Living side-by-side with an AI

2022 is, undoubtedly, the year of the AI. It started with DALL-E, then GitHub Copilot, and now—ChatGPT. AI is here to stay, whether you like it or not, and we ought to find a way to live with it.



Earlier this month, the OpenAI team released [ChatGPT](https://chat.openai.com/)—a GPT-3 based AI chat assistant that can do a lot of things like drafting a blog post idea, writing essays, explaining various topics, writing code etc. I’ve shared my initial impression about it in my previous blog post—[[ChatGPT, AI, and the future of tech]]. In the past week, I’ve used ChatGPT to perform some mundane tasks, which I usually use Google for. In this post, I want to raise some questions on how we, humans, are going to co-exist with AI.

# Immediate impact of ChatGPT
In my previous post, I said that I don’t think software engineers or content creators will lose their jobs, at least not in the near future. I still stand behind this opinion. However, as with any technological progress, there are those who are going to be impacted—for good and for bad.

When the assembly line was introduced, and later on—perfected with automation, people who used to do what robots do now—lost their jobs. That’s the price we pay for technological advancement. Mundane, and repetitive jobs—are always the first in–line to be replaced. Things like low quality content production for SEO purposes, or researching keywords for blog posts. All these can be easier done by ChatGPT with minor corrections from a human expert.

![ChatGPT generates me some keywords for a blog post#859x728](https://res.cloudinary.com/dvp7olno1/image/upload/v1670781640/yieldcode/chatgpt-keywords-for-blog-post_t5obtt.webp)

Yet, there are always people who look for low-hanging fruits. In today's world—content is highly rewarded. No matter what niche you take, be it software engineering or self development, the successful people in that niche produce content. Blog posts, podcasts, YouTube videos, books, courses etc. You can say we do it for ourselves, but the truth is—we all, including myself, want to share our content with the world. Those who write for themselves—keep a journal, not a blog. We all do it for different reasons. Some to learn, others to teach. Some to share with others, others to enrich themselves. I’m not the one to judge, and I believe people should be paid for their knowledge—if they desire so.

However, the thing with new content producers is they think it’s easy. Just slap in some words in a text editor and publish it to WordPress, or film yourself talking to a camera and upload to YouTube. But in reality—content production is a hard job that involves not only the ability to write or speak, but also the ability to distribute your content and build an audience.

And while most of the creators take the hard route of producing content, there are people who will always try to find shortcuts. Like with money making—some people go to work every day, while others try to set up a *get-rich-fast* scams. And the big beneficiaries of AI technologies today—are those who are looking for shortcuts.

I’m relatively active in communities like HackerNews and Reddit, and I can see the explosion of ChatGPT content on those platforms. I even had an answer to one of my questions, copy–pasted from ChatGPT. Granted, the author of the answer disclosed he/she used ChatGPT. [Notion](https://www.notion.so/), the highly popular note–taking app, that I no longer use, recently released [Notion AI](https://www.notion.so/product/ai)—an ability to generate content right inside the editor itself, leveraging GPT-3 model. They did not work on the countless issues that their users have with the software, such as lack of offline support or E2EE—instead, they’ve put their efforts into AI.

And it’s a smart move because I believe there will be a huge demand for AI, due to the important role of content creation in our current society. Websites, like StackOverflow, have now implemented a [temporary policy](https://meta.stackoverflow.com/questions/421831/temporary-policy-chatgpt-is-banned) to ban ChatGPT generated content. Getty [bans](https://www.theverge.com/2022/9/21/23364696/getty-images-ai-ban-generated-artwork-illustration-copyright) AI generated content. And I’ve read somewhere that Google is working on, or already have, a policy to punish websites that use primarily AI generated content. I can’t find the source, so don’t quote me on that.

## The human touch
From all the examples I’ve seen, and based on my personal experience—ChatGPT is often times wrong. It gets a lot of the things right, but usually make a mistake here and there. And this is one of the dangers of using its raw output. A person, who has no understanding of the topic, can not effectively use ChatGPT. I can ask ChatGPT how a nuclear bomb works, but I’m not a nuclear scientist to verify that the answer is correct. I can, however, go on Quora or Reddit and spam answers at a rate of one per minute, hoping that 90% of them will be correct—while ripping the benefits.

