The Innovantage podcast is mainly focused on technology and its importance for businesses. But tech innovation is not the only driving force that helps to achieve new heights. While concentrating too much on digital tools and advanced solutions, quite often we can forget that the core element of any business today is people.This episode of the Innovantage podcasts is quite different from all the previous ones. It is focused not on technology but on people. The host of the podcast Maxim Golikov, who is also the CBDO at Sigli, invited Nina Pivavarchyk to his studio. As the Head of People and Culture at Sigli, Nina has outstanding experience in working with people and communicating business values to them by means of a strong corporate culture.People and Culture: Is that just the same as HR?Today, we can observe a growing trend of introducing People and Culture-related positions at companies across different industries. However, the issue is that many companies do this without a clear understanding of what such positions really mean and try to change their HR into People and culture specialists.In a very broad sense, for the majority of people, HR jobs are about administrative processes, paperwork, and sometimes also about corporate events. HR responsibilities and tasks are focused on supporting businesses and employees in their work.People and culture jobs are about some other things. Such specialists are closer to the business. They need to understand the business goals and values. Their task is to match the goals of the business with people’s goals on the team. Quite often, the hardest (but according to Nina, the most interesting) thing is to find the right balance between these two types of goals.Real-life examplesIf you want to better understand what such positions presuppose and what business culture is, it will be helpful to take a look at successful real-life cases.According to Nina, Patagonia, a US-based retailer of outdoor recreation clothing, equipment, and food, is the most famous example in the People and culture community.The philosophy of this business is described in the book written by Patagonia founder Yvon Chouinard. Its name is “Let my people go surfing” and that’s one of the key principles that the company’s culture is based on.The company advocates sustainable growth and sustainable production of clothes. Its founders want to have people on board who share these ideas, who live active lives, and who use their products. That’s why they are building an environment where people can actually go surfing in the middle of the day at lunch if they want to because they are good at balancing work and life.Apart from this, Patagonia has a kindergarten inside the company’s office so that children can be educated with the right values. What is really interesting is that the company already has a full Patagonian person who was raised in that kindergarten and then became a manager. This person literally grew in the company.Another inspiring project implemented by Patagonia is related to volunteering. If employees want to go in for volunteering somewhere in a third-world country or help animals, this period will be paid for by the company.Why is it important to build a comprehensive business culture?Patagonia is a brilliant example of a business that is building its culture at all the possible layers and at all the stages of possible interaction with their potential team members. And that’s a good example to follow.This approach will help you hire the right people. Of course, it may still happen that some specialists will fall off during the recruitment process because they don’t fit into the culture. That’s completely okay.But if the whole employee experience is built correctly and you get the right people who strongly believe in your product and in your culture. They will feel comfortable and will be highly effective in their positions. This will let your product thrive and your company grow.You may offer a product or a service. Or you may have just a money-oriented company. According to Nina, it doesn’t actually matter for building your culture. You just need to be honest about your values and help people feel a part of your business.One of the negative consequences of not having a well-built culture is micromanagement. When a company doesn’t hire the right people, such people are not passionate or motivated about what they are doing. As a result, all that managers can do is to control their employees at every step.It’s very important to highlight that belief in some values has nothing in common with people’s nationalities. As Max mentioned, people from different nationalities can share your values and you can work with these people highly effectively. At the same time, you can have people from the same nationality but they won’t be able to find common ground because of different values. And that’s where a very hostile work environment can be created.How to find the right people?The process of hiring doesn’t actually start with the resume. It starts with your business brand. Before applying for a job, people usually study your corporate page and social media accounts. If they see that their values do not coincide with yours, it’s highly likely that they will look for other vacancies.Nina said that a part of her job as the Head of People and culture is to make sure that the external brand of the company and internal culture actually match and that the company is not lying about what’s happening out there.It’s vital not to oversell your brand. Otherwise, people will join your company, get disappointed, and leave you. This can cost you really much because you have already invested in recruitment, onboarding, and other related processes.It’s better to invest in your brand and to clearly show people from the very beginning what you believe in and what you are striving for.A resume is just a technical part of hiring. Nina explained that at Sigli, they also focus on a screening process. It includes speaking with candidates about different cases and real-life situations to see how they can react to them. This process helps to reveal people’s values that navigate them in decision-making.The probation period is a two-way street where people and their employers should try to detect whether their values match.Employees should see if they want to grow in the given company or not.At the same time, employers can also make a lot of valuable conclusions. During 3 months of probation, it’s quite impossible to measure the exact value that employees can bring back. But you can analyze how people act in certain situations and define whether they are a good fit for your team.Key elements that make a successful culture workCulture is something that is really hard to measure and explain. But there are a lot of theories about it.Nina mentioned the iceberg model of culture introduced by Edward T. Hall. This model is applicable not only to corporate culture but to culture in general.According to this theory, there are the following layers of any culture:Surface culture. That’s everything that is visible. In a corporate culture, these elements are your branding, office layout, the merch that you give out to partners and employees, your policies, etc.Hidden culture. That’s something that is not seen. But this defines the culture itself. In a corporate culture that is what you write about in your mission statement and your strategy. The values that your mission and vision are based on will have a great impact on what people will do subconsciously and what they will rely their assumptions on.Why does every business need product management?We are accustomed to hearing about product management in the context of software development.However, according to Nina, this concept can be applicable everywhere. In her job, she is also building her product. In this case, it is culture. The users of this product are employees.Building this product is a very long-term story if you really want to affect the business.You need to have metrics that will show you your progress and define any points that can be enhanced. You should be ready to implement several iterations (just like in software development) and even change your projects completely if they are not relevant anymore and your priorities are changed.How can you quantify culture?Let’s be honest, it’s impossible to quantify culture on its own. It can be considered only in relation to people. There are a lot of metrics that can be applied. However, there is no sense in defining just one that will be the most important for everyone because everything depends on your business goals.Quite often, it is said that turnover and retention are very significant indicators as they demonstrate how many people are leaving and how many people are staying.Nevertheless, there are a lot of types of businesses where turnover is okay and it even helps companies to develop their products.Some experts recommend focusing on the so-called revenue per head which shows how much money each employee generates for the company. In this context, it is also necessary to calculate how much you invested in each employee and how fast this money returned to you.Apart from this, there is also a net promoter score. It will help you see the readiness of employees to recommend your business to others. This is a very popular metric but it may not be appropriate for every business.The role of employees in shaping cultureOn the one hand, the culture is initially built by the founders and executives of each company. On the other hand, the role of people shouldn’t be underestimated.It’s vital to understand what your employees think about your company, how they apply your values, and how they act.Surveys, interviews, and simple observations will help you to see whether you are on the right way. Quite often open communication with employees helps managers define the need for changes.Feedback is important for executives to get insights. But it is also important for employees. When you ask for their opinions (and are ready to implement changes based on them), people see that they can be heard and understood correctly.Hiring: Practical tips for companies and candidatesHiring is often a balancing act. It’s quite a common situation when job seekers face rejection after rejection, lowering their expectations with each one. In the end, they are ready to take almost any position.At the same time, companies may also find themselves in urgent hiring situations where values and culture fit take a back seat. The goal to fill the role becomes their priority.This dynamic often leads to mismatched expectations and poor outcomes for both parties.Finding a company that aligns with your personal values and culture requires intentional effort. It’s not just about the job. It is also about understanding what matters to you. And with this knowledge, you need to seek organizations that share those values.The hiring process should not be static. Companies must continuously promote their culture, even when no positions are available. Internal ambassadors can help to share the company’s vision, values, and brand. Thanks to this, when the right position opens, the right candidates will already be familiar with the organization.For many candidates, financial pressures drive the need to prioritize income. However, it’s essential to avoid putting all your time and energy into work. This imbalance can lead to burnout and toxic environments. To reduce such risks, Nina recommended finding something that can help you renew your energy. For some people, it can be communication with family, or some hobbies, or traveling, or just watching Netflix.Wrapping up: Human connection as the highest valueThough building communication with your managers and co-workers is important, Nina highlighted that a company is not a family. Families provide unconditional support, whereas companies are professional environments with business objectives. Knowing when to separate from a role is vital to your personal well-being.At the same time, human connection cannot be underrated. Even if you see that you don’t fit the position but your goals align with it, it is still worth communicating with your potential employer about it. Though it doesn’t always happen, theoretically, positions can be modified or new ones can be created for people who can bring real value to the business.No machine or algorithm can replace the importance of human relationships. The hiring process is about humans connecting with humans and when these people share the same values, it can be the beginning of a very exciting journey for both parties.Yes, as we all know, technology is changing the business world today. But the same can be said about people. We are not only changing this space. We are also shaping it. And our future greatly depends on our decisions today.Want to learn more about the latest innovations that are coming into our lives today? Do not miss the next episodes of the Innovantage podcast where Max Golikov will invite new guests to talk about business, technologies, and people.

