AI_bookmarks

cost

GraphCast is now the most accurate 10-day global weather forecasting system in the world, and can predict extreme weather events further into the future than was previously possible.

A staggering gender gap has opened in the adoption of ChatGPT: Women are 20 percentage points less likely to use ChatGPT compared to men in the same occupation.

Suno's mission is to make it possible for everyone to make music. We imagine a future where music is a bigger, more valuable, and more meaningful part of people's lives than it even is today. Technology enables a future where the whole world can explore, create, and be active participants in an art form most have only ever consumed. From professional musicians seeking inspiration to friends and family writing songs for each other, we are exploring new ways to create, listen to, and experience music. So far, more than 12 million people are engaging with music in new ways that wouldn't be possible without Suno. We see this as early but promising progress. Major record labels see this vision as a threat to their business. Each and every time there's been innovation in music — from the earliest forms of recorded music, to sampling, to drum machines, to remixing, MP3s, and streaming music — the record labels have attempted to limit progress. They have spent decades attempting to control the terms of how we create and enjoy music, and this time is no different. So, it is perhaps not a surprise that on June 24th, members of the Recording Industry Association of America, which represents the major record labels, filed a lawsuit against Suno, alleging that the data used in training our music generation technologies infringed on the copyrights of the major record labels that they represent. This lawsuit is fundamentally flawed on both the facts and the law, and is nothing more than yet another instance where they chose litigation over innovation. For starters, the major record labels clearly hold misconceptions about how our technology works. Suno helps people create music through a similar process to one humans have used forever: by learning styles, patterns, and forms (in essence, the "grammar" or music), and then inventing new music around them. The major record labels are trying to argue that neural networks are mere parrots — copying and repeating — when in reality model training looks a lot more like a kid learning to write new rock songs by listening religiously to rock music. Like that kid, Suno gets better the more our AI learns. We train our models on medium- and high-quality music we can find on the open internet — just as Google's Gemini, Microsoft's Copilot, Anthropic's Claude, OpenAI's ChatGPT, and even Apple's new Apple Intelligence train their models on the open internet. Much of the open internet indeed contains copyrighted materials, and some of it is owned by major record labels. But, just like the kid writing their own rock songs after listening to the genre — or a teacher or a journalist reviewing existing materials to draw new insights — learning is not infringing. It never has been, and it is not now. The timing of this lawsuit was somewhat surprising. When this lawsuit landed, Suno was, in fact, having productive discussions with a number of the RIAA's major record label members to find ways of expanding the pie for music together. We did so not because we had to, but because we believe that the music industry could help us lead this expansion of opportunity for everyone, rather than resisting it. Whether this lawsuit is the result of over-eager lawyers throwing their weight around, or a conscious strategy to gain leverage in our commercial discussions, we believe that this lawsuit is an unnecessary impediment to a larger and more valuable future for music. This is particularly the case because Suno is a new kind of musical instrument, one that enables a new kind of creative process for everyone and opens new business opportunities for the industry. Suno is designed for original music, and we prize originality, both in how we build our product and in how people use it. People who use Suno are using the product to create their own, original music. They are not trying to recreate an existing song that can be heard somewhere else on the internet for free. But, even if they were trying to copy existing music, we have myriad controls in place to encourage originality and prevent duplicative use cases. We do so more aggressively than any other company in the industry, including other startups. Some of our originality-guarding features include checking for and preventing copyrighted content in audio uploads, and disallowing artist-based descriptions in requests to generate music. Why do we work to encourage originality? We do this because it makes for a more fun and engaging experience to create entirely original compositions on Suno. We do it because we think it makes Suno incredibly valuable to be a place where new musical talent can shine. AI allows anyone to realize the songs in their head, regardless of the money, equipment, or connections that they have. The future is an explosion of new artists that are creating music in new ways, building fan bases, finding new reasons to smile, and getting famous. We hope that the major record labels realize that we can build a stronger foundation

Comparing Traditional AI vs. Generative AI models shows that both are trained on large datasets. However, TA relies on rules and patterns, whereas Gen AI captures the essence of human-created content.

general purpose technology

Know your students: High-quality learning is fundamentally relational, not transactional, despite higher education looking increasingly transactional in nature (I won't rant about that issue here). While getting to know students can be challenging in large cohorts and/or with a high reliance on sessional staff, finding ways to connect with students individually can make a significant difference in promoting academic integrity and provide insight into the individual trajectories students are on in their learning. Yes, I know, much easier said than done.Be transparent about AI use: Whether using a system of "lanes," like Professor Danny Liu’s two-lane approach, the multi-lane highway approach outlined by UNSW’s Professor Alex Steel, the AI Assessment Scale developed by Leon Furze, Dr Mike Perkins, Dr Jasper Roe SFHEA and Dr. Jason MacVaugh or another framework, be absolutely explicit about what constitutes appropriate and inappropriate use of AI in your units/subjects. My sense is that coordinators are best placed to make these calls, which takes us back to the awareness-raising piece above. Clear guidelines can help students navigate this new terrain ethically but this kind of guidance is often lacking (partly because we have been trying to figure all this out, of course).Ask students to show their working: If students are permitted to use AI tools, require them to document their process. The calculator analogy doesn’t work with generative AI for a range of reasons but ‘show us your working’ is a useful heuristic here, as it was when calculators appeared on the scene. This approach could include sharing the prompts they used, the outputs they received, and how they incorporated this information into their final work. This approach not only discourages misuse but also helps students develop critical skills in working with AI and gives us some insight into how these tools can be used in the tasks we assign.Engage in conversations with students as assessment: Consider incorporating more oral assessments or discussions into your assessment. While this may be challenging in large cohorts, even small-scale implementation can provide valuable insights into students' understanding and thought processes that may not be evident in written work alone. I have resisted this one because of the challenges I face in implementing this approach in a cohort of 250 students, but I have changed my mind on this and will give it a go. I have been convinced by the argument that we can learn a lot more about how a student is going in a 15-minute chat with them than in spending an hour or more looking at the distant echoes of their progress in a written artefact.