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AI Creation Tools Will Change the Way We Create, Engage and Interact in 2023

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Can You Use AI-Generated Art in Your Digital Marketing and Content Efforts?

AI tools are going to have a much bigger influence over many aspects of online communication in 2023, in good ways and bad. And while everyone’s excited to check out what they might look like in various art styles, and how generative systems can reduce their content creation workload, it is worth noting the various impacts, and how these tools can be of both benefit and detriment in your digital marketing efforts.

Content creation

Maybe the most obvious usage of AI creation tools for marketing, as noted, is text-based content, with tools like ChatGPT potentially making it much easier to quickly create massive clusters of blog posts and web pages to help in your SEO efforts.

And it likely can help in this respect. The outputs of ChatGPT, and other text generation tools, are generally readable, competent summaries of a given subject, and with the right text inputs, they can be aligned to certain keywords that will help to ensure that your website meets what Google’s crawlers are seeking, in regards to relevant queries.

But you do need to consider a couple of things. For one, ‘competent’ and ‘good’ are not the same thing, and your visitors will notice.

In the example above, the writing here is fine, the language is functional – it does all the things that it needs to do. I can edit it a bit more to freshen it up, but at base, it’ll probably work.

I suspect that many, many websites will take this approach in future, and they’ll end up using generic overviews like this on their web pages. Which is likely not much worse than the current state of the web – I mean, these tools take in examples from across the current web, then repurpose the language into new presentations, so logically, it’s a very close replica of everything else online.

The end result, then, is that people – like, actual, real humans – will be less and less engaged by the generic, which could open up more opportunity for better copy to stand out, and make your brand more of a useful, helpful resource along the same lines.

Essentially, these tools can be useful for replicating what’s already out there, but if you want anything fresh or new, or even engaging, you’re probably better off creating your own.

But if SEO is your goal, and you want to cut down on time, then this could be an option.

In terms of legal use, you can use ChatGPT outputs, though the requirements do note that you have to make it clear that the content was created by AI, not a human, on the page.

There’s also this qualifier:

“Due to the nature of machine learning, Output may not be unique across users and the Services may generate the same or similar output for OpenAI or a third party. For example, you may provide input to a model such as “What color is the sky?” and receive output such as “The sky is blue.” Other users may also ask similar questions and receive the same response. Responses that are requested by and generated for other users are not considered your Content.

So, you could also end up copying someone else’s work, which, in itself, can lead to Google penalties for duplicate content.

You can probably get around this by conducting a search using the generated text, or using a tool like Copyscape, but essentially, if you’re putting your text output into the hands of a machine learning system, there could be some usage concerns – aside from pretty bland copy.

I’ve played with ChatGPT a bit, and I’ve tried it out with various article ideas and concepts. None of those outputs, on my reading, have been something that I would publish. The text is fine, it’s, again, functional. But when you’re training a system on billions of pages of bland text from the web, what it’ll ultimately produce, understandably, is going to also be bland, flat prose.

Basically, if you’re okay with your website being like every other web page, if you’re happy with the reading experience of brochures and blog posts, and if you think other brands in your niche have fine text elements, then this is probably fine to you too.

There are different purposes and approaches, but you can’t expect an AI system to give you engaging insight.

Image Production

The other big AI tool usage is in visual creation, and getting tools like Dall E to produce your visuals for you, based on prompts.

DALL E examples

And again, it works. You enter what you want, and Dall E will generally come up with at least a few examples that will probably work for what you need.

But the same as text, it won’t be amazing, in general. Functional, yes, it will do the things, but also likely a little bit off, with frayed edges, weird text, strange eyes, etc.

Similar to text outputs, you can legally use image AI outputs for whatever you like, as any image that you create has technically never existed before you entered your text prompt. So, the copyright technically goes to the creator, and as it was your prompt that ‘created’ it, that’s you.

There’s a heap of ways in which this can be useful, and valuable, and many people are already using AI generated artwork to accompany their blog posts and content. It can be useful, but again, it’s literally generic – each images you get is based on a generalization of every other image that the system can find, based on your text prompts.

It depends on how you go about it, and what you want to use these visuals for, but similar to text, there can be duplication concerns (Dall E advises users not to create images of public figures, for example, to avoid misrepresentation concerns), while various legal cases are now being filed over the misappropriation of artists’ work (there’s currently no legal framework that truly covers this type of use).

