Another dinobaby post. No AI unless it is an image. This dinobaby is not Grandma Moses, just Grandpa Arnold.
The big AI tech outfits are rushing to tell the world, “Our models can break out of our systems. Trust us for sure, please.” I don’t know about you, but my trust is the US big AI tech outfits is fragile. Your mileage may be vary. (Enjoy the ride, please.)
Thanks, MidJourney. Good enough.
Tom’s Hardware published an interesting write up with the snappy title “AI Creates 16 New Viruses That Never Existed in Nature after Learning DNA’s Pattern from 9 Trillion Nucleotides — Experts Warn Such Applications Are Way Ahead of Necessary Guardrails.” The write up states:
Researchers say the phages can overcome bacterial resistance
I remember my advanced biology class. That’s the one in which the impressive Camile B. pounded out A+ work without looking bothered at all. It also sounds a bit like the alleged gain of function work allegedly funded by the US allegedly not really a US project in the facility not far from a popular food market in Wuhan. (I have been to Wuhan. Lots of people. Lots of alleged activity.)
The write up says:
… researchers trained a genomic AI model to design complete DNA sequences for viruses, then chemically built the results and watched some come to life. Of 285 AI-generated viral genomes tested, 16 successfully assembled into functioning viruses capable of infecting bacteria and reproducing.
Yep, we do it because we can. It’s science. Plus, don’t worry:
The scientists say the new viruses were completely harmless to humans, noting that replicating the same results in pathogens known to affect humans would be a different ballgame. They were also careful not to train the AI on any data from organisms known to affect humans. However, the study inadvertently shows that highly dangerous applications are a possibility. Experts worry that such studies are way ahead of necessary guardrails and regulations. AI has also been known to fly off the rails autonomously. An OpenAI agent recently went rogue and hacked Hugging Face.
Several observations from this dinobaby are warranted:
Guardrails are tough to set up when one does not know exactly what one is guarding against.
Researchers probably should be [a] supervised and [b] have a cheat sheet that lists dot points for ethical behavior
Any technology can be used for beneficial and detrimental purposes. Just a reminder of the two-edged sword trope.
Net net: I wonder if the non US smart software can perform in a similar manner. Nah, no other LLM researchers would be this creative and innovative. Well, maybe?
On September 3, 2026, something interesting seems to have taken place. OpenAI, Anthropic, xAI, and Google learned that their AI systems experience a digital heart failure. The hearts began beating again, but the leadership of these firms had a headache, and I am not sure it has gone away.
While the affected frontier models go down occasionally, having all four experience interruptions in the same short period is practically unheard of. Claude reports 99.4 percent uptime for its services over the last 90 days and last reported a similar three-hour “partial outage” on August 24. OpenAI reports 99.63 percent uptime for ChatGPT and 100 percent uptime for ChatGPT Codex in the same period. ChatGPT’s so-called “Work Mode” reported an hours-long period of “elevated latency” on August 31.
…on Thursday, as OpenAI’s ChatGPT, Anthropic’s Claude, and SpaceXAI’s Grok near-simultaneously, and somewhat mysteriously, experienced significant, prolonged outages.
Yep, mysterious.
Let’s stop and ask a handful of questions not addressed in these two cited write ups:
What happened to the cloud providers and their systems? Don’t infrastructure operators have smart software tuned to identify and fail over immediately? Everyone’s favorite BAIT (big AI tech company) outfit not only owns its vertical stack, the firm owns a couple of state-of-the-art cyber security systems. What went wrong in the engineering, design, and implementation of these smart defense mechanisms? I think I know the answer. A lawyer will help craft a plausible deniability statement or tell the clients with the problematic system, “Keep you lip zipped.”
What about the tech bloggers and YouTube experts? Where are those deep dives into the technical issues that caused for BAITs to go south at approximately the same time? (Is it possible that analyses have been posted and remain unindexed in order to convert the story into the information equivalent of a squirrel dashing into a roadway only to be squished by a Ford F-150’s front tire?)
