Enhancing Educational Support with GenAI: How Lyceo is Integrating AI into its Learning Framework
18
October
2024
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The ongoing teacher shortage in the Netherlands is a growing concern, creating disruptions that impact the quality of education and limiting students’ future opportunities. With some classes and even entire school days being canceled, and certain subjects no longer taught, education has taken a hit. As a response, many parents have turned to private tutoring or homework assistance for their children, while schools increasingly seek external educational services. Among these, Lyceo has emerged as the largest provider.
As more and more schools rely on Lyceo, the company is able to leverage AI technology to address various educational challenges and automate tasks. With the introduction of the Lyceo GenAI learning tool, the company’s virtual tutors will be able to support students by answering questions and providing timely feedback on assignments. The tool will offer personalized insights, highlighting students’ strengths and identifying areas where they can improve. By considering diverse learning preferences and abilities, Lyceo can create tailored teaching strategies and resources for each student. This technology not only provides real-time explanations but also extends continuous support, even during late-night study sessions. This self-paced approach is particularly beneficial for those students who prefer to study according to their own schedules.
Additionally, Lyceo’s GenAI-powered chatbots will enhance customer service by assisting parents in obtaining answers immediately. The chatbots are designed to provide information and perform tasks. The informative chatbots will deliver pre-set information to help parents with questions about pricing or suitable programs tailored to a student’s needs. In contrast, task-based chatbots are programmed to handle specific requests, such as scheduling tutoring sessions for students.
However, integrating GenAI into Lyceo’s business model involves considerable investment. The costs for implementing generative AI can range from minimal to several million euros, depending on the specific use case and scale. While smaller companies may benefit from free versions of generative AI tools, like ChatGPT, Lyceo will likely need to invest in customized AI services to develop the online learning tool and sophisticated chatbots tailored to their needs.
The potential benefits make this investment worthwhile, enabling Lyceo to improve its educational support services and continue to meet the evolving demands of schools, students and parents.
NutriNet – a personal assistant for your grocery shopping
18
October
2024
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Have you ever taken hours browsing around supermarkets searching for the most nutritious food options? Did it take you too much time figuring out which recipes to cook best, with the groceries you bought? Struggles when dealing with food are numerous, starting from choosing products with appropriate nutrients simply not knowing enough recipes.
Therefore, we introduce NutriNet – a mobile application that is going to make shopping and meal planning easier!
NutriNet simplifies grocery shopping and meal planning, by analyzing which products and recipes fit the users’ desired grocery item wishes, nutrition values and store preferences best. NutriNet aims to address the challenges being implicit to personalized nutrition and helps consumers make healthier food choices by simplifying the grocery shopping and meal planning process. This shall be done by eliminating the need to perfectly understand all nutrition values or to search for numerous recipes. NutriNet completes all these tasks for you in real-time and provides clear and accessible recommendations for you.
• Real-time personal assitant: NutriNet acts as a multifunctional application providing value to consumers by solving various food related problems in real-time. Appearing as a chatbot, it is aimed at taking general grocery shopping lists or meal wishes as input query, combined with preferences for food characteristics (e.g., nutrients, allergies) and grocery stores. It then provides brand specific and personalized grocery shopping lists, as well as meal recommendations, if so desired. Moreover, grocery items can also be added into the initial grocery shopping list query, by scanning them with the integrated AR tool. The product will then be detected visually and thus will be integrated into the shopping list input query.
• Long-term customer engagement: NutriNet distinguishes from competition by providing personalized and customized advice. This is possible, as NutriNet consists of a database, having incorporated stock and product information of the major supermarkets in the Netherlands. In contrast, classic applications, which try to meet similar needs (e.g., meal recommendation) rather focus on counting nutrients for the purpose of short-term weight loss, instead of personalizing grocery shopping lists and meal recommendation to enable a healthier lifestyle for users. Those applications are usable on short term but are proven to have low adherence over the time (Chen et al., 2015).