But they can be valuable, in saving time and money, and it is worth at least checking out Dall E, and other visual creation tools, to see what they produce, based on your prompts.

AI Tweets?

This, at least in my opinion, is where things could get bland pretty quick.

This week, some new tools have emerged aligned with Twitter specifically, which can produce AI generated tweets and replies for your account/s.

Some are using ChatGPT, for example, to produce ‘thinkfluencer’ type tweets, and essentially automate their online persona – which seems problematic, in various ways. What if somebody actually wants to speak to you in person, based on your tweets, and you’re not who you’ve represented in these exchanges? What possible opportunities could arise from using automated tweets, which are not your original thoughts or ideas?

But worse than this, as you’ll note in the above example, such tools could also be used to create tweets complaining about a company.

How long will it be till bot armies are using these processes to attack competitors, at the behest of paying brands? Using generative AI, you could quickly come up with thousands of variations of credible, seemingly real complaints about a business, which could then influence public perception, and harm a brand’s reputation online.

That seems like a more effective, and less detectable approach than the current cut and paste messaging that many bot networks employ.

I suspect that this will become a bigger problem in 2023, as more brands realize the potential for trashing competitors via AI created tweets.

How you police that, I don’t know – especially given Twitter’s ongoing problems in dealing with bot accounts.

AI Generated 3D Models

The latest AI generation tools on the market now also enable users to create 3D models based on simple prompts, which could have big implications for the next stage.

3D models created by AI

As reported by TechCrunch:

OpenAI open sourced Point-E, a machine learning system that creates a 3D object given a text prompt, can produce 3D models in one to two minutes on a single Nvidia V100 GPU.

The next stage of digital connection, be it in the metaverse, VR or AR environments, will all require 3D models, as a means to build the experience, and facilitate interaction in multi-dimensional spaces.

The challenge in this sense is that it requires development expertise and skill, it requires years of knowledge of 3D creation and rendering in order to build these objects and experiences.

Unless it doesn’t.

Every platform with an interest in the next stage is now developing simplified 3D creation tools, with a particular focus on helping brands to scan in their products for AR/VR promotions.

What if AI could do that for you? What if, based on simple prompts, and maybe 2D image examples, AI tools could eventually help you build a corpus of 3D objects for such use?

There’s huge potential here, and it’s becoming increasingly possible to imagine a VR world where you would be able to build entirely new experiences before your eyes, based on simply vocalizing what you want to see.

It’s amazing to consider the potential, and how this could change the way we interact. And while the results of AI tools won’t always blow you away just yet, the fact that these processes are even possible at all is significant, and points to massive opportunities in future use.

AI tools are going to keep getting better, they’ll keep improving, which will lead to more use cases and potential over time. The only restriction, at least right now, is that they can only base their outputs on things that already exist – which means that they’ll always, by design, be at least somewhat generic.

But someday soon, that too will change, and AI tools will be able to create all new content and concepts, beyond what we already know.

Which seems a little scary, but it could also be the true ‘unlock’ that shifts these tools into entirely new territory.

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Twitter faces lawsuit by advisory firm for $1.9 million in unpaid bills

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Twitter faces lawsuit by advisory firm for $1.9 million in unpaid bills

US-based advisory firm Innisfree M&A Incorporated sued Twitter on Friday in New York State Supreme Court, seeking about $1.9 million compensation for what it says are unpaid bills. Reuters File Photo

New York: US-based advisory firm Innisfree M&A Incorporated sued Twitter on Friday in New York State Supreme Court, seeking about $1.9 million compensation for what it says are unpaid bills after it advised the social media company on its acquisition by Elon Musk last year.

“As of December 23, 2022, Twitter remains in default of its obligations to Innisfree under the agreement in an amount of not less than $1,902,788.03,” the lawsuit said.

Twitter and a lawyer for Innisfree did not respond to queries.

Elon Musk in October closed the $44 billion deal announced in April that year and took over microblogging platform Twitter.

In January 2023, Britain’s Crown Estate, an independent commercial business that manages the property portfolio belonging to the monarchy, said that it had begun court proceedings against Twitter over alleged unpaid rent on its London headquarters.

Advertising spending on Twitter Inc dropped by 71% in December, data from an advertising research firm showed, as top advertisers slashed their spending on the social-media platform after Musk’s takeover.

