26 Jan Exciting Applications for Generative AI
ChatGPT3: The new buzz word for 2023 is here (whether you like it or not), and we are all trying to grasp what this might mean for society, business and humanity. The power that Generative AI tools have is astounding, allowing people to use the internet to create rather than just move content.
Right now, people everywhere are gleefully using generative AI to write a marketing campaign, build an app, contest a parking fine and (probably) cheat a little with their homework!
Generative AI will become part of everyone’s toolbox, and learning how to leverage and harness its capabilities is no different than learning how to Google effectively. Here is a look at the landscape and 8 areas where people are currently building the fastest:
Some of the earliest applications of Generative AI have been to augment human writing or replace human writing. There is a whole world of opportunity for Generative AI to achieve automated content generation (articles, blog posts, or social media posts); to improve content quality (AI models are able to learn fast what’s good); to create more varied content (AI models can generate a variety of content types) and also create personalized content (imagine AI models that can generate personalized writing – books, advertising copy, screenplays – based on the preferences of individual users). This could help big companies run more efficiently – or empower ordinary people to compete with these companies.
As Dominik Angerer, the co-founder of the content management software Storyblok, says that “generative AI will impact the way people brainstorm and develop content ideas…. Why should an article only be available in long-form when AI can provide a summary? Think of AI as an easy way to generate multiple headlines, images you can use right away without buying assets, and even a full page of content at the press of a button that a human can then iterate on.”
The future for text is probably in vertical-specific writing assistants, where general writing models (e.g Chat GPT) get fine-tuned (see above) for very specific use cases. There are potentially hundreds of these use cases, but some that are flourishing are:
Copywriting and Writing
Writing SEO content on websites and writing advertising copy is a perfect use case for generative ai, given the short nature of the text. Big companies have already been built in this area.
Sales and Customer Relations
One exploding area is companies using Generative AI to help salespeople write better cold outbound emails or generally deal better with customers. The idea is that if a trained model can reduce the time taken to write a good outbound email from 10 minutes to 2 minutes, that’s a huge saving.
Knowledge and Research
One of the biggest topics in generative AI is its impact on search (Google faces a huge innovator’s dilemma here: does it disrupt itself?). There are reports that Microsoft is planning to add OpenAI’s generative AI-powered ChatGPT to its Bing search engine.
There is also more general knowledge of organizational systems, such as Mem, a self-organizing workplace (a generative AI version of Notion), or Glean, which is automation for finance teams. Pragma is another one, aiming to centralize all your organization’s knowledge base for easy reference.
Conversational AI / Chatbots
LLMs are increasingly being used at the core of conversational AI or chatbots. Spending 5 minutes playing with Chat GPT and you can see why that’s powerful, and a huge leap from the V1 of chatbots that was a startup craze a few years ago. These can be used in customer service, clearly, but also potentially in health (communicating with doctors or therapists); education (teaching people online); HR (corporate training or onboarding) or in travel (coming up with a travel itinerary).
Legal Support or Writing
Legal documents are painful and expensive to put together, so it’s easy to imagine a future where documents are done (either partly or completely) by fine-tuned legal language models. Already there is PatentPal, which automates part of the writing in patent applications. There are also companies such as Darrow, which uses AI to discover legal violations, and Do Not Pay, which is using generative AI to fight for consumer rights.
Maximilian Vocke, a lawyer at the law firm Osborne Clarke, says that one of the lawyer’s core tasks, drafting documents, could become “secondary” thanks to Generative AI, at least in terms of simpler, more standardized cases. He adds that with data banks of case law and commercial registries becoming digitized and many widely used legal templates being provided online,” there is more material than ever for AI to work with and use, for example, in legal due diligence.
Some coders have been (perhaps unwisely!) showing how Chat GPT can do their jobs for them with ease. Maybe generative AI will not replace coders just yet, but it’s certainly making them more efficient: GitHub Copilot is now generating nearly 40% of code in the projects where it is installed.
So Generative AI could go a long way to making coders better and faster. Another opportunity may be opening up access to coding for consumers – allowing them to learn to prompt rather than learn to code.
Maximilian Eber, the founder of Taktile, a company that helps fintech companies test and deploy decision-making models, says: “Code generation (Codex/GitHub Copilot) will be one of the first applications of Generative AI with a substantial enterprise footprint because making developers more productive has such obvious value for businesses.”
Generative AI first truly captured the public’s imagination when OpenAI unveiled a system called DALL-E, which lets people generate photo-realistic images simply by describing what they wanted to see. It was startling to play with (and a lot of fun). So as well as disrupting writing and coding, generative AI is also disrupting the visual world. This is, of course, a huge opportunity for startups. Here are some of the key areas that are being attacked:
Imagine you could type in prompts such as “next-generation Nike trainers” or “more durable car bonnet” and a programme would build you sketches and 3D design models? High-fidelity renderings from rough sketches and prompts are already a reality, but getting better all the time. This could work for physical products, but also for digital products such as apps (e.g “design me a dating app”) This can extend to areas such as kitchen design, architecture and complex engineering projects as well.
Related to the above but there are also an enormous number of startups just generating images from text. Some of the biggest are MidJourney, DALL-E and Stability AI. Interesting here is their impact on the creator economy and ordinary artists as well as how these image models get integrated into other products (e.g image generation + text generation to make beautifully designed books or content). Also the disruption of stock photo libraries like Getty (why not just generate your own) and image software like the Adobe suite. This is also a marketing application here as well, very similar to the one described above for text.
The top-10 pop music charts are pretty formulaic. Why can’t generative AI write them? Why can’t we get Stephen Fry to narrate ALL our audio books at the click of a button? We are not so far away from any of this. In the recent documentary The Andy Warhol Diaries, filmmakers used generative technology to recreate the late artist’s voice (with access to only 3 minutes of audio of him speaking). New software allows producers to change a singer’s tone of voice and even change a lyric with text with a button.
One core category is the creation of music from scratch, allowing for infinite copyright-free music for commercial use. This might be background music – but it may also be the next global hit song!
There are also a host of startups that can create speech from scratch, either mimicking others or building sophisticated text-to-voice programs.
If you can make images, then you can also make videos. It is easy to see how “make me an oil painting of a man made out of fruit in the style of Rembrandt” can become “make me a Pixar-style movie about a little boy who does not want to grow up” or “make me a safety training video for my oil refinery”.
Runway, Tavus, Fliki and others are text-to-video converters that allow you to create video (or audio) content using AI voices. Some of the core use cases here are for corporate training videos, promising to cut down time to make a video from weeks to minutes.
Making a Movie
Meta and Google have both announced software that converts text prompts into short videos; another tool, Phenaki, can do whole scenes. None of these video generators has been released to the public yet, but the company D-ID offers an AI app that can make people in still photos blink and read from a script, and some have been using it to animate characters created by Midjourney.
Synthetic data is information that’s artificially generated rather than produced by real-world events. It’s powerful because many fields need enormous quantities of data (like training AI models!) which can be difficult and expensive to get.
Synthetic data generation is powered by deep generative algorithms, which use data samples as training data, learn the correlations, statistical properties and data structures. Once trained, the idea is that the algorithm can generate data that is statistically and structurally identical to the original training data. All this is arguably not really an application lawyer, but a deep model, but is still an exciting area for progress.
Gaming and 3D
A longstanding dream in generative AI has been to create dynamic digital 3D environments – whole worlds of objects, spaces, and characters that can interact together. This could be used to create synthetic worlds and enormous amounts of synthetic data, but also new content for games and metaverses as well as digital twins of the physical world.
Already in gaming, there are big advances here, with LLMs able to produce textures and non-playable characters. As yet, there are no text-to-game engines (games are complicated) but there are startups edging this way such as Versed. It’s a huge prize. It takes tens of thousands of hours of manpower to make one hour of games, which is tough for even big studios. Generative AI could lead to a new era of creativity in gaming, a trend that Robolox and Unity have already started.
7. Biology and Science
Companies such as Insilico and Deepmind have been using machine learning to speed up science with the help of generative AI. Will we end up in a world where we can say: Computer, design me a harder type of concrete, or Computer, look at my cancer and design me the best chemo drug to fight it…
8. Other Ideas
there is an opportunity to integrate generative AI into the whole ecommerce stack. For example: Hey AmazonGPT, can you make me some custom wallpaper for my hall, based on this image? Also, what happens when Shopify stores get pre-loaded with generative AI tools?
This ecommerce point speaks to another key trend: the growth of multi-modal models, those that can understand several domains (e.g. text and image) based on a shared model.
A New Type of Job: Prompt Engineering.
As AIs become more and more powerful, the ability to get the most out of them (communicate with them?) becomes a job in itself. There is already at least one great business, Promptbase, which is a marketplace for great prompts. But will prompting become the ultimate high-level coding language?
There is a lot of talk about search being disrupted, but Sam Lessin makes the point that the real problem with Gmail is it’s hard to search. What could another LLM-focused email client do to help people access, use and category decades’ worth of data on themselves?
Companies will spring up fighting back against generative AI, not just in text but also in video protecting IP and warning about deep fakes. Ciarán O’Mara, the co-founder of the ML vision company Protex, says: “I think there will be big winners in the ethics space. This could be solving the problem of the Generative AI outputs being biased or controversial or its potential to controversially disrupt the art industry. Talented artists are having their unique style used to train a model that outputs a body of work that they get no credit for.”
Generative AI and Blockchain
If anyone can create art in any style at the touch of the button, what does that mean for authenticity? Is this an opportunity for Web3-powered art and NFTs? Isaac Kamlish, the co-founder of NFT primary minting platform Fair.xyz, says: “We are consistently seeing visual art being democratized through generative AI. We therefore anticipate artist provenance to play an even more valuable role. A Damien Hirst will only feel like a Damien Hirst if it’s created by him (even if it looks like his style!). People will lean more into the who, what and the why rather than judging art purely on aesthetic. We believe that blockchain will be the vehicle to drive this change.”
Generative AI and Content Management Systems
Dominik Angerer, the co-founder of the content management software and firstminute portfolio company Storyblok, sees how it’s important for the infrastructure layer of content: “Generative AI will impact the way people brainstorm and develop content ideas…. Why should an article only be available in long-form when AI can provide a summary? Think of AI as an easy way to generate multiple headlines, images you can use right away without buying assets, and even a full page of content at the press of a button that a human can then iterate on.”
WIthout a doubt, there is extraordinary hype around generative AI. While most are obsessed with it, there are some that are chiming in with notes of caution as well, comparing it to the web3 bonanza of the past few years that led to some not-so-great outcomes.
François Chollet at Google says: “The current climate in AI has so many parallels to 2021 web3 it’s making me uncomfortable. Narratives based on zero data are accepted as self-evident. Everyone is expecting as a sure thing “civilization-altering” impact (& 100x returns on investment) in the next 2-3 years”
There are also big questions about how to build defensibility in this area when, as discussed above, models become commoditized and data moats can be hard to maintain. Finally, there is a big question on how much of the value at all will go to incumbents or big tech companies.
Still, with that note of caution, it’s impossible to ignore a new area of technology that is so wide-reaching and touches so much of the economy and, intrinsically, what makes us human.
This article is from: How to Make Money From Generative AI by firstminute Capital