The Future of Business: AI Essential Role of In-Context Learning
In-Context Learning: A breakthrough in AI revolutionizing business operations. Improve customer service, automate tasks, personalize marketing, and make better decisions. Adapt for the future!
By Andrew & Otzai (our futuristic AI caveman)
In Today’s Caveminds Journey…
We bring some fire for your weekend!
🚀 From Dusk to Dawn: ChatGPT’s Gives Way to the real AI Epoch
💼 Business Buzz: Adapting Your Strategies for the LLM Evolution
📈 Actionable Use Cases: The Fast Lane to an In-Context Learning Future!
Alright, let’s get started!
LLMs are here and while all of you on Twitter are watching people build tools on top of ChatGPT and bragging about quick 💰, be glad that we are inviting you to dive deeper than surface level and learn about what the big boys and girls are doing with AI.
The Real AI Dawn: Adapting Your Strategies for the LLM Evolution
While we all gloat about the simplicity to login to our favorite LLM and ask questions until our ♥️’s content - not everyone is excited about the ease of use.
Consider Amazon's blanket restriction against Bard, ChatGPT, and other LLMs usage for its employees.
The reason? One word: security.
Is this a mistake? No. It’s going to become the norm.
Because what many do not realize is that when www was created, the same security problems existed. Well, it was simpler back then - there were very few computers 💾 connected to the internet - but very quickly firewalls were built. And the enterprise companies were protected 🔒
LLM’s pose the same risk. Instead of being restricted on which www you can search inside of your companies firewalls - Amazon is restricting which LLMs you can use. Because anything you type into the LLM is always and forever public.
Even Law Offices are restricting the use of LLM’s. Why? Because if the lawyer types in confidential information to formulate the agreement; well, that is a violation of their attorney-client confidentiality pledge. 🤝
So don’t go thinking lawyers will be extinct soon ☠️ — the only thing that will happen is their prices will decrease as software agencies build out private LLM’s for their offices. (More on this soon.)
But what about all the tools on Twitter that are “making life easier” for lawyers, accountants, tax advisors, content writers, film makers, and every person’s job ever created? 🤔
Well, those will all become use cases for agencies and internal enterprise teams of how to build secure tools for real world use cases.
I am not saying that all tools are junk 🗑️. But the Lindy effect proves that if you build something in a month - it will die in a month. Things need to be built to last for customers that plan to use your product for years - not microseconds.
So if “new tools” will die, how will AI advance?
Bard is now released public to the world and destroys any tool that was created on ChatGPT promoting a “fine-tuned” data set. I’m looking at those Jasper-style “make me something in my brand voice” tools by uploading all my website content and then hitting “Give me a hero image”. Double 🥱🥱
So, what’s going to happen?
Something that no one saw coming. 👀
It is larger than ChatGPT, it is not human-created, and it is shocking data scientists. It also, in theory, should replace most tools you see being built on top of ChatGPT because, well, it's emergent.
It’s called “In Context Learning” and Enterprise clients are drooling over its capabilities. 🤤
What is in-context learning❓
In-context learning is a type of machine learning where an AI model learns to perform a task by observing examples of the task being performed in context. This is in contrast to traditional machine learning, where an AI model is trained on a dataset of labeled examples of the task being performed.
Kind of like asking an intern to label all the movies in Netflix 🍿by genre. 🥱
Previously, you would need to hire a team of laborers to go into Netflix and label movie, after movie, after movie, after movie of alllll the genres that exist.
– What even is Neo Noir anyways?
Then, after it’s properly labeled, finally a data scientist can come in and create predictions on the dataset so that after your done watching “10 Things I hate about you” you can roll right into “Never Been Kissed.”
– Yes, 90s movies are the best.
With in-context learning, you can instead train your AI model by observing examples of how humans are labeling those movies based on the context of the movie itself.
Maybe, the machine notices that if the word “Love” appears more than 4 times then it’s a Romantic Movie. But it is not doing this in post-production. The machine is inferring the data from only a few labels that the human provided. Then predicting the rest of the labeling by some other content it discovers about the movie itself - sometimes even things humans don’t notice.
Maybe there are millions of movies and after 20 labels, the machine can start self-labeling the genres. That is a ridiculous time-saving tasks to prepare information for prediction models.
This. Is. MindBlowing. 🤯
That is great, but how can I use In-Context Learning for my Small Biz?
Regardless of whether you're running a mom-and-pop shop or a software startup, there are still ways you should be educating yourself to prepare your biz.
– Because if you don’t your competitors will.
