#Explainable AI Market Size

[gatorbites-imagines]

Hi! Hi! Fiesta time requesting to ya and was hoping if can place this ask here. I made sure to read you're rules so if I do somthing wrong then ignore my ask.

So Yautja's know that humans do not have strong instics as they do but they have certain things the Yautja don't have. Like uncanny valley.

So in this, the Yautja is with their human when they suddenly freeze. When they ask their human what's wrong, they don't awnser, just stearing off at somthing that they see. The Yautja can smell the fear and panic off of them.

What does the Yautja do?

Please please please please ignore this if I went aginst you're rules! Have a good day/night

Male Yautja OC (Bako) x male reader

Headcanons

I imagined this as Bako, who was mentioned a few times in my last yautja post, which you can read here.

Bako is a very chill Yautja compared to others. Hes already had multiple offspring and is still in his prime. It gives him a good amount of confidence and comfort in himself.

It also makes him a bit of a tease to his ooman lover, throwing you over his shoulder or just moving you around as he pleased, unless it really annoys you when he does.

He loves the size difference between you as well. You’ll catch him pressing his orange scaled hand against your own every now and then just to look at the difference. Bako always grumbles happily a about it.

But just because he’s more chill than most Yautja doesn’t mean he isn’t as active and aware as everyone else, he’s just great at hiding it behind an easygoing facade. Dating a normal ooman definitely makes him even more on edge and protective.

Hed try to teach you how to at least defend yourself or how to sharpen your instincts enough to protect yourself. You might not be able to kill another yautja in their prime, but you will be able to maul them enough to give you time to get away. Then he will hunt them down and present their skull to you.

Seeing you with a weapon also makes him grumble even more, arms crossed over his chest and his yellow eyes sparkling as he watches you use different firearms. Especially the firearms hes specially kitted for you to fit your hands and size.

If you take an interest in camoflague hed be more than happy to show you too, since hes mastered the art. Even without all his gear, Bako is able to melt into the background with ease after years of practice.

Having a more colorful shade in his scales meant he had to be really good at what he did, or he would have died one way or another. He just has to figure out how to really blend the different colors on your human skin.

But even with all this, Bako is always weary like any Yautja worth their salt should be. This is also why he notices pretty much immediately that you are weirded out or weary about something.

Having a Yautja partner can be pretty damn annoying sometimes with how protective and possessive they’ll be. Even if you guys are walking through what’s supposed to be a peaceful market, you still find Bako almost glued against your back.

Maybe you spot a species that just looks… uncomfortably human. But not really. You know like those ai robots that have skin that doesn’t really fit, or they blink too slowly and more too stiffly.

It makes you freeze for a moment, immediately sending alarm bells ringing inside Bakos head. There should be no reason for you to freeze, his clan had come to this market for years and it should be safe.

But smelling the discomfort and uncomfortable fear from you makes his mandibles flare under his mask, looking down at you for a moment to see where you are looking, before snapping his head in that direction, ready to kill.

Of course, you end up having to hold him back and explain that no, that alien didn’t say or do anything, yes, you were okay. It was just a weird human survival reaction.

You end up having to explain uncanny valley to him, and how once upon a time, humans developed pattern recognition for survival reasons.

This makes sense to Bako after you explain. He mentions something about other species that looked like humans coming to earth, to hunt humans, so of course you guys developed survival instincts against them.

This has you thinking “excuse me, what?” because what did he mean by that. of course, Bako just shrugs and goes “I thought you knew” and keeps you guys moving, as if he didn’t just drop that bomb on you.

Bako keeps being extra protective the rest of the day, as if just the smell of your fear keeps him on edge. Just in case, ya know? What if something jumps out of the shadows at you? You never knew out here. You just have to accept it, and accept all the cuddles later.

[tzifron]

I’ve been rereading the late anthropologist David Graeber’s Bullshit Jobs, which persuasively makes the case that the corporate world is happy to nurture inefficient or wasteful jobs if they somehow serve the managerial class or flatter elites—while encouraging the public to harbor animosity at those who do rewarding work or work that clearly benefits society. I think we can expect AI to accelerate this phenomenon, and to help generate echelons of new dubious jobs—prompt engineers, product marketers, etc—as it erodes conditions for artists and public servants.

A common refrain about modern AI is that it was supposed to automate the dull jobs so we could all be more creative, but instead, it’s being used to automate the creative jobs. That’s a pretty good articulation of what lies at the heart of the AI jobs crisis. Take the former Duolingo worker who was laid off as part of the company’s pivot to AI.

“So much will be lost,” the writer told me. “I was a content writer, I wrote the questions that learners see in the lessons. I enjoyed being able be creative. We were encouraged to make the exercises fun.” Now, consider what it’s being replace with, per the worker:

“First, the AI output is very boring. And Duolingo was always known for being fun and quirky. Second, it absolutely makes mistakes. Even on things that you would think it could get right. The AI tools that are available for people who pay for Duolingo Max often get things wrong—they have an ‘explain my mistake’ tool that often will suggest something that’s incorrect, sometimes the robot voices are programmed to speak the wrong language.”

This is just a snapshot, too. This is happening, to varying degrees, to artists, journalists, writers, designers, coders—and soon, perhaps already, as Thompson’s story points out, it could be happening to even more jobs and lines of work.

Now, it needs to be underlined once again that generative AI is not yet the one-size-fits-all agent of job replacement its salesmen would like it to be—far from it. A recent SalesForce survey reported on by the Information show that only one-fifth of enterprise AI buyers are seeing good results, and that 61% of respondents report a disappointing return on investment for AI or even none at all.

Generative AI is still best at select tasks that do not require consistent reliability—hence its purveyors taking aim at art and creative industries. But all that’s secondary. The rise of generative AI, linked as it is with the ascent to power of the American tech oligarchy, has given rise to a jobs crisis nonetheless.

We’re left at a crossroads where we must consider nothing less than what kind of jobs we want people to be able to do, what kind of work and which institutions we think are important as a society, and what we’re willing to do to protect them—before the logic of generative AI and the jobs crisis it has begotten guts them to the bone, or devours them altogether.

[probablyasocialecologist]

This is it. Generative AI, as a commercial tech phenomenon, has reached its apex. The hype is evaporating. The tech is too unreliable, too often. The vibes are terrible. The air is escaping from the bubble. To me, the question is more about whether the air will rush out all at once, sending the tech sector careening downward like a balloon that someone blew up, failed to tie off properly, and let go—or more slowly, shrinking down to size in gradual sputters, while emitting embarrassing fart sounds, like a balloon being deliberately pinched around the opening by a smirking teenager. But come on. The jig is up. The technology that was at this time last year being somberly touted as so powerful that it posed an existential threat to humanity is now worrying investors because it is apparently incapable of generating passable marketing emails reliably enough. We’ve had at least a year of companies shelling out for business-grade generative AI, and the results—painted as shinily as possible from a banking and investment sector that would love nothing more than a new technology that can automate office work and creative labor—are one big “meh.” As a Bloomberg story put it last week, “Big Tech Fails to Convince Wall Street That AI Is Paying Off.” From the piece: Amazon.com Inc., Microsoft Corp. and Alphabet Inc. had one job heading into this earnings season: show that the billions of dollars they’ve each sunk into the infrastructure propelling the artificial intelligence boom is translating into real sales. In the eyes of Wall Street, they disappointed. Shares in Google owner Alphabet have fallen 7.4% since it reported last week. Microsoft’s stock price has declined in the three days since the company’s own results. Shares of Amazon — the latest to drop its earnings on Thursday — plunged by the most since October 2022 on Friday. Silicon Valley hailed 2024 as the year that companies would begin to deploy generative AI, the type of technology that can create text, images and videos from simple prompts. This mass adoption is meant to finally bring about meaningful profits from the likes of Google’s Gemini and Microsoft’s Copilot. The fact that those returns have yet to meaningfully materialize is stoking broader concerns about how worthwhile AI will really prove to be. Meanwhile, Nvidia, the AI chipmaker that soared to an absurd $3 trillion valuation, is losing that value with every passing day—26% over the last month or so, and some analysts believe that’s just the beginning. These declines are the result of less-than-stellar early results from corporations who’ve embraced enterprise-tier generative AI, the distinct lack of killer commercial products 18 months into the AI boom, and scathing financial analyses from Goldman Sachs, Sequoia Capital, and Elliot Management, each of whom concluded that there was “too much spend, too little benefit” from generative AI, in the words of Goldman, and that it was “overhyped” and a “bubble” per Elliot. As CNN put it in its report on growing fears of an AI bubble, Some investors had even anticipated that this would be the quarter that tech giants would start to signal that they were backing off their AI infrastructure investments since “AI is not delivering the returns that they were expecting,” D.A. Davidson analyst Gil Luria told CNN. The opposite happened — Google, Microsoft and Meta all signaled that they plan to spend even more as they lay the groundwork for what they hope is an AI future. This can, perhaps, explain some of the investor revolt. The tech giants have responded to mounting concerns by doubling, even tripling down, and planning on spending tens of billions of dollars on researching, developing, and deploying generative AI for the foreseeable future. All this as high profile clients are canceling their contracts. As surveys show that overwhelming majorities of workers say generative AI makes them less productive. As MIT economist and automation scholar Daron Acemoglu warns, “Don’t believe the AI hype.”

