philip lelyveld The world of entertainment technology

IT’S THE CREATOR ECONOMY

... By Hollywood standards, Dax Shepard is not a huge star. He started this podcast, in his attic on a lark, on his own, after a recent movie, CHiPS, failed. I don’t italicize “on his own” because starting a podcast is either hard or brave. Five million available podcasts is proof that starting a podcast is neither of those. I use italics because podcasting is the perfect example of a large and influential Media segment that almost no one thinks is part of the Creator Economy. And that proves the core thesis of this piece: The Creator Economy is both very different and much bigger than most people think. ...

Yes, Joe Rogan is a millionaire. But he wasn’t when he started his podcast - now the biggest in the world - and he was making $30 million per year on that indie podcast long before Spotify came along. Which is precisely why Spotify wanted him so badly, and why so many huge Media companies are clamoring to do enormous deals with similar Creators like the Smartless guys. They know something I’ve been professing for a while: Increasingly, Creators are generating audiences just as large, and far more engaged, as gatekeeper-led content. ...

See the full story here: https://eshap.substack.com/p/its-the-creator-economy

Here’s how OpenAI will determine how powerful its AI systems are

OpenAI has created an internal scale to track the progress its large language models are making toward artificial general intelligence, or AI with human-like intelligence, a spokesperson told Bloomberg.

Today’s chatbots, like ChatGPT, are at Level 1. OpenAI claims it is nearing Level 2, defined as a system that can solve basic problems at the level of a person with a PhD. Level 3 refers to AI agents capable of taking actions on a user’s behalf. Level 4 involves AI that can create new innovations. Level 5, the final step to achieving AGI, is AI that can perform the work of entire organizations of people. ...

In May, OpenAI dissolved its safety team after the group’s leader, OpenAI cofounder Ilya Sutskever, left the company. Jan Leike, a key OpenAI researcher, resigned shortly after claiming in a post that “safety culture and processes have taken a backseat to shiny products” at the company. While OpenAI denied that was the case, some are concerned about what this means if the company does in fact reach AGI. ...

See the full story here; https://www.theverge.com/2024/7/11/24196746/heres-how-openai-will-determine-how-powerful-its-ai-systems-are

The AI-focused COPIED Act would make removing digital watermarks illegal

PhilNote: I like that it appears to have teeth, but there is a good chance that, as a slow-moving NIST process, by the time it is developed and deployed industry will say the market is too big to implement it and it is unenforceable anyway. Oh yeah, also it will "stifle innovation."

A bipartisan group of senators introduced a new bill to make it easier to authenticate and detect artificial intelligence-generated content and protect journalists and artists from having their work gobbled up by AI models without their permission.

The Content Origin Protection and Integrity from Edited and Deepfaked Media Act (COPIED Act) would direct the National Institute of Standards and Technology (NIST) to create standards and guidelines that help prove the origin of content and detect synthetic content, like through watermarking. It also directs the agency to create security measures to prevent tampering and requires AI tools for creative or journalistic content to let users attach information about their origin and prohibit that information from being removed. Under the bill, such content also could not be used to train AI models.

Content owners, including broadcasters, artists, and newspapers, could sue companies they believe used their materials without permission or tampered with authentication markers. State attorneys general and the Federal Trade Commission could also enforce the bill, which its backers say prohibits anyone from “removing, disabling, or tampering with content provenance information” outside of an exception for some security research purposes. ...

See the full story here: https://www.theverge.com/2024/7/11/24196769/copied-act-cantwell-blackburn-heinrich-ai-journalists-artists?mc_cid=442e730f2e&mc_eid=3ce5196977

OpenAI’s new superalignment

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OpenAI's new superalignment team, which (over the next four years) will dedicate 20% of OpenAI’s compute resources to solving alignment challenges, will be co-led by Ilya Sutskever and Jan Leike. The team will focus on developing scalable training methods, validating alignment models, and conducting adversarial testing to ensure the AI systems align with human intent and do not go rogue.