The banks that had provided $13 billion in financing last year for the Tesla chief executive’s acquisition of Twitter abandoned plans to sell the debt to investors because of uncertainty around the social media company’s fortunes and losses, according to media reports.

Recently, Twitter made its first interest payment on a loan that banks provided to help finance Musk’s purchase of the social media company last year.

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Twitter Expands Access to Twitter Blue, Announces New Incentives for Signing Up

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Twitter Expands Access to Twitter Blue, Announces New Incentives for Signing Up

Twitter is making its next big push on Twitter Blue subscriptions, as Elon Musk and Co. look to build Twitter Blue into a more significant revenue driver for the app.

First off, Twitter has now expanded Twitter Blue access to Saudi Arabia, France, Germany, Italy, Portugal and Spain, which will enable millions more Twitter users to potentially sign-up for a verification tick.

I mean, most probably won’t, going on what we’ve seen thus far, but it will likely swell Twitter Blue sign-ups by another few thousand, adding more cash to Twitter’s coffers.

Twitter’s also looking to further incentivize Blue sign-up by offering revenue share for ads shown in reply threads.

The idea here is that if users write interesting tweets, they would get compensated for the discussion they generate – but you need to be signed up to Twitter Blue to get it.

Elon hasn’t shared any further info on potential revenue split or process at this stage.

Twitter’s also looking to bring back an improved Spaces/podcast experience, as a Twitter Blue exclusive, while Musk has also hinted at allowing some users to avoid having to pay for basic API access, when it becomes unavailable next week, if they sign-up.

Oh, and Twitter’s gold checkmarks for business? Yeah, they’re likely going to be expensive if you want them.

Can’t imagine many brands are going to fork out $12,000 a year for a profile badge, along with $50 per staff member you want to add.

But maybe, Elon and Co. have some more tricks up their sleeve here, and they’ll eventually offer more incentives for businesses to sign-up.

But right now, that’s pretty steep.

And also, ‘legacy’ checkmarks will apparently be gone within the next few months.

All of these elements combined could juice Twitter Blue take-up, though it’s still hard to see it becoming the major contributor to Twitter’s revenue as Elon envisions.

At present, based on third-party tracking, the new Twitter Blue program looks to have around 300,000 subscribers, bringing in an extra $2.4 million per month, and $7.2 million per quarter.

Which is pretty good – but again, it’s still a long way from where Twitter wants subscription revenue to be.

When initially outlining his Twitter 2.0 reformation plans, Musk said that he wants to make subscription revenue around 50% of Twitter’s overall intake. That would serve two purposes – if the majority of users sign-up, Twitter can then use Twitter Blue as a form of ‘payment verification’, meaning that those accounts that don’t have a blue tick are increasingly likely to be bots. It would also reduce Twitter’s reliance on ads, which would give Musk more freedom to make moderation decisions as he likes, without considering potential ad placement concerns.

But in order to do this, Twitter needs a lot more users to sign up.

Twitter’s revenue in Q2 2022, the last time it publicly reported its numbers, was $1.18 billion, meaning that Twitter Blue would need to be bringing in around $590 million per quarter to meet that 50% goal.

Which is about 81x what Twitter Blue is currently bringing in, while at 300k sign-ups, that’s also only 0.12% of Twitter’s active user base that’s currently paying for a blue tick.

That’s likely why Twitter is making a new push on the program, in a bid to jack those numbers up, and maybe, in combination with businesses that do end up forking over $1k per month, it could become a more significant element in Twitter’s revenue make-up.

But 50% of revenue still seems like a lofty goal.

It’s also still confusing as to why anyone would pay, because as soon as you do, you’re devaluing the whole point of the verification checkmark in the first place.

The initial blue ticks were designed to delineate noteworthy users and organizations, which Twitter didn’t always get right, but for the most part, you knew that a blue tick account was likely someone who had relevant, authoritative things to say.   

Now, it’s just anyone who can afford it, and with Twitter looking to increase the reach of tweets from Blue accounts, that also means that the app is increasingly becoming more ‘pay to play’ for regular users, with the blue ticks becoming increasingly meaningless from a functional perspective.

And the logic behind them becomes more diluted with every person who signs up. Eventually, all the blue checkmark will mean is that this person can afford to pay – and who cares? Why do they need a blue tick, from a user perspective, to show that they have enough money to spend?

It sort of feels like the NFT trend of 2021, but worse, because it’s replacing an existing system that did serve a purpose.

