Windows 11: Loved and Wanted? Sure As Long As No One Thinks about MSFT Security Challenges
January 10, 2022
I hold the opinion that the release of Windows 11 was a red herring. How does one get the tech pundits, podcasters, and bloggers to write about something other than SolarWinds, Exchange, etc.? The answer from my point of view was to release the mostly odd Windows 10 refresh.
Few in my circle agreed with me. One of my team installed Windows 11 on one of our machines and exclaimed, “I’m feeling it.” Okay, I’m not. No Android app support, round corners, and like it, dude, you must use Google Chrome, err, I mean Credge.
I read “Only 0.21%, Almost No One Wants to Upgrade Windows 11.” Sure, the headline is confusing, but let’s look at the data. I believe everything backed by statistical procedures practiced by an art history major whose previous work experience includes taking orders at Five Guys.
The write up states:
According to the latest research by IT asset management company Lansweeper, although Windows 10 users can update Windows 11 for free, it is currently only 0.21%. Of PC users are running Windows 11.
I am not sure what this follow on construction means:
At present, Windows 11 is very good. Probably the operating system with the least proportion.
I think the idea is that people are not turning cartwheels over Windows 11. Wasn’t Windows 10 supposed to be the last version of Windows?
I am going to stick with my hypothesis that Windows 11 was pushed out the door, surprising Windows experts with allegedly “insider knowledge” about what Microsoft was going to do. The objective was to deflect attention from Microsoft’s significant security challenges.
Those challenges have been made a little more significant with Bleeping Computer’s report “Microsoft Code Sign Check Bypassed to Drop Zloader.”
Is it time for Windows 12, removing Paint, and charging extra for Notepad?
Possibly.
Stephen E Arnold, January 10, 2022
Perhaps Someone Wants to Work at Google?
January 7, 2022
I read another quantum supremacy rah rah story. What’s quantum supremacy? IBM and others want it whatever it may be. “Google’s Time Crystals Could Be the Greatest Scientific Achievement of Our Lifetimes” slithers away from the genome thing, whatever the Nobel committee found interesting, and dark horses like the NSO Group’s innovation for seizing an iPhone user’s mobile device just by sending the target a message.
None of these is in the running. What we have it, according to The Next Web, is what may be:
the world’s first time crystal inside a quantum computer.
Now the quantum computer is definitely a Gartner go-to technology magnet. Google is happy with DeepMind’s modest financial burn rate to reign supreme. The Next Web outfit is doing its part. Two questions?
What’s a quantum computer? A demo, something that DARPA finds worthy of supporting, or a financial opportunity for clever physicists and assorted engineers eager to become the Seymour Crays of 2022.
What’s a time crystal? Frankly I have no clue. Like some hip phrases — synaptic plasticity, phubbing, and vibrating carbon nanohorns, for instance — time crystal is definitely evocative. The write up says:
Time crystals don’t give a damn what Newton or anyone else thinks. They’re lawbreakers and heart takers. They can, theoretically, maintain entropy even when they’re used in a process.
The write up includes a number of disclaimers, but the purpose of the time crystal strikes me as part of the Google big PR picture. Whether time crystals are a thing like yeeting alphabet boys or hyperedge replacement graph grammars, the intriguing linkage of Google, quantum computing, and zippy time crystals further cements the idea that Google is a hot bed of scientific research, development, and innovation.
My thought is that Google is better at getting article writers to make their desire to work at Google evident. Google has not quite mastered the Timnit Gebru problem, however.
And are the Google results reproducible? Yeah, sure.
Stephen E Arnold, January 7, 2022
A New Spin on Tech Recruitment
January 7, 2022
“Knock Knock! Who’s There? – An NSA VM” is an interesting essay for three reasons.
First, it contains a revealing statement about the NSO Group:
Significant time has passed and everyone went crazy last week with the beautiful NSO exploit VM published by Project Zero, so why not ride the wave and present a simple NSA BPF VM. It is still an interesting work and you have to admire the great engineering that goes behind this code. It’s not everyday that you can take a peek at code developed by a well funded state actor.
I noticed that the write up specifically identifies the NSO Group as a “state actor.” I think this means that NSO Group was working for a country, not the customers. This point is one that has not poked through the numerous write ups about the Israel-based company.