What are the BAIT ourfits saying? Yeah, not much, right? Does the cat have your PR departments tongue?
I haven’t been in a statistics class in more than half a century. The idea that four unrelated companies’ smart software glitches at about the same time on the same day seems unlikely. Is there an undisclosed or previously unknown dependency sparking this dumpster fire? Did an outside actor send a signal to test the efficacy of its “kill US AI services” switch? I can think of a couple of countries who might have explored developing such a software device.
I used the free pen I snagged at the Staunton, Virginia, Comfort24 Hotel. I roughed out some numbers. I recast these into one of my analogies. For four unrelated AI firms to experience an outage at roughly the same time on the same day of the week is like a public park with four kid-oriented merry go rounds rotating 24 hours a day. Each merry go rounds dumps kids off. The chance that I could look at the four rotating merry go rounds and see each of the four dumping a kid simultaneously is about one in 6,000,000. For me, the coincidence is sufficiently improbable to say, “Yo, we have a shared dependency or we have a bad actor testing a kill switch.”
In my typical dinobaby fashion, I wish to offer a few observations. [a] Sheer chance is unlikely to produce this type of event. [b] A bad actor working from a position with tools that permits a kill actio to occur seems worth considering. [c] The baloney about security and reliability needs a footnote to two to allay the concerns of suspicious individuals like me.
Net net: We just received a signal. Who will stand up and say, “Okay, folks this is what really happened.” Tip: Don’t hold your breath.
Nokia had the budget, talent, and urgency to fight the iPhone. One hidden bottleneck shows why AI won’t make companies faster unless they rethink the work.
Thanks, MidJourney. Good enough.
Here’s the passage I found suggestive:
Compiling is where the code that a programmer writes gets turned into something the phone can actually run….The 48 hours were for one programmer’s piece. The full build, which required gathering code from every team and turning it into a working version of the operating system, took up to two weeks—two weeks to learn whether a change had worked or whether it would break something.
Working on the IBM mainframe at my undergraduate college in 1962 operated on a similar schedule. But for a a mobile phone company, an engineer probably expected a snappier turnaround. I am okay with a two-day cycle, but I am a dinobaby. In 2007, the developers wanted hours maybe? Nope, I am wrong. The Inc. articles dispenses the gospel:
At Google, a comparable build would take under 20 minutes. And Google’s engineers were complaining that 20 minutes was too slow.
And we know what Google has achieved with its mobile phone, don’t we?
The write up includes a useful statement of what I call “workflow time”; to wit:
So why do big companies feel so slow? Usually, it’s not because the people are lazy. We saw already, it’s that everyone all waiting on someone else. And the wait never ends because the parts of a company run on different clocks:
Finance closes the books four times a year.
R&D plans its bets five years out.
Customer support hears from customers every day.
The online store learns something new every second.
Those clocks never line up. But they aren’t meant to.
My view of this anecdote and the comment about “clocks” is that rapid information flows have different impacts on each work task. Online allows the finance department to eliminate handwritten ledgers with spreadsheets. Computational chemists can use software to identify, model, and discard lots of potential chemical combinations.
What happens when smart software becomes available for the discrete work tasks? I have observed three impacts:
The shared understanding that emerges from old fashioned and slow work is eroded. The result is that errors can persist simply because no one is around to provide comments or criticism. The “manager” and leadership are useless because the old slow work methods allowed information to diffuse, some drifting upwards to the top dogs.
The speed with which outputs are delivered overwhelm many people, yes, even those who are MBAs and lawyers. The output from a simple online query (forget AI for a moment) means that most people look at the first few outputs and say, “Hey, I understand.” Sure, these people do.
The arrival of AI adds two additional factors: [a] “Normal” workers don’t have to think, ask colleagues, or doubt what the AI says. It’s expensive. The boss loves it. Go with it. And [b] top flight workers see that AI can allow them to push out the less intelligent colleagues, maybe take credit for something and get a promotion to a cushy job, or start up a side gig because of the free time the AI delivers.