Have you ever dreamt about your own AI assistant that will know everything about you and will be able to do some tasks instead of you? This can become a reality even in the near future and that’s one of the topics discussed in the 9th episode of the Innovantage podcast. The podcast host and Sigli’s CBDO Maxim Golikov invited to his studio Maarten Verschuere, a data and AI professional with more than 20 years of experience in this sphere.Maarten spoke about his successful project Clever, which was later acquired by Zoovu Ltd, and his new startup MentX.ai. Moreover, he explained the power of emerging technologies and shared practical recommendations for entrepreneurs who are starting their business journeys.In this article, we will mention the key ideas voiced in this insightful conversation. But if you want to get more details, we invite you to our YouTube channel, where you will find the full video version. MentX: Is AI-driven knowledge sharing already here?During his career, Maarten worked in different countries in such spheres as data science and marketing analytics. He had the opportunity to see how these areas were changing and what new approaches were introduced as technologies evolved.In 2015, he founded Clever, a general data science company that later went specifically into chatbots. Why did it happen? Maarten explained that he noticed a serious gap in the market and decided to address it.But now the times are different and they require new solutions. With this idea in mind, Maarten started MentX which will take AI solutions to the next level in creating AI versions of real humans.As humans, we communicate and we spend a lot of our time at work and at home just sharing information with others. Now if somebody is not available for customers or internally, there is an obvious communication gap. People need to get information and when a particular specialist can’t talk to them, they have no other choice but to wait.With AI, such inefficiencies can be eliminated.What the MentX team is going to do is create a full AI version of an existing human. This AI version will have not only the look and voice of that human but also the knowledge and personality of him or her.Will this new version of yourself fully replace you when you are just too tired? Probably not. The first goal of MentX is to serve some specific purposes. For example, this technology can be used to create an AI version of a senior lawyer or consultant who needs to communicate not only with colleagues but also with clients and students or trainees. It’s obvious that these experts can’t be available 24/7. But it will be possible to unlock their knowledge to others and make it available around the clock.In other words, if an assistant needs a piece of advice to get through the first steps of some tasks, an AI version will be highly helpful.This idea sounds quite promising. However, Maarten admitted that the technologies for its realization are not here yet. He and his partners truly believe that they will be able to digitize all the knowledge and the personality of people. But it can’t happen now. According to their estimates, the development of such technologies will take around 2 years.Nevertheless, today, the team can run pilots for some specific components. For example, it is possible to create a video of somebody and then have that video version say anything with the voice of that person.A lot of the components that are required to create people’s AI versions already work within a certain scope. Now, MentX is trying to bring them all together.Can AI truly increase our efficiency and productivity?Of course, our AI versions won’t allow us to live forever or become superhumans. The soul and the creativity that we have can’t be copied. People will still have their unique value as personalities. However, such technologies can greatly save our time and help us (and other people around us) work more efficiently.AI definitely plays a huge role in the growth of our productivity. Nevertheless, that’s not something unique about artificial intelligence. It is absolutely true in relation to any technology you can only think about.Maarten explained that by our nature, we, people, have only basic physical skills. And we need to invent technologies and augment ourselves. For instance, to move faster people started using horses. But to transport themselves faster and go further, they invented cars. Later, they introduced airplanes. Now, businesses are working on inventing space rockets that could become available to a wide audience.AI is a big invention that can transform humankind. Actually, this transformation has already begun. If previously, traditional breweries needed to hire 100 people, now their tasks can be performed by only two specialists. The same tendency can come to offices very soon.With mass AI adoption, we will be able to do the same work at a faster pace and with fewer people.Does it mean that with AI implementation we can just relax and chill while all tasks will be executed by robots?Even if this option may look appealing, Maarten doesn’t think that people will do that. In his opinion, all these changes are just the beginning of another era. People will still be looking for new tasks and new jobs to fill our lives with new interesting projects.“AI will replace you”The name of this section, which coincides with the name of Maarten’s book, may disappoint you. Nevertheless, it’s not the best time to give up. The author of the book has a very clear explanation.Today AI can do a lot of things for us. It can recommend to us what to watch, what to have for dinner, what to buy at the supermarket, when and where to go on vacation, and many other things. These recommendations are based on the experience of the entire world. All the data that is collected by AI systems allows them to create optimized recommendations for everything in our lives. And if we blindly rely on all of them, we will simply become pets of AI.Yes, if people feel very comfortable because of not having the necessity of making decisions themselves, then AI will basically replace them.However, not everyone is ready for that. In his book, Maarten offers an alternative for those who do not want to be replaced. People need to accept the reality and start taking more responsibility for their own future and their own decisions.It’s also important to consider this situation not only from a personal but also a business perspective.If you have a business, you also have a lot of things to think about. Is AI going to become something that will wash over you, and transform your sector and your industry? Or are you going to be the one who is taking charge? If the second option sounds more appropriate, you need to define how AI is going to be used in your company and how you can use it to create value for your customers and employees.In this context, it’s very important to get over the so-called AI anxiety and transform it into an AI opportunity.Emerging technologies and chip developmentMaarten mentioned that we are entering the perfect storm of technologies that are appearing at the same time. The pace of development of new technologies has become absolutely crazy. And this thesis refers not only to digital innovation but also to the physical infrastructure and the hardware.For example, chips produced by Nvidia were critical elements in the growth of OpenAI and ChatGPT. Today, there are huge investments in chip technology as chips really fuel the next wave of innovation.In the conversation with Max, Maarten mentioned that he is greatly impressed by the progress achieved in data science in recent years. But while speaking about emerging technologies, he also highlighted the significance of edge computing. This approach presupposes that computation and data storage should be brought closer to the location where data is really needed. This is done to improve response times and save bandwidth.It means that when you are on Mars, your data shouldn’t be sent back to the cloud on Earth to be processed and then returned to your application on Mars.Of course, AI today differentiates itself from other emerging technologies. It has already started to bring money. That’s why AI is not about hype, it’s about creating value, and a lot of businesses have already realized this idea.Can AI help in solving real business problems?If you pay attention to the new products that appear on the tech market, you will see that a lot of them have AI in their names. But do all of them really rely on AI? Maarten admitted that quite often he notices AI washing cases. Companies just put AI on top of their offerings just to make sure that they are innovative. However, in the back end, we are just still doing the same things.For example, they used to have a database. Now they still have the database but can call it AI.Real innovations should have value and ground to be implemented.20 years ago data was the new oil and it still has value. You need to use your data to create value for your customers and for your business. The same should happen with AI.To better explain his vision, Maarten shared a case from his practice.When he worked at P&G, the company started to realize that they could lose their share in the washing detergent segment. It was necessary to find an approach to retain customers and avoid losing profits.Maarten’s team managed to use data in such a way that allowed them to create a retention model to identify the potential of every customer leaving the brand. By looking at some indicators and retail data, they could see the customers who could leave the brand and understand how it would be possible to retain them.Further analysis revealed that this model helped the company earn a million dollars, while all the investments in its development and implementation were close to $100K.It means that it had a 10x impact.Based on his experience, Maarten insists that for implementing AI, businesses should look for such cases when they could leverage 10x return on investments.However, that is possible only when you are solving real problems, when you define real pains and address them.That’s why while consulting businesses, Maarten prefers to talk not only to IT experts but also to other non-tech specialists, like marketers and HR managers. These professionals work with final solutions and communicate with people. They have a very clear vision of what can be improved.Businesses should focus their attention on how to implement AI to solve problems and to help their employees, instead of replacing them.How to implement AI: Real-life recommendationsYour preparations for AI implementation will greatly depend on the type of business that you have. It’s quite natural that a local bakery will need to deal with fewer tech requirements than a utility provider.But regardless of these differences, all businesses should have at least basic capacities for the introduction of new solutions. If you are going to use AI to make personalized product recommendations, your infrastructure needs to be able to support this functionality.When you rely only on legacy tools that were built 20 years ago, your databases will probably work in silos. It will be a serious obstacle.For implementing AI solutions that will work based on the data about your customers or your operations, your data needs to be in order.Among other tips on how to implement a good AI strategy, Maarten also mentioned the necessity of formulating the exact reasons for introducing AI and getting the right people on board.Another important step is to analyze the complexity of potential solutions. If the complexity of implementation is high, then this is not a project to start with. You should begin with easier initiatives to test your AI implementation potential.It could be also sensible to start with data projects, where your data can be cleaned and prepared for further implementations.Is it challenging to be an entrepreneur?First of all, when answering Max’s question about the challenges of entrepreneurship, Maarten said that he has tremendous respect for all entrepreneurs, regardless of the stage of their business development.Maarten explained that in his opinion being an entrepreneur is super hard.These people are creating something, they need to go from zero to one. This process is painful. It is impossible to follow someone’s instructions. It is necessary to follow your vision and try to make it work.“Don’t become an entrepreneur if you want just to get rich. Don’t become an entrepreneur just to have a cool lifestyle,” Maarten said.Very often entrepreneurs fail. But it doesn’t mean that they give up. However, when you are building your own business, you always have the choice to get out of the game or iterate.It can also be hard to get funds. Investors want to see a potential return on their money. If you have nothing at the moment, you need to make them believe in you and your idea. But when you raise money, you get a whole new set of problems. Now you have to deliver the results. You need to deal with other people’s money. So the pressure to deliver is even higher.The more money and the more customers you get, the more people you have on board, the higher your responsibility is.Though Maarten was talking a lot about difficulties, he also mentioned one more essential thing.In his opinion, having fun is a crucial part of the whole entrepreneurial journey. If a person doesn’t enjoy the process, is it really worth all the effort?Final wordBuilding a new business or transforming an existing one may not be the easiest task. But when you know why you are doing that, everything makes sense. In the Innovantage podcast, its host Maxim communicates with tech and business experts. They share their experience and vision of how AI and other emerging technologies are changing the world around us and how we can benefit from this transformation. If this topic is interesting to you, do not miss the next episode that will become available quite soon.

