Features of AI Companion

Language models developed by OpenAI, Anthropic, and Meta open up new possibilities for accurate and high-quality AI-powered transcription. For example, Zoom, which introduced its AI Companion for subscribers, has trained this system using a vast array of employee meetings from various organizations, significantly enhancing the potential for successful outcomes.

Using natural language processing, AI synthesizes long discussions and organizes them into summaries, highlighting the most critical points. One of the key applications of this tool is meeting summaries, which can be generated at any time during a meeting, not just after it ends. On platforms like Zoom, for instance, users can access this feature whenever they need it. Additionally, AI can create idea boards, visualizing the expressed thoughts.

With the phrase “catch me up,” the AI Companion can take notes of the conversation that might have been missed or misunderstood during the live meeting, helping users stay on track with the current discussion.

Another feature is the ability to capture the emotional tone of the meeting. After the meeting, machine learning systems can summarize the overall atmosphere, noting the emotions expressed by different participants, identifying moments of tension, and more.

Yet another functionality is identifying the most active speaker. Although the system doesn’t provide exact speaking time for each participant, the generated transcript can offer a clear idea of who contributed most to the discussion.

The Significance of Transcription with AI Companion

As in many fields, AI tools automate certain processes, saving time in the process. This means that during or after a meeting, there’s no need to finish taking notes or try to capture the details you may have missed. AI captures all the meeting’s details, creating thorough and comprehensive notes that cover everything.

By eliminating the need for multitasking, AI allows participants to focus entirely on the ongoing discussion, fully engaging in the conversation without the distraction of taking meeting notes.

Another crucial aspect is that AI-generated notes are created even when the conversation isn’t being recorded, using temporary transcripts to generate summaries. These AI-generated notes are valuable for those who missed the meeting, providing an unbiased account of the conversation. This is important, as different colleagues might emphasize different issues or solutions discussed during the session.

It’s worth noting that transcription tools aren’t only useful in business meetings, where they’re especially convenient. Emotional tracking is also valuable in business contexts, as it helps identify whether a client is interested in a proposal or assesses participants’ moods during conflict resolution.

However, this tool is also beneficial in universities, where lectures often present a large volume of information, making it hard to note everything. It’s especially helpful for students with health issues that make note-taking difficult.

In journalism or publishing, this tool can help structure public speeches, important announcements, or material from meetings and conferences.

Nonetheless, this technology requires a clearly defined structure and thematic consistency to ensure that the summary is of high quality.

Final Word

In a world where time is increasingly scarce, transcription with AI Companion becomes a valuable tool that saves time and allows us to focus on more demanding tasks—like engaging in meaningful discussions and fully participating in various meetings. Sources: Zoom, Wired

AI Voice Assistants as Customer Service Tools

High-quality customer service is one of the key standards in the hospitality industry. While AI tools as hospitality phone agents are not yet as widespread as consumer-oriented generative AI tools, they represent another niche that significantly contributes to the automation of business processes.

In restaurants, these AI-powered tools are typically available 24/7 via phone and can provide consultations on general topics such as dress codes, seating arrangements, cuisine, food allergens, and they also manage table reservations.

What Do Virtual Host Offer?

AI integration is especially advantageous for restaurants, as this automation saves time for the administration by answering questions that are often already covered on the restaurant’s website. Every customer’s call is answered promptly, speeding up the reservation process and enhancing the overall customer experience.

Moreover, this helps avoid issues related to human error. Employees might be overwhelmed or tired, which can lead to distractions during interactions with potential customers. As a result, reservations might get mixed up or double-booked, requiring a call to the customer to cancel or reschedule, which usually leads to dissatisfaction.

Machine learning also uses predictive analytics, drawing on statistics to forecast future needs and optimize the restaurant’s operations. It can even personalize recommendations for customers, predict which tables are most often selected, forecast spikes in customer traffic, or anticipate last-minute reservation cancellations or no-shows based on past data.

In real time, AI can also be integrated to analyse waiting lines, estimate wait times, and provide updates via SMS or apps, improving the efficiency of queue management in the future.

Additionally, AI voice integration into reservation systems allows for automated reminders or confirmations of existing reservations, ensuring that all available tables are utilized efficiently. If a spot opens up, it can be quickly rebooked for another customer.

Using virtual hosts in dining establishments not only improves the business’s internal processes and efficiency but also offers significant benefits to customers, which is the key focus. Faster responses, personalized offers, and recommendations help enhance the guest experience and elevate it to a higher level.

