In our previous blog post, we delved into the ESG implications of generative AI. We discussed the potential risks associated with the use of this frontier technology, such as the creation of deepfakes, the environmental impacts of training the large models that underpin generative AI, and concerns around privacy and security. Despite these concerns, however, there are many positive possibilities of generative AI that are worth exploring, which will be the focus of this blog post.
One area where generative AI could have a significant impact is in the creative industries. With the ability to generate highly realistic images, videos, and audio, generative AI has the potential to revolutionize the way that art is created and consumed. One example of generative AI being used in the creative industry is in the creation of digital art. Artist Robbie Barrat has used generative AI to create unique art pieces that blend traditional artistic techniques with cutting-edge technology. His work often involves feeding data into a generative AI model, which then creates images or videos that are completely unique and unexpected. The L’Avant Galerie Vossen brought together Robbie with famous French painter Ronan Barrot. Their exhibition “BARRAT/BARROT: Infinite Skulls – An unprecedented encounter between a painter and an artist researcher in artificial intelligence” featured an “infinite” number of skulls. 450 paintings of skulls which Ronan Barrot painted over the last few years were digitally scanned so that Robbie Barrat could train a neural network to create new images of skulls from these works. The resulting collaboration – or confrontation – between its digital and analog creators marked the birth of an entirely new realm of creative partnership [1, 2, 3].
Another example is the AI-generated music created by companies such as Amper Music, a platform that uses generative AI to create custom music tracks for users. Users can specify the genre, tempo, and mood they want for their track, and the AI generates a completely original piece of music that matches their criteria. Take a look at the Break Free Official Music Video – Composed with AI | Lyrics by Taryn Southern. This has the potential to revolutionize the way that music is created, making it easier, cheaper, and more accessible for independent artists to produce high-quality tracks .
Thirdly, generative AI has been used to create highly realistic images and videos for advertising and marketing purposes. For example, Coca Cola recently launched its “Create Real Magic” AI platform to generate original artwork with iconic creative assets from the Coca-Cola archives. Built exclusively for Coca-Cola by OpenAI and Bain & Company, it is the first platform of its kind to combine the capabilities of GPT-4, which produces human-like text from search engine queries, and DALL-E, which produces images based on text . Take a look!
Another area where generative AI could have a significant impact is in healthcare. With the ability to analyze vast amounts of data, generative AI could be used to develop new treatments and drugs that are more effective and targeted than those that currently exist. For example, researchers at MIT have developed a generative AI model that can analyze millions of chemical compounds to identify potential drug candidates for specific diseases. This could significantly accelerate the drug discovery process and lead to the development of new treatments for diseases that are currently incurable .
Another example of generative AI being used in healthcare is the research conducted by Insilico Medicine, a Hong Kong-based biotechnology company. Insilico Medicine used generative AI to develop a potential drug candidate for idiopathic pulmonary fibrosis, a fatal lung disease. The company used generative AI to analyze large amounts of data on various compounds and their effects on the disease, ultimately identifying a promising drug candidate for further study. This process, which typically takes years, was completed in just 18 months thanks to their use of generative AI .
Our final healthcare example comes from researchers at Stanford University, who used generative AI to identify potential new antibiotics. The researchers trained a generative AI model on existing antibiotics and their chemical structures, and then used the model to generate thousands of new compounds with similar structures. They then tested these compounds and identified several promising candidates that could be effective in treating antibiotic-resistant bacteria. This process demonstrates the potential of generative AI to accelerate drug discovery and develop new treatments for diseases .
Improve General Human Productivity
Generative AI also has the potential to improve the efficiency and accuracy of many industries, such as finance and logistics, such as logistics route optimization. According to a study by DHL, optimizing delivery routes can result in significant cost savings and reduced greenhouse gas emissions. Generative AI can be used to analyze data on traffic patterns, delivery addresses, and other factors to determine the most efficient routes for delivery vehicles to take. This can help reduce fuel consumption, shorten delivery times, and improve overall supply chain efficiency .
In the finance industry, generative AI can be used to improve fraud detection and prevention. By analyzing large amounts of data, generative AI models can identify patterns and anomalies that may indicate fraudulent activity. For example, JPMorgan Chase has developed a generative AI model that can analyze a wide range of data sources, including social media and news articles, to identify potential cases of fraud. This has the potential to significantly reduce the financial losses associated with fraud, while also improving the overall security of financial systems .
