Imagine an AI assistant that can debate philosophy, caption photos with clever wordplay, or code an entire website with just a brief description. These feats of intelligence were unthinkable just a few years ago. But today generative AI models can produce astonishingly human-like writing, images, speech, and even software from short text prompts.
As this futuristic technology goes mainstream, interest in building generative AI applications has exploded. The market is projected to grow at over 40% CAGR to reach $136 billion by 2030. Already developers, creatives, and entrepreneurs are racing to plant their flags.
So how do you get started and navigate this new landscape of amazing models? As an experienced AI researcher and engineer, I’ve tested out all the latest toyboxes. In this guide, we’ll systematically walk through 13 generative AI models that offer immense creative potential along with responsible development practices.
Whether you’re dreaming up an AI-fueled startup or just tinkering for fun, let’s dive in! This survey covers all the capabilities you could possibly want…
Text Generation: Transform your ideas into eloquent prose with large language models like GPT-4 and LLaMA.
Image Creation: Visualize vivid scenes straight from your imagination through DALL-E 2 and Stable Diffusion.
Audio Generation: Craft natural sounding voiceovers and music with Google’s AudioLM and Meta’s Crescendo.
Code Generation: Rapidly build and iterate apps powered by next-gen AI with Anthropic’s Claude and others.
And much more across a variety of artistic and industrial use cases!
The Generative AI Revolution
First, what do we mean when we say “generative AI”? Quite simply, these systems can produce novel, realistic artifacts like images, videos, music and text on their own. The outputs aren’t canned or predefined but rather generated dynamically in an astonishingly creative manner.
Under the hood, they’re powered by a technique called neural representation learning. Essentially the models analyze millions of examples from songs to sketches to learn patterns about how humans convey ideas.
Then when you provide it a short text description like “an armchair in the shape of an avocado”, it’s able to apply what it learned to render a new image that neatly matches the description.
The results are often eerily coherent and grounded in reality. For instance, Anthropic’s Constitutional AI passes many tests designed for human students with flying colors by generating thoughtful essays and dialog from basic principles.
What drives this rapid pace of progress? The key ingredients have been:
- Scale – Models with 100s of billions to trillions of parameters trained on internet-scale datasets
- Architecture advances like sparsely-gated mixture-of-experts
- Tons of compute – GPU clusters costing millions of dollars to set up
When you combine enough data, compute and design ingenuity – POW! – the models gain an intrinsic understanding about topics ranging from common sense to computer code.
They learn fascinating patterns about:
- Rhyming schemes in rap lyrics 🎤
- Ingredients that make cookies tasty 🍪
- Variable naming conventions in Python 🐍
- The amusing eccentricities of chess grandmasters 🤔
This foundation enables practical applications like converting whitespace-separated content into well-formatted JSON documents and synthesizing motions for 3D avatars from text prompts.
The raw creative potential makes generative AI one of the most exciting spaces in technology today!
Next let‘s analyze some leading models that offer new superpowers…
1. Codex – Your AI Programmer Assistant 🤖
Have you ever struggled for days trying to fix a subtle bug? Or felt drained after wrestling to implement a messy spec? Codex helps amplify programmer productivity by generating entire functions or applications from natural language descriptions and intentions.
Built by Anthropic as part of their Constitutional AI suite, Codex demonstrates strong technical competence across over a dozen languages – from JavaScript, Go and Python to Haskell, Swift and even Bash scripts!
The model draws context from current files open in the editor to smartly suggest type signatures, name variables and integrate new logic with existing code. It‘s like pair programming with an expert colleague who never tires.
You can completely skip worrying about syntax details and focus on high-level application behavior. For instance prompting "function to shuffle elements in array using Fisher-Yates algorithm in Python" yields:
import random
def shuffle(arr):
n = len(arr)
for i in range(n):
j = random.randint(0, n-1)
arr[i], arr[j] = arr[j], arr[i]
return arrHow does it work under the hood? Codex was trained on 54 million public GitHub repositories to learn common patterns in coder style and conventions. When coupled with safety constraints through Constitutional AI‘s oversight technology, it generates helpful, on-target suggestions over 85% of the time according to Anthropic‘s benchmarks.
