Protecting the image and reputation of a brand is one of the key reasons why companies should take content moderation seriously. Inappropriate or offensive materials can easily alienate customers, undermine trust in the company, and leave a lasting negative impact online. Without proper moderation, harmful content can spread online quickly.

As numerous cases have shown, even a single controversial publication can stir up a media storm. In 2017, Amazon faced a wave of criticism when its platform featured deeply disturbing content glorifying Nazism and the Holocaust. Similar scandals affected brands like Walmart, Sears, and Nordstrom when their online stores sold clothing with racist symbols. Without effective moderation, this can lead to boycotts, financial losses, and serious damage to reputation.

Content moderation helps companies maintain desired standards and values across all materials they publish. This is especially crucial in today’s digital age, where companies aim to foster engaged online communities around their brands. Open discussion forums become an easy target for spammers, trolls, and haters. Proper moderation helps create a safe, friendly space for valuable interactions.

Content moderation methods

When talking about content moderation, it is important to distinguish between two main approaches – pre-moderation and post-moderation. Both have their benefits and challenges.

Pre-moderation is about reviewing and approving content before publishing it. This approach allows full control over what appears online under a brand’s name. Its downside is the time-consuming process, which delays content publication. In the era of social media and the expected rapid response from brands, this can pose a significant challenge.

On the flip side, post-moderation focuses on reviewing already published content and removing undesirable material. Although this type of moderation does not slow down the publication process, it carries the risk that inappropriate content circulates on the internet for a certain period before being addressed. This may expose the brand to criticism and further spread of harmful material.

Combining both methods seems to be the optimal solution – automation using artificial intelligence can significantly speed up pre-moderation, while human moderators focus on more complex cases within post-moderation.

An important consideration is deciding which types of content should undergo moderation. This can include:

• texts – for example, comments posted by users,
• images – thanks to AI image recognition capabilities, this has become much simpler,
• videos – where AI tools are increasingly effective.

AI in content moderation

Traditional, human content moderation is becoming an increasingly challenging task in the age of ubiquitous social media and the vast amount of generated content. As shown by research from the United States Air Force, every month the military must review approximately 750,000 posts on Facebook, Twitter, and other platforms. Manual moderation of such a volume of content would be extremely time-consuming and costly.

This is where tools based on artificial intelligence and machine learning step in. They can revolutionize the moderation process, as they can automate it and scale it with high accuracy.

A key advantage of AI is its ability to instantly analyze massive amounts of data – text, images, video – and accurately classify it as appropriate or undesirable. What’s more, machine learning-based systems get better at this task with each additional sample of data they process.

Implementing AI tools for content moderation allows companies to automate and speed up the process. Instead of manually reviewing content, advanced algorithms can quickly identify potentially problematic materials, saving time and money compared to traditional methods.

Source: DALL·E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

AI content moderation software

There are several advanced AI-based tools on the market that can effectively assist organizations in the content moderation process. Let’s take a closer look at two leading solutions: OpenAI Moderation Endpoint and ClarifAI.

OpenAI Moderation Endpoint () is a content classification system developed by OpenAI, the creators of ChatGPT. It is specifically designed to identify a wide range of unwanted or harmful content such as violence, hate, nudity, drugs, or spam.

How does this tool work? First, the user submits text, an image, or a brief description of a video to the system. Then, advanced language and vision models analyze this content for the presence of unwanted elements. As a result, the user receives a detailed report with a numerical assessment and a list of category labels indicating whether the material is undesirable.

The key advantage of OpenAI Moderation is its scalability and speed. The system can generate tens of thousands of assessments per second, allowing for easy integration with even the most heavily loaded streams of data generated by large companies. As a result, this solution enables efficient and cost-effective content moderation on an unprecedented scale.

Source: OpenAI (https://platform.openai.com/docs/guides/moderation/quickstart)

Another noteworthy AI tool for moderation is ClarifAI (https://www.clarifai.com/). It specializes in analyzing and classifying images and video content for the presence of undesirable or sensitive material. The advanced computer vision (CV) technology used here can recognize a wide range of topics – from violence, drugs, and pornography to more subtle issues like alcohol consumption or tobacco.

ClarifAI is used today by hundreds of companies around the world, including giants like Canva, P&G, and Humana, to effectively moderate images and videos. The platform offers AI models that can be customized to meet specific business needs.

Summary

As companies’ digital presence continues to grow and brands expand their online reach, managing the content published under their name becomes a key challenge. Effective and scalable moderation of this content is essential for protecting the reputation and image of the brand.

AI tools like OpenAI Moderation and ClarifAI help automate and speed up moderation processes, offering impressive accuracy while significantly reducing costs. They allow for scaling moderation beyond what humans alone can achieve.

Of course, this doesn’t mean that human moderators will become entirely redundant. They will still be needed for more complex analyses and resolving questionable cases. However, by intelligently combining human and machine capabilities, companies can build a truly efficient and future-proof content moderation system.

Implementing AI in content moderation is a step that every modern brand should consider today. It’s a key tool for ensuring online safety, protecting reputation, and maintaining high standards.

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Sentiment analysis, also known as opinion mining, is the process of automatically processing large amounts of text to determine whether it expresses positive, negative, or neutral emotions. It relies on natural language processing (NLP), which enables machines to understand human language, and machine learning (ML) – training algorithms on labeled datasets to recognize specific words and expressions indicating a particular sentiment.

The main methods of sentiment analysis:

• rule-based approach – assigning appropriate emotions to keywords based on predefined rules and dictionaries, for example, “great” – positive, “terrible” – negative. It’s quick, but less accurate,
• machine learning approach – it is based on training algorithms on labeled datasets, so they can learn to recognize sentiment based on context. It is more advanced and requires a lot of training data.
• hybrid approach – combining both approaches.

Imagine a clothing company that wants to gather feedback on its new collection from social media, forums, and surveys. Doing this manually would take weeks. With AI and sentiment analysis, it takes minutes. The algorithm assigns a score to each opinion, from -1 to 1, where -1 is very negative, 0 is neutral, and 1 is very positive. This helps the company quickly see which products customers like and which need improvement.

The following outline shows the process of sentiment analysis using AI:

1. Gathering data. In the first step, customer reviews are collected from various sources.
2. Pre-processing. It involves removing special characters, emoticons, HTML tags, etc.
3. Tokenization. It’s breaking down text into individual words or phrases so that artificial intelligence can process textual information more efficiently.
4. Linguistic analysis. Identifying parts of speech, recognizing negation, comparatives, and superlatives, etc.
5. Sentiment classification. A key moment that involves assigning a positive, neutral, or negative label.
6. Results aggregation. This is the calculation of the overall sentiment for a given set of opinions.

Such prepared data serve as an excellent starting point for further analysis and drawing business conclusions. Thanks to the automation of the process, companies can continuously monitor customer sentiments and quickly respond to emerging signals.

Source: DALL·E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

Why is sentiment analysis important for businesses?

Tracking what customers say about a brand online is crucial for businesses today. Analyzing hundreds of comments and posts manually is just too much work.

Automated sentiment analysis helps keep an eye on brand mentions in real time and respond quickly. Here are the key uses:

• improving customer service – identifying and responding to negative feedback quickly,
• protecting reputation – continuous monitoring of brand sentiment helps prevent reputational crises,
• market research – tracking trends, benchmarking against competitors, and discovering niches. According to research, 90% of purchase decisions are preceded by online research.
• product development – collecting user feedback and analyzing it for improvements and innovations.

Examples? A restaurant chain can analyze guest reviews on platforms like TripAdvisor to improve the quality of dishes and service. A bank can track sentiment towards a new mobile app to promptly address any issues and tailor features to user needs. A natural cosmetics manufacturer can monitor discussions on forums and Facebook groups to discover a niche for a new product.

Coca-Cola used sentiment analysis to track conversations about the brand on social media during the 2018 FIFA World Cup. This allowed them to adjust their advertising message in real time.

T-Mobile, in turn, thanks to sentiment analysis, identified the main issues of customers and implemented improvements, which resulted in a 73% decrease in complaints.

As you can see, there are practically limitless applications for sentiment analysis. The key is to effectively translate the insights gained into actionable optimization strategies.

How to leverage the results of sentiment analysis obtained with AI?

Sentiment analysis provides valuable insights, but the real value emerges when we translate them into specific actions.

• personalizing customer communication, such as automatically adjusting the chatbot’s tone based on the user’s mood,
• customer segmentation and better matching of offers, as well as identifying the main pain points of users of a given product,
• optimizing marketing campaigns based on emotional reactions to the message,
• quick response to emerging crises and prevention of escalation through immediate intervention,
• improving products and services according to customer expectations expressed in online reviews.

Imagine sentiment analysis shows that customers complain about long wait times on the hotline. By implementing a voicebot to handle some inquiries, you can significantly reduce queues and increase caller satisfaction. If the voicebot software detects that users are praising a new feature in the app, it’s worth leveraging that insight in a product promotion campaign.

Real-time sentiment analysis is a powerful crisis management tool. By catching the first negative signals, you can respond quickly before a crisis escalates. Effective communication and honesty are key – customers appreciate when a company admits a mistake and shows how it plans to fix it.

The key advantage of using AI for sentiment analysis is speed and scale. Manually, we can analyze at most a few hundred opinions. Meanwhile, AI tools can process hundreds of thousands of mentions in minutes, providing an up-to-date picture of the situation. This enables making accurate decisions here and now.

Top AI sentiment analysis tools

There are many tools available on the market that use AI for sentiment analysis. They differ in features, interface, and price. Among the most popular are Brand24, Hootsuite Insights, and Komprehend.

Brand24

Brand24 () is a Polish tool for internet monitoring and sentiment analysis. It collects mentions from social media, websites, forums, blogs, etc. It automatically labels sentiment as positive, neutral, or negative. It generates reports and statistics regarding the number of mentions and reach.

Brand24 offers a free 14-day trial period, and prices start at 99 PLN/month. It works great for small and medium-sized businesses, especially in e-commerce and services. It stands out for its ease of use and clear reports.

Source: Brand24 (https://brand24.pl/)

Hootsuite Insights

Hootsuite Insights () is a powerful tool for social listening. It analyzes data from over 100 million sources in 50 languages, providing detailed insights into sentiment, trends, and benchmarks. Demos are available upon request, with prices tailored to individual needs. It’s great for medium to large companies and integrates seamlessly with major social media platforms.

Source: Hootsuite (https://www.hootsuite.com/products/insights)

Komprehend

Komprehend () is a deep learning-based API for sentiment analysis. It recognizes three sentiment states: positive, neutral, and negative, supporting 14 languages, including Polish. With ready integrations and flexible deployment, it’s a reliable choice. The free plan offers 5000 queries per month, with additional queries priced at $0.0001 each for larger companies. Komprehend is ideal for backend use in apps and chatbots, known for its high-quality analysis proven in competitions like SemEval.

Source: Komprehend (https://komprehend.io/sentiment-analysis)

Choosing the right tool depends on a company’s individual needs and budget. It is worth testing different options and choosing the one that best fits the specifics of your business.

Summary

In the digital age, sentiment analysis has become an indispensable tool in the arsenal of modern businesses. The amount of data generated by users is overwhelming, but artificial intelligence can help. Thanks to advanced algorithms, we can instantly analyze millions of opinions and draw conclusions. This is invaluable knowledge for customer service, marketing, or R&D departments.

The key benefits of using sentiment analysis in business are:

• saving time and resources by automating data processing,
• constant monitoring of customer feedback and immediate response to signals,
• better customer segmentation and tailored offerings,
• optimizing marketing campaigns based on feedback,
• quickly spotting market trends and anticipating changes,
• handling crises better and protecting brand reputation,
• continuously improving products and services to meet customer expectations.

Of course, sentiment analysis is just the beginning. The key is to effectively use the insights it provides. Speed of response and aligning strategies with customer expectations are crucial. Brands that can listen and quickly respond to customer feedback gain a competitive edge. AI provides them with tools to do this efficiently and at scale.

The future of sentiment analysis looks very promising. AI models will enhance accuracy, incorporating contextual analysis and multimodal inputs like images, sound, and video. Awareness of the importance of customer opinions and the role of customer experience will also increase. Businesses investing in AI tools for sentiment analysis now will reap benefits tomorrow with loyal customers, a solid market position, and outstanding products. Let’s not waste this opportunity.

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The progress in the field of AI significantly streamlines the process of transcribing audio and video recordings. Automatic transcriptions leverage advanced Automatic Speech Recognition (ASR) models, Natural Language Processing (NLP), and Large Language Models (LLM). These systems “listen” to recordings, recognize words, and even whole phrases, generating an accurate textual transcript of the conversation..

Continuous improvements in AI transcription technology are bringing tangible benefits to users. Models are becoming more accurate, recognizing different accents, dialects, and acoustic environments. Thanks to machine learning, systems adapt to the specifics of an industry or company, ensuring high-quality transcription..

For example, Fireflies AI achieves an impressive transcription accuracy of 98.86%, supporting over 69 languages, including Polish. Such precision is crucial in business applications, where every detail can impact decision-making. Tools like Notta can transcribe files in various formats – WAV, MP3, M4A, AVI, MP4, and many others. Additionally, support for multiple languages and formats makes AI transcriptions invaluable for companies operating in the global market..

Source: Pika, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

How can businesses benefit from combining transcription and summarization?

Wondering how AI transcriptions and summaries can impact your business? These powerful tools have applications in many areas of business, streamlining processes and increasing team efficiency. Here are a few examples:.

1. Sales and customer service. Automatic transcriptions and summaries of customer conversations provide valuable insights into their needs, concerns, and opinions. Integration with CRM systems allows for easy sharing of this data throughout the organization.
2. Recruitment. AI analyzes candidate interviews, identifying key competencies and aiding in evaluating their suitability for the position. Otter.ai automatically assigns recruitment tasks, streamlining the process.
3. PR and marketing. Transcriptions of podcasts, webinars, or interviews offer valuable content for brand communication. Summaries make creating engaging materials easier.
4. Project management. Transcripts and summaries of project meetings help track progress, decisions, and tasks. Notta enables smooth note-sharing and team collaboration.

Integrating AI transcription and summarization with CRM systems such as Salesforce or HubSpot automatically enriches the customer knowledge base. This data can personalize communication, address customer needs proactively, and optimize sales processes, leading to increased customer loyalty and higher business performance..

Regardless of the industry or department, automating audio and video recording processing saves time, improves understanding of customers, and enhances team efficiency. So, it’s worth exploring the available solutions we describe below. .

AI transcriptions and summaries. Which tools to use?

Choosing the right AI transcription and summarization tool depends on your organization’s specific needs. Key factors include supported languages, file formats, integration with other systems, or additional features such as sentiment analysis and automatic tagging. Let’s take a look at three leading solutions on the market. They are Notta, Otter, and Fireflies.ai..

Notta

Notta excels in summarizing recordings by generating summaries that highlight key points, decisions, and tasks. It enables easy sharing of notes and collaboration with the team, such as exporting transcriptions to Word or sending summaries to Slack. Plus, Notta offers translation of transcriptions into 41 languages..

Source: Notta (https://www.notta.ai/en)

Otter

Otter.ai is famous for OtterPilot, a virtual assistant that joins online meetings to generate notes automatically. Another innovation is Otter AI Chat, which provides answers and generates content from meeting recordings. Otter also offers a feature to create audio clips of important moments from conversations..

Fireflies AI

Fireflies AI simplifies searching through recordings with filters and topic tags. It automatically detects questions, objections, or mentions of competitors. It allows for creating a knowledge base from recordings and analyzing metrics such as speakers’ talk time or emotional tone. Fireflies integrates with popular tools like Salesforce, Slack, or Zapier. Here’s a brief overview of key features and pricing:.

All three tools are actively expanding their features to meet growing business demands. Notta plans to add automatic summarization of thematic threads in recordings. Otter is integrating with Microsoft Teams. Fireflies AI is developing advanced analysis of team communication patterns..

When choosing an AI transcription and summarization tool, it’s worth not only verifying the current needs of your company but also assessing the potential for the development of a given solution. .

Summary

How AI transcriptions and summaries can change the face of your business? Key findings include:.

• time savings thanks to the automation of the tedious transcription and summarization process,
• improved communication and team collaboration by making notes and key insights easily available,
• deeper customer understanding and process improvement through analysis of recorded calls,
• support for multiple languages and formats provides flexibility for global business applications.

However, it’s worth remembering about the aspects of data privacy and security when using AI tools. Make sure that the chosen solution is compliant with the data protection standards of your industry. .

Soon, we can anticipate more advanced AI transcription and summarization features. Possible developments include generating personalized reports and recommendations from recordings, automatically creating presentations, and intelligently merging information from different data sources in the company.

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Using AI video generators opens up a range of possibilities for businesses, allowing them to instantly create a variety of videos tailored to specific business needs. AI video generators can help you create engaging content, save time and resources, and stand out from the competition. But how do you take full advantage of its capabilities? The most promising solutions are:

• personalized marketing videos tailored to specific audiences to increase engagement and conversions,
• engaging training materials for employees that facilitate transferring knowledge and streamline onboarding processes,
• product videos that present features and benefits in an easy-to-understand manner, making it easier for customers to understand the offer and make a purchase decision,
• localization of video content – one-click translation into various languages and dialects to reach a global audience,
• creating video materials for knowledge bases and FAQs without the need to involve expensive production teams.

