Twenty-Five Eye-Opening 2023 Predictions About Generative AI And ChatGPT Including A Splash Of AI Ethics And AI Law Tossed In

Bigger, better, and badder.

That’s the overall gist of what is going to happen with Artificial Intelligence (AI) throughout the upcoming year of 2023.

We will see AI that gets bigger in the sense of being more encompassing and able to do things that previously had not been especially viable for AI to do. Bigger also applies to the notion that more people will become aware of AI and there will be increasing worldwide attention to AI.

When talking about better, that’s the watchword for the fact that AI will further amaze us all at how well AI works, seemingly appearing to be somewhat human-like in various ways (leading regrettably to more false claims of AI sentience and other outrageously nonsensical balderdash contentions about today’s AI, see my assessment at the link here). Many pundits and journalists often stop at the mention of AI as becoming bigger and better, thus opting by default or due to AI-illiteracy to not acknowledge the other side of this coin, namely that AI is also going to be badder (shall we say, ostensibly bad to the bone).

We are going to have a lot more AI that is atrocious.

As per my ongoing and extensive coverage of AI Ethics and AI Law, see the link here and the link here, the pervasiveness of AI that embodies unethical behaviors and exhibits undue biases and discriminatory practices will go off the charts. Sad to say that despite all the exhortations and warnings by those of us in the AI Ethics and AI Law realm, the pace of adverse AI is going to continue to stampede ahead. A bit of uplifting solace is that we are making a dent in these unsettling matters and therefore it is abundantly worthwhile to ardently pursue AI For Good and seek to curtail or mitigate AI For Bad.

Keep on trucking.

For my predictions about AI in 2023, I’ll focus on the hottest AI topic these days consisting of Generative AI. You see, one of the most newsworthy advances that rose to the surface in 2022 has to do with something broadly referred to as Generative AI and especially has gained widespread prominence due to a recently released AI app known as ChatGPT, see my overarching explanation and analysis about generative AI and ChatGPT at the link here.

Due to widely expressed interest in the topic, I did a follow-up piece that closely explored the qualms that this type of AI is going to end up undercutting for example student learning by enabling and altogether luring students into using AI to write their essays, see my assessment of that controversy at the link here. I also did a seasonally flavorful piece about whether Santa is real so that I could further illuminate the upsides and downsides of generative AI and ChatGPT, see the link here.

To catch you up, generative AI is a type of AI that composes text as though the text was written by the human hand and mind. All you need to do is enter a prompt, such as a sentence like “Tell me about Abraham Lincoln” and generative AI will provide you with an essay about Lincoln. Your first thought might be that this does not seem like a big deal. You can easily do an online search of the Internet and readily find tons and tons of essays about President Lincoln.

The kicker in the case of generative AI is that the essay is ostensibly unique and has an original composition. If you were to try and find the AI-produced essay online someplace, you would be unlikely to discover it. Generative AI makes use of a complex mathematical and computational formulation that has been set up by examining patterns in written words and stories across the web. As a result of examining thousands and millions of written passages, the AI is able to spew out new essays and stories that are a mishmash of what was found. By adding in various probabilistic functionality, the resulting text is pretty much unique in comparison to what has been used in the training set.

That’s why there has been an uproar about students being able to cheat when writing essays outside of the classroom. A teacher cannot merely take the essay that deceitful students assert is their own writing and seek to find out whether it was copied from some other online source. Overall, there won’t be any definitive preexisting essay online that fits the AI-generated essay. All told, the teacher will have to accept that the student wrote the essay as an original piece of work. I address in my article about these concerns some of the ways that this might be combatted, see the link here.

You can absolutely expect that the topic of Generative AI is going to grab ahold of headlines throughout 2023. No doubt about it.

Some AI insiders find this a bit oddish or beguiling since generative AI research has been ongoing for several years now. Why didn’t anyone seem to care about generative AI before this recent emergence of unbridled interest? The reason that this spilled over into widespread public awareness was partially due to ChatGPT being released for public use. Up until recently, most of the generative AI apps were being used by AI insiders and not particularly made available to the public at large. When ChatGPT quickly had a million sign-ups to use the AI app, all of a sudden a lot of people were experiencing generative AI.

They expressively used social media to talk about it.

The talk itself went way over the top. It is the best thing since sliced bread, many harkened aloud. This is sure proof that AI is sentient or on the verge of sentience, some proclaimed. It changes everything and disrupts everything, came the cries of those that believed this to be a revolutionary AI innovation. On and on the fawning went.

I dare say that the world as we know it is still pretty much the same. Sure, we can find all kinds of fascinating and useful uses for today’s generative AI. Kudos to the AI advancements being made. But let’s be more down to earth about this. We are making improvements in AI, one step at a time. Each step tends to increase the amazement factor. Yet, just to be clear, today’s AI is not sentient and we aren’t on the cusp of AI sentience. I’ll be saying more about this in my column during 2023.

I also want to address another consideration that frequently comes up. A type of unfortunate polarization has seemingly invaded AI these days. Here’s what I mean. I just said that generative AI is not sentient. For some people, those are fighting words. They get enraged as though I am denigrating the amazement factor of generative AI. Daring to question the nature of generative AI becomes a kind of triggering point.