The power of communities like StackOverflow or Reddit—is the positive feedback loop we get from our fellow humans. The ability to audit a solution, or provide multiple answers from multiple people—is priceless in the world of self development and knowledge acquiring. People make mistakes—that’s why we have protocols in place, as well as fellow human beings, to correct our mistakes. ChatGPT is free, for now. If I want to read AI generated content, I can use ChatGPT directly. And so, flooding the internet or the above communities with AI generated content, makes it harder to maintain the quality of the content as well as getting **real** human answers that come from their personal experience and knowledge.

# What is creativity?
When we talk about AI generated content, we need to talk about creativity. If we put aside for a moment, the fact that ChatGPT produces inaccurate content in most cases, one could ask then—*what's the difference between a content written by a human and a content created by a machine?*

Creative process is not something that can be described mathematically. Humans, usually, describe their creative process as *a-ha!* moments. Something like: *I was taking a shower, while thinking about the solution to that problem, and then it hit me! I need to use [JavaScript](/tag/javascript)!*

We then go on and do research on that topic, by reading materials, and eventually summarizing the entire process in a unit of production—be it a system design, a piece of code or art. If we look beyond technical fields, and gravitate towards more creative fields like writing or singing—we can learn that many writers or poets produce content based on their personal experience and background. A complicated child might become a rapper who will rap about his though childhood, which might resonate with children in a similar situation. A WW1 veteran—might write about his horrible experience during the war, as a way to ease his pain and help him cope with that experience, while passing onto the reader the horrors of those events.

The word creativity in itself is associated with something human. AI has no experience. It has no notion of past. It’s not having childhood traumas. Nor it can think like a human can. Therefor, I believe it has no creativity—it can only remaster. And there is something humanly when we look at art or read books and can *feel* the same things the artist or the author were feeling. I can’t have sympathy towards machine. I usually kick my robot vacuum cleaner when it’s stuck. *Please don’t call the robots abuse hot line*.

![ChatGPT talks about creativity#878x680](https://res.cloudinary.com/dvp7olno1/image/upload/v1670783062/yieldcode/chatgpt-on-creativity_rzjz94.webp)

## Something from nothing
One could, rightfully, ask—*Humans rarely create anything from nothing. How is that different from an AI?*

I do want to point out that **there are** great discoveries made by humans. My point is not to say that all we create—is a remix of existing things. But as I’ve said earlier, most of what we do create—is based on past knowledge and experience. A student of Rembrandt will be inclined to create art in a similar style to his teacher—unless he or she decides to explore other styles or artists. The same goes to all other types of content creation. What I write—is based on what I read and the experience I have.

The difference between me and AI—is that I have context and understanding. I know that `2+2=4`. A good mathematician, which I am not, also knows *why*. AI—does not. It simply fed this fact into its neural network. If you feed it with another *fact*, it will produce answers based on that fact. This creates a negative feedback loop. I’ll talk a bit later about the negative feedback loop.

## What is copyright?
When we talk about creativity and remastering—we have to talk about copyright. As I’ve said in the beginning of this post—2022 is the year of the AI. During a very short period of time, something like 3–4 months, we've gone from *closed-behind-doors-deep-learning-ai-that-beats-humans-in-go-or-chess*, towards AI that generates art, code, and poems.

But you know what’s missing? AI that generate music or videos. Yes, there is [OpenAI Jukebox](https://openai.com/blog/jukebox/), but it didn’t get as much attention as DALL-E, Stable Diffusion, or ChatGPT. While the results of it are amazing—they are not on the same level as other AI tools. And there is no, yet, AI that can generate video content such as movies. I think it’s related to the fact, that existing AI models are trained on existing data. Access to royalty free music or films—is limited. GitHub copilot was trained on public GitHub repositories, while ChatGPT was trained on crawling the internet. Those are huge datasets. GitHub has nearly 128 million of public repositories; while the GPT-3 model was trained on nearly 400 billion websites.

