Microsoft and Palantir: Moving Up to Higher Impact Levels
August 20, 2024
Microsoft And Palantir Sell AI Spyware To Us Government
While AI is making the news about how it will end jobs, be used for deep fakes, and overturn creativity industries, there’s something that’s not being mentioned: spyware. The Verge writes about how two big technology players are planning to bring spyware to the US government: “Palantir Partners With Microsoft To Sell AI To The Government.”
Palantir and Microsoft recently announced they will combine their software to power services for US defense and intelligence services. Microsoft’s large language models (LLMs) will be used via Azure OpenAI Service with Palantir’s AI Platforms (AIP). These will be used through Microsoft’s classified government cloud environments. This doesn’t explain exactly what the combination of software will do, but there’s speculation.
Palantir is known for its software that analyses people’s personal data and helping governments and organizations with surveillance. Palantir has been very successful when it comes to government contracts:
“Despite its large client list, Palantir didn’t post its first annual profit until 2023. But the AI hype cycle has meant that Palantir’s “commercial business is exploding in a way we don’t know how to handle,” the company’s chief executive officer Alex Carp told Bloomberg in February. The majority of its business is from governments, including that of Israel — though the risk factors section of its annual filing notes that it does not and will not work with “the Chinese communist party.””
Eventually the details about Palantir’s and Microsoft’s partnership will be revealed. It probably won’t be off from what people imagine, but it is guaranteed to be shocking.
Whitney Grace, August 20, 2024
An Ed Critique That Pans the Sundar & Prabhakar Comedy Act
August 16, 2024
This essay is the work of a dumb dinobaby. No smart software required.
I read Ed.
Ed refers to Edward Zitron, the thinker behind Where’s Your Ed At. The write up which caught my attention is “Monopoly Money.” I think that Ed’s one-liners will not be incorporated into the Sundar & Prabhakar comedy act. The flubbed live demos are knee slappers, but Ed’s write up is nipping at the heels of the latest Googley gaffe.
Young people are keen observers of certain high-technology companies. What happens if one of the giants becomes virtual and moves to a Dubai-type location? Who has jurisdiction? Regulatory enforcement delayed means big high-tech outfits are more portable than old-fashioned monopolies. Thanks, MSFT Copilot. Big industrial images are clearly a core competency you have.
Ed’s focus is on the legal decision which concluded that the online advertising company is a monopoly in “general text advertising.” The essay states:
The ruling precisely explains how Google managed to limit competition and choice in the search and ad markets. Documents obtained through discovery revealed the eye-watering amounts Google paid to Samsung ($8 billion over four years) and Apple ($20 billion in 2022 alone) to remain the default search engine on their devices, as well as Mozilla (around $500 million a year), which (despite being an organization that I genuinely admire, and that does a lot of cool stuff technologically) is largely dependent on Google’s cash to remain afloat.
Ed notes:
Monopolies are a big part of why everything feels like it stopped working.
Ed is on to something. The large technology outfits in the US control online. But one of the downstream consequences of what I call the Silicon Valley way or the Googley approach to business is that other industries and market sectors have watched how modern monopolies work. The result is that concentration of power has not been a regulatory priority. The role of data aggregation has been ignored. As a result, outfits like Kroger (a grocery company) is trying to apply Googley tactics to vegetables.
Ed points out:
Remember when “inflation” raised prices everywhere? It’s because the increasingly-dwindling amount of competition in many consumer goods companies allowed them to all raise their prices, gouging consumers in a way that should have had someone sent to jail rather than make $19 million for bleeding Americans dry. It’s also much, much easier for a tech company to establish one, because they often do so nestled in their own platforms, making them a little harder to pull apart. One can easily say “if you own all the grocery stores in an area that means you can control prices of groceries,” but it’s a little harder to point at the problem with the tech industry, because said monopolies are new, and different, yet mostly come down to owning, on some level, both the customer and those selling to the customer.
