How to use artificial intelligence in business: A brief guide to application areas
Imagine walking into your office and finding a technology that could predict customer behaviors, automate tedious tasks, and help you make better business decisions in seconds. That’s not science fiction anymore – it’s the reality of artificial intelligence in today’s business world. As companies worldwide race to harness AI’s potential, understanding how to implement this technology effectively has become crucial for staying competitive. 1. Introduction to AI in Business The business landscape is experiencing a seismic shift thanks to artificial intelligence. With the global AI market valued at almost $196.63 billion in early 2024 (source: synthesio.io), it’s clear that AI isn’t just another tech trend – it’s becoming the backbone of modern business operations. AI in business represents a fundamental transformation in how companies operate, make decisions, and interact with customers. From small startups to multinational corporations, organizations are leveraging AI to automate routine tasks, analyze vast amounts of data, and unlock new opportunities for growth and innovation. What makes AI particularly powerful in the business context is its ability to learn and improve over time. Machine learning algorithms can identify patterns in data that humans might miss, while natural language processing enables more natural interactions between computers and people. These capabilities are revolutionizing everything from customer service to supply chain management. The projected growth to $738.80 billion by indicates that AI adoption isn’t slowing down – it’s accelerating. For businesses, this means that understanding and implementing AI isn’t just about staying current; it’s about future-proofing your organization. The companies that adapt now will be better positioned to thrive in an increasingly AI-driven marketplace. Key areas where AI is making significant impacts include: Customer experience and personalization Process automation and efficiency Data analysis and decision-making Risk management and fraud detection Product development and innovation As we delve deeper into the practical applications of AI in business, remember that successful implementation isn’t about replacing human workers – it’s about augmenting human capabilities and freeing up time for more strategic, creative work. 2. Areas of your business where you should implement artificial intelligence With 83% of companies claiming AI as a top priority (explodingtopics.com), understanding where to implement artificial intelligence for businesses has become crucial. Let’s explore the key areas where artificial intelligence business applications can create the most significant impact. 2.1 AI in Marketing: Beyond Basic Automation The role of artificial intelligence in business is particularly transformative in marketing. AI-powered tools analyze customer behavior patterns, preferences, and engagement metrics to create highly targeted campaigns. These systems can predict customer needs before they even arise, enabling proactive marketing strategies. Marketing teams can leverage AI to: Personalize content and product recommendations Optimize ad spending and campaign performance Create data-driven customer segments Generate and test marketing copy Predict customer lifetime value 2.2 Add Intelligent Support Systems to Your Customer Service Business with artificial intelligence transforms customer service operations through smart automation. Modern AI-powered chatbots can handle multiple customer inquiries simultaneously, providing 24/7 support while maintaining consistent quality. These systems learn from each interaction, continuously improving their responses and understanding of customer needs. The use of artificial intelligence in business support systems has shown remarkable results in reducing response times and improving customer satisfaction. These tools can automatically route complex queries to human agents while handling routine questions independently, creating a seamless support experience. 2.3 Improve operations and supply chain management Artificial intelligence for businesses is revolutionizing supply chain management through predictive analytics and real-time optimization. AI systems can forecast demand patterns, optimize inventory levels, and identify potential disruptions before they impact operations. Smart algorithms analyze historical data, weather patterns, and market trends to make accurate predictions about supply chain needs. This proactive approach helps businesses maintain optimal inventory levels while reducing storage costs and preventing stockouts. 2.4 Equip Your HR with Analytical and Recruitment Tools based on Artificial Intelligence With 97 million people expected to work in the AI space by 2025 (explodingtopics.com), HR departments need AI-powered tools to handle the evolving workforce. AI streamlines recruitment by screening resumes, identifying top candidates, and even conducting initial interviews through chatbots. These tools also help in: Predicting employee turnover Identifying skill gaps Personalizing employee development plans Automating administrative tasks Analyzing workplace satisfaction 2.5 Detect fraud and unnecessary spending in your finance department Artificial intelligence is a game-changer in financial management, offering advanced fraud detection and expense monitoring capabilities. AI systems can analyze vast amounts of transaction data in real-time, identifying irregular patterns that may indicate fraudulent activity. These tools go beyond traditional rule-based systems, using machine learning algorithms to adapt and recognize new types of threats. In addition to fraud prevention, AI helps finance teams: Monitor spending trends and flag outliers Automate expense reporting and approval processes Forecast cash flow and financial risks Optimize budgeting and cost management strategies 2.6 Make your decision-making and data analysis processes more efficient AI-driven analytics tools transform raw data into actionable insights, enabling faster and more informed decision-making. With AI technology projected to generate $15.7 trillion in revenue by 2030 (explodingtopics.com), businesses that leverage AI for data analysis gain a significant competitive advantage. These systems can process vast amounts of data in real-time, identifying patterns and trends that humans might miss. They can also generate predictive models that help businesses anticipate market changes and adapt their strategies accordingly. 2.7 Improve Your Training Programs AI-powered learning platforms adapt to individual learning styles and pace, creating personalized training experiences for employees. These systems track progress, identify areas where learners struggle, and automatically adjust the difficulty level to optimize learning outcomes. The technology can also: Generate customized learning paths Provide real-time feedback Create interactive training scenarios Track and analyze learning effectiveness Automate assessment and certification processes Through these various applications, artificial intelligence is fundamentally changing how businesses operate and compete in the modern marketplace. As the US AI market heads toward $299.64 billion by 2026 (explodingtopics.com), organizations that strategically implement AI across these areas will be better positioned for future success. 3. Concluding Thoughts on the Future of AI in Business The integration of artificial intelligence and business management represents a fundamental shift in how companies operate and compete. As businesses using artificial intelligence continue to demonstrate remarkable success, it’s crucial to understand that this transformation requires strategic planning and commitment. 