Second, the write up walks through a method associated with the National Security Agency. In terms of technical usefulness, one could debate whether the write up contains old news or new news. The information does make it clear that there are ideas for silent penetration of targeted systems. The targets are not specific mobile phones. It appears that the targets of the methods referenced and the sample code provided are systems higher in the food chain.
Third, the write up is actually a recruitment tool. This is not novel, but it is probably going to lead to more “look how smart and clever we are, come join us” blandishments in the near future. My hunch is that some individual, eager to up their games, will emulate the approach.
Is this method of sharing information a positive or negative? That depends on whom one asks, doesn’t it?
Stephen E Arnold, January 7, 2022
Datasets: An Analysis Which Tap Dances around Some Consequences
December 22, 2021
I read “3 Big Problems with Datasets in AI and Machine Learning.” The arguments presented support the SAIL, Snorkel, and Google type approach to building datasets. I have addressed some of my thoughts about configuring once and letting fancy math do the heavy lifting going forward. This is probably not the intended purpose of the Venture Beat write up. My hunch is that pointing out other people’s problems frames the SAIL, Snorkel, and Google type approaches. No one asks, “What happens if the SAIL, Snorkel, and Google type approaches don’t work or have some interesting downstream consequences?” Why bother?
Here are the problems as presented by the cited article:
- The Training Dilemma. The write up says: “History is filled with examples of the consequences of deploying models trained using flawed datasets.” That’s correct. The challenge is that creating and validating a training set for a discipline, topic, or “space” is that new content arrives using new lingo and even metaphors instead of words like “rock.” Building a dataset and doing what informed people from the early days of Autonomy’s neuro-linguistic method know is that no one wants to spend money, time, and computing resources in endless Sisyphean work. That rock keeps rolling back down the hill. This is a deal breaker, so considerable efforts has been expended figuring out how to cut corners, use good enough data, set loose shoes thresholds, and rely on normalization to smooth out the acne scars. Thus, we are in an era of using what’s available. Make it work or become a content creator on TikTok.
- Issues with Labeling. I don’t like it when the word “indexing” is replaced with works like labels, metatags, hashtags, and semantic sign posts. Give me a break. Automatic indexing is more consistent than human indexers who get tired and fall back on a quiver of terms because who wants to work too hard at a boring job for many. But the automatic systems are in the same “good enough” basket as smart training data set creation. The problem is words and humans. Software is clueless when it comes to snide remarks, cynicism, certain types of fake news and bogus research reports in peer reviewed journals, etc. Indexing using esoteric words means the Average Joe and Janet can’t find the content. Indexing with everyday words means that search results work great for pizza near me but no so well for beatles diet when I want food insects eat, not what kept George thin. The write up says: “Still other methods aim to replace real-world data with partially or entirely synthetic data — although the jury’s out on whether models trained on synthetic data can match the accuracy of their real-world-data counterparts.” Yep, let’s make up stuff.
- A Benchmarking Problem. The write up asserts: “SOTA benchmarking [also] does not encourage scientists to develop a nuanced understanding of the concrete challenges presented by their task in the real world, and instead can encourage tunnel vision on increasing scores. The requirement to achieve SOTA constrains the creation of novel algorithms or algorithms which can solve real-world problems.” Got that. My view is that validating data is a bridge too far for anyone except a graduate student working for a professor with grant money. But why benchmark when one can go snorkeling? The reality is that datasets are in most cases flawed but no one knows how flawed. Just use them and let the results light the path forward. Cheap and sounds good when couched in jargon.
What’s the fix? The fix is what I call the SAIL, Snorkel, and Google type solution. (Yep, Facebook digs in this sandbox too.)
My take is easily expressed just not popular. Too bad.
- Do the work to create and validate a training set. Rely on subject matter experts to check outputs and when the outputs drift, hit the brakes, and recalibrate and retrain.
- Admit that outputs are likely to be incomplete, misleading, or just plain wrong. Knock of the good enough approach to information.
- Return to methods which require thresholds to be be validated by user feedback and output validity. Letting cheap and fast methods decide which secondary school teacher gets fired strikes me as not too helpful.
- Make sure analyses of solutions don’t functions as advertisements for the world’s largest online ad outfit.