Hopefully, most will understand that we can chew gum and speak at the same time. It is perfectly fine to on the one hand relish that which generative AI has so far attained, and which will continue to flourish and be extended, while also being level-headed about what it cannot yet do. In addition, we sorely need to look for and contend with the adverse properties that generative AI brings to the fore. Let’s put aside the rose-colored glasses and give this all a sobering assessment.

Based on the media-spurred accolades about generative AI, you would almost think that generative AI is the only kind of AI that there is.

Please do realize that there are a lot of other types of AI and a slew of other AI development and advancements taking place. Nonetheless, yes, I am expecting Generative AI to be the darling for most of 2023. It is like a flashy car that catches our eye. Meanwhile, a wide variety of other AI efforts are being diligently pursued and important accomplishments are being racked up. Those won’t likely be especially heralded and will remain somewhat under the hood and behind the scenes (though, notably, I will be covering them, so keep watching and reading, thanks).

I am going to focus then on Generative AI for my 2023 predictions. If you carefully read between the lines, you’ll also be getting a larger worldview of what will be occurring with AI in 2023 too. I tried to condense all the myriad of AI leaps and bounds for 2023 into a set of twenty-five key elements. To make that twenty-five into readily digestible chunks, I put together five categories. Each category contains five predictions. I will explain the five categories and then go into each category and the respective predictions.

Without further ado, let’s jump into the AI for 2023 prediction extravaganza.

Are you ready?

I hope so.

The Categories For My Predictions Of 2023

First, consider my five devised categories:

• Five Categories For Predictions About AI In 2023
• Category #1: My Top 5 Generative AI Text-to-Outbounds Predictions
• Category #2: My Top 5 Generative AI Outbounds-To-Inbounds Predictions
• Category #3: My Top 5 Generative AI Under-The-Hood Machinations Predictions
• Category #4: My Top 5 Generative AI Business-Making Buzz Predictions
• Category #5: My Top 5 Generative AI Ethics And AI Law Considerations Predictions

Allow me a moment to explain those categories.

When using generative AI, you typically enter into the AI a text prompt that is then used by the AI to generate some form of output. The prompt can produce output that is text, such as the instance of you asking for the life story of Abraham Lincoln as your prompt, and voila, an entire essay about Lincoln is generated by the AI.

Some people refer to this as a text-to-text mode. You input your text and then get some text as an output from the AI. I often refer to this as text-to-essay since it seems to resonate better with people in that you are usually getting an entire essay as a result of your customarily relatively short-length text prompt entry. You enter text and you get an entire essay back. In any case, it is indeed text as input and text that is produced as output, so you can either refer to this as text-to-text or text-to-essay. I’ll use those catchphrases interchangeably herein.

Turns out that you can have other kinds of outputs too.

For example, earlier in 2022 there was a big brouhaha about using text to generate art, see my coverage and analysis at the link here (the hearty and heated discussions continue to arise). You entered a text prompt such as asking to see a frog wearing a hat while sitting on a chimney, and the AI-generated that kind of artistic rendering. People were agog with this.

Not everyone was quite so excited. This type of AI is often computationally trained on artwork that is all across the Internet. As such, it could be that your prized artwork got swept up in the AI computational calculations and the outputs being produced by the AI resemble your art. We are heading toward troubling times as to how Intellectual Property (IP) rights pertaining to art and also text are going to be upended by this type of AI, for more of my analysis on this matter see the link here.

The typical types of output today from generative AI consist of output that is purely text or that is pure art. One or the other, but not both occurring at the same time.

When I refer to output that is art, most people tend to think of artistry such as paintings or drawings. Another form of art-related output consists of AI-generating photorealistic images. Think of those as looking like snapshots or pictures that you take via your smartphone camera. To distinguish conventional art from photorealistic art-like outputs, I’ll conveniently herein refer to them as text-to-art versus text-to-photorealistic images. Not everyone goes along with dividing those into two types of output, but I find it helpful when discussing the topic all told.

A generalized way to depict or describe generative AI is to say that this kind of AI usually takes text as input and produces some form of output that might vary in terms of the mode being used. We have text-to-text or what I like to say is text-to-essay. There is text-to-art. I like to say that there is text-to-art and there are text-to-photorealistic images. In essence, these are all texts to some form of outbounds.

The reverse direction is also getting traction.

Here’s the deal.

Suppose you provide some artwork to a generative AI app. The AI app might be set up to produce text that is intended to textually describe an artwork. For example, I opt to paint a depiction of a frog wearing a hat that is sitting on a chimney. I decide next to scan in my beloved artwork. The AI app attempts to essentially explain or describe my drawing. Thus, the output might be that the AI app indicates I have painted a frog wearing a hat that is sitting on a chimney.

This is an example of art-to-text.

Whereas I had earlier said that the outbounds were potentially art or photorealistic images, we can instead consider those modes to instead (or also) be potentially used for inputs into a generative AI app. An AI app might be set up for art-to-text, which I would also say usually includes or is accompanied by a capability to do photorealistic-image-to-text.

Today’s generative AI is usually devised as one flavor of these multitudes of flavors.

A given generative AI app might do text-to-text, and nothing more. A different generative AI app might do text-to-art, and nothing more. Yet a different generative AI app might do art-to-text, and nothing more. They are each siloed in the sense that they are each a one-trick pony.

You’ll be pleasantly surprised to know that I am setting you up for a big reveal about 2023 and AI. Since you are now likely perched on the edge of your seat, I suppose that I ought to share with you the reveal.

We are heading toward what I refer to as multi-X or multi-modal generative AI.

