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with large language models (like chatGPT), and AI art generation - everything we know about tech in the next few years, maybe changing drastically
Runs a Low Code UI test automation startup ( https://uilicious.com/ ) and is part of the GPUJS team ( https://gpu.rocks/ )
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Introducing a very wide range of topics, and ideas. From code to games, in a light hearted way
A proposed solution for all parties ...
Dev to CTO - How do I become a 10x / Rockstar developer?
Key learning points for founders and VCs on implementing LLM AI. And a framework on how to do the same for your startup (or portfolio)
This article focuses more on the current state of AI, on 5th Dec 2022. As a means of briefing others outside this industry, what has been going on recently, and what are its limitations. With a focus on recent developments in chatGPT, over long-term speculations.
Recommended music for the article - GITS Stand Alone Complex: Be Human youtube music, spotify
In 2022 artificial intelligence (AI) has made some major strides in its capabilities. Largely in the two following area
Prompt to text generation
Prompt to image generation
Collectively, these "prompt-to-output" models are designed to take a prompt (or a question) and generate a finished product in either text (like a clickbait headline, or programming code) or image form (an illustrated art image, or stock images) based on that prompt.
This type of AI is incredibly powerful due to how simple it is to use, and has the potential to revolutionize many different fields, and can literally feel like magic.
An example would be how a single prompt like
Can be used to generate the following image
Or write business plans for a website
Or even be used to help generate entire novels
Because the full scope of its capabilities requires you to try it, to better understand its potential for yourself. I would highly recommend giving these services a try, while it's still free at https://chat.openai.com/chat (it may turn into a paid service in the future)
Yes, but that includes dreaming up lies.
In an extremely convincing fashion that even experts can find it hard to tell the difference.
It can also be very stubborn in how it presents itself, so as hard as I tried to get it to help me write this article, unfortunately, it just couldn't be "me".
In a weird way, you should view these AI as a unique assistant who ...
is a naive smart kid
who is slightly high or dreaming in the clouds
have very unique and flawed views
and is very willing to bluff their way, in providing a response, even if they dun have the answers
If you ever dealt with such an individual, you know that there are random nuggets of pure gold wisdom that they will say from time to time, and bulls#!t you will need to filter, as it dreams up the answer.
The later act of filtering, is potentially the biggest danger of this new technology.
As it can be very very difficult, because of how convincing the AI structure its answer, if you are not an expert in the respective field.
For example, as an experiment, I asked it questions in domains i had zero (or even negative) expertise, like cooking or chemistry. And I realised I have nearly no capability, in validating its bluffs.
This is despite my best attempts to google and fact-check it at times, where it may give sometimes answers that are slightly different, but I have honestly no idea if it's right or wrong. Unless I try it myself.
Which can potentially be dangerous, seeing as how it can get things wrong in my area of expertise.
Yes, but it is not thinking, it is dreaming.
To elaborate, let’s start with an oversimplified explanation on how these AI models fundamentally work.
First the AI is given a large dataset of articles or images from various sources, it uses this data to provide the foundation of its knowledge, loosely connected together. This is then trained together with possible prompts and responses given by the user.
Once this is completed, we have a working neural network or trained model with all this loosely connected data. Frozen in time.
When a prompt or question is given, the AI is not thinking of an answer or deliberating from the trained model. In a way, it is hallucinating the answer using the snapshot of knowledge it has.
Now, lets use a human analogy instead
The human analogy, would be your brain right now, with all your past knowledge, or experiences in school or in life. Snapshot and recorded, in your head. Minus your emotions, and your feelings, or anything hormonal related.
When a prompt is given, imagine it as being told to you, before going to sleep into a dream. For example "go buy milk tomorrow, when you wake up".
For most folks, sleep is when they as an individual have little to no control with how the story goes (ignoring the lucky few lucid dreamers).
In this state, your mind in its dream state, starts to take that last phrase that is stuck in your head, and starts dreaming a story around that phrase.
Where you are an individual inside a dream, and your brain the dream world doing its best to be as convincing as possible that its real and not a dream world, continuously writing the story back and forth with your interactions and actions.
All while using the snapshot of all your past knowledge and experience, in your brain. To make your dreamworld as realistic as possible
Flipping it back around
You, with your prompts is a participant in a dream. With the AI being the dream world, trying its best to be, as convincingly as possible, a good dream. Where it answer your questions in a very believable manner.
And just like your dream world, it can be at times entirely realistic, and factually accurate, or as imaginary (and perhaps exciting) as it wants to be
As such, this dreamlike state, allows it both to make basic math mistakes with high confidence
To hilariously answer questions as a cat when instructed
To very impressively, even emulating interactions with a virtual machine (rather accurately!)
It's difficult to predict exactly how the advancements in AI will impact the future of work, but there are some potential scenarios to consider.
One thing that seems clear is that the current wave of "prompt-to-output" models is having its "iPhone revolution moment". Just as the iPhone changed how everyday people interact with software and ushered in the era of smartphones, prompt-to-output models have the potential to change how we work and interact with tools.
It has already started fundamentally changing industries, today. Especially in SEO copywriting, and stock illustration/photo industries.
The next wave could manifest in waves of AI assistant work tools and practical enhancements to existing tools, ultimately making automation more accessible and lowering the barrier of entry for performing tasks.
Regardless of the forms of changes, this is a significant development and every startup founder (including me) and product owner should be taking note of how to integrate AI into their business. Just as the smartphone/iPhone era made entire industries and tools obsolete and created new domains of app-based tools, the rise of AI has the potential to do the same.
However, it's important to note that not all tools will be impacted by AI. Some industries, may not see much change beyond a new skin or UI. Only time will tell which side your business falls on.
PS: if you are feeling FOMO, and is in the tech industry, chill - the iPhone and app revolution happened in months and years, not days. Do go on with your life, and your holiday plans.
As long as you are not ignoring the potential changes, and making steps toward it - you are good.
If you are outside the tech industry, keep a lookout, and be ready to learn new tools, across the next few years - that may change your industry. And be prepared to learn and use them to your advantage.
~ Until next time 🖖 live long and prosper
The article was originally posted on the author’s substack here:
https://substack.tech-talk-cto.com/p/introducing-ai-chatgpt-and-how-it