Another area where generative AI can improve efficiency is in inventory management. By analyzing data on sales trends, customer behavior, and other factors, generative AI can help businesses optimize their inventory levels, reducing the amount of excess inventory that is wasted or sold at a discount. This can result in significant cost savings, as well as a reduction in waste and environmental impact, as demonstrated by IBM .
Generative AI could also be used to create highly realistic simulations and virtual environments for a range of educational purposes, especially when combined with virtual reality (VR) technology. These immersive experiences can engage students in subjects that they may find challenging or uninteresting in traditional classroom settings. By providing interactive and engaging experiences, generative AI can help improve students’ understanding and retention of complex subjects.
For example, physics simulations created with generative AI can provide students with a hands-on experience of scientific concepts that are difficult to visualize or demonstrate in a traditional classroom setting. Students can manipulate virtual objects and observe how they behave in different conditions, allowing them to gain a deeper understanding of complex concepts like gravity or thermodynamics.
Similarly, historical recreations created with generative AI can transport students to different time periods and allow them to experience historical events in an immersive and engaging way. Students can explore virtual environments and interact with virtual characters to gain a better understanding of historical contexts and events. This can be particularly useful for students in areas where physical resources, such as museums or historical sites, are limited.
For example, VR simulations can provide students with a virtual tour of a science laboratory or a factory, allowing them to see how different processes and experiments are carried out in a realistic setting. This can be particularly useful for students who may not have access to physical resources or who may be unable to travel to different locations.
By providing immersive and engaging experiences, generative AI can help improve students’ understanding and retention of complex subjects and provide educational opportunities for students in areas where physical resources are limited .
While we deep-dived into four specific areas, these are just a few examples of the many possible positive applications of generative AI. Below is a non-exhaustive list of additional applications where generative AI is likely to have a favorable impact:
- Natural disaster prediction: can be used to predict natural disasters such as earthquakes, floods, and wildfires, allowing for early warnings and preparation.
- Climate change analysis: can be used to analyze data on climate change and predict future impacts, informing policy decisions and actions.
- Cybersecurity: can be used to identify and prevent cyber-attacks by analyzing network traffic and detecting anomalies.
- Urban planning: can be used to optimize urban planning by analyzing data on population density, traffic patterns, and environmental impact, leading to more sustainable and efficient cities.
- Agriculture optimization: can be used to optimize agriculture by analyzing weather patterns, soil quality, and crop yields, leading to increased productivity and sustainability.
It is clear that generative AI has the potential to create significant positive benefits across a range of industries. As with any emerging technology, it is important to approach the development and deployment of generative AI with a sense of responsibility and a focus on ethical considerations. In our final blog post of this series, we will explore the specific impacts we expect generative AI to have in Japan. See you next time!
1. Beal, V. (2021). The 5 positive effects of AI in the workplace. TechGenyz. https://www.techgenyz.com/2021/05/21/positive-effects-of-ai-in-the-workplace/
3. Hookway, J. (2021). AI Is Changing the Art World. The Wall Street Journal. https://www.wsj.com/articles/ai-is-changing-the-art-world-11627758000
6. Jin, W., Barzilay, R., & Jaakkola, T. (2018). Junction tree variational autoencoder for molecular graph generation. Proceedings of the 35th International Conference on Machine Learning, PMLR 80:2546-2555. http://proceedings.mlr.press/v80/jin18a.html
7. Insilico Medicine. (2022). Insilico Medicine announces preclinical data on potential idiopathic pulmonary fibrosis treatment. Retrieved from https://insilico.com/phase1
8. Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., … & Collins, J. J. (2020). A deep learning approach to antibiotic discovery. Cell, 180(4), 688-702. doi: 10.1016/j.cell.2020.01.021
9. DHL. (2022). Optimizing Deliveries for Reduced Emissions and Costs. Retrieved from https://www.dhl.com/global-en/home/press/press-archive/2022/artificial-intelligence-saves-costs-and-emissions-by-optimizing-packaging-of-shipments-for-dhl-supply-chain-customers.html
10. JPMorgan Chase. (2021). JPMorgan Chase develops AI fraud detection system.
11. IBM. (n.d.). Using AI for Inventory Optimization. Retrieved from https://www.ibm.com/products/inventory-optimization-ai
12. (2021) “How AI Is Revolutionizing Education And Changing The Way We Learn” and can be accessed at the following link: https://www.forbes.com/sites/forbestechcouncil/2021/08/29/how-ai-is-revolutionizing-education-and-changing-the-way-we-learn/?sh=25d5b5c55123