Directories Have Value
November 29, 2024
Why would one build an online directory—to create a helpful reference? Or for self aggrandizement? Maybe both. HackerNoon shares a post by developer Alexander Isora, “Here’s Why Owning a Directory = Owning a Free Infinite Marketing Channel.”
First, he explains why users are drawn to a quality directory on a particular topic: because humans are better than Google’s algorithm at determining relevant content. No argument here. He uses his own directory of Stripe alternatives as an example:
“Why my directory is better than any of the top pages from Google? Because in the SERP [Search Engine Results Page], you will only see articles written by SEO experts. They have no idea about billing systems. They never managed a SaaS. Their set of links is 15 random items from Crunchbase or Product Hunt. Their article has near 0 value for the reader because the only purpose of the article is to bring traffic to the company’s blog. What about mine? I tried a bunch of Stripe alternatives myself. Not just signed up, but earned thousands of real cash through them. I also read 100s of tweets about the experiences of others. I’m an expert now. I can even recognize good ones without trying them. The set of items I published is WAY better than any of the SEO-optimized articles you will ever find on Google. That is the value of a directory.”
Okay, so that is why others would want a subject-matter expert to create a directory. But what is in it for the creator? Why, traffic, of course! A good directory draws eyeballs to one’s own products and services, the post asserts, or one can sell ads for a passive income. One could even sell a directory (to whom?) or turn it into its own SaaS if it is truly popular.
Perhaps ironically, Isora’s next step is to optimize his directories for search engines. Sounds like a plan.
Cynthia Murrell, November 29, 2024
Apple: Another Problem Becoming Evident
November 25, 2024
Apple is a beast in Big Tech with its cult of loyal devotees, technology advancement (especially in mobile devices), and Apple TV. Apple TV invested big money in developing original content for its streaming service and has garnered many accolades, but it’s a misnomer in the entertainment industry. Why? ArsTechnica has the lowdown on that: “Apple TV+ Spent $20B On Original Content. If Only People Actually Watched.”
Apple spent $20 billion to make a name for itself in the prominent streaming wars. While its original content shows have loyal followings, Nielsen says that its attracted only 0.3% of US eyeballs. Bloomberg wrote: “Apple TV+ generates less viewing in one month than Netflix does in one day.”
Ouch! Here are some numbers to support that statement:
“Apple doesn’t provide subscriber numbers for Apple TV+, but it’s estimated to have 25 million subscribers. That would make it one of the smallest mainstream streaming services. For comparison, Netflix has about 283 million, and Prime Video has over 200 million. Smaller services like Peacock (about 28 million) and Paramount+ (about 72 million) best Apple TV+’s subscriber count, too.”
Apple only has 259 shows compared to Netflix’s 18,000. Also Apple’s marketing efforts are minimal, but the company has used big names like Leonardo DiCaprio, Reese Witherspoon, Idris Elba, and Martin Scorsese. Here are some more numbers for comparisons sake:
“To put this into perspective, Apple spent $14.9 million on commercials for Apple TV+ in October 2019 versus $28.6 million on the iPhone, per iSpot.TV data cited by The New York Times. Online, Apple paid for 139 unique digital ads for Apple TV+ in October 2019 compared to 245 for the iPhone (about $1.7 million versus about $2.3 million), per data from advertising analytics platform Pathmatics cited by The Times.”
Apple plans to raise its viewership by licensing its content to foreign marketplaces and adopting more common streaming practices. These include bundling through Comcast and Amazon Prime Video.
Apple had smart intentions but its lackluster performance begs its intelligence in the entertainment department. Apple sure didn’t replicate the success Steve Jobs had by investing in Pixar.
Whitney Grace, November 25, 2024
China Smart, US Dumb: LLMs Bad, MoEs Good
November 21, 2024
Okay, an “MoE” is an alternative to LLMs. An “MoE” is a mixture of experts. An LLM is a one-trick pony starting to wheeze.
