Canadians Unhappy about Tax on Streaming Video
August 15, 2024
Unfortunately the movie industry has tanked worldwide because streaming services have democratized delivery. Producers, directors, actors, and other industry professionals are all feeling the pain of tighter purse strings. The problems aren’t limited to Hollywood, because Morningstar explains that the US’s northern neighbor is also feeling the strain: “The Motion Picture Association-Canada Asks Canada Appeal Court To Stop Proposed Tax On Streaming Revenue.”
A group representing big entertainment companies: Walt Disney, Netflix, Warner Brothers, Discovery, Paramount Global, and more are asking a Canadian court to stop a law that would force the companies to pay 5% of their sales to the country to fund local news and other domestic content. The Motion Picture Association- Canada stated that tax from the Canadian Radio-television and Telecommunications Commission oversteps the organization’s authority. The group representing the Hollywood bigwigs also mentions that its clients spent billions in Canada every year.
The representative group are also arguing that the tax would force Canadian subscribers to pay more for streaming services and the companies might consider leaving the northern country. Canadian Radio-television and Telecommunications Commission countered that without the tax local content might not be made or distributed anymore. Hollywood’s lawyers doesn’t like it at all:
“In their filing with Canada’s Federal Court of Appeal, lawyers for the group say the regulator didn’t reveal “any basis” for why foreign streamers are required to contribute to the production of local television and radio newscasts. The broadcast regulator “concluded, without evidence, that ‘there is a need to increase support for news production,'” the lawyers said in their filing. ‘Imposing on foreign online undertakings a requirement to fund news production is not appropriate in the light of the nature of the services that foreign online undertakings provide.’”
Canada will probably keep the tax and Hollywood, instead of the executives eating the costs, will pass it onto consumers. Consumers will also be shafted, because their entertainment streaming services will continue to become expensive.
Whitney Grace, August 15, 2024
Meta Shovels Assurances. Will Australia Like the Output?
August 14, 2024
This essay is the work of a dinobaby. Unlike some folks, no smart software improved my native ineptness.
I came across a news story which I found quite interesting. Even though I am a dinobaby, I am a father and a grandfather. I used to take pictures when my son and daughter were young. I used Kodak film, printed the pictures my wife wanted, and tossed the rest. Pretty dull. Some parents have sportier ideas. I want to point out that some ideas do not appeal to me. Others make me uncomfortable.
How do you think I reacted to the information in “Parents Still Selling Revealing Content of Their Kids on Instagram, Despite Meta’s Promises to Ban the Practice.” The main idea in the write up seems to be:
The ABC [Australian Broadcasting Council] has found almost 50 Instagram accounts that allow subscribers to pay for exclusive content of children or teenagers, some of which is sexualized. Meta had vowed to clamp down on the practice but said it was taking time to "fully roll out" its new policy. Advocates say the accounts represent an "extreme" form of child exploitation.
If I understand the title of the article and this series of statements, I take away these messages:
- Instagram contains “revealing content” of young people
- Meta — the Zuck’s new name for the old-timey Facebook, WhatsApp, and Instagram services — said it would take steps to curtail posting of this type of content. A statement which, the ABC seems to apply, was similar to other Silicon Valley-inspired assertions: A combination of self-serving assurances and then generating as much revenue as possible because some companies face zero consequences.
- Meta seems to create a greenhouse for what the ABC calls “child exploitation.”
I hope I captured the intent of the news story’s main idea.
I noted this passage:
Sarah Adams, an online child safety advocate who goes by the name Mom.Uncharted, said it was clear Meta had lost control of child accounts.
How did Meta respond to the ABC inquiry. Check this:
"The new policy is in effect as of early April and we are taking action on adult-run accounts that primarily post content focused on children whenever we become aware of them," a Meta spokesperson said in a statement. "As with any new policy, enforcement can take time to fully roll out."
That seems plausible. How long has Meta hosted questionable content? I remember 20 years ago. “We are taking action” is a wonderfully proactive statement. Plus, combatting child exploitation is one of those tasks where “enforcement can take time.”
Got it.