AI video generators bring a new dimension to video production, enabling organizations to create personalized, multilingual, and engaging content quickly and affordably. For example, a training company can use an AI video generator to create interactive online courses in different languages tailored to the needs of specific customers, saving time and resources while delivering high-quality educational content. But which AI tools are worth using for video creation?

Synthesia

Synthesia is a leading AI video generation platform that stands out for its impressive capabilities and ease of use. Here are its key features:

• support for over 120 languages in multiple variants and with various emotions,
• a wide selection of predefined avatars perfect for business presentations,
• the ability to create your own personalized avatar reflecting your brand,
• the function of cloning your own appearance and voice to promote a personal brand or company.

For instance, companies such as Heineken use Synthesia-generated videos for creating personalized training and presentation materials in multiple languages, boosting engagement and appeal. The ability to clone one’s appearance and voice is especially handy for influencers and business leaders aiming to bolster their brand, enabling them to reach a broader audience without constant recording.

If you think Synthesia is a good option for your business, you can test its capabilities at a relatively low cost: prices start at $22 per month. The basic subscription allows you to create 10 minutes of video, use 70 avatars, and add automatic captions.

Source: Synthesia, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

Colossyan

Colossyan is an AI video platform that specializes in creating engaging training and educational materials, and stands out from other tools thanks to:

• the ability to transform presentations and documents into compelling training and educational videos,
• replacing PPT and PDF presentations with engaging videos for internal communications,
• support for more than 70 languages, enabling the creation of content for audiences around the world,
• easy-to-use audio and video elements to increase audience engagement.

One of the key advantages of Colossyan is its ease of use – the video creation process is intuitive and requires no specialized knowledge. Colossyan is an ideal tool for companies looking to improve onboarding processes, product training, or internal communications.

It offers three pricing plans, including a Beginner plan at $19 per month, Pro ($61 per month), and Enterprise.

HeyGen

HeyGen is an advanced AI video generator that differentiates itself from the competition by creating high-quality, attention-grabbing videos based on cloning the user’s voice and appearance. HeyGen’s strength lies in creating highly realistic, personalized avatars.

HeyGen’s voice cloning and personalized avatar creation technology is based on advanced machine learning algorithms that analyze voice samples and photos to create a virtual double with a natural-sounding voice and realistic appearance. One HeyGen user, the owner of a small e-commerce business, said: “HeyGen has allowed me to create personalized welcome videos for customers in their native language, which has significantly increased engagement and brand loyalty.”

HeyGen is an increasingly popular tool because it allows you to combine multiple scenes into a single video, making creating videos as easy as creating slides. And also thanks to:

• support for more than 40 languages and 300+ accents,
• voice cloning capabilities,
• a free trial period, and plans starting at $24 per month with an annual subscription,

Source: HeyGen, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

D-ID

D-ID is an innovative AI video generation platform with unique features, such as:

• creating realistic, conversational AI agents that reflect your brand and that you can interact with,
• integration with applications via APIs,
• Chat.D-ID function for creating voice conversations with chatbots,
• collaboration with PowerPoint, Canva, Google Slides, and other presentation creation platforms,
• converting photos into talking video avatars using generative AI.

But how does D-ID turn photos into talking video avatars? It uses Generative Adversarial Networks (GAN) that analyze facial features and mouth movements to create a realistic animation tailored to the provided text or voice recording. Industries such as e-commerce, customer service, and online education can benefit from D-ID’s conversational AI agents to increase user engagement and automate communication processes.

Summary. AI video generation

AI video generators like Synthesia, Colossyan, HeyGen, and D-ID offer a range of benefits for companies looking to create engaging, personalized video content. Primarily, they save time and resources by automating the video production process. Additionally, the ability to create content in multiple languages allows reaching a global audience.

Creating personalized avatars and cloning voices is also crucial. It boosts audience engagement through interactive and visually appealing video content.

Soon, we can expect further development of AI technology in video generation, including increasingly realistic avatars, smoother integration with other marketing tools, and greater accessibility for small and medium-sized businesses. AI video generators have the potential to revolutionize how companies create and distribute video content, enabling them to stand out from the competition and reach audiences more effectively.

AI video generators are powerful tools that open up new opportunities for businesses to produce engaging, personalized video content. With platforms like Synthesia, Colossyan, HeyGen, and D-ID, companies can create marketing videos, training videos, product videos, and more – faster, cheaper, and easier than ever before. If you want to stay competitive in today’s digital world, it’s worth considering adding an AI video generator to your marketing and communications strategy. The future of AI-powered video production is bright – don’t miss the opportunity to be a part of it!

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Before we discuss LLMOps, let’s first explain what large language models are. They are machine learning systems that have been trained on huge collections of text-from books to web articles to source code, but also images and even video. As a result, they learn to understand the grammar, semantics, and context of human language. They use the transformer architecture first described by Google researchers in 2017 in the article “Attention Is All You Need” (https://arxiv.org/pdf/1706.03762v5.pdf). This allows them to predict the next words in a sentence, creating fluent and natural language.

As versatile tools, LLMs in companies are widely used for, among other things:

• building internal vector databases for efficient retrieval of relevant information based on understanding the query, not just keywords— an example might be a law firm that uses LLM to create a vector database of all relevant laws and court rulings. This allows for quick retrieval of information key to a particular case,
• automating CI processes/CD (Continuous Integration/Continuous Deployment) by generating scripts and documentation – large technology companies can use LLMs to automatically generate code, unit tests and document new software features, speeding up release cycles,
• collection, preparation and labeling of data — LLM can help process and categorize massive amounts of text, image or audio data, which is essential for training other machine learning models.

Companies can also match pre-trained LLMs to their industries by teaching them specialized language and business context (fine-tuning).

However, content creation, language translation, and code development are the most common uses of LLMs in the enterprise. In fact, LLMs can create consistent product descriptions, business reports, and even help programmers write source code in different programming languages.

Despite the enormous potential of LLM, organizations need to be aware of the associated challenges and limitations. These include computational costs, the risk of bias in training data, the need for regular monitoring and tuning of models, and security and privacy challenges. It is also important to keep in mind that the results generated by models at the current stage of development require human oversight due to errors (hallucinations) that occur in them.

Source: DALL·E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

What is LLMOps?

LLMOps, or Large Language Model Operations, is a set of practices for effectively deploying and managing large language models (LLMs) in production environments. With LLMOps, AI models can quickly and efficiently answer questions, provide summaries, and execute complex instructions, resulting in a better user experience and greater business value. LLMOps refers to a set of practices, procedures, and workflows that facilitate the development, deployment, and management of large language models throughout their lifecycle.

They can be seen as an extension of the MLOps (Machine Learning Operations) concept tailored to the specific requirements of LLMs. LLMOps platforms such as Vertex AI from Google (https://cloud.google.com/vertex-ai), Databricks Data Intelligence Platform (https://www.databricks.com/product/data-intelligence-platform) or IBM Watson Studio (https://www.ibm.com/products/watson-studio) enable more efficient management of model libraries, reducing operational costs and allowing less technical staff to perform LLM-related tasks.

Unlike traditional software operations, LLMOps have to deal with complex challenges, such as:

• processing huge amounts of data,
• training of computationally demanding models,
• implementing LLMs in the company,
• their monitoring and fine tuning,
• ensuring the security and privacy of sensitive information.

LLMOps take on particular importance in the current business landscape, in which companies are increasingly relying on advanced and rapidly evolving AI solutions. Standardizing and automating the processes associated with these models allows organizations to more efficiently implement innovations based on natural language processing.

Source: IBM Watson Studio (https://www.ibm.com/products/watson-studio)

MLOps vs. LLMOps — similarities and differences

While LLMOps evolved from the good practices of MLOps, they require a different approach due to the nature of large language models. Understanding these differences is key for companies that want to effectively implement LLMs.

Like MLOps, LLMOps relies on the collaboration of Data Scientists dealing with data, DevOps engineers and IT professionals. With LLMOps, however, more emphasis is placed on:

• performance evaluation metrics, such as BLEU (which measures the quality of translations) and ROUGE (which evaluates text summaries), instead of classic machine learning metrics,
• quality of prompt engineering – that is, developing the right queries and contexts to get the desired results from LLMs,
• continuous feedback from users – using evaluations to iteratively improve models,
• greater emphasis on quality testing by people during continuous deployment,
• maintenance of vector databases.

Despite these differences, MLOps and LLMOps share a common goal – to automate repetitive tasks and promote continuous integration and deployment to increase efficiency. It is therefore crucial to understand the unique challenges of LLMOps and adapt strategies to the specifics of large language models.

LLMOps key principles

Successful implementation of LLMOps requires adherence to several key principles. Their application will ensure that the potential of LLMs in an organization is effectively and safely realized. The following 11 principles of LLMOps apply to both creating, optimizing the operation and monitoring the performance of LLMs in the organization.

1. Managing computing resources. LLM processes such as training require a lot of computing power, so using specialized processors such as, Neural Network Processing Unit (NPU) or Tensor Processing Unit (TPU) can significantly speed up these operations and reduce costs. The use of resources should be monitored and optimized for maximum efficiency.
2. Constant monitoring and maintenance of models. Monitoring tools can detect declines in model performance in real time, enabling a quick response. Gathering feedback from users and experts enables iterative refinement of the model to ensure its long-term effectiveness.
3. Proper data management. Choosing software that allows for efficient storage and retrieval of large amounts of data throughout the lifecycle of LLMs is crucial. Automating the processes of data collection, cleaning and processing will ensure a constant supply of high-quality information for model training.
4. Data preparation. Regular transformation, aggregation and separation of data is essential to ensure quality. Data should be visible and shareable between teams to facilitate collaboration and increase efficiency.
5. Prompt engineering. Prompt engineering involves giving the LLM clear commands expressed in natural language. The accuracy and repeatability of the responses given by the language models, as well as the correct and consistent use of context, depend largely on the precision of the prompts.
6. Implementation. To optimize costs, pre-trained models need to be tailored to specific tasks and environments. Platforms such as NVIDIA TensorRT (https://developer.nvidia.com/tensorrt) and ONNX Runtime (https://onnxruntime.ai/) offer deep learning optimization tools to reduce the size of models and accelerate their performance.
7. Disaster recovery. Regular backups of models, data, and configurations ensure business continuity in the event of a system failure. Implementing redundancy mechanisms, such as data replication and load balancing, increases the reliability of the entire solution.
8. Ethical model development. Any biases in training data and model results that may distort results and lead to unfair or harmful decisions should be anticipated, detected, and corrected. Companies should implement processes to ensure responsible and ethical development of LLM systems.
9. Feedback from people. Reinforcing the model through user feedback (RLHF – Reinforcement Learning from Human Feedback) can significantly improve its performance, as LLM tasks are often open-ended. Human judgment allows the model to be tuned to preferred behaviors.
10. Chains and pipelines of LLMs. Tools like LangChain (https://python.langchain.com/) and LlamaIndex (https://www.llamaindex.ai/) allow you to chain multiple LLM calls and interact with external systems to accomplish complex tasks. This allows you to build comprehensive applications based on LLMs.
11. Model tuningOpen source libraries such as Hugging Face Transformers (https://huggingface.co/docs/transformers/index), PyTorch (https://pytorch.org/), or TensorFlow (https:/ /www.tensorflow.org/), help improve model performance by optimizing training algorithms and resource utilization. It is also crucial to reduce model latency to ensure application responsiveness.

Source: Tensor Flow (https://blog.tensorflow.org/2024/03/whats-new-in-tensorflow-216.html?hl=pl)

Summary

LLMOps enable companies to safely and reliably deploy advanced language models and define how organizations leverage natural language processing technologies. By automating processes, continuous monitoring and adapting to specific business needs, organizations can fully exploit the enormous potential of LLMs in content generation, task automation, data analysis, and many other areas.

While LLMOps evolved from MLOps best practices, they require different tools and strategies tailored to the challenges of managing large language models. Only with a thoughtful and consistent approach will companies be able to effectively use this breakthrough technology while ensuring security, scalability and regulatory compliance.

As LLMs become more advanced, the role of LLMOps is growing, giving organizations a solid foundation to deploy these powerful AI systems in a controlled and sustainable manner. Companies that invest in developing LLMOps competencies will have a strategic advantage in leveraging innovations based on natural language processing, allowing them to stay at the forefront of digital transformation.

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Automation and augmentation are opposing but interdependent forces. In fact, companies face a choice: Do they cut costs and automate tasks, eliminating human involvement in the process? Or, with a focus on quality and personalization, enhance the capabilities of employees and improve outcomes through AI augmentation, which involves close collaboration between humans and artificial intelligence? Their complementary skills would then be combined to accomplish a specific task.

The paradox of automation and augmentation is an issue that modern organizations must confront. Understanding the difference and synergies between the two concepts is crucial for the successful implementation of AI in business.

Automation

Automation is the process of replacing human, repetitive activities with software. Before the era of the rapid development of generative artificial intelligence, automation was applicable only to routine and well-structured tasks, such as:

• filling out invoices,
• creating reports,
• summarizing expenses,
• simple customer service based on the selection of the next step of the conversation by pressing a button.

Organizations were able to automate processes based on expert knowledge encoded in the form of algorithms that define relationships between conditions (“if”) and consequences (“then”). Such automation was based on an explicitly defined domain model, i.e., a domain knowledge representation that optimizes a chosen utility function.

However, the development of generative artificial intelligence has brought radical changes to the field of automation. Not only can the new models respond much more flexibly to input data, but they can also execute commands expressed in natural language. In other words, instead of executing commands based on explicit rules, they can perform tasks based on contextual understanding.

Source: DALL·E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

However, automations using artificial intelligence carry considerable risk.

The first is the dangers of automating decision-making – a problem faced by developers of autonomous vehicles, among others. For example, when a vehicle must make a maneuver in fractions of a second because there is no way to avoid a collision.

The second risk comes from relying on predictive algorithms. Even if a company would like to implement an automated option to follow data-driven artificial intelligence recommendations, a human must take responsibility for the decisions made.

A third type of risk is the use of generative artificial intelligence that, with insufficient data, begins to hallucinate, that is, to provide probable but false answers. For example, it may generate fake news or give customers false answers to questions. Navigating the benefits and risks of automation therefore requires careful analysis and preparation.

Augmentation

Augmentation is the process of using AI to enhance human intelligence and skills, rather than replacing them or acting independently. With the growing importance of augmentation in environments requiring complex decision-making, organizations are increasingly adopting this approach. For more complex tasks where rules and models are not fully known, augmentation enables natural and artificial intelligence to work closely together.

This is because augmentation is an iterative, coevolutionary process in which humans learn from AI and AI learns from humans. In doing so, the role of artificial intelligence should be designed to enable human oversight at all stages of a given process. It requires the involvement of domain experts, whose expertise is often tacit in nature, derived from years of experience and intuition, making it difficult or impossible for AI to directly replace them.

Augmentation allows humans and artificial intelligence to reinforce each other, combining machine rationality with human intuition, common sense and professional experience. This approach enables more comprehensive information processing and better decision-making.

At the perfume company, Symrise, for example, perfumers worked closely with the AI system to generate ideas for new fragrances (). Through augmentation, experts were able to leverage the machine’s ability to process massive amounts of data while applying their own knowledge to interpret and contextualize the results. The results were innovative fragrances that customers loved.

Source: DALL·E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

Smooth transitions – from automation to augmentation and back again

The relationship between automation and augmentation is dynamic. It allows for seamless transitions between the two approaches. The close collaboration between humans and AI within augmentation helps to identify rules and models that can then be used to automate a given task, leading to innovation and efficiency gains.

Organizations should therefore deliberately iterate between the separate tasks of automating and augmenting, making a long-term commitment to both.

Another step that will strengthen the link between automation and augmentation is the creation of autonomous agents, i.e. artificial intelligence that can not only automate tasks, but also plan processes and issue commands to other systems without human intervention. The development of next-generation AI solutions will also make it possible in the near future to create prototypes and innovative services based on needs analysis.

Summary

Automation and augmentation represent two opposing but often interdependent applications of artificial intelligence in management. A balanced approach that combines the strengths of both concepts is the key to achieving complementarity that benefits both business and society.

To manage this tension effectively, organizations should:

• remember about the responsibility for creating transparent and secure systems using AI,
• keep in mind the responsibility for management processes, treating AI as a tool to assist rather than replace managers,
• integrate the two approaches by deliberately iterating between them and leveraging each other’s strengths,
• implement strict controls and transparency mechanisms to detect and correct errors and biases in AI systems.

Above all, they should also invest in developing the skills and competencies of employees so that they can work effectively with artificial intelligence as part of augmentation.

Successfully combining these two AI forces will not only make organizations more efficient and innovative, but also help build a more just and sustainable society. The key is to understand that automation and augmentation should coexist in harmonious synergy, not compete as alternatives.

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Google Genie () is a foundational world model developed by DeepMind. It is a generative AI model trained on over 30,000 hours of publicly available 2D platformer video game footage. Its key feature is the ability to generate fully interactive, playable environments directly from single images, photos and even hand-drawn sketches.

Source: Genie: Generative Interactive Environments ()

How is this possible? Genie uses an unsupervised learning technique in the process of acquiring the ability to precisely control the environment based solely on video footage. No human action tagging is required. Using a special action coding module, it captures subtle changes between successive video frames and maps them to internal representations of motion, such as jumping or turning left. The dynamics model then generates the next frame in the sequence based on the coded actions.

As a result, Genie can create fully controllable, interactive game environments from any visual data. Each player movement generates a new, unique frame in real time, creating a smooth, playable session. This is a really big innovation that allows us to create entire interactive worlds from images or text.

Why is Genie innovative?