Consequently the Nokia case illustrates that work tasks can prevent innovation. The present day’s struggles with AI help explain some of the perils of AI.
Net net: Organizations will make decisions based on what AI says. Many of those decisions will be be bad, and the impact of those decisions will be evident more quickly. It took time for Nokia to lose its zip. Today those organization-killing effects will surface more and more quickly. Look around and ask:
Why are air line companies struggling with schedules, fares, and customer service?
Why are customers unable to reach a human at many firms?
Why do mixed messages about health flow from different governmental agencies?
Why do those self-driving taxis end up in Cow Hollow?
Go fast and break things means that nothing will work as expected. Just like at Nokia years ago. Toss in AI and the failures will continue to skyrocket. No problem. Just blame the different clocks for the disease.
Hundreds of user conversations with Anthropic’s popular artificial intelligence (AI) chatbot Claude were found to have been available to essentially anyone using Google or other web browsers. Links to the chats, some of which included personal and work information, would show up if a user of a search engine like Google used a site-specific search term. The searches showed Claude chats for which a user had decided to “share” a link had been saved by search engines like Google, leaving them accessible to the broader public.
Two members of big AI tech leadership find the idea of editorial responsibility unacceptably stupid. Thanks, MidJourney. Good enough.
Now who is to blame? Anthropic’s position about sharing is similar to OpenAI’s; that is, the user is responsible for any sharing. And what about the Google? The Beeb offers this statement from that estimable firm:
A spokesman for Google made clear to the BBC that the company does not control “what pages are made public on the web,” saying instead that action comes from websites. “We give site owners clear controls to decide whether pages can be crawled or indexed, and we always respect those directives.”
I love this approach to smart software and indexing, often without permission, content accessible via the Internet. The users are to blame. But for Google, it is abundantly clear that despite more than 100,000 full time equivalents, the firm is not able to control what pages are made public on the web. I include a site called Altenen in my lectures for LE and intel professionals. This is an interesting site, and you may want to check out the firm’s tutorials and for-fee information about credit and bank card theft. Google just can’t control what pages are made public on the web if the Beeb’s story is spot on. Oh, I locate the Altenen outfit via a Google search. I would not advise providing this site name to one’s teeny boppers.
I was disappointed that the Beeb did not point out that more than 80 percent of online queries flow to the Google. And in Denmark, the GOOG hits 99 percent of the search traffic. Content in Google becomes the content from a couple of billion people. Yep, no control.
Google says these changes could endanger user privacy and security.
That’s clear. The EU will take steps for Google to share search data over which it has no control. Furthermore, if Google were to be required to share its data, the chief problem would be “user privacy and security.”
As a dinobaby, I am growing weary of the behaviors of the big tech outfits. The “it’s easier to say I am sorry than ask for permission” combined with the “move fast and break things” is for me a matter of concern. Others find the behavior beyond reproach. If there’s a problem, it’s my fault and yours. And whatever we do, we can’t control content.
Another dinobaby post. No AI unless it is an image. This dinobaby is not Grandma Moses, just Grandpa Arnold.
Venture Beat, a quite unusual online publication, published “Enterprise AI Is Entering An Evaluation Gap: Agents Are Gaining Autonomy Faster Than Companies Can Verify Them. ” Is this a write up in the old Amazing Tales magazine or a serious analysis of people who are looking for a way to cut expenses, increase revenue, and land a big bonus payday? The answer: The article is a business and technical analysis prepared by a hard working professional with a track record of covering fast-changing subjects. Therefore, let’s take a look at the write up.
The good news is that software agents armed with “intelligence” haven’t become fully autonomous… yet. What is happening is that enterprise AI teams are giving their AI agents more freedom as their confidence in automated testing is collapsing. Is this one of those inconvenient paradoxes?