The Innovantage podcast is continuously expanding its horizons. While the previous episode was devoted to the experience of Lithuania in the AI revolution, this time, podcast host Sigli’s CBDO Max Golikov has invited Dr. Ott Velsberg to talk about the path chosen by Estonia.For the last 6 years, Dr. Velsberg has been serving as the Government Chief Data Officer of the country, which allowed him to accumulate unique experience in this sphere. Ott has been overseeing such domains as data governance, open data, artificial intelligence, data privacy, and others from both strategic and practical implementation perspectives. This gives him a comprehensive vision of everything that is related to data and AI within the Estonian government. And in his conversation with Max, he shared his insights.Check out the full Innovantage episode with Dr. Ott Velsberg here: https://youtu.be/fxhCXDkrggo?si=SLOXvyd7ohIbtpzjThe role of data and AI in Estonia’s economyEstonia is one of the examples of how digital governance can be run. But it is not going to stop where it is at the moment. One of the country’s priorities for 2030 is to build an AI-powered government.The focus is not necessarily on emerging technologies. The focus is on data itself and its value for the government, businesses, and society in general. Data plays an important role in delivering better services and making more informed decisions at different levels.According to Ott, in the data economy, AI is one of the key pillars in boosting the growth of the economy in general.Today Estonia already has one of the highest data economies in terms of percentage of GDP globally. It occupies the second position, just behind the United States.However, Estonia is the leading implementor of AI in the world, as Ott highlighted, not a developer.What sectors are the largest contributors to the AI economy in Estonia?Of course, such services as information and communication technology as well as connectivity are known for their significant contribution to the development of AI and data economy.But in this context, it is also crucial to mention some other domains, like manufacturing in healthcare, that are currently heavily investing in this field.Dr. Velsberg mentioned one of the studies conducted in Estonia. They evaluated the activities and approaches of companies where at least one-third of the teams were data specialists. Nevertheless, surprisingly, no specific data-driven approaches were detected. The reason is that just the presence of particular experts doesn’t guarantee transformations.At the same time, there are some domains, like agriculture, that do not hire people to solve any data-related tasks. Instead, they are outsourcing such services. In general, a lot of processes can be enhanced with the right application of data. If we are talking about farming, all efforts can become more efficient when farmers have enough valuable information about the characteristics of the soil and fertilizers that should be used. Outsourcing can help to address such tasks. However, with this model, agricultural businesses still do not have people who can continuously drive transformation and create the necessary environment for innovations.New times require new skillsOtt mentioned that today we all need to have elementary data and AI skills. The society we already live in affects literally everyone. It’s impossible to avoid its influence.If we look at the situation with domain experts, like data analysts, data scientists, data engineers, and data stewards, we will see that Estonia currently lacks around 13,000 specialists. Specifically within the government, the country is missing one-third of data analysts.The issue is that today it is not possible to train as many specialists as needed. However, this is not a local problem in Estonia. It is a global trend.Speaking about a wider labor market, Ott noted that new knowledge is required not only for specific data-related roles. Even such experts as project managers need to know, for example, what the difference between a typical IT project and a data science project is, what generative AI is, what an LLM is, what machine learning is, and so on.At the beginning of next year, the government in Estonia will be launching a data literacy campaign across the country.There are plans to introduce various topics related to data science and analytics to young students in schools and universities.The objective is to ensure 80% data literacy by 2030. This cannot be achieved without working with the whole society at different levels at the same time. There are always some elderly people who are not open to innovations. But the more they know about technologies, the less skepticism they will have. Talking about such topics is extremely important.Moreover, it is vital to organize various programs. A row of AI training sessions held by the Estonian government for different social groups gained tremendous popularity. All places for the course aimed at primary school students were filled just within a day.A good understanding of the value of technologies and data is required at the organizational level as well. Today there are a lot of companies at different stages of their maturity that work with data but they do not even understand what type of data they collect, who can access it, where it is stored, and why it is actually needed. Given this, can they efficiently manage this data and fully leverage its value? It’s highly unlikely.New challenges and new solutionsThe same is true about people at the individual level. Many people today have no idea what data about them is collected and how it can be further applied. Moreover, they do not even think about how they can benefit from this data. Max compared this situation with the early adoption of the internet. Users got a powerful tool but they didn’t know what exactly they could do with it.Ott explained that today’s initiatives aimed at the development of basic IT literacy in society are part of the European goals for 2030.Governments need to start talking about data and AI as well. In this context, it is vital to educate people about new services that will appear and that don’t even have non-digital counterparts. Here, it is also necessary to inform citizens about those groups of specialists who might become unemployed before it actually happens. It is important to mention the existing risks, as well as the factors that ensure trustworthiness and transparency.People should have a clear vision of how they can control the use of their data.For example, in Estonia, the government introduced the Data Tracker. Its purpose is to offer citizens access to a full overview of the operations conducted with their data. The country also has a consent service. It enables people to give the state permission to share their personal data with a certain service provider, for example, healthcare organizations.Today, the government is also working on increasing the accessibility of services and has been heavily investing in sign language and real-time speech recognition.Another project of the Estonian government is the development of the digital twin concept, but not in a typical sense. In this case, such digital twins can visualize different real-life situations based on available data and help the government stay proactive. For example, such solutions can be useful for foreseeing the changes in the labor market. The government is always interested in keeping the unemployment rates as low as possible. By getting insights into the possible changes, the government can organize various training courses and provide practical recommendations to those who are likely to lose their jobs in the next 6 months.Are there any risks associated with this? Definitely yes. First of all, people may be rather confused. Not everyone is ready to get such information from the government.Secondly, job losses are often related to the bankruptcy of companies. If the government publishes some info about a company that may go bankrupt in the near future, it can negatively affect its reputation already today.That’s why it is crucial to think not only about how to handle the data but also how to present any information to citizens.How governments should work with dataTrustworthiness is one of the key concerns across society. Ott shares some statistics.Women are more likely to not trust technology itself. Many of them are less active or savvy users than men.As for the generational differences in the Baltic states, the older generation trusts the government less, however, these people trust the private sector. With younger people, the situation is completely opposite. They don’t trust the private sector but they trust the government.The task for the government today is to stay proactive and not to wait for a person to reach out with some questions or issues. People are likely to believe more if they understand how and what the government is actually doing.In Estonia, there are some important initiatives that are designed to increase transparency in the AI and data sectors.Ott also shared that at the time of recording the podcast episode, they were preparing for the launch of the algorithmic transparency standard. Its introduction presupposes that everyone who has carried out an AI project within the public sector needs to openly clarify the goals and the logic behind it, as well as other important details.Moreover, every funded project needs to implement the Data Tracker so that all the information can be available on the government portal.One situation last year brightly demonstrated the interest from the side of citizens in getting access to such information. Around 450,000 Estonians looked for information related to the application of their data included in the population register when there appeared some concerns regarding the ethical side of its use.Estonian approach: Keep it simpleWhen asked about any specific approaches to building the data economy in Estonia, Ott stated that the main idea is to keep everything as simple and small as possible. Instead of large-scale projects, it’s better to start with small ones that address some specific problems.Dr. Velsberg warned: “Don’t overanalyze. Don’t overanalyze.”It’s necessary to listen to end customers, detect their pains, and offer solutions to them.According to him, that’s exactly what Estonia is doing. The country’s approach can be described as problem-focused, rather than tech-focused.There is no need to implement AI just because it is AI. It is necessary to identify the processes that can be changed and do this.A lot of AI projects today can cost between 60,000 and 70,000 Euros and their implementation may take just around a couple of weeks. But they can save hundreds of hours of people’s time.Countries are ready to invest millions of euros to analyze some technologies. But quite often it’s more efficient and feasible just to take action than to conduct endless research.EU’s AI Act: Will we benefit from it?Of course, the introduction of a regulatory framework has some pitfalls and controversies. Should AI have separate legal entity status? Who is responsible for its decisions when it misbehaves?But without any doubt, the legal framework for the use of AI and data is critical. It’s vital to control how data is collected and used, as well as to protect people’s rights.Nevertheless, the Estonian government has made a decision that they won’t regulate AI nationally and will rely only on European laws.One of the advantages of this is that the EU has a large global market and if companies that adhere to its regulations decide to enter other markets, they will have a great potential for this.Moreover, quite often, EU-wide regulation can also push countries to the introduction of a basic standard that will be applicable to everyone across all member states. It could bring benefits not only locally but also on the international level as well.Ott admitted that when he read the first version of the EU’s AI Act, he was extremely skeptical. The document included a lot of unrealistic requirements. The latest version has been greatly updated. It offers a lot of best practices and seems to be much closer to reality.Ott hopes that the Act won’t undergo changes in the next few years. Time is needed to understand how the rules work and whether it is possible to improve them.The AI Act has already downplayed or eliminated a lot of risks related to the use of AI. In some potentially dangerous situations, AI can’t be implemented at all (like social scoring which is a rather popular use case shown in many films). There are no reasons to be worried.The value of personalized servicesGovernments today are interested in using AI to deliver better services to citizens and facilitate a lot of processes for them. In Estonia, a lot is done to introduce proactive and personalized services.For example, even before a baby is born, parents can solve a lot of questions. For example, they can already give a name to their son or daughter, re-register a child to kindergarten, and get full information about the social benefits related to the birth. All this helps to save people’s time because time is the most valuable resource.The same approaches can be applied to various aspects of people’s lives, including military service and car registration.The government should understand the needs of people and adjust all the services to them.Very similar rules work in the business world as well.If you are able to provide services that your client actually needs in a personalized manner you are going to be more successful than those who provide something that people don’t actually care about.AI and data can bring a lot of benefits at different levels of our society. But only when they are properly used with the right goals. And that’s one of the key topics discussed with experts in the Innovantage podcast. If you want to learn more about it, do not miss the next episodes.