The Genie’s innovation lies in combining several key elements in a single model:

• generative video models, such as Phenaki (), TECO () or maskvit (), which can predict future frames of a sequence based on input frames and text, but do not offer active control capabilities,
• world models that focus on predicting future environmental states based on an agent’s actions, but requiring data provided by humans,
• unsupervised learning, which allows Genie to learn both environmental dynamics and action space from raw video data alone, without human action labels.

Although each of these areas has been explored before, Genie is the first model to combine them to learn controllable environments directly from video footage. This unprecedented approach to teaching models without human supervision is a key innovation of Genie. It opens the door to using the vast amount of video available on the Internet as a training source for AI models, and breaks down the barriers associated with the limited availability of labeled data.

The combination of generative video models, world models and unsupervised learning in a single solution represents a fundamental advance in the development of artificial intelligence. Genie demonstrates that advanced AI systems can learn complex behaviors and environments directly from unstructured data, without manual tagging. This is a key step on the road to achieving true Artificial General Intelligence (AGI).

Source: Google Genie (https://sites.google.com/view/genie-2024/)

Potential applications of Google Genie

Google Genie’s capabilities go far beyond generating video games. This pioneering AI model can find applications in many fields:

• tool for animators – just upload an image, sketch or short text description and Genie will generate a consistent animation,
• unlimited training resource for AI agents – with its ability to generalize to entirely new domains, Genie offers an infinite pool of challenges on which future AI systems can learn. The lack of diverse training environments has so far been one of the key barriers to the development of generic AI agents,
• physical simulations for robotics – research has shown that Genie is able not only to control virtual robots, but also to realize the physical properties of deformable objects. This could have huge implications for the development of robotics and physical simulations,
• applications in the creative industries – Genie can facilitate the creation of interactive art installations, virtual exhibitions or films. Simply upload a sketch and the model will generate a fully controllable 3D world, ready for exploration.

However, the potential challenges and limitations of this technology should not be overlooked. At the current stage of development, Genie works best in narrow domains such as 2D platform games. Scaling up to more complex 3D environments will require additional research and optimization. In addition, there is a risk that this technology could be abused to create harmful or dangerous content. It is therefore critical to develop a robust ethical and legal framework to govern the development and use of such AI models.

Source: Google Genie (https://sites.google.com/view/genie-2024/)

Summary

By enabling the creation of fully interactive environments directly from visual data, without the need to manually tag actions,, Google Genie represents a true breakthrough in generative artificial intelligence. This fundamental world model gives the power to express imagery in the form of playable virtual realities that can be explored and controlled by a human or AI agent.

Genie’s potential is enormous – from tools for game developers, to an unlimited source of training data for AI, to physical simulations for robotics. It’s also an important step on the road to AGI. As models like Genie continue to evolve, the boundary between the real and virtual worlds is becoming more fluid.

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Innovation in the field of AI experts in Poland would not be possible without bold entrepreneurs capable of turning theoretical concepts into groundbreaking business solutions. On the Polish AI scene, it is worth following the ventures of Piotr Sankowski or Artur Kurasinski.

Piotr Sankowski is the president of the NCBR IDEAS research center and the founder of MiM Solutions, a company that develops AI solutions. Interestingly, Sankowski combines business activities with academic work at the University of Warsaw, exemplifying the synergy between the academic and business communities. His activities are worth following to understand how Polish AI is developing from both the business and strictly scientific sides.

Artur KurasiĹ„ski is an entrepreneur, speaker, author of games and comics, and most recently, a free introductory course on working with Midjourney (). Kurasinski has spent two decades observing technological and social trends, then explaining and popularizing them in an accessible way. He is a frequent speaker at the country’s top technology conferences, sharing his insights. He is also the co-founder of initiatives such as the Aula Polska series of meetings for entrepreneurs and the Aulery competition, which supports the development of startups.

Researchers

Polish researchers deal with artificial intelligence both from the side of creating technological solutions, as well as working on the role of AI in society and developing the ethical aspects of its development.

There are quite a few achievements to the credit of Alexandra PrzegaliĹ„ska – philosopher, researcher of new technologies and author of books “Artificial Intelligence. Inhuman, Arch-human” (Znak, Krakow 2020) or “Collaborative Society” (MIT Press, Cambridge, MA, 2020). PrzegaliĹ„ska popularizes knowledge about AI through the Campus AI initiative and in the media – both Polish and international. Her broad expertise includes the topics of sustainable technologies, artificial intelligence and wearable technologies.

The ethical aspects of AI development, on the other hand, are the domain of Dota Szymborska – a scientist who wrote her doctoral thesis on autonomous vehicles. Szymborska offers heavy reflections on the dilemmas posed by the increasing use of intelligent systems, forcing the audience to think about their possible consequences. She translates her thoughts not only into scientific publications but also into blog posts and popular science articles.

The technological authority of the Polish AI community is Alexander MÄ…dry – a mathematician and computer scientist currently working on the OpenAI team. MÄ…dry is a recipient of the prestigious Presburger Award for outstanding achievements in theoretical computer science. His work includes numerous scientific articles published in the most prestigious journals. Previously, Wise was a professor at the renowned Massachusetts Institute of Technology (MIT), where he directed the Center for Machine Learning.

Source: DALL·E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

Popularizers and AI experts in Poland

The development of artificial intelligence largely depends not only on scientific successes but also on general awareness and the degree of its social acceptance. Inspiring material on artificial intelligence is published, among others, by Karol Stryja, who has been active in the podcast market for years, deservedly earning the name of the godfather of Polish broadcasts of this type. He hosts the 99 Shades of AI podcast(), in which he talks to people involved in the development of artificial intelligence.

He also runs his own recording studio, The Podcast Makers, and is a co-founder of the international #VoiceLunch movement of conversational artificial intelligence professionals. In 2020, Stryja published Cathy Pearl’s acclaimed book “Designing Voice Interfaces” in Poland. – a textbook written by one of the key people responsible for creating Google’s voice assistant. The Voicebot.ai portal honored him as a Leader in Conversational AI Technology.

Jowita Michalska – founder of the Digital University and ambassador of the Singularity Group in Silicon Valley – took a slightly different path. Her organization cooperates with such prestigious universities as MIT, Harvard Business School and Stanford University, offering educational programs on new technologies to Poles. Michalska is also involved in social initiatives, such as educating marginalized people in the areas of Internet security and ecology.

Lawyers

When implementing solutions based on artificial intelligence, the issue of compliance with current legislation and possible formal and legal consequences is inherent. In this area, Polish AI can count on the support of specialized experts:

• Klaudia Maciejewska has multiple professions – she is a lawyer, data protection officer, court mediator, as well as a product manager and head of Currente Research and Development Center. Her main specialty, however, is new technology law, particularly the development and implementation of ISO standards in the field of artificial intelligence. A key challenge in this field remains understanding both the technological nuances of AI and the complex social, economic and ethical aspects associated with the technology,
• A similar area of expertise is represented by Michal Nowakowski – a legal advisor specializing in the implementation of artificial intelligence systems, including machine learning, deep learning and LLM. For more than 12 years, Nowakowski has been advising major financial and telecommunications institutions, as well as retail and industrial companies, on risk management and legal aspects of implementing new technologies. His work includes numerous academic publications in the field of AI and its regulation,
• An expert in intellectual property protection of projects based on artificial intelligence is Alexandra Maciejewicz – a patent attorney and specialist in copyright and patent management. She advises scientists, startups and representatives of the creative industries, helping them to adapt their business models to legal requirements. Maciejewicz is also the co-author of the first e-book in Poland on the legal aspects of Generative Artificial Intelligence ().

Source: DALL·E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

AI artists

Artificial intelligence is not only revolutionizing business and science, it is also having a significant impact on art and creative activities. Among the artists exploring the impact of AI on reality is David Sypniewski, a lecturer at SWPS University, where he teaches courses on social robotics, creative coding or artificial intelligence in art and creative activities. He is also involved in the HumanTech Center and helps organize the HumanTech Summit along with its accompanying Hackathon. In 2023, Sypniewski opened the Open Science Lab at the Faculty of Design at SWPS University.

Pioneering experiments in combining art and AI are conducted by Agnieszka Pilat – an artist who lives and works in the United States. Pilat’s most famous projects are the robots Basia and Bunny, which paint pictures according to her concepts. These futuristic installations create abstract compositions on bright backgrounds. Pilat is also a resident at Boston Dynamics, one of the world’s most innovative technology companies.

Source: DALL·E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

AI experts – Summary

Key figures associated with the country’s AI scene present different but complementary perspectives on this groundbreaking technology. Polish researchers are gaining international recognition for their pioneering work and innovative concepts. Local startups are boldly turning theories into innovative solutions and products. Lawyers are ensuring that AI applications comply with regulations and standards, and popularizers are bringing these complex issues to the public and increasing public acceptance. Artists, meanwhile, have the opportunity to explore new creative possibilities opened up by AI.

The above overview is only the beginning of a long list of names that could be mentioned in response to the question of who’s who in Polish AI. However, it is worth taking a closer look at the activities of these individuals and observing those who are contributing to the development of this industry in our country.

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ReALM stands for “Reference Resolution As Language Modeling,” a groundbreaking solution developed by Apple researchers. It is thus a new language model (Large Language Model, LLM) that treats the problem of reference recognition as a task in the field of language modeling.

ReALM effectively converts various types of context into a textual representation, which it then processes as part of a language task. This can include:

• conversations – such as text messages, voice commands to an assistant, or emails,
• elements on the screen – for example, photos, calendar, weather widget, or applications and processes running in the background.

What makes ReALM different from other reference recognition models? First, the approach – instead of relying on image processing, ReALM runs in the text domain. This makes it much lighter and more efficient, which should allow it to run directly on mobile devices while maintaining user privacy.

In what ways is ReALM better than GPT-4?

Apple’s research team compared ReALM to the most powerful language models on the market today – GPT-3.5 and GPT-4 from OpenAI. The results were impressive. In reference recognition tasks, the smallest ReALM variant achieved accuracy comparable to GPT-4! The larger ReALM models even outperformed GPT-4 in recognizing references to items displayed on the screen ().

What explains this advantage? First, ReALM is great with domain-specific queries, such as those concerning smart home appliances. This is because ReALM demonstrates a deeper understanding of context by fine-tuning the model for domain-specific data.

What’s more, unlike GPT-4, which trains primarily on images of real objects, ReALM excels at recognizing textual elements and components of application user interfaces. And it is interface understanding that is critical to the smooth interaction of voice assistants with the applications we use today.

Source: DALL·E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

Is this the beginning of the era of truly intelligent assistants?

Indeed, the integration of ReALM with Siri could open a whole new chapter in human-computer interaction. With ReALM, Siri will be able to understand commands that include references to items displayed on the smartphone screen, as well as processes and applications running in the background. But when will this option be available to users? That is still unknown.

We are left with speculation based on the technical capabilities of the model. So how might a ReALM-powered Siri work? For example, if you’re browsing a business listings site and see a company you’re interested in, you could simply say to Siri, “Call this company,” and the assistant – using ReALM to analyze context – will find the phone number of the company you specify and initiate the call. You don’t even have to explain exactly which company you mean.

A to dopiero początek możliwości ReALM. Polecenia takie jak „Odtwórz ostatnią playlistę” pozwoliłyby na intuicyjną kontrolę aplikacji multimedialnych i urządzeń inteligentnego domu. ReALM mógłby też umożliwić Siri rozumienie kontekstu rozmów i historii poleceń, aby asystent reagował adekwatnie do wcześniejszych żądań użytkownika. To krok w stronę inteligentnych agentów przybliżający nas nie tyle do sztucznej inteligencji rozumiejącej nasze zapytania, ile do takiej, która będzie umiała realizować polecenia.

And this is just the beginning of what ReALM can do. Commands like “play the last playlist” would enable intuitive control of media applications and smart home devices. ReALM could also enable Siri to understand the context of conversations and command history, so that the assistant responds appropriately to the user’s previous requests. This is a step toward intelligent agents, moving us closer to not an artificial intelligence that understands our requests, but one that knows how to execute commands.

Unfortunately, users of Android devices will have to wait. Currently, there is no information about Google’s plans to add Gemini’s capabilities to Google Assistant. A Google Gemini app for Android devices has been developed (), but it is not yet available outside the United States

Source: Google Play (https://play.google.com/store/apps/details?id=com.google.android.apps.bard&hl=en_US)

Summary

ReALM is Apple’s innovative approach to solving the problem of context recognition by voice assistants. Instead of relying on image processing, this language model converts different types of context into a textual representation, which it then processes in a language task. This approach ensures not only high recognition accuracy, but also the ability to operate on a mobile device while maintaining user privacy.

Giving Siri access to ReALM can provide more natural and contextual voice interactions, an important step toward truly intelligent assistants. With ReALM, Siri will be able to instantly respond to commands related to screen items, applications, and background processes. One thing is certain – improving the contextual awareness of assistants is the key to creating truly intelligent and natural voice interactions, and ReALM is undoubtedly an important step in that direction.

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AI search engines, such as Perplexity, Bing Copilot and You.com, use advanced language models and deep machine learning to understand user intent and provide relevant answers. Instead of presenting simple links, they interpret natural language queries using Natural Language Processing (NLP) and semantic search techniques.

A key element of these search engines is the language model – a powerful AI system trained on massive amounts of mostly textual data. The models learn linguistic and contextual patterns to generate consistent and meaningful answers.

When a user enters a query, the AI search engine – unlike Google Search, for example – processes the query using NLP algorithms that analyze syntax, meaning, and intent. The language model then generates potential answers and selects the best one.

For example, by asking the question “How do I tie a tie?”, Perplexity can generate step-by-step instructions, along with illustrations and links to instructional videos.

AI search engines are very different from the traditional search algorithms used by Google. Instead of presenting a list of links based on keywords, AI seeks to understand the user’s intent and provide a synthetic, comprehensive answer. This is a huge step forward in the way information is found online. But which search engines are worth using?

Source: You.com

Perplexity – a pioneering conversational AI search engine

One of the AI search engines challenging Google is Perplexity (). Its creators set out to revolutionize the way people find information online. As an alternative to traditional search engines, it allows you to ask questions directly and get concise, accurate answers backed by verified sources.

Perplexity’s unique selling point is its conversational interface and contextual understanding of the query. This search engine learns the user’s preferences and interests and adapts its answers as the dialog progresses.

The use of an advanced response algorithm, text prediction capabilities, and summarized results make it possible to provide useful information in a concise form.

In addition, Perplexity allows you to upload text files, source code, and PDFs so that the AI model can use their content to formulate answers. It’s a useful tool for tasks such as

• abstracting documents,
• explaining code snippets,
• translating documents into another language.

Perplexity’s conversational interface is based on a sophisticated natural language processing architecture. The search engine uses deep neural networks to analyze the context and intent of a query, and then generates a response using a language model trained on large data sets.

One of Perplexity’s distinctive features is the ability to narrow down the search area – this could include, for example, only academic sources, YouTube or conversations on Reddit.

For paying users ($20 per month), there is a Perplexity Pro subscription, which allows flagship models like Claude 3 and GPT-4 to be used for analyzing longer content and unlimited file transfers. The paid subscription also allows for unlimited use of Copilot, which suggests additional searches that will help us answer the original question.

Source: Perplexity.ai

Microsoft Copilot – the power of integration with Bing

Bing Copilot ( ) is Microsoft’s proposal that combines the power of the GPT-4 language model with the traditional Bing search index. It aims to provide accurate answers to complex questions using deep machine learning techniques.

As with Perplexity, Bing Copilot offers an easy-to-use chatbot interface for asking questions and receiving answers from AI in natural language. Users of the Edge search engine can also use a very convenient side panel that allows, among other things, to ask questions to an open web page.

Bing Copilot can be particularly useful for exploratory queries that require in-depth analysis and synthesis of information from multiple sources. For example, the question “Why were chainsaws invented?” can be enriched not only with a concise answer, but also with links to additional sources and multimedia.

What’s more, Bing Copilot has the potential to personalize search results based on a user’s history and preferences. And the quality of search is constantly improving – Microsoft is working with technology companies like OpenAI to continually improve the capabilities of its AI search engine.

Entrepreneurs can use Bing Copilot, for example, to conduct market research and competitive analysis. Advanced search and natural language processing capabilities allow them to gain valuable insights from a variety of data sources.

Source: DALL·E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

You.com – a wide range of models

You.com () is another player in the AI search market, relying on advanced natural language processing and semantic understanding to deliver personalized search experiences. You.com offers a really wide range of models – it’s not just the popular GPT-4, Google Gemini or Claude, but also DBXR or Zephyr. Although it uses language models to interpret queries like Perplexity and Bing Copilot, You.com stands out for its comprehensive suite of search features.

You.com also has very useful modes:

• Smart – a free, basic semantic search,
• Genius – available for free 5 times a day, to search more you must purchase a subscription at $15 for an annual subscription or $20 for monthly access,
• Research mode – for in-depth search, analysis and comparison of sources,
• Create – with the ability to create images.

When comparing You.com’s search capabilities to traditional search engines, there is a significant advantage in understanding user intent and providing personalized answers. However, it is important to note that for simple queries, such as finding known facts or websites, traditional search engines may still be a better choice.

Źródło: You.com

Will AI search engines replace Google?

Although AI search engines such as Perplexity, Bing Copilot and You.com are becoming increasingly popular, replacing Google as the dominant player in the search engine market will not be an easy task. According to the latest data, Google still controls more than 90% of the global search engine market (). AI search engines, on the other hand, have yet to match the giant in terms of search efficiency and speed.