About 50% of enterprises have deployed an AI agent or LLM that passed internal evaluations, but they are still failing on the customer facing side. The logical thing to do would be to slow automation but 66% of respondents to a VB Pulse survey said they deployed some production without human oversight. Autonomy is coming sooner than humans are reviewing it. With layoffs and employees dropping out of “real” work, I am not sure that there are enough motivated, informed humans to review agentic software. In my experience, generating code really fast is a core semi-competency.
The article states:
“It also fits a broader thesis that will be explored at VB Transform 2026: enterprises ship agents first, while the control layers around identity, evaluation, cost, context and orchestration are arriving later. The next year will be a retrofit cycle, with buyers shifting budget toward the systems that make agentic deployments governable and dependable.”
Is an agent ready to be turned loose because it completes a task in one successful run? Sure it does. The goal of AI is to achieve repeatable results so that customers don’t experience any hiccups on their end. Every failure should be a regression test to make the AI agent better. Somehow American Airlines garbled by email and displayed an old phone number for me. I called the special number for AAdvantage members. I told the AI agent I needed to update my account. The system did not understand “update my account.” After five repeats telling me to state what I wanted to do, the smart software said, “If you want us to call you in 45 minutes, press one.” I did. In fact, I went through this drill several times. Did the smart software agent get someone to call me? Nope. I wrote a handwritten letter to the leadership of American Airlines, but I don’t think anyone will contact me. The agentic system worked at least once. Therefore, the system is perfect for paying customers.
I don’t think there are enough human hours to review every AI agent action, but:
“w-risk actions such as drafting internal summaries or categorizing documents can tolerate broader autonomy. Financial transactions, customer communications, code deployment, access-control changes and data deletion need stricter thresholds, repeated consistency tests, policy checks, rollback mechanisms and clear human escalation paths. The risk isn’t evenly distributed by company size, either. Larger enterprises — those with 2,500 or more employees — are moving toward zero-human deployment fastest, at 70% versus 64% for smaller companies, and they’re also shipping more agents that go on to fail a customer, at 54% versus 48%.”
I think this means that management must manage agents. The customer centric enterprise teams need to get their acts together and create an evaluation system so bad agents are not foisted on paying customers. Will that happen, Venture Beat?
Another dinobaby post. No AI unless it is an image. This dinobaby is not Grandma Moses, just Grandpa Arnold.
These BAIT outfits are indeed remarkable. In my lingo, as you may recall, means “big AI tech.” In my lectures, I like to point out that these companies are becoming increasingly similar. They have realized that the convergence creates the idea opportunity for one big winner to emerge, a second place finisher with a shot at about 60 percent of the revenues of the winner, and the rest of the BAIT outfits fighting over remaining revenue. Therefore, folks, its cage match time.
Apple on Friday [July 10, 2026] sued OpenAI in federal court in Northern California, alleging trade secret theft, saying that the artificial intelligence lab took the iPhone maker’s intellectual property in order to develop its own consumer hardware.
How did this work? Did busy little software daemons prowl through Apple’s digital information? Did OpenAI’s smart software launch a thousand and one agents to suck down any content connected to the expanded term “Apple”? Did Sam AI-Man show up in the Apple parking lot, handing out offers to happy Applers?
Nope.
Apple alleged that OpenAI’s hardware chief, Tang Tan, who is a former Apple vice president, has directed Apple employees interviewing at OpenAI to share Apple secrets as part of the interviewing process. Tan is named as a defendant in the suit. “He has directed job candidates still working for Apple to bring ‘actual parts’ from Apple to their interviews for ‘show and tell’ sessions in which he and his team at OpenAI can elicit still more Apple confidential information,” Apple said in the filing. Apple alleged that OpenAI coached departing Apple employees in how to evade security processes when leaving the iPhone maker, and that Chang Liu, a former employee who joined OpenAI, stole an Apple laptop. Liu is named as a defendant in the suit.