Additionally, OpenAI is collaborating with industry leaders like Anthropic, Google, and Microsoft through the Frontier Model Forum. This initiative aims to advance AI safety research, identify best practices, and facilitate information sharing among policymakers, academia, and civil society. The Forum will focus on developing standardized evaluations and benchmarks for frontier AI models to ensure their responsible development and deployment. ...

July 12, 2024 issue https://shellypalmer.com/blog/

Will K-pop’s AI experiment pay off?

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The music video features an AI-generated scene, and the record might well include AI-generated lyrics too. At the launch of the album in Seoul, one of the band members, Woozi, told reporters he was "experimenting" with AI when songwriting.

“We practised making songs with AI, as we want to develop along with technology rather than complain about it," he said.

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Her worry though, is that a whole album of AI generated lyrics means fans will lose touch with their favourite musicians.

"I love it when music is a reflection of an artist and their emotions," she says. "K-pop artists are much more respected when they’re hands on with choreographing, lyric writing and composing, because you get a piece of their thoughts and feelings. ...

“What I've learned by hanging out in Seoul is that Koreans are big on innovation, and they're very big on ‘what's the next thing?’, and asking, ‘how can we be one step ahead?’ It really hit me when I was there,” he says.

“So, to me, it's no surprise that they're implementing AI in lyric writing, it's about keeping up with technology.” ...

See the full story here: https://www.bbc.com/news/articles/c4ngr3r0914o

AI’s understanding and reasoning skills can’t be assessed by current tests

PhilNote: this is a really nerdy paper on the various approaches that researchers are taking to determine whether and when an AI "understands" what it was doing. It goes into the flaws of each technique. The conclusion is that an 'understanding test' is a complex moving target that we may never fully solve. For me the most interesting and disturbing finding from one of their evaluations was "Surprisingly, when the researchers investigated the models’ answers at each sub-step, they found that even when the final answers were right, the underlying calculations and reasoning — the answers at each sub-step — could be completely wrong."

... But “AI surpassing humans on a benchmark that is named after a general ability is not the same as AI surpassing humans on that general ability,” computer scientist Melanie Mitchell pointed out in a May edition of her Substack newsletter. ...But “AI surpassing humans on a benchmark that is named after a general ability is not the same as AI surpassing humans on that general ability,” computer scientist Melanie Mitchell pointed out in a May edition of her Substack newsletter. ... But “AI surpassing humans on a benchmark that is named after a general ability is not the same as AI surpassing humans on that general ability,” computer scientist Melanie Mitchell pointed out in a May edition of her Substack newsletter. ...

The Winograd Schema Challenge, or WSC, was proposed in 2011 as a test for intelligent behavior of a system. Though many people are familiar with the Turing test as a way to evaluate intelligence, researchers had begun to propose modifications and alternatives that weren’t as subjective and didn’t require the AI to engage in deception to pass the test (SN: 6/15/12).

Instead of a free-form conversation, WSC features pairs of sentences that mention two entities and use a pronoun to refer to one of the entities. Here’s an example pair:

Sentence 1: In the storm, the tree fell down and crashed through the roof of my house. Now, I have to get it removed.

Sentence 2: In the storm, the tree fell down and crashed through the roof of my house. Now, I have to get it repaired.

A language model scores correctly if it can successfully match the pronoun (“it”) to the right entity (“the roof” or “the tree”). The sentences usually differ by a special word (“removed” or “repaired”) that when exchanged changes the answer. Presumably only a model that relies on commonsense world knowledge and not linguistic clues could provide the correct answers.

But it turns out that in WSC, there are statistical associations that offer clues. Consider the example above. Large language models, trained on huge amounts of text, would have encountered many more examples of a roof being repaired than a tree being repaired. A model might select the statistically more likely word among the two options rather than rely on any kind of commonsense reasoning. ...