6 August 2024

[anhed-nia]

Oh holy shit, I almost forgot my book is officially out today! If someone could source or make for me a BUY MY BOOK! BUY MY BOOK! gif from The Critic that would be pretty awesome. This has been in my mind, and then in the works for such a long time now that the idea of the actual street date totally eluded me, and I have to use this gif that @moviesludge made me to explain what that's like:

Anyway I'm very grateful for the chance to work on something so (literally) novel that turned out to be so personal and serious, and I'm also grateful for the great guys at Encyclopocalypse, and for the incredible support and encouragement of Vincenzo Natali who I am very fortunate to know.

Before I even remembered that this was happening today, I woke up on this excellently chilly pre-Fall morning to a little treat I got myself in the mail:

It has cute little illos on the story title pages and everything. My dark secret is that I haven't read much horror fiction since I was a kid, and I was thinking about how if I had a time machine I would probably use it to go back to my childhood library to immerse myself in their seemingly endless stacks of pulp horror novels. I wasn't generally allowed to watch horror movies but I could read whatever I wanted, and the content of some of those books was just unimaginably sleazy and profane, the kind of thing you could never put in a movie because no one would let you and you could probably never afford it anyway. Of course a lot of them couldn't possibly live up to their great covers--that's sort of a lost art (and no it isn't just because of AI)--but it was still fun to find out.

This is my long way of asking you, John Q. Public, what are your favorite works of horror fiction from before 1990 or so? And don't pick any Stephen King, please just assume that we have both enjoyed some King. Feel free to reblog with specific great covers if you can find them.

[bestaiimagegenerator]

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[thosearentcrimes]

Read an AI hype preprint because apparently I am not very discerning with my time. There's a lot that's kinda funny about it.

So the title of this paper is "People cannot distinguish GPT-4 from a human in a Turing test". The purpose of the title is evidently not to provide a clear reference for other researchers, but rather to produce AI hype and be reproduced in credulous headlines, as we'll see shortly.

The paper opens with a brief history and presentation of the Turing test. The important features are that the interrogator questions an AI and a human and is tasked with identifying the AI and has 5 minutes to do so. Then it presents the testing setup they used. 500 people are recruited, they are split into five categories of 100 each (making the percentage signs redundant), 400 interrogators, who talk to GPT-4, GPT-3.5, ELIZA, or a human (from the remaining 100) for five minutes and then are asked to determine if they talked to a human or not. It is a two-player setup. Why, though? You just explained that the Turing test was formulated on the premise of three participants, what is the reasoning for departing from that? Oh, ok, here it is:

We used a two-player formulation of the game, where a single human interrogator conversed with a single witness who was either a human or a machine. While this differs from Turing’s original three-player formulation, it has become a standard operationalisation of the test because it eliminates the confound of the third player’s humanlikeness and is easier to implement

WHAT? The "confound" of the third player's humanlikeness is the point! That is part of the premise of the test, it's trying to test/compare humanlikeness. Margarine marketers are more honest than this. By the end of the Introduction section of this paper titled "People cannot distinguish GPT-4 from a human in a Turing test" the authors have explained that they did not administer a Turing test because they were pretty sure that if they did GPT-4 would fail and they wanted a positive result so they designed their own test it could succeed instead. This is just outright fraud!

It's also kind of weird to talk about "GPT-4" succeeding at the test, because it was in fact a specific elaborate prompt of GPT-4, selected on the basis of prior research that had found it the most effective strategy. I mean, it needs to be prompted with something and it's not entirely clear to me why I think something more neutral without specific listed strategies would be a more honest implementation but I do. I guess it's because the way this finding is presented it's claiming that the AI is good at deception, and I mean it just really isn't. The AI didn't come up with the idea of doing an exploratory study with a wide variety of prompts (or for that matter, come up with the prompts themselves) and then using the ones that worked best, that was the researchers. The researchers who have pretty obviously rigged the whole study, in fact.

There is also some possible arguable dishonesty in the way the situation was presented to the human participants. They say the participants were told they would be put in conversation with either a human or a machine. Now, unless they were told more than is mentioned, I contend those people would have been entitled to treat this as an implication that those were equally likely possibilities, especially given the analogy with the actual Turing test, in which the population sizes of humans and machines are necessarily identical. Note also that all in all the interrogators turned out just a little under 50% human verdicts. In fact, the probability that they would be assigned to speak to a machine was 75%. If the interrogators had been told that there was only a 25% probability they would be assigned to speak to a human, would they have been more critical? Would they have assigned closer to 25% human verdicts? Even if it weren't that much closer, I suspect it would bring the GPT-4 success rate below 50%, which was treated as an important benchmark for god knows what reason. It might also bring the human success rate below 50%, of course. All of this could have been avoided by simply running the actual Turing test, but again, GPT-4 would have failed so they couldn't do that.

I wouldn't be too surprised if this paper did get accepted without major revisions, science is as vulnerable to hype cycles as everyone else, but if it does then what an indictment of the field (hm, actually, what field, I don't think "dicking around with GPT" has really been standardized yet).

[notwiselybuttoowell]

In Europe, Pringles has 34 active flavours in seven can sizes (one of which is called “David” for reasons no one can explain). Not all of these flavours are available in every European country – prawn cocktail only really sells in the UK and Ireland, while bacon is found in most places except Belgium, the Netherlands and strongholds of vegetarianism Austria, Denmark and Sweden. Salt and vinegar has spread everywhere except Norway and Italy. “They don’t have the habit of doing vinegar on their crisps; they just eat them plain with salt,” says Julie Merzougui, lead food designer at Kellanova. If an employee in Italy wanted to explore bringing salt and vinegar to the market, they could – they’d simply have to ask. As of yet, they haven’t.

Multiple times a year, Pringles releases limited-edition flavours known internally as “insanely accurate analogues” – Merzougui and Peremans come up with these for Europe. “People think we have the dream job,” Merzougui says (she has dark hair, round glasses and an easy laugh, a personality akin to an experimental flavour – perhaps a chorizo Pringle). Peremans, who has worked at the company for 26 years, has a salt and pepper beard and a Salt & Shake personality. He speaks quietly and pragmatically, but has a subtle playful streak: “My young son, he wants to become my successor.”

Like Lay’s, Pringles starts with data – in Asia, the company uses a Tinder-like tool with 200 consumers at a time, asking them to swipe left or right on potential flavours. Lucia Sudjalim, a senior Pringles developer in Asia, says she does a lot of “social media listening”, observing trends among influencers and bloggers. Kellanova also uses AI, which Merzougui says can predict trends up to 10 years in advance. Things aren’t always this sophisticated though – both Lay’s and Pringles also look at what’s on the shelves in countries they want to break into, copying flavours and identifying gaps to fill.

Yet just because the world wants a flavour doesn’t mean it’s made. In December 2020, scotch egg sales soared in the UK after Conservative ministers ruled the snack a “substantial meal” (providing punters with an excuse to be in the pub under Covid-19 lockdown rules). Peremans was challenged to make scotch egg Pringles and pulled it off; Merzougui says they tasted “really authentic”. Ultimately, however, the potential order volume was not high enough to justify a production run. (This, incidentally, is why it’s hard to get Salt & Pepper Pringles in the UK, even though they’re delicious.)

Another unreleased flavour was part of a collaboration with Nando’s that petered out for reasons Peremans is unsure about. Sometimes, logistics get in the way: the perfectly blended seasoning might clog the machines or create too much dust, causing sneezing fits in the factory. Belgian legislation mandates that every seasoning has to be put through a dust explosion test – it is set alight in controlled conditions to ensure it won’t blow up.

Inside the plant, manager Van Batenburg shows me giant cube-shaped bags of seasonings that arrive ready to be cascaded on to the crisps. At the end of his video presentation, he made a passing comment that rocked my world. We were talking about other crisp companies, big name competitors. “In essence,” he said, “they’re using the same seasoning houses we do.”

I leave Belgium with the names of three seasoning houses Pringles work with. At home, I discover that their websites are obscure – they speak of flavours and trends, but don’t even mention Pringles. I haven’t so much stumbled upon a conspiracy as been invited into it, but I am still shocked. After two months’ cajoling by the Pringles team, two representatives from a seasoning house agree to speak – but only on the condition of total anonymity, in line with their contractual obligations.

“It’s quite secretive,” food scientist Reuben admits via Zoom, wearing a pink shirt and a thoughtful expression (the only crisp I can compare him to is a Quaver). “Everyone has their own crown jewels that they protect.”

As a marketer, Peggy has always found the company’s secrecy “strange”. She speaks clearly, in a way that is reminiscent of a teacher or a steadfast multigrain snack. “It’s always been a bit of a puzzle to me … I was like, ‘Why aren’t we shouting about this?’ But I was told, ‘Oh, no, we have to keep it very quiet.’”

This is because – just as Van Batenburg hinted in Belgium – the seasoning house Reuben and Peggy work for provides flavours for Pringles and Lay’s, as well as other brands. When asked whether their clients know, Reuben says, “They do and they don’t.” “It’s just not really talked about,” Peggy adds. However, this doesn’t mean that a Salt & Vinegar Pringle is flavoured with the same seasoning as a Salt & Vinegar Lay’s. In fact, the seasoning house is strictly siloed to guarantee exclusivity. Reuben’s team work on the Pringles account; the team making flavours for PepsiCo is in an entirely different country. “So the recipe, if you will, of the Pringles salt and vinegar can’t be seen by the other team,” Reuben says.

[mariacallous]

The latest in a series of duels announced by the European Commission is with Bing, Microsoft’s search engine. Brussels suspects that the giant based in Redmond, Washington, has failed to properly moderate content produced by the generative AI systems on Bing, Copilot, and Image Creator, and that as a result, it may have violated the Digital Services Act (DSA), one of Europe’s latest digital regulations.

On May 17, the EU summit requested company documents to understand how Microsoft handled the spread of hallucinations (inaccurate or nonsensical answers produced by AI), deepfakes, and attempts to improperly influence the upcoming European Parliament elections. At the beginning of June, voters in the 27 states of the European Union will choose their representatives to the European Parliament, in a campaign over which looms the ominous shadow of technology with its potential to manipulate the outcome. The commission has given Microsoft until May 27 to respond, only days before voters go to the polls. If there is a need to correct course, it may likely be too late.

Europe’s Strategy

Over the past few months, the European Commission has started to bang its fists on the table when dealing with the big digital giants, almost all of them based in the US or China. This isn’t the first time. In 2022, the European Union hit Google with a fine of €4.1 billion because of its market dominance thanks to its Android system, marking the end of an investigation that started in 2015. In 2023, it sanctioned Meta with a fine of €1.2 billion for violating the GDPR, the EU’s data protection regulations. And in March it presented Apple with a sanction of €1.8 billion.

Recently, however, there appears to have been a change in strategy. Sanctions continue to be available as a last resort when Big Tech companies don’t bend to the wishes of Brussels, but now the European Commission is aiming to take a closer look at Big Tech, find out how it operates, and modify it as needed, before imposing fines. Take, for example, Europe’s Digital Services Act, which attempts to impose transparency in areas like algorithms and advertising, fight online harassment and disinformation, protect minors, stop user profiling, and eliminate dark patterns (design features intended to manipulate our choices on the web).

In 2023, Brussels identified 22 multinationals that, due to their size, would be the focus of its initial efforts: Google with its four major services (search, shopping, maps, and play), YouTube, Meta with Instagram and Facebook, Bing, X (formerly Twitter), Snapchat, Pinterest, LinkedIn, Amazon, Booking, Wikipedia, Apple’s App Store, TikTok, Alibaba, Zalando, and the porn sites Pornhub, XVideos, and Stripchat. Since then, it has been putting the pressure on these companies to cooperate with its regulatory regime.

The day before the Bing investigation was announced, the commission also opened one into Meta to determine what the multinational is doing to protect minors on Facebook and Instagram and counter the “rabbit hole” effect—that is, the seamless flood of content that demands users’ attention, and which can be especially appealing to younger people. That same concern led it to block the launch of TikTok Lite in Europe, deeming its system for rewarding social engagement dangerous and a means of encouraging addictive behavior. It has asked X to increase its content moderation, LinkedIn to explain how its ad system works, and AliExpress to defend its refund and complaint processes.

A Mountain of Laws …

On one hand, the message appears to be that no one will escape the reach of Brussels. On the other, the European Commission, led by President Ursula von der Leyen, has to demonstrate that the many digital laws and regulations that are in place actually produce positive results. In addition to the DSA, there is the Digital Markets Act (DMA), intended to counterbalance the dominance of Big Tech in online markets; the AI Act, Europe’s flagship legislation on artificial intelligence; and the Data Governance Act (DGA) and the Data Act, which address data protection and the use of data in the public and private sectors. Also to be added to the list are the updated cybersecurity package, NIS2 (Network and Information Security); the Digital Operational Resilience Act, focused on finance and insurance; and the digital identity package within eIDAS 2. Still in the draft stage are regulations on health data spaces and much-debated chat measures which would authorize law enforcement agencies and platforms to scan citizens’ private messages, looking for child pornography.

Brussels has deployed its heavy artillery against the digital flagships of the United States and China, and a few successful blows have landed, such as ByteDance’s suspension of the gamification feature on TikTok Lite following its release in France and Spain. But the future is uncertain and complicated. While investigations attract media interest, the EU’s digital bureaucracy is a large and complex machine to run.

On February 17, the DSA became law for all online service operators (cloud and hosting providers, search engines, e-commerce, and online services) but the European Commission doesn’t and can’t control everything. That is why it asked states to appoint a local authority to serve as a coordinator of digital services. Five months later, Brussels had to send a formal notice to six states (Cyprus, Czechia, Estonia, Poland, Portugal, and Slovakia) to urge them to designate and fully empower their digital services coordinators. Those countries now have two months to comply before Brussels will intervene. But there are others who are also not in the clear. For example, Italy’s digital services coordinator, the Communications Regulatory Authority (abbreviated AGCOM, for Autorità per le Garanzie nelle Comunicazioni, in Italian), needs to recruit 23 new employees to replenish its staff. The department told WIRED Italy that it expects to have filled all of its appointments by mid-June.

The DSA also introduced “trusted flaggers.” These are individuals or entities, such as universities, associations, and fact-checkers, committed to combating online hatred, internet harassment, illegal content, and the spread of scams and fake news. Their reports are, one hopes, trustworthy. The selection of trusted flaggers is up to local authorities but, to date, only Finland has formalized the appointment of one, specifically Tekijänoikeuden tiedotus- ja valvontakeskus ry (in English, the Copyright Information and Anti-Piracy Center). Its executive director, Jaana Pihkala, explained to WIRED Italy that their task is “to produce reports on copyright infringements,” a subject on which the association has 40 years of experience. Since its appointment as a trusted flagger, the center’s two lawyers, who perform all of its functions, have sent 816 alerts to protect films, TV series, and books on behalf of Finnish copyright holders.

… and a Mountain of Data

To assure that the new commission is respected by the 27 states, the commission set up the DSA surveillance system as quickly as possible, but the bureaucrats in Brussels still have a formidable amount of research to do. On the one hand, there is the anonymous reporting platform with which the commission hopes to build dossiers on the operations of different platforms directly from internal sources. The biggest scandals that have shaken Meta have been thanks to former employees, like Christopher Wylie, the analyst who revealed how Cambridge Analytica attempted to influence the US elections, and Frances Haugen, who shared documents about the impacts of Instagram and Facebook on children’s health. The DSA, however, intends to empower and fund the commission so that it can have its own people capable of sifting through documents and data, analyzing the content, and deciding whether to act.

The commission boasts that the DSA will force platforms to be transparent. And indeed it can point to some successes already, for example, by revealing the absurdly inadequate numbers of moderators employed by platforms. According to the latest data released last November, they don’t even cover all the languages spoken in the European Union. X reported that it had only two people to check content in Italian, the language of 9.1 million users. There were no moderators for Greek, Finnish, or Romanian even though each language has more than 2 million subscribers. AliExpress moderates everything in English while, for other languages, it makes do with automatic translators. LinkedIn moderates content in 12 languages of the European bloc—that is, just half of the official languages.

At the same time, the commission has forced large platforms to standardize their reports of moderation interventions to feed a large database, which, at the time of writing this article, contains more than 18.2 billion records. Of these cases, 69 percent were handled automatically. But, perhaps surprisingly, 92 percent concerned Google Shopping. This is because the platform uses various parameters to determine whether a product can be featured: the risk that it is counterfeited, possible violations of site standards, prohibited goods, dangerous materials, and others. It can thus be the case that several alerts are triggered for the same product and the DSA database counts each one separately, multiplying the shopping numbers exponentially. So now the EU has a mass of data that further complicates its goal of being fully transparent.

Zalando’s Numbers

And then there’s the Big Tech companies’ legal battle against the fee they have to pay to the commission to help underwrite its supervisory bodies. Meta, TikTok, and Zalando have challenged the fee (though paid it). Zalando is also the only European company on the commission’s list of large platforms, a designation Zalando has always contested because it does not believe it meets the criteria used by Brussels. One example: The platforms on the list must have at least 45 million monthly users in Europe. The commission argues that Zalando has 83 million users, though that number, for example, includes visits from Portugal, where the platform is not marketed, and Zalando argues those users should be deducted from its total count. According to its calculations, the activities subject to the DSA reach only 31 million users, under the threshold. When Zalando was assessed its fee, it discovered that the commission had based it on a figure of 47.5 million users, far below the initial 83 million. The company has now taken the commission to court in an attempt to assure a transparent process.

And this is just one piece of legislation, the DSA. The commission has also deployed the Digital Markets Act (DMA), a package of regulations to counterbalance Big Tech’s market dominance, requiring that certain services be interoperable with those of other companies, that apps that come loaded on a device by default can be uninstalled, and that data collected on large platforms be shared with small- and medium-size companies. Again, the push to impose these mandates starts with the giants: Alphabet, Amazon, Apple, Meta, ByteDance, and Microsoft. In May, Booking was added to the list.

Big Tech Responds

Platforms have started to respond to EU requests, with lukewarm results. WhatsApp, for instance, has been redesigned to allow chatting with other apps without compromising its end-to-end encryption that protects the privacy and security of users, but it is still unclear who will agree to connect to it. WIRED US reached out to 10 messaging companies, including Google, Telegram, Viber, and Signal, to ask whether they intend to look at interoperability and whether they had worked with WhatsApp on its plans. The majority didn’t respond to the request for comment. Those that did, Snap and Discord, said they had nothing to add. Apple had to accept sideloading—i.e., the possibility of installing and updating iPhone or iPad applications from stores outside the official one. However, the first alternative that emerged, AltStore, offers very few apps at this time. And it has suffered some negative publicity after refusing to accept the latest version of its archenemy Spotify’s app, despite the fact that the audio platform had removed the link to its website for subscriptions.

The DMA is a regulation that has the potential to break the dominant positions of Big Tech companies, but that outcome is not a given. Take the issue of surveillance: The commission has funds to pay the salaries of 80 employees, compared to the 120 requested by Internal Market Commissioner Thierry Breton and the 220 requested by the European Parliament, as summarized by Bruegel in 2022. And on the website of the Center for European Policy Analysis (CEPA), Adam Kovacevich, founder and CEO of Chamber of Progress, a politically left-wing tech industry coalition (all of the digital giants, which also fund CEPA, are members), stated that the DMA, “instead of helping consumers, aims to help competitors. The DMA is making large tech firms’ services less useful, less secure, and less family-friendly. Europeans’ experience of large tech firms’ services is about to get worse compared to the experience of Americans and other non-Europeans.”

Kovacevich represents an association financed by some of those same companies that the DMA is focused on, and there is a shared fear that the DMA will complicate the market and, in the end, benefit only a few companies—not necessarily those most at risk because of the dominance of Silicon Valley. It is not only lawsuits and fines, but also the perceptions of citizens and businesses that will help to determine whether EU regulations are successful. The results may come more slowly than desired by Brussels as new legislation is rarely positively received at first.

Learning From GDPR and Gaia-X

Another regulatory act, the General Data Protection Regulation (GDPR), has become the global industry standard, forcing online operators to change the way they handle our data. But if you ask the typical person on the street, they’ll likely tell you it’s just a simple cookie wall that you have to approve before continuing on to a webpage. Or it’s viewed as a law that has required the retention of dedicated external consultants on the part of companies. It is rarely described as the ultimate online privacy law, which is exactly what it is. That said, while the act has reshaped the privacy landscape, there have been challenges, as the digital rights association Noyb has explained. The privacy commissioners of Ireland and Luxembourg, where many web giants are based for tax purposes, have had bottlenecks in investigating violations. According to the latest figures from Ireland’s Data Protection Commission (DPC), 19,581 complaints have been submitted in the past five years, but the body has made only 37 formal decisions and only eight of those began with complaints. Noyb recently conducted a survey of 1,000 data protection officers; 74 percent were convinced that if privacy officers investigated the typical European company, they would find at least one GDPR violation.

The GDPR was also the impetus for another unsuccessful operation: separating the European cloud from the US cloud in order to shelter the data of EU citizens from Washington’s Cloud Act. In 2019, France and Germany announced with great fanfare a federation, Gaia-X, that would defend the continent and provide a response to the cloud market, which has been split between the United States and China. Five years later, the project has become bogged down in the process of establishing standards, after the entry of the giants it was supposed to counter, such as Microsoft, Amazon, Google, Huawei, and Alibaba, as well as the controversial American company Palantir (which analyses data for defense purposes). This led some of the founders, such as the French cloud operator Scaleway, to flee, and that then turned the spotlight on the European Parliament, which led the commission to launch an alternative, the European Alliance for Industrial Data, Edge and Cloud, which counts among its 49 members 26 participants from Gaia-X (everyone except for the non-EU giants) and enjoys EU financial support.

In the meantime, the Big Tech giants have found a solution that satisfies European wishes, investing en masse to establish data centers on EU soil. According to a study by consultancy firm Roland Berger, 34 data center transactions were finalized in 2023, growing at an average annual rate of 29.7 percent since 2019. According to Mordor Intelligence, another market analysis company, the sector in Europe will grow from €35.4 billion in 2024 to an estimated €57.7 billion in 2029. In recent weeks, Amazon web services announced €7.8 billion in investments in Germany. WIRED Italy has reported on Amazon’s interest in joining the list of accredited operators to host critical public administration data in Italy, which already includes Microsoft, Google, and Oracle. Notwithstanding its proclamations about sovereignty, Brussels has had to capitulate: The cloud is in the hands of the giants from the United States who have found themselves way ahead of their Chinese competitors after diplomatic relations between Beijing and Brussels cooled.

The AI Challenge

The newest front in this digital battle is artificial intelligence. Here, too, the European Union has been the first to come up with some rules under its AI Act, the first legislation to address the different applications of this technology and establish permitted and prohibited uses based on risk assessments. The commission does not want to repeat the mistakes of the past. Mindful of the launch of the GDPR, which in 2018 caused companies to scramble to assure they were compliant, it wants to lead organizations through a period of voluntary adjustment. Already 400 companies have declared their interest in joining the effort, including IBM.

In the meantime, Brussels must build a number of structures to make the AI Act work. First is the AI Council. It will have one representative from each country and will be divided into two subgroups, one dedicated to market development and the other to public sector uses of AI. In addition, it will be joined by a committee of technical advisers and an independent committee of scientists and experts, along the lines of the UN Climate Committee. Secondly, the AI Office, which sits within Directorate-General Connect (the department in charge of digital technology), will take care of administrative aspects of the AI Act. The office will assure that the act is applied uniformly, investigate alleged violations, establish codes of conduct, and classify artificial intelligence models that pose a systemic risk. Once the rules are established, research on new technologies can proceed. After it is fully operational, the office will employ 100 people, some of them redeployed from General Connect while others will be new hires. At the moment, the office is looking to hire six administrative staff and an unknown number of tech experts.

On May 29, the first round of bids in support of the regulation expired. These included the AI Innovation Accelerator, a center that provides training, technical standards, and software and tools to promote research, support startups and small- and medium-sized enterprises, and assist public authorities that have to supervise AI. A total of €6 million is on the table. Another €2 million will finance management and €1.5 million will go to the EU’s AI testing facilities, which will, on behalf of countries’ antitrust authorities, analyze artificial intelligence models and products on the market to assure that they comply with EU rules.

Follow the Money

Finally, a total of €54 million is designated for a number of business initiatives. The EU knows it is lagging behind. According to an April report by the European Parliament’s research service, which provides data and intelligence to support legislative activities, the global AI market, which in 2023 was estimated at €130 billion, will reach close to €1.9 trillion in 2030. The lion’s share is in the United States, with €44 billion of private investment in 2022, followed by China with €12 billion. Overall, the European Union and the United Kingdom attracted €10.2 billion in the same year. According to Eurochamber researchers, between 2018 and the third quarter of 2023, US AI companies received €120 billion in investment, compared to €32.5 billion for European ones.

Europe wants to counter the advance of the new AI giants with an open source model, and it has also made its network of supercomputers available to startups and universities to train algorithms. First, however, it had to adapt to the needs of the sector, investing almost €400 million in graphics cards, which, given the current boom in demand, will not arrive anytime soon.

Among other projects to support the European AI market, the commission wants to use €24 million to launch a Language Technology Alliance that would bring together companies from different states to develop a generative AI to compete with ChatGPT and similar tools. It’s an initiative that closely resembles Gaia-X. Another €25 million is earmarked for the creation of a large open source language model, available to European companies to develop new services and research projects. The commission intends to fund several models and ultimately choose the one best suited to Europe’s needs. Overall, during the period from 2021 to 2027, the Digital Europe Program plans to spend €2.1 billion on AI. That figure may sound impressive, but it pales in comparison to the €10 billion that a single company, Microsoft, invested in OpenAI.

The €25 million being spent on the European large language model effort, if distributed to many smaller projects, risks not even counterbalancing the €15 million that Microsoft has spent bringing France’s Mistral, Europe’s most talked-about AI startup, into its orbit. The big AI models will become presences in Brussels as soon as the AI Act, now finally approved, comes into full force. In short, the commission is making it clear in every way it can that a new sheriff is in town. But will the bureaucrats of Brussels be adequately armed to take on Big Tech? Only one thing is certain—it’s not going to be an easy task.

[vipu14]

Maximize Digital Presence: Essential Digital Marketing Strategies for 2024

In the ever-evolving digital landscape, staying ahead of the curve is essential for businesses looking to maximize their digital presence. As we move into 2024, several digital marketing strategies stand out as crucial for achieving success. Here’s a comprehensive guide to the essential digital marketing strategies for the year ahead.

1. Leverage AI and Automation

Personalized Customer Experiences

Artificial Intelligence (AI) and automation tools have revolutionized how businesses interact with customers. By utilizing AI-driven analytics, you can gain deeper insights into customer behavior, allowing for highly personalized marketing campaigns. Chatbots, personalized email marketing, and predictive analytics can enhance user experience and drive conversions.

Efficiency in Campaign Management

Automation tools can streamline your marketing efforts, from scheduling social media posts to managing email campaigns. This not only saves time but also ensures that your marketing activities are consistent and timely.

2. Focus on Content Quality and Relevance

High-Value Content Creation

Content remains king in 2024, but the focus is on quality and relevance. Create high-value content that addresses your audience’s pain points and provides actionable insights. This includes blog posts, whitepapers, videos, and infographics.

SEO Optimization

Search engine optimization (SEO) is critical for visibility. Ensure your content is optimized for search engines by using relevant keywords, creating engaging meta descriptions, and utilizing internal and external links. Voice search optimization is also becoming increasingly important as more users rely on voice-activated devices.

3. Harness the Power of Social Media

Platform-Specific Strategies

Different social media platforms cater to different demographics and user behaviors. Develop platform-specific strategies to maximize engagement. For instance, use Instagram for visual storytelling, LinkedIn for professional content, and TikTok for short, engaging videos.

Social Commerce

Social commerce is on the rise, with platforms like Instagram and Facebook offering in-app shopping experiences. Leverage these features to provide seamless shopping experiences directly within social media platforms, boosting sales and customer satisfaction.

4. Invest in Video Marketing

Short-Form and Live Videos

Short-form videos and live streaming continue to dominate the digital space. Platforms like TikTok, Instagram Reels, and YouTube Shorts are perfect for creating engaging, bite-sized content. Live videos offer real-time interaction with your audience, fostering a sense of community and trust.

Educational and Explainer Videos

Videos that educate or explain complex concepts can position your brand as an authority in your industry. Create tutorials, product demonstrations, and behind-the-scenes content to engage and inform your audience.

5. Embrace Omnichannel Marketing

Consistent Messaging Across Channels

Omnichannel marketing ensures that your customers have a seamless experience across all digital and physical channels. Consistent messaging and cohesive brand experiences across websites, social media, email, and in-store interactions can significantly enhance customer loyalty.

Integrated Customer Data

Integrate customer data from various touchpoints to create a unified view of the customer journey. This helps in delivering personalized experiences and improving overall customer satisfaction.

6. Prioritize Data Privacy and Security

Transparency and Trust

With increasing concerns about data privacy, it’s crucial to prioritize transparency and build trust with your audience. Clearly communicate your data policies and ensure that customer data is protected.

Compliance with Regulations

Stay updated with data protection regulations such as GDPR and CCPA. Ensure your marketing practices comply with these regulations to avoid legal issues and build credibility with your audience.

Conclusion

As digital marketing continues to evolve, staying ahead requires a strategic approach and a willingness to adapt to new trends and technologies. By leveraging AI and automation, focusing on content quality, harnessing social media, investing in video marketing, embracing omnichannel strategies, and prioritizing data privacy, you can maximize your digital presence in 2024 and beyond. Stay proactive, stay informed, and watch your digital marketing efforts thrive in the coming year.

[updated-reviews]

Elevate Your Marketing Videos: The Power of AI Text-to-Speech with Different Voices

In today's fast-paced digital world, capturing audience attention is more crucial than ever. Marketing videos have become a cornerstone of successful marketing campaigns, offering a dynamic and engaging way to connect with your target audience. However, creating high-quality video content can be a time-consuming and expensive endeavor, especially when it comes to professional voiceovers.

This is where the magic of AI text-to-speech (TTS) technology comes in. Imagine a world where you can transform your marketing scripts into captivating voiceovers with just a few clicks. AI text-to-speech allows you to do just that, offering a powerful and versatile tool for businesses of all sizes. By leveraging the power of AI, you can create professional-sounding voiceovers in a variety of styles and languages, all at a fraction of the traditional cost.

Beyond the Human Voice: Unveiling the Versatility of AI Text-to-Speech (AI text to speech different voices)

Gone are the days of being limited to a single voice narrator. AI text-to-speech technology boasts a vast library of AI voices, each offering unique characteristics and personalities. This opens up a world of possibilities for your marketing videos. Imagine tailoring the voiceover to perfectly match the tone and style of your brand. Need a friendly and approachable voice for a product explainer video? AI has you covered. Creating a high-energy commercial? No problem! The variety of AI voices allows you to select the perfect narrator to resonate with your target audience and enhance the overall message of your video.

But the versatility of AI text-to-speech goes beyond just voice selection. Many platforms allow you to fine-tune the speaking style, adjusting the pace, pitch, and even adding emphasis for dramatic effect. This level of control empowers you to craft the ideal voiceover that seamlessly integrates with the visuals of your video, creating a truly immersive experience for viewers.

Crafting the Perfect Tone: How AI Creates Emotionally-Charged Voiceovers (convert text to speech with emotions AI)

The human voice is a powerful tool for conveying emotions. A skilled voiceover artist can inject the right amount of enthusiasm, authority, or warmth to captivate the audience. But what if you could achieve the same level of emotional resonance with AI? Believe it or not, AI text-to-speech technology is rapidly evolving to incorporate emotional intelligence.

Some advanced platforms allow you to choose from a range of pre-programmed emotional styles, such as joyful, persuasive, or urgent. This allows you to tailor the emotional delivery of your voiceover to perfectly compliment the message you're trying to convey. Imagine a heartwarming ad for a charity using a gentle and compassionate voice, or a product demonstration packed with excitement and energy. AI text-to-speech empowers you to evoke the desired emotions in your audience, fostering a deeper connection and ultimately driving results.

Elevate Your Reach: Expanding Your Audience with Multilingual AI Voices (AI text to speech for marketing videos)

The global marketplace offers a vast pool of potential customers. However, language barriers can often present a significant hurdle for marketing campaigns. AI text-to-speech technology breaks down these barriers by offering a multilingual solution. Many platforms support a wide range of languages, allowing you to create voiceovers in the native tongue of your target audience. This not only enhances the overall understanding and engagement of your videos but also demonstrates a commitment to catering to a global audience.

Imagine reaching new markets and expanding your brand awareness without the need for expensive voiceover translations. AI text-to-speech provides a cost-effective and efficient way to localize your marketing videos, ensuring your message resonates across borders.

From Budget-Friendly Options to Premium Solutions: Choosing the Best AI Text-to-Speech Software (best AI text to speech software)

The beauty of AI text-to-speech technology lies in its accessibility. A variety of options are available, catering to different needs and budgets. For those just starting out, several free AI text-to-speech converters (free AI text to speech converter) offer basic functionality. These platforms can be a great way to experiment with AI voiceovers and see if they align with your marketing strategy. However, keep in mind that free options may have limitations in terms of voice selection, audio quality, and customization features.

For businesses seeking a more professional and feature-rich solution, several premium AI text-to-speech software providers exist. These platforms offer a wider range of voices, advanced control over audio parameters, and even integration with text to speech API with AI for seamless workflow integration with your video editing software. While premium options come with a cost, the investment can pay off handsomely, allowing you to create high-quality marketing videos that truly stand out from the crowd.

[nickgerlich]

What's In Your Cart?

It seems like ancient history now, but the way Americans shop for groceries started to change in a big way back in 1988. That’s when the first Walmart Supercenter opened in Washington Missouri. It was a novel concept for Americans, because it combined groceries with dry goods and general merchandise.

While Walmart cannot take credit for the innovation, since there had been so-called hypermarkets doing so on an even larger scale in Europe and Asia in years prior, they can take credit for nailing it here. Walmart did such a good job that they systematically started replacing the majority of their older stores in favor of these behemoths, which in larger metros clock in at 200,000 square feet. In Amarillo, the stores on Coulter Road and Grand Street are both of this size, having opened in 1992. They have downsized versions for smaller markets, like here in Canyon, but the concept is the same.

Walmart did its time building its grocery business, and in 2001 became the nation’s biggest food retailer, with sales of $56 billion. It has not let go of that title.

Today, though, Walmart has laid claim to yet another honor in the grocery business: They are now the second-biggest e-grocery retailer, accounting for 36% of sales. Only Amazon, with nearly double Walmart’s e-grocery sales, is bigger.

And for yet another find-a-positive-in-a-pandemic exercise, ponder just how fast Walmart pivoted during COVID, jumping on curbside pickup and home delivery faster and better than its brick-and-mortar competitors. That advantage is still playing out today as the chain continues to grow its online grocery presence.

I see it every time I go to Walmart for groceries. Now let me explain something. In Canyon we have only Walmart and United, the latter of which is owned by Albertson’s and is slated for further acquisition under the Kroger umbrella. United has considerably higher prices on the things I buy, and lacks many of the items I want that Walmart routinely stocks. I’m not cheap. It just looks that way.

But when I am in our Walmart, I constantly see order pickers pushing their huge carts stacked with blue plastic bins. They are fulfilling orders for both curbside and delivery, and sometimes there are so many of them in the aisles that it becomes hard to shop with my little buggy. Like this last week, when there was a major traffic jam right in front of the pizza and pasta section, which, as my luck would have it, was where I needed to be. It’s kind of like driving on the freeway. You get out of the way of the massive trucks, even if you have right-of-way.

As for me, I have yet to order groceries online, aside from some specialty items I have procured through Amazon. I am just old-school enough, as my father taught me, to want to squeeze that lettuce, thump that melon, etc., before I make my selection. I don’t want tomatoes with bruises, nor broccoli that is starting to turn yellow. Only I can control for that, and that means in-person.

But I know the time is coming. I am no Luddite; I’m just picky. Maybe I should do a few test orders with non-perishable items, just to see how they do. Then maybe add some onions or cauliflower. I might just like this. Still, it’s going to be hard to let go of old habits and practices. My father was the grocery shopper in my family, and I watched him closely. He was a master at his craft.

Back in the Canyon Walmart, I think it might be time for them to consider opening a dark store, which looks like, feels like, and smells like a traditional store, but is not open to the general public. In the grocery biz, a dark store would be where all curbside and delivery orders are filled, alleviating the brick-and-mortar store from all those cumbersome trolleys.

Good for Walmart in responding best during the challenges of a pandemic. It paid dividends, and apparently enough people made the switch to online grocery shopping then that it is now habit for many. Just don’t run me over in the pizza and pasta aisle while you’re busy filling those orders.