3.1 The Importance of Patience and Persistence in Achieving Success with AI Implementing artificial intelligence technology in business is not an overnight process. Success requires a methodical approach and realistic expectations. Organizations must understand that AI systems need time to learn, adapt, and deliver optimal results. The initial implementation phase might present challenges, but persistence is key to achieving long-term benefits. The journey of integrating artificial intelligence technologies in business typically involves: Careful assessment of current processes Gradual implementation in phases Continuous monitoring and adjustment Regular training and upskilling of staff Measuring and analyzing results systematically Companies that maintain patience during this transition period often see more sustainable and meaningful results. It’s essential to focus on building a strong foundation rather than rushing to implement every available AI solution at once. 3.2 Staying Ahead: The Need for Ongoing Innovation and Adaptation The rapid evolution of artificial intelligence in the business world demands a commitment to continuous innovation. Organizations must stay informed about emerging technologies and be ready to adapt their strategies accordingly. This involves creating a culture that embraces technological change and values ongoing learning. To maintain a competitive edge, businesses should: Regularly assess new AI technologies and their potential applications Invest in employee training and development Foster a culture of innovation and experimentation Build flexible systems that can adapt to new AI capabilities Monitor industry trends and competitor implementations The future of artificial intelligence and business management lies in the ability to balance innovation with practical implementation. While early adoption of new technologies can provide competitive advantages, it’s equally important to ensure that these implementations align with business objectives and deliver tangible value. The successful integration of artificial intelligence in the business world requires a delicate balance between ambition and pragmatism. Organizations that can maintain this balance while staying committed to their AI journey will be better positioned to thrive in an increasingly technology-driven marketplace. The key is to remain adaptable while maintaining a clear vision of how AI can serve your business objectives and drive sustainable growth. 4. How TTMS Can Help Implement Artificial Intelligence in Your Business At TTMS, we specialize in integrating artificial intelligence (AI) into business operations, providing tailored solutions that enhance efficiency and foster innovation. Here’s how we can assist you in implementing AI within your organization: 4.1 AI-Driven Business Solutions We deliver advanced AI solutions designed to boost operational efficiency and decision-making across various industries. Our services include: Intelligent Chatbots and Virtual Assistants: Automate customer service operations with our AI-powered chatbots, capable of managing multiple inquiries simultaneously and offering 24/7 support while maintaining consistent quality. Advanced Analytics: We transform raw data into actionable insights, enabling faster and more informed decision-making. Our AI-driven analytics tools process vast amounts of data in real-time, identifying patterns and trends that are often missed. 4.2 Industry-Specific AI Implementations Our team has a proven track record of deploying AI solutions tailored to specific industry needs. For example: Pharmaceuticals: We implemented an AI system integrated with Salesforce CRM for Takeda Pharma, automating RFP analysis to improve bidding processes and decision-making speed. Legal Services: We created an AI-based support system for a law firm, automating the analysis of legal documents, mediation preparation, and contract generation to improve workflow and service quality. Manufacturing: We deployed an AI-driven SEO meta optimization solution in Adobe Experience Manager for a mechatronic manufacturer. By automating meta tag generation and analyzing user behavior, we significantly improved search visibility and boosted marketing efficiency. Corporate Training: We implemented an AI-powered system for corporate training programs, personalizing learning paths, automating assessments, and customizing teaching materials to meet specific employee needs. 4.3 Comprehensive AI Integration Services We provide end-to-end AI integration services, including: Consultation and Strategy Development: Assessing your business needs to develop a customized AI implementation strategy. System Design and Implementation: Designing and deploying AI systems tailored to your operational requirements. Training and Support: Offering training for your team and ongoing support to ensure seamless integration and optimal performance of AI solutions. At TTMS, we are committed to helping your business leverage cutting-edge AI technologies to automate routine tasks, enhance decision-making, and maintain a competitive edge in the market. Let us guide you through your AI journey and unlock the full potential of your business. Contact us now! Read our case studies on implementing AI technology: AI-Driven SEO Meta Optimization in AEM: Stäubli Case Study Case Study – AI Implementation for Court Document Analysis Using AI in Corporate Training Development: Case Study Pharma AI – Implementation Case Study at Takeda Pharma Case Study: How Migration to AEM Platform Accelerated Growth in EMEA Market How do companies actually use AI? The role of artificial intelligence in business spans across multiple departments and functions. Companies primarily implement AI to automate repetitive tasks, analyze large datasets, and enhance decision-making processes. Modern artificial intelligence business applications include predictive analytics for sales forecasting, automated customer service systems, and intelligent inventory management solutions. These implementations help businesses reduce operational costs while improving efficiency and accuracy. How can AI be used in business? The use of artificial intelligence in business can be implemented in several ways: Customer behavior analysis and prediction Process automation and workflow optimization Real-time market trend analysis Quality control and maintenance prediction Employee productivity tracking and enhancement Risk assessment and fraud detection Supply chain optimization Personalized marketing campaigns What is an example of AI in business? A prime example of artificial intelligence for businesses is the implementation of intelligent CRM systems. These platforms use AI to: Predict customer purchasing patterns Automate email marketing campaigns Identify sales opportunities Provide personalized product recommendations Track customer satisfaction metrics Generate detailed customer insights reports How to use AI to transform your business? Transforming your business with artificial intelligence requires a strategic approach: Assess your current business processes Identify areas where AI can add value Start with small, manageable projects Measure and analyze results Scale successful implementations Provide ongoing training for staff Continuously optimize and adjust The key is to begin with clear objectives and gradually expand your AI initiatives as you see positive results. How can artificial intelligence (AI) change your business? Business with artificial intelligence experiences transformative changes across multiple dimensions: Improved Operational Efficiency: AI automates routine tasks, reducing human error and increasing productivity Enhanced Customer Experience: Personalized interactions and faster response times Better Decision Making: Data-driven insights for strategic planning Increased Revenue: New opportunities through predictive analytics and market insights Reduced Costs: Streamlined processes and optimized resource allocation Innovation Acceleration: Faster product development and market adaptation Competitive Advantage: Enhanced ability to respond to market changes quickly The impact of AI varies by industry and organization size, but its potential to drive significant business transformation is universal. Success lies in choosing the right applications and implementing them strategically to align with your business goals.
Read
Will Artificial Intelligence (AI) Take Over Lawyers’ Jobs?
Automation, robotics, and algorithms used to be associated primarily with manufacturing. However, the rapid development of artificial intelligence (AI) is increasingly affecting industries once deemed resistant to automation—like the legal sector. Should lawyers worry that AI will “take” their jobs? What are the real risks and opportunities associated with implementing AI in law firms and corporate legal departments? 1. In Which Areas Do Lawyers Already Use AI? 1.1 Document and Contract Analysis AI-driven tools are increasingly helping lawyers sift through thousands of legal documents, rulings, or regulations at high speed. They excel at quickly locating relevant information in large data sets—work that used to require many hours or even days of lawyer time. 1.2 Preliminary Drafting of Documents and Contracts Advanced AI systems can generate templates for agreements or litigation documents based on existing data and patterns. This frees lawyers from repetitive, routine tasks and allows them to focus on more complex, strategic issues. 1.3 Searching for Precedents and Rulings Rather than manually poring over hundreds of judgments, lawyers can rely on AI tools to quickly find the most relevant precedents and legal interpretations. This approach supports stronger legal arguments in court cases and negotiations. 1.4 Predicting Case Outcomes Some emerging tools analyze big data and case histories to estimate the likelihood of winning or losing a lawsuit. While these algorithms remain in development, they can help attorneys decide on the best litigation strategy or whether to settle a case. 2. Can AI Really Replace Lawyers? While AI can take over many repetitive and relatively straightforward tasks, the core aspects of a lawyer’s work—interpreting legal nuances, negotiating, providing strategic counsel, and considering the social or business context—are far harder to automate fully. Creativity and Complex Argumentation Although AI tools can generate text or recommendations based on patterns, they still lack the full capacity to produce original legal concepts or intricate arguments that weigh social and psychological factors. Risk Assessment and Advisory Services Lawyers do more than interpret laws; they serve as advisors who must weigh various scenarios and consequences for their clients. AI can provide statistics and models, but it’s ultimately a person who evaluates risk and suggests the best course of action for a particular situation. Court Representation and Client Interaction Direct communication with clients, conflict resolution, and negotiating in teams all require interpersonal skills and nuanced judgment that remain the domain of human lawyers. 3. How Can Lawyers Leverage AI to Their Advantage? Instead of viewing AI solely as a threat, it’s worth seeing it as a tool that can streamline lawyers’ work and give them a competitive edge: 3.1 Time and Cost Savings AI tools help lawyers quickly analyze documents or draft basic agreement templates, freeing up time and reducing service costs. 3.2 Higher Quality Services Automating repetitive tasks allows lawyers to focus on work that requires specialized expertise. This enhances service quality and fosters greater client satisfaction. 3.3 New Specializations The rise of AI also brings new legal challenges related to data protection, the ethics of technology use, and regulations governing algorithms. Specialists in these areas will be in high demand. 3.4 Global Market Opportunities AI solutions can facilitate international expansion and make document translation more efficient, enabling law firms to serve clients worldwide. 4. Does the Future Mean Fewer Lawyers? It’s likely that the demand for lawyers performing only simple, repetitive tasks will decline over time. On the other hand, the need for highly skilled attorneys who combine legal knowledge with technological or business acumen is set to grow. Evolution of the Legal Profession The legal profession adapts as technology advances. Today’s legal experts must understand the basics of AI and how to harness algorithms in practice. Lawyers who specialize in AI regulations—like data privacy, intellectual property, or anti-discrimination laws—will become even more valuable. Collaboration with AI, Not Replacement The most probable scenario is one of collaboration between people and machines. AI will support attorneys in data analysis and document preparation, but final legal decisions, context interpretation, and strategic thinking will remain in human hands. 5. Conclusion Will AI take over lawyers’ jobs? Probably not in a way that replaces them entirely. However, it will undoubtedly change the nature of legal work. Lawyers will use AI tools to handle routine tasks more swiftly and effectively. As a result, demand for some repetitive jobs will diminish, but there will be a greater need for highly qualified legal experts who understand and collaborate with advanced technologies. AI thus serves as a catalyst for the evolution of the legal industry rather than a threat that will eliminate the lawyer’s profession. From the perspective of both seasoned attorneys and newcomers to the legal field, staying informed about technological developments and continuously upgrading skills will be crucial for providing top-tier services in a rapidly changing environment. And what do the lawyers themselves think about these concerns? We invite you to listen to the remarks of Natalia Lener-Bobek, a legal counsel collaborating with our Sawaryn & Partners law firm, and a member of the Working Group on Artificial Intelligence (GRAI) at the Ministry of Digital Affairs. You can read more about our collaboration with the Sawaryn & Partners law firm here. https://youtu.be/Y41D2QuHDjc
Read
Everything You Wanted to Know About ChatGPT But Were Afraid to Ask
Artificial intelligence is becoming an increasingly common part of our daily lives, and ChatGPT is one of the most popular AI-powered tools. We have gathered the most important questions about this leading large language model (LLM) to address any doubts and demonstrate how to use it effectively. 1. What is ChatGPT, and how does it work? ChatGPT is a language model developed by OpenAI, based on the advanced GPT (Generative Pre-trained Transformer) architecture. Its purpose is to generate text in response to user queries. The model has been trained on vast amounts of textual data, enabling it to conduct conversations, answer questions, create creative content, and solve complex problems. ChatGPT operates by predicting the most likely answers to given questions. It analyzes the input data, leverages linguistic patterns, and uses context to generate responses that appear natural and logical. Its advanced capabilities lie in understanding language nuances and tailoring responses to the user’s specific needs. 2. Ok, but how does ChatGPT REALLY work? How does ChatGPT generate responses to user questions? We will now describe the procedure the model follows. This explanation is for the most curious readers—step by step, with technical details about the model’s mechanism, natural language processing, and the response generation process. 2.1 Input Data Processing Użytkownik wprowadza zapytanie w formie tekstu. Model traktuje tę treść jako sekwencję tokenów, które są podstawowymi jednostkami analizy. Tokeny to fragmenty tekstu, które mogą być pojedynczymi słowami, znakami interpunkcyjnymi lub ich kombinacjami. Example: For the sentence “How does ChatGPT work?” the tokens might look as follows:: How does Chat GPT Work ? 2.2 Encoding the Query Each token is transformed into a numerical representation (vector) using a pre-trained word embedding mechanism. This vector representation reflects the semantic meaning of each token in a multi-dimensional numerical space, taking into account the context of the tokens within natural language usage. 2.3 Processing Through Transformer Layers The GPT architecture consists of multiple transformer layers that process input data. Each layer performs the following operations: 2.3.1 Self-Attention Mechanism The model analyzes which tokens in the input are related to each other and to what extent. The self-attention mechanism calculates attention scores for each pair of tokens, enabling the model to understand the context of the provided data. Example: In the query “How does ChatGPT work?”, the model identifies that the word “work” has a significant relationship with “ChatGPT”. 2.3.2 Normalization and Processing Through Feed-Forward Networks After attention analysis, each token passes through feed-forward neural networks, which perform further computations and enhance contextual understanding. 2.4 Generating Output Token Predictions After processing through all the layers, the model generates probabilities for each possible output token, taking into account the previously generated tokens and the context of the entire query. At this step, the softmax activation function is applied to produce a probability distribution over all possible tokens. Example: The model predicts that the most likely next token after “ChatGPT” is “works,” followed by “on,” and so on. 2.5 Decoding Strategies for Selecting Responses To generate a coherent response, the model employs one of several decoding strategies, each with distinct characteristics: Greedy Decoding: Selects the token with the highest probability at each step. This approach is fast but can lead to suboptimal results because it does not consider the broader context of the sequence. Beam Search: Explores multiple possible sequences simultaneously to find the one with the highest overall probability. This method balances accuracy and computational complexity, often yielding better results than greedy decoding. Sampling: Randomly selects tokens based on their probabilities, allowing for more creative and varied outputs. By adjusting the temperature parameter, users can control the randomness—lower values make responses more focused, while higher values make them more diverse and imaginative. 