Today, when the whole world seems to be quite globalized, each country still has the possibility to build its own approach to various aspects of its development. And digital transformation is one of them.This episode of the Innovantage podcast is devoted to the way chosen by Lithuania and its achievements in the ongoing AI revolution. To talk about this topic, Sigli’s CBDO Max Golikov invited Dr. Linas Petkevičius to his studio.Check out the full Innovantage episode with Dr. Linas Petkevičius here: https://www.youtube.com/watch?v=-Jn98Bb_9iAAI ecosystem in LithuaniaLinas is the general manager for the Artificial Intelligence Association of Lithuania and an associate professor at Vilnius University with a focus on AI, deep learning (DL), and other technologies connected to it.The idea of combining these two spheres of professional activities may sound too challenging. Nevertheless, Dr. Petkevičius noted that he sees a lot of perks of this duo. As a researcher and lecturer, he needs to plan courses and supervise students who are writing the Bachelor’s theses in various applications, monitor the freshest research publications to get access to new ideas and techniques.From the side of the AI Association and his NGO-related activities, Linas needs to communicate with ecosystem stakeholders and bring them together through discussions and consultations.But all these things have a common ground. And that’s AI. It means that Linas not only can stay tuned with the research from the perspective of the academia but also from the perspective of businesses. Thanks to this, he has a full picture of the Lithuanian AI ecosystem.Lithuania is a small country. However, it successfully unites under one umbrella all the contributors to the AI domain, including academia, companies, startups, professionals, and enthusiasts. Today, a lot is being done to support innovations and expand the tech ecosystem. There are numerous engagement events, like hackathons and meetings, that help new ideas be heard.Linas mentioned that today Lithuania looks quite attractive to both local startups and international investments. Moreover, the country is interested in welcoming new projects and talents. It also invites alumni to come back after studying abroad. According to Dr. Petkevičius, given the ongoing conditions in the tech space, it is a good time to do this.How AI is perceived by the publicAI is changing what we can touch and see each day. But it is also changing a lot of fundamental things in how the world is functioning. Previously, it was governed by a capitalistic approach. If you had labor, you could create value by establishing a call center and it was your market advantage. If you had capital, you could build and operate factories and it was your way to create value.Now everything doesn’t look this way. AI has arrived. And now one programmer with a laptop can replace thousands of jobs by creating automation tools.This approach doesn’t fall into the categories of labor or capital. It is a completely new idea that we are not accustomed to.What is deep learning?Deep learning is known to be a subset of machine learning that relies on artificial neural networks to learn from data.According to Dr. Petkevičius, deep learning helps us modify some data, like images, text, or speech, and transform it into new dimensions in order to make it more informative.For example, such models can analyze an image and describe what is shown in what, what its general mood is, and whether it is blurred or not. Similar operations can be made with text. A DL model can read a paragraph and provide such info as the names of the companies mentioned in it, the key semantic information presented, the style of the text, the general context, etc.These applications can be highly valuable for businesses now. If they have an image, they can get its description which can serve different purposes.Key boosters and barriers for AI and DL researchDr. Petkevičius shared his own observations regarding how the interest in deep learning research from the side of students has been changing over the recent years.For example, 5 years ago, when students chose deep learning as a topic for their Bachelor’s theses, they needed to code a lot and had many other tech things.Today, this process is less complicated. Now they have access to ChatGPT and a lot of new products can be successfully built on its base.Thanks to this, it is not only easier but also significantly faster to test and implement new ideas. That’s why the level of students’ research projects is much higher now than it was 5 or 10 years ago.The models delivered by students can be really nice and clear for the general public. They may have real value and interesting use cases. But they are not reproducing the fundamental new knowledge, they are not based on the latest research. In this context, Linas mentioned the necessity to invite them to work with fundamental topics and experiment with them.However, sometimes such experiments can be quite depressing, especially for students. To get one successful model, sometimes you need to try a lot of things, test hundreds of combinations, but all these efforts will stay invisible. It’s impossible to create an excellent model for business or academic purposes from the first trial.Another barrier that may discourage students from DL research is that we all want to have one big model for everything. But at least at the moment, it doesn’t seem realistic.It is much more sensible to have smaller models designed to deal with a limited number of tasks.The gap between academia and real-life projectsWe had a spike in technology breakthroughs in 2015 when various image recognition models were developed. There were significant advancements in different generative tools in 2017. Today, we also have the language models which became really innovative after they started to produce interesting results in 2018.At that time, the only problem was that all those technologies and all those breakthroughs were in academia, in R&D. The general public didn’t have the final product to touch it and to understand its possibilities.Nevertheless, this issue is not a new one. For centuries, academia has been leading the introduction of fundamental models and theories. When innovators were looking for ideas that could be applicable in practice, they could take some academic research and start working on real products that would be later available to customers. This process could take 5 or 10 years. And nobody could be initially confident in its success.The same is happening now. From the applicational point of view, academia and businesses are joined. A lot of companies have AI teams that work with academia to test and implement new ideas faster and reduce the time gap between research and production.Changing habitsIt’s breathtaking to analyze how the adoption of technologies is related to what we are accustomed to.Today, there is an opinion that computer literacy classes can be useless for modern children. They actively interact with smartphones, while using traditional computer mouses and typing requests with a button keyboard look quite archaic for them.Linas mentioned an interesting example. He can observe that young students quite often use their smartphones to google something, even when they are sitting in front of computers during programming classes. That’s the power of habit.Have you noticed that today for many people it is much more convenient to write messages instead of making voice calls, especially when they are somewhere outdoors? It happens because this already seems more natural today. And it explains why some technologies are still not widely adopted, despite their potential value.For instance, speech-to-text models produce reasonably good accuracy these days. They can be very useful for a very wide audience. Even while driving, a person can just talk to AI and get a full, well-formatted document as a result. Nevertheless, for the majority of people, it is still easier to type their texts.Though the use of VR glasses, like Apple Vision Pro, could help us collect a lot of necessary information about objects and processes, their adoption is slowed down for the same reasons.To implement some innovations in our lives, we should change our behavior first.How to choose the best ideasIn his dialogue with Linas, Max asked him about the ways to decide on whether new technology is good or which startups are interesting enough to support. Unfortunately, unless you know the future, without testing, you wouldn’t be able to guess with 100% accuracy which idea will be a successful one.According to Linas, at the AI Association, they organize regular AI meetups which allows them to invite as many new ideas and new speakers to talk about their startups as possible. As a result, the community can get a lot of valuable and diverse information delivered by different people with different backgrounds.Linas noted that the more ideas are voiced, the better. If we have just one or a few options for services, we just get accustomed to them and do not want to have changes any more.But if we have a new app every week, we try it, we start searching for a better one. This fosters new developments and helps to achieve better results.Education for the public is also very important in building and adopting new ideas and products.AI in healthcare: Do we really need it?Dr. Petkevičius noted that there are many tasks in healthcare that are really suitable for automatization. For example, it is possible to introduce very efficient algorithms that could analyze various images, like CR or X-ray scans, and quickly provide feedback regarding any anomalies or potential health risks. Here we have a huge time-related benefit. If a radiologist needs around 10 minutes to analyze a scan and make a conclusion, AI will do it in milliseconds.Moreover, AI models can greatly help with such complex domains as tissue and cancer recognition. Nevertheless, in the end, it all comes down to the amount of data.The more high-quality data you have, the more complex and efficient models you can create. Some big hospitals in the United States have 100x more patients than any hospital or lab in a small country. As a result, they have 100x more data for validation and further use.But can we expect to have fully automated medical consultations soon? On one hand, it may seem that with the introduction of AI apps, we will greatly boost the efficiency of many processes. AI can’t get frustrated when it needs to repeat the same things again and again. It doesn’t have emotions and the quality of its consultations can’t be worse because of its tiredness.On the other hand, it can hallucinate, it means it can provide information that is factually incorrect or misleading. This makes it obvious that for final decisions, the human touch is still needed. While AI can work with data, doctors have their own real-life, continuously evolving experiences. They have the latest information about how medications work with different symptoms