In any event, Twitter’s not backing away from its Blue subscription plan, and its hopes of maximizing revenue intake, in any way it can, to keep the company afloat.

Which, given the extra debt it’s been saddled with in the Elon deal, is even tougher than ever – but maybe, in combination with everything else, subscriptions will form enough of an extra income stream to meaningfully contribute to its plans.



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Novak Djokovic, Rafael Nadal and Roger Federer: Born or made great?

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The Big 3 have won a total of 56 Grand Slams in their career.

Ecogastronomy, puppet arts, viticulture and enology, influencer marketing, or bakery science. In 2022, you can become anything you want and there are even specialized undergraduate degrees to help you gain all the relevant skills at university. Essentially, you can now be academically trained in any subject and learn practically everything you need to excel at your job.

In the context of sports, and particularly tennis, this is no different. There are plenty of degrees you can pursue to complement your career as an athlete, physiotherapist, or coach with useful knowledge about the human body, anatomy, and health.

This basically means that professional tennis players of the 21st century can complement their extraordinary talent and training routine with a relevant education and an elite team of professional and eminent physiotherapists, coaches, PR, and strategists. Ultimately, players have countless tools that can help them win matches, stay healthy, and be well-liked by the press and the fans.

You can find these ‘A teams’ all around the tour nowadays: players of the former next gen have taken advantage of their early success to incorporate experts on every specialty into their team and others like Carlos Alcaraz or Holger Rune have come directly in the tour alongside first-class teams headed by former World No. 1 and Slam champion Juan Carlos Ferrero and respected coach Patrick Mouratoglou respectively.

Understandably, tennis legends who have been on tour for almost two decades have progressively adapted to the quest for perfection too. You must remember Novak Djokovic’s radical diet change mid-career or Rafael Nadal’s loyal sports doctor for most of his injury-prone career.

21st-century professional tennis players have learned it all as far as tennis skills are concerned. In fact, objectively any top-100 player can produce Djokovesque cross-court backhands or Nadalese down-the-line forehands any time – we have seen rallies of the highest level in practices, Challengers and junior tournaments.

So, one must think that if every player on the tour can produce top-level tennis and is surrounded by the perfect team, what is stopping them from winning 20+ Grand Slam titles like Nadal, Roger Federer, and Djokovic?


Nadal, Federer and Djokovic — the Big 3

Roger Federer, Rafael Nadal and Novak Djokovic in discussion at the 2022 Laver Cup.
Roger Federer, Rafael Nadal and Novak Djokovic in discussion at the 2022 Laver Cup.

The Big 3 — Rafael Nadal, Roger Federer and Novak Djokovic — are living proof that in life there are things you just can’t learn, despite our self-help books saying otherwise. Tennis is different from other mainstream sports in that it remains an individual and extremely mental sport.

These three players belong at a higher level than anyone else, and it is not only the 63 combined Slam titles that separate them from their opponents. It is clearly not their physical form either, quite the opposite currently. It is the ability to remain serene, focused, confident, and indifferent to the crowd, pressure, and expectations, to play one point at a time, whether it is a break or a championship point, and to extract it from the surrounding context.

Being the best of all time does, however, not imply being the better player in all matches. We don’t have to go far back to find an example of a time when Nadal and Djokovic were the clear underdogs in a match. For instance, in Wimbledon 2022 we saw Nadal win a match with an abdominal tear and an average 80-mph serve speed (on a grasscourt!) against Taylor Fritz, a top American player in his best-ever season.

In essence, the three GOATs have had the ability to know how to win even when they are the worst players on the court, and if that greatness is something we all could learn or train for, it would stop being called so and we would see it more often.

Whether it is the experience, intelligence or just intrinsic and unique talent that has led to Big 3’s unprecedented achievements we won’t ever exactly know and, I am afraid, they are giving no opportunity to the so-called Next Gen to even dream of replicating their record book and help us make sense of what it takes to become a tennis master.

In any case, we can only feel extremely fortunate to have lived on the same timeline as the greatest trivalry in sports history. All of us, but the Next Gen, can only hope Nadal and Djokovic do not follow Federer’s retirement path anytime soon. And one only needs to watch their last matches against each other to (rightfully) assume that might not happen anytime soon.

What is the foot injury that has troubled Rafael Nadal over the years? Check here

Poll : Who will end up with most Grand Slam titles?

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