2.6 Considering Context in Extended Interactions If the conversation involves multiple stages, the model employs a context retention mechanism. The entire conversation history is processed as a sequence of input tokens, enabling the model to generate coherent and logical responses that take previous messages into account. 2.7 Postprocessing The generated response may undergo additional postprocessing steps, such as: Grammar or stylistic corrections to enhance clarity and polish the text. Content filtering to ensure compliance with ethical guidelines and safety policies (e.g., avoiding offensive or inappropriate responses). 2.8 Response Presentation The generated response is returned to the user in text format. This entire process is fast enough to make the interaction feel like it happens in real-time. The ChatGPT model does not “understand” language in the human sense but relies on statistical patterns found in the data it was trained on. As a result, it generates responses that appear natural and logical while continuously improving through iterative enhancements and updates. 3. What key technologies are used in the functioning of the ChatGPT model? What technologies underpin ChatGPT, and what roles do they play in generating responses? Transformer: The core of GPT architecture, enabling simultaneous processing of entire data sequences. This approach differs significantly from traditional sequential methods like LSTMs (Long Short-Term Memory networks) or RNNs (Recurrent Neural Networks). Transformers efficiently analyze dependencies between distant elements in text, which is essential for understanding context and generating logical responses. Attention Mechanism: A crucial component for understanding context and relationships between tokens. It calculates “weights” for each pair of words in the text, allowing the model to identify which data fragments are most relevant to the given context. This mechanism dynamically adjusts the meaning of tokens based on their surroundings in the text. Tokenization: The process of dividing text into smaller units called tokens. Tokens can represent entire words, fragments of words, or even individual characters, depending on the tokenization method. Tokens are the fundamental units on which the model operates, and their transformation into numerical representations is a key step in processing input data. Pre-Trained Embeddings: Input data is transformed into vector representations using pre-trained embeddings. These embeddings represent the meaning of tokens in a multi-dimensional numerical space, based on knowledge acquired during the training process. This enables the model to recognize complex relationships and similarities between words, even if they appear in slightly different forms than in the training data. As a result, this process improves language understanding and allows for generating responses that are more accurate and aligned with user intent. 4. How to Use ChatGPT in Daily Life? ChatGPT can be utilized in various aspects of daily life, such as: Task Planning: Ask ChatGPT to prepare a daily schedule or remind you of important events. Content Creation: Use it to write emails, articles, blog posts, or creative stories. Learning Assistance: ChatGPT can explain complex concepts, summarize readings, or provide tips for homework. Information Searching: Quickly obtain answers to various questions without browsing the internet. Problem Solving: Seek advice on tackling challenging situations, whether in work or personal life. To effectively use ChatGPT, it’s beneficial to clearly formulate your questions and, when necessary, follow up with additional questions to obtain more detailed responses. 5. How to Ask ChatGPT Questions to Get the Best Answers? To maximize ChatGPT’s potential, it’s crucial to phrase questions clearly and precisely. Here are some tips: Avoid vague phrasing: Instead of asking “What is it?”, be more specific, such as “What is a black hole?” Provide context: If your question concerns a specific topic, include additional information and create context, e.g., “In quantum physics, what does the uncertainty principle mean?” Ask one question at a time: Instead of asking several questions in one message, break them into smaller parts to receive more detailed and specific answers. Use examples: If you’re asking about something complex, providing an example helps clarify your query and improves the accuracy of the response. 6. What are the most common applications of ChatGPT? ChatGPT is used in various fields, such as: Customer service: Automatically responding to customer queries, generating quotes, or providing technical support. Education: Creating interactive lessons, generating math examples, or providing homework assistance. Entertainment: Writing stories, jokes, poems, or game scenarios. Business: Generating reports, assisting in creating marketing strategies, or analyzing data. Health: ChatGPT can help find health information but does not replace professional medical consultations. In all these fields, ChatGPT helps save time and improve efficiency. 7. Is ChatGPT safe, and what are its limitations? ChatGPT is designed with safety principles in mind. Its responses are becoming increasingly accurate. However, there are a few aspects to keep in mind: Precision limitations: The model may generate responses that sound logical but are not factually correct. Such hallucinations remain a challenge. Lack of understanding: ChatGPT does not “understand” topics in a human sense but predicts the most probable responses. User-provided data: Avoid sharing confidential information, as ChatGPT is not designed to handle sensitive data. OpenAI continuously works on improving safety by introducing new models and updates. When using ChatGPT, remember that it is a supportive tool and not a substitute for human analysis. 8. What new features does the Pro version of ChatGPT offer? The Pro version of ChatGPT introduces several improvements and new features, such as: Higher performance: Access to faster responses, even during peak hours. Better quality content generation: Utilizing the GPT-4 model, which provides more advanced responses and better context understanding. Plugin support: The Pro version allows integration with additional tools, such as data analysis or web search. Advanced chat history management: Pro users can manage saved conversations more easily. 9. What are the benefits of using GPT-4 compared to GPT-3.5? GPT-4 surpasses GPT-3.5 in several key areas: Precision: GPT-4 generates more coherent and accurate responses. Better understanding of complex queries: It can analyze more challenging issues and respond more effectively to multi-part questions. Wider range of applications: It supports more advanced business and scientific use cases. Multi-language support: GPT-4 handles translations and multilingual queries better. We have already discussed the differences between ChatGPT versions in several articles: The Evolution of ChatGPT from OpenAI: From GPT-1 to GPT-4o | TTMS What are the differences between ChatGPT 4o and 4.0? GPT 4o as a Content Creation Tool | TTMS The New Era of ChatGPT: How does o1-preview differ from GPT-4o? | TTMS 10. How to use new features, such as the mobile app mode? ChatGPT is available not only on computers but also in mobile applications. Using these features is simple: Download the app on Android or iOS from the appropriate store. Log in to your OpenAI account. Use features such as typing or dictating questions, accessing chat history, and integrating with additional plugins. The mobile app provides convenient access to the tool anywhere, increasing its usability. 11. What are ChatGPT’s limitations in business? While ChatGPT can be extremely helpful in business (as discussed here: ChatGPT for Business: Practical Applications and Use Cases | TTMS), it also has its limitations: Lack of specialized knowledge: The model does not always provide answers tailored to specific industry requirements. Need for verification: Generated responses may contain inaccuracies, so it is always worth checking them. Limitations in context understanding: ChatGPT may struggle to understand complex relationships between data. Despite these limitations, when used appropriately, ChatGPT can improve efficiency and save time in many business processes. 12. Why should you use ChatGPT in creative work? ChatGPT is an incredibly versatile tool that can significantly enrich and streamline the creative process in many aspects. With its content generation and language analysis capabilities, it serves as valuable support for creators at every stage of their work. First and foremost, ChatGPT inspires creativity. It generates ideas for various forms of expression, such as literary texts, scripts, poems, or visual content. For example, a writer stuck at a dead end can ask for suggestions on continuing the story, while a visual artist can request ideas for describing an image. This makes the creative process more fluid, and new perspectives can inspire the creator to develop their work in ways previously unconsidered. Additionally, ChatGPT significantly accelerates content creation. It is useful for writing articles, advertisements, or social media posts, where time is crucial. For example, a marketer can receive a catchy headline or promotional content tailored to a specific audience within seconds. A journalist can ask for help formulating a lead for an article or quickly gathering basic information on a given topic. ChatGPT also excels as an editor. It can analyze existing texts and suggest stylistic, grammatical, or logical improvements. For instance, an editor working on a lengthy essay can ask for the text to be reviewed for clarity or coherence of arguments. This makes corrections more precise, and the text gains a professional polish. It is worth noting that ChatGPT is a supportive tool and does not replace human creativity. The creator retains full control over the final shape of their work, treating the model as a partner providing raw material that requires refinement. For example, an author can use ideas generated by ChatGPT, but their ultimate form depends solely on the author. Not all content generated by the model is successful. For instance, poems may appear clumsy, lacking rhyme or rhythm (especially noticeable in Polish). Why is that? Read the next section. 13. Why does ChatGPT write poor poetry? If you’ve ever tried to create a simple birthday rhyme for a friend, compose a catchy, rhyming story, or write a simple song using ChatGPT, you’re likely familiar with the feeling of disappointment after reading the results. The poor-quality poems generated by ChatGPT are the result of several key technological limitations, particularly evident in languages like Polish: Lack of deep understanding of poetic structure: ChatGPT relies on analyzing language patterns rather than truly understanding the rules of poetry creation. Rhyme, rhythm, syllable counts in lines, or meter are complex rules requiring precise execution. The model cannot consistently apply these principles because it does not “understand” the concept of poetic harmony—it analyzes text as a sequence of tokens, not as a cohesive literary piece. Limitations in training data: In the training data used for the model, poetry constitutes a relatively small percentage. For Polish poems, the dataset is even more limited, leaving the model without sufficient patterns to correctly replicate the specific elements of poetry characteristic of the Polish language. Polish language as a challenge for AI models: Polish is a highly inflectional language, making rhymes more difficult to achieve compared to less grammatically complex languages like English. Additionally, Polish rhythm often requires matching stress and syllable counts in lines, which is more challenging for the model to generate. Statistical approach to text generation: The model generates text based on predictions of which tokens most commonly appear in certain contexts. In poetry, which often requires creative rule-breaking, this approach leads to banal or chaotic results. Rhymes are selected randomly, and rhythm is neglected because the model focuses more on semantics than on form. Lack of iterative improvement: Poets often revise their works multiple times, refining each line and rhyme. ChatGPT generates text in a single pass, without iterative analysis or refining the content, which further impacts coherence and finesse. As a result, ChatGPT may produce works that sometimes sound interesting but generally lack the literary precision and emotional depth that are the essence of poetry. It’s best to treat these attempts as inspiration or drafts rather than finished pieces. 14. How to effectively use ChatGPT for personalized AI interactions? Personalizing your interactions with ChatGPT allows for better results and increased efficiency when using the tool. Here are some practical tips: Set a conversation goal: Explain to ChatGPT what you want to achieve. For example, “I want you to write a formal email” or “I need a creative scenario for an advertising campaign.” Adjust response parameters: In applications with advanced settings like temperature, you can control response creativity. A higher temperature value (e.g., 0.8) generates more creative and diverse responses, while a lower one (e.g., 0.2) makes responses more logical and concise. Max tokens: You can limit the length of responses to get shorter and more concise answers or allow the model to generate longer, more exhaustive responses. Example prompts: “Provide 3 title suggestions for an article about artificial intelligence”—a specific question that limits the breadth of the response. “Explain to me how artificial intelligence works as if you were talking to a 10-year-old”—clearly defining the style and level of detail. Recall context: If your question pertains to an earlier ChatGPT response, refer to it, e.g., “You mentioned data analysis. What tools can I use for this?” Create a user profile: Let ChatGPT know your preferred response style. For example, “Respond to me in simple sentences” or “Be analytical.” The tool will remember this style for the duration of the session. Experiment with tone and response style: “Respond in a formal tone.” “Explain this humorously.” “Prepare a list of bullet points.” Iteration: If you’re not satisfied with the response, ask for revisions, e.g., “Rephrase this more professionally” or “Clarify the second part of the response.” Advanced techniques: System commands (in advanced versions): Some versions of ChatGPT allow you to define roles, e.g., “Act as a marketing expert” or “You are a biology teacher.” Contextual modules: In Pro applications or those with API support, you can load predefined modules or data to increase response accuracy in specific fields. Using these techniques, ChatGPT interactions become more effective and tailored to your individual needs. The better you define your expectations, the higher the chance of receiving a response that meets your requirements. 15. What are the best ChatGPT prompts to boost productivity and creativity? Example prompts: Planning: “Help me create a daily schedule with priorities.” Creativity: “Write an essay about friendship in a romantic style.” Data analysis: “Prepare a summary of key trends in e-commerce based on the following data.” Pro tip: Always test different phrasings of the same query to see which yields the best results. 16. What ethical considerations should be taken into account when using ChatGPT to generate content? Using AI comes with ethical challenges such as: Copyright: Ensure that generated content does not infringe on the rights of other creators. Education: Do not use AI to cheat, for instance, by generating entire academic papers. Disinformation: Avoid publishing unverified information generated by the model. Best practices include labeling content as AI-generated and verifying its accuracy before publication. 17. How to use ChatGPT in education without promoting academic dishonesty? ChatGPT can be a valuable tool for learning if used responsibly: Helping understand topics: The model can simplify complex concepts. Creating study questions: You can ask for sample exam questions. Knowledge verification: Use AI to check your answers but not as your sole source of information. 18. What are ChatGPT’s limitations in providing medical or legal advice? ChatGPT can provide general information but does not replace consultations with a doctor or lawyer. Lack of timeliness: The model might not reflect the latest regulations or research. Lack of personalization: It has no access to patient data or the context of legal cases. Always verify information with a specialist. 19. How can I use ChatGPT to improve my writing and communication skills? ChatGPT is an excellent tool for enhancing writing skills: Text editing: Ask for style improvements or error corrections. Writing workshop: Generate sample paragraphs on a given topic to inspire your text structure. Conversation simulations: Conduct dialogue simulations to improve communication skills. 20. What are the best practices for integrating ChatGPT with business processes? Integrating ChatGPT can enhance business operations in many areas: Customer service: Automating responses to inquiries. Content creation: Generating proposals, reports, or presentations. Decision support: Data analysis and preparation of summaries. It is advisable to implement AI in stages to minimize disruptions to ongoing processes. 21. How does ChatGPT manage user data and ensure privacy? Data security is a priority for OpenAI: No data storage: The model does not save information after the session ends. Encryption: All data sent to the model is encrypted. Always avoid entering sensitive personal information. This guide will help you use ChatGPT effectively, avoid potential pitfalls, and maximize its value. However, remember that AI is a tool – its effectiveness depends on how well you use it. 22. How does ChatGPT generate images? ChatGPT itself does not generate images as it is a language model. However, OpenAI offers a tool called DALL·E, which generates images based on textual descriptions. In ChatGPT Plus (powered by GPT-4) and versions with active visual functions, DALL·E is available as an additional tab in the ChatGPT interface. You can use this feature to create images by providing detailed descriptions, e.g., “a winter landscape with pine trees and falling snow.” ChatGPT will help you formulate the description, and DALL·E will generate the image based on it. This feature works in OpenAI applications (on the web and mobile apps) in appropriate versions with active access to this functionality. 23. How does ChatGPT help with presentations? ChatGPT can assist in creating presentation content but does not generate ready-made multimedia files, such as PowerPoint or Google Slides presentations. The model can suggest slide layouts, recommend headings, create discussion points, or write full slide texts based on a given topic. For instance, you can ask: “What slides should be included in a presentation about environmental protection?”, and ChatGPT will suggest the structure and content. However, the final materials must be created manually in presentation programs. 24. ChatGPT: How to upload an image? Currently, ChatGPT does not support directly uploading images to the chat interface as it is a language-focused model. However, in the latest versions, such as ChatGPT Plus and ChatGPT Pro, an advanced voice mode with vision (Advanced Voice Mode with Vision) has been introduced, enabling real-time image analysis. To use this feature: Log in to your ChatGPT Plus or Pro account. Activate voice mode: Click the microphone icon in the chat interface to start a voice conversation. Share an image: During the conversation, you can enable the camera or share your screen to allow ChatGPT to analyze the provided images. Note that this feature is primarily available in ChatGPT mobile apps and may not yet be implemented in all regions. If you lack access to this feature, it is worth regularly checking for app updates and OpenAI announcements regarding new capabilities and extensions to ChatGPT functionality.
