Google Hits Microsoft in the Nose: Alleges Security Issues
April 15, 2022
The Google wants to be the new Microsoft. Google wanted to be the big dog in social media. How did that turn out? Google wanted to diversify its revenue streams so that online advertising was not the main money gusher. How did that work out? Now there is a new dust up, and it will be more fun than watching the antics of coaches of Final Four teams. Go, Coach K!
The real news outfit NBC published “Attacking Rival, Google Says Microsoft’s Hold on Government Security Is a Problem.” The article presents as actual factual information:
Jeanette Manfra, director of risk and compliance for Google’s cloud services and a former top U.S. cybersecurity official, said Thursday that the government’s reliance on Microsoft — one of Google’s top business rivals — is an ongoing security threat. Manfra also said in a blog post published Thursday that a survey commissioned by Google found that a majority of federal employees believe that the government’s reliance on Microsoft products is a cybersecurity vulnerability.
There you go. A monoculture is vulnerable to parasites and other predations. So what’s the fix? Replace the existing monoculture with another one.
That’s a Googley point of view from Google’s cloud services unit.
And there are data to back up this assertion, at least data that NBC finds actual factual; for instance:
Last year, researchers discovered 21 “zero-days” — an industry term for a critical vulnerability that a company doesn’t have a ready solution for — actively in use against Microsoft products, compared to 16 against Google and 12 against Apple.
I don’t want to be a person who dismisses the value of my Google mouse pad, but I would offer:
- How are the anti ad fraud mechanisms working?
- What’s the issue with YouTube creators’ allegations of algorithmic oddity?
- What’s the issue with malware in approved Google Play apps?
- Are the incidents reported by Firewall Times resolved?
Microsoft has been reasonably successful in selling to the US government. How would the US military operate without PowerPoint slide decks?
From my point of view, Google’s aggressive security questions could be directed at itself? Does Google do the know thyself thing? Not when it comes to money is my answer. My view is that none of the Big Tech outfits are significantly different from one another.
Stephen E Arnold, April 15, 2022
AI Helps Out Lawyers
April 11, 2022
Artificial intelligence algorithms have negatively affected as many industries as they have assisted. One of the industries that has benefitted from AI is law firms explains Medium in: “How Artificial Intelligence Is Helping Solve The Needs Of Small Law Practitioners.” In the past, small law firms were limited in the amount of cases they could handle. AI algorithms now allow small law practices to compete with the larger firms in all areas of laws. How is this possible?
“The latest revolution in legal research technology ‘puts a lawyer’s skill and expertise in the driver’s seat…’ New artificial intelligence tools give lawyers instant access to vast amounts of information and analysis online, but also the ability to turn that into actionable insights. They can be reminded to check specific precedents and the latest rulings, or be directed to examine where an argument might be incomplete. That leaves the lawyers themselves to do what only they can: think, reason, develop creative arguments and negotiation strategies, provide personal service, and respond to a client’s changing needs.”
Lawyers used to rely on printed reference materials from databases and professional publications. They were limited on the number of hours in a day, people, and access to the newest and best resources. That changed when computers entered the game and analytical insights were delivered from automated technology. As technology has advanced, lawyers can cross reference multiple resources and improve legal decision making.
While lawyers are benefitting from the new AI, if they do not keep up they are quickly left behind. Lawyers must be aware of current events, how their digital tools change, and how to keep advancing the algorithms so they can continue to practice. That is not much different from the past, except it is moving at a faster rate.
Whitney Grace, April 11, 2022
Why Be Like ClearView AI? Google Fabs Data the Way TSMC Makes Chips
April 8, 2022
Machine learning requires data. Lots of data. Datasets can set AI trainers back millions of dollars, and even that does not guarantee a collection free of problems like bias and privacy issues. Researchers at MIT have developed another way, at least when it comes to image identification. The World Economic Forum reports, “These AI Tools Are Teaching Themselves to Improve How they Classify Images.” Of course, one must start somewhere, so a generative model is first trained on some actual data. From there, it generates synthetic data that, we’re told, is almost indistinguishable from the real thing. Writer Adam Zewe cites the paper‘s lead author Ali Jahanian as he emphasizes:
“But generative models are even more useful because they learn how to transform the underlying data on which they are trained, he says. If the model is trained on images of cars, it can ‘imagine’ how a car would look in different situations — situations it did not see during training — and then output images that show the car in unique poses, colors, or sizes. Having multiple views of the same image is important for a technique called contrastive learning, where a machine-learning model is shown many unlabeled images to learn which pairs are similar or different. The researchers connected a pretrained generative model to a contrastive learning model in a way that allowed the two models to work together automatically. The contrastive learner could tell the generative model to produce different views of an object, and then learn to identify that object from multiple angles, Jahanian explains. ‘This was like connecting two building blocks. Because the generative model can give us different views of the same thing, it can help the contrastive method to learn better representations,’ he says.”
Ah, algorithmic teamwork. Another advantage of this method is the nearly infinite samples the model can generate, since more samples (usually) make for a better trained AI. Jahanian also notes once a generative model has created a repository of synthetic data, that resource can be posted online for others to use. The team also hopes to use their technique to generate corner cases, which often cannot be learned from real data sets and are especially troublesome when it comes to potentially dangerous uses like self-driving cars. If this hope is realized, it could be a huge boon.
This all sounds great, but what if—just a minor if—the model is off base? And, once this tech moves out of the laboratory, how would we know? The researchers acknowledge a couple other limitations. For one, their generative models occasionally reveal source data, which negates the privacy advantage. Furthermore, any biases in the limited datasets used for the initial training will be amplified unless the model is “properly audited.” It seems like transparency, which somehow remains elusive in commercial AI applications, would be crucial. Perhaps the researchers have an idea how to solve that riddle.
Funding for the project was supplied, in part, by the MIT-IBM Watson AI Lab, the United States Air Force Research Laboratory, and the United States Air Force Artificial Intelligence Accelerator.
Cynthia Murrell, April 8, 2022
Ethical Behavior and the Ivy League: Redefinition by Example
April 5, 2022
First, MIT and its dalliance with the sophisticated Jeffrey Epstein. Then there was Harvard and its indifference to an allegation of improper interpersonal behavior. Sordid details abound in this allegedly accurate report. Now Yale. The bastion of “the dog”, the football game, Skull and Bones, etc., etc.
“A Former Yale Employee Admits She Stole $40 Million in Electronics from the University” makes clear that auditing, resource management, and personnel supervision are not the esteemed institution greatest strengths.
I gave a talk at Yale a decade ago. The subject was Google, sparked because one of the Yale brain trust found my analysis interesting. Strange, I thought, at the time. No one else cares about my research about Google’s systems and methods. I showed up and was greeted as though I was one of the gang. (I wