The future of organizations consists of digital twins: Transform with artificial intelligence and strengthen sectors such as healthcare and finance. Artificial Intelligence (AI) is more than just ChatGPT. Although 2023 brought AI into the public consciousness thanks to OpenAI's chatbot breakthrough, AI has been quietly evolving for decades, waiting for the right moment to shine. Today, it's a very different kind of technology—capable of simulating, creating, analyzing, and even democratizing, pushing the boundaries of what's possible in virtually every industry.
But what exactly can AI do, and how should companies integrate it into their strategies? Let's dive into the potential, use cases and challenges of AI from an IT strategic perspective.
AI is capable of incredible feats such as simulating reality (via Deep Learning and Reinforcement Learning), creating new content (with models such as GPT and GANs), and predicting outcomes by analyzing massive data sets. Sectors such as healthcare, finance and security are already feeling the impact:
These examples are just the tip of the iceberg. From real estate and insurance to customer service and the legal system, AI has the ability to revolutionize almost every aspect of our lives.
One of the most intriguing applications of AI is the creation of digital twins . By simulating reality with operational data, companies can safely explore the impact of AI before deploying it at scale. Digital twins can represent a pilot, judge or even a digital credit rating agency, allowing companies to mitigate risk and gradually integrate AI into their operations.
When companies want to embrace AI, they should consider questions such as “buy, use open source, or build your own?” and “how do we empower our current employees with AI tools?” It is crucial to view AI as a way to enhance—not replace—human skills. The ultimate goal is to create augmented advisors that support decision-making without sacrificing the human touch.
With great power comes great responsibility. The EU AI Act , which came into effect in 2024, aims to balance innovation with fundamental rights and security. Companies must proactively consider bias in AI models, data privacy and the ethical implications of deploying such technologies.
Consider using synthetic data generated by GANs to address bias, and leverage tools like SHAP or LIME to build more explainable AI systems. We need AI that supports human goals and values—technology that can improve lives rather than endanger them.
AI already determines how we live and work. According to Gartner, six of the top ten technology trends for 2024 related to AI. Forrester predicts that the AI market will reach $227 billion by 2030. Companies now need to figure out how to take AI out of the labs and into practical use cases.
The future is not about replacing humans, but about creating a world where personal AIs collaborate with enterprise AIs , augmenting human capabilities and transforming industries. The vision is clear—embrace AI responsibly and harness its power for a more efficient and enriched future.
In the world of software development, outdated code can be a barrier to innovation and growth. Legacy code is often built from decades of patches, workarounds, and updates that were once functional but are now difficult to maintain. Fortunately, there's a new player that can help development teams modernize this code: artificial intelligence (AI). AI allows companies to clean up, document, and even convert legacy code to more modern programming languages faster, more efficiently, and more accurately.
Legacy code, written in outdated languages or with outdated structures, presents several challenges:
Modernizing legacy code with AI not only gives companies the opportunity to take advantage of new technologies, but also to minimize risk and save costs. With AI it is possible to gradually transform a legacy codebase into a modern, future-proof infrastructure, without losing the underlying functionality.
In a world where technology is developing rapidly, companies can gain a valuable advantage through AI, by updating outdated code and positioning themselves as innovative players in their field. Modernizing legacy code is now not only feasible, but also cost and time efficient
Need help coaching and implementing AI to modernize legacy code? Complete the contact form and I will be happy to explain more. On average, a modernization process with AI is 5 times faster
Generative AI (genAI) is developing at breakneck speed. Where we once only dreamed about technology that can match human creativity, today we see applications that surprise and inspire us. From text generation to artificial image and video production: genAI opens doors to new possibilities in diverse sectors, from marketing and entertainment to healthcare and education. In this article we discuss the most groundbreaking developments and look at what the future may bring.
The latest genAI models such as OpenAI and DALL-E have become multimodal. This means they can combine different types of input, such as text and images, to generate more complex and creative outcomes. For example, with DALL-E you can now generate images based on text descriptions, helping creative professionals to visualize their ideas immediately. These multimodal models make it easier to push boundaries between different creative disciplines.