Dr “Stay In Your Lane” Gerlich

Audio Blog

[introvertedwolf]

oh my GOD i have so many thoughts on this shit.

first of all, HORRENDOUS marketing. Colossal Biosciences is doing INSANELY scummy shit, and they have tried to "bring back" creatures like woolly mammoths and dodos with no real success. i'd bet my bottom dollar they're trying to use this language for this project as a "proof of concept" to sell to investors, because THEY DID NOT DO SHIT.

they edited, by their own admission, 14 total genes, making 20 changes between them, using CRISPR technology (which is entirely it's own mess of a situation and probably doesnt give us the degree of control its usually sold as, but thats an entirely different can of worms)

but i need to make it clear. MOST OF THOSE CHANGES. WERE TO COLOR. other changes were to ear size, body size, and fur texture.

THEY MADE TIMBER WOLVES. THAT ARE WHITE. AND CALLING THEM DIRE WOLVES.

and if that wasn't bad enough, Dire Wolf were Not related to wolves. they're not in the same genus. dire wolves are most closely related to african jackals and african painted dogs than anything else currently alive. theyre more closely related to my niche canine blorbo The Dhole than any extant wolf species, particularly north american ones.

and lets be clear, even with jackals being the most related currently alive species, jackals are still more closely related to timber wolves than either one is to the dire wolf.

there are LITERALLY MILLIONS of genes worth of difference between grey wolves and jackals, and millions MORE between jackal and dire wolf. and they edited FOURTEEN. and CALLED IT A DIRE WOLF.

dire wolf were only Called that because they had similar skeletal shapes and pack hunting behavior, but currently we think they'd look more like a jackal or coyote than anything else. and reconstructing COLOR? spending MOST OF YOUR EDITS ON COLOR? this is not at all what a direwolf would look like, even remotely, and it CERTAINLY wont tell us anything about how they BEHAVED.

they make claims about how they want to release these animals into the wild (to their "rightful place" as their website says), but the ecological niche that dire wolf filled no longer exists. dire wolf are a species of megafauna, who specialized in hunting other megafauna. the only living species of megafauna is moose, and modern timber wolves, bear and humans are known for hunting those just fine. if you released a dire wolf into the north american wilds, it would compete with endangered timber wolves for resources.

its one thing to save a species from wild extinction that experienced it recently, where its niche was not otherwise filled, but wolves DID fill that niche. introducing a dire wolf at this point would have the same issues as introducing an invasive species. this was not a species that RECENTLY went extinct in the wild. there is no longer space to support an animal like a dire wolf in the wild.

they say they're "committed to transparency," but they refused to release the 2 complete dire wolf genomes they used to splice into the pups DNA. when it comes to welfare, they've refused to explain what species of surrogate they used (most speculate it was a domestic dog) or what care conditions for those are/were.

this is a flagrant marketing ploy, its total BS.

and not for nothing, the Colossal Biosciences website uses AI generated images.

this company is so frustratingly misleading. They did not bring back the direwolf (Aenocyon dirus). They modified a modern grey wolf (Canis lupus) into having some direwolf morphology. There has been no de-extinction. This is pure hype slop. As a friend said "these are dire wolves the same way La Croix is a fruit".

[ajay-adm777]

Highlights from AI’s Next Frontier: A Global Economic Revolution for People

Artificial Intelligence has long been seen as the future. But in 2025, it’s no longer a future concept — it’s a present force, actively reshaping global economies, industries, and societies. What’s different now is that we’re no longer just discussing AI as a tool to automate or optimize. We’re entering AI’s next frontier, where its role expands beyond business productivity to become a catalyst for inclusive economic transformation — a people-first revolution.

This blog explores the key developments that define this shift and explains how AI is set to reshape employment, entrepreneurship, education, and global economic equity. This is not just about machines; it's about how AI is being designed and deployed to uplift people.

1. From Automation to Empowerment

The early narratives around AI largely focused on job displacement and automation. While automation remains part of the equation, the focus has now shifted to how AI can empower individuals — not replace them.

In 2025, AI tools are now embedded in the hands of everyday workers, freelancers, small business owners, educators, and students. These tools don’t just perform tasks; they enhance human creativity, decision-making, and productivity.

For instance, a graphic designer can now use generative AI to produce 10 design concepts in seconds. A solo entrepreneur can build a brand, run marketing campaigns, and manage customer service with AI copilots. AI has evolved into a collaborative partner — and that’s changing the economic landscape from the bottom up.

2. The Rise of the AI-Powered Individual Economy

What the gig economy did for flexibility, the AI-powered individual economy is doing for capability. People are becoming one-person enterprises, equipped with tools that would have previously required a full team.

Platforms are now emerging that allow creators, consultants, developers, and service providers to offer AI-enhanced services globally — from personalized tutoring to AI-driven business analysis. The barriers to entry in many industries are falling fast.

As AI continues to democratize access to skills and technology, individuals from underserved regions and low-income backgrounds are finding new ways to generate income, access global markets, and build sustainable livelihoods.

3. Small Businesses Become Smarter, Faster

In traditional economies, scale often equated to strength. Larger companies could afford better technology, hire specialized talent, and outspend competitors. AI is flipping that script.

In 2025, small and medium-sized businesses (SMBs) are now competing on equal footing with large corporations thanks to AI. From automated accounting to intelligent supply chain tools, small enterprises are operating leaner and smarter than ever before.

AI is also enhancing customer personalization, enabling even a local business to deliver Amazon-level service. This is fueling a new wave of entrepreneurship across emerging markets, where business owners are skipping outdated legacy systems and going straight to intelligent platforms.

4. AI-Driven Education and Skill Transformation

One of the most transformative aspects of AI’s economic revolution is its impact on learning and skills development. Personalized AI tutors, adaptive learning platforms, and real-time feedback systems are reshaping how people gain knowledge.

Millions are now accessing microlearning modules, coding bootcamps, and language lessons through AI-driven platforms — often for free or at very low cost. This is particularly powerful in regions where access to quality education has been limited.

Governments, NGOs, and private companies are investing heavily in AI for education, aiming to close skill gaps, prepare youth for future industries, and promote lifelong learning. The result is a global workforce that's becoming more agile, informed, and ready for the demands of an AI-first economy.

5. New Jobs, New Industries

While some traditional jobs are being phased out, AI is creating new ones at an accelerating pace. Roles like AI ethics officers, prompt engineers, virtual experience designers, and data explainers didn’t exist just a few years ago — now, they’re in high demand.

Moreover, entirely new industries are emerging. Synthetic media, digital healthcare, climate tech, and AI-powered agriculture are opening fresh opportunities for innovation and employment. People are not just adapting to AI; they’re building with it.

The key challenge ahead is ensuring that reskilling efforts keep pace with this transformation. Fortunately, AI is also playing a role in guiding career transitions through intelligent career coaching and customized learning journeys.

6. AI and Economic Inclusion

One of the most promising aspects of AI’s global revolution is its potential to address economic inequality. AI tools are enabling financial inclusion by powering microloans, risk assessment, and digital banking in regions where traditional infrastructure has failed.

Farmers in rural communities can now use AI-powered apps to get weather forecasts, optimize crop yields, and connect directly to markets. Women in underserved areas are using AI-driven platforms to launch digital businesses from home. People with disabilities are accessing AI-enabled tools for communication, mobility, and job training.

This is a shift from AI for profit to AI for participation — where the focus is not just on GDP growth, but on human growth.

7. Public Policy and AI Governance

To truly unlock the benefits of AI for people, thoughtful governance is essential. Around the world, policymakers are waking up to the dual responsibility of promoting innovation while protecting human rights, data privacy, and fair access.

In 2025, several international frameworks have been introduced to ensure that AI systems are transparent, explainable, and free from harmful bias. Economic policies are also being revised to include AI-readiness indexes, digital infrastructure investment, and social protections for workers affected by disruption.

The global dialogue is moving toward AI as a public good, and this framing is helping shift focus from corporate dominance to societal benefit.

8. The Ethics of Economic AI

With great power comes great responsibility. As AI begins to make decisions that affect people’s economic lives — from credit approvals to job matches — ethical considerations must remain front and center.

The next frontier involves embedding values like fairness, accountability, and inclusivity into the core of AI systems. Businesses are increasingly expected to practice responsible AI development, and consumers are becoming more vocal about transparency and ethical use.

An economy built on AI must also be built on trust.

9. Global Collaboration and Innovation

AI’s economic impact is not confined to Silicon Valley or major tech hubs. Collaboration across countries, cultures, and sectors is now essential. Nations are investing in joint innovation labs, open-source AI models, and public-private partnerships that share benefits more equitably.

This spirit of global collaboration ensures that the AI revolution doesn't widen the gap between rich and poor, but rather brings the world closer through shared progress and mutual support.

Final Thoughts: A People-First Revolution

The story of AI’s next frontier is no longer about machines — it’s about human potential. We are witnessing the beginning of a global economic revolution that places people at the center, with AI acting as an enabler, not a replacement.

For entrepreneurs, educators, workers, and governments, the message is clear: those who embrace AI thoughtfully and ethically will shape the economic future. It’s not just about keeping up with the technology — it’s about leading with purpose, inclusion, and innovation.

At businessinfopro.com, we believe in tracking these pivotal shifts and helping our readers navigate this new economy with clarity and confidence. The AI revolution is here — and it’s for everyone.