A generative AI app that is multi-X or multi-model is able to do a range of inbound and outbound modes. You can enter text and produce a text essay. If you wish, you can instead enter art and get an essay produced. You can enter text and get artwork produced. It is a mix-and-match selection of your choosing.

We will also see the blending of these modes.

I enter text such as asking the generative AI to tell me about the style of the famous painter Rembrandt and also show me a frog wearing a hat on a chimney as painted in the style of Rembrandt. The generative AI app will then provide two outputs, consisting of a text essay accompanied by a rendered painting as per your request.

Another example would be that I feed my hand-done artwork into a generative AI app and furthermore enter text asking that the AI app redo my artwork so that it looks as though Rembrandt did it, plus I want the AI app to compare the two pieces of artwork. The AI app will produce the requested variant of my artwork and then explain in an essay how my original artwork and the AI-generated version compare to each other.

I trust that you can envision how exciting this is going to get in 2023.

I’d like to add icing to that cake if I might do so.

Another mode that is going to shock the world in 2023 will be the mode of video.

Sit down for this revelation.

You will be able to enter text such as asking the AI app to produce a video that showcases a car racing on a track that keeps going in circles. Based on your text description alone, a video of this will be devised by the AI. That would be text-to-video.

There will also be video-to-text.

You feed a video into a generative AI app. The AI app produces an essay that describes the video. Allow me to clarify. This is not an audio transcription of what is said in the video. Nope. This is an essay-style description such as the video consists of a car that is shown on a track and keeps going round and round.

Various AI researchers are already working on the text-to-video and video-to-text modes. It is hard stuff. During 2023, you’ll be seeing bits and pieces of these advances. At first, the look and feel will be rather stiff and simplistic. You might be tempted therefore to write it off as unworthy. Don’t be so quick to judge.

I can also predict with a reasonable level of certainty that some will wail that this use of text-to-video is the death knell for Hollywood. Well, the death knell for anyone that produces videos, including those that post videos for a living as YouTube influencers. Presumably, no need to deal with all the arduous aspects of producing videos, just instead enter text and get those videos entirely generated. No hard work is involved.

We are years away from that kind of top-notch cohesive video output produced by the mere entry of text. The dream for some is that you could enter a script in text format, wait for the generative AI app to do its stuff, and you’d get the output of an Oscar-winning movie. Lest you think I am referring to some form of animated video, realize that this is going to include a video that has what appears to be people in it. Via the added use of deepfakes, you could produce a video with your favorite movie star, with them appearing to talk, move, sing and dance.

Hold onto your hats for that day.

Anyway, we will see some toe-dipping going on in 2023 that will foretell the future of this type of generative AI.

All of this will have dramatic and quite vital AI Ethics and AI Law considerations. Naturally, that’s why I have included AI Ethics and AI Law as one of my five categories for grouping my 2023 set of predictions.

We are primed to get into the categories and take a look at my selected five crucial predictions per category. In the end, I’ll show them all listed together and then lumped together, doing so for ease of showcasing them all.

Category #1: My Top 5 Generative AI Text-to-Outbounds Predictions

Let’s begin by exploring the text-to-outbounds category.

Here are my predictions for 2023 in this category:

• Category #1: My Top 5 Generative AI Text-to-Outbounds
• 1.1) Text-to-Art Gets More Sensibly Artistic
• 1.2) Text-to-Photorealistic-Image Gains Deeper Fakery
• 1.3) Text-to-Essay Overcomes Some Hallucinations And Guffaws
• 1.4) Text-to-Video Becomes The Next Big Thing
• 1.5) Text-to-X Transmutes Into Multi-X Multi-Modal All In One

A quick elaboration for you.

1.1 Text-to-Art Gets More Sensibly Artistic. Text-to-art generative AI will get better at producing artistic outputs. Trying to discern whether the artistry was made by a human artist versus AI is going to be nearly impossible. Debates about whether this art is “true art” will arise anew. A decrying that this is going to put human artists out of work is going to persist. A contention also will be that this is art without a soul. Another is that this is art without any semblance of creativity due to having been devised by AI. The counterargument will be that art is art, generally suggesting that any semblance of a soul is in the eye of the beholder and not how the art was generated or produced. On the creativity front, this too will be hotly debated since the randomness and computational complexity of the generative AI will produce art that in the eye-of-the-beholder might seem just as creative if not more so than other or some human artists. Let the artist’s philosophical games ensue.

1.2 Text-to-Photorealistic-Image Gains Deeper Fakery. You undoubtedly already know that there is an enormous amount of handwringing about the advent of deepfakes. People opt to edit a photo of a real person and make it look as though the person is doing something that they didn’t actually do. This raises all manner of disinformation, misinformation, and potentially defamatory and other concerns. Generative AI will up the ante. You will be able to merely enter a text prompt that indicates the name of the celebrity or other named person, and indicates what you want the imagery to depict, and the AI will produce a photorealistic image for you. You can then tell the AI to refine it, doing so until it is exactly a perfected deepfake. Hurrah for AI (assuming that the deepfake is made for positive and beneficial purposes), or perhaps yet another miserable and altogether exploitable use of AI (assuming the deepfake is made for nefarious purposes).