Blue chip consulting firms flip this comment around. The points Ed makes are the recommendations and tactics the would-be monopolists convert to action plans. My reaction is, “Thanks, Silicon Valley. Nice contribution to society.”
Ed then gets to artificial intelligence, definitely a hot topic. He notes:
Monopolies are inherently anti-consumer and anti-innovation, and the big push toward generative AI is a blatant attempt to create another monopoly — the dominance of Large Language Models owned by Microsoft, Amazon, Google and Meta. While this might seem like a competitive marketplace, because these models all require incredibly large amounts of cloud compute and cash to both train and maintain, most companies can’t really compete at scale.
Bingo.
I noted this Ed comment about AI too:
This is the ideal situation for a monopolist — you pay them money for a service and it runs without you knowing how it does so, which in turn means that you have no way of building your own version. This master plan only falls apart when the “thing” that needs to be trained using hardware that they monopolize doesn’t actually provide the business returns that they need to justify its existence.
Ed then makes a comment which will cause some stakeholders to take a breath:
As I’ve written before, big tech has run out of hyper-growth markets to sell into, leaving them with further iterations of whatever products they’re selling you today, which is a huge problem when big tech is only really built to rest on its laurels. Apple, Microsoft and Amazon have at least been smart enough to not totally destroy their own products, but Meta and Google have done the opposite, using every opportunity to squeeze as much revenue out of every corner, making escape difficult for the customer and impossible for those selling to them. And without something new — and no, generative AI is not the answer — they really don’t have a way to keep growing, and in the case of Meta and Google, may not have a way to sustain their companies past the next decade. These companies are not built to compete because they don’t have to, and if they’re ever faced with a force that requires them to do good stuff that people like or win a customer’s love, I’m not sure they even know what that looks like.
Viewed from a Googley point of view, these high-technology outfits are doing what is logical. That’s why the Google advertisement for itself troubled people. The person writing his child willfully used smart software. The fellow embodied a logical solution to the knotty problem of feelings and appropriate behavior.
Ed suggests several remedies for the Google issue. These make sense, but the next step for Google will be an appeal. Appeals take time. US government officials change. The appetite to fight legions of well resourced lawyers can wane. The decision reveals some interesting insights into the behavior of Google. The problem now is how to alter that behavior without causing significant market disruption. Google is really big, and changes can have difficult-to-predict consequences.
The essay concludes:
I personally cannot leave Google Docs or Gmail without a significant upheaval to my workflow — is a way that they reinforce their monopolies. So start deleting sh*t. Do it now. Think deeply about what it is you really need — be it the accounts you have and the services you need — and take action. They’re not scared of you, and they should be.
Interesting stance.
Several observations:
- Appeals take time. Time favors outfits like losers of anti-trust cases.
- Google can adapt and morph. The size and scale equip the Google in ways not fathomable to those outside Google.
- Google is not Standard Oil. Google is like AT&T. That break up resulted in reconsolidation and two big Baby Bells and one outside player. So a shattered Google may just reassemble itself. The fancy word for this is emergent.
Ed hits some good points. My view is that the Google fumbles forward putting the Sundar & Prabhakar Comedy Act in every city the digital wagon can reach.
Stephen E Arnold, August 16, 2024
A Familiar Cycle: The Frustration of Almost Solving the Search Problem
August 16, 2024
This essay is the work of a dumb dinobaby. No smart software required.
Search and retrieval is a difficult problem. The solutions have ranged from scrolls with labels to punched cards and rods to bags of words. Each innovation or advance sparked new ideas. Boolean gave way to natural language. Natural language evolved into semi-smart systems. Now we are in the era of what seems to be smart software. Like the punch card systems, users became aware of the value of consistent, accurate indexing. Today one expects a system to “know” what the user wants. Instead of knowing index terms, one learns to be a prompt engineer.
Search and retrieval is not “solved” using large language models. LLMs are a step forward on a long and difficult path. The potential financial cost of thinking that the methods are a sure-fire money machine is high. Thanks, MSFT Copilot. How was DEFCON?