It’s important to remember that successfully integrating new technologies requires proper preparation and testing. There are still cases where AI voice assistant may not understand a question or the query falls outside its programmed scope.

Data protection is also a critical issue here. It’s essential to choose quality, trusted data protection measures to ensure the safety of customer information.

Final Word

Virtual host enables restaurants to create a high-quality and elevated hospitality experience by automating certain processes and fully integrating AI. As a result, the importance of machine learning systems is steadily growing, becoming a vital component of successful business operations.

Sources: Loman.ai, Forbes, Wired

What is Web Scraping?

Web scraping is a process of extracting desired information from a specific website and exporting it into a more convenient format for the data collector. Although this process can be performed manually, it is more often conducted using automated tools. While web scraping is legal, it can become unlawful when attempting to access data that is not publicly available.

In simpler terms, the process works by assigning URL codes to a web scraper before data extraction. The scraper then retrieves the necessary data and exports it into a preferred format, such as CSV, Excel, or JSON.

The Opportunities and Risks of Data Scraping

AI-powered web-scraping tools, such as GPTBot or Google-Extended – commonly referred to as bots – are used to scan websites and collect data, helping improve language models (LLM) and AI technology in general. This plays a significant role in refining AI models to ensure accurate results.

However, there is increasing focus on how AI might impact ethical standards and data protection. As a result, web scraping is often viewed as controversial, and some website owners may choose to block bots from scraping their data.

Various tools are available to help monitor AI bots collecting data and to identify those attempting to conceal illegal data access. The concern arises primarily from fears that AI-powered information retrieval tools might misuse available information and illegally gather data from websites, which also increases the risk of compromising sensitive data.

For example, creators of journalistic content aim to preserve the uniqueness and reliability of their work, and they do not want AI systems to use their data, which increases the risk of manipulation. According to Palewire’s research, 47% of news websites already block AI bots.

Another area of concern is retail. Each e-commerce website builds its brand by creating unique content, and information retrieval poses a risk to their competitive advantage and intellectual property if it is used without consent.

On the other hand, blocking bots can be inconvenient for the websites themselves. In some cases, using specific bot-blocking tools can reduce a site’s visibility and hinder accessibility. Additionally, such a decision distances the site from contributing to the development of high-quality machine learning tools, as there is a risk of losing valuable data that could enhance AI models and drive progress.

Final Word

The new age of technology compels us to seek ways that ensure respectful and fair collaboration between website owners and web crawlers. Gradually, tools are emerging that will help find a balance, allowing owners to decide whether they truly want to block all AI bots and potentially hinder technological advancement. It is also likely that a form of agreement will emerge, providing web crawlers with specific terms of use and responsibilities for granting access to content creator information.

Sources: Parsehub, Yoast, Wired

The Role of AI-Generated Content in Search Engine Performance

With the release of Gemini, a multimodal language model, Google made significant progress as a response to Microsoft’s ChatGPT. It was immediately clear that Gemini would be integrated into Bard and SGE results, inevitably impacting search engine outcomes and potentially displacing human-created content.

This has important implications for website operators, as they anticipate a rise in “no-click”searches, leading to a loss of traffic. This is because SGE will generate sufficiently detailed answers to users’ queries, eliminating the need to click on links to individual websites. For example, in the U.S., where SGE has been active the longest, it has been observed that websites have experienced traffic losses ranging from 20% to 64%, depending on the domain and industry.

A greater threat has been identified for smaller brands with limited budgets, as Google aims to promote larger brands that can allocate substantial budgets. It is anticipated that this will directly affect those who create informational and editorial content since high-ranking results were only granted to a few major websites, including Reddit.

It has inevitably been discussed that human-created content will find it difficult to compete with AI-generated results, which are presented at the top of the query results. To achieve higher rankings, human-created content will need to meet even higher quality, uniqueness, and thoroughness standards. SEO strategies will need to be even more finely optimized to stand a chance of appearing in AI-generated summaries.

Some, in response to the looming threats, have already started developing strategies to find new opportunities for expanding search. There has been talk about optimizing newer search channels, allowing users to discover content on other platforms such as YouTube, social media, or e-commerce platforms.

Did Expectations Surpass Reality?

Despite various concerns about the potential for drastic changes in search systems, reality turned out to be somewhat different. AI-generated content has not yet surpassed or completely filled search systems.

Human-generated content remains a crucial aspect, offering more uniqueness, emotion, and authenticity. Users still tend to focus more on articles created by humans rather than AI-generated content.

It’s true that these changes have indeed impacted search, but so far, they have not been as significant as expected. According to Semrush, Google remains dominant, and AI search has not had the effect that many anticipated. Retail search trends remain stable, although the rise of AI is being observed.

Based on a study byNeil Patel, it can be highlighted that traffic metrics for an article vary depending on whether it was created by AI or by a human. A specific case showed that traffic for AI-generated content fluctuated, while traffic for human-generated content steadily increased over five months.

Another experiment focused on how users react to and how much time they spend reading materials that were labelled as AI-generated, AI-assisted but modified, or left as human-created product. The results revealed that people spent the most time and showed the highest levels of trust and engagement with human-generated materials.

Final Word

While machine learning systems have not yet replaced human resources, their impact remains significant. No one doubts that AI will continue to advance, bringing even more opportunities for change in the future. Sources: NEILPATEL, Proficio, The Drum, X

Top AI Headshot Generators

One of important aspect is that AI has made significant progress, and from previously illogical and incoherent images, it can now generate highly refined photos. By providing a simple existing image, you can achieve the desired result. Thus, it is an excellent tool for businesses of all sizes to create a solid image or represent their team.

Not only does this save time and resources, but it also plays a role in privacy protection, as real team photos are not being published. When choosing an AI headshot generator, there are several factors to consider. First, speed. In this case, free tools are often the fastest.

Second, accuracy or realism. While the quality of results depends on each individual case, some tools are known for their high accuracy, while others might produce more ethereal images. Finally, the intended use – whether it’s for large companies that require various additional features or smaller businesses needing only basic functionality at a good price.

1.      Portrait Pal

This tool uses advanced machine learning technology, making it an excellent choice for those seeking high-quality results. Original photos are analysed using a deep learning model, namely stable diffusion. It is a paid tool that can deliver the final result in 30 minutes without requiring additional editing.

2.      BetterPic

Although the realism is slightly lower compared to the first tool mentioned, for an additional fee, it offers customization options, such as changing clothes, hairstyles, or the photo background.

3.      Aragon.ai

A professional AI headshot generator, whose algorithms were developed by researchers who worked with Meta and Microsoft. It delivers versatile results within two hours. A distinctive feature is that Aragon.ai allows users to input textual comments about the photo, such as background or outfit descriptions.

4.      MyEdit

A good option for those looking for a free tool. Users get 3 free credits per day to generate professional photos. The process involves uploading a photo and selecting 8 preferred photo styles, with suggestions generated within 10 minutes. The generator also includes other tools like background removal, background generation, and converting photos into avatars or sketch/cartoon styles.

5.      PixelPose

Another AI headshot generator, PixelPose is ideal for those seeking exceptional quality. One reason is its modernity, which ensures precise image reproduction and creates a solid overall impression.

6.      Dreamwave

This AI headshot generator offers unlimited background and outfit options, allowing for a wide variety of looks. The photos are highly realistic, but the price is one of the highest on the market. In addition, the price in this app is presented only after uploading photos.

7.      PFPMaker AI

Another free alternative for those seeking a budget-friendly option. It allows users to crop their preferred photos and change the background colour. It focuses on speed and maintaining quality, with the free version offering 90 generated photo options.

8.      HeadshotPro

A simple and traditional photo generator that creates sufficiently professional images. The company offers a team package for creating a consistent image for employees. Generated photos can be edited, but most styles are neutral, making it less suitable for more creative solutions.

9.      StudioShot

This tool focuses not on the number of suggestions but on the ability to refine the generated image through detailed editing. However, this approach lengthens the time it takes to deliver the final result, which can take up to two days.

10.  LightX

An online photo editor with a free AI Person Photoshoot tool that can be used to create business photos. It is very user-friendly, making it perfect for individual and personal needs.

Final Word

The demand for photo generators in the market is enormous. Identifying specific needs becomes a critical factor in choosing the right tool. Some may require a professional AI headshot generator offering high-quality results, while others may find a simpler, more economical tool with basic features sufficient.

Sources: CyberLink, The Hollywood Reporter, RollingStone

AI Voice Features

In September, OpenAI announced that advanced voice mode (AVM) functionality would be available at the ChatGPT Plus and Teams levels, allowing AI-generated voices to sound more natural. This change comes with the introduction of the enhanced voice mode, and ChatGPT will offer a total of nine voices.

AI voice technology is focused on creating human-like speech using cutting-edge methods. Advanced voice assistant tools are not only capable of understanding and deciphering spoken words but also of analysing context, recognizing the speaker’s tone and emotions, and providing an appropriate response based on these factors.