I don’t believe knowledge or art should be safeguarded, unless the author decides so. But I do believe that people have the right to retain copyrights for their work. I’m not a copyright lawyer, so I don’t know exactly where is the line between original work and remixed work. But I know for sure that I can not publish a YouTube video with Britney Spears song in it. This video will be removed, for respect to Britney’s copyright. The same respect that other authors, or open source developers—have. Is it right to train an AI model on an open source project, just because it’s open source? Is it right to train an AI model on a website, just because it’s publicly available? Is it moral to charge money for [an app](https://apps.apple.com/us/app/lensa-ai-photo-video-editor/id1436732536) that uses an AI model, which is, most likely, trained on a publicly available art, that generates avatars for you—without giving credit or attribution back to the artists that “inspired” the AI model?

I don’t know. I know there is a [lawsuit against copilot](https://githubcopilotlitigation.com/). And I know that we **must** have a discussion as to how we should train those AI models. The fact that some datasets used to train the models—are public, does not mean it can be used without implications, credits, or attributions. The fact that one owns a bunch of legally obtained DVDs with movies, does not grant them the right to open a private movie theater. Images, articles, and blogs posts—sometimes have licenses. And those licenses should be respected by humans as well as AI. And unless we start to raise awareness for this issue, and have an open discussion—we are, potentially, violating the rights of content producers and artists.

# Negative Feedback Loop
I’ve mentioned it earlier, and I want to expand on this topic. In one of my previous posts, [[The software industry is broken]], I’ve discussed the fact that software today—is not as optimized as it used to be. One of the reasons I’ve outlined—is the fact that writing software became easier, and in a way *dumber*. Developers today don’t need to know how memory is managed. There are high level languages that manage it for you. And so, as tools become more available to the masses—they’re ought to be simplified. And simplification, often times, leads to loss or deterioration of knowledge.

A similar thing can, and will, happen to our knowledge if we do not control the spread of AI content. AI is great today because of two reasons—(1) the engineers behind it and (2) the entire knowledge of human beings. Every time an AI generates a piece of code, art, or a text paragraph—it’s because there is work made or knowledge shared by humans, that the AI was able to train on.

I’ve said earlier that, as of today, ChatGPT gets most of the things right, and some things wrong. If we don't set up protocols in place to distinguish human generated content from AI generated content, AI generated content will flood the internet. Look at how the standards of beauty have changed, because everyone is posting unrealistic photos of themselves, by using a bunch of photo retouching, auto-magical, tools.

There are people who embrace the easy model of producing free content. And by flooding the internet with potentially inaccurate content, we will not only bias the future training process of AI—we will damage the future knowledge of human beings.

> The real danger is not that computers will begin to think like men, but that men will begin to think like computers. 

— Sydney J. Harris

# Conclusion
I tend to think of myself as a realist. Although, if you ask some of the people who know me well—they might say I’m leaning towards pessimism. AI is here. We’ve opened that box. As with every tool ever created by human beings—it can be used for good, and it can be used for bad. People, usually, tend to focus on the good. I’m not going to unwrap my entire philosophical views here, but I tend to, typically, focus on the bad.

We reward content creation. Aside from Google, the 4 most visited websites are YouTube, Facebook, Twitter, and Instagram ([source](https://en.wikipedia.org/wiki/List_of_most_visited_websites)); and the 3 most downloaded apps are TikTok, Instagram, and Facebook. Those are all content creation tools. I’ve read somewhere that today's kids, when you ask them who they want to be when they grow up, answer *a YouTuber* or a *TikTok/Instagram influencer*. And we value knowledge.

ChatGPT, DALL-E, Stable Diffusion, Midjourney, Copilot etc. Those are all great tools for both content creation and knowledge sharing. Every one of us will have to find a place for them in our daily toolbox. ChatGPT crossed 1 million users in less than a week—this is a clear sign that it’s not just a tool that will be forgotten soon.

> The striking thing about the reaction to ChatGPT is not just the number of people who are blown away by it, but who they are. These are not people who get excited by every shiny new thing. Clearly something big is happening.