1.3 Text-to-Essay Overcomes Some Hallucinations And Guffaws. One of the most notable downsides of today’s generative AI is that it can potentially produce erroneous outputs. For example, suppose the produced essay about the life of Lincoln indicated that he used to fly around the country in his private jet. You and I know that this is silly and patently incorrect. The thing is, people reading the outputted essays won’t necessarily know that somewhere in the narrative could be false statements. Sometimes the errors are due to how the AI originally computationally did the pattern matching across the Internet, while in other cases other factors come to play. When AI goes a bit mathematically awry, the AI field tends to call this an AI hallucination, which is coined terminology that I earnestly disagree with and have said we should avoid this kind of false anthropomorphizing, see my analysis at the link here.

The key point is that we are going to have to contend with generative AI that produces misleading or outright false outputs. In some cases, the produced essay might contain a subtle and marginally false claim, while in other instances it could be drastically incorrect. Imagine asking a generative AI app to produce a recipe for pumpkin pie, and the generated essay includes a step that tells you to add poison to the batch. The person that follows the instructions might not realize that a poison is the indicated ingredient if perhaps the item is listed under some other naming. Not good.

Disturbingly, generative AI might be a fast path to producing vast and insidiously immersed disinformation and misinformation. It gets worse too. Here’s how. Assume that people will generate all manner of essays via generative AI. They proceed to post those essays onto the Internet. Nobody has especially screened those essays to make sure they are free of errors. The amount of added clutter that we end up adding to the Internet begins to multiply manyfold because people can easily use generative AI to create textual content for them. Ultra-massive amounts of disinformation and misinformation pile onto the piles we already have as made directly by human hands. Yikes, the Internet gets even worse than it already is in terms of suspicious content.

I’ll somewhat shift gears and bring up a pertinent aspect specifically about ChatGPT. As I’ve discussed in my other posts about ChatGPT, see the link here and the link here, there was a concerted effort by the AI developers to try and reduce the bad stuff outputs. For example, they used a variant of what is known as RLHF (Reinforcement Learning from Human Feedback), whereby before they released the AI to the public, they had hired humans to examine various outputs and indicate to the AI whether there were things wrong with those outputs such as perhaps showcasing biases, foul words, and the like. By providing this feedback, the AI app was able to adjust computationally and mathematically toward reducing the emitting of such content. Note that this isn’t a guaranteed ironclad method and there are still ways that such content can be emitted by the AI app.

You might find of interest that ChatGPT is based on a version of a predecessor AI app known as GPT-3, see my discussion at the link here. ChatGPT is considered to be a slightly next step, referred to as GPT-3.5. It is anticipated that GPT-4 will likely be released in the Spring of 2023. Presumably, GPT-4 is going to be an impressive step forward in terms of being able to produce seemingly even more fluent essays, going deeper, and being an awe-inspiring marvel as to the compositions that it can produce.

I bring this up because there is a potential Achilles heel to these better and bigger generative AI apps. If any AI vendor makes available a generative AI app that spews out foulness, this could dash the hopes of those AI makers. A spillover can also cause all generative AI to get a serious black eye. People will indubitably get quite upset at foul outputs, which has happened many times already and led to boisterous societal condemnation backlashes toward AI.

1.4 Text-to-Video Becomes The Next Big Thing. I earlier herein discussed text-to-video. As mentioned, this is being pursued in research labs and you can expect to see some quite interesting and attention-grabbing announcements in mid-2023. The better stuff will likely be unveiled toward the end of 2023.

1.5 Text-to-X Transmutes Into Multi-X Multi-Modal All-In-One. I earlier herein discussed the notion of having generative AI that can go to and from a multitude of output or input modes, which I’m calling multi-X or multi-modal generative AI. These will be rolling out in 2023. I’d guess that this will cause quite a splash of interest and generate more buzz about AI.

Category #2: My Top 5 Generative AI Outbounds-To-Inbounds Predictions

Let’s next explore the outbounds-to-inbounds category.

Here are my predictions for 2023 in this category:

• Category #2: My Top 5 Generative AI Outbounds-To-Inbounds
• 2.1) Art-to-Text Gets Abundantly Descriptive
• 2.2) Photorealistic-Image-to-Text Catches The Essentials
• 2.3) Essay-to-Text Does Remarkable Recaps
• 2.4) Video-to-Text Makes Impressive Baby Steps
• 2.5) Multi-X Multi-Modal Tries To Do Reverse Splits

A quick elaboration for you.

2.1 Art-to-Text Gets Abundantly Descriptive. As earlier mentioned, we will see heightened AI capabilities at taking art as input and then producing an essay that describes the inputted artwork. The essay can be somewhat customized by the person using generative AI. For example, you could tell the AI app to produce a summary of artwork or instead instruct the AI to be overtly profuse and generate a lengthy gushing elaboration.

2.2 Photorealistic-Image-to-Text Catches The Essentials. As mentioned earlier, we will also have generative AI that produces essays about inputted photos. These first versions will be not quite as impressive as the art-oriented ones. Don’t worry, these AI apps will be markedly improved and do better in 2024.

2.3 Essay-to-Text Does Remarkable Recaps. Many people using generative AI apps do not realize that most of these AI apps provide a feature wherein you can feed an essay into the AI and get as output a summary of the essay. For example, you can take a lengthy article that someone has written, feed it as a prompt into the AI app, and ask the AI app to produce a recap or summary. Not all generative AI apps do this, plus some have restrictions on the length of the inputs. In any case, the odds are that we’ll by the end of 2023 have people regularly using generative AI to produce summaries for posting on the Internet or use in other ways.