• Personalized recommendations: NutriNet leverages generative AI to offer accurately personalized recommendations. Users can simply enter their preferences, while prompting a grocery list or a meal, such as gluten-free or high in protein, and the generative AI powered application will provide accurately personalized results. However, personalization and raising awareness for healthy foods are not the only purposes of NutriNet. It also addresses sustainability issues that supermarkets are facing. By gathering consumer purchase and search data in the application, consulting services can be offered to supermarkets, enabling them to plan ordering and stockholding processes more efficient. Hence, supermarkets should be able to reduce food waste due to overstocking on long-term.
NutriNet offers a 2-sided network platform, yielding important value to both, consumers as well as supermarkets.
Contributors
574051 – Duong Dao 728070 – David Wurzer 738898 – David Do 562387 – Roxi Ni
References
Chen, J., Berkman, W., Bardouh, M., Ng, C. Y. K., & Allman-Farinelli, M. (2019). The use of a food logging app in the naturalistic setting fails to provide accurate measurements of nutrients and poses usability challenges. Nutrition, 57, 208-216.
From Dense Texts to Dynamic Videos: The Synopsis.ai Web App
17
October
2024
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Team 6: Noah van Lienden, Dan Gong, Ravdeep Singh & Maciej Wiecko.
Ever found yourself staring blankly at a 50-page academic paper, wondering if there’s a faster, more engaging way to grasp the key points? What if that dense text could transform into a lively video, complete with animations and a friendly narrator? Welcome to the future of learning with our Synopsis.ai web app!
The Education Technology (EdTech) market is skyrocketing. In 2023, the global EdTech market hit a whopping $144.6 billion and is projected to triple by 2032. With advancements in AI, augmented reality (AR), virtual reality (VR), and more, the way we learn is evolving faster and changing day to day. Generative AI is the new superstar in the EdTech universe. Tools like Scholarcy are helping students by turning lengthy texts into bite-sized summaries. But let’s face it—reading summaries can still feel like, well, reading. How great would it be if you could watch a video instead?
Enter Synopsis, the groundbreaking web app that’s set to revolutionize how we digest academic content. Synopsis uses advanced AI to convert scholarly articles into short, engaging videos. It’s like having your own personal explainer video for every complex paper you need to read. You can customize these videos and choose either a lecture format or an animated video format. Furthermore, users can select their desired video length, content granularity and even add subtitles!
All this new content is not only wonderful for student learning with our web app, but also Researches, Educators and even Content Creators! All these different users can have different uses of our platform, and can each bring value in new ways to themselves, or even to others!
So how does this magic work behind the scenes? Synopsis leverages state-of-the-art AI models like GPT-4 and BERT, fine-tuned on vast academic datasets. It collaborates with AI research institutions to stay ahead of technological advancements and works with designers to create customizable templates and animations. While there are tools that summarize texts or create videos, none combine both in an educational context. Synopsis fills this market gap by offering a seamless solution that transforms academic articles into personalized video summaries.
In a world where attention spans are dwindling, and visual content reigns supreme, Synopsis is poised to make a significant impact. By making learning more accessible and enjoyable, it’s not just keeping up with the future of education—it’s helping to shape it!
Using Generative AI as My Marathon Training Coach
11
October
2024
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I want to run a marathon next summer, so I turned to ChatGPT to help me out. Not with the running part, but mostly everything else. With generative AI, I can have a personal marathon coach that understands my needs, optimizes my training plan, and continually adapts as my fitness level evolves.
Setting Up My AI Coach
To create my AI coach, I used the MyGPT function offered by OpenAI. It allows you to customize a ChatGPT model to your liking by providing sets of instructions and information. I instructed it to act as my professional marathon coach who understands my goals, fitness level, and the specific challenges of marathon training. I explained my current abilities, body metrics, and general athletic history. I also provided my time goal, the marathon course info, and the date of the event. Moreover, I uploaded important data:
Running metrics for current, regular runs (distance, time, heart rate zone info, cadence, pace, split pace, effort level, etc.)