— **Paul Graham**, Computer Scientist, Entrepreneur, Venture Capitalist, and Author, <cite>[Twitter](https://twitter.com/paulg/status/1598698665337561088)</cite>

AI here to stay—and more will come. But together with the adoption of those tools into our daily toolboxes—we need to have a discussion about topics such as creativity, attribution, respect for human work and the effects those tools might have on the future of humanity and our knowledge.

#WrittenByHuman 

2022 is, undoubtedly, the year of the AI. It started with DALL-E, then GitHub Copilot, and now—ChatGPT. AI is here to stay, whether you like it or not, and we ought to find a way to live with it.



ChatGPT, AI, and the future of tech

Unless you’ve been living under a rock, you probably heard about OpenAI releasing ChatGPT for a free research preview.



After the release, a bunch of different posts across Reddit and Hacker News started to emerge — outlining all the possible reasons why AI and ChatGPT will now replace jobs such as: Product managers, software engineers, tech bloggers etc. Usage of ChatGPT exploded, leading to sites like StackOverflow — to implement temporary policy to ban ChatGPT generated answers ([source](https://meta.stackoverflow.com/questions/421831/temporary-policy-chatgpt-is-banned)).

Look — I’m a technology lover. I like messing with technology; I like using technology to improve my life. I automate certain tasks using software; I adore my robotic vacuum and window cleaners, and after getting a dishwasher last year — I don’t understand why I didn’t get one earlier. But, I, personally, can’t share the enthusiasms or doom–like prophecies that we all, are soon, going to be replaced by AI.

# Understanding AI
I’m not an AI engineer, and apart from messing a little with artificial neural networks and genetic algorithms many years ago, my AI knowledge is very limited. According to my understanding, AI, and especially the GPT–3 model — built upon feeding on initial content. This means that those models are analyzing existing texts or source code — and learn to generate meaningful content based on that. According to Wikipedia, GPT–3 was trained mostly on crawled websites ([source](https://en.wikipedia.org/wiki/GPT-3#Training_and_capabilities)). The famous GitHub CoPilot was trained on source code from GitHub repositories. And so, in my uneducated opinion, current AI models are limited by the knowledge we — humans — have created.

# Taking AI for a ride
At first I was skeptical, but ChatGPT was topping Reddit and Hacker News front pages, as well as talked a lot among my colleagues, so I’ve decided to take it for a ride. It’s open for public, for a free research preview: https://chat.openai.com/.

## Hey AI – write me a blog post
Before jumping to find whether I’ll be replaced as software engineer, I was wondering will I be replaced as a blogger. On Wednesday, I’m going to publish a new blog post that talks about cursor based pagination. You should totally subscribe to my [newsletter](/newsletter) to be notified when it’s published, as well as stay updated about any future posts. But in the meantime, I wanted to see — would ChatGPT be able to generate a blog post on that topic. And so I’ve asked AI the following:

> write a blog post about cursor based pagination and comparison with traditional pagination, including examples for usage as well as cons and pros of each approach

It generated a nice post, but when it came to comparing pros and cons — the results became messy. It listed 3 pros for cursor based pagination, and while the first two were okay-ish, the third one is a big miss:

> It’s more user-friendly. With traditional pagination, it’s difficult for the user to know how many pages there are in total, or to go directly to a specific page. With cursor based pagination, the user can see the cursor value and use it to navigate to a specific position in the dataset.

This is a complete lie. There is an easy way to both (1) know how many pages are there in total, and (2) go directly to a specific page. We’ve been doing it for decades. Moreover, depending on the implementation, the user is not always able to know and use the cursor as he desires. On complex datasets — cursors are opaque and meaningless to the user.

You could say that this is a mistake that can be easily fixed by a human later on, but wait! There’s more! When ChatGPT generated the cons of cursor based pagination, it wrote this:

> It’s less predictable. In traditional pagination, the user always knows how many records will be on each page. With cursor based pagination, the number of records on each page can vary depending on how many records have been added or deleted.

The first sentence contradicts the pro outlined earlier. If I know how many records are on each page, I can know how many pages there are. The second sentence — is not entirely true, in fact it is possible (and recommended) to include a limit on the amount of records you retrieve using any type of pagination.