2.4 Video-to-Text Makes Impressive Baby Steps. I earlier mentioned that we’ll be seeing some video-to-text generator AI apps. I’d bet that once these get relatively good at doing appropriate textual essays about an inputted video, a lot of people will be eagerly making use of this functionality. I say this because rather than having to watch an hour-long video, it would be handy to have a written description of what the video conveys, such that you can just breeze through the written essay and then decide if you want to laboriously view the video. Humans do this type of written depiction by hand, right now, while in 2023 and into 2024, we will increasingly use generative AI to do this for us.

2.5 Multi-X Multi-Modal Tries To Do Reverse Splits. I earlier mentioned this capability of doing multi-X or multi-modal as the input, for which then the generative AI app reverse engineers the input and can split things out for us. Suppose I provide a drawing of Lincoln as input, and I ask to get this turned into a video about the life of Lincoln, along with an essay that goes along with the video. Nifty.

Category 3: My Top 5 Generative AI Under-The-Hood Machinations

Let’s next explore the under-the-hood machinations category.

Here are my predictions for 2023 in this category:

• Category 3: My Top 5 Generative AI Under-The-Hood Machinations
• 3.1) Prompt Engineering Establishes Footholds
• 3.2) Chain Of Thought Protocol Advances Toward Convention
• 3.3) Real-time Internet-Connected Generative AI Blooms
• 3.4) Sensible Coupling Of Internet Search And Generative AI Flourishes
• 3.5) Zero-Shot Generative AI Glimmers And Simmers

A quick elaboration for you.

3.1 Prompt Engineering Establishes Footholds. The manner in which you enter a text prompt can radically produce a different essay on output. In a sense, there are good ways and not-so-good ways to write a text prompt. Some pundits are proclaiming that we will need to train humans in how to write good prompts, for which they will have the vaunted title of a prompt designer or prompt engineer. Though this might occur in the short-term, in the medium and long-term the AI will be enhanced to do handholding when people enter prompts. The days of humans having that task will be numbered, mark my words.

3.2 Chain Of Thought Protocol Advances Toward Convention. When you enter a text prompt into a generative AI app, sometimes the AI is set up to allow you to create a kind of thread of discussion with the AI. You enter a prompt. The AI responds with some output. You then refer to the output and ask or indicate to do something else with it. This goes on repeatedly. For example, I ask the AI app to produce a life story about Lincoln. Upon seeing the essay produced, I enter a subsequent prompt that says to focus the essay on the Civil War. A new essay is generated. I then tell the AI app to only cover the Gettysburg Address. Etc.

In some instances, this prompt upon prompt can materially alter the essays being generated. Though I don’t like the naming of this, due to the anthropomorphizing involved, many AI insiders tend to refer to this as a chain of thought protocols (in my view, the even worse moniker is that this is a chain of thought “reasoning” as though akin to human reasoning). Anyway, I do believe that this chain of thought approach has some interesting technological possibilities, and I am anticipating more AI work to advance on this in 2023.

3.3 Real-time Internet-Connected Generative AI Blooms. Some of the generative AI is based on scanning the Internet as of some particular cutoff date, such as ChatGPT was established as a cutoff in 2021. There are several reasons for this. One is that the computational effort to do real-time access to the Internet and feed this into the generative AI for producing real-time results can be onerous. People are expecting to get their generated results in seconds, whereas real-time computational scanning of the Internet could push this into minutes, hours, or even days. Another concern is that if real-time Internet-accessed info is used, this might not be as readily caught if it contains foul content, whereas with a generative AI that is stopped in time you have a better chance of during training getting those aspects possibly cornered. And so on.

The good news is that where there is a will, there is a way. All kinds of computational trickery and cleverness can be used to contend with a desire to do real-time Internet-connected generative AI. You’ll see this start happening in 2023.

3.4 Sensible Coupling Of Internet Search And Generative AI Flourishes. I previously covered in one of my columns on generative AI and ChatGPT that some are loudly sounding an alarm that Google and other search engine companies will be forced out of business due to generative AI ostensibly taking on the Internet search chore. I pointed out that this is one of those Mark Twain moments whereby the death of search engines is quite prematurely being proffered. My viewpoint is that we will have a side-by-side coupling of search engines and generative AI. Recall too that I’ve pointed out the unnerving facet of generative AI producing so-called AI hallucinations and other foul outputs. We don’t expect our search engines to do this, and thus it makes sense to keep to the sidekick role for now the generative AI, such that it doesn’t taint an already well-respected and huge ad-revenue generating highly-trusted search engine (see my further discussion at the link here).

3.5 Zero-Shot Generative AI Glimmers And Simmers. Most of today’s generative AI was crafted by doing extensive scanning across the Internet. This takes gobs of computational processing. Generally, if you bring up a topic in your text prompt that is not one that was previously covered by some scanned content, you will get either a brisk and potentially vacuous output or simply an indication that the generative AI has nothing to say about that topic. Another approach entails what is sometimes referred to as a zero-shot. This suggests that an AI app can pontificate on a topic without necessarily having to extensively be pre-trained on that topic. You can expect to see the zero-shot generative AI getting a glimmer and simmering into something substantive during 2023.

Category 4: My Top 5 Generative AI Business-Making Buzz

Let’s next explore the business-making buzz category.

