we say omnichannel, we mean interacting with the audience in several related channels.

The company collects information from different touch points in order to know the context of each customer: when and with what issue a person applied, what he or she bought and how often. This helps the brand provide a high level of service, and in return receives income and [**a loyal audience**](customer-loyalty).

For omnichannel marketing, you need a tool that will combine several channels. [**Altcraft Platform**](/en) is a software that will allow you to automate online communications and collect customer data in a single window. The service integrates with various sources and supports such features as sending emails and syncing customer audiences in advertising accounts. It also collects analytical data.

<Img 
img="/uploads/omnikanalnyj_marketing_d78f8418e2.png"
alt="Omnichannel marketing as a trend in 2023"
/> 

## First party data priority

Google's new [**3rd party data**](first-second-and-third-party-data) collection rules, as well as [**GDPR and CCPA**](cdp-in-the-world-without-cookies) regulations, are forcing companies to change the way they collect and store information. Now the priority is the data that businesses receive directly from users: names, phone numbers, addresses, demographics, data on user behavior on the site or in the app. This information is accurate and useful for personalizing marketing offers and content.

Data storage is a serious responsibility for a company. This is the reason why, along with the trend to collect your own databases, more attention is also paid to the secure storage of information. Modern [**CDPs**](/en) must quickly collect data from different sources and ensure its privacy. There should be no leaks of information that can result in loss of reputation.

## Augmented and Virtual Reality

AR will continue to conquer the digital world in 2023. The [**mobile app**](mobile-app-as-a-marketing-channel) market for augmented reality [**reached $16 billion**](https://www.statista.com/statistics/282453/mobile-augmented-reality-market-size/) in 2022 and will grow to $21 billion next year. AR is used to improve the customer experience. Users try on clothes or cosmetics online through the app, see how furniture and other items look in the interior. Visibility increases sales and simplifies the customer journey.

<Img 
img="/uploads/VR_realnost_9c7fdb6086.png"
alt="AR and VR as a trend in 2023"
/>

Virtual reality is not inferior to augmented reality. The VR market in 2022 is [**$12 billion**](https://www.statista.com/statistics/1221522/virtual-reality-market-size-worldwide/), in 2023 it will be $16 billion.

## Metaverses

AR and VR technologies influence the development of the [**metaverses**](what-are-metaverses). Metaverses are digital spaces that simulate the real world. Marketers will continue to "catch" customers in virtual stores and sell brand products through advertising in the digital space.

This format is relevant for the younger generation, which is looking for a new experience in the metaverse. For example, users have already spent [**$500 million**](https://www.banklesstimes.com/metaverse-statistics/) on virtual real estate.

Well-known brands are already investing and developing in the metaverse. For example, Nike has been investing in the digital space for a long time. At the end of 2022, it announced that the company's developers had created their own metaverse .Swoosh. Fans will be able to buy their favorite sneakers or even participate in their creation.

In the future, the time that users spend in Metaverse will increase. By 2026, 25% of people will go there for an hour or more every day. Also, the trend towards online education will increase the audience of the "parallel world". Universities will begin to think about working in a virtual format.

## Chatbots

In 2023, [**chatbots**](https://altcraft.com/blog/the-benefits-of-chatbots-for-businesses) are also in trend, because it is more profitable for businesses to respond to users 24/7 and not depend on the work schedule of managers. Bots will become smarter due to artificial intelligence. They will begin to communicate with customers in a more personalized way and even help solve complex tasks  (for example, money transactions in banks).

Robots will better understand human speech. Thus, the number of chatbots with the function of [**a voice assistant**](https://www.softwebsolutions.com/resources/chatbot-trends-2023.html) will grow. Voice search in ecommerce will continue to evolve and improve.

## Artificial Intelligence

AI is taking over targeted advertising, content creation, email marketing, automating sales. Technologies already generate creative ads without a designer, optimize campaigns and predict user behavior, which enhances the personalization of service and sales. [**According to statistics**](https://www.idc.com/getdoc.jsp?containerId=US49571222#:\~:text=IDC%20forecasts%20the%20overall%20AI,Automation%20Research%20with%20IDC%27s%20software), the AI software market will grow to $791 billion by 2025.

<Img 
img="/uploads/iskusstvennyj_intellekt_e1fa859a97.jpg"
alt="AI as a trend in 2023"
src="An example of the work of the Deep Dream project from Google based on the work of Van Gogh"
/>

## NFT

The technology of non-fungible [**NFT**](https://altcraft.com/blog/what-is-nft) tokens will remain popular in the coming year. Brands are already selling replicas of their real-life items as NFTs and creating virtual-only fashion collections. The technology "records" the authorship of the content, which is important for protecting the rights of content makers and increasing the value of digital items. Now, when customers buy digital products (pictures, virtual clothes or accessories), they secure ownership and protect themselves from theft of the object.

The cost of NFT is growing, so the rental of tokens is developing. Now you can simply rent a digital outfit or avatar instead of buying it.

<Img 
img="/uploads/nft_b1158a9bc0.jpg"
alt="NFT as a trend in 2023"
src="NFT platform for beginners OpenSea"
/>

## User-generated content

The content that users themselves create for the brand works like a new word of mouth. Promotion through customers includes a product photo, product unboxing, reviews and special hashtags, comments. The advantage of such content is nativeness. People perceive the posts of other users as recommendations. [**84% of customers**](https://everyonesocial.com/blog/user-generated-content-statistics/) say they trust other people's posts more than ads. This is how social proof works.

## Internet of Things

Internet of Things describes everyday household devices that connect via a network to smartphones and computers. These can be alarm clocks, lighting fixtures, robotic vacuum cleaners, and even cars.

Now there are 13 billion smart things in operation, in 2023 there [**will be 15 billion**](https://www.statista.com/statistics/1183457/iot-connected-devices-worldwide/), and by 2030 — 29 billion. The items themselves are unlikely to learn to sell or advertise anything, but they can collect and transmit data about user behavior to marketers. Brands will use the collected data to predict the actions of customers and enhance the personalization.

## Podcasting

[**Podcasts**](https://altcraft.com/blog/podcast-marketing) will continue to play an important role in brand promotion. "Radio of the 21st century" is very popular. On average, one person listens to eight audio broadcasts per week. And all over the world, about [**383 million users choose podcasts**](https://www.demandsage.com/podcast-statistics/). This is an easy way to consume content in transport, during training, and so on. Companies create their own podcasts or sponsor releases from authors and make collaborations. Advertising is smoothly embedded in the conversation of the speakers, so it is less annoying for users. The podcasting industry is projected to [**grow to $4 billion by 2024**](https://www.theverge.com/2022/5/10/23065056/podcasting-industry-iab-report-audacy-earnings-patreon-pulitzer).

<Img 
img="/uploads/image6_b5ed571465.png"
alt="Podcasting as a trend in 2023"
src="Google Podcasts"
/>

## Conclusion

In 2023, companies will continue to implement AR and VR technologies to sell products that can be tested virtually. Marketing will have to master the metaverses and look for new techniques there. For example, create NFT products of their brand.

1st party data that includes information companies collect from customers will play an important role. Brands will improve customer experience by using chatbots, artificial intelligence and the Internet of things. Omnichannel marketing is a valuable data resource next year.

Short videos and podcasts will remain in the content trend. Meanwhile, user-generated content will become a popular promotion method.

User-generated content, NFT, metaverses, 1st party data - read about these and other digital marketing trends in 2023.

trendy-digital-marketinga.png

Technology generates trends: NFT, metaverses, user-generated content. What else awaits us next year?

What Trends Will be Relevant in Digital Marketing in 2023

Digital Marketing Trends in 2023

<Img
img="/uploads/benefits_of_chatbots_for_businesses_776h517_2dddff90e2.jpg"
alt="Rule-based chatbot"
/>

The digital age has led to high customer expectations. From eCommerce, healthcare, marketing, or finance, instant communication is essential in every business. Today, chatbots provide businesses in different industries with the capability to keep customers engaged and satisfied.