I read “LLM Progress Is Slowing — What Will It Mean for AI?.” The write up makes clear that some of the excitement of smart software which can makes sense of natural language queries (prompts) has lost some of its shine. This type of insight is one that probably existed when a Babylonian tablet maker groused about not having an easy way to stack up clay tablets for the money guy. Search and retrieval is essential for productive work. A system which makes that process less of a hassle is welcomed. After a period of time one learns that the approach is not quite where the user wants it to be. Researchers and innovators hear the complaint and turn their attention to improving search and retrieval … again.
The write up states:
The leap from GPT-3 to GPT-3.5 was huge, propelling OpenAI into the public consciousness. The jump up to GPT-4 was also impressive, a giant step forward in power and capacity. Then came GPT-4 Turbo, which added some speed, then GPT-4 Vision, which really just unlocked GPT-4’s existing image recognition capabilities. And just a few weeks back, we saw the release of GPT-4o, which offered enhanced multi-modality but relatively little in terms of additional power. Other LLMs, like Claude 3 from Anthropic and Gemini Ultra from Google, have followed a similar trend and now seem to be converging around similar speed and power benchmarks to GPT-4. We aren’t yet in plateau territory — but do seem to be entering into a slowdown. The pattern that is emerging: Less progress in power and range with each generation.
This is an echo of the complaints I heard about Dr. Salton’s SMART search system.
The “fix” according to the write up may be to follow one of these remediation paths:
- More specialization
- New user interfaces
- Open source large language models
- More and better data
- New large language model architectures.
These are ideas bolted to the large language model approach to search and retrieval. I think each has upsides and downsides. These deserve thoughtful discussion. However, the evolution of search-and-retrieval has been an evolutionary process. Those chaos and order thinkers at the Santa Fe Institute suggest that certain “things” self organize and emerge. The idea has relevance to what happens with each “new” approach to search and retrieval.
The cited write up concludes with this statement:
One possible pattern that could emerge for LLMs: That they increasingly compete at the feature and ease-of-use levels. Over time, we could see some level of commoditization set in, similar to what we’ve seen elsewhere in the technology world. Think of, say, databases and cloud service providers. While there are substantial differences between the various options in the market, and some developers will have clear preferences, most would consider them broadly interchangeable. There is no clear and absolute “winner” in terms of which is the most powerful and capable.
I think the idea about competition is mostly correct. However, what my impression of search and retrieval as a technology thread is that progress is being made. I find it encouraging that more users are interacting with systems. Unfortunately search and retrieval is not solved by generating a paragraph a high school student can turn into a history teacher as an original report.
Effective search and retrieval is not just a prompt box. Effective information access remains a blend of extraordinarily trivial activities. For instance, a conversation may suggest a new way to locate relevant information. Reading an article or a longer document may trigger an unanticipated connection between ant colonies and another task-related process. The act of looking at different sources may lead to a fact previously unknown which leads in turn to another knowledge insight. Software alone cannot replicate these mental triggers.
LLMs like stacked clay tablets provide challenges and utility. However, search and retrieval remains a work in progress. LLMs, like semantic ad matching, or using one’s search history as a context clue, are helpful. But opportunities for innovation exist. My view is that the grousing about LLM limitations is little more than a recognition that converting a human concept or information need to an “answer” is a work in progress. The difference is that today billions of dollars have been pumped into smart software in the hope that information retrieval is solved.
Sorry, it is not. Therefore, the stakes of realizing that the golden goose may not lay enough eggs to pay off the cost of the goose itself. Twenty years ago search and retrieval was not a sector consuming billions of dollars in the span of a couple of years. That’s what is making people nervous about LLMs. Watching Delphi or Entopia fail was expensive, but the scale of the financial loss and the emotional cost of LLM failure is a different kettle of fish.
Oh, and those five “fixes” in the bullet points from the write up. None will solve the problem of search and retrieval.