Here are some ways to future-proof your business:
🫂 Improve customer service:
In-context learning can be used to train AI models to answer customer questions in a more accurate and efficient way. This can free up human customer service representatives to focus on more complex tasks.
👨💻 Personalize marketing campaigns:
In-context learning can be used to track customer behavior and preferences to personalize marketing campaigns. This can help businesses to target their marketing efforts more effectively and improve their return on investment (ROI).
✅ Automate tasks:
In-context learning can be used to automate tasks that are currently performed by humans. This can free up human employees to focus on more strategic and value-added activities.
🎯 Make better decisions:
In-context learning can be used to analyze data to identify trends and patterns. This information can be used to make better decisions about business operations.
Here are specific examples of how a biz can use it…
🥐☕A small coffee shop could use in-context learning to train an AI model to recommend coffee drinks to customers based on their past orders and preferences.
A boutique clothing store could track in-store customer behavior to personalize marketing campaigns and product recommendations. 👗🛍️
💼📊An accounting firm could use in-context learning to automate tasks such as data entry and billing, freeing up human employees to focus on more complex tasks.
Here are the steps on how to set up an on-premise LLM with in-context learning:
Choose an LLM platform. Options range from commercial offerings such as Google AI's LLM and OpenAI's GPT-3 to open-source solutions like Hugging Face's Transformers.
Install the LLM platform on your on-premise server. This process will vary based on your chosen platform.
Configure the LLM platform, including setting up environment variables like the path to the data directory and the model parameters.
Collect data. Depending on your needs, this could include text, images, or audio data.
Label the data. This step involves assigning labels to each piece of data, which will guide the LLM in learning the desired task.
Train the LLM. This process could take hours or even days, depending on the data set's size and the task's complexity.
Deploy the LLM. Make it available to users via a web server or a mobile app.
This is when you pull out your reading glasses 🥸 and read the fine print.
LLMs are heading towards a private future. — Hear me out…
✔️ Secure models will be built inside of network firewalls and provide executive assistant-level help to all employees inside a company.
✔️ Workflows will be created, classifications will exist and prompting will be rampant.
✔️ All inside a company firewall. And all built internally for that specific company.
You see, it's important — ahem, vital — that none of this information leaks.
How a company “prompts” will be IP. How a company obtains data for it’s LLM will be IP. How a company filters data for their clients and customers will be IP.
So Lawyers will definitely be needed to protect this new paradigm. – Told you we would come back to talking about Lawyers.
🤔 But what do you need to know about your multi-thousandaire company (or millionaire for those lucky few)?
Big-Tech, Big Products
Big products are being built to “suck up” all the ChatGPT tools that you see in the wild today.
These big products are coming from companies such as Facebook, HuggingFace, Microsoft, and Google and they will be deployed inside their ecosystems.
For instance, Adobe will replace all of your "MidJourney" apps with its own LLM trained to produce Photoshop-style image editing.
Google will incorporate search capabilities into Google Docs for quick HR, Legal, and Engineering workflow creation. Not only the text but the nuances of your business language.
Dropbox will offer its own LLM to their enterprise customers, providing a secure and scalable environment for work.
As for Amazon? Well - they already have many many many internal LLMs doing specific tasks. They have for years - it’s nothing new for them to figure out how to keep data secure. What is new, is that their LLMs can get smarter through in-context learning based on the new discoveries in AI.
So keep playing with your SelfGPT apps and photoGPT tools. 🤣 I am not sure about the future of Copy.AI. It’s even hard to see a use case for it with Bard now.
But then again, people still use Whereby even though Zoom, Google, Teams, and whatever Meta calls their video conferencing tool exists. 🤷♂️
Surely some will last. But most will die as enterprises get warmed up with their newfound abilities to perform in-context learning on their large datasets.
As for agencies?
👉 Start reading CoHere blogs and learn how Pinecone works - because that is the future of development IMO.
So, what's the AI buzz about?
It's all about harnessing the power of Language Learning Models applied in In-Context Learning. 🤝
It's not just about jumping on the AI bandwagon – privacy and data security matter too. So, instead of a wild west scenario with public LLMs, envision secure, customized AI tools sitting snug within organizational firewalls, protecting IP and supercharging productivity.
The advent of in-context learning is a game-changer, streamlining data labeling and breathing life into smarter customer service, personalized marketing, automation, and decision-making. Whether you're a big shot, a budding business, or a startup on the rise, the key to staying ahead in this ever-evolving AI landscape is adaptability. - And remember, you'll still need your lawyers! 🧑💼
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