Read
What’s New in Microsoft Teams: November 2024
Microsoft Teams is continuously evolving, delivering new features and improvements that enhance user experience and optimize daily workflows. From Teams Rooms enhancements to chat and channel upgrades, and improved meeting and calling functionalities, Microsoft Teams aims to support both remote and in-office teams in dynamic work environments. As a Microsoft partner, our company, TTMS, is excited about the possibilities these updates bring to foster better collaboration and user convenience. Here’s an overview of the latest features and how they benefit Teams users. 1. Easier Management of Information and Teams A new info panel in 1:1 and group chats enables quick access to key resources, including a participant list, pinned messages, shared files, and a search option. This feature is particularly helpful for users who frequently revisit important information or need to search through chat histories when collaborating on projects. Additionally, the revamped view of teams and channels provides a clear organizational structure, with options to filter and manage projects, and access analytical insights. Now, as the number of channels and teams grows, employees can more easily find the information they need. A screenshot of this new team and channel structure, highlighting its management options, will make it easier for users to understand these updates. 2. Improved Meeting and Webinar Experience Teams now offers notifications of completed meeting summaries in the Activity feed, allowing users to quickly review key points even if they couldn’t attend the meeting. Adding a screenshot of this notification will illustrate how simple it has become to manage such summaries. Meeting organizers also have new controls over admitting participants from the lobby, deciding who has access to the meeting. This flexibility is especially useful in larger meetings, as organizers can grant access rights to co-organizers and presenters. A screenshot of these settings can help illustrate how easy it is to manage meeting access. Thanks to voice isolation for MacOS, remote workers can now enjoy clearer sound by eliminating background noise. Using AI, this feature filters out unwanted sounds, providing a distraction-free meeting experience – a function especially valued by remote teams. 3. New Tools for Managing Large-Scale Events For organizers of large-scale events like Town Halls, webinars, and training sessions, Microsoft Teams now offers enhanced tools to streamline the entire process of managing participants and communications. One of the key improvements is the ability to integrate with external email platforms, enabling organizers to handle event-related communication from within Teams in a much more efficient manner. This integration allows organizers and co-organizers to send a range of messages, such as invitations, reminders, follow-ups, and updates, directly to participants’ inboxes, eliminating the need to switch between multiple platforms. For instance, organizers can set up automated reminders for registrants, confirm attendance, or even send personalized thank-you notes after the event, ensuring that participants remain engaged throughout the event lifecycle. This streamlined approach enhances the overall experience, allowing hosts to focus more on content delivery and less on administrative tasks. In addition to communication improvements, Teams also provides expanded control settings for admitting attendees. With these new controls, organizers can specify who has permission to allow participants into the event from the lobby, whether it’s just the organizer or also co-organizers and presenters. This flexibility is particularly useful for managing large or complex sessions where different team members might need to take on specific roles, ensuring a smooth and efficient start to each meeting or presentation. These updates not only enhance the logistical side of event management but also create a more polished experience for attendees, who benefit from timely communications and efficient event handling. A few well-placed screenshots of the integration with email platforms and the lobby management controls can effectively illustrate how these tools make large-scale event management simpler and more effective in Teams. 4. Teams Phone: Professional Call Queue Management For organizations that rely on phone calls, the new Queues app in Teams Phone offers significant benefits. Agents can make calls on behalf of call queues and auto attendants, while supervisors have real-time visibility into call statistics and can monitor conversations. This feature will be particularly valuable to customer service teams, and screenshots of the Queue app’s interface can help illustrate the management possibilities it brings to customer support. 5. Digital Signage in Microsoft Teams Rooms The new digital signage feature in Teams Rooms on Windows allows companies to leverage unused screens in meeting rooms to display dynamic content, such as company communications, news, guidelines, or announcements. Integration with providers like Appspace and XOGO further expands the scope of this feature. A screenshot showing an example of this dynamic signage will demonstrate how companies can maximize their workspace for internal communication. For organizations that use multiple meeting platforms, Microsoft Teams Rooms now allows users to join meetings on other platforms such as Google Meet, Zoom, and Cisco Webex. This cross-platform support is especially useful for teams working with clients and partners who use various solutions. A screenshot of an inter-platform meeting in progress can help visualize this new functionality. 6. Custom Branding for Meetings with Frosted Glass Effect The frosted glass effect for meeting backgrounds is a feature that allows companies to add a subtle, professional touch to their Teams meetings by displaying a blurred logo or brand element in the background. This effect can enhance the look of your meetings, providing a clean and sophisticated branded experience. Here’s a quick guide on how to apply this effect: Open Microsoft Teams and start a new meeting or join an existing one. Once in the meeting, click on the More Actions button (represented by three dots). Select Apply background effects from the dropdown menu. In the background effects menu, choose or upload a custom image with your logo that includes a frosted or blurred effect. If needed, you can design a background in a graphics editor to add your logo with a frosted effect. Apply the chosen background by clicking Preview to check how it looks and then select Apply to set it as your background for the meeting. This setup helps your branding look modern and appealing, while maintaining a minimalist and unobtrusive presence in your meetings. 7. Summary The latest Microsoft Teams updates provide companies with innovative tools to enhance efficiency, user experience, and workplace positivity. From the new info panel to the Queue app and frosted glass branding, Microsoft’s enhancements align with the evolving needs of modern businesses. At TTMS, as a Microsoft partner, we offer comprehensive support for Microsoft 365 solutions, enabling seamless collaboration, data security, and tailored productivity tools. These recent innovations represent another step towards excellence in providing solutions that not only support teamwork and workflow but also strengthen brand integrity and trust. Contact us now! Explore our previous articles about Microsoft Teams: TTMS Blog – The World Through the Eyes of IT Experts What is the new info panel in Microsoft Teams, and how can it help me? The info panel in 1:1 and group chats allows quick access to essential resources, such as a participant list, pinned messages, shared files, and a search option. This feature is especially useful for users who frequently need to revisit key information, making it easier to locate details without scrolling through long chat histories. This panel streamlines project collaboration and communication within Teams, helping teams work more efficiently. How do meeting organizers manage large-scale events like webinars more effectively now? Microsoft Teams has introduced enhanced tools for large-scale event management, such as integrating external email platforms directly within Teams. This integration allows organizers to send event-related messages like invitations, reminders, and follow-ups straight to participants’ inboxes. Additionally, organizers now have more control over lobby permissions, making it easier to manage participant access during large meetings, ensuring smoother event operations. What benefits does the Queues app in Teams Phone bring to customer service teams? The new Queues app allows customer service agents to handle calls more efficiently, including making calls on behalf of queues and monitoring call data. Team leaders can track call statistics and monitor conversations in real-time, which helps in managing high call volumes. This feature is especially valuable for customer service teams, as it provides essential tools for effective call management and customer support. How can companies use the frosted glass effect for branding in Teams meetings? The frosted glass effect enables companies to add a subtle branding element to their Teams meetings by displaying a blurred logo or brand element in the background. This effect helps companies maintain a professional and clean look during meetings, without overwhelming the space with branding. Users can activate this effect in their background settings by selecting or uploading a custom image with a frosted effect, adding a modern and polished touch to their online presence. How does the digital signage feature work in Teams Rooms, and what are its benefits? The digital signage feature in Teams Rooms allows companies to use idle screens in meeting rooms to display dynamic content, such as company news, announcements, and guidelines. This feature, available on Windows, integrates with providers like Appspace and XOGO, enabling organizations to maximize the value of their meeting spaces for internal communication. It’s a great way to make efficient use of equipment and to keep employees informed and engaged within the workplace.
Read
The New Era of ChatGPT: What Makes o1-preview Different from GPT-4o?
On September 17, 2024, OpenAI unveiled its new AI models, o1-preview and o1-mini, designed to tackle complex reasoning tasks more effectively than their predecessors, such as GPT-4o. The new models bring a focus on deeper thinking and problem-solving in fields like science, math, and coding. But how do these models really compare to GPT-4o? Let’s dive into the innovations behind o1-preview and o1-mini and explore where GPT-4o might still come out on top.
Read
AI in Sports – How Artificial Intelligence Supports Athletes and Coaches?
“Poland will formally bid to host the Olympic Games” – these were the words we heard from Prime Minister Donald Tusk in mid-August 2024. While the target is set for 2040 or 2044, and Tusk added that “time will tell if this is a realistic goal,” we will use this as an opportunity to reflect on the future of sports. The future of sports, and certainly of the Olympics, is inextricably linked with artificial intelligence. How is AI supporting sports today? You can read about it in the article below. What will sports look like in 2040? We don’t know for sure, but AI will undoubtedly play a major role, both in supporting athletes, coaches, referees, and organizers, as well as meeting the demands of the most discerning fans. A detailed “case study” on this topic already appeared on our blog some time ago – if you haven’t yet read my article about how AI supported the Paris Olympic Games, make sure to catch up! How does AI support athletes, coaches and entire teams? Artificial intelligence-based solutions are already helping to optimize training, refine tactical elements, and minimize the risk of injury. Let’s focus on a few examples illustrating where AI support for people who make a living from sports is heading – players and coaches. How does AI support players? Digital twin, or how to copy a heart Based on current knowledge of anatomy, physiology and biochemistry, specialists have the ability to look into many biometric parameters of athletes and use them to predict the condition of the body, but only to a certain extent. What if we created a virtual replica of a human organ and stimulated it with training loads? This question was asked by scientists who recently created interactive, three-dimensional models of our “components”. They can be subjected to tests and experiments simulating training or competitions. Also, predictions such as “if I sleep an hour longer, will I be able to break my marathon record?” seem extremely valuable. That is why representatives of various disciplines are already using so-called digital twins (Digital BioTwin). The most famous one at the time was the American runner Desiree Linden. Scientists “copied” her heart and circulatory system to be able to check their behavior at the limit of human performance. They created a “living” computer simulation that takes input to use AI and machine learning to provide accurate predictions of how the organ might function under certain scenarios, even very specific ones (such as reduced sleep or an unusual diet). The “digital twin” technology is the work of two companies: Tata Consultancy Services (TCS), which created the “heart” of Des Linden, and Dassault Systèmes, which provided expertise in computational models. There is no doubt that “digital twins” will also find their application in medicine. AstraZeneca is already testing the impact of cardiology and diabetes drugs on them. The idea itself is not foreign to other areas of life, where predicting processes with minimal risk is worth its weight in gold. We are talking about industry, energy, construction, transport and aviation, where a “digital twin” can be used to monitor, optimize and simulate real processes and objects. Based on technologies such as IoT, artificial intelligence, big data and computer simulations, digital twins enable precise management and prediction of potential problems. Power Bra, or What a Footballer Wears Under His Shirt If you happen to watch football matches and don’t turn off the TV after the 90th minute, there’s a good chance that you’ve noticed – while changing shirts – the black vests that encircle the players’ chests. They are part of advanced data analysis systems that collect information about the players’ physical parameters, such as heart rate, running speed, distance covered, or current position on the pitch thanks to GPS. The data is then analyzed by artificial intelligence algorithms that help coaches and medical staff better understand the players’ physical condition, monitor their training load, avoid injuries, and optimize preparation for matches. The leaders in the production of vests are Catapult Sports and STATSports. While the above companies focused on football, footballers from the NFL (National Football League) have also received a dedicated solution. Thanks to the combination of forces of Amazon Web Services (AWS) and the NFL, the Digital Athlete technology was created. It uses artificial intelligence and machine learning to create a comprehensive picture of the player experience, allowing teams to understand what individual players need to stay healthy, recover quickly and perform at their best. In the 2023/24 season, the technology was used by all 32 NFL clubs. The above solutions are examples of so-called wearable technology, i.e. technology that athletes wear and collect the necessary data. It should be noted here that processing it without the help of artificial intelligence systems would be virtually impossible. AI interprets this very complex data, discovers patterns and provides insights that analysts are not able to capture so efficiently and quickly. Devices monitoring and analyzing player parameters are springing up like mushrooms after the rain. Some of the more interesting ones are worth mentioning: Intel 3D Athlete Tracking (3DAT), which uses AI and cameras to analyze athletes’ movements in real time, enabling detailed monitoring and optimization of technique, and the WHOOP band, which creates recommendations for training and rest based on collected physiological data. AI algorithms against injuries In the NBA (but also in the NFL hockey league and football) for several years, artificial intelligence algorithms have been successfully used to predict the risk of injuries. Advanced machine learning models are used to analyze data on the health and performance of players, including injury history, game intensity, time spent on the pitch, and other physical metrics. An example of such a system is METIC (Multiple bidirectional Encoder Transformers for Injury Classification), which uses deep learning technology to analyze sequences of past matches and injuries, predicting the risk of future injuries. Models such as METIC work by processing large data sets using transformers (a technology originally developed for natural language processing) to more accurately predict injuries. Another example is Zone7, which works with sports teams to provide AI algorithms that predict injuries based on data from devices worn by players, such as GPS and accelerometers. Thanks to this data, it is possible to predict, for example, the risk of Achilles tendon rupture, which allows coaches and medical staff to take preventive measures, such as regeneration, reducing the intensity of training or so-called pre-rehabilitation exercises (anti-injury). Such solutions allow teams not only to minimize the risk of injury, but also to optimize player performance, which can have huge financial and sporting significance. In the NBA, losses due to player injuries reach tens of millions of dollars per year. Artificial Intelligence, Natural Emotions – How Does AI Detect Them and Use Them to Improve Results? Can AI feel emotions? Emotions are a complex set of physiological and psychological responses to external stimuli. Machines do not have consciousness or even the biological basis for having “feelings”. However, AI can be taught to read these emotions