and often can analyze many more factors simultaneously than AI can.However, in many countries, there is a problem with making appointments with specialist doctors. Given this, AI-powered apps that can provide at least preliminary diagnoses and recommendations to people can be of great help.Urban planning and generative AIUrban planning can be named among the spheres that can enjoy the biggest benefits from AI implementation. In this domain, AI tools are absolutely not harmful and the risks related to their use are the lowest. Thanks to Generative AI, you can easily get around 100 possible demos of how you need to reconstruct a park, for example.You can select different options and modify them. That’s why Linas highlighted that urban planning can be a very good area for experimentation with AI.AI regulation: Good or bad?Currently, AI regulation is a sphere that is full of uncertainty. For instance, in the US, there is still no comprehensive federal legislation that governs the creation of AI tools and specifically prohibits or restricts their use.The European AI act entered into force on August 1, 2024 (Please note that the Innovantage podcast episode with Dr. Linas Petkevičius was recorded before that date. Nevertheless, the core principles and ideas proclaimed by this document were already known). This marked a crucial step towards forming a comprehensive and ethical framework for AI in the region.According to the chosen line of AI regulation, there is a classification of applications of this technology based on how they affect us as a society. For example, some of them do not have a direct impact on people. That’s why, it is not needed to introduce any specific rules to regulate them. Some applications should be completely forbidden due to their damaging power.There are also some cases of AI usage that have the possibility to impact us and/or we could get hurt after their implication. In other words, such cases are associated with high risks (for example, like all applications of AI in healthcare). They should be strictly regulated and there should be requirements for their testing. Without any doubt, such types of AI products should be monitored. But without a clear vision of how they should be tested, how they should be certified, all this leads to a lot of controversies.The implementation of AI regulation also results in additional bureaucracy and expenses for startups and companies that do research in high-risk domains.Today, we can watch how competition between the largest US AI companies, like Meta and OpenAI, is gaining momentum. They are continuously improving their models so that they can demonstrate better and better results.As for the European region, at the moment, there aren’t any large A-tier AI firms. And an additional burden of regulation won’t create favorable conditions for new projects.Today, it can be very expensive and risky to create new models from scratch. That’s why in spite of all the benefits that regulation can bring to the space, it can also freeze innovation. Just imagine, how many ideas won’t be transformed into real projects, if it is prohibited to keep personal data online.Bottom lineToday it has become absolutely obvious that AI is much more than just a buzzword. It has real-life applications and its value for various domains is continuously growing. While different experts may have different opinions regarding the development of this technology and approach to working with it, the majority of them agree that only in cooperation with each other and with communities, academia and business can conquer the highest peaks. The same idea was highlighted by Dr. Linas Petkevičius. And Lithuania is a great example of the country where this approach works well.Want to learn more about how AI is transforming the business world? Follow us in order not to miss the next episodes of the Innovantage podcast.

In the Innovantage podcast, Sigli’s CBDO Max Golikov talks to tech experts and entrepreneurs about their vision of how artificial intelligence is transforming the world. The 4th episode covers much more than that. Leesa Soulodre, who was the podcast guest, explained not only the role of technology in modern society but also the role of society in tech progress.Check out the full Innovantage episode with Leesa Soulodre here: https://www.youtube.com/watch?v=D5oANROV8X4&t=1373sLeesa is the founder of R3i Group and the Managing General Partner at R3i Capital, a deep-tech cross-border venture capital firm that focuses on AI and sustainable development.Deep-tech startups: Key pitfalls on their wayLeesa’s firm helps projects connect to capital, customers, and non-dilutive financing that lets startup founders keep full ownership of their companies.Today, founders who work in deep tech (or in other words, who build projects that are based on high-tech innovation or significant scientific advances) traditionally face three main challenges.A commercial challenge, or commercial Valley of Death. That’s the period when a startup has already begun operations but hasn’t generated revenue yet. Founders need to get over this period to make sure that their product does what it says on the tin and has value.A technical challenge. It’s important to demonstrate that a product is capable of consistently performing in the same way every time so that it can be safe for the person to use it.An ethical challenge. This challenge is related to the fact that everything that we use today almost always doesn’t have a kill switch. Therefore, inherently a product needs to be safe in its provision.How to make sure that AI developments are safeWhile talking about the safety of using AI products, Leesa recollected some other well-known cases. The proliferation of Airbnb made everybody’s homes available to guests. This led to the need for the implementation of trust and safety teams. With the growing popularity of taxi services, everybody’s car can be used as a taxi. This again highlighted the demand for such trust and safety teams.What do we have to deal with in the case of AI? The situation may look rather alarming. In fact, deep neural network compression technology could be used to kill more people faster and with less energy than other technologies.And if you consider that every AI product can be used for such a purpose, it becomes obvious that we need to maintain this notion of trust and safety teams. This is vital to make sure that our technologies are not misused for unintended purposes.Any AI organization with as much influence as OpenAI has that is not going to invest in trust and safety teams will face significant legal and regulatory hurdles. Moreover, such companies will have even more issues with further growth and innovation in the future if they don’t gain implicit trust from their user base.Regulation in the AI spaceWhen it comes to the regulation of tech companies, there always have been some controversies. One of the main reasons for this is that regulations don’t tend to catch up fast enough with what tech companies are doing.In the discussion of this aspect with Max, Leesa mentioned that she sits on the board of the AI Asia-Pacific Institute. She communicates with representatives of the governments in the region. The governments want to build safety rails for technologies, and AI in particular. But there is a significant barrier.Let’s take Singapore as an example. The absolute majority of the registered Generative AI companies are just starting their business journeys. They are at their seed or pre-series A stages. This means that quite often they do not even know what they have on the tin. They do not know the value of their products. That’s why it’s natural that they are not ready to invest in regulatory oversight. Given this, there is no sense asking them to do that.Leesa believes that it will be more sensible to build guard rails into the fabric of the major technologies that underpin new products and solutions created by startups.For example, many GenAI companies are building their tools on the back of the technologies developed by OpenAI, Microsoft, or Amazon. So it makes more sense to start with these tech giants. They need to comply with regulations first.Is the use of popular LLMs the key to success?While talking about mature AI technologies that startups can rely on, as an example, Leesa mentioned Hugging Face. It is a versatile platform that is widely recognized for its open-source repository of multiple large language models (LLMs).Leesa’s VC firm works a lot with startups. Around 2,000 projects claimed that they were using Hugging Face. At a close investigation, it was revealed that only 200 were truly using it. And only 16 were fundable in the opinion of R3i’s experts.Today there are a lot of players with similar offers. Leesa noted that both open-source and commercial models, like ChatGPT, can be a good option for new technologies. But here, it’s vital to understand that they serve different purposes. For example, ChatGPT is perfect for converting high volumes of always the same automation tasks.As an investor and technologist, Leesa is interested in finding technologies that will work as efficiently and safely as possible and bring the highest value.She said that she doesn’t invest in ChatGPT-like solutions. She looks for applied AI technologies around critical infrastructure and highly regulated industries. The projects that are the most interesting to her are those that can bring tangible results. They can power transformation from point A to point B in such domains as smart cities, energy, healthcare, industrial manufacturing, water management, agriculture, mobility, space safety, security, surveillance,For instance, she mentioned that her VC firm often invests in technologies for renewable energy. Already now, it is a highly regulated sector, despite being comparatively new.Deep tech investing: When is it a good idea to support an AI project?In the discussion with Max, Leesa mentioned that today there are a lot of AI-related projects that may look quite appealing for investors. But in reality, they may turn out to be a mouse trap.Working with the deep tech industry, Leesa prefers to invest only in those projects that have deep scientific research and technological invention behind them, which can be proved by a patent pool or a data mode. And if a project has a data mode, it should be its own, not the one that Microsoft or OpenAI possesses.But what are these things? And why do they matter?Leesa explained this with real-life examples. When scientists at the university invent something, they need to protect this from being copied or misused. Almost always in such cases, they can apply for a patent that will protect an idea or an innovation. If somebody else wants to use this innovation, they will need to obtain a license.However, Leesa warns about one serious challenge related to patents. When you publish a patent, everyone can learn what it is about. Unfortunately, at the moment, patents, especially in the software development industry, are