Read more about this: https://businessinfopro.com/ais-next-frontier-a-global-economic-revolution-for-people/

[sutraai-driventransformation]

Turning Compliance Into Confidence: Why Human‑Centric AML and Fraud Risk Management Matters

Every headline about money‑laundering fines or sophisticated payment fraud is a reminder: the threat is real, and the cost of inaction keeps rising. Yet most teams still experience AML compliance as a maze of spreadsheets, alerts, and last‑minute fire drills. At Sutra Management, we believe it doesn’t have to be that way. Done right, AML and fraud risk management becomes a source of strength—protecting your reputation while giving you the freedom to focus on growth.

The Compliance Catch‑22

Regulators demand rigour, regulators demand speed—and they rarely give you more people or budget to achieve both. Meanwhile, customers expect a seamless experience with zero friction. Somewhere in the middle sits your compliance team, juggling:

Ever‑shifting global regulations

Mountains of transactional data that never stop growing

Pressing questions from the business: “Can we launch this product tomorrow?”

The result can feel like a constant game of whack‑a‑mole, where each solved alert spawns three new ones.

Our Philosophy: Technology + Empathy

Behind every suspicious transaction is a story—sometimes an innocent one, sometimes a red flag. Behind every keyboard is a human being balancing deadlines, risk appetite, and brand promise. That’s why our approach pairs advanced analytics with a human touch:

Unified View of Risk – We knit fraud detection and AML monitoring into a single FRAML‑style workflow, so you never chase the same problem twice.

Risk‑Based Prioritisation – One‑size‑fits‑all rules create alert fatigue. We fine‑tune thresholds to your products, geographies, and customer segments—surfacing what truly matters.

Explainable AI – Black‑box decisions erode trust. Our models highlight the exact patterns that triggered an alert, helping analysts close cases faster and auditors sleep easier.

Continuous Coaching – Tools only succeed when people believe in them. We run hands‑on training that turns “extra work” into “easier work,” building a culture where compliance feels empowering.

What “Good” Looks Like

🔹 Fewer False Positives Targeted rules and machine‑learning models cut noise, freeing analysts to investigate genuine risk.

🔹 Faster Investigations A single dashboard pulls KYC data, network links, and sanction hits into one place—no more tab‑hopping.

🔹 Stronger Governance Real‑time MI dashboards give leadership instant insight into exposure, trends, and staffing needs.

🔹 Happier Customers Smarter screening means fewer onboarding delays and smoother transaction flows.

A Day in the Life With Sutra’s Platform

09:05 – An unusually large wire transfer pings the system. AI cross‑references device location, historical behaviour, and watch‑list data in seconds. 09:06 – The risk score is high, but the analyst sees exactly why—an address match to a newly sanctioned entity. No guesswork, no dead‑end digging. 09:20 – Analyst files an STR directly from the case screen, automatically attaching evidence and narrative. 09:30 – Case closed, coffee still hot.

Multiply that efficiency across hundreds of daily alerts, and the ROI speaks for itself.

Built to Scale With You

Whether you’re a fintech racing toward Series C or a multinational bank modernizing legacy system, our modular architecture grows as you do—new markets, new products, no problem. And because regulations never stand still, our policy library and rule sets update continuously, keeping you ahead of tomorrow’s headlines.

Ready to Make Compliance Your Competitive Edge?

Financial crime isn’t slowing down, but neither are we. If you’re tired of compliance that drains resources instead of adding value, let’s talk about a partnership where AML and fraud risk management fuel your confidence—not your anxiety.

🔗 Explore Sutra Management’s AML compliance solutions: https://sutra-management.com

[alias-milamber]

All this is true, but I'm going to add some shading to the post-2000s dot-com pre-2020 area (2005 to 2015 was my startups phase). One of the lessons investers learned after the first dot-com boom was that public perception was less important than users. A lot of the boom folded when companies like pets.com and the first versions of delivery companies and eshops were gaining massive public perception by throwing money at superbowl ads and Times Square billboards, and then showing that millions upon millions of people were visiting their website. The investers didn't realise at the time that this was a soft metric, the numbers they didn't ask for were how many users bought things. Browsed things. Put things into their cart. Showed any interest in buying anything.

So the next revolution was Monthly Active Users, and really it still is. The only two numbers the investors ever care about are MAU and revenue, and revenue can wait. MAU must go up. If MAU goes down, or remains stagnent, or doesn't go up fast enough, your entire company management will be consumed by meetings with and explainations to investors. Nothing that isn't MAU related can get done at all until MAU is growing higher, faster. There was a break in 2007 with the credit crunch, and for a few years a revenue path was more important. But then, MAU.

MAU MAU MAU. Like an unfed cat.

This means there's no scope for solidifying what the product is, if you don't go directly towards the biggest possible markets with the most users, you're going to have to justify that. There's no scope for maintaining company culture - you just have to grow (Which is part of why Startup company culture doesn't generally scale). Once you let in the vulture capitalists, you are no longer the product you were building. You are a balloon, and if you do not grow, you will not be able to afford to stay the size you are.

If you aren't doing what the investors want, they will pressure you into hiring a Business Development Officer, usually freshly off a larger company they own, who will be inserted to the company to ensure initiatives are more "business focused". Most of the non-founder early staff will find their roles and ability to affect change curtailed, if they're not unexpectedly "downsized" for not meeting targets previously unknown. The founding CEO will "step down" to focus more on "the things that matter in the company" and the Business Development Officer will take on the CEO role, and withing a year the company will have pivoted to a new investor-friendly industry (like is currently happening to LLM, Web3, "AI"; but previously was "Social Networks", "Web 2.0", e-commerce, microblogs, photo sharing, blog platforms, video, web portals, social internet, etc etc) and the founders will have moved to "exciting new oppertunities".

If you do what the investors want, either you become the next big thing (Google, Facebook), are bought by the previous big thing and kept around (Blogger, Instagram), are bought by a bigger thing and absorbed like a tub of mealworms into which someone has dropped a steak (Occulus, anything Twitter bought). Even if you become the next big thing, you will become primarily an advertising company whose original product is just a way to feed those ads to people.

If anyone wants to know why every tech company in the world right now is clamoring for AI like drowned rats scrabbling to board a ship, I decided to make a post to explain what's happening.

(Disclaimer to start: I'm a software engineer who's been employed full time since 2018. I am not a historian nor an overconfident Youtube essayist, so this post is my working knowledge of what I see around me and the logical bridges between pieces.)

Okay anyway. The explanation starts further back than what's going on now. I'm gonna start with the year 2000. The Dot Com Bubble just spectacularly burst. The model of "we get the users first, we learn how to profit off them later" went out in a no-money-having bang (remember this, it will be relevant later). A lot of money was lost. A lot of people ended up out of a job. A lot of startup companies went under. Investors left with a sour taste in their mouth and, in general, investment in the internet stayed pretty cooled for that decade. This was, in my opinion, very good for the internet as it was an era not suffocating under the grip of mega-corporation oligarchs and was, instead, filled with Club Penguin and I Can Haz Cheezburger websites.

Then around the 2010-2012 years, a few things happened. Interest rates got low, and then lower. Facebook got huge. The iPhone took off. And suddenly there was a huge new potential market of internet users and phone-havers, and the cheap money was available to start backing new tech startup companies trying to hop on this opportunity. Companies like Uber, Netflix, and Amazon either started in this time, or hit their ramp-up in these years by shifting focus to the internet and apps.

Now, every start-up tech company dreaming of being the next big thing has one thing in common: they need to start off by getting themselves massively in debt. Because before you can turn a profit you need to first spend money on employees and spend money on equipment and spend money on data centers and spend money on advertising and spend money on scale and and and

But also, everyone wants to be on the ship for The Next Big Thing that takes off to the moon.

So there is a mutual interest between new tech companies, and venture capitalists who are willing to invest $$$ into said new tech companies. Because if the venture capitalists can identify a prize pig and get in early, that money could come back to them 100-fold or 1,000-fold. In fact it hardly matters if they invest in 10 or 20 total bust projects along the way to find that unicorn.

But also, becoming profitable takes time. And that might mean being in debt for a long long time before that rocket ship takes off to make everyone onboard a gazzilionaire.

But luckily, for tech startup bros and venture capitalists, being in debt in the 2010's was cheap, and it only got cheaper between 2010 and 2020. If people could secure loans for ~3% or 4% annual interest, well then a $100,000 loan only really costs $3,000 of interest a year to keep afloat. And if inflation is higher than that or at least similar, you're still beating the system.

So from 2010 through early 2022, times were good for tech companies. Startups could take off with massive growth, showing massive potential for something, and venture capitalists would throw infinite money at them in the hopes of pegging just one winner who will take off. And supporting the struggling investments or the long-haulers remained pretty cheap to keep funding.

You hear constantly about "Such and such app has 10-bazillion users gained over the last 10 years and has never once been profitable", yet the thing keeps chugging along because the investors backing it aren't stressed about the immediate future, and are still banking on that "eventually" when it learns how to really monetize its users and turn that profit.

The pandemic in 2020 took a magnifying-glass-in-the-sun effect to this, as EVERYTHING was forcibly turned online which pumped a ton of money and workers into tech investment. Simultaneously, money got really REALLY cheap, bottoming out with historic lows for interest rates.

Then the tide changed with the massive inflation that struck late 2021. Because this all-gas no-brakes state of things was also contributing to off-the-rails inflation (along with your standard-fare greedflation and price gouging, given the extremely convenient excuses of pandemic hardships and supply chain issues). The federal reserve whipped out interest rate hikes to try to curb this huge inflation, which is like a fire extinguisher dousing and suffocating your really-cool, actively-on-fire party where everyone else is burning but you're in the pool. And then they did this more, and then more. And the financial climate followed suit. And suddenly money was not cheap anymore, and new loans became expensive, because loans that used to compound at 2% a year are now compounding at 7 or 8% which, in the language of compounding, is a HUGE difference. A $100,000 loan at a 2% interest rate, if not repaid a single cent in 10 years, accrues to $121,899. A $100,000 loan at an 8% interest rate, if not repaid a single cent in 10 years, more than doubles to $215,892.

Now it is scary and risky to throw money at "could eventually be profitable" tech companies. Now investors are watching companies burn through their current funding and, when the companies come back asking for more, investors are tightening their coin purses instead. The bill is coming due. The free money is drying up and companies are under compounding pressure to produce a profit for their waiting investors who are now done waiting.

You get enshittification. You get quality going down and price going up. You get "now that you're a captive audience here, we're forcing ads or we're forcing subscriptions on you." Don't get me wrong, the plan was ALWAYS to monetize the users. It's just that it's come earlier than expected, with way more feet-to-the-fire than these companies were expecting. ESPECIALLY with Wall Street as the other factor in funding (public) companies, where Wall Street exhibits roughly the same temperament as a baby screaming crying upset that it's soiled its own diaper (maybe that's too mean a comparison to babies), and now companies are being put through the wringer for anything LESS than infinite growth that Wall Street demands of them.

Internal to the tech industry, you get MASSIVE wide-spread layoffs. You get an industry that used to be easy to land multiple job offers shriveling up and leaving recent graduates in a desperately awful situation where no company is hiring and the market is flooded with laid-off workers trying to get back on their feet.

Because those coin-purse-clutching investors DO love virtue-signaling efforts from companies that say "See! We're not being frivolous with your money! We only spend on the essentials." And this is true even for MASSIVE, PROFITABLE companies, because those companies' value is based on the Rich Person Feeling Graph (their stock) rather than the literal profit money. A company making a genuine gazillion dollars a year still tears through layoffs and freezes hiring and removes the free batteries from the printer room (totally not speaking from experience, surely) because the investors LOVE when you cut costs and take away employee perks. The "beer on tap, ping pong table in the common area" era of tech is drying up. And we're still unionless.

Never mind that last part.

And then in early 2023, AI (more specifically, Chat-GPT which is OpenAI's Large Language Model creation) tears its way into the tech scene with a meteor's amount of momentum. Here's Microsoft's prize pig, which it invested heavily in and is galivanting around the pig-show with, to the desperate jealousy and rapture of every other tech company and investor wishing it had that pig. And for the first time since the interest rate hikes, investors have dollar signs in their eyes, both venture capital and Wall Street alike. They're willing to restart the hose of money (even with the new risk) because this feels big enough for them to take the risk.

Now all these companies, who were in varying stages of sweating as their bill came due, or wringing their hands as their stock prices tanked, see a single glorious gold-plated rocket up out of here, the likes of which haven't been seen since the free money days. It's their ticket to buy time, and buy investors, and say "see THIS is what will wring money forth, finally, we promise, just let us show you."

To be clear, AI is NOT profitable yet. It's a money-sink. Perhaps a money-black-hole. But everyone in the space is so wowed by it that there is a wide-spread and powerful conviction that it will become profitable and earn its keep. (Let's be real, half of that profit "potential" is the promise of automating away jobs of pesky employees who peskily cost money.) It's a tech-space industrial revolution that will automate away skilled jobs, and getting in on the ground floor is the absolute best thing you can do to get your pie slice's worth.

It's the thing that will win investors back. It's the thing that will get the investment money coming in again (or, get it second-hand if the company can be the PROVIDER of something needed for AI, which other companies with venture-back will pay handsomely for). It's the thing companies are terrified of missing out on, lest it leave them utterly irrelevant in a future where not having AI-integration is like not having a mobile phone app for your company or not having a website.

So I guess to reiterate on my earlier point:

Drowned rats. Swimming to the one ship in sight.

[wdcsuae]

Understanding AI Architectures: A Guide by an AI Development Company in UAE

In a world where screens rule our day, Artificial Intelligence (AI) quietly drives most of the online tools we now take for granted. Whether it's Netflix recommending the next film, a smartphone assistant setting reminders, or stores guessing what shirt you might buy next, the trick behind the curtain is the framework-the architecture.

Knowing how that framework works matters to more than just coders and CTOs; it matters to any leader who dreams of putting AI to work. As a top AI company based in the UAE, we think it is time to untangle the idea of AI architecture, explain why it is important, and show how companies here can win by picking the right setup for their projects.

What Is AI Architecture?

AI architecture is simply the plan that lines up all the parts of an AI system and shows how they talk to one another. Think of it as the blueprint for a house; once the beams are in place, the system knows where to read data, learn trends, decide on an action, and respond to people or other software.

A solid architecture brings four quick wins:

speed: data is processed fast

growth: the platform scales when new tasks arrive

trust: sensitive details are kept safe

harmony: it plugs into tools the business already uses

Because goals, data amounts, and launch settings vary, every model-whether machine learning, deep learning, NLP or something else-needs its own twist on that blueprint.

Core Layers of AI Architecture

Whether you're putting together a chatbot, a movie recommender, or a smart analytics dashboard, most projects rest on four basic layers.

1. Data Layer Every AI starts with data, so this layer is ground zero. It handles:

Input sources, both structured tables and messy text

Storage options, from classic databases to modern data lakes

Cleaning tools that tidy and sort raw bits into useable sets

In the UAE, firms juggle Arabic, English, and several dialects across fields like finance and tourism, so keeping fast, local data clean can make-or-break a project.

2. Modelling Layer Next up, the brains of the operation live here. Data scientists and engineers use this stage to craft, teach, and test their models.

Major pieces include:

Machine-learning algorithms, such as SVMs, random forests, or gradient boosting

Deep-learning networks, like CNNs for images or Transformers for text

Training platforms, with tools from TensorFlow, Keras, or PyTorch

An AI shop in Dubai or Abu Dhabi tunes this layer to local patterns, legal rules, and industry demands-whether that's AML flags for banks, fast scans for hospitals, or fair-value estimates for buyers.

3. Serving Layer After the models finish training, they must be put into action and made available to users or business tools. This step includes:

APIs that let other software talk to the model

Places to run the model (on-site, in the cloud, or a mix)

Speed tweaks so answers come back fast

In a fast-moving market like the UAE, especially in Dubai and Abu Dhabi, a slow reply can turn customers away. That makes this layer so important.

4. Feedback and Monitoring Layer AI systems are not plug-and-play for life; they learn, drift, and need care. This layer keeps things fresh with:

Watching how the model performs

Collecting feedback from real-world results

Re-training and rolling out new versions

Without that routine check-up, models can grow stale, skewed, or just plain useless.

Popular AI Architectures in Practice:

Lets highlight a few AI setups that companies across the UAE already count on.

1. Client-Server AI Architecture Perfect for small and mid-sized firms. The model sits on a server, and the client zips data back and forth through an API.

Use Case: Retail chains analyze shopper behavior to better place stock.

2. Cloud-Native AI Architecture Built straight into big clouds such as AWS, Azure, or Google Cloud. It scales up easily and can be deployed with a few clicks.

Use Case: Fintech firms sifting through millions of records to spot fraud and score loans.

3. Edge AI Architecture Edge AI moves brainpower right onto the gadget itself instead of sending every bit of data to faraway cloud servers. This design works well when speed is vital or when sensitive info cant leave the device.

Use Case: Think of smart cameras scanning mall hallways or airport lounges in the UAE, spotting unusual behavior while keeping footage onsite.

4. Hybrid AI Architecture Hybrid AI blends edge smarts with cloud muscle, letting apps react quickly on a device but tap the cloud for heavy lifting when needed.

Use Case: A medical app that checks your heart rate and ECG in real time but uploads that data so doctors can run big-pattern analysis later.

Challenges to Consider While Designing AI Architectures

Building a solid AI backbone is not as simple as plug-and-play. Here are key hurdles firms in the UAE often encounter.

Data Privacy Regulations

With the UAE tightening digital-security rules, models must meet the Personal Data Protection Law or face fines.

Infrastructure Costs

Top-notch GPUs, fast storage, and chilled racks add up fast. A skilled UAE partner will size the setup wisely.

Localization and Multilingual Support

Arabic-English chatbots have to handle dialects and culture cues, which means fresh, on-the-ground training, not off-the-shelf data.

Talent Availability  

Brilliant models need more than code; they rely on data engineers, AI researchers, DevOps pros, and industry insiders speaking the same language.

How UAE Businesses Can Profit from Custom AI Setups?

Across the UAE, artificial intelligence is spreading quickly-from online government services to real-estate apps and tourism chatbots. Picking or creating a custom AI setup delivers:

Faster decisions thanks to real-time data analysis

Better customer support through smart, automated replies

Lower costs via predictive maintenance and lean processes

Higher revenue by personalizing each users journey

Partnering with a seasoned local AI firm gives you technical skill, market know-how, rule-following advice, and lasting help as your project grows.