Stephen E Arnold, August 16, 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
AI Research: A New and Slippery Cost Center for the Google
August 7, 2024
This essay is the work of a dumb humanoid. No smart software required.
A week or so ago, I read “Scaling Exponents Across Parameterizations and Optimizers.” The write up made crystal clear that Google’s DeepMind can cook up a test, throw bodies at it, and generate a bit of “gray” literature. The objective, in my opinion, was three-fold. [1] The paper makes clear that DeepMind is thinking about its smart software’s weaknesses and wants to figure out what to do about them. And [2] DeepMind wants to keep up the flow of PR – Marketing which says, “We are really the Big Dogs in this stuff. Good luck catching up with the DeepMind deep researchers.” Note: The third item appears after the numbers.
I think the paper reveals a third and unintended consequence. This issue is made more tangible by an entity named 152334H and captured in “Calculating the Cost of a Google DeepMind Paper.” (Oh, 152334 is a deep blue black color if anyone cares.)
That write up presents calculations supporting this assertion:
How to burn US$10,000,000 on an arXiv preprint
The write up included this table presenting the costs to replicate what the xx Googlers and DeepMinders did to produce the ArXiv gray paper:
Notice, please, that the estimate is nearly $13 million. Anyone want to verify the Google results? What am I hearing? Crickets.
The gray paper’s 11 authors had to run the draft by review leadership and a lawyer or two. Once okayed, the document was converted to the arXiv format, and we the findings to improve our understanding of how much work goes into the achievements of the quantumly supreme Google.
Thijs number of $12 million and change brings me to item [3]. The paper illustrates why Google has a tough time controlling its costs. The paper is not “marketing,” because it is R&D. Some of the expense can be shuffled around. But in my book, the research is overhead, but it is not counted like the costs of cubicles for administrative assistants. It is science; it is a cost of doing business. Suck it up, you buttercups, in accounting.
The write up illustrates why Google needs as much money as it can possibly grab. These costs which are not really nice, tidy costs have to be covered. With more than 150,000 people working on projects, the costs of “gray” papers is a trigger for more costs. The compute time has to be paid for. Hello, cloud customers. The “thinking time” has to be paid for because coming up with great research is open ended and may take weeks, months, or years. One could not rush Einstein. One cannot rush Google wizards in the AI realm either.
The point of this blog post is to create a bit of sympathy for the professionals in Google’s accounting department. Those folks have a tough job figuring out how to cut costs. One cannot prevent 11 people from burning through computer time. The costs just hockey stick. Consequently the quantumly supreme professionals involved in Google cost control look for simpler, more comprehensible ways to generate sufficient cash to cover what are essentially “surprise” costs. These tools include magic wand behavior over payments to creators, smart commission tables to compensate advertising partners, and demands for more efficiency from Googlers who are not thinking big thoughts about big AI topics.
Net net: Have some awareness of how tough it is to be quantumly supreme. One has to keep the PR and Marketing messaging on track. One has to notch breakthroughs, insights, and innovations. What about that glue on the pizza thing? Answer: What?
Stephen E Arnold, August 7, 2024
Agents Are Tracking: Single Web Site Version
August 6, 2024
This essay is the work of a dumb humanoid. No smart software required.
How many software robots are crawling (copying and indexing) a Web site you control now? This question can be answered by a cloud service available from DarkVisitors.com.
The Web site includes a useful list of these software robots (what many people call “agents” which sounds better, right?). You can find the list of about 800 bots as of July 30, 2024) on the DarkVisitors’ Web site at this link. There is a search function so you can look for a bot by name; for example, Omgili (the Israeli data broker Webz.io). Please, note, that the list contains categories of agents; for example, “AI Data Scrapers”, “AI Search Crawlers,” and “Developer Helpers,” among others.
The Web site also includes links to a service called “Set Up Your Robots.txt.” The idea is that one can link a Web site’s robots.txt file to DarkVisitors. Then DarkVisitors will update your Web site automatically to block crawlers, bots, and agents. The specific steps to make this service work are included on the DarkVisitors.com Web site.