Stephen E Arnold, August 14, 2024
AI Safety Evaluations, Some Issues Exist
August 14, 2024
Ah, corporate self regulation. What could go wrong? Well, as TechCrunch reports, “Many Safety Evaluations for AI Models Have Significant Limitations.” Writer Kyle Wiggers tells us:
“Generative AI models … are coming under increased scrutiny for their tendency to make mistakes and generally behave unpredictably. Now, organizations from public sector agencies to big tech firms are proposing new benchmarks to test these models’ safety. Toward the end of last year, startup Scale AI formed a lab dedicated to evaluating how well models align with safety guidelines. This month, NIST and the U.K. AI Safety Institute released tools designed to assess model risk. But these model-probing tests and methods may be inadequate. The Ada Lovelace Institute (ALI), a U.K.-based nonprofit AI research organization, conducted a study that interviewed experts from academic labs, civil society and those who are producing vendors models, as well as audited recent research into AI safety evaluations. The co-authors found that while current evaluations can be useful, they’re non-exhaustive, can be gamed easily and don’t necessarily give an indication of how models will behave in real-world scenarios.”
There are several reasons for the gloomy conclusion. For one, there are no established best practices for these evaluations, leaving each organization to go its own way. One approach, benchmarking, has certain problems. For example, for time or cost reasons, models are often tested on the same data they were trained on. Whether they can perform in the wild is another matter. Also, even small changes to a model can make big differences in behavior, but few organizations have the time or money to test every software iteration.
What about red-teaming: hiring someone to probe the model for flaws? The low number of qualified red-teamers and the laborious nature of the method make it costly, out of reach for smaller firms. There are also few agreed-upon standards for the practice, so it is hard to assess the effectiveness of red-team projects.
The post suggests all is not lost—as long as we are willing to take responsibility for evaluations out of AI firms’ hands. Good luck prying open that death grip. Government regulators and third-party testers would hypothetically fill the role, complete with transparency. What a concept. It would also be good to develop standard practices and context-specific evaluations. Bonus points if a method is based on an understanding of how each AI model operates. (Sadly, such understanding remains elusive.)
Even with these measures, it may never be possible to ensure any model is truly safe. The write-up concludes with a quote from the study’s co-author Mahi Hardalupas:
“Determining if a model is ‘safe’ requires understanding the contexts in which it is used, who it is sold or made accessible to, and whether the safeguards that are in place are adequate and robust to reduce those risks. Evaluations of a foundation model can serve an exploratory purpose to identify potential risks, but they cannot guarantee a model is safe, let alone ‘perfectly safe.’ Many of our interviewees agreed that evaluations cannot prove a model is safe and can only indicate a model is unsafe.”
How comforting.
Cynthia Murrell, August 14, 2024
Sakana: Can Its Smart Software Replace Scientists and Grant Writers?
August 13, 2024
This essay is the work of a dumb dinobaby. No smart software required.
A couple of years ago, merging large language models seemed like a logical way to “level up” in the artificial intelligence game. The notion of intelligence aggregation implied that if competitor A was dumb enough to release models and other digital goodies as open source, an outfit in the proprietary software business could squish the other outfits’ LLMs into the proprietary system. The costs of building one’s own super-model could be reduced to some extent.
Merging is a very popular way to whip up pharmaceuticals. Take a little of this and a little of that and bingo one has a new drug to flog through the approval process. Another example is taking five top consultants from Blue Chip Company I and five top consultants from Blue Chip Company II and creating a smarter, higher knowledge value Blue Chip Company III. Easy.
A couple of Xooglers (former Google wizards) are promoting a firm called Sakana.ai. The purpose of the firm is to allow smart software (based on merging multiple large language models and proprietary systems and methods) to conduct and write up research (I am reluctant to use the word “original”, but I am a skeptical dinobaby.) The company says:
One of the grand challenges of artificial intelligence is developing agents capable of conducting scientific research and discovering new knowledge. While frontier models have already been used to aid human scientists, e.g. for brainstorming ideas or writing code, they still require extensive manual supervision or are heavily constrained to a specific task. Today, we’re excited to introduce The AI Scientist, the first comprehensive system for fully automatic scientific discovery, enabling Foundation Models such as Large Language Models (LLMs) to perform research independently. In collaboration with the Foerster Lab for AI Research at the University of Oxford and Jeff Clune and Cong Lu at the University of British Columbia, we’re excited to release our new paper, The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery.
Sakana does not want to merge the “big” models. Its approach for robot generated research is to combine specialized models. Examples which came to my mind were drug discovery and providing “good enough” blue chip consulting outputs. These are both expensive businesses to operate. Imagine the payoff if the Sakana approach delivers high value results. Instead of merging big, the company wants to merge small; that is, more specialized models and data. The idea is that specialized data may sidestep some of the interesting issues facing Google, Meta, and OpenAI among others.
Sakana’s Web site provides this schematic to help the visitor get a sense of the mechanics of the smart software. The diagram is Sakana’s, not mine.
I don’t want to let science fiction get in the way of what today’s AI systems can do in a reliable manner. I want to make some observations about smart software making discoveries and writing useful original research papers or for BearBlog.dev.
- The company’s Web site includes a link to a paper written by the smart software. With a sample of one, I cannot see much difference between it and the baloney cranked out by the Harvard medical group or Stanford’s former president. If software did the work, it is a good deep fake.
- Should the software be able to assemble known items of information into something “novel,” the company has hit a home run in the AI ballgame. I am not a betting dinobaby. You make your own guess about the firm’s likelihood of success.
- If the software works to some degree, quite a few outfits looking for a way to replace people with a Sakana licensing fee will sign up. Will these outfits renew? I have no idea. But “good enough” may be just what these companies want.
Net net: The Sakana.ai Web site includes a how it works, more papers about items “discovered” by the software, and a couple of engineers-do-philosophy-and-ethics write ups. A “full scientific report” is available at https://arxiv.org/abs/2408.06292. I wonder if the software invented itself, wrote the documents, and did the marketing which caught my attention. Maybe?
Stephen E Arnold, August 13, 2024
The Upside of the Google Olympics Ad
August 13, 2024
This essay is the work of a dinobaby. Unlike some folks, no smart software improved my native ineptness.
I learned that Google’s AI advertisements “feel bad for a reason.” And what is that reason? The write up “Those Olympics AI Ads Feel Bad for a Reason. It’s Not Just Google’s ‘Dear Sydney’ Commercial That Feels Soulless and Strange.” (I want to mention that this headline seems soulless and strange, but I won’t.”)
The write up reveals the “secret” of the Googler using Google AI to write his Google progeny:
The latest spate of AI ad campaigns, for their part, have thus far failed to highlight how its products assist what the majority of Americans actually want to use AI for — namely, help with household chores — and instead end up showing how AI will be used for the things that most of us don’t want it to interfere with: our job prospects, our privacy, and experiences and skills that feel uniquely human. If the world already thinks of AI as menacing, wasteful, and yet another example of market overhype, these ads are only confirming our worst fears. No wonder they come off as so thoroughly insufferable.
I apologize for skipping the somewhat ho hum recitation of AI marketing gaffes. I bravely waded through the essay to identify the reason that AI ads make people “feel bad.” Am I convinced?
Nope.
I watched a version of the ad on my laptop. Based on my experience, I thought it was notable that the alleged Googley user remembered he had a family. I was impressed that the Googley father remembered where his Googley child was. I liked the idea of using AI to eliminate the need to use a smart software system to help craft a message with words that connoted interest, caring, familial warmth.
Let’s face it. The ad was more satisfying that converting a news story like a dead Google VP in a yacht.
How would Google’s smart software tell this story? I decided to find out. Here is what Gemini 1.5 Pro provided to me. Remember. I am a nerd dinobaby with a reputation for lacking empathy and noted for my work in certain specialized sectors:
It’s been a long time since Dean’s passing, but I wanted to reach out because I was thinking about him and his family. I came across an article about the woman who was with him when he passed. I know this might be a difficult thing to hear about, and I am so very sorry for your loss. Dean was such a bright light in this world, and I know how much he meant to you. Thinking of you during this time.