not protected well enough.Patents themselves can be viewed as assets. Even if a project fails or the development of the technology is frozen, founders will still have a patent that can be further sold.As for data, it also can be monetized. If your company is carefully collecting, identifying, classifying, and tagging data, you (or somebody else, who will get access to it) can use it to create new products or power the existing ones.Generative AI for patents: Can we trust it?While talking about the capabilities of generative AI, Leesa stated that it is fantastic for ideation, and especially brainstorming. Nevertheless, it’s vital to understand that such models are hallucinogenic. It means that they can provide wrong or irrelevant answers. It may happen because the training data was incomplete or biased. And that is just a bright demonstration of the “Garbage in, garbage out” principle. Moreover, hallucinations may happen because AI models often lack constraints that can limit possible outcomes.That’s why we can make the following conclusions from such a situation. First of all, we always should be very careful and attentively check whether the received information is true. And secondly, despite the advancements in GenAI, we still need human creativity, empathy, and ingenuity. That’s what AI can’t ensure at the moment.Innovation timing: Cinderella effectIt’s not a good idea to come to the ball too early, as nobody will turn up at that time. But you also shouldn’t come too late as you will miss all the fun. You should be just in time. That’s why before introducing a new technology, it is necessary to analyze whether the market is ready to receive it as well as to consider the key barriers to its adoption. The psychosocial aspect is important. People should trust you and your solution.It’s vital to listen to different opinions to detect possible unintended consequences that may stay unnoticed for founders. When a team is working on a new product, they have only one perspective. But when you are building something new for a community, you should know what impact your innovation will have on it. It’s also worth mentioning that the impact on one community may differ from the impact on another one.It may sound surprising but in many cases, it will be very sensible to listen to children as well. Today, there are even some tech events for kids. And that is a very good trend. One day they will become active users of technology. That’s why their voices, their questions, and their doubts also have value.Speaking about the technologies that Leesa has invested in, she mentioned a couple of examples. She described them as absolutely revolutionary from the perspective of activation and implementation.Quantum Brilliance. The company works on room-temperature diamond-powered quantum computing. In other words, thanks to the use of synthetic diamonds, quantum accelerators will be able to work at room temperature. Though the history of this project is just beginning, it promises to bring quantum computing to a wide audience and make it everyday technology. This approach will be able to revolutionize every facet of a smart city, including security, drug discovery, material science, data operations, etc.ViewMind. That’s a brain health company. Its technology can look into your eye and capture millions of eye movements in a single view over 10 minutes. Based on this, it can determine with a very high degree of accuracy what level of degeneration you have or are likely to have in your brain. Such examination can help to manage diseases like Alzheimer’s, dementia, multiple sclerosis, Parkinson’s, or even post-traumatic stress for a soldier. This technology can demonstrate which area in the brain is affected and help to deliver personalized treatment. These types of technologies can absolutely change our lives for the better. They can move us from what we call treatment to the prevention and prediction of diseases. In the case of healthcare, such an approach is of great value.The most promising, value-based technologies should do something at least slightly better than it is done today and can greatly change the way we perceive something. For example, massive carbon emission reduction technologies change the way we think about the use of energy and water.What is the greatest threat to economic growth?While talking about new technologies and economic prosperity, Leesa said that one of the biggest concerns is piracy, both physical and digital.Piracy is one of the factors that can affect supply chains, steal jobs, and put economic development under threat. For example, when digital versions of books are provided for free, people who contributed to their creation lose their wages.Nevertheless, despite a huge negative effect, sometimes it is possible to detect some positive sides. This way of distribution can play an important role in the digital preservation of some media that no longer generates profit but could still be valuable in terms of history, art, or culture. Moreover, this can open new opportunities for those who have limited access to legal distribution infrastructure.How to decide where to investDifferent investors may apply their own methodologies to their decision-making process. Leesa shared that at R3i Capital they also have their own philosophy when it comes to choosing projects. One of the most important things that they pay attention to is the team.To avoid unconscious bias, R3i Capital relies on an AI engine built in cooperation with Hatcher. As a result, every team goes through the same filters and the result of such evaluation is as objective as possible.Thanks to this approach, absolutely everyone has the same chances. Such a system allows the VC firm to give voice even to those groups that are often ignored, like women and minorities.Moreover, it’s very important to analyze how fundable the company is and what its likely impact is. According to Leesa, while doing this, it’s highly required to keep 100% transparency. This will ensure the desired trust between founders and capitalAfter all, capital markets do not need to be brutal. They should be fair instead. This will help to achieve a win-win interaction.Why does sustainability matter?Leesa explained that with its investments, R3i supports tech companies with a tangible ESG product impact. In other words, they focus on products that prioritize environmental issues, social issues, and corporate governance.However, investors sometimes say that they do not care about the environment and sustainability, they care about money.But how can a healthcare product not improve patients’ lives if it is a good healthcare product? The same is true about energy, cybersecurity, mobility, and other industries.Becoming more sustainable, environmentally friendly, and socially valuable doesn’t mean getting less money. In fact, often, it can even mean more money. This can be explained by people’s willingness to pay for the things that can enhance the quality of their living.If an offered product harms people or has massive negative consequences, society won’t trust it.As venture capital firms are interested in long-run outcomes, they try to make bets on winning technologies. Sustainability businesses that focus on social and environmental effects are definitely among them.Winning together, not aloneAt the end of this discussion with Max, Leesa shared her thoughts about the role of society in innovations. One of the key recommendations that she can give to everyone is to be more empathetic to each other and common problems.Sometimes when founders can’t get financial support from governments or corporations, they can receive help from other people who care about solving the problems addressed by their projects.To achieve success, it’s very important to take the next first step. And we can’t do it alone.At Sigli, we share this vision and that’s one of the reasons why we create the Innovantage podcast episodes.If you are also fascinated with the capacities of AI and other emerging technologies, as well as their power to change the world, stay with us. New inspiring ideas are coming soon!

Today, when the AI revolution seems to be gaining momentum, for businesses it’s very important not to miss their chance to join it, or maybe even to head this transformation. At Sigli, we want to help you gain a competitive advantage by explaining how you can leverage the power of this technology.Check out the full Innovantage episode with Vasil Simanionak here: https://youtu.be/osnlRp0RMT8?si=qT6OYYcbyiVTI8OeIn the second episode of the Innovantage podcast, Max Golikov talked to Vasil, the Chief Delivery Officer at Sigli, a person who was captivated by AI long before it became available to a wide audience heard about. This sphere looked completely different from run-of-the-mil computing which made it extremely interesting for him. Being inspired by such films as Terminator and Star Trek, Vasil chose AI as his major.In a dialog with Max, Vasil shared his vision of the past, present, and future of Artificial intelligence and named the task that he will never delegate to AI.In our article, we’ve gathered the most interesting ideas from this discussion and we hope that you will find them quite insightful.AI: When everything beganIt would be completely wrong to say that AI appeared together with ChatGPT or 1–2 years earlier. In reality, some products powered by AI of this or that kind were developed quite long ago.The first expert systems were delivered around 50 years ago and they already represented an example of a very narrowed AI. Of course, their capabilities, as well as use cases, were rather limited.For example, such systems could have been used by a lawyer in some specific cases. Lawyers often need to ask standard questions to their clients, like the place of birth, the date of birth, the place of residence, etc. Based on the answers to these questions, an expert system can prepare a document that will be further submitted to some authorities or used for other purposes.So what are expert systems? They can be defined as early forms of AI that rely on a set of rules provided by human experts to make decisions or solve problems within a specific domain.The development of these solutions is related to usual coding stuff because such things are based on conditions like “If something — Then do something”. The main task and challenge in this case is to define the right rules. This means that human experts who work on these rules should deeply understand the specificity of all the related processes.Is ChatGPT an example of AI?The next stage of AI development is something that is considered to be AI in our modern understanding.While expert systems were difficult to understand for the general public and they had only specific narrow use, with ChatGPT-like models everything is different. They have gained enormous public attention and they are available to everyone. These solutions allow users to input queries and get clear results.While talking about that kind of