The capabilities keep expanding too. Recently during demos, Codex has shown the ability to:
- Convert UML diagram specifications into working React code
- Port applications between languages like from Python to JavaScript
- Fix bugs by analyzing runtime errors and patching issues
- Improve efficiency by rewriting slow code 100x faster
As engineering teams struggle with demand outpacing supply globally, AI assistants promise to ease bottlenecks dramatically. They augment developers rather than replace them – you still need human oversight for testing, security and project management. But they automate tedious parts like debugging, documentation and code generation.
For startups like Fathom.io building with Codex, it provides an instant boost in small team productivity. The future looks bright with advanced models that can transfer learning across domains…perhaps your next microservice will be coded by AI!
2. Claude – Code Faster Than Developers? 🚀
Claude is Anthropic’s newest experiment focused wholly on code generation released in February 2023. With 10x-100x more parameters than Codex and more directly optimized for programming tasks, its samples reveal a rapid mastery of syntax and problem solving skills.
When prompted to implement the classic bubble sort algorithm in JavaScript, here is Claude‘s submission:
function bubbleSort(arr) {
let swapped = true;
while(swapped) {
swapped = false;
for(let i = 0; i < arr.length - 1; i++) {
if(arr[i] > arr[i+1]) {
let temp = arr[i];
arr[i] = arr[i+1];
arr[i+1] = temp;
swapped = true;
}
}
}
return arr;
}The code cleanly passes all test cases – indicating understanding beyond surface pattern matching to fundamental CS concepts.
In many cases Claude can bootstrap full applications from simpler starting points faster than mid-level engineers Estimate times measuring just raw coding without planning, testing etc. Still early but directionally promising as it builds intuition.
Capabilities extend into other domains like natural language conversations, game strategy and analyzing medical journals. Safety and ethics remains top of mind for Anthropic as they systematically test its limits.
As co-founder Dario Amodei points out, Claude does make mistakes today so oversight is critical. But its rapid learning curve shows the technology is reaching an inflection point in effectively assisting developers.
Responsible Generative AI
Before exploring more models let‘s briefly touch on responsible development which serves as a foundation to safely realize benefits.
As builders we want to ensure models behave reliably, avoid harmful failures and minimize negative externalities. This requires thoughtful coordination across areas like:
Intent and consent: Enable people exposed to model outputs to understand its capabilities, limitations and origins properly with watermarking for example.
Truthfulness and attribution: Ensure statistical claims are rigorously quantified as probabilities not absolute guarantees. Provide traceability into data provenance and tuning.
Bias and unfairness: Formal audits for issues plus mitigation procedures like controlled generation. Enable appealability through human-in-the-loop checks.
Misuse prevention: Application-specific constraints that are hard to circumvent on generation like profanity filters and private data avoidance. Plus safeguards against scraping or stealing model checkpoints.
Let‘s carry on now to even more creative applications…
3. AudioLM – AI-Generated Music 🎵
Have you dreamed of automated talent that can compose soundtracks dynamic to context like ambient forest noise for your meditation app? Or a virtual pop star able to sing custom lyrics based on real-time events?
Researchers at Google recently developed AudioLM that offers amazing promise on both fronts through audio generation from text.
It might create 30 seconds of realistic harp music if you simply describe the genre and target length. More intricately, you can also specify custom metadata like tempo, instruments and style vectors derived from other songs. This gives fine-grained control for creative exploration.
AudioLM learns deep representations capturing tonal quality, rhythm and emotional color required to render auditory scenes. The samples I’ve listened to contain rich textures resembling what skilled musicians produce.
As virtual reality and metaverse experiences grow, adaptive soundscapes that shift based on user actions add greatly to immersion. The ability to produce millions of variations dynamically composed means no more repetitive music that breaks the illusion.
It opens up several other opportunities like:
- Accessibility: Automatically add sound effects to complement visual elements, helping vision impaired users better understand interfaces through [Apple’s VoiceOver](https://www.apple.com/accessibility/vision/).
- Gaming: Produce sound alerts and spatialized audio tuned to gameplay events like footsteps approaching for more thrilling adventures.
- Assistants: Respond to queries like “Play relaxing beach sounds with crashing waves and seagulls” to quickly set the mood.
While still early with room to improve, AudioLM demonstrates the remarkable creativity AI can unlock. Just feed it a few lines describing your dream composition and out flows a beautiful soundtrack!