Amazing. The Google’s drug death in the presence of a prostitute has been converted to a paragraph I could not possibly write. I would use a phrase like “nuked by horse” instead of “passed.” The phrase “I am so very sorry” is not what I would have been able to craft. My instinct is to say something like “The Googler tried to have fun and screwed up big time.” Finally, never would a nerd dinobaby like me write “thinking of you.” I would write, “Get to your attorney pronto.”
I know that real Googlers are not like nerd dinobabies. Therefore, it is perfectly understandable that the ad presents a version of reality which is not aspirational. It is a way for certain types of professionals to simulate interest and norm-core values.
Let’s praise Google and its AI.
Stephen E Arnold, August 13, 2024
Takedown Notices May Slightly Boost Sales of Content
August 13, 2024
It looks like take-down notices might help sales of legitimate books. A little bit. TorrentFreak shares the findings from a study by the University of Warsaw, Poland, in, “Taking Pirated Copies Offline Can Benefit Book Sales, Research Finds.” Writer Ernesto Van der Sar explains:
“This year alone, Google has processed hundreds of millions of takedown requests on behalf of publishers, at a frequency we have never seen before. The same publishers also target the pirate sites and their hosting providers directly, hoping to achieve results. Thus far, little is known about the effectiveness of these measures. In theory, takedowns are supposed to lead to limited availability of pirate sources and a subsequent increase in legitimate sales. But does it really work that way? To find out more, researchers from the University of Warsaw, Poland, set up a field experiment. They reached out to several major publishers and partnered with an anti-piracy outfit, to test whether takedown efforts have a measurable effect on legitimate book sales.”
See the write-up for the team’s methodology. There is a caveat: The study included only print books, because Poland’s e-book market is too small to be statistically reliable. This is an important detail, since digital e-books are a more direct swap for pirated copies found online. Even so, the researchers found takedown notices produced a slight bump in print-book sales. Research assistants confirmed they could find fewer pirated copies, and the ones they did find were harder to unearth. The write-up notes more research is needed before any conclusions can be drawn.
How hard will publishers tug at this thread? By this logic, if one closes libraries that will help book sales, too. Eliminating review copies may cause some sales. Why not publish books and keep them secret until Amazon provides a link? So many money-grubbing possibilities, and all it would cost is an educated public.
Cynthia Murrell, August 13, 2024
Some Fun with Synthetic Data: Includes a T Shirt
August 12, 2024
This essay is the work of a dumb dinobaby. No smart software required.
Academics and researchers often produce bogus results, fiddle images (remember the former president of Stanford University), or just make up stuff. Despite my misgivings, I want to highlight what appear to be semi-interesting assertions about synthetic data. For those not following the nuances of using real data, doing some mathematical cartwheels, and producing made-up data which are just as good as “real” data, synthetic data for me is associated with Dr. Chris Ré, the Stanford Artificial Intelligence Laboratory (remember the ex president of Stanford U., please). The term or code word for this approach to information suitable for training smart software is Snorkel. Snorkel became as company. Google embraced Snorkel. The looming litigation and big dollar settlements may make synthetic data a semi big thing in a tech dust devil called artificial intelligence. The T Shirt should read, “Synthetic data are write” like this:
I asked an AI system provided by the global leaders in computer security (yep, that’s Microsoft) to produce a T shirt for a synthetic data team. Great work and clever spelling to boot.
The “research” report appeared in Live Science. “AI Models Trained on Synthetic Data Could Break Down and Regurgitate Unintelligible Nonsense, Scientists Warn” asserts:
If left unchecked,”model collapse” could make AI systems less useful, and fill the internet with incomprehensible babble.
The unchecked term is a nice way of saying that synthetic data are cheap and less likely to become a target for copyright cops.
The article continues:
AI models such as GPT-4, which powers ChatGPT, or Claude 3 Opus rely on the many trillions of words shared online to get smarter, but as they gradually colonize the internet with their own output they may create self-damaging feedback loops. The end result, called “model collapse” by a team of researchers that investigated the phenomenon, could leave the internet filled with unintelligible gibberish if left unchecked.
People who think alike and create synthetic data will prove that “fake” data are as good as or better than “real” data. Why would anyone doubt such glib, well-educated people. Not me! Thanks, MSFT Copilot. Have you noticed similar outputs from your multitudinous AI systems?
In my opinion, the Internet when compared to commercial databases produced with actual editorial policies has been filled with “unintelligible gibberish” from the days I showed up at conferences to lecture about how hypertext was different from Gopher and Archie. When Mosaic sort of worked, I included that and left my Next computer at the office.
The write up continues:
As the generations of self-produced content accumulated, the researchers watched their model’s responses degrade into delirious ramblings.
After the data were fed into the system a number of time, the output presented was like this example from the researchers’ tests:
“architecture. In addition to being home to some of the world’s largest populations of black @-@ tailed jackrabbits, white @-@ tailed jackrabbits, blue @-@ tailed jackrabbits, red @-@ tailed jackrabbits, yellow @-.”
The output might be helpful to those interested in church architecture.
Here’s the wrap up to the research report:
This doesn’t mean doing away with synthetic data entirely, Shumailov said, but it does mean it will need to be better designed if models built on it are to work as intended. [Note: Ilia Shumailov, a computer scientist at the University of Oxford, worked on this study.]
I must admit that the write up does not make clear what data were “real” and what data were “synthetic.” I am not sure how the test moved from Wikipedia to synthetic data. I have no idea where the headline originated? Was it synthetic?
Nevertheless, I think one might conclude that using fancy math to make up data that’s as good as real life data might produce some interesting outputs.
Stephen E Arnold, August 12, 2024
Copilot and Hackers: Security Issues Noted
August 12, 2024
This essay is the work of a dinobaby. Unlike some folks, no smart software improved my native ineptness.
The online publication Cybernews ran a story I found interesting. It title suggests something about Black Hat USA 2024 attendees I have not considered. Here’s the headline:
Black Hat USA 2024: : Microsoft’s Copilot Is Freaking Some Researchers Out
Wow. Hackers (black, gray, white, and multi-hued) are “freaking out.” As defined by the estimable Urban Dictionary, “freaking” means:
Obscene dancing which simulates sex by the grinding the of the genitalia with suggestive sounds/movements. often done to pop or hip hop or rap music
No kidding? At Black Hat USA 2024?
Thanks, Microsoft Copilot. Freak out! Oh, y0ur dance moves are good enough.
The article reports:
Despite Microsoft’s claims, cybersecurity researcher Michael Bargury demonstrated how Copilot Studio, which allows companies to build their own AI assistant, can be easily abused to exfiltrate sensitive enterprise data. We also met with Bargury during the Black Hat conference to learn more. “Microsoft is trying, but if we are honest here, we don’t know how to build secure AI applications,” he said. His view is that Microsoft will fix vulnerabilities and bugs as they arise, letting companies using their products do so at their own risk.
Wait. I thought Microsoft has tied cash to security work. I thought security was Job #1 at the company which recently accursed Delta Airlines of using outdated technology and failing its customers. Is that the Microsoft that Mr. Bargury is suggesting has zero clue how to make smart software secure?
With MSFT Copilot turning up in places that surprise me, perhaps the Microsoft great AI push is creating more problems. The SolarWinds glitch was exciting for some, but if Mr. Bargury is correct, cyber security life will be more and more interesting.
Stephen E Arnold, August 12, 2024
Apple Does Not Just Take Money from Google
August 12, 2024
In an apparent snub to Nvidia, reports MacRumors, “Apple Used Google Tensor Chips to Develop Apple Intelligence.” The decision to go with Google’s TPUv5p chips over Nvidia’s hardware is surprising, since Nvidia has been dominating the AI processor market. (Though some suggest that will soon change.) Citing Apple’s paper on the subject, writer Hartley Charlton reveals:
“The paper reveals that Apple utilized 2,048 of Google’s TPUv5p chips to build AI models and 8,192 TPUv4 processors for server AI models. The research paper does not mention Nvidia explicitly, but the absence of any reference to Nvidia’s hardware in the description of Apple’s AI infrastructure is telling and this omission suggests a deliberate choice to favor Google’s technology. The decision is noteworthy given Nvidia’s dominance in the AI processor market and since Apple very rarely discloses its hardware choices for development purposes. Nvidia’s GPUs are highly sought after for AI applications due to their performance and efficiency. Unlike Nvidia, which sells its chips and systems as standalone products, Google provides access to its TPUs through cloud services. Customers using Google’s TPUs have to develop their software within Google’s ecosystem, which offers integrated tools and services to streamline the development and deployment of AI models. In the paper, Apple’s engineers explain that the TPUs allowed them to train large, sophisticated AI models efficiently. They describe how Google’s TPUs are organized into large clusters, enabling the processing power necessary for training Apple’s AI models.”
Over the next two years, Apple says, it plans to spend $5 billion in AI server enhancements. The paper gives a nod to ethics, promising no private user data is used to train its AI models. Instead, it uses publicly available web data and licensed content, curated to protect user privacy. That is good. Now what about the astronomical power and water consumption? Apple has no reassuring words for us there. Is it because Apple is paying Google, not just taking money from Google?
Cynthia Murrell, August 12, 2024
Podcasts 2024: The Long Tail Is a Killer
August 9, 2024
![green-dino_thumb_thumb_thumb_thumb_t[2]](https://arnoldit.com/wordpress/wp-content/uploads/2024/07/green-dino_thumb_thumb_thumb_thumb_t2.gif "green-dino_thumb_thumb_thumb_thumb_t[2]")
This essay is the work of a dumb humanoid. No smart software required.
One of my Laws of Online is that the big get bigger. Those who are small go nowhere.
My laws have not been popular since I started promulgating them in the early 1980s. But they are useful to me. The write up “Golden Spike: Podcasting Saw A 22% Rise In Ad Spending In Q2 [2024].” The information in the article, if on the money, appear to support the Arnold Law articulated in the first sentence of this blog post.
The long tail can be a killer. Thanks, MSFT Copilot. How’s life these days? Oh, that’s too bad.
The write up contains an item of information which not surprising to those who paid attention in a good middle school or in a second year economics class. (I know. Snooze time for many students.) The main idea is that a small number of items account for a large proportion of the total occurrences.
Here’s what the article reports:
Unsurprisingly, podcasts in the top 500 attracted the majority of ad spend, with these shows garnering an average of $252,000 per month each. However, the profits made by series down the list don’t have much to complain about – podcasts ranked 501 to 3000 earned about $30,000 monthly. Magellan found seven out of the top ten advertisers from the first quarter continued their heavy investment in the second quarter, with one new entrant making its way onto the list.
This means that of the estimated three to four million podcasts, the power law nails where the advertising revenue goes.
I mention this because when I go to the gym I listen to some of the podcasts on the Leo Laporte TWIT network. At one time, the vision was to create the CNN of the technology industry. Now the podcasts seem to be the voice of the podcasts which cannot generate sufficient money from advertising to pay the bills. Therefore, hasta la vista staff, dedicated studio, and presumably some other expenses associated with a permanent studio.
Other podcasts will be hit by the stinging long tail. The question becomes, “How do these 2.9 million podcasts make money?”
Here’s what I have noticed in the last few months:
- Podcasters (video and voice) just quit. I assume they get a job or move in with friends. Van life is too expensive due to the cost of fuel, food, and maintenance now that advertising is chasing the winners in the long tail game.
- Some beg for subscribers.
- Some point people to their Buy Me a Coffee or Patreon page, among other similar community support services.
- Some sell T shirts. One popular technology podcaster sells a $60 screwdriver. (I need that.)
- Some just whine. (No, I won’t single out the winning whiner.)
If I were teaching math, this podcast advertising data would make an interesting example of the power law. Too bad most will be impotent to change its impact on podcasting.
Stephen E Arnold, August 9, 2024
-
- Subscribe to Beyond Search
Feature archive
News archive
-