Google, Apple, Amazon, GitHub, OpenAI, Facebook, and other organizations are at the top of the list when people think about AI innovations. We forget about other countries and universities experimenting with the technology. Tencent is a China-based technology conglomerate located in Shenzhen and it’s the world’s largest video game company with equity investments are considered. Tencent is also the developer of Hunyuan-Large, the world’s largest MoE.
According to Tencent, LLMs (large language models) are things of the past. LLMs served their purpose to advance AI technology, but Tencent realized that it was necessary to optimize resource consumption while simultaneously maintaining high performance. That’s when the company turned to the next evolution of LLMs or MoE, mixture of experts models.
Cornell University’s open-access science archive posted this paper on the MoE: “Hunyuan-Large: An Open-Source MoE Model With 52 Billion Activated Parameters By Tencent” and the abstract explains it is a doozy of a model:
In this paper, we introduce Hunyuan-Large, which is currently the largest open-source Transformer-based mixture of experts model, with a total of 389 billion parameters and 52 billion activation parameters, capable of handling up to 256K tokens. We conduct a thorough evaluation of Hunyuan-Large’s superior performance across various benchmarks including language understanding and generation, logical reasoning, mathematical problem-solving, coding, long-context, and aggregated tasks, where it outperforms LLama3.1-70B and exhibits comparable performance when compared to the significantly larger LLama3.1-405B model. Key practice of Hunyuan-Large include large-scale synthetic data that is orders larger than in previous literature, a mixed expert routing strategy, a key-value cache compression technique, and an expert-specific learning rate strategy. Additionally, we also investigate the scaling laws and learning rate schedule of mixture of experts models, providing valuable insights and guidance for future model development and optimization. The code and checkpoints of Hunyuan-Large are released to facilitate future innovations and applications.”
Tencent has released Hunyuan-Large as an open source project, so other AI developers can use the technology! The well-known companies will definitely be experimenting with Hunyuan-Large. Is there an ulterior motive? Sure. Money, prestige, and power are at stake in the AI global game.
Whitney Grace, November 21, 2024
Management Brilliance Microsoft Suggests to Customers, “You Did It!”
November 21, 2024
No smart software. Just a dumb dinobaby. Oh, the art? Yeah, MidJourney.
I read an amusing write up called “Microsoft Says Unexpected Windows Server 2025 Automatic Upgrades Were Due to Faulty Third-Party Tools.” I love a management action which points the fingers at “you” — Partners, customers, and anyone other than the raucous Redmond-ians.
Good enough, MidJourney. Good enough.
The write up says that Microsoft says:
“Some devices upgraded automatically to Windows Server 2025 (KB5044284). This was observed in environments that use third-party products to manage the update of clients and servers,” Microsoft explained. “Please verify whether third-party update software in your environment is configured not to deploy feature updates. This scenario has been mitigated.”
The article then provides a translation of Microsoftese:
In other words, it’s not Microsoft – it’s you. The company also added the update had the “DeploymentAction=OptionalInstallation” tag, which patch management tools should read as being an optional, rather than recommended update.
Several observations:
- Pointing fingers works in some circumstances. Kindergarten type interactions feature the tactic.
- The problems of updates seem to be standard operating procedure.
- Bad actors love these types of reports because anecdotes about glitches and flaws say, “Come on in, folks.”
Is this a management strategy or an indicator of other issues?
Stephen E Arnold, November 21, 2024
After AI Billions, a Hail, Mary Play
November 19, 2024
Now it is scramble time. Reuters reports, “OpenAI and Others Seek New Path to Smarter AI as Current Methods Hit Limitations.” Why does this sound familiar? Perhaps because it is a replay of the enterprise search over-promise and under-deliver approach. Will a new technique save OpenAI and other firms? Writers Krystal Hu and Anna Tong tell us:
“After the release of the viral ChatGPT chatbot two years ago, technology companies, whose valuations have benefited greatly from the AI boom, have publicly maintained that ‘scaling up’ current models through adding more data and computing power will consistently lead to improved AI models. But now, some of the most prominent AI scientists are speaking out on the limitations of this ‘bigger is better’ philosophy. … Behind the scenes, researchers at major AI labs have been running into delays and disappointing outcomes in the race to release a large language model that outperforms OpenAI’s GPT-4 model, which is nearly two years old, according to three sources familiar with private matters.”
One difficulty, of course, is the hugely expensive and time-consuming LLM training runs. Another: it turns out easily accessible data is finite after all. (Maybe they can use AI to generate more data? Nah, that would be silly.) And then there is that pesky hallucination problem. So what will AI firms turn to in an effort to keep this golden goose alive? We learn:
“Researchers are exploring ‘test-time compute,’ a technique that enhances existing AI models during the so-called ‘inference’ phase, or when the model is being used. For example, instead of immediately choosing a single answer, a model could generate and evaluate multiple possibilities in real-time, ultimately choosing the best path forward. This method allows models to dedicate more processing power to challenging tasks like math or coding problems or complex operations that demand human-like reasoning and decision-making.”
OpenAI is using this approach in its new O1 model, while competitors like Anthropic, xAI, and Google DeepMind are reportedly following suit. Researchers claim this technique more closely mimics the way humans think. That couldn’t be just marketing hooey, could it? And even if it isn’t, is this tweak really enough?
Cynthia Murrell, November 19, 2024
AI and Efficiency: What Is the Cost of Change?
November 18, 2024
No smart software. Just a dumb dinobaby. Oh, the art? Yeah, MidJourney.
Companies are embracing smart software. One question which gets from my point of view little attention is, “What is the cost of changing an AI system a year or two down the road?” The focus at this time is getting some AI up and running so an organization can “learn” whether AI works or not. A parallel development is taking place in software vendors enterprise and industry-centric specialized software. Examples range from a brand new AI powered accounting system to Microsoft “sticking” AI into the ASCII editor Notepad.
Thanks, MidJourney. Good enough.
Let’s tally the costs which an organization faces 24 months after flipping the switch in, for example, a hospital chain which uses smart software to convert a physician’s spoken comments about a patient to data which can be used for analysis to provide insight into evidence based treatment for the hospital’s constituencies.
Here are some costs for staff, consultants, and lawyers:
- Paying for the time required to figure out what is on the money and what is not good or just awful like dead patients
- The time required to figure out if the present vendor can fix up the problem or a new vendor’s system must be deployed
- Going through the smart software recompete or rebid process
- Getting the system up and running
- The cost of retraining staff
- Chasing down dependencies like other third party software for the essential “billing process”
- Optimizing the changed or alternative system.
The enthusiasm for smart software makes talking about these future costs fade a little.
I read “AI Makes Tech Debt More Expensive,” and I want to quote one passage from the pretty good essay:
In essence, the goal should be to unblock your AI tools as much as possible. One reliable way to do this is to spend time breaking your system down into cohesive and coherent modules, each interacting through an explicit interface. A useful heuristic for evaluating a set of modules is to use them to explain your core features and data flows in natural language. You should be able to concisely describe current and planned functionality. You might also want to set up visibility and enforcement to make progress toward your desired architecture. A modern development team should work to maintain and evolve a system of well-defined modules which robustly model the needs of their domain. Day-to-day feature work should then be done on top of this foundation with maximum leverage from generative AI tooling.
Will organizations make this shift? Will the hyperbolic AI marketers acknowledge the future costs of pasting smart software on existing software like circus posters on crumbling walls?
Nope.
Those two year costs will be interesting for the bean counters when those kicked cans end up in their workspaces.
Stephen E Arnold, November 18, 2024
Salesforce to MSFT: We Are Coming, Baby Cakes
November 18, 2024
No smart software. Just a dumb dinobaby. Oh, the art? Yeah, MidJourney.
Salesforce, an outfit that hopped on the “attention” bandwagon, is now going whole hog with smart software. “Salesforce to Hire More Than 1,000 Workers to Boost AI Product Sales” makes clear that AI is going to be the hook for the company for the next hype cycle riding toward Next Big Thing theme park.
The write up says:
Agentforce is a new layer on the Salesforce platform, designed to enable companies to build and deploy AI agents that autonomously perform tasks.
Now that’s a buzz packed sentence: “Layer,” sales call data as a “platform”, “AI agents”, “autonomously”, and smart software that can “perform tasks.”
The idea is that sales are an important part of a successful organization. The exception is that monopolies really don’t need too many sales professionals. Lawyers? Yes. Forward deployed engineers? Yes. Marketers? Yes. Door knockers? Well, probably fewer going forward.
How does the Salesforce AI system work? The answer is simple it seems:
These AI agents operate independently, triggered by data changes, business rules, pre-built automations, or API signals.
Who writes the rules? I wonder if AI writes it own rules or do specialists get an opportunity to demonstrate their ability to become essential cogs in the Salesforce customers’ machines?
What do customers do with smart Salesforce? Once again, the answer is easy to provide. The write up says:
Companies such as OpenTable, Saks and Wiley are currently utilizing Agentforce to augment their workforce and enhance customer experiences. Over the past two years, Salesforce has focused on controlling sales expenses by reducing jobs and encouraging customers to use self-service or third-party purchasing options.
I think I understand. Get rid of pesky humans, their vacations, health care, pension plans, and annoying demands for wage increases. Salesforce delivers “efficiency.”
I am not sure what to make of this set of statements. Underpinning Salesforce is a database. The stuff on top of the database are interfaces. Now smart software promises to deliver efficiency and obviously another layer of “smart stuff” to provide what software and services have been promising since the days of the punched card.
Smart software, like Web search, is a natural monopoly unless specific deep pocket outfits can create a defensible niche and sell enough smart software to that niche before some other company eats their lunch.
But that’s what some companies do? Eat other individual’s lunch. So whose taking those lunches tomorrow? Amazon, Google, Microsoft, or Salesforce? Maybe the lunch thief will be a pesky start up essentially off the radar of the big hungry dogs?
With AI development shifting East, is the Silicon Valley AI way the future. Heck, even Google is moving smart software to London which is a heck of a lot easier flight to some innovative locations.
Hopefully one of the AI companies can convert billions in AI investment into new revenue and big profits in a sprightly manner. So far, I see marketing and AI dead ends. Is Salesforce, as the long gone Philco radio company used to say, “The leader”? On one hand, Salesforce is hiring. On the other, get rid of employees. Okay, I think I understand.
Stephen E Arnold, November 18, 2024
Will Tim Apple Vacation in Sochi?
November 18, 2024
![dino-orange_thumb_thumb_thumb_thumb_[1]](https://arnoldit.com/wordpress/wp-content/uploads/2024/11/dino-orange_thumb_thumb_thumb_thumb_1.gif "dino-orange_thumb_thumb_thumb_thumb_[1]")
No smart software. Just a dumb dinobaby. Oh, the art? Yeah, MidJourney.
I love the idea that “It’s just business.” Forget special operations, people falling from windows high above the cobbles, and wheeling and dealing with alleged axis of evil outfits. Focus on doing what is going to sell product. That is the guiding light.
Immanuel Kant, in the midst of pondering his philosophical treatise about ethics, considers the question of the apple on his desk. Thanks, MidJourney. Good enough.
I read the allegedly accurate write up “Apple Removes Another RFE/RL App at Request of Russian Regulator.” The story reports as actual factual:
Roskomnadzor notified Apple that the Russian Service app contains materials from an organization whose activities in Russia have been declared “undesirable.”
What did Apple do (hey, that’s a t-shirt slogan, WDAD)? According to the the cited article:
U.S. technology giant Apple has notified RFE/RL that it has removed another of its apps following a request from Russia’s media regulator, Roskomnadzor. The newly removed RFE/RL app is that of the Russian Service, which in turn hosts the websites of its regional projects Siberia.Realities and North.Realities. Apple had previously removed the apps for RFE/RL’s Kyrgyz Service and Current Time, the Russian-language TV and digital network run by RFE/RL.
The acronym RRE/RL is GenX speak for Radio Free Europe and Radio Liberty. In case you are not familiar with these efforts, the US government funds the broadcasting organizations. The idea is to provide “real” news, information, and analysis (insight) to avid listeners in Eastern Europe, Central Asia, and the Middle East.
The write up adds:
RFE/RL President Stephen Capus called the decision “yet another example of how the Russian government sees truthful reporting as an existential threat.” Besides RFE/RL’s apps, Apple also removed or hid several Russian-language podcasts produced by independent journalists.
From my point of view, the US government wants to provide information to citizens in some countries. Russian authorities do not want that information to flow to residents of those countries. So the Russian authorities told Apple to remove an app which allowed iPhone owners to access certain information deemed unsuitable to citizens in some countries of interest to Russia. I think I am following this … mostly.
Then Apple said, “No problem.” The extremely well-loved Cupertino, California, outfit removed the applications offensive to Russian authorities. Then — let me get this straight in my dinobaby brain — Apple notified Radio Free Europe and Radio Liberty professionals, “Yo, dudes, we rolled over for Vlad and his agents.”
Have I got this right? Apple wants to government to disseminate the information Russia does not like. That’s helpful, Apple.
Several observations:
- Is Apple more powerful in terms of information dissemination than Google and its allegedly reviled video service which has notable performance problems in Russia and parts of the Russian Federation?
- Is the US government supposed to amp up its dissemination of information to the affected nation states? (Well, thanks for the guidance Apple.)
- Is Apple supporting the US government or actively assisting a nation state whose leadership continues to talk about nuclear bombs and existential threats to Mr. Putin’s home base?
My hunch is that Apple, like a handful of other commercial entities, perceives itself as a nation state. The pesky government officials — regardless of where they lives —have to be kept happy. The real objective is keeping those revenues flowing.
Did Immanuel Kant cover this angle in his “Groundwork for the Metaphysics of Morals”? Oh, the apple on Kant’s desk has disintegrated.
Stephen E Arnold, November 18, 2024
Microsoft: That Old Time Religion Which Sort of Works
November 15, 2024
Having a favorite OS can be akin to being in a technology cult or following a popular religion. Apple people are experienced enthusiasts, Linux users are the odd ones because it has a secret language and handshakes, while Microsoft is vanilla with diehard followers. Microsoft apparently loves its users and employees to have this mantra and feed into it says Edward Zitron of Where’s Your Ed At? in the article, “The Cult Of Microsoft.”
Zitron reviewed hundreds of Microsoft’s internal documents and spoke with their employees about the company culture. He learned that Microsoft subscribed to “The Growth Mindset” and it determines how far someone will go within the hallowed Redmond halls. There are two types of growth mindset: you can learn and change to continue progressing or you believe everything is immutable (aka the fixed mindset).
Satya Nadella even wrote a bible of sorts called Hit Refresh that discusses The Growth Mindset. Zitron purports that Nadella wants to setup himself up as a messianic figure and used his position to claim a place at the top of the bestseller list. How? He “urged” his Microsoft employees to discuss Hit Refresh with as many people as possible. The communication methods he had his associates use was like a pyramid scheme aka a multi-level marketing ploy.
Microsoft is as fervent of following The Growth Mindset as women used to be selling Mary Kay and Avon products. The problem, Zitron reports, is that it has little to do with actual improvement. The Growth Mindset can’t be replicated without the presence of the original creator.
“In other words, the evidence that supports the efficacy of mindset theory is unreliable, and there’s no proof that this actually improves educational outcomes. To quote Wenner Moyer:
‘MacNamara and her colleagues found in their analysis that when study authors had a financial incentive to report positive effects — because, say, they had written books on the topic or got speaker fees for talks that promoted growth mindset — those studies were more than two and half times as likely to report significant effects compared with studies in which authors had no financial incentives.’
Turning to another view: Wenner Moyer’s piece is a balanced rundown of the chaotic world of mindset theory, counterbalanced with a few studies where there were positive outcomes, and focuses heavily on one of the biggest problems in the field — the fact that most of the research is meta-analyses of other people’s data…”
Microsoft has employees write biannual self-performance reviews called Connects. Everyone hates them but if the employees want raises and to keep their jobs then they have to fill out those forms. What’s even more demeaning is that Copilot is being used to write the Connects. Copilot is throwing out random metrics and achievements that don’t have a basis on any facts.
Is the approach similar to a virtual pyramid scheme. Are employees are taught or hired to externalize their success and internalize their failures. If something the Big Book of MSFT provides grounding in the Redmond way.
Mr. Nadella strikes me as having adopted the principles and mantra of a cult. Will the EU and other regulatory authorities bow before the truth or act out their heresies?
Whitney Grace, November 15, 2024
A Digital Flea Market Tests Smart Software
November 14, 2024
Sales platform eBay has learned some lessons about deploying AI. The company tested three methods and shares its insights in the post, “Cutting Through the Noise: Three Things We’ve Learned About Generative AI and Developer Productivity.” Writer Senthil Padmanabhan explains:
“Through our AI work at eBay, we believe we’ve unlocked three major tracks to developer productivity: utilizing a commercial offering, fine-tuning an existing Large Language Model (LLM), and leveraging an internal network. Each of these tracks requires additional resources to integrate, but it’s not a matter of ranking them ‘good, better, or best.’ Each can be used separately or in any combination, and bring their own benefits and drawbacks.”
The company could have chosen from several existing commercial AI offerings. It settled on GitHub Copilot for its popularity with developers. That and the eBay codebase was already on GitHub. They found the tool boosted productivity and produced mostly accurate documents (70%) and code (60%). The only problem: Copilot’s limited data processing ability makes it impractical for some applications. For now.
To tweak and train an open source LLM, the team chose Code Llama 13B. They trained the camelid on eBay’s codebase and documentation. The resulting tool reduced the time and labor required to perform certain tasks, particularly software upkeep. It could also sidestep a problem for off-the-shelf options: because it can be trained to access data across internal services and within non-dependent libraries, it can get to data the commercial solutions cannot find. Thereby, code duplication can be avoided. Theoretically.
Finally, the team used an Retrieval Augmented Generation to synthesize documentation across disparate sources into one internal knowledge base. Each piece of information entered into systems like Slack, Google Docs, and Wikis automatically received its own vector, which was stored in a vector database. When they queried their internal GPT, it quickly pulled together an answer from all available sources. This reduced the time and frustration of manually searching through multiple systems looking for an answer. Just one little problem: Sometimes the AI’s responses were nonsensical. Were any just plain wrong? Padmanabhan does not say.
The post concludes:
“These three tracks form the backbone for generative AI developer productivity, and they keep a clear view of what they are and how they benefit each project. The way we develop software is changing. More importantly, the gains we realize from generative AI have a cumulative effect on daily work. The boost in developer productivity is at the beginning of an exponential curve, which we often underestimate, as the trouble with exponential growth is that the curve feels flat in the beginning.”
Okay, sure. It is all up from here. Just beware of hallucinations along the way. After all, that is one little detail that still needs to be ironed out.
Cynthia Murrell, November 14, 2024
-
- Subscribe to Beyond Search
Feature archive
News archive
-