Apple Watch training data
Calendar data
This data ensures that the model can give more specific advice and training plans. For example, it uses my previous data to set pace targets considering terrain and what heart rate zone I should be in for a particular session.
Phased Training Plan
The AI-generated plan broke my preparation into phases, each with a specific focus. The initial phase was about building endurance with consistent mileage and easy runs. Later phases introduced speed work and tempo runs to improve stamina and pace. The AI explains the purpose behind each phase, which keeps me motivated and committed.
The phased approach means I’m not overwhelmed. Instead of seeing it as one long journey, I focus on each phase, trusting that the AI has mapped out the best way to build my fitness gradually. Each phase has unique challenges and goals, which helps me stay focused and structured. Here is an example of what it created:
Phase 2: Strength & Speed Building (Weeks 11–20)
Goal: Increase mileage, speed, and introduce hill work. Continue to improve cadence and manage heart rate.
Weekly Structure:
5 days running:
1 long run (gradually increasing distance)
1 interval session (e.g., 4 x 1 km at 5:00/km)
1 tempo run at marathon pace (5:40–5:50/km)
2 easy runs (HR < 150 bpm)
1–2 days cross-training or strength training focused on core and lower body strength.
Example Week 15:
Monday: Rest or cross-training
Tuesday: 8 km easy run (HR < 150 bpm)
Wednesday: Interval run: 5 x 1 km at 4:55–5:05/km with 2-min recovery
Thursday: Cross-training (strength-focused)
Friday: 8 km tempo run at marathon pace (5:40/km)
Saturday: 18 km long run (pace 6:10–6:30/km)
Sunday: Rest or mobility work
Long Run Progression:
Week 11: 18 km at 6:10/km
Week 14: 22 km at 6:00–6:20/km (HR under 160 bpm)
Week 17: 26 km at 6:10/km with last 3 km at marathon pace (5:40/km)
Week 20: 30 km long run (gradual build, HR < 160 bpm)
End of Phase Goal:
Cadence: Improve to around 165–170 SPM.
Long Runs: 30 km long run with a fast finish.
Tempo Pace: Consistent pacing at 5:40/km for up to 12 km.
How AI Adjusts Based on Real-Time Data
The AI adjusts based on real-time data from my Apple Watch, including cadence, heart rate, pace, and distance. If my heart rate is higher than usual during long runs, the AI might recommend more rest or adjust upcoming workouts. On strong days, it suggests more intensity or extra mileage. These adjustments keep my plan in sync with my body’s needs. The AI doesn’t just follow a set schedule—it adapts continuously to prevent burnout or injuries and to push me when I’m ready.
Personalized Insights & Accountability
The AI can provide insights into my performance, spotting patterns I might not notice. I can ask it to analyze my recent running data and give me advice if it is necessary to adjust the training plan. For example, when my pace stagnated, the AI suggested changes—more recovery days and different speed workouts. This helped me break through the plateau, improving my pace within weeks. These insights are invaluable and something I wouldn’t easily notice on my own.
Looking Ahead: My Marathon Journey Continues
With about eight months until the marathon, I’m excited to continue building on the progress I’ve made. Having a personalized plan gives me confidence that I will be well-prepared when race day arrives. Generative AI has transformed my training goals. It’s personalized, adaptive, and insightful. It helps me understand my capabilities, push limits, and gain confidence in my training approach.
Have you ever considered using AI to help with your fitness goals? I’d love to hear your thoughts or experiences—feel free to share in the comments below!
AI Meets the Kitchen
10
October
2024
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When I was younger, I used to dislike cooking. It always seemed overwhelming to me since there were so many different techniques, recipes and ingredients. I was often confused, especially when trying to come up with meals based on whatever I had at home. However, when I began exploring different generative AI tools, my perspective shifted completely. These AI tools transformed cooking into something enjoyable and creative rather than a chore I needed to finish as soon as possible.
One of the most valuable tools in my cooking journey has been ChatGPT. Instead of spending hours browsing cooking websites or watching YouTube videos, I can simply ask ChatGPT what to cook using the ingredients I have at home. It generates personalized recipe ideas along with step-by-step instructions, which saves me time and reduces food waste by helping me make the most of what’s on hand. Additionally, I use it to create customized meal plans tailored to my dietary goals, such as increasing protein intake or following a gluten-free diet. Below is an example of a weekly meal plan I’ve created with ChatGPT’s help.
I also believe that generative AI can be a useful tool for menu and recipe creation in restaurants. By generating novel recipes based on specific criteria like ingredient availability or dietary preferences, AI helps restaurants stay competitive and offer unique menu options. It can assist chefs by suggesting adjustments, reducing the time needed for recipe development. AI simplifies parts of the creative process, allowing restaurants to consistently introduce new dishes while reducing the pressure on the culinary teams.
As generative AI continues to evolve, its potential to revolutionize at-home cooking and the restaurant industry continues to grow. Whether you are a beginner like myself or a professional chef, I believe that generative AI can enhance everyone’s cooking experience.
Hollywood vs. AI – Who will win the battle?
19
September
2024
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Ask ChatGPT, Gemini, or any other generative AI bot to create a story; the output will always amaze you. You will get a compelling storyline with the most detailed descriptions of scenes and characters. When you’re doing this for fun, this might sound like a dream. But for professionals in the creative industries, this seems more like a nightmare.
Strike upon strike
The tension between generative AI and creative professionals hit the world when actors and writers announced major strikes in Hollywood. One of the prominent reasons was that generative AI might replace their careers in the upcoming (near) future. According to the BBC[1], creative professionals advocated for better pay, working conditions, and job security in the age of generative AI. The strikes performed by over 160.000 creative professionals caused delays for many movie projects and caused major monetary damage. Framestore, the company behind the special effects in Marvel movies, saw a decrease of 46.7 million[2] US dollars in its revenue.
Discussion
In my opinion, the strength of generative AI and the strength of creative professionals need to be combined into a collaborative relationship. At this point, AI will inevitably replace some job roles, whether it’s in the creative industry or not. Thus, it is important to work together and to combine the strengths of both sides. For actors, I believe that the reassurance against the use of AI is justified. Their faces or voices should not be used without their consent for movies created with AI. A more controversial opinion from my side might be that writers need to adapt to the current circumstances. It is not completely new that jobs are replaced by new technologies. Think of factory workers in car assembly lines being replaced by robots. As emphasized earlier, a collaborative relationship is needed to both foster the talents of writers and the strength of generative AI. New job types and roles could come into existence which would optimise the output of creative companies when humans and computers collaborate.
What do you think? What is your opinion on the use of generative AI in the creative industries? Should it be halted or should it be enabled?
The Present & The Future of AI-Generated Videos – a Discussion with Synthesia
22
October
2023
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Imagine a world where you can create captivating, high-quality video content at the speed of thought. AI-generated videos are now transforming this vision into a reality. These digital platforms can turn basic scripts – and generate the scripts themselves, as well – into stunning masterpieces, adapting to various languages and global audiences through diverse avatars and instant translation options.
To delve into this cutting-edge technology, I chose to test out Synthesia, a leading AI video platform, and a unicorn with a $1 billion valuation (UCL, 2023). With features like automatic script-to-video conversion, multilingual support, and customization (Synthesia – AI Video Generation Platform, n.d.) as well as an impeccable customer list, I was intrigued.
I delved deep into researching the company and the ethics of AI-generated content, generated two demo videos, and had an interview with Borys Khodan, the Head of Paid Media at Synthesia, for a comprehensive research about the company’s direction and ethical considerations.
The Synthesia Experience
To start with, the overall customer experience for demo videos at the moment seems heavily targeted towards B2B. The clients list of Synthesia features well-known companies, including Amazon, Zoom, BBC etc., and not individuals. Organizations collaborating with Synthesia often use it to create how-to videos, used for learning & training, IT, sales, and marketing teams. There are a ton of features available for them. What truly caught my attention is the effortless video updates – if a script of a video has to be changed, a simple script tweak yields a new video almost instantly, sparing companies from the costly ordeal of re-recording content from scratch. The potential savings here are impressive and certainly make a difference in the corporate landscape. Moreover, Synthesia has plans to expand its offerings for individuals (B2C), introducing advanced 3D avatars and other features for additional use cases, as confirmed by Borys.
My personal experience involved choosing one of Synthesia’s demo video options, “Sales Pitch.” I edited the standard text and received the video via email within minutes. It was an impressive process, though there was a minor glitch. While the AI handled the standardized text perfectly, it struggled to pick up the word “Resume” as a noun and mispronounced it as the verb, when I attempted to generate a sample pitch for a potential employer. However, in the paid version, there must be tools to easily adjust the videos. When I tried the “compliment” option to generate another video, it worked smoothly – sending a personalized video to me directly without any hiccups. It was quite fun to watch the text that I wrote in 15 seconds come to life in a video!
AI-Generated Video: Sales Pitch
Industry Overview
In this rapidly evolving landscape, competition among AI-driven video generation platforms is fierce, leading to heightened ethical concerns. Lots of companies scape the web for data – sometimes, using personal data for training their algorithms to get ahead of the competition. But for Synthesia, the path is clear – no shortcuts, no compromises. The company’s founders have committed substantial resources to ensure ethical data procurement, even if it may mean encountering short-term delays and additional expenses. The company wants to be sure that the data they use to train their models on are ethical, legal, and are not violating any privacy laws (Browne, 2023). This approach may shield Synthesia from potential legal repercussions stemming from unethical or illegal data sources.
Ethical Implications
This ethical lens extends to the creation of AI-generated videos, and brings us to the questions of authorship, artistic authenticity, and ownership. What if AI is requested to generate violent or inappropriate content? What happens when an AI can replicate the voice and likeness of a person without their consent? The concept of deepfakes, where AI manipulates content to deceive and misinform, also poses a clear danger, and there are many opportunities and threats in that direction (Collins, 2023). I asked some of these questions to Borys in a fruitful discussion about the future of AI-generated videos.
“We maintain very high ethical standards and closely monitor all content generated by our customers. If the content violates any of our policies, such as featuring violence, harmful behavior, offensive language and more, the video would not be generated,” Borys said. “Our high ethical standards aren’t just nice to have; they are core to our mission and set us apart in a competitive market.”
Unfortunately, not everyone in the industry follows these standards, making the next few years crucial for determining who will succeed, and what legal requirements will evolve. For now, there are no current regulations requiring content to be labeled as AI-generated, but that may change in the future.
Conclusion
AI-generated videos hold immense potential for reshaping the corporate landscape. Synthesia serves as an inspiring example of this transformation, and working even with their demo product was seamless, quick and simple. However, harnessing the potential of the technology to the fullest extent requires unwavering adherence to strict ethical standards of all the companies in the sector, reminiscent of Synthesia’s own commitment. When wielded responsibly, AI-generated videos stand as a remarkable testament to the synergy of technology and business solutions, marking an important step into the future.
My previous post talked about my experiences with using Generative AI tools for learning languages. I received a comment on the blog which talked about if there will even be a need to learn languages when advanced technology can eventually translate any language with near perfection in real-time into your ears. This image attached is an AI’s imagination of people who know no common languages communicating with the help of an instant translator earpiece. This is a daunting proposition, that there will be fewer and fewer language diversity as times go on due to the domination of the internet by a few languages, mostly English.
Generative AI is a lot different from a mere translator application as it can understand the nuances of a language and translate in a much more accurate way. But as more of these tools are majorly trained in English, the lingua franca of the world, there is a possibility of widening gap between English and some European languages vs the rest of the languages (Sharma, 2023). ChatGPT for example could reinforce the dominance of English, and result in reduced richness of language as it does not currently function well with non-European languages.