Stephen E Arnold, December 22, 2021
Microsoft Has a Digital Death Star and Windows 11
December 21, 2021
If you are not familiar with Microsoft’s digital Death Star, you will want to watch the story in the December 26, 2021, Dark Cyber video news program. You can find it in the mini player at this link. More than a year after the SolarWinds’ security misstep became public, the Redmond giant can digitally slay the 1,000 malefactors responsible for some data exfiltration. Quick.
My hunch has been that Microsoft rolled out Windows 11 as part of a red herring campaign. The idea may have been that Windows 11 would capture the attention of “real” journalists, thus reducing the blow torch directed at the Microsoft enterprise software processes. It seems to have worked. No one I have spoken with knows much about the Death Star meme and quite a few people are excited about Windows 11.
ZDNet remains firmly in the camp of writing about Windows 11. Why not? Users who want to use a browser other than Edge or a specialized software to perform a specific PDF function find that some noodling is required. Windows 11 is supposed to be simpler better cheaper faster more wonderfuler, right?
“8 Harsh Realities of Being a Windows 11 User” presents a distinguished lecturer’s view of some Windows 11 foibles. Let’s take a quick look at three of the eight and then circle back to the year long wait for digital retribution against the 1,000 engineers who created the SolarWinds’ misstep and made the Softies look inept and sort of silly in the security department.
Reality 1. The Browser Lock In
Microsoft does not want a Windows 11 user to load up a non Microsoft browser. I find this amusing because Edge is not really Microsoft code. Microsoft pulled out what I call soft taco engineering; that is, the Chrome engine is wrapped in a tortilla crafted in the kitchens of Microsoft Café 34. I am a suspicious type; therefore, I think the browser lock in is designed to make darned sure the geek bloggers and the “real” journalists have something to Don Quixote.
Reality 5. Control Panel / Settings Craziness
Okay, where is the widget to have the weird File Explorer show me “details”? And what about Display controls? I have a couple of places to look now. That’s helpful. Exactly what is the difference between a bunch of icons grouped in one place under one jargonized name? I am not sure about the logic of this bit of silliness, but, hey, one has to do more than clean the microwave in the snack area or hunt for the meeting room on the campus. (Where did the alleged interpersonal abuses take place? Is there a Bing Map for that?)
Reality 8. What Runs Windows 11?
Now if there is a super sized red herring being dragged over the SolarWinds’ misstep it is this one: Will my PC run Windows 11? Lame? You bet, but we are in the distraction business, not in the useful software business. Subscribe and pay now for the greatness which may not run on your PC, you computer dolt. But why? Maybe SolarWinds’ stuff saying, “Look here, not there.”
You have to navigate to the distinguished lecturer’s cited post for Realities of 2, 3, 4, 6, and 7. There are more Dusies too.
Now the circle back: SolarWinds’s misstep is still with us and Microsoft. At least I can understand Windows 11 as a quick and dirty distraction. Can users?
Stephen E Arnold, December 21, 2021
Semantics Have Become an Architecture: Sounds Good but
December 17, 2021
Semantic Architecture Is A Big Data Cash Grab
A few years ago, big data was the hot topic term and in its wake a surge of techno babble followed. Many technology companies develop their own techno babble to peddle their wares, while some of the jargon does have legitimate means to exist. Epiexpress has the lowdown on one term that does have actual meaning: “What Is Semantic Architecture, And How To Build One?”
The semantic data layer is a system’s brain or hub, because most data can be found through a basic search. It overlays the more complex data in a system. Companies can leverage the semantic layer for business decisions and discover new insights. The semantic layer uses an ontology model and enterprise knowledge graph to organize data. Before building the architecture, one should consider the following:
“1. Defining and listing the organizational needs
When developing a semantic enterprise solution, properly-outlined use cases provide the critical questions that the semantic architecture will answer. It, in turn, gives a better knowledge of the stakeholders and users, defines the business value, and facilitates the definition of measurable success criteria.
2. Survey the relevant business data
Many enterprises possess a data architecture founded on data warehouses, relational databases, and an array of hybrid cloud systems and applications that aid analytics and data analysis abilities
In such enterprises, employing relevant unification processes and model mapping practices based on the enterprise’s use cases, staff skill-sets, and enterprise architecture capabilities will be an effective approach for data modeling and mapping from source systems.3. Using semantic web standards for ensuring governance and interoperability
When implementing semantic architecture, it is important to use semantic technology such as graph management apps to be middleware. Middleware acts as organizational tools for proper metadata governance. Do not forger that users will need tools to interact with the data, such as enterprise search, chatbots, and data visualization tools.