Here are my predictions for 2023 in this category:

• Category 4: My Top 5 Generative AI Business-Making Buzz
• 4.1) Personalization And Cascading Of Generative AI Is The Next Mighty Hook
• 4.2) Breakthroughs Appear For Generative AI Speed And Efficiencies
• 4.3) Synthetic Data Emerges From The Shadows And Does Good
• 4.4) Flimsy Generative AI Starts To Spoil The Barrel
• 4.5) Wild Mishmash Of Generative AI Apps With Scams Included

A quick elaboration for you.

4.1 Personalization And Cascading Of Generative AI Is The Next Mighty Hook. Most of the generative AI apps tend to be generic with respect to the person using the AI app. The AI app doesn’t know you. Anything you enter is treated the same as if entered by anyone else. Some of the generative AI apps do allow you to save a thread that you can return to later on, thus, in a modest way allowing for a modicum of being aware of your presence. I’m expecting that in 2023 we will see a personalization capacity added to generative AI. Your particular interests and style of prompting will become a pattern tracked by the AI app and be used to hone responses to how you prefer them to be composed. Also, you can expect that the cascading of a generative AI output into other generative AI will also become relatively popular and commonplace in 2023.

4.2 Breakthroughs Appear For Generative AI Speed And Efficiencies. A thorny issue facing the AI makers that are allowing their generative AI to be used by the general public is the question of the costs involved. In the instance of ChatGPT, the cost is currently being eaten by the AI maker during this freebie sampling period. Part of the indicated basis for having opted to cut off the sign-ups of ChatGPT at a million people was that the cost per transaction is notable and chewing up the dough. In addition, as these generative AI apps get bigger and tussle with more and more data, along with possibly being real-time Internet-connected, there is going to be a fervent need for speed. From a computer scientist purist perspective, finding ways to make generative AI faster and more computationally efficient is exciting and handy. The same kinds of breakthroughs in this particular domain can likely apply to a wide variety of other computing platforms and systems. Expect this to play out in 2023.

4.3 Synthetic Data Emerges From The Shadows And Does Good. There is real data and there is synthetic data. An example of real data would consist of scanning the Internet for information such as the life of Lincoln. Synthetic data is when you essentially make up data for the purposes of training your AI. Rather than bearing the cost and effort of scanning for real data, you sometimes do things to create data that will be plentiful at the push of a button. In a sense, it is faked data, though usually based on some grounding that is real. The use of synthetic data for aiding the training and use of generative AI will be an emerging trend during 2023.

4.4 Flimsy Generative AI Starts To Spoil The Barrel. This is a sad face topic about generative AI. Now that generative AI has gotten its fifteen minutes of fame via the likes of ChatGPT, a lot of other AI makers are wanting to get into the same game. To make things abundantly clear, there are indeed already many bona fide generative AI apps that have been kept quietly under wraps or that the tech vendor was worried would get into trouble if the AI’s potential propensity to sometimes produce foulness was revealed while put into public use. Those generative AI apps are going to soon be marketed so that everyone will know that there is more than one mover and shaker in town. The limelight will shine upon many.

This though will also have a downside. There will be some generative AI rushed into the public eye. These flimsy versions are going to be rife for producing foul outputs. People will get upset. Whether society can distinguish between one maker’s generative AI versus another will be a big question. The flimsy versions could spoil the whole barrel. We will need to wait and see how this plays out in 2023.

4.5 Wild Mishmash Of Generative AI Apps With Scams Included. I have more than just a sad face on this one, it is a tooth-grinding grimacing face. In an upcoming column, I will be discussing how generative AI can be used to do evildoing, such as having the AI produce malware for you. All you need to do is tell the generative AI to do so, even if you have no clue how to code up malware on your own, and the generative AI app will produce the devious code. I realize that maybe this seems techie nerdish, so let’s consider other evil acts. Suppose you want to try and scam somebody, such as those emails that tell people you are a prince with lots of money and all you need is their bank account number to send them a zillion dollars to hold for you. Generative AI can help you come up with and devise such essay-based scams. I guess that’s why we can’t have any new toys.

Category 5: My Top 5 Generative AI Ethics And AI Law Considerations

Let’s next explore the AI Ethics and AI Law considerations category.

Here are my predictions for 2023 in this category:

• Category 5: My Top 5 Generative AI Ethics And AI Law Considerations
• 5.1) Monetization of Generative AI Struggles For Dough
• 5.2) Adverse Carbon Footprint Undercuts Generative AI Accolades
• 5.3) Generative AI Toxic Transgressions Bodes For Grand Condemnations
• 5.4) European Union AI Act (AIA) Enacts With Ballyhoo And Gotchas
• 5.5) USA Algorithmic Accountability Act Sits But Stirs Into Consciousness

A quick elaboration for you.

5.1 Monetization of Generative AI Struggles For Dough. I have an important question for you. How will people be able to make money off of providing generative AI apps? We don’t know for sure yet that these are truly money-making apps. Would you be willing to pay a transaction fee or a subscription fee to have access to a generative AI app? Maybe yes, maybe not. Some people are only having fun and playing with generative AI just for kicks, therefore the cost would presumably need to be on par with other forms of online fun such as using online games. Others are trying to more seriously use generative AI more for doing work-related tasks. For example, in my AI Lab, we have been experimenting with and adapting generative AI for use by attorneys in performing legal tasks such as putting together a legal brief. Lots and lots of ideas are floating around about how to leverage generative AI to make a buck. The odds are that 2023 is going to be the show-me-the-money year as to whether there are viable ways to turn generative AI into real-world money-makers. Follow the money, as they say.