When analysing AI-generated voices, several technical aspects can be discussed. The creation of these voices involves three main methods:

First, machine learning algorithms. These are significant because they enable systems to continuously learn from data, improving AI outcomes over time. The datasets incorporate a large amount of detailed linguistic models, including phonetic structures and speech dynamics. This allows AI-generated voices to refine their sound, making them as similar as possible to human speech, without standing out due to differences in phonetics or intonation.

Second, natural language processing (NLP). This is perhaps the most crucial technological aspect of AI voice, as it directly impacts the understanding and interpretation of human speech. NLP enables AI to transform specific data, which may be expressed in numbers or facts, into narratives that sound highly natural. This opens up the possibility for synthetic voices to speak in complex sentences and incorporate advanced language features, regardless of word similarities or ambiguities.

Third, the speech synthesis method. These methods are at the core of AI and a prerequisite for machines to process text. First, syntax is involved. NLP algorithms break down sentences into structural data and segments that the system can process. Then comes sentiment analysis, which essentially reads between the lines to understand the overall tone.

Different synthesis methods can be used. For example, concatenative synthesis, where recorded speech is combined, parametric synthesis, which creates mathematical models, and one of the most advanced methods, neural TTS, which uses deep learning models–neural networks.

One crucial aspect of this technology’s advancement remains its continuous interaction with humans. By communicating in everyday tasks, machine systems learn and, as they improve, can better understand queries and provide accurate responses.

AI Voice Technologies from Different Companies

For instance, Google has introduced a tool called WaveNet, which is also capable of generating speech that sounds natural and is considered one of the leaders in this field, although it has weaker capabilities in analysing contextual information.

Microsoft has also developed Azure Cognitive Services. While its voice naturalness is slightly behind the two companies mentioned, it remains an excellent AI voice synthesis option, especially well-suited and easily integrated into various Microsoft products.

Since OpenAI uses advanced methods and incorporates the most innovative neural networks in its AI voice technology, the generated voices sound extremely realistic, capturing natural speech rhythm and tone after conducting high-quality context and emotion analysis. This sets OpenAI’s tools apart from others.

Final Word

The development of artificial intelligence opens up new solutions and brings us closer to the substitution of human resources, as it can now easily mimic human voices. This solution brings countless new discoveries and innovations to both businesses and everyday life, while also presenting certain challenges that lie ahead. Sources: AutoGPT, Google, Microsoft, OpenAI, Podcastle, TechCrunch

How Does AI Create a Music Playlist?

The first to experience this new feature on Spotify were app subscribers in the U.K. and Australia. Now, the AI-generated playlist function is also available to users in the U.S., Canada, Ireland, and New Zealand.

Although still in beta, users can already test out this innovation. The tool works by allowing users to describe the playlist they want to generate, such as romantic songs for a dinner at home, or a party playlist for friends. AI then generates a playlist that reflects the described mood. Notably, the descriptions don’t need to be brief, they can include creative ideas.

There is also the option to refine and further edit the description to improve the playlist. Additionally, the descriptions don’t have to be purely textual – they can be enhanced with visual elements such as emojis, colours, or even references to movie characters to help convey the desired playlist mood.

What Opportunities Does Spotify’s AI Playlist Feature Offer?

One of the main benefits of applying AI to playlist generation is the significantly reduced time spent searching for new music or songs that match a specific mood or theme. The process becomes faster and more efficient, as machine learning immediately suggests songs based on both the description provided and the user’s previous activity history.

Analysing past activity ensures that the playlist is personalized for the user, making it more likely to meet their preferences and expectations. Another key aspect is that AI continuously learns from users’ habits and requests, becoming more accurate over time in generating content.

The vast array of available music also means that AI can analyse large amounts of data to select the most fitting suggestions. This increases the likelihood of expanding one’s musical tastes and discovering new, up-and-coming artists – something that, from a different perspective, benefits the artists themselves by improving the visibility of lesser-known or emerging musicians.

Can We Fully Rely on an AI-Generated Playlist?

One of the important risks associated with AI in various fields, including music, is that its great potential for personalization may lead to a limited perspective. Much like social media algorithms create “filter bubbles,” AI in the music industry could lead to users being confined to a specific musical bubble, where over time they may view music from only one angle, limiting exposure to other genres or styles.

Moreover, it’s important to remember that AI is not infallible – it can make mistakes or misunderstand the deeper contexts of a user’s search or their emotional expression, resulting in playlists that don’t match the user’s expectations.

Additionally, in this version of Spotify, AI will not create playlists based on political messages or descriptions containing terms like “deceptive” or “dangerous.”

Final Word

Artificial intelligence is gradually becoming one of the new age’s DJ alternatives, offering its own generated music playlists. This is yet another tool that enhances user experience and opens new possibilities in the music industry.

Sources: Spotify, TechCrunch, Fast Company

What is Open Source AI?

Open-source AI platforms are software and toolkits that are available to everyone. The source code of these platforms is publicly accessible, enabling users to customize the software based on their needs, collaborate with others, and share improvements.

The growing integration of machine learning into businesses encourages the adoption of these innovations, allowing automation of processes, enhanced data analysis, and the creation of new products. Open-source AI provides an accessible, cost-effective space that doesn’t rely on proprietary systems, making it a popular choice across various fields.

Open-source AI platforms are already transforming many sectors, significantly improving their performance. In healthcare, they are used in medical image analysis, such as X-rays and MRIs. In retail, they personalize customer experiences and integrate chatbots, while in the automotive industry, open-source AI is helping to develop self-driving technologies.

15 Open Source AI Platforms

A wide variety of open-source AI tools exist today. Below are 15 of the best platforms available:

1. TensorFlow

TensorFlow is a highly popular, free, open-source platform designed for creating and training machine learning models. It is used across various industries, including Gmail and Google Photos. TensorFlow is flexible, allowing users to retrain existing models or build new ones from scratch, and it offers a range of instructional videos.

2. PyTorch

PyTorch is a machine learning library that utilizes Python and operates as a one-stop solution for turning ideas into functional applications.

3. Keras

Designed for fast experimentation with deep neural networks, Keras works well with other libraries like TensorFlow. It’s a great choice for both beginners and experts creating new prototype ideas.

4. OpenAI

OpenAI, a leader in AI innovation, offers tools like OpenAI Gym, which are used for testing and developing reinforcement learning algorithms. It’s one of the most popular platforms for research and learning.

5. Rasa

Rasa is used to build conversational AI, such as chatbots and virtual assistants. Its machine learning technology simplifies understanding natural language responses and enables complex dialogue commands.

6. Scikit-learn

Known for its consistency and beginner-friendly nature, Scikit-learn integrates Python and provides numerous machine learning and statistical modelling tools.

7. Apache MXNet

Apache MXNet focuses on being a flexible and efficient open-source deep learning system, capable of handling various tasks. It’s suitable for both research and production.

8. CNTK

CNTK (Microsoft Cognitive Toolkit) is a deep learning toolkit backed by Microsoft, supporting services like Cortana and Azure.

9. Caffe

Caffe excels as a high-performance deep learning system, making it ideal for tasks such as image classification and real-time object detection.

10. Theano

Theano integrates deeply with NumPy and is known for its efficient gradient calculations. This platform allows developers to create new machine learning algorithms and handle large-scale mathematical computations.

11. OpenNN

OpenNN is a C++ class library specifically designed for building and using neural networks, making it a great option for researchers.

12. H2O.ai

H2O.ai is an open-source machine learning platform that supports various algorithms and integrates easily with Hadoop and Spark. It also simplifies the process of building predictive analytics models.

13. MLflow

MLflow is a platform for managing the entire machine learning lifecycle, from data preparation to deploying production models.

14. Shogun

Shogun is a machine learning library offering a wide range of unified machine learning methods, designed to solve large-scale machine learning problems.

15. Ludwig

Ludwig is a toolkit that allows users to train and test deep learning models without writing code. It’s perfect for non-experts who need to train cutting-edge models with minimal setup.

Final World

Open-source AI opens vast opportunities to enhance business projects. It offers accessibility that encourages experimentation and the discovery of the best possible solutions. However, it’s important to note that companies must invest in knowledge and expertise to effectively use these freely available platforms. With the right approach, open-source AI can become a key component in driving business success and innovation.

Sources: Digital Ocean, Telnyx, Medium, GeeksforGeeks, HubSpot, Qodex

The lies of LLM’s

LLMs lie or “hallucinate” in a few different ways: not only can they give a misleading answer, and mix truth and fiction together, but can also make up completely fake people, events, or articles. It has been an issue between ChatGPT creating false data about people and EU laws regarding the personal data of individuals.