In-context learning means AI models become better at understanding the context and nuances of what you ask, without needing additional training. This makes them directly applicable in real-time situations, such as customer service. Adaptive AI, which can adapt based on feedback and usage patterns, ensures that AI continues to improve at providing personalized answers and services.
The genAI community is becoming increasingly open, with companies like Meta and Hugging Face making their models public. This allows developers to experiment with these advanced AI systems themselves and contribute to improvements. The open-source community plays an important role in solving problems such as bias and ethical issues, through input from diverse users worldwide.
Traditionally, powerful AI models such as genAI require a lot of computing power and energy. Innovations in AI architectures, such as more efficient neural networks and dedicated AI chips, make it possible to run large AI models on a smaller scale and at a lower cost. This makes genAI solutions more accessible to smaller companies and individual users.
While genAI was previously mainly applied to text, the latest developments in image and video technology are impressive. Models like Midjourney and Runway offer users the ability to generate high-quality images and even video clips. This is particularly useful for marketing and advertising, where visually appealing content plays a major role. New AIs can even imitate human movements, allowing actors or animated characters to move lifelike in generated environments.
With the rise of powerful genAI models, ethical issues are also emerging, such as copyright, privacy and the impact of AI on jobs. More and more companies and governments are working on guidelines to ensure responsible use of AI. OpenAI, for example, introduced features such as 'safeguarding' to prevent unintended results in image generation. It also looks at ways to make AI more transparent for users, so that they know when and how AI is used.
GenAI is increasingly finding its way into everyday software tools, such as word processors, design software and browsers. Google and Microsoft are integrating AI features into their Google Workspace and Microsoft Office suites respectively, helping users work smarter and faster. This integration ensures that AI support is immediately available in the workflow of millions of people, which can significantly increase productivity.
With the speed at which genAI is developing, we can expect even more groundbreaking applications soon. Think of AI assistants that not only respond, but can also help proactively by taking over tasks, advanced holographic images that are almost indistinguishable from the real thing, and AIs that work together to solve complex problems.
Companies will also increasingly apply AI in business processes. A company can train multiple agents with a specific task and have them work together as a team. Currently, AI is mainly a very suitable assistant. One that works quickly and is, for example, very good at writing, checking and debugging computer code.
Generative AI is here to stay and plays a crucial role in the future of technology and creativity. Whether it's companies using genAI to create innovative products, or individuals looking to increase their productivity, the possibilities are endless and the future looks promising.
Developments in artificial intelligence (AI) raise questions about what lies ahead. A recent white paper by Leopold Aschenbrenner paints a fascinating picture of the current situation and what may lie ahead. Here are some key insights shaping the future of AI, based on an analysis of the trends and challenges.
The progress in AI is unprecedented. In just a few years, we have gone from GPT-2, which was comparable to a preschooler in understanding, to GPT-4, which has reached the capabilities of a smart high school student. This development has been driven by exponential growth in computing power, algorithmic efficiency and innovative techniques such as reinforcement learning. This trend is expected to continue, which could lead to AI systems that function as professional researchers or engineers by 2027.
After human levels of intelligence, the next step is super intelligence. This transition can be accelerated by AI's ability to improve itself. The implications are enormous: from economic transformations to existential risks. Aschenbrenner emphasizes that this intelligence explosion could be a turning point, where control and security are crucial to prevent disasters.
The enormous infrastructure required for these AI systems is already being prepared. Companies invest billions in data centers, GPUs and electricity to provide the required computing power. This mobilization of resources marks an industrial shift similar to historical war efforts, but now focused on technological dominance.
The economic implications of AI are profound. AI sectors are expected to drive much of the global economic growth, mainly through automation, productivity increases and the creation of new markets. At the same time, there is a risk of major economic inequality, leaving countries and companies without access to advanced AI. According to Aschenbrenner, governments and companies must work together to close this gap, by encouraging education, innovation and fair distribution of resources.
A key challenge is the security of AI models and data. The risk of sensitive technologies falling into the wrong hands, such as hostile states, is a major threat. The document calls for stronger security measures and better policies to mitigate such risks.
One of the biggest scientific challenges is developing methods to make AI systems work in accordance with human values, even as they become much smarter than us. This is referred to as “superalignment”. Whether or not to achieve superalignment can lead to unforeseen and potentially catastrophic consequences.
In addition to technological challenges, there is a geopolitical dimension. Countries such as China and the United States are vying for dominance in AI. Whoever wins this race will have a decisive advantage not only economically but also militarily. It is therefore vital that democratic societies work together to ensure a free and stable world order.
The prospects outlined in this paper are both exciting and disturbing. They require attention, action and cooperation. To seize the opportunities of AI and manage the risks, we must invest in research, policy and international cooperation. As the document puts it, the future isn't just something that happens to us—it's something we shape together.
What do you think? Are we ready for the challenges and opportunities that AI brings us? Read more ?
Data obviously plays a crucial role in companies that are digitizing. But as the demand for high quality and large amounts of data increases, we often encounter challenges such as privacy restrictions and a lack of sufficient data for specialized tasks. This is where the concept of synthetic data emerges as a breakthrough solution.
Synthetic data is data that is generated artificially rather than by real events or processes. This data is often created using algorithms and techniques from artificial intelligence (AI), such as machine learning models. The goal of synthetic data is to mimic real data as closely as possible in terms of statistical properties and patterns.
Example: A synthetically generated room
Although it offers many benefits, there are also challenges. Ensuring the quality and accuracy of this data is crucial. Inaccurate synthetic datasets can lead to misleading results and decisions. In addition, it is important to find a balance between the use of synthetic data and real data to get a complete and accurate picture. Furthermore, additional data can be used to reduce imbalances (BIAS) in a data set. Large language models use generated data because they have already read the Internet and need more training data to improve.
Synthetic data is a promising development in the world of data analysis and AI. They offer a solution to privacy problems and improve data availability. They are also invaluable for training advanced algorithms. As we further develop and integrate this technology, it is essential to ensure the quality and integrity of the data so that we can leverage the https://netcare.nl/service/consultancy/full potential of synthetic data.
Need help applying AI effectively? Make use of our consultancy services
For years, robots in industry have ensured that simple work can be automated. So far this has not led to higher unemployment, but that is expected to change.
With the arrival of drones and self-driving cars, the entire transport sector, police and army will also be robotized. GenAI and artificial intelligence in general will Normally, additional prosperity will lead to the creation of new jobs higher up in the added value chain. Artificial Intelligence will work against this process because AI can also provide value here.
The extra prosperity will therefore end up with a few, the owners and managers of (large) companies. Initially, the difference between rich and poor will continue to increase. First, the less educated will lose their jobs and there will be no replacements for them. In the Netherlands, they will end up in the safety net of unemployment law and social assistance. In other countries such as the USA this will lead to abject poverty much more quickly. It is therefore not difficult to imagine that this could lead to enormous discontent and perhaps even revolutions. Hopefully this is just an interim period in which policymakers make adjustments so that everyone can benefit from increased prosperity. Drawing up and implementing effective policy is crucial to shape this transition.
But ultimately this development cannot be stopped, simply because it is possible and a lot of money and power can be achieved with AI and robotization.
If, ultimately, the highly educated are also forced into unemployment by artificial intelligence, the government will be forced to intervene. This can be done by redistributing prosperity between the (by then) super-rich and the unemployed. Because the national government will no longer have sufficient influence over multinationals, this requires cooperation. Let's assume the positive and they will eventually manage to get this done. We will then live with a lot of freedom, free time and prosperity until the last job is replaced by smarter robots. At that moment or just before, the economy as we know it disappears and everything is free. Robots make everything including the extraction of raw materials and because they do not demand anything in return, they do this free of charge, 24 hours a day, 365 days a year. The prices of products and services therefore fall further and further until they ultimately reach zero.
The economy has disappeared, being rich is no longer useful because everything is free.
Will a shadow economy arise, as now exists between the underworld and the upper world, or will we try to distinguish ourselves in other ways? At the moment I don't know, what I do know is that the above scenario is realistic and that we must be prepared for the period between now and the disappearance of the economy as well as the period afterwards.
But if we handle it properly, we can achieve exactly what we have always wanted. More free time and enough income to live a beautiful and healthy life. I think that idea is worth continuing to invest in innovation. I'm curious if we together .
In the world of artificial intelligence, one of the biggest challenges is developing AI systems that are not only intelligent, but also act according to ethical standards and values that match those of humans. One approach to this is to train AI using law books and case law as a basis. This article explores this method and looks at additional strategies to create an AI with human-like norms and values. I also made this suggestion on behalf of the Dutch AI coalition to the Ministry of J&V in a strategy paper that we wrote on behalf of the ministry.
The idea of training an AI based on law books and case law is based on the concept that laws are a reflection of the collective norms and values within a society. By having an AI analyze these legal texts, the system can gain insight into what is socially acceptable and what behavior is prohibited.
Using GANs to Identify Gaps
Generative Adversarial Networks (GANs) can serve as an instrument to discover gaps in legislation. By generating scenarios that fall outside existing laws, GANs can reveal potential ethical dilemmas or unaddressed situations. This allows developers to identify and address these gaps, giving the AI a more complete ethical data set to learn from. Of course, we also need lawyers, judges, politicians and ethicists to fine-tune the model
While law training provides a solid starting point, there are some important considerations:
To develop an AI that truly resonates with human ethics, a more holistic approach is needed.
1. Integration of Cultural and Social Data
By exposing the AI to literature, philosophy, art and history, the system can gain a deeper understanding of the human condition and the complexity of ethical issues.
2. Human Interaction and Feedback
Involving experts from ethics, psychology and sociology in the training process can help refine the AI. Human feedback can provide nuance and correct where the system falls short.
3. Continuous Learning and Adaptation
AI systems must be designed to learn from new information and adapt to changing norms and values. This requires an infrastructure that allows for constant updates and retraining.
4. Transparency and Explainability
It is crucial that AI decisions are transparent and explainable. This not only facilitates user trust, but also allows developers to evaluate ethical considerations and adjust the system where necessary.
Training an AI based on law books and case law is a valuable step towards developing systems with an understanding of human norms and values. However, to create an AI that truly acts ethically in a manner similar to humans. This requires a multidisciplinary approach. By combining legislation with cultural, social and ethical insights, and by integrating human expertise into the training process, we can develop AI systems that are not only intelligent, but also wise and empathetic. Let's see what the future can bring
Additional Resources:
For a larger project at Essent, we built an artificial intelligence robot, among other things. The project includes the integration of (live) chat via the website with both employees and an AI robot. In addition, integration with CRM to record survey data.
This blog is mainly about the AI piece, which turned out to be a nice piece of the puzzle to build a robot within the allotted time that was bilingual (French and Dutch) and also recognizes emotion, so that if customers become too emotional they can be pushed through. to flesh and blood employees.
We used, among other things, Google's Neural Network for natural language. A nice API that Google itself also uses and has a high ability to interpret the content of a conversation. NetCare has had to add a lot of functionality, such as self-learning and a nice feature that is particularly useful for chat and provides links about products and services, so that you quickly get the right information. The logic for learning is still work in progress and requires a lot of data to analyze.
The applications of our robot are very extensive, so we see many opportunities to continue with this topic. We are currently building our own chat bot for our MyVox demo site that can help interested parties further. In the long term, we also want to make the robot talk and it could be a pleasant and good conversation partner who is always helpful, never gets tired and knows everything about the company, the products and the employees.
We have now also further developed the artificial intelligence robot and integrated it into our own website. You can talk to AIR via the live chat at the bottom that comes on automatically after 10 seconds. He knows Wikipedia like the back of his hand.
We are therefore looking for market parties that we can help improve their services. Interested? Contact us and make an appointment without obligation. We would be happy to give a demo and explain what we can do for your organization.
Meet AIR, our own artificial intelligence robot
https://youtu.be/Gt7q7lrBLbc