Well, that’s subtle. a human thought up the ploy. Information flowed at the humanoid to humanoid level. Tips and tricks to sidestep “security” flew like a hungry turkey (yes, turkeys eat apples, but turkeys do not climb apple trees. Like job hoppers from Apple to OpenAI, the turkeys just go for easy pickings in my opinion).
Okay, I want to make sure I understand this. Apple found itself the defendant in some interesting knowledge transfer litigation. Examples, include the Masimio medical device matter, the VirnetX dispute, the University of Wisconsin (go badger Alumni Research Foundation), and some others. I am not lawyer, so my recollection may be off base. Please, keep in mind that I am a dinobaby and averse to doing legal research.
Assume that one of these instances of Apple taking another firm’s intellectual property is true. Do you find it interesting that Apple is greatly aggrieved that a fellow BAIT outfit is asking potential employees a question like this: “Okay, Sally, do you want some oat milk for your coffee? No, okay. Tell me what expertise you have and include a couple of simple examples?”
Yeah, HR and interviewers never ask this type of question. Let’s stick with the oat milk interrogatory. That’s much more useful in today’s go fast and break things world. Am I correct?
The CNBC story concludes:
Apple is seeking damages, injunctions, and an order to force OpenAI to stop using its trade secrets.
Okay, but I have one question: What’s a trade secret? I usually ask questions when I know the answer or I have a reasonable sense of the answer. Does that mean a person who answers my question has revealed a secret when I already know the answer or have a good idea of the answer?
“That’s not the point,” squawks a legal eagle. I agree. I don’t want to be baited by a BAIT. The unfolding legal drama will be interesting to watch. Snacking on apples in a court may not be permitted.
Give up? The answer is, “Whatever those with access to the AI system’s training sets, threshold settings, and software wrappers.”
Humans have driven AI’s development since the onset, but that might be about to change says Anthropic in the press release: “When AI Builds Itself: Our Progressive Toward Recursive Self-Improvement And Its Implications.”? When AI can autonomously drive towards improvement it is called “recursive self-improvement.? It could be closer on the horizon than expected.? When the 2026 benchmarks for Anthropic are measured against 2021-2025, engineers are delivering 8 times more code each quarter.? What does this mean? Buckle up. The article asserts:
“The technical trends discussed in this piece suggest that AI systems are going to become much more capable in coming years. These trends have huge implications. AI that can build itself would be a major development in the history of technology—one that could bring enormous good for the world in science, healthcare, and beyond. But full recursive self-improvement also might increase the risks of humans losing control over AI systems. If systems are capable of fully building their own successors, the ways we secure them, monitor them, and shape their behavior all grow much more important.”
Claude writes the majority of Anthropic’s code. In 2025, Claude ran code instead of suggest it for engineers to copy and paste.? ? The algorithm is also getting better at research. For example, on Claude Code sessions, when the the algorithm is asked to find mistakes, it can detect when engineers veered off course
If Claude is programming Anthropic, do humans have a role anymore?
“The evidence suggests that the human role is narrowing at each step in the AI development process. Once human- and AI-authored code quality reach parity, humans will stop writing code entirely, and shift to only reviewing it. But if they can’t review code as quickly as Claude can generate it, human review will become the bottleneck to AI development. Similarly, once Claude can run experiments, the question shifts towards ‘Which of these experiments is worth running?’ Put simply: the doing (i.e., writing the code, running the experiment, producing the result) now costs almost nothing in human time, even if it still has costs in compute. An area of human comparative advantage, for now, is research taste and judgment, including choosing which problems matter, which results to trust, and when an approach is a dead end.”
There are possible futures where this particular trend could stall, AI could see compounding efficiency gains, or the algorithms become entirely capable of full recessive self-improvement.
What are we going to do? The write up says:
“If it were possible to effectively slow the development of this technology to give ourselves more time to deal with its immense implications, we think that would likely be a good thing. But if a slowdown simply lets the least cautious actors catch up technologically, it could leave everyone less safe. Without a global coordination mechanism, companies and governments will have to make difficult decisions about safety while under competitive and geopolitical pressures.”
Don’t let the bad actors get ahead. They’ll take advantage of everyone if they do. We guess the only thing to do is push forward and hope for the best while wearing white hats…maybe gray.
The “AI failed” information continues to appear in my newsfeed. Most we ignore, but due to the ubiquity of an outfit called Starbucks (and the “bucks” word is germane to the business model for hot water sales) jumped into the output of a Thermoplan AG device. How did that dip into the warm bath of AI marketing work out?
“A “unique synthesis” of on-device computer vision, 3D spatial intelligence, and augmented reality. Wave a tablet at a shelf of milk jugs and syrups, and the count appears, validated in AR. Starbucks’ chief technology officer framed it as freeing partners from a time-intensive task so they could get back to making drinks and connecting with customers.”
It was removed in May 2026. The job for AI was “simple” like most work processes analyzed by “let’s increase margin” crazed MBAs and flexible bean counters. The system was supposed to count milk cartons. Eliminating manual counting would free up Excel projected hours. Presumably the time would be used for more productive activities like turning out more expensive hot water and writing in physician scrawl a customer’s name on a cup with a mermaid image.
How did that AI handle the simple job? Yeah, not very well. As anyone who has worked in a busy foody type of environement, when the rush arrives, life is pretty crazy. The system worked great in a store with a tidy, clean shelf, decent lighting, controlled SKUs. When caffeine semi withdrawl Type As crowd the counter, smart software did not work too well.
No modern “leadership” admits mistakes. Starbucks didn’t consider it a failure instead Automated Counting is labeled as more of a learning experience than anything. It’s a carefully crafted statement from Starbucks HQ to output what I call smarmy talk. Yeah, I interpreted the AI counting milk anecdote as another memorable moment for an outfit which sells hot water.
Does this AI moment prove anything? Yep:
Real world actions are different from those demonstrated in a controlled setting.
Modern leadership believes in Tooth Fairies, silver bullets, and the outputs of BAIT acolytes (BAIT is our lingo for big AI tech outfits).
Probability is good for some things but not for other applications.
My view is that modern management is gullible and believes the the next big thing will wave a magic wand over some business problems. News flash: There is no tooth fairy.
Chatbots are expected to take on monotonous tasks thus freeing up employees for other work. A problem, however, is that most chatbots still fail at basic facts and Big Tech companies are still launching agents to handle more complex tasks. Here is what will happen:
“As AI agents become more integrated into the economy, companies and entities that deploy them will benefit disproportionately compared to those that cannot, Nick Srnicek, a senior lecturer in digital economy at King’s College London, told Rest of World. ‘We will see new inequalities of access, scale, quality and trust: divides between those who have agents and those who don’t; those who have good agents and those who have bad agents; those who have many agents and those who have few agents; and those who can trust their agents and those who cannot,’ he said.”
China and Singapore apparently introduced frameworks to regulate AI to focus on accountability and safety. Chinese local governments are boasting single-employee companies that rely on AI chatbots. India is even embracing AI to cut costs and scale quicker.
Better chatbots are already advancing companies, saving them money, and advancing in the markets. Another problem is that if governments and companies rely on chatbots, they can be canceled at anytime without notice. There are also security concerns:
Matthew Sharp, a research affiliate at the Oxford Martin AI Governance Initiative [said]:
‘The same infrastructure can become a surveillance layer if the data flows, defaults, and oversight are wrong,” Sharp said. “The safeguards around consent, purpose limitation, auditability, and political independence would need to be real, not merely architectural.’”
My fear is that “good enough” is the new standard of excellence. A race to dumbing down. Outstanding.
Is American big tech becoming a bit of a problem in the UK? Probably not, some of the BAIT folks would say. BAIT is my jargon for big AI tech. Perhaps “attitude” may be more of an issue? The do-what-we-tell-you approach may be unpalatable in some countries. After working in the UK on a number of projects over the years, in my experience saying “excuse me” when someone steps on your shoes indicates a different mental approach that stepping on a person’s toe and snarling excuse me at the individual whose 10 pound shoe shine has been besmirched.