The problem of LLMs inaccuracy rules out the possibility of ChatGPT taking over tasks that require precision and accuracy. A study on ChatGPT as a diagnostic tool in medicine revealed ChatGPT does not give factual correctness, despite the great amount of information that is used to train the model. After 150 Medscape case challenges have been put to LLM, it answered only about half (49%) of the cases correctly, making this tool untrustworthy for medical counsel.

Why does it happen?

AI models learn from massive datasets filled with text, images, etc., and are trained to identify and replicate patterns in data. Because it focuses on statistical correlations rather than understanding semantics, depending on the prompt it may “hallucinate”.

Where context and understanding are needed, for example, by asking to provide legal advice or generate a scientific explanation, the LLMs might give back an answer that sounds confident but is actually misleading, contains errors or even made-up facts.

In addition, the L in LLMs stands for “large”, meaning the model deals with a massive quantity of internet data, which contains both accurate and inaccurate information. The LLM‘s are designed to predict the next word or sentence based on the learned patterns, not to verify the truthiness of it. Also, due to the generative nature of LLMs, they can combine patterns in unexpected ways which may lead to misinformation.

New method to avoid LLM’s “hallucinations”

To prevent overconfidence about incorrect predictions, LLMs need calibration. During calibration, the LLM’s level of confidence is aligned with its accuracy. A model that is well-calibrated should be more confident about a correct prediction, and less confident about a wrong one.

Recently researchers from MIT and the MIT-IBM Watson AI Lab presented a method of calibration called “Thermometer”. This method is supposed to make the process of calibration better while applying a more versatile technique.

The labeled datasets that were used for calibration before are now changed by an auxiliary model that runs on top of an LLM to calibrate it. The labeled data is used to train the Thermometer, but after that, this model can generalize to new tasks of similar category without needing additional datasets.

“As long as we train a Thermometer model on a sufficiently large number of tasks, it should be able to generalize well across any new task, just like a large language model, it is also a universal model”, – says Maohao Shen, the main author of a study on Thermometer, a calibration model that may help to fix the problem of overconfident inaccuracy in large language models.

Sources: University of Maryland, MIT Sloan Teaching & Learning Technologies, MIT News, nyob, Plos One.

In the Shadow of Cyberattacks

Cybersecurity refers to any technology, practice, or policy aimed at preventing digital attacks and mitigating their potential impact. The focus in this field is on computer systems, programs, devices, data, or financial assets.

Meanwhile, cyberattacks can be triggered for various reasons, ranging from minor theft to acts that could be considered warfare. They are intentional efforts to steal, alter, or destroy data and programs by unlawfully accessing a computer network.

An example of a significant breach is the 2023 MOVEit cyberattack, where servers were compromised, and data from more than 2,000 organizations — about 60 million people’s information, including that of organizations like British Airways and BBC — was leaked. Unfortunately, this was one of the largest attacks, not only due to the number of affected individuals but also due to the financial damage and long-term impact.

Another example is the 2014 Yahoo breach, with even graver consequences as over 500 million data were compromised and stolen. The situation worsened when the company concealed the breach until 2016.

The 2020 SolarWinds cyberattack is regarded as one of the biggest information technology security breaches of the 21st century, as it caused severe vulnerabilities in the supply chain and affected thousands of organizations, including the U.S. government.

These examples show the massive damage such attacks can cause, underscoring the urgent need to find ways to reduce the risks posed by these threats.

AI as a Cyber Shield

Traditional cybersecurity relies on manual analysis, which can be carried out by a single specialist, posing the risk of human error. Moreover, such analysis can take a significant amount of time. In contrast, AI can analyse vast amounts of data, recognize patterns, and generate insights based on the data analysed.

One of the key areas is behavioural analysis. For a cyberattack to manifest, malicious behaviour must emerge in a program, which well-trained artificial intelligence — often AI-based extended detection and response (XDR) tools — can identify, respond to more quickly, and alert about suspicious behaviour.

Moreover, AI can not only instantly detect suspicious activity but also respond automatically to the emerging threat. It can also create automated solutions for repetitive tasks related to threat management.

It is essential to note that AI tools can be used not only for information technology security prevention but also by cybercriminals to generate fake emails with fraud links or even to develop malware. Therefore, it is necessary to consider these risks as well.

Final Word

Artificial intelligence plays a crucial role in cybersecurity, primarily in the areas of prevention and assistance. However, we must acknowledge that AI is also becoming a tool for cybercriminals.

Nonetheless, future forecasts indicate even greater advancements in machine learning systems and deep neural networks, which will enable more effective prevention and combat against digital attacks.

Sources: IBM, TechTarget, SOPHOS