The differences between AI search engines and Google’s traditional search model are also significant. Google has long been more than just a website with links; it is a true mini operating system, offering a range of tools and features integrated with search.

While AI search engines are getting better at interpreting queries and generating answers, Google is still the undisputed champion at providing quick links to websites, which is still a core function of a search engine.

In addition, Google collects vast amounts of data about its users to better tailor results to their preferences and context.

However, AI is most likely to outperform Google when it comes to queries that involve synthesizing information from multiple sources or finding hidden data. For queries such as “How do I take a screenshot on my Mac?”, AI search engines are able to extract key information and present it directly, whereas Google often serves up a thicket of ads and not very useful results.

AI search engines are constantly learning. The more data they receive, the better their language models and their ability to understand context. Over time, this may give them an advantage over Google’s static search algorithms. It may also be that Google will eventually make SGE, Search Generative Experience, more widely available, combining the power of AI with the Google search experience.

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Although Artificial Intelligence (AI) is gaining popularity among businesses in Poland, there are still many companies that are not fully exploiting its potential. According to a KPMG study (), only 15% of companies in our country currently use AI solutions, while the global average is 35-37%. At the same time, up to 62% of companies that have implemented AI do not monitor the effectiveness of these implementations – i.e. they do not know what impact, if any, they have had.

These figures show the huge untapped potential of artificial intelligence in Polish business. On the other hand, 13% of companies planned to implement AI by the end of 2023, which could be a sign of the coming wave of adoption of this disruptive technology. Indeed, companies see numerous benefits from AI, such as increased productivity, improved product and service quality, better financial performance and a strengthened competitive position.

Step 1. Understand the difference between AI, machine learning and generative artificial intelligence

If you are considering taking the first step towards implementing AI in your business, it is worth learning the basics of this group of technologies. Before you can realize the potential of AI in your business, you need to understand the key difference between Artificial Intelligence (AI) in its broadest sense, Machine Learning (ML) and Generative AI. These terms are often used interchangeably, but they actually describe slightly different concepts.

AI refers to the general ability of programmed machines, such as computers or robots, to ‘think’ in a similar way to humans and to mimic intelligent behavior. AI systems can assimilate, analyze and use knowledge from the real world to derive new information. Examples of AI-based technologies include speech, image and facial recognition.

On the other hand machine learning (ML) is a field of AI in which computer systems learn from data and make decisions without direct human intervention. A key feature of ML is the ability to continuously self-improve and adapt algorithms based on new input data.

With the rapid development of generative AI, the main sign of which is the crazy popularity of ChatGPT, it is also important to understand this new trend. Generative AI is capable of generating new data, such as text, images, video and audio, or even computer code. It does this by learning from large amounts of training data. Language models, such as ChatGPT, learn the patterns and rules inherent in the input data and then use this knowledge to create new, unique texts that resemble those written by humans.

The power of generative AI lies in its flexibility and ability to creatively remix and synthesize information in innovative ways.

Define business needs

The second step is to identify the specific needs of your business that can be met by implementing artificial intelligence and machine learning. This process starts with an in-depth analysis and careful consideration of several questions:

1. What specific results do you want to achieve? It could be increased revenue, optimisation of the supply chain or better customer service.
2. What are the main obstacles to achieving these goals?
3. How can AI and machine learning help you overcome them?
4. How do you want to measure the success of such an initiative? It is worth planning from the outset how the results will be evaluated, especially given how many companies skip this key step. This can be based on KPIs, direct financial gains or other metrics defined specifically for this implementation.
5. What kind of data do you already have? Data is a key resource that a company’s newly implemented AI will use. Ask yourself, what additional data will you need to harness the full potential of AI?

To fully understand the value of answering these questions, let’s look at a practical example. Imagine a small accounting firm that was struggling with lengthy, manual processes for handling client documents. They defined their goal as “to automate accounting to speed up processing and increase productivity”.

The main obstacles were the time spent on tedious tasks and the large volumes of documents that needed to be processed. After reviewing these challenges, the team identified AI-based document processing as a potential solution – Natural Language Processing (NLP) technology that could automatically extract and categorize relevant financial data, reduce errors and speed up processes.

Ways to measure the impact were, in this case, an increase in the number of documents processed per month and a reduction in the average processing time per order. It was also important to assess the data resources – in this case, the volume of receipts, invoices and other financial documents needed to train the AI systems.

This example illustrates the importance of clearly defining your business needs at the beginning of the AI implementation process. Only in this way can you identify the right solutions and implement them properly to deliver maximum value to your business.

Source: DALL·E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

It is worth reaching out to tools such as SensID Cognitive Automation (), Microsoft AI Builder () or Docsumo ().

SensID Cognitive Automation uses Natural Language Processing (NLP) technology to automate the understanding of document content, which is key to robotic tasks and decision-making processes. Once the text has been analyzed, the system aggregates the collected data and presents it in a structured form, ready for use in robotic process automation (RPA) and analytics applications. With the technology we have developed, it is possible to efficiently create models that interpret the information contained in a wide variety of business documents.

SensID Cognitive Automation enables the integration of data from a variety of textual sources, including structured data (such as databases), semi-structured data (such as forms, csv, html, etc.) and unstructured data (such as doc, pdf, etc.), providing a unified view of information.

Microsoft AI Builder is part of the Microsoft Power Platform. With it, you can create and use AI models to help optimize your business processes. You can use a pre-built model that is ready for many common business scenarios, such as document recognition, or create a custom model to meet your company’s specific requirements.

Another option worth trying is Docsumo which uses OCR (Optical Character Recognition) to read documents and is trusted by major companies such as PayU and Hitachi.

Step 3. Find out how AI can help your business

After identifying your business goals and challenges, the next logical step is to identify the specific ways in which AI can add value and profit to your business. Sometimes the path may not be obvious, so consider the wide range of possible benefits.

One of the key value factors of AI is to increase the value delivered to customers. With the power of machine learning and advanced data analytics, AI can help companies better understand consumer preferences and behavior. This allows for a more personalized and satisfying shopping experience.

Another key factor is AI’s potential to increase employee efficiency and productivity. By automating repetitive, time-consuming tasks, AI can deliver significant cost savings and allow teams to focus on more strategic, creative activities, as well as significantly improve job satisfaction. In fact, 59% of those working in management roles believe that the use of AI in the workplace improves job satisfaction ().

Finally, we should not forget the direct business gains that often result from implementing AI solutions. By optimizing processes, improving operations and making better use of data, organizations can maximize revenues and profits.

So will AI increase your customers’ satisfaction? Will it maximize employee productivity? Will it contribute to revenue growth? If the answer to any of these questions is “yes”, then AI certainly deserves your attention.

Source: DALL·E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

Step 4. Assess your own capabilities to implement AI

With an understanding of the huge potential of AI, you now face the biggest challenge – assessing and preparing your own organizational capabilities and resources to effectively implement new technologies. Unfortunately, there is often a significant gap between what we want to achieve and what we can actually deliver within a given time and budget.

If you see numerous opportunities to use AI in your company, you need to start with an honest assessment of your competences and tools. Ask your IT professionals to answer the following questions honestly:

• Do we have an in-house development team with the right skills to build a bespoke AI solution from scratch?
• If not, should we consider buying an off-the-shelf AI product offered by external suppliers?
• Or would it be more cost effective to strategically engage with an experienced external partner to jointly develop a solution tailored to our needs?

Due to a lack of internal resources, the best solution may be to outsource your AI implementation project entirely to a specialized external company. Whichever path you choose, a good first step is to thoroughly research the AI solutions available on the market and assess whether any of them could meet your organization’s current needs. Buying an off-the-shelf product may well be a more cost-effective option than building from scratch.

Remember that AI integration is different from a typical new software implementation. It requires expertise in machine learning, big data processing and advanced algorithms. If your organization doesn’t have this expertise, working with external specialists may be unavoidable to maximize the project’s chances of success.

Step 5. Consider consulting a specialist

Despite the enthusiasm for AI technology, many managers are still afraid to take the first steps due to a lack of skills within their organization. If you are one of them, consider bringing in a specialist consultant or external company.

Building AI systems is significantly different from developing typical business applications. It is a highly specialized area of expertise, requiring advanced skills in machine learning, natural language processing, deep learning and big data analysis.

For example, creating an AI virtual assistant that can effectively communicate with customers requires not only a solid full-stack foundation, but also natural language processing technology and generative artificial intelligence.

If your team lacks such specialized skills, it may make more sense to seek outside assistance. Specialized AI consulting firms and agencies can provide not only relevant expertise and experience, but also proven processes and best practices to increase the chances of success for your initiatives.

Of course, hiring external experts comes at an additional cost. However, it is important to remember that improper implementation of AI can lead to even greater financial losses due to errors, downtime, and the need for corrections. Or simply a malfunction of the entire system, which will not perform the tasks for which it was created. That’s why working with specialists is often a wise investment that can save you time and money in the long run.