The basic service is free. However, if you want analytics and a couple of additional features, the cost as of July 30, 2024, is $10 per month.
An API is also available. Instructions for implementing the service are available as well. Plus, a WordPress plug in is available. The cloud service is provided by Bit Flip LLC.
Stephen E Arnold, August 6, 2024
MBAs Gone Wild: Assertions, Animation & Antics
August 5, 2024
Author’s note: Poor WordPress in the Safari browser is having a very bad day. Quotes from the cited McKinsey document appear against a weird blue background. My cheerful little dinosaur disappeared. And I could not figure out how to claim that AI did not help me with this essay. Just a heads up.
Holed up in rural Illinois, I had time to read the mid-July McKinsey & Company document “McKinsey Technology Trends Outlook 2024.” Imagine a group of well-groomed, top-flight, smooth talking “experts” with degrees from fancy schools filming one of those MBA group brainstorming sessions. Take the transcript, add motion graphics, and give audio sweetening to hot buzzwords. I think this would go viral among would-be consultants, clients facing the cloud of unknowing about the future. and those who manifest the Peter Principle. Viral winner! From my point of view, smart software is going to be integrated into most technologies and is, therefore, the trend. People may lose money, but applied AI is going to be with most companies for a long, long time.
The report boils down the current business climate to a few factors. Yes, when faced with exceptionally complex problems, boil those suckers down. Render them so only the tasty sales part remains. Thus, today’s businesss challenges become:
Generative AI (gen AI) has been a standout trend since 2022, with the extraordinary uptick in interest and investment in this technology unlocking innovative possibilities across interconnected trends such as robotics and immersive reality. While the macroeconomic environment with elevated interest rates has affected equity capital investment and hiring, underlying indicators—including optimism, innovation, and longer-term talent needs—reflect a positive long-term trajectory in the 15 technology trends we analyzed.
The data for the report come from inputs from about 100 people, not counting the people who converted the inputs into the live-action report. Move your mouse from one of the 15 “trends” to another. You will see the graphic display colored balls of different sizes. Yep, tiny and tinier balls and a few big balls tossed in.
I don’t have the energy to take each trend and offer a comment. Please, navigate to the original document and review it at your leisure. I can, however, select three trends and offer an observation or two about this very tiny ball selection.
Before sharing those three trends, I want to provide some context. First, the data gathered appear to be subjective and similar to the dorm outputs of MBA students working on a group project. Second, there is no reference to the thought process itself which when applied to a real world problem like boosting sales for opioids. It is the thought process that leads to revenues from consulting that counts.
Third, McKinsey’s pool of 100 thought leaders seems fixated on two things:
gen AI and electrification and renewables.
But is that statement comprised of three things? [1] AI, [2] electrification, and [3] renewables? Because AI is a greedy consumer of electricity, I think I can see some connection between AI and renewable, but the “electrification” I think about is President Roosevelt’s creating in 1935 the Rural Electrification Administration. Dinobabies can be such nit pickers.
Let’s tackle the electrification point before I get to the real subject of the report, AI in assorted forms and applications. When McKinsey talks about electrification and renewables, McKinsey means:
The electrification and renewables trend encompasses the entire energy production, storage, and distribution value chain. Technologies include renewable sources, such as solar and wind power; clean firm-energy sources, such as nuclear and hydrogen, sustainable fuels, and bioenergy; and energy storage and distribution solutions such as long-duration battery systems and smart grids.In 2019, the interest score for Electrification and renewables was 0.52 on a scale from 0 to 1, where 0 is low and 1 is high. The innovation score was 0.29 on the same scale. The adoption rate was scored at 3. The investment in 2019 was 160 on a scale from 1 to 5, with 1 defined as “frontier innovation” and 5 defined as “fully scaled.” The investment was 160 billion dollars. By 2023, the interest score for Electrification and renewables was 0.73. The innovation score was 0.36. The investment was 183 billion dollars. Job postings within this trend changed by 1 percent from 2022 to 2023.