There is hope though as it has been observed that Generative AI apps have been able to comprehend languages that they were never initially trained on. The process of how AI is able to pick up languages is not explained currently however there are attempts to explain it using the way humans pick up languages. Awareness of a single languages predisposes humans to learn a second language merely through immersion in the second language, as we’re able to recognize patterns in the language to understand its structure. The same has been considered in the case of Generative AI which is able to recognize patterns in languages as well, suggesting that as AI learns more languages, it may be easier for it to catch on even more (Eliot, 2023). However, the resources available for certain languages far exceed that of others and the risk of losing linguistic diversity is still exemplified in the age of Generative AI due to this difference.
As a language enthusiast myself, I see myself reliant on Google lens to translate even the smallest of foreign language words when I am in a new country, which removes the need to even read or type it into the translator, thereby reducing all the interaction I could have with the new language. Do you have any experience where you find yourself using lesser linguistic power as Generative AI can do it for you?
The emergence of ChatGPT plugins has been a turning point in the realm of conversational AI. Not only do plugins enhance the built-in capabilities of ChatGPT but also expand the horizons of possibilities. The plugins have significant implications for various industries and sectors. However, as with any AI advancement, there are both pros and cons to consider. In this blog post, I delve deeper into my personal experience with ChatGPT plugins, discuss their real-world applications, and explore the ongoing debates surrounding their usage.
What are Plugins?
Currently, the plugins are only available in the Plus version of ChatGPT. In a nutshell, they are technically software add-ons that extend the existing capabilities of the original ChatGPT model. Plugins can serve a variety of purposes and can connect the model to external data sources, thus, increasing the accuracy of responses. They are not developed by the OpenAI itself, but rather are enhancement tools made by human beings and “submitted” into the ChatGPT ecosystem. For instance, the plugins can enable ChatGPT to draft emails, conduct web searches, summarise documents etc.
The User Experience
My personal journey with ChatGPT plugins started very recently, way after they were introduced. Yet, the experience has been quite enlightening. Not only have these plugins made my interactions with ChatGPT more dynamic but have also allowed for my productivity and efficiency to skyrocket. Though there are only around 1,000 of them available, I am very far from having explored all of them. Among the ones I have had the pleasure of working with, a few have stood out due to their utility in my personal and professional lives:
1. KeyMate.AI Search
KeyMate.AI has truly been a game-changer for me. The plugin basically acts like a missing link between ChatGPT and Google Search. It helped me reduce the time I spent on web research and increase its efficiency. For instance, while working on a market analysis for a work project, I used KeyMate.AI to quickly generate an overview of the market and biggest players. It can also help you make your investment decisions by providing real-life data and trends!
As someone who has a keen interest in data analytics, the Wolfram plugin has been interesting to explore. In short, it allows for performing quick complex calculations and data analysis right within the ChatGPT interface. It also has access to curated knowledge. While I have not yet used it for a specific work I “played around” with it and tried to see what transformations of different Pokemons look like.
I am a huge “fact-nerd”, I love constantly googling for information and questions that come to my mind. Most of the time I end up reading through a Wikipedia page, but those pages are quite lengthy. Thus I started using the plugin and it is now my absolute go-to for quick and efficient information lookup. As you can see below, I have used it to find out about absolutely different concepts and historical events in a very efficient way.
While the capabilities of the plugins are impressive, they have their own drawbacks. Here are some of the most notable ones I (and others) are concerned with:
1. Personal data exposure. One of the main pressing concerns is the ethical implications and data safety. Given plugins are made by third-party developers, the personal data of the users is available to parties outside of the ChatGPT ecosystem.
2. Speed of generation. In short, plugins are slow. It takes a while to generate responses. When it comes to looking up information on the web, you are better off using the search engines directly for short questions (e.g. Microsoft stock price). However, once your search becomes slightly more extensive, the benefit of plugin-generated answers tends to exceed the cost of time spent waiting.
3. Hallucinations. While in the plugins amount of hallucinated (i.e. fake) answers is reduced, they are not fully eliminated. This is due to the fact that the underlying language learning model (LLM) behind ChatGPT itself is still prone to generating hallucinations.
What does the Future Hold?