## What Is a Chatbot?

You've likely encountered this already. Have you gone to a website, and a little chat window pops up with a generic profile picture and name to go along with it? That's a chatbot, usually. Sometimes it's a real person, but that's extremely rare owing to logistics. Generally, you're encountering a chatbot.

<Note>
A chatbot is a software application that uses Artificial Intelligence (AI) and Natural Language Processing (NLP) to process and understand human intentions and provide guidance to desired outcomes. It works as a virtual assistant to enhance your customer experience.
</Note>

Basically, chatbots for marketing often leverage existing data to understand submitted queries. They also analyze appropriate answers based on their training period. And with machine learning technology, they learn context and improve their efficiency.

## How Do Chatbots Work?

Chatbots use advanced machine learning and NLP algorithms to learn from existing data. Typically, NLP is the ability for computers to process and understand human speech and respond in human language. It's designed to make the interactions mimic communication between two people.

### Types of Chatbots

- **Rule-based Chatbots**

Rule-based chatbots require human input to choose predefined options. And since the selections are limited, these chatbots tend to be the slowest in terms of customer service.

<Img
img="/uploads/rule_based_chatbots_7936fbde73.gif"
alt="Rule-based chatbot"
/>

- **Intellectually Independent Chatbots**

Intellectually independent chatbots rely on machine learning to understand users' inputs and queries. These bots are highly trainable, and you can train them to process certain phrases and keywords that require responses. Independently, the bots will also learn complex queries and generate appropriate responses.

<Img
img="/uploads/Bo_A_Erica_AI_17c0aecd89.jpg"
alt="AI chatbot"
/>

Let's take a closer look at the difference between rule-based chatbots and AI chatbots.

<Table>
| **Rule-based chatbots** | **AI chatbots** |
| ------ | ------ |
| Powered by keywords | Programmed with NLP and Machine Learning |
| Can’t capture typos | Employ NLP to understand customer inputs |
| Navigation-focused and incapable of learning from interactions | Use machine learning to get “smarter” over time |
| The interactions feel robotic rather than conversational | Engage naturally in human-like personalized conversations |
| Need to update rules in order to optimize the bot performance | It will continuously improve without much further assistance |
| The implementation process is faster and less complicated | The implementation process is more complicated and longer |
| Difficult to scale but works well if you have a very small scope | Highly scalable due to a broader range of decision-making skills |
</Table>

Utilizing rules-based chatbots and AI chatbots can facilitate a variety of outcomes which provide clear paths to sales avenues for customers. When considering what chatbots are used for, look into infrastructural needs. Artificial intelligence chatbots may be a little bit too ambitious for your operation. Sometimes you just need something that generates preset alternatives for clientele. At other times, you need AI capable of having a conversation. It all depends on the needs of your business overall. 

## Benefits of Chatbots for Businesses

**1. They Give Your Business a Friendly Face**

Chatbots can help your business create a friendly and relatable face for better customer interactions. For your audience, the chatbot becomes the first [**touchpoint**](marketing-attribution-models) and can be more personal than communication through the phone or mail.

Chatbot personality can have a significant influence on user experience, and it plays an important role in customer perceptions. To develop the appropriate chatbot personality for your company, you have to consider factors like brand identity and audience preferences.

To get started, consider the phrases and words that you use when marketing to your audience. The chatbot persona should mirror this language. And while you can’t appeal to everyone, making a persona that’s consistent with your brand voice can address audience preferences.

**2. Constant Availability**

Unlike traditional customer service, chatbots for websites are always available 24/7. It provides support to your customers on weekends and at late hours. Interestingly, AI chatbots are not overwhelmed by a multitude of requests. By design, it can process multiple requests at the same time and maintain accuracy.

Whenever there's a query that a chatbot can't handle, a customer can always forward the question to a human respondent.

Outside the normal business hours, customers can leave a message for customer service to attend to the next business day. But within business hours, the complicated queries can be handled by human employees.