While I am amazed by the ability to generate, *generally*, good structured and informative post — it’s far from replacing a human, such as myself, who performed a good research combined with personal, and professional experience.

## sudo ai – generate me some code
I’m safe with writing blog posts. Let’s see if I can be replaced as a software engineer.

I’ve asked ChatGPT to generate me some code:

> Write a function that streams data from external API endpoint and caches it, using typescript and nodejs

And it produced the following result:

```typescript
import * as request from 'request';
import * as fs from 'fs';

// Function for streaming data from external API endpoint and caching it.
async function streamData(url: string, cacheFile: string) {
	return new Promise((resolve, reject) => {
		// Check if the cache file exists.
		fs.access(cacheFile, fs.constants.F_OK, (err) => {
			if(err) {
				// Cache file does not exist, so create it and steam data from the API
				const fileStream = fs.createWriteStream(cacheFile);
				request(url)
					.pipe(fileStream)
					.on('finish', () => {
						resolve(true)
					})
					.on('error', (err) => {
						reject(err);
					});
			} else {
				// Cache file exists, so read data from the cache file.
				fs.readFile(cacheFile, 'utf8', (err, data) => {
					if(err) {
						reject(err);
					} else {
						resolve(data);
					}
				});
			}
		});
	});
}
```

ChatGPT suggested using a deprecated module — `request`. Also, the code itself will fail linting because `streamData` is defined as `async` but there is no need for that function to be async, since the code uses callback style `fs` and has no `await` statements. Nitpicking, I know.

When I asked ChatGPT, what is wrong with the above code — it was smart enough to output this:

> One potential issue with this code is that it uses the `request` module, which has been deprecated and is no longer recommended for use. Instead of using the `request` module, the code should use a more modern HTTP client, such as the `axios` library, to make HTTP requests.
> 
> Another potential issue with this code is that it does not handle errors properly. In the case where the `request` module throws an error, the code does not reject the promise, which means that the error will be silently ignored. To fix this, the code should add a `catch` block to the `request` call, and reject the promise with the error if one occurs.

It was able to catch the deprecated `request` module, but said nothing on the unnecessary `async` keyword, or suggested to use `fsPromises` instead of callbacks for a more readable code.

# Programming is more than just writing code
For some reason, people keep thinking that a programmer's job is to write code, while in reality — our job is translating client requirements into code as well as designing, and maintaining complex systems. Writing new, fresh code — is relatively small part compared to maintenance and modification of existing code.

Questions, such as *how to sort an array*, were long ago solved with all possible algorithms. ChatGPT is no different from Google search for such question, except for being more convenient than performing your own search, while potentially producing wrong or outdated code. The power of communities such as StackOverflow — is that answers are audited and updated. I keep getting notification about new comments or updates to a [question](https://stackoverflow.com/questions/28459418/use-of-put-vs-patch-methods-in-rest-api-real-life-scenarios) I’ve asked more than 7 years ago!

Don’t get me wrong — those are amazing results. I’m happy for the OpenAI team, and the people behind GPT–3 are, undoubtedly, incredible and talented engineers. But I believe that current AI models are limited by our existing knowledge. They are unable to **create** new things. They are able to **remix** existing knowledge. Takes bits from different sources, and *try* to put them in context. Those tools, in their current state, can be a great addition to other tools like searching in Google or using StackOverflow. But I don’t believe we are anywhere close to having an AI that can truly create new things — and thus replacing us in our jobs. Don’t trust me? At least trust an AI, then.

![ChatGPT on replacing software engineers#890x417](https://res.cloudinary.com/dvp7olno1/image/upload/v1670269643/yieldcode/ai_replace_me_r0rokp.webp)

#WrittenByHuman

Unless you’ve been living under a rock, you probably heard about OpenAI releasing ChatGPT for a free research preview.



How technology affects our day to day lives

Israel is experiencing a collapse of the transportation system. Public transport is unreliable and under-developed, and many people rely on their cars to get from point A to point B. And with the increase of vehicles on the road — comes the increase in traffic. A commute of 4.8 km, which usually takes about 19 minutes without traffic — now takes me anywhere between 40 and 50 minutes.