Stop burning fossil fuels? Well, not quite. But the “save the whales” meme is embedded in the verbiage. Confused? That may be the point. What’s the fix? Hire McKinsey to help clarify your thinking.
AI plays the big gorilla in the monograph. The first expensive, hairy, yet promising aspect of smart software is replacing humans. The McKinsey report asserts:
Generative AI describes algorithms (such as ChatGPT) that take unstructured data as input (for example, natural language and images) to create new content, including audio, code, images, text, simulations, and videos. It can automate, augment, and accelerate work by tapping into unstructured mixed-modality data sets to generate new content in various forms.
Yep, smart software can produce reports like this one: Faster, cheaper, and good enough. Just think of the reports the team can do.
The third trend I want to address is digital trust and cyber security. Now the cyber crime world is a relatively specialized one. We know from the CrowdStrike misstep that experts in cyber security can wreck havoc on a global scale. Furthermore, we know that there are hundreds of cyber security outfits offering smart software, threat intelligence, and very specialized technical services to protect their clients. But McKinsey appears to imply that its band of 100 trend identifiers are hip to this. Here’s what the dorm-room btrainstormers output:
The digital trust and cybersecurity trend encompasses the technologies behind trust architectures and digital identity, cybersecurity, and Web3. These technologies enable organizations to build, scale, and maintain the trust of stakeholders.
Okay.
I want to mention that other trends range from blasting into space to software development appear in the list. What strikes me as a bit of an oversight is that smart software is going to be woven into the fabric of the other trends. What? Well, software is going to surf on AI outputs. And big boy rockets, not the duds like the Seattle outfit produces, use assorted smart algorithms to keep the system from burning up or exploding… most of the time. Not perfect, but better, faster, and cheaper than CalTech grads solving equations and rigging cybernetics with wire and a soldering iron.
Net net: This trend report is a sales document. Its purpose is to cause an organization familiar with McKinsey and the organization’s own shortcomings to hire McKinsey to help out with these big problems. The data source is the dorm room. The analysts are cherry picked. The tone is quasi-authoritative. I have no problem with marketing material. In fact, I don’t have a problem with the McKinsey-generated list of trends. That’s what McKinsey does. What the firm does not do is to think about the downstream consequences of their recommendations. How do I know this? Returning from a lunch with some friends in rural Illinois, I spotted two opioid addicts doing the droop.
Stephen E Arnold, August 5, 2024
Google and Its Smart Software: The Emotion Directed Use Case
July 31, 2024
This essay is the work of a dumb humanoid. No smart software required.
How different are the Googlers from those smack in the middle of a normal curve? Some evidence is provided to answer this question in the Ars Technica article “Outsourcing Emotion: The Horror of Google’s “Dear Sydney” AI Ad.” I did not see the advertisement. The volume of messages flooding through my channels each days has allowed me to develop what I call “ad blindness.” I don’t notice them; I don’t watch them; and I don’t care about the crazy content presentation which I struggle to understand.
A young person has to write a sympathy card. The smart software is encouraging to use the word “feel.” This is a word foreign to the individual who wants to work for big tech someday. Thanks, MSFT Copilot. Do you have your hands full with security issues today?
Ars Technica watches TV and the Olympics. The write up reports:
In it, a proud father seeks help writing a letter on behalf of his daughter, who is an aspiring runner and superfan of world-record-holding hurdler Sydney McLaughlin-Levrone. “I’m pretty good with words, but this has to be just right,” the father intones before asking Gemini to “Help my daughter write a letter telling Sydney how inspiring she is…” Gemini dutifully responds with a draft letter in which the LLM tells the runner, on behalf of the daughter, that she wants to be “just like you.”
What’s going on? The father wants to write something personal to his progeny. A Hallmark card may never be delivered from the US to France. The solution is an emessage. That makes sense. Essential services like delivering snail mail are like most major systems not working particularly well.
Ars Technica points out:
But I think the most offensive thing about the ad is what it implies about the kinds of human tasks Google sees AI replacing. Rather than using LLMs to automate tedious busywork or difficult research questions, “Dear Sydney” presents a world where Gemini can help us offload a heartwarming shared moment of connection with our children.
I find the article’s negative reaction to a Mad Ave-type of message play somewhat insensitive. Let’s look at this use of smart software from the point of view of a person who is at the right hand tail end of the normal distribution. The factors in this curve are compensation, cleverness as measured in a Google interview, and intelligence as determined by either what school a person attended, achievements when a person was in his or her teens, or solving one of the Courant Institute of Mathematical Sciences brain teasers. (These are shared at cocktail parties or over coffee. If you can’t answer, you pay the bill and never get invited back.)
Let’s run down the use of AI from this hypothetical right of loser viewpoint:
- What’s with this assumption that a Google-type person has experience with human interaction. Why not send a text even though your co-worker is at the next desk? Why waste time and brain cycles trying to emulate a Hallmark greeting card contractor’s phraseology. The use of AI is simply logical.
- Why criticize an alleged Googler or Googler-by-the-gig for using the company’s outstanding, quantumly supreme AI system? This outfit spends millions on running AI tests which allow the firm’s smart software to perform in an optimal manner in the messaging department. This is “eating the dog food one has prepared.” Think of it as quality testing.
- The AI system, running in the Google Cloud on Google technology is faster than even a quantumly supreme Googler when it comes to generating feel-good platitudes. The technology works well. Evaluate this message in terms of the effectiveness of the messaging generated by Google leadership with regard to the Dr. Timnit Gebru matter. Upper quartile of performance which is far beyond the dead center of the bell curve humanoids.
My view is that there is one positive from this use of smart software to message a partially-developed and not completely educated younger person. The Sundar & Prabhakar Comedy Act has been recycling jokes and bits for months. Some find them repetitive. I do not. I am fascinated by the recycling. The S&P Show has its fans just as Jack Benny does decades after his demise. But others want new material.
By golly, I think the Google ad showing Google’s smart software generating a parental note is a hoot and a great demo. Plus look at the PR the spot has generated.
What’s not to like? Not much if you are Googley. If you are not Googley, sorry. There’s not much that can be done except shove ads at you whenever you encounter a Google product or service. The ad illustrates the mental orientation of Google. Learn to love it. Nothing is going to alter the trajectory of the Google for the foreseeable future. Why not use Google’s smart software to write a sympathy note to a friend when his or her parent dies? Why not use Google to write a note to the dean of a college arguing that your child should be admitted? Why not let Google think for you? At least that decision would be intentional.
Stephen E Arnold, July 31, 2024
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Bots Have Invaded The World…On The Internet
July 23, 2024
Robots…er…bots have taken over the world…at least the Internet…parts of it. The news from Techspot is shocking but when you think about it really isn’t: “Almost Half Of All Web Traffics Is Bots, And They Are Mostly Malicious In Nature.” Akamai is the largest cloud computing platform in the world. It recently released a report that 42% of web traffic is from bots and 65% of them are malicious.
Akamai said that most of the bots are scrapper bots designed to gather data. Scrapper bots collect content from Web sites. Some of them are used to form AI data sets while others are designed to steal information to be used in hacker, scams, and other bad acts. Commerce Web sites are negatively affected the most, because scrapper bots steal photos, prices, descriptions, and more. Bad actors then make fake Web sites imitating the real McCoy. They make money by from ads by ranking on Google and stealing traffic.
Bots are nasty little buggers even the most benign:
“Even non-malicious scraping bots can degrade a website’s performance, impact search engine metrics, and increase computing and hosting costs.
Companies now face increasingly sophisticated bots that use AI algorithms, headless browser technology, and other advanced solutions. These new threats require novel, more complex mitigation approaches beyond traditional methods. A robust firewall is now only the beginning of the numerous security measures needed by website owners today.”