While the ChatGPT plugin ecosystem is still in its infancy, I believe it is immensely promising. With the evolvement of this technology, we can expect to see more sophisticated, faster, and user-friendly plugins. However, it is important to have a balanced approach towards innovation and consider both its benefits and challenges. While OpenAI has yet to let another genie out of the bottle with its introduction of a plugin ecosystem, it is the users and developers who in my opinion hold sole ownership of the technology’s future.
Have you had an experience with ChatGPT plugins or have some thoughts on the topic? Happy to hear more in the comment section below!
Generative AI: Increasing efficiency of drug design
16
October
2023
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Since the pandemic, the pharmaceutical industry has been redefining and reinventing its operations and implementing more digital innovations. Generative artificial intelligence is one of the digital technologies promising to accelerate and improve the drug discovery process. The drug discovery process is extensively complicated and requires significant investments of time and money. Realizing new drugs take, on average, between 12 to 18 years, costing approximately $2.6 billion. Eventually, only 10% of drugs make it to clinical trials. Several companies are extensively researching the possibilities of integrating generative AI to improve the efficiency of their drug discovery processes (GlobalData Healthcare, 2023). My interest in (holistic) health, medical innovations, and breakthroughs stems from my own medical journey, searching for remedies for my chronic condition. The possibilities shown by the technique of generative AI to support the development of new drug discovery genuinely intrigue me. Seeing through the years how innovations widen the possibilities in the medical landscape is exciting to me.
Companies train their artificial intelligence to inspect vast and complex chemical and biological data sets. Subsequently, generative models process all this data to locate new targets for treating diseases and create new molecular structures with suitable properties. The input of scientists is to look for specific molecules with particular characteristics to transform these into new drugs (Nouri, 2023).
One of the companies to use generative AI to discover new drugs is Insilico Medicine. Insilico Medicine uses its generative AI platform, Pharma.AI, in each step during the drug discovery process. Traditional discovery of a drug for idiopathic pulmonary fibrosis would have cost over $400 million over six years. However, with the use of their generative models, the cost was $40 million, and the first phase of clinical trials began after 2.5 years. The generative approach to designing drugs thus increases time and cost efficiency (Yao, 2023).
Insilico Medicine even received IND approval from the U.S. Food and Drug Administration (FDA) to start their drug in the clinical validation stage(Insilico Medicine receives IND approval for novel AI-designed USP1 inhibitor for cancer, 2023). Besides, they have several other AI-designed drugs in their pipeline. Their lead drug for progressive idiopathic pulmonary disease is a breakthrough for entirely generated AI drugs since it is in phase II patient trials (Nouri, 2023).
Even though generative-designed drugs have a promising outlook, they also have barriers. Regulatory and ethical considerations may limit the design of drugs with the use of generative AI. Additionally, datasets must be high quality and large enough for machine learning (GlobalData Healthcare, 2023).
I believe using generative AI to design drugs will mature since it can shorten the discovery process of drugs and increase cost-efficiency. What do you think the future of healthcare will look like now that drugs can be created more efficiently with generative AI? Do you regard generative AI as a prescription for success in the future of healthcare?
References
GlobalData Healthcare. (2023, 3 augustus). Generative AI has the potential to revolutionise drug discovery. Pharmaceutical Technology. https://www.pharmaceutical-technology.com/comment/generative-ai-revolutionise-drug-discovery/?cf-view&cf-closed
Insilico Medicine receives IND approval for novel AI-designed USP1 inhibitor for cancer. (2023, 25 mei). EurekAlert! https://www.eurekalert.org/news-releases/990417
Nouri, S. (2023, 5 september). Generative AI drugs are coming. Forbes. https://www.forbes.com/sites/forbestechcouncil/2023/09/05/generative-ai-drugs-are-coming/
Yao, R. (2023, 27 juni). Insilico Medicine uses generative AI to accelerate drug discovery | NVIDIA blog. NVIDIA Blog. https://blogs.nvidia.com/blog/2023/06/27/insilico-medicine-uses-generative-ai-to-accelerate-drug-discovery/