Semantic babble?
Whitney Grace, December 17, 2021
Will The Google PR Carpet Bombing about AI Deliver Victory?
December 13, 2021
The short answer is, “Yes.” The mass market and niche content marketing is the obvious part of the program. There are some less obvious characteristics which warrant some attention. Run a query for Snorkel. What does one get? Here’s my result page on December 9, 2021:
The pictures are for scuba snorkels. But the first hit is not an advertisement, at least one that an entity compensated the Google to publish. The number one with a bullet is the Snorkel AI company. There you go. That’s either great performance from the circa 1998 algorithm, spectacular SEO, or something of interest to some entity at the Google.
What happens if I run a query for “AI”? Here’s what I saw on December 9, 2021
Amazon bought an ad and linked to its free AI solutions. The number one hit is:
The Google.
So what? Nudging, great SEO, some engineer’s manicured hand on the steering wheel?
I do know that most people have zero idea about smart software. What’s my source for this glittering generality? Navigate to “Survey Suggests 84% of Americans Are Illiterate about AI — So Here’s a Quiz to Test Your Own AI IQ.”
Those nudges, the PR, and the search results may amount to something; for example, framing, reformation, and dissemination of what the Google magical “algorithms” find most relevant. Google wants to win the battle for its approach to really good training data for machine learning. Really wants to win.
Stephen E Arnold, December 13, 2021
Google into Healing
December 9, 2021
How does one explain healing? For the Google the answer is with a Year in Search video. “Google Is All about Healing in its Year In Search Video for 2021” reports:
According to Google Trends, searches on climate change have hit a record high number in 2021. Google’s Year In Search video has captured that with the search of ‘how to help our planet.’ Other positive search examples like ‘ways to help your community’ and ‘how to be yourself’ have also captured the attention of Google and are included in the video with appropriate examples.
Okay, climate change. And Covid. Google even shares some of its user data, according to the write up:
The Year In Search video is accompanied by an interactive site where you can see all of the top global Google searches month-by-month in nine different categories. Of course, you can also check out Google Trends where you can see data for a specific country or region.
What about repairing that relationship with Dr. Timnit Gebru and others who raised questions about Google’s AI methods and motives?
Oh, that’s an unfair question. No healing there. Plus only the really important information is worthy of the Year in Search video.
Stephen E Arnold, December 9, 2021
Smart Software Is Innovative: Two Marketing Examples, You Doltish Humanoids
December 7, 2021
I zipped through the news releases, headlines, and emails which accumulate in my system. I spotted two stories. Each made the case that smart software — created by humans — is discerning information humans had not previously known or had revealed. This assertion has some interesting implications. There are issues associated with Kurt Gödel-type thinking and the Star Trek think which has launched billions of smart phones.
Here’s the first article. It’s called “AI Is Discovering Patterns in Pure Mathematics That Have Never Been Seen Before.” That’s a clickable title. The write up asserts:
In a newly published study, a research team used artificial intelligence systems developed by DeepMind, the same company that has been deploying AI to solve tricky biology problems and improve the accuracy of weather forecasts, to unknot some long-standing math problems.
DeepMind is pretty much Google. Google is a fan of Snorkel methods. These procedures use minimal training and then let math learn. The outputs are — well — Googley. You know solving the big problems of life like online advertising, reducing the costs of alternative methods of training smart software, and dealing with the legal hassles associated with the alleged “AI cabal” and Timnit Gebru.
The second article is “AI Generates Hypotheses Human Scientists Have Not Thought Of.” The write up says:
One of the benefits of machine learning systems is the way that they can look for patterns and scenarios that programmers didn’t specifically code them to look out for – they take their training data and apply the same principles to new situations. The research shows that this sort of high-speed, ultra-reliable, large-scale data processing can act as an extra tool working with mathematicians’ natural intuition. When you’re dealing with complex, lengthy equations, that can make a significant difference.
What’s interesting is that the write up does not link the researchers with DeepMind. But it appears that the mathematician András Juhász has worked with Googley DeepMind. See “DeepMind AI Collaborates with Humans on Two Mathematical Breakthroughs.”
The first item cited in this blog post appeared on December 4, 2021. The second appeared in October 2021.
My thought is that the Google is injecting rah rah messages about its Snorkel-type approach into highly regarded publications. My hope is that Dr. Timnit Gebru’s and her work gets equal coverage.
Why? The Google wants to be the big dog in certain smart software dog sled pulling. But inbreeding has its downsides; including, bias. PR firms and rah rah marketers are not sensitive to such mathematical oddities as “drift” in my experience. From peer reviewed articles to the open market for “great ideas”, information marches forward on the wheels of propaganda and factual reformation it does, it does.
Stephen E Arnold, December 6, 2021
What Company Is the Leader in Search Powered by Artificial Intelligence? One Answer May Surprise You. It Did Me.
November 30, 2021
Give up? The answer is Lucidworks, “the leader in AI-powered search.” You can get the gull story from Unite.ai and the article “Will Hayes, CEO of Lucidworks – Interview Series.” What’s “AI”? I don’t know, and the answer is not provided from @IAmWillHayes’ comments. What’s “search”? I don’t know because no specific definition is provided. (Search is a blanket word, covering everything from the open source Lucene in policeware solutions to whiz-bang, patented real time methods for time series data from Trendalyze. And we must not forget the generous offerings of “search” for eDiscovery, product supplier data, chemical structures, streaming video files, code libraries, and mysterious content like the interesting information in encrypted Signal and Telegram interactions. Search at Lucidworks is different it seems.
I noted this statement:
Lucidworks takes mission-critical business problems and solves them with search.
I assume that Lucidworks is disconnected from Dassault Systèmes search based applications approach. There is a 2011 book titled “Search Based Applications: At the Confluence of Search and Database Technologies.” The author is Dr. Gregory Grefenstette with assistance from Laura Wilber. The Lucidworks’ assertion struck me as one more example of marketing hoo hah disconnected from what came before. At least, the Dassault technology was original, not a recycling of open source software.
Here’s another statement offered as an original insight:
Lucidworks offers products and applications for commerce, customer service, and the workplace that use AI and machine learning to solve search. Fusion, our flagship product, uses AI extensively through every stage of enriching data—during ingest and at query time, for understanding user intent, and personalizing results that match that intent.
I want to point out that the Paris-based firm Polyspot used almost the exact same language (both French and English) to describe the company’s approach to information access. Here’s what Bloomberg says about the now repositioned company:
PolySpot SAS develops and publishes enterprise software. The Company’s products offer search and information access solutions designed to improve business and ensure that companies can access the data they need, regardless of their structure, format or origin. PolySpot markets its products internationally.
Dis Yogi Berra or Yogi Bear say: “It’s déjà vu all over again.” I go with the cartoon bear. The aphorism applies to Lucidworks in my opinion.
Lucidworks also does chatbots, fits into the connected experience cloud (CXC), and compounds “value.” Okay. The company, according to @IAmWillHayes, is “leader in next-generation search solutions and we have an exciting roadmap of cloud products coming in the near future.”
I wonder what outfits like Algolia, Coveo, Sphinx Search, and even the heroic X1 think about this assertion. What will Google’s revolving door search experts make of Lucidworks’ bold assertion? What about the crafty laborers in AWS search vineyards who watch the competitors gun for the Bezos bulldozer? What about the innovators working on the somewhat frightening IBM search solution? Maybe Microsoft will just pull a “Fast Search” and buy Lucidworks to beef up its incredible array of finding systems?
My hunch is that Lucidworks has to deal with its backers who want their money back plus some upside. Mix in the harsh market realities of many options, some free or low cost, and others bundled with purpose built solutions like Voyager Labs’ software and what do you get?
I am not sure about your answer. My answer is, “Recycling marketing lingo, ideas, and assertions which are decades old?” Will AI, machine learning, and CXC pull a rabbit from the search magician’s hat?
Maybe. But the investors who have injected more than $200 million into the company may want more than a magic show. And what is “search” and “AI” anyway? Solr with a new outfit from Amazon?
Stephen E Arnold, November 30, 2021
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