Akamai should have dedicated part of their study to investigate the Dark Web. How many bots or law enforcement officials are visiting that shrinking part of the Net?
Whitney Grace, July 23, 2024
Looking for the Next Big Thing? The Truth Revealed
July 18, 2024
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This essay is the work of a dinobaby. Unlike some folks, no smart software improved my native ineptness.
Big means money, big money. I read “Twenty Five Years of Warehouse-Scale Computing,” authored by Googlers who definitely are into “big.” The write up is history from the point of view of engineers who built a giant online advertising and surveillance system. In today’s world, when a data topic is raised, it is big data. Everything is Texas-sized. Big is good.
This write up is a quasi-scholarly, scientific-type of sales pitch for the wonders of the Google. That’s okay. It is a literary form comparable to an epic poem or a jazzy H.L. Menken essay when people read magazines and newspapers. Let’s take a quick look at the main point of the article and then consider its implications.
I think this passage captures the zeitgeist of the Google on July 13, 2024:
From a team-culture point of view, over twenty five years of WSC design, we have learnt a few important lessons. One of them is that it is far more important to focus on “what does it mean to land” a new product or technology; after all, it was the Apollo 11 landing, not the launch, that mattered. Product launches are well understood by teams, and it’s easy to celebrate them. But a launch doesn’t by itself create success. However, landings aren’t always self-evident and require explicit definitions of success — happier users, delighted customers and partners, more efficient and robust systems – and may take longer to converge. While picking such landing metrics may not be easy, forcing that decision to be made early is essential to success; the landing is the “why” of the project.
A proud infrastructure plumber knows that his innovations allows the home owner to collect rent from AirBnB rentals. Thanks, MSFT Copilot. Interesting image because I did not specify gender or ethnicity. Does my plumber look like this? Nope.
The 13 page paper includes numerous statements which may resonate with different readers as more important. But I like this passage because it makes the point about Google’s failures. There is no reference to smart software, but for me it is tough to read any Google prose and not think in terms of Code Red, the crazy flops of Google’s AI implementations, and the protestations of Googlers about quantum supremacy or some other projection of inner insecurity the company’s genius concoct. Don’t you want to have an implant that makes Google’s knowledge of “facts” part of your being? America’s founding fathers were not diverse, but Google has different ideas about reality.
This passage directly addresses failure. A failure is a prelude to a soft landing or a perfect landing. The only problem with this mindset is that Google has managed one perfect landing: Its derivative online advertising business. The chatter about scale is a camouflage tarp pulled over the mad scramble to find a way to allow advertisers to pay Google money. The “invention” was forced upon those at Google who wanted those ad dollars. The engineers did many things to keep the money flowing. The “landing” is the fact that the regulators turned a blind eye to Google’s business practices and the wild and crazy engineering “fixes” worked well enough to allow more “fixes.” Somehow the mad scramble in the 25 years of “history” continues to work.
Until it doesn’t.
The case in point is Google’s response to the Microsoft OpenAI marketing play. Google’s ability to scale has not delivered. What delivers at Google is ad sales. The “scale” capabilities work quite well for advertising. How does the scale work for AI? Based on the results I have observed, the AI pullbacks suggest some issues exist.
What’s this mean? Scale and the cloud do not solve every problem or provide a slam dunk solution to a new challenge.
The write up offers a different view:
On one hand, computing demand is poised to explode, driven by growth in cloud computing and AI. On the other hand, technology scaling slowdown poses continued challenges to scale costs and energy-efficiency
Google sees that running out of chip innovations, power, cooling, and other parts of the scale story are an opportunity. Sure they are. Google’s future looks bright. Advertising has been and will be a good business. The scale thing? Plumbing. Let’s not forget what matters at Google. Selling ads and renting infrastructure to people who no longer have on-site computing resources. Google is hoping to be the AirBnB of computation. And sell ads on Tubi and other ad-supported streaming services.
Stephen E Arnold, July 18, 2024
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