No ratings yet.

I recently came across Artificial Intelligence Virtual Artist (AIVA) which helps create music and offers a lot of customizations. As someone who has always wanted to make music, but has never been able to learn any instruments, this offered an avenue to a lot of possibilities, and I wanted to see what AI can offer us. Hence, I made an account on AIVA and was quite fascinated by the various options it provided.

Firstly, it allowed composing a track in the following ways:

1. You could provide it with a style which could vary from lo-fi, techno, sync-wave, etc., duration and the number of compositions and it will create new music for you.
2. You could provide it with a chord progression or upload an already existing one. It also suggests a chord progression, or you can give it a prompt to generate one. (The prompt was very fascinating as I provided it with an overview, and it gave a decent chord progression.)
3. A step-by-step process where you could customize everything from styles, chord progressions, composition layers, instruments, provide prompts.
4. Upload an influence or an existing music file

I started with providing it with a simple style to create a small lo-fi playlist as this is the kind of music I listen to a lot while working, and it was able to give a quick 30 second clip which was probably not the best lo-fi track but still better than I expected.

Next, I tried giving it more details and seeing what the AI could when provided with more details. I used the chord progression function, and it allowed me to generate a chord progression using a prompt. I provided it with the prompt to use the same chord progression as used in the song, “Sound of Silence” by Simon and Garfunkel. The results were quite fascinating. While the chord progression wasn’t exactly copied, but still the music generated took me by surprise and was worth listening to.

Lastly, I experimented with other genres and more detailed experiments using Daft Punk as the style, and asking the AI to generate a fast-tempo Daft Punk styled song, and I was actually able to get quite an interesting song that was worth listening to. It allowed working with composition layers, changing song durations, and also generating multiple tracks.

Experimenting with AIVA was quite fun and if not for the limit on their free subscription, I would have experimented more. I believe that with adding more customised and user-friendly interfaces where someone with minimal music knowledge could generate music clips through mere prompts, customization around instruments and also adding your own music and mixing and matching, GenAI could really transform the creative process of music making. It would also be interesting if we could compare human produced music and AI generated music side by side and see the similarities and differences.

We are living in exciting times, and it would be interesting to see how the future evolves, and how the human and machine made music combines to evolve the creative process.

No ratings yet.

Artificial intelligence (AI) is revolutionizing the healthcare sector, and federated learning (FL) is emerging as a powerful approach. Working with one of the pharma giants during my work experience, I’ve observed how federated learning enables cross collaboration across institutions offering a much needed solution to the privacy concerns that have plagued the medical industry. This has also been observed lately with various US medical centers and institutes, partnering with NVIDIA and Google Cloud to develop powerful federated learning models. Federated learning has also emerged as a powerful approach across various other sectors like automotive, manufacturing and media.

The healthcare industry generates vast amounts of data daily, from electronic health records to medical imaging. However, this data is often distributed across different institutions and subject to strict privacy regulations. Federated learning addresses these issues by decentralizing the AI models and sharing only the updated weights rather than the raw data.

One of the most significant applications of FL in healthcare is in medical imaging analysis. Radiology departments across multiple hospitals can collaborate to train AI models for tasks like tumour detection or disease classification without exchanging patient scans, thus making their operations more efficient. This approach not only preserves patient privacy but also leads to more robust and generalizable models.

Despite its promise, FL in healthcare faces several challenges. Ensuring the security of model updates, dealing with diverse data across institutions, and managing the computational resources required for distributed learning are ongoing areas of research. Additionally, regulatory frameworks need to evolve to accommodate this new paradigm of data collaboration.

As FL technology matures, we can expect to see more real-world implementations in healthcare. From improving diagnostic accuracy to optimizing hospital operations, there are many potential applications. Moreover, FL could play a crucial role in preparing for future health crises by enabling rapid, collaborative development of AI models for disease detection and outbreak prediction.

In conclusion, federated learning is causing significant disruption in the healthcare sector by addressing longstanding challenges in data privacy and collaboration. As this technology continues to evolve, it promises to accelerate the development of AI solutions that can improve patient outcomes, enhance clinical decision-making, and transform healthcare delivery on a global scale.

References:

1. Kaissis, G., Makowski, M., Rückert, D., & Braren, R. (2020). Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence, 2(6), 305-311.
2. Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50-60.
3. Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., … & Cardoso, M. J. (2020). The future of digital health with federated learning. NPJ Digital Medicine, 3(1), 1-7.