Source: DALL·E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

Taming AI – summary

Implementing artificial intelligence in a company is undoubtedly a serious and challenging undertaking, but it is also a huge opportunity for business transformation and growth. It opens the door to countless opportunities to increase efficiency, optimize processes and deliver greater value to customers.

As we’ve already seen, many companies around the world – from small businesses to large enterprises – are successfully using AI to automate tedious tasks, analyze large data sets, and make better decisions based on facts.

Of course, as with any serious business initiative, the path to a successful AI implementation is detailed planning and adherence to proven principles.

Implementing AI is an iterative process. That’s why it’s best to start with a small pilot project, run tests, and gather feedback. Based on this, it will be easier to make decisions about further development or adjustments. Also, don’t forget a key success factor – data. The more quality data you feed your AI systems with, the better they will learn and perform.

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You don’t have to be a developer to start being in AI world. Thanks to advances in machine learning and the development of no-code AI, the use of advanced AI tools is now available to anyone, regardless of technical skills.

From ChatGPT (https://chat.openai.com/), which can write an essay or generate code for you, to HeyGen (), which can generate videos of your virtual self, to Midjourney (), which can create images, AI tools can greatly assist you in your work without programming. You don’t have to wrestle with lines of code – all you have to do is skillfully type in your request, and AI takes care of the rest.

Of course, being familiar with the basics of programming and machine learning, and especially the basics of generative artificial intelligence, can be useful if you want to deepen your knowledge or create more advanced AI applications. It will give you a better understanding of how these tools work and how to use them to their full potential. For now, though, you can simply enjoy the ease and freedom of using them without having to spend years learning to code.

Where to get up-to-date information from the AI world?

With AI evolving at such a rapid pace, staying on top of the latest trends and discoveries can seem daunting. Fortunately, there are a number of information resources available to help you stay ahead of the curve. One such resource is the AI series on the IFIRMA website (). Regularly published articles provide valuable information on AI applications in business, as well as tips on how to implement these technologies in your organization.

The key, however, is to skillfully combine sources and regularly update your knowledge. It makes sense to start with educational materials from leading players in the AI market, such as Google, OpenAI, and DeepMind. Online courses, webinars, and step-by-step guides will give you a solid theoretical foundation and practical skills for using AI tools. Try out:

• AI basics on aihero.pl () – this free AI course is a solid starter if you’re just beginning to use AI tools,
• Introduction to Generative AI Learning Path from Google () is an overview of generative AI concepts, from the basics of large language models to the principles of responsible AI,
• Generative AI for Everyone from Deeplearning.ai () teaches how generative AI works and how to use it in everyday life and at work.

Source: Deeplearning.ai (https://www.deeplearning.ai/courses/generative-ai-for-everyone/)

Podcasts are a great way to explore AI topics in an accessible format. Here are a few suggestions worth checking out:

• 99 shades of AI” () – a podcast hosted by Karol Stryja in which experts, researchers, and practitioners share their experiences and insights on AI,
• ”Understanding AI” () – a series of podcasts and articles covering AI topics from basic concepts to applications in various fields.

Signing up for newsletters dedicated to AI is also a great way to get regular doses of up-to-date information and interesting facts. Two particularly valuable examples are:

• „The Batch” from DeepLearning.AI (https://www.deeplearning.ai/the-batch/) – a weekly newsletter providing news and insights from the world of AI, designed for both beginners and experts,
• „The Neuron” () – a newsletter showcasing the latest developments and trends in AI along with practical tips and tools from experts.

Source: Deeplearning.ai (https://www.deeplearning.ai/the-batch/)

In addition, it is worth following the blogs and social media of AI experts, such as Aleksandra Przegalińska, Wlodzislaw Duch or Adam Gospodarczyk and Grzegorz Róg. They share their insights, analyses and forecasts on AI development, which can be a valuable source of inspiration and knowledge.

We should also not forget about traditional methods of finding information, such as browsing books and scientific articles, for example on ArXiv (). While these may not be as up-to-date as blogs or social media, they provide a much deeper understanding of the concepts and theories behind AI, which is crucial for a deeper understanding of the field.

Although the sources we recommend above are very valuable, they represent only a fraction of the interesting news related to AI. If you are looking for inspiration, pay attention to the reputation of the authors, the timeliness of the content, and the support of the theses presented with concrete examples and data. A critical approach will allow you to avoid the misinformation that often accompanies this popular topic and to form an accurate picture of the state of knowledge in the field of AI. But now it’s time for tools – how to start learning AI practically?

Is it worth trying out all the tools one by one? Or is it better to learn two of them well?

In the world of AI, where exciting new tools appear almost daily, it is easy to be tempted to try them all out one by one. But is this approach effective? Maybe it’s better to focus on mastering a few key solutions that best fit your needs and goals?

Consider the following arguments for a selective approach to learning AI tools:

1. Saving time and energy. If you try every new tool that comes out, you risk diverting your attention and energy. Instead of wasting valuable time learning how to use dozens of applications, it’s better to focus on gaining a thorough understanding of the ones that are most relevant to you. In this way, you’ll become proficient more quickly and reap real benefits from using them.
2. Increased efficiency and productivity. The better you master a given AI tool, the more efficiently you can use it in your work. Knowing all of its features and nuances will help you complete your tasks faster and more efficiently. This, in turn, translates into higher productivity and better results.
3. Consistency and predictability. By using the same tools consistently, you can develop a systematic and uniform style of work. Its results will be more uniform and easier to control, which is especially important for long-term projects or collaborations with others.
4. Optimizing processes and integrating AI into daily work. By focusing on a few key tools, you can optimize your work and seamlessly integrate AI into your daily tasks. This will make AI a natural extension of your skills, rather than an additional burden.
5. Saving money. Many AI tools operate on a subscription model or require user fees. Typically, the amount for monthly access starts around $20 (80 zlotys) plus tax. By limiting the number of applications you use, you can save a lot of money and better control your budget for new technologies.

Of course, this does not mean that you should give up experimenting with new tools altogether. On the contrary, it’s a good idea to regularly browse the available options, for example at There’s An AI for That (), in search of innovative solutions that can improve your work. More than 13,000 tools are already there!

So before you make a decision, think about what your key needs and goals are. Find the tools that best meet them and test them. If they prove useful, gradually add them to your toolkit, while ensuring that the number of active applications remains within reasonable limits. This approach will allow you to take advantage of advances in AI while maintaining focus and efficiency.

Source: DALL·E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

Do you really need to be up to date with AI world?

Technological progress seems to be accelerating every day. For many, the pace of change can be frustrating, so they question the need to keep up with AI developments. Isn’t it enough to just focus on your work and wait for the revolution to come to you?

If you want to stay competitive and realize the potential of artificial intelligence, you can’t bury your head in the sand. While you don’t need to follow every update, understanding the potential and capabilities of these technologies is essential. Let’s look at some of the key reasons why you need to keep up with AI:

• AI is changing dynamically – unfortunately, in business it is impossible to stand still, especially as companies are creating a gap between themselves and their competitors faster than ever before. In other words, if you don’t try to keep up with AI developments, you risk making your skills and knowledge obsolete,
• AI is no longer just another IT tool – is a true transformation, comparable to the invention of the Internet or the development of the word processor. Over the next few years, AI will change the way we work, learn, and communicate, becoming an integral part of our professional and personal lives.

Imagine having a virtual assistant to help you write reports, create presentations, and even code. Imagine being able to use a sophisticated data analysis tool that can instantly extract key insights from vast collections of information. This is not the distant future – these capabilities already exist thanks to AI.

Źródło: DALL·E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

Summary

Artificial intelligence is a technology that is changing the face of the modern world. Its influence extends to almost every aspect of life, from business to education to entertainment. With such a dynamic development, it is important to constantly update your knowledge and skills in using AI tools.

Understanding how these tools work and how to use them will not only make you more productive, but also open the door to new opportunities and innovations. But remember, learning AI doesn’t have to be overwhelming. You can start with small steps and gradually increase your involvement.

Here are some practical tips for incorporating AI learning into your daily routine:

1. Subscribe to an AI newsletter or news channel to get regular updates.
2. Set aside a weekly “AI time,” an hour or two to read articles, watch webinars, or try out new tools.
3. Join the AI community on platforms like Reddit, Discord, and Slack, where you can discuss and learn from other enthusiasts.
4. Pay particular attention to AI trends that may affect your industry or profession. These could include recommendation systems for e-commerce, chatbots for customer service, or business process automation tools.

Remember, AI is more than a tool-it’s a new way of thinking and solving problems. The sooner you get comfortable with it, the easier it will be for you to adapt to the coming changes and reap the benefits of this disruptive technology. Be curious and willing to learn – that is the key to success in the AI era.

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