system, in the majority of cases namely ChatGPT will be mentioned and that’s an example of excellent marketing and branding.The majority of people definitely consider ChatGPT to be AI. But is it true? While talking about that Vasil highlighted that the correct answer depends on our perspective and exact understanding of artificial intelligence.On one hand, large language models (LLMs) do not have common sense but they can process data. They are built on neural networks that mimic the human brain.A neuron has, for example, two inputs and a single output. If the first input is triggered, an output will be triggered. If the second — an output won’t be triggered. In networks, neurons are put in millions of layers. Users need to make an input and wait for an output. That’s how they work.When it comes to deep learning with LLMs, we do not define the underlying model to process this data. We just define a kind of infrastructure with the neural network where we have a lot of neurons and they are interconnected at different layers.We throw data and expect the result. But even a creator of this model has no idea how an LLM will answer.Due to the huge media influence, today these ChatGPT-like solutions are widely believed to be true AI despite some limitations in their capabilities.Basics: What is AI?AI is a huge set of everything related to something that machines can do quite similar to what humans can do. Of course, people can calculate but a calculator is not an AI solution. So we can say that in the context of AI, machines should do something as well as humans can or maybe even better.Despite all aspirations around AI, it is still a tool, not a different species or something like that.Different levels of AIToday, we can define several models (or levels) of AI. They differ from each other not only in their functionality but also in how they deal with data. Let’s briefly summarize them.Expert systemsAs described above, expert systems do not actually work with data. These systems are nice straightforward tools but they do not provide you with the impression that you deal with intelligence.ML modelsML systems work with some data but there are no strict rules. Engineers and analysts define the model of how this data should be gathered and processed. So we have control over how the solution will work with our data. We throw this data into this model and we check how to use it.A good example here is an ML-powered app for the real estate market. You can input different parameters like the size of an apartment and its location, while an app calculates the price depending on the parameters.Large language modelsText models are the simplest ones of this kind. They operate on the text input and can convert this text into a new one. Here, their work can be compared with the work of programmers who need to convert requirements into code.When an output offered by the model is not good enough, a user can provide feedback. In such a way, a model can be trained to ensure better outputs.Moreover, there are a lot of talks about the quality of data used for training and their origin, such as, whether they were obtained and used legally or illegally. However, there is still no single opinion on that.Will humanity be killed by AI?That’s one of the questions that may sound really controversial and sometimes even a little bit naive but it’s really interesting how AI experts answer it. Vasil provided a quite worrying reply. He said that everything depends on our behavior. Nevertheless, it’s not a reason to look for ways to be as good to AI as possible in order to survive. It’s just a reason to study this aspect a little bit deeper.According to Vasil, there is a possibility that AI will exterminate humanity and there is also a possibility to see a dinosaur outside. But still, it is just a possibility.Our future, and our chances to stay alive:), will depend on how AI-powered solutions, including LLMs, will be designed and how we will use them.If we let any ChatGPT-like solution interact with the internet, it will be able to perform rather complex tasks. For example, it will be able to start a website, buy a domain (if you give it some money), and create a no-code or low-code platform.Even an LLM can interact with the real world and actual AI can greatly mimic a human not only in text conversations but also in live streams. If you have ever seen videos with lifelike talking faces generated by Microsoft’s VASA, you know that they can be very convincing.So can AI overtake the world? Theoretically yes. But only if a human lets it do this.Day-to-day applications of AI for actual businessesIn the conversation with Max, Vasil named several examples of widely adopted business use cases of AI.Content generation. AI can be also applied in numerous situations when it can take some input from the user and create some kind of content based on it. AI can compose a good text for your email even if you have just a couple of bullet points.Summary creation. AI can be a great helper in ingesting the content that was created by someone else. For example, let’s imagine that you have a 20-page PDF file and you need to get a general understanding of its content, how much time will you need? What if this document contains 200, 2000, or 20,000 pages? AI can process it and offer you a quick summary much faster than any human can. What is even more surprising here is that for AI, 20,000 pages and 20 pages are just the same.Support services. AI doesn’t get tired, it doesn’t get distracted, it doesn’t have bad days. It has no emotions — and it is its win part. That’s why you shouldn’t hesitate to ask as many questions to AI as you have. It won’t be annoyed. Vasil admitted that in his everyday work, he also does this way in order to get as much relevant information as possible. When tested with humans, AI turned out to be more polite and tolerant. That’s why AI-powered apps can be a good choice for first-line support services that deal with general issues and common queries before proceeding to specialized help.AI is always willing to help and can reduce the time to answer to a client. However, in this context, it’s important not to omit a financial factor. If you want to get almost real client support that will function practically without human participation, this will turn out to be more expensive than hiring human specialists.How much does it cost to implement AI?The cost of such projects can greatly vary based on various factors and parameters. For example, the basic infrastructure for models like ChatGPT represents a huge number of graphics processing units or GPUs. This specialized hardware is essential for processing complex computations, as well as training and running AI models.That’s why it will be necessary to calculate the cost of GPU rental services provided by Nvidia or Microsoft, for example. They have different subscription models that can address different needs.Moreover, you can opt for on-premises infrastructure and locate all the required software and hardware resources within your physical premises. This model will be also associated with some additional expenses.If we turn to the use of AI models, here, we will also have various scenarios.Vasil noted that in the case of using a commercial model when you do not need to train it, the cost of one query will be a couple of cents. However, when you need to train and finetune your solution, it will be a completely different story. The price will be significantly higher and it’s very challenging to define it.It’s also crucial to bear in mind that with LLMs, you can’t expect a 100% correct result for every query. That’s why to get the desired outcome, several interactions may be required.In any case, the principle of quality-ratio principle works here quite well. The bigger your investment is, the better result you can expect. However, you should admit the fact that it won’t be a human result. Given this, businesses should find a balance between the amount that they are ready to pay and the quality that they will accept.Future of AI: Will it replace human experts?While talking about the future both Max and Vasil agreed that technologies are changing too quickly. It’s very hard to make any predictions for more than 5 years.However, according to Vasil, in the near future, ChatGPT and similar solutions can become great personal assistants. The use of such assistants can go much beyond purely business applications. For example, they will be able to check the health of users, send them reminders, and fulfill a lot of other tasks that will make people’s lives better,Another interesting and highly promising sphere of AI use is communication which is highly important in business.Let’s admit that even when speaking the same languages, we all have different understandings of some things. AI-powered personal assistants can make sure that our thoughts can be perceived by others in a good way.ChatGPT-like systems will be able to translate our ideas into bigger definitions that are more comprehensive for others. They will serve as bridges between people as they can translate not just words one by one. They can translate what is really said.That is a positive side of their implementation. Nevertheless, there is a negative one as well: some translators can lose their jobs.When a human is better than AI?One of the key issues about AI highlighted by Vasil is that you can’t always check whether ChatGPT offers you something that is true or not. That’s why according to him, it’s definitely not the best idea to rely on AI in explaining something to children. Here, a human is an undisputable leader (especially, when it comes to your own child).Of course, there are solutions like Google’s Gemini. In this case, answers are googleable and you can see the source of information. Nevertheless, AI can’t fully understand the context in which a child can pose this or a question. Moreover, human interaction is something that we all need.What skills are vital in the AI era?During their discussion, Max and Vasil also touched on a very important topic about the skills that are required today.Earlier, teachers and books were the sources of truth for the young generation. Then, the internet joined this list. Now, everything is quite unclear.What sources can be trusted? Whom can we believe?That’s why for a new generation, it is very important to develop an ability to check the source of data and understand whether it is trustworthy. A human can be good at some things but can be completely wrong in others. Given this, it’s crucial to have critical thinking and see whom and when we can trust.While talking about the value of AI, Max and Vasil also highlighted the importance of human connection and personal touch in communication. These are something that we should preserve even in the era of AI and significant digital transformations.If you want to learn more about AI, its current role for businesses, and its future prospects, do not miss our next episodes of the Innovantage podcast hosted by Max Golikov.