For some researchers, the fact that LLMs are passing benchmarks so easily simply means that more comprehensive benchmarks need developing. For instance, researchers might turn to a collection of varied benchmark tasks that tackle different facets of common sense such as conceptual understanding or the ability to plan future scenarios. ...

But others are more skeptical that models performing great on the benchmarks necessarily possesses the cognitive abilities in question. If a model tests well on a dataset, it just tells us that it performs well on that particular dataset and nothing more, Elazar says.  ...

Taking a different approach to testing

Systematically digging into the mechanisms required for understanding may offer more insight than benchmark tests, Arakelyan says. That might mean testing AI’s underlying grasp of concepts using what are called counterfactual tasks. In these cases, the model is presented with a twist on a commonplace rule that it is unlikely to have encountered in training, say an alphabet with some of the letters mixed up, and asked to solve problems using the new rule. ...

To try to get a better sense of language understanding, the team compared how a model answered the standard test with how it answered when given the same premise sentence but with slightly paraphrased hypothesis sentences. A model with true language understanding, the researchers say, would make the same decisions as long as the slight alteration preserves the original meaning and logical relationships. ...

But for a sizable number of sentences, the models tested changed their decision, sometimes even switching from “implies” to “contradicts.” When the researchers used sentences that did not appear in the training data, the LLMs changed as many as 58 percent of their decisions.

“This essentially means that models are very finicky when understanding meaning,” Arakelyan says. This type of framework, unlike benchmark datasets, can better reveal whether a model has true understanding or whether it is relying on clues like the distribution of the words. ...

Surprisingly, when the researchers investigated the models’ answers at each sub-step, they found that even when the final answers were right, the underlying calculations and reasoning — the answers at each sub-step — could be completely wrong. This confirms that the model sometimes relies on memorization, Dziri says. Though the answer might be right, it doesn’t say anything about the LLM’s ability to generalize to harder problems of the same nature — a key part of true understanding or reasoning. ...

In truth, a perfect AI evaluation might never exist. The more language models improve, the harder tests will have to get to provide any meaningful assessment. ...

See the full paper here: https://www.sciencenews.org/article/ai-understanding-reasoning-skill-assess

Stop Trying to Sell Gamers What They Don’t Want

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Every year, the Game Developers Conference in San Francisco has a main floor filled with every manner of game development tools and platforms. Among these are inevitably Web3 companies trying to explain to an audience of completely uninterested Web2 developers how easy it is to tokenize in-game items and how many transactions per second their layer 2 scaling solution has. But never once has a single of these developers heard from their communities “I really wish my magic spells were NFTs.” ...

Video game players do not care about NFTs. They don’t care about ownership of in-game assets. They don’t care about faster and cheaper blockchains. They fundamentally don’t care at all about the underlying tech stack behind the games they play. ...

Since Web3 developers care about decentralization and ownership of assets, they mistakenly believe everyone else must as well. But the average gamer doesn’t have a clue they don’t “own” the video game skins or items they have bought or received through gameplay.  ...

Then, what do gamers care about? Well, playing an enjoyable game goes without saying. Beyond that, they actually care about many of the same things that those in Web3 care about. The most important of these being community, data, and extensibility. Fortunately, these are all problems Web3 technologies are perfectly set up to solve. ...

One only needs to glance at things like esports tournaments or speed-running stats to see how important data is for players. ... Developers use analytics for in-game activity like balancing or tracking player behaviors, but also for finding what demographics and marketplaces are best for advertising and selling. ..

Finally we have extensibility, or the ability of these systems to be expanded upon. Gamers adore user-generated content. They love every aspect of it, from community tournaments to fan art, to custom maps to secondary marketplaces. ...

See the full story here: https://www.coindesk.com/consensus-magazine/2024/07/10/stop-trying-to-sell-gamers-what-they-dont-want/

Taiwan central bank says no timetable for launching digital currency

... Taiwan's central bank has been working on a pilot for a government-run digital currency, to allow people to use a digital wallet and make payments without using a debit or credit card.