5.2 Adverse Carbon Footprint Undercuts Generative AI Accolades. I’ve previously discussed in my columns that one worry about the burgeoning use of AI is that devising and running these computationally intensive apps consumes a lot of computer processing power, see my analysis at the link here. To the surprise of many, there is a carbon footprint associated with AI. We need to weigh the benefits of AI against the societal costs of the carbon footprint. Expect to see AI Ethics and AI Law rising to bring greater awareness about the AI carbon footprint, including potentially enacting laws about the need to report on and publicly disclose carbon production regarding AI and what is being done to mitigate it. Nothing in life is free.

5.3 Generative AI Toxic Transgressions Bodes For Grand Condemnations. I’ve already mentioned several times herein that the generative AI of today can produce foul outputs. All it will take is for some of the generative AI in 2023 to produce outrageously biased commentary or other foulness and a societal backlash might suddenly erupt. When this happens, and it will, I am at least hoping that added attention to AI Ethics will be a kind of silver lining in that cloud. You can also bet that the impetus to forge new AI-related laws will likely be sparked by these unsavory occurrences. Regulators and legislators will get riled up.

5.4 European Union AI Act (AIA) Enacts With Ballyhoo And Gotchas. I’ve written extensively about the EU AI Act (AIA) that is being drafted and revised, see my coverage at the link here. This will be by far the most significant new law about AI and will have monumentally sweeping effects. I am betting it will finally get enacted in 2023. Among the many controversies about this law is that it takes a risk-based approach to classify AI systems. In brief, there are four classifications consisting of (a) unacceptable risk, (b) high risk, (c) limited risk, and (d) minimal risk. Some believe that this is the best way to cope with AI from a legal perspective. Others disagree and assert that the risk framework is going to be untenable and create all manner of confusion and trickery by those that make or field AI. I have my own opinions on this, as discussed in my column postings. In any case, if indeed the EU AIA passes in 2023, you can certainly anticipate that there will be a whole lot of ballyhoo involved. We will all be waiting with bated breath to see how things go. Will this law aid in putting a lid on AI For Bad, or will it become an unintended killer of AI For Good, or end up somewhere in between? Stay tuned to 2023.

5.5 USA Algorithmic Accountability Act Sits But Stirs Into Consciousness. The United States has been slowly and gradually tussling with a bill in Congress that would be a large-scale AI law, known as the Algorithmic Accountability Act. I’ve discussed the draft, and also covered other associated federal and state AI-related legislative efforts (and at the local levels too, such as the New York City law requiring AI auditing, see my discussion at the link here). You might especially find of interest my analysis of the AI Bill of Rights that was released by the White House in 2022, see the link here. If the EU AIA passes in 2023, the odds are that this will awaken and fuel the US legislative efforts. At the same time, some will press for waiting to see how things go with the EU AIA before proceeding headstrong into a USA AI law. Partially, the push in the US would be accelerated if any big-time generative AI or other notable AI snafus caught widespread attention across the country. All in all, my prediction is that though the US effort will be stirred, I don’t see much movement forward until after the 2024 elections. Until then, the hustle and bustle of dealing with a large-scale AI law won’t seem worthwhile, unless of course some demonstrative bad thing happens with AI and an outcry makes the pursuit a sudden hot priority.

Conclusion

Exciting times are coming in 2023 for AI.

You won’t want to miss the fireworks. The future is revealing itself, day by day, week by week, and month by month. Along this rocky path, there will be a lot of pronouncements that sentient AI is here. I ask that you read the fine print on those claims.

To help you glean all of my predictions in one fell swoop, here I list them by each category:

Category #1: My Top 5 Generative AI Text-to-Outbounds

• 1.1) Text-to-Art Gets More Sensibly Artistic
• 1.2) Text-to-Photorealistic-Image Gains Deeper Fakery
• 1.3) Text-to-Essay Overcomes Some Hallucinations And Guffaws
• 1.4) Text-to-Video Becomes The Next Big Thing
• 1.5) Text-to-X Transmutes Into Multi-X Multi-Modal All In One

Category #2: My Top 5 Generative AI Outbounds-To-Inbounds

• 2.1) Art-to-Text Gets Abundantly Descriptive
• 2.2) Photorealistic-Image-to-Text Catches The Essentials
• 2.3) Essay-to-Text Does Remarkable Recaps
• 2.4) Video-to-Text Makes Impressive Baby Steps
• 2.5) Multi-X Multi-Modal Tries To Do Reverse Splits

Category #3: My Top 5 Generative AI Under-The-Hood Machinations

• 3.1) Prompt Engineering Establishes Footholds
• 3.2) Chain Of Thought Protocol Advances Toward Convention
• 3.3) Real-time Internet-Connected Generative AI Blooms
• 3.4) Sensible Coupling Of Internet Search And Generative AI Flourishes
• 3.5) Zero-Shot Generative AI Glimmers And Simmers

Category #4: My Top 5 Generative AI Business-Making Buzz

• 4.1) Personalization And Cascading Of Generative AI Is The Next Mighty Hook
• 4.2) Breakthroughs Appear For Generative AI Speed And Efficiencies
• 4.3) Synthetic Data Emerges From The Shadows And Does Good
• 4.4) Flimsy Generative AI Starts To Spoil The Barrel
• 4.5) Wild Mishmash Of Generative AI Apps With Scams Included