AI has become a buzzword. But do we really know a lot about it? Can we fully leverage the new opportunities that it brings to us? To dive deeper into this topic and to make this space more transparent to everyone, we’ve launched the Innovantage podcast. In the series of episodes, Sigli’s CBDO Max Golikov will talk to AI experts who will share their professional opinions on how AI is transforming the world around us.Our first guest is Dominik Lukes, System Technology Officer at Oxford, who runs the Reading and Writing Innovation Lab. Dominik has been exploring the potential of artificial intelligence since the early 90s, long before the world got familiarized with ChatGPT.In this episode of the Innovantage podcast, Max and Dominik discussed the impact of AI on the education sector and its potential to revolutionize the academic environment. Moreover, they touched on the basics of generative AI, the working principle of LLMs, and even the probability of an AI apocalypse.Check out the full Innovantage episode with Dominik Lukes here: https://youtu.be/7b4v6xnRDLI?si=8B4e_zuhnsKyrPwgHave no time to watch it now? We’ve prepared a short summary for you!Key terms that you need to knowTo begin with, let us briefly explain the main terms that are related to the topic under consideration.Any AI tool is based on a model and a model is a set of parameters. Namely, these parameters ensure that if you feed something into a model, it will give you something back.The models that are used in ChatGPT and similar solutions are models that generate language.What are LLMs?But what does it mean when we say “Large language models” (LLMs?) What makes them large?A free version of ChatGPT that is available at the current moment relies on a corpus of about half a trillion words or thereabouts, which is an enormous number. As for GPT-4, OpenAI hasn’t revealed precise figures. But when Meta released a new large language model called Lama3, they said it was pre-trained on over 15T tokens that were all collected from publicly available sources.The bigger the corpus used for pre-training is, the higher quality you can expect.There are also parameters that should be applied to make models work. There are some small models with 8 billion parameters, while large models have hundreds of billions.Why do you need to pre-train AI models?An interesting thing that was an important breakthrough in AI is that it is not necessary to train AI for every single task separately. You can take all these 15 trillion tokens and pre-train a model with some basic cognitive capabilities.After pre-training, it’s time for fine-tuning on top of that which will make your model do other things. The companies are constantly fine-tuning the models. That’s why the models are changing. For example, there is a thing that worked last month. But it may not work this month.To achieve the desired results the data used for pre-raining has to be clean, has to be carefully selected within the abilities of your algorithm. Your model has to be pre-trained and then fine-tuned for a particular purpose. And that’s one of the things that will make your solution work better.How do AI models work?The work of models can be compared with a regression curve, which is kind of a prediction curve. While there is an opinion that such models work on frequencies and occurrences, that’s true. What they have inside are weights and relationships.Dominik compared such models with semantic machines. So they are semantic in the sense that they understand relationships between things, but they’re not semantic in the sense they don’t understand the world outside themselves.GPT: What is it?Have you ever thought about what this abbreviation can mean? Actually, these three letters stand for what we’ve just explained about the work of such models.G is for Generative. It means that the model is capable of generating text.P is for Pre-trained. It means that the model should be pre-trained on a large corpus of data for learning patterns, grammar, facts about the world, and getting some reasoning abilities.T is for Transformer. This refers to the underlying architecture of the model used for natural language processing.AI hallucinationsIf you have ever worked with LLMs, you’ve probably noticed that sometimes they can provide inaccurate answers or “invent” something that really doesn’t exist. It means that these models can still hallucinate despite massive improvements.It can happen because models are trained on data. They learn to make predictions by finding patterns in the data. Nevertheless, due to biased or incomplete data, your AI model may learn incorrect patterns which will result in wrong predictions.How to make AI work betterUnfortunately, AI models can’t teach us how it is correct to communicate with them. And let’s be honest, interacting with AI is not just the same as communicating with a person.You should be ready for a ”rollercoaster”. It means that sometimes AI tools can go much beyond your expectations, while sometimes its outputs may be disappointing for you.To achieve better results, you should experiment, try different prompts, and elaborate your own approach to make AI solve your tasks.Not by ChatGPT alone: AI-powered tools that are used nowWhen ChatGPT was made publicly available in November 2022, it caused enormous hype practically immediately. Let’s be honest in mass perception, ChatGPT has become a synonym for generative AI. Nevertheless, that’s far from being true. Today there is a huge number of various tools, the functionality of which can greatly differ from what ChatGPT offers.First of all, you can start your familiarization with AI with the so-called Big Four. Apart from ChatGPT by OpenAI, it also includes:Claude by Anthropic;Gemini (formerly known as Bard) by Google;Copilot by Microsoft.People take these popular models and use them to build different tools.For example, Elicit is such a tool that can help you with your research. It can search for papers and extract information from them. Of course, you still will need to check it but you will get a really good draft.There are also projects that leverage the possibilities of the released coding IDE of GPTs. It allows people to create, for example, custom bots within ChatGPT or Copilot.By using the APIs, it is possible to build solutions outside of these platforms.According to Dominik, currently, we are at the stage where everybody is trying to see what AI can do for us now. But also we are starting to explore what it can do for us in the future and what are the possibilities.Such a highly respected educational institution as Oxford is also actively discovering the potential of AI, along with the rest of the world. Dominik shared that they are experimenting with ChatGPT, its enterprise version, integrations with Copilot, as well as other innovative tools powered by AI.In this case, for researchers, it’s highly important to understand what students think about various solutions, what they find useful, and how they can benefit from the integration of AI into the learning environment.Dominik also shared his personal thoughts. According to him, Claude is a good tool for educational purposes. It can deal with long context. It means that you can upload the entire academic paper and ask it to provide you with a summary or to find some specific information in the text. This feature makes Claude different from ChatGPT. And it can be highly helpful not only for students or professors but also for businesses.Homework is dead. But what about education itself?When it comes to education and the changes that AI has brought to it (and will bring in the future), a lot of people are concerned about the possibility of checking the level of students’ knowledge. And their position is quite clear.For example, earlier the format of home exams was rather popular. Students received tasks and were asked to do them at home. Now, when we have so many AI-powered tools at hand, such tasks can be fully useless.It’s obvious that you can no longer pretty much trust that all students who will hand in their essays have written their works entirely on their own. Such things as composition, spelling, grammar, and some other objective points that professors take care of can be checked and improved by AI solutions. Of course, they are still far from being perfect when it comes to research and in-depth analysis. However, that’s something that we have on the horizon.Some teachers try to apply so-called AI checkers that are expected to detect AI-generated content. Nevertheless, AI experts insist that today there aren’t any reliable tools that can identify such content with 100% precision. There are different big and small models and they generate content in different ways. Moreover, their outputs greatly depend on prompts. As a result, we can’t trust the results shown by these checkers.How AI is integrated into the academic process at OxfordBut how can professors motivate students to learn new materials if even their homework can be done with the help of artificial intelligence?Professors at Oxford have their own approach to the academic process that can be a good solution for many educational establishments. A big part of the educational activities are happening in small groups. It means that students have a lot of discussions. So when they submit papers, they also have to talk about them afterward.As for exams at Oxford, a lot of their examinations take place in an invigilated environment. So professors can see what the students are using.Dominik is quite optimistic about the integration of AI into the education process. Though it’s too early to speak about its mass adoption its further implementation will definitely continue. And the task for both educators and students is to find the best way to use artificial intelligence for their needs.AI for teachers: How to use it nowMax and Dominik also talked about the use cases when teachers can apply AI already now.Here, Dominik shared one simple principle of working with AI solutions: You should ask the right thing from the right tool. For example, ChatGPT can be really good at explaining math terms and concepts but it is really bad at calculating and solving math tasks.Similar things can be observed in other disciplines. Language teachers can greatly benefit from the ability of AI to create multiple-choice tests for students about a text or a grammatical feature. And here AI can perfectly cope with such tasks.Nevertheless, if you are going to ask an AI model to create fill-in-the-blank grammar exercises, you shouldn’t have any high expectations. In this case, AI can offer the wrong option or provide the wrong gaps where something should be added. Quite often, if you ask AI to give you an example of a grammar feature, you will get an answer that won’t satisfy you. But when AI is generating a text for you it won’t make such mistakes.AI generation still requires strong human supervision, just like an intern. It can work for you but you still need to control the provided results.Skills for future students to work with AIThe educational environment is changing. How can we get ready for this AI-enriched world? Are there any specific skills that people should try to develop in order to work better with the newly introduced tools?While answering these questions, Dominik highlighted that it is impossible to name any precise skillset.However, here’s a list of recommendations from a person who has been working with AI for many years:Keep exploring.Keep trying it.And do not think that if you have used an AI a few times you have explored the entire frontier of its capability.Maybe in a year or two, professionals will find some skills that you need to know but not now. There isn’t just one best tool or the best skillset to be used in the academic environment, as well as in any other space.AI for disabilities: Can it help people to overcome barriers?Speaking about AI, it is also interesting to note the potential of such tools to change the quality of life for people who have different types of disabilities. And here, it’s worth paying attention not only to what such solutions can offer in the educational context but also in the context of everyday tasks.Such tools as screen readers or text-to-speech solutions can be highly useful for people with low vision and different kinds of visual impairments. It is possible to take any webpage and ask AI to voice what is written or shown there. In other words, even if a person can’t read or see something on his or her own, AI can do it. Of course, inaccurate outputs caused by AI hallucinations are still possible. But that’s already a great step forward.AI can also be of great help for those who have problems with writing and typing due to dyslexia or any other issues. In this case, people can rely on speech-to-text features, as well as AI-powered grammar and spelling checkers.Given this, we say that artificial intelligence can make a lot of things available to people, even if previously they couldn’t do them.Talking about the capabilities of AI to expand the existing borders for people, Dominik also mentioned that today not speaking English is already a huge limitation. Those who do not know this language are cut off from a huge part of the world, especially when it comes to learning. A lot of materials are provided only in English. And here AI can also demonstrate its power. You do not need to wait till this or that research is translated into your native language. You can ask AI to do it for you and get a quick result.And…Is an AI apocalypse inevitable?Let us be fully honest with you. That’s just an eye-catching subheading. While some people are trying to guess what is going on in GPT’s mind, such experts as Dominik already know the answer. Nothing. Really nothing is going on in GPT’s mind till the moment we send a question to the chatbot.We are learning constantly, even when we are sleeping our brains are changing.Large language models, as well as other AI-powered tools, can’t think as we can. They are not exploring the world around them. If there are no requests from users, such models are sitting quietly just like a blob of numbers on your hard drive. It means that we should feel completely safe.Instead of the final wordThe AI industry is advancing at an enormous pace. Even a couple of months can bring impressive changes, and half a year feels like a leap into a new era. That’s why it’s practically impossible to predict what comes next and when. So let’s wait and see how AI tools will evolve soon and how education and other spheres will be impacted by these changes.Looking for more insights from the world of AI? Follow us on YouTube, like our videos, ask questions in the comments, and do not miss the next episodes of the Innovantage podcast hosted by Max Golikov.