When it’s morning, I tend to commute in “automatic mode”. I do what my navigation software tells me, pay attention to the environment, and slowly but painfully navigate my way via the traffic. The navigation software I use, Waze, is so called — smart. It is able to predict traffic buildups and re-route me to a less busy road. And since pretty much everyone in Israel uses Waze — the algorithm is very good. Many times I find myself taking a longer route, anywhere between 6 km to 8 km, which suppose to be faster (at least according to Waze) than my original, heavily traveled, route.

On a sunny morning commute — I had an epiphany. Technology has a huge effect on our day-to-day life. Let me explain.

# Urban design
Urban design fascinates me. We don’t think a lot about urban design in our day-to-day lives, but it is an interesting occupation.

If you’ve ever been in a European city, you probably visited the so called — City Center. It is usually a pedestrianized zone, with a “main” street on which the town hall, the towns’ market, and sometimes a church, are located. It is surrounded by smaller streets, which all eventually lead to the main street. I don’t know if you have noticed — but you can’t get lost here. No matter what route you take — you always reach the main street.

The reason behind this goes back in time when cities were small and people hanged out on the main street near the town hall and the city market. Farmers from all around the town — came to the market to sell their goods.

As towns grew and became cities, new neighborhoods were built to accommodate the growth. And then came the industrial revolution and zoning.

## Zoning
During the industrial revolution, everybody was building factories. Factories are usually big, ugly, smelly, and noisy. People don’t want to live near them, so they started to settle far away. This is why cities today have, so called, industrial zones. During working hours — those are filled with people (workers) and very lively. But after the working day ends, or during the weekend, they are soulless.

Apart from industrial zones, cities have other zones. Zones for multi-story residential neighborhoods; zones for single home detached units; business zones etc. And those zones are designed to accommodate those who live or work there. Multi-story neighborhoods for example are designed to be accessible by a big amount of people. Single story neighborhoods will be less accessible, and in extreme cases like the US — not accessible at all by public transportation, only by car. Business centers will be built around major transportation hubs like trains stations or bus terminals, to accommodate a huge amount of working people that are coming from all corners of the city, and sometimes even from nearby cities.

Mixed zoning exists as well, but as you can imagine — all those zones are designed around the people who live, work and recreate there. And people can, usually, choose where to live. Some value the city vibe and decide to live close to business centers in high rise buildings, while others might value smaller communities and decide to live in a single family homes far from the noise.

# But… Technology
Here I was. Sitting in my car, in traffic, in a neighborhood designed around single family homes. Together with me there were tens of other cars. And for a moment I thought to myself — *did the people who decided to live there — knew they will, eventually, end up living in a car clogged neighborhood?*

I was never in this neighborhood before, nor it is the most optimal route to get from my home to my workplace. At least not according to graph theory, on which all navigation systems are built. But not to Waze. Waze does not calculate the shortest possible route. It calculates the shortest possible route, **considering traffic**. And sometimes, in order to avoid traffic, the best route is through quiet neighborhoods that were designed for minimal car traffic.

## Technology is not a panacea
My point is not to complain about Waze. I’m here to outline a bigger problem — we, as humanity, reached to a point where we throw “Technology”, “AI” and “Machine Learning” at every problem we have.

*Horrible traffic? — Let's build AI navigation software!*

*Satellites internet? — Let's lunch tens of thousand of satellites in low orbit, creating light pollution.*

*Bad and expensive taxi services? — Let's create a non-taxi service where everybody can be a driver!*

*Money system bad!!! — Let’s create a decentralized financial system that emits ~27.4 million tons of CO2. [^1]*

And everybody is okay with it. Because if you are not — you are anti progress. And technology is always associated with progress. But as with any solution, there are good technological solutions and there are bad technological solutions. And they need to be evaluated and their impact on our lives should be assessed. And in some cases, it might turn out to be that technology — is not the best solution for the problem.

[^1]: https://earthjustice.org/features/cryptocurrency-mining-environmental-impacts

During this time and age, we default to technological solution to every problem. Often times - without taking into considereation what effect those solutions might have on our day-to-day lives.

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