**3. Instant Response to a Customer**

By providing real-time interaction, you can ensure instant service to your customers. The studies confirm that businesses can react three times faster than when using human customer service. That's one of the reasons why chatbots help businesses increase customer satisfaction by [**24%**](https://www.intercom.com/blog/the-state-of-chatbots/).

**4. Gathering Customer Data And Provide Accurate Marketing Insights**

Customer service chatbots can qualify relevant business leads by collecting relevant data that can be used by your sales teams for sales conversions. Ideally, chatbots can direct customers towards desired products based on their queries.

And since we're in the customer age, your customers expect personalized responses to their queries. Chatbots are designed to tap into customer history and generate the most appropriate responses to questions. Most importantly, the data helps your marketing team to send [**personalized messages**](/features/personalization) and offers.

**5. Chatbots Can Increase Your Sales**

If your business can offer the appropriate information and offers to potential customers, your [**conversion**](conversion-funnel-stages-and-optimization) rates can increase significantly. Chatbots for business can provide answers to website visitors throughout the customer journey. Most importantly, it will advise and guide customers when selecting products. According to a recent survey, businesses that use chatbots increased their sales by [**67%**](https://www.intercom.com/blog/the-state-of-chatbots/). 

## Social Media Chatbots

Generally, a website chatbot is a great tool for improving customer service and increasing sales. But you need not only rely on website options. In recent years, chatbots in messengers and social networks have been increasingly implemented by companies from a wide variety of industries. The relevance of the solution is confirmed by the fact that a very large and active audience is concentrated on [**social media**](self-marketing-how-to-promote-yourself-on-social-media). And that audience is your potential consumers.

Let's consider social media like [**Telegram**](updates-telegram-bot-facebook-ads-manager), as an example. Text and image files can be sent through this platform, and "bots", as they've come to be known, are popping up all over the place. You might send bulk messages to Telegram subscribers using chatbots. Varying buttons can be put in images, text can be added, you can have images in your messages, and basically design them just as you like. When the time comes, you can schedule it to be sent as fits your needs; this can be narrowed down to the specific time on the specific day when you need that message sent. After sending, stats can be explored to determine effectiveness. It's a great way to determine how effective your outreach is.

## Final Words

Artificial intelligence has ushered in limitless capabilities for businesses. And chatbots have proved to be an incredible invention for any business. Chatbots offer continuous customer support and recommendations to help you faster. While chatbot technology is useful for automation, it's prudent to balance chatbot automation with the human touch.

A chatbot is a software application that uses AI and NLP to process and understand human intentions and provide guidance to desired outcomes.

benefits of chatbots for businesses 776х517.jpg

Unlike traditional customer service, chatbots for websites are always available 24/7. It provides support to your customers on weekends and at late hours. 

AI and Rule-Based Chatbots | How to Use It for Your Business

The Benefits of Chatbots for Businesses

uzum-case.png

Reduced push notification opt-out rates, increased OR, saved working hours

bnb-bank-kejs-v-menyu.png

The open rate for automatic Viber messages is around 60%, and MAU growth for the year is +19%

All user information is systematically collected in a unified customer data platform

Distributing messages across various communication channels: email, SMS, push notifications, messengers

Promo codes, bonuses, and points for building long-term customer relationships

You can create and edit any HTML template, as well as save your own custom blocks

Personalized distribution of messages to clients through communication channels

Conducting experiments and optimizing marketing strategies

Tracking the effectiveness of marketing campaigns to make informed decisions

Connecting external systems to the platform and synchronizing data with company databases

Creating questionnaires, surveys, and feedback forms of any level of complexity

The ability to communicate with your clients anytime and anywhere

Increasing user awareness of your company's offers

Customer Data Platform

Omnichannel Marketing

Loyalty programs

HTML Email Builder

Personalized mailings

A/B/n Testing

Real-time analytics

Integration and WebHooks

Forms and Surveys

Messenger Communication

Pop-Up Windows

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