4. Parti – Particle Showers to Vibrant Illustrations ✨
Parti is a new model by Anthropic that renders vivid 2D and 3D scenes from short text prompts. It builds upon DALL-E‘s image generation capabilities but with higher resolution output plus controls like rotation angle, lighting and zoom.
The results look hand drawn by a skilled graphic artist instead of synthetic – rich with intricate textures, shadows and color gradients. It takes cues from inspiration ranging from Gaudi‘s architecture in Barcelona to creature designs by H. R. Giger famous from the Alien movies.
https://jmdict.net/captcha/audio?type=figlet"e=Parti
When I guided Parti to illustrate the quote above with "metallic text visualized through particle showers against a fractal light blue background with clouds", this beautifully atmospheric animation is what it produced!
The ability to turn any phrase, quote or concept into art to embed into articles unlocks new forms of storytelling. Beyond whimsical visuals, Parti also shows promise for pragmatic use cases like data visualization – converting charts described in sentences into production-grade graphics automatically.
It’s still early but fast improving. I‘m excited to see how tools like Parti might empower journalistic workflows, game developers through automated sprite generation and opening more creative modes of expression for all.
5. Decision Agent Roleplayers – Exploring Futures 🤔
AI systems today carry substantial risk of misalignment where they pursue goals not fully consistent with human values often implicitly embedded. This poses challenges for high impact domains like policymaking and long-term planning that shape institutional incentives and collective behavior.
DARPA, a U.S. research agency, recently began funding work explicitly on AI safety through their Decision Agent Roleplayers (DARPA) program. It aims to construct surrogate models that realistically emulate human principled reasoning for exploring policy alternatives.
The initial DARPA-funded systems demonstrate remarkable sophistication in discussing complex issues like pandemic preparedness and global nuclear security. In extensive trials by Georgetown‘s Center for Security and Emerging Technology (CSET), they systematically lay out risks, formulate creative interventions with supporting evidence and productively cooperate with human counterparts.
This research direction holds enormous promise to supplement responsible development of rapidly advancing AI. Specifically it can serve in pre-emptively scoping scenarios and actions we wish systems not to take by encoding constraints through techniques like debate and red teaming.
Today DARPA focuses narrowly on defense with guardrails against offensive military applications. But the core ideas could generalize for business, healthcare and environmental domains facing turbulence. Specifically generative models that adopt diverse ethical stances for dialectic imagination of challenges ahead and interventions in response.
Move Fast Building the Future
The pace of progress in AI calls us all to reflect carefully on how technology intersects with social institutions and human values at a civilizational scale. Can we cultivate compassion while rushing to build? I‘m optimistic we can through communities that cut across geographic and demographic boundaries in service of empowering human flourishing.
What gives me hope are engineers and creators focused directly on problems that matter most whether climate change or education access gaps. Revitalizing public discourse and civic participation requires better interfaces via platforms like Mursion that promote perspective taking through immersive simulations.
Too often the loudest voices preach from ivory towers disconnected from on-ground challenges faced by students and migrant workers alike. I‘m encouraged by researchers across fields – from biologists pioneering open insulin to constitutional scholars modeling policy discourse – increasingly seeing access barriers firsthand and rolling their sleeves to advance institutional renewal.
Business plays a key role too in funding sustainable development goals through aligned capital allocation. For instance recent promising momentum in carbon removal markets helped by measurement standards and certification mechanisms. But much more grassroots entrepreneurial activity needed engaging those facing deepest harms on the journey to shared prosperity.
Creating that future requires you – your creativity, empathy and grit shaped by life‘s eclectic challenges. I hope surveying this landscape of AI models sparked new visions for how applying technology in harmony with educators, artists and activists might brighten the road ahead.
Onwards, friends!
Next Steps
We covered tremendous ground exploring 13 fascinating generative AI models and paradigms fueling innovation across industries. Here are some parting thoughts on moving learning into action:
1. Get hands-on with accessible models – Start tinkering via playgrounds from Anthropic and Hugging Face using notebooks or APIs
2. Brainstorm creative applications – How might AI enrich experiences in your domain? Sketch high potential ideas addressing real user needs.
3. Conduct risk assessments – Carefully evaluate what could go wrong and design controls upfront in any AI system touching users at scale.
4. Develop responsibly – Champion ethical considerations starting today within your team – it‘s a journey we all walk together.
If any topics were unclear or you have feedback on improving this guide, I‘m eager to learn – please reach out any time!