based on behavioral expressions. This assumption was made by the creators of software – researchers from the Technical Institute in Karlsruhe and the University of Duisburg in Germany – used to recognize the emotional states of tennis players. Their body language during matches is analyzed using convolutional neural networks (CNN). Convolutional neural networks are mainly used to analyze grid-structured data, such as images. They work by processing data in a hierarchical manner, using convolutional layers to automatically recognize important features such as edges, shapes, and patterns, which are then used for classification or further analysis. Why do tennis coaches need to read players’ emotions? Based on this, the coach can individualize training, respond more quickly to negative emotions such as frustration or stress, to keep the player focused during the match. Technology can also help prevent burnout by monitoring emotions and adjusting training load. This allows the coach to analyze how emotions affect performance, which supports the creation of more effective training and match strategies. Can AI read emotions better than humans? Let’s look at the numbers: the AI model achieved 68.9% accuracy, which is a similar result. This is an excellent result, which means that the “emotion reading” aspect can be simply outsourced to AI. From Elite Sports to Everyday Use Many solutions based on AI algorithms, and tested in competitive sports, have found their application in amateur sports. It has been a long time since fans of running, cycling or team sports have been taking advantage of training innovations, and most often equipment innovations. Thanks to the dynamic rapid development of artificial intelligence and “wearable” technology, i.e. devices worn by athletes that collect and analyze data in real time, many solutions have reached “home”, allowing amateurs to take a more advanced approach to training and monitoring progress. One of the key examples is the development of mobile applications for monitoring physical activity, such as Strava, Nike Run Club, or Garmin Connect. These applications, thanks to integration with devices such as smartwatches and fitness bands, allow amateurs to track parameters such as speed, distance, heart rate or calories burned. Additionally, advanced AI algorithms analyze data to provide personalized training advice, plan regeneration and avoid overtraining. An example is the WHOOP wristband, which uses biometric data to advise users when it is best to train and when to rest. Another example is motion analysis techniques, originally used in sports such as track and field or football. In projects such as Intel 3D Athlete Tracking (3DAT), AI algorithms analyze athletes’ movements in real time, allowing them to improve their technique and optimize movement. Today, amateurs can use similar technologies, for example through running analysis applications such as RunScribe or Stryd, which help improve their technique and minimize the risk of injury. The same is true of the solution from Zone7 (Zone7 started out working with professionals), which, using data from GPS devices and accelerometers, predicts the risk of injury and suggests changes to training intensity. An interesting application worth taking a look at is Swing Vision, available for amateur tennis players who are also… iPhone users. Players can monitor statistics such as the location of shots, ball speed, precision, and length of rallies. In addition, the app offers a video replay function with analysis of technique, footwork and match strategy. Thanks to this, players can improve their technique, increase the effectiveness of their strikes and better prepare for sparring and training. The accuracy of the app is impressive – it reaches 97 percent. All these technologies, which were originally reserved for top-level athletes, are now available to a wide group of users. Thanks to them, amateur trainers can benefit from precise data that was available only in the most advanced training centers a few years ago. This is proof of how AI is changing sports – from competitive to amateur, providing everyone with access to modern tools that improve performance and health. How does VAR work? VAR (Video Assistant Referee) uses video technology and AI-powered tools to assist referees in making key decisions during matches. AI helps analyze video feeds from multiple cameras to highlight critical incidents such as goals, penalties, and offsides. The AI system processes the data quickly to assist referees in reviewing the play in real-time, but the final decision is always made by the on-field referee. How does Hawk-Eye work? Hawk-Eye uses AI algorithms to track the ball’s trajectory in sports like tennis, football, and cricket. The system employs cameras that feed data into AI models, which then calculate the ball’s exact position and predict its future movement. This enables precise decisions about whether the ball was “in” or “out,” and the AI ensures fast and reliable results, which are essential for fair play. What features does SwingVision offer? SwingVision utilizes AI to analyze tennis matches by tracking ball speed, shot placement, and rally length. The AI-powered app processes video footage to provide detailed insights into player performance, footwork, and match strategy. With AI-generated video replays and real-time feedback, SwingVision helps players refine their techniques and improve their game more efficiently. What is a "digital twin" and how does AI play a role? A “digital twin” is an AI-powered virtual replica of a physical body or organ. In sports, AI models use real-time data from athletes to simulate their physical responses to different training regimens or scenarios. AI helps in predicting potential outcomes, such as how an athlete’s heart will respond under intense strain, allowing for more personalized training programs and injury prevention strategies. How does WHOOP work? WHOOP is a wearable device that collects physiological data like heart rate, sleep patterns, and strain. AI analyzes this data to provide personalized insights and training recommendations. The AI engine tracks trends over time, advising athletes on when to push harder or when to rest, helping them avoid overtraining and optimize recovery for peak performance. How does IBM Watson for Tennis work? IBM Watson for Tennis leverages AI to analyze vast amounts of match data, including player performance, statistics, and fan engagement. AI algorithms provide real-time insights, predict match outcomes, and even offer strategy suggestions based on player tendencies. Watson’s AI capabilities help both players and coaches make smarter, data-driven decisions during matches. How does Wyscout utilize AI? Wyscout uses AI to analyze football player performance by processing game footage and player statistics. AI algorithms break down player movements, tactical decisions, and match patterns, providing in-depth analysis for scouts and coaches. This AI-driven scouting platform helps clubs identify talent, assess skills, and make informed decisions about player transfers and team strategies. How does METIC work? METIC (Multiple bidirectional Encoder Transformers for Injury Classification) uses AI to predict the risk of injuries in sports like basketball and football. AI-powered deep learning models analyze past injury data, match intensity, and player performance to forecast potential injuries. This allows coaches and medical teams to adjust training loads and reduce injury risks based on AI-generated predictions. How does the Oura app leverage AI? The Oura app, connected to the Oura Ring, uses AI to analyze physiological data such as heart rate, body temperature, and sleep quality. AI algorithms interpret this data to provide personalized recommendations for recovery, fitness, and sleep improvement. The AI-powered insights help users make informed decisions about their health and daily activity, enhancing overall performance and well-being.
Read