Category #5: My Top 5 Generative AI Ethics And AI Law Considerations

• 5.1) Monetization of Generative AI Struggles For Dough
• 5.2) Adverse Carbon Footprint Undercuts Generative AI Accolades
• 5.3) Generative AI Toxic Transgressions Bodes For Grand Condemnations
• 5.4) European Union AI Act (AIA) Enacts With Ballyhoo And Gotchas
• 5.5) USA Algorithmic Accountability Act Sits But Stirs Into Consciousness

Now that I’ve shown them all together based on their categories, let’s go ahead and remove the categories and show the list as purely twenty-five predictions about AI for 2023:

My Twenty-Five Predictions About AI In 2023

• Text-to-Art gets more sensibly artistic
• Text-to-Photorealistic-Image gains deeper fakery
• Text-to-Essay overcomes some hallucinations and guffaws
• Text-to-Video becomes the next Big Thing
• Text-to-X transmutes Into Multi-X Multi-Modal all-in-one
• Art-to-Text gets abundantly descriptive
• Photorealistic-Image-to-Text catches the essentials
• Essay-to-Text does remarkable recaps
• Video-to-Text makes impressive baby steps
• Multi-X Multi-Modal tries to do reverse splits
• Prompt engineering establishes footholds
• Chain Of Thought protocol advances toward convention
• Real-time Internet-connected generative AI blooms
• Sensible coupling of Internet search and generative AI flourishes
• Zero-Shot generative AI glimmers and simmers
• Personalization and cascading of generative AI is the next mighty hook
• Breakthroughs appear for generative AI speed and efficiencies
• Synthetic data emerges from the shadows and does good
• Flimsy generative AI starts to spoil the barrel
• Wild mishmash of generative AI apps with scams included
• Monetization of generative AI struggles for dough
• Adverse carbon footprint undercuts generative AI accolades
• Generative AI toxic transgressions bode for grand condemnations
• European Union AI Act (AIA) enacts with ballyhoo and gotchas
• USA Algorithmic Accountability Act sits but stirs into consciousness

Since you might be tempted to discuss my predictions with others, I am going to go ahead and include a final listing of the twenty-five predictions and number them.

The numbering is solely intended as a convenient means to refer to the predictions. I say this because the numbering does not imply or indicate anything regarding a semblance of priority or importance. Thus, do not interpret the numbering as though the first one is somehow more or less important than the twenty-fifth one listed. They are all considered equal weight in this listing.

My Twenty-Five Predictions About AI In 2023 (numbering shown for ease of reference)

1) Text-to-Art gets more sensibly artistic

2) Text-to-Photorealistic-Image gains deeper fakery

3) Text-to-Essay overcomes some hallucinations and guffaws

4) Text-to-Video becomes the next Big Thing

5) Text-to-X transmutes Into Multi-X Multi-Modal all in one

6) Art-to-Text gets abundantly descriptive

7) Photorealistic-Image-to-Text catches the essentials

8) Essay-to-Text does remarkable recaps

9) Video-to-Text makes impressive baby steps

10) Multi-X Multi-Modal tries to do reverse splits

11) Prompt engineering establishes footholds

12) Chain Of Thought protocol advances toward convention

13) Real-time Internet-connected generative AI blooms

14) Sensible coupling of Internet search and generative AI flourishes

15) Zero-Shot generative AI glimmers and simmers

16) Personalization and cascading of generative AI is the next mighty hook

17) Breakthroughs appear for generative AI speed and efficiencies

18) Synthetic data emerges from the shadows and does good

19) Flimsy generative AI starts to spoil the barrel

20) Wild mishmash of generative AI apps with scams included

21) Monetization of generative AI struggles for dough

22) Adverse carbon footprint undercuts generative AI accolades

23) Generative AI toxic transgressions bode for grand condemnations

24) European Union AI Act (AIA) enacts with ballyhoo and gotchas

25) USA Algorithmic Accountability Act sits but stirs into consciousness

If there is feedback expressed that readers would like me to provide a sequenced list such as by most important or most likely, I’ll do so in a subsequent column.

Well, there you have it, my AI predictions for 2023.

In my Forbes column throughout 2023 (as will be posted at the link here), I will be discussing these meaty AI topics at length, and we’ll see how right or off-target I end up being. You can be assured that I will be forthright about this.

Some final remarks for now.

Peter Drucker, the legendary management guru, stated that the best way to predict the future is to create it. I implore all of us that are in AI to keep that sage bit of wisdom in mind. We need to mindfully abide by AI Ethics and AI Law, or else the future of AI is not going to be as rosy as we might imagine.

The great science fiction writer, Isaac Asimov, said that science fiction writers foresee the inevitable, and although problems and catastrophes may be inevitable, solutions are not. I ask that AI researchers and AI developers take to heart that they need to be mindfully thinking about how their AI might exhibit or portend either directly or indirectly and whether by intent or by happenstance, an emergence of real and harmful problems and catastrophes (for my analysis of dual-use AI that can be simply and regrettably switched readily into so-called Doctor Evil projects, see the link here). A duty-bound vow and requirement by all should be to find societally acceptable solutions to accompany such AI-adverse conundrums.

Finally, and a quite fitting comment, Yogi Berra humorously declared: “It’s tough to make predictions, especially about the future.”

So too can be said about predicting the future of AI.