"Although the bank currently has no timetable for issuing central bank digital currency, in the process of continuous research and experimentation it is already improving the processing efficiency and innovative application of the payment system," it said in a report to parliament. ...

See the full story here: https://www.digitalnationaus.com.au/news/taiwan-central-bank-says-no-timetable-for-launching-digital-currency-609529

Why Bill Gates says AI Superintelligence requires some self-awareness

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Reporting on and writing about AI has given me a whole new appreciation of how flat-out amazing our human brains are. While large language models (LLMs) are impressive, they lack whole dimensions of thought that we humans take for granted. Bill Gates hit on this idea last week on the Next Big Idea Club podcast. Speaking to host Rufus Griscom, Gates talked at length about  “metacognition,” which refers to a system that can think about its own thinking. Gate defined metacognition as the ability to “think about a problem in a broad sense and step back and say, Okay, how important is this to answer? How could I check my answer, and what external tools would help me with this?”

The Microsoft founder said the overall “cognitive strategy” of existing LLMs like GPT-4 or Llama was still lacking in sophistication. “It’s just generating through constant computation each token and sequence, and it’s mind-blowing that that works at all,” Gates said. “It does not step back like a human and think, Okay, I’m gonna write this paper and here’s what I want to cover; okay, I’ll put some text in here, and here’s what I want to do for the summary.” ...

HOW THE SUPREME COURT’S LANDMARK CHEVRON RULING WILL AFFECT TECH AND AI 

... As Axios’s Scott Rosenberg points out, the removal of the Chevron Doctrine may make passing meaningful federal AI regulation much harder. Chevron allowed Congress to define regulations as sets of general directives, and left it to the experts at the agencies to define the specific rules and settle disputes on a case-by-case basis at the implementation and enforcement level. Now, it’ll be on Congress to hash out the fine points of the law in advance, doing their best to anticipate disputes that might arise in the future. And that might be especially difficult with a young and fast-moving industry like AI. ...

But there’s no guarantee that the courts will rise to the challenge. Just look at the high court’s decision to effectively punt on the constitutionality of Texas and Florida regulations governing social networks’ content moderation. “Their unwillingness to resolve such disputes over social media—a well-established technology—is troubling given the rise of AI, which may present even thornier legal and Constitutional questions,” Mercatus Center AI researcher Dean Ball points out. ...

See the full story here: https://www.fastcompany.com/91150606/bill-gates-ai-superintelligence

The Future of AR Beyond the Vision Pro Is Already Brewing

I recently flew out to Long Beach, California, for the AWE augmented and virtual reality conference, but I left my mixed reality VR devices — the Apple Vision Pro and Meta Quest 3 — back in New Jersey. Instead I took two pairs of smart glasses: Meta's Ray-Bans and Xreal's Air 2 Pro. I took photos and made calls with the Ray-Bans. I watched movies on the plane with Xreal. And I didn't miss those chunky VR goggles one bit. ...

These gadgets don't offer up anything like the full-fledged mixed reality that can happen in the Vision Pro or Quest 3, but their increasing utility points to a future of augmented reality beyond bulky headsets. ...

Meanwhile, AWE reminded me that better lenses, displays and hand tracking are coming but still face real challenges. How will future glasses offload all their processing? What about the battery? ...

The Meta Quest 3 and Vision Pro were scattered everywhere around AWE's expo floor in plenty of peripheral and software demos. That's because they both support hand tracking, and they combine camera feeds of the real world with overlays of virtual graphics to mix reality surprisingly well.  ...

Ultraleap, a company that already has hand-tracking technology on existing VR and AR headsets, is testing a smaller, more power-efficient event camera technology — which only senses rough changes in light and movement as opposed to specific details — that could last for hours on smaller glasses while looking for hand micro gestures, similar to what the Apple Vision Pro does with more power-hungry infrared. ...

See the full story here: https://www.cnet.com/tech/computing/the-future-of-ar-beyond-vision-pro-is-already-brewing/