Thanks, Midjourney. I did not know knees could speak. Good enough, of course.
I noted a BBC report a couple of weeks ago. The story “Millions’ Of Pounds Saved By Replacing Palantir Tech In Refugee System” suggests that the Palantir Technologies Tolkien infused intelware was not working for The Homes for Ukraine. That group replaced Palantir’s seeing stone with software designed by its own team. What began as a free service morphed into a US government procurement scale invoice.
Instead of paying the bill, the Ministry of Housing, Communities, and Local Government (MHCLG) said its new in-house system was equipped to handle high standards of security and was more flexible. The BBC pointed out that is was proud to have supported the project and helped to resettle 157,000 refugees displaced from the Russia “special operation.” The decision to step away from Palantir was celebrated:
“That message may be particularly welcome to those who have criticized Palantir and its contracts across UK public services – including with the NHS, the Ministry of Defense (MoD), the Financial Conduct Authority and 11 police forces. Some argue the firm’s success is because its tech is badly needed and works well. But others contend Palantir’s involvement with US immigration enforcement and Israel’s military, as well as the beliefs of its two most prominent founders, make it an unsuitable partner. There are also concerns the UK is relying too much on large US tech suppliers.”
Palantir has accused Sadiq Khan of “putting politics above public safety” after the London mayor blocked its £50m contract with the Metropolitan police in a move that has also led to tensions inside Labor over its involvement with the US tech company. Louis Mosley, who heads Palantir in the UK and Europe, accused Khan of politicizing procurement after he rejected a two-year deal for Scotland Yard to use AI to process intelligence in criminal investigations, as first revealed by the Guardian. Mosley said: “What Londoners value is not being mugged, not being raped by a serving police officer.”
According the the article, Palantir has been raising the hackles of some in the UK. Here’s how the Guardian presents Palantir’s sales suavity:
Last year, when the company’s chief executive, Alex Karp, was challenged that “Palantir kills Palestinians” in Gaza, he said: “Mostly terrorists, that’s true.” Khan’s stance puts him at odds with the UK government which has a £330m NHS England deal with Palantir and a £240m deal with the Ministry of Defense.
Instead of beating the anti-BAIT tambourine, the article presents this:
Ministers say they are aware of the need for less reliance on foreign AI companies as the technology becomes increasingly applied in the delivery of public services. Kyle said: “We need to have more British AI companies that can do those kinds of things, which is why I’ve taken equity stakes in British AI firms and British tech firms, so that we can scale them up much, much faster.”
I think this is part of the “pardon me” offered by the person who is looking at a ruined shoe shine. The polite phrase does not mean, “Hey, you baseball-cap wearing American, I am at fault.” Nope, the excuse me means that the rude oaf is a social menace. But it sounds nice, doesn’t it?
Stephen E. Arnold monitors search, content processing, text mining
and related topics from his high-tech nerve center in rural Kentucky.
He tries to winnow the goose feathers from the giblets. He works with colleagues
worldwide to make this Web log useful to those who want to go
"beyond search". Contact him at sa [at] arnoldit.com. His Web site
with additional information about search is arnoldit.com.
Another dinobaby post. No AI unless it is an image. This dinobaby is not Grandma Moses, just Grandpa Arnold.
Another dinobaby post. No AI unless it is an image. This dinobaby is not Grandma Moses, just Grandpa Arnold.
Another dinobaby post. No AI unless it is an image. This dinobaby is not Grandma Moses, just Grandpa Arnold.
Another dinobaby post. No AI unless it is an image. This dinobaby is not Grandma Moses, just Grandpa Arnold.
Another dinobaby post. No AI unless it is an image. This dinobaby is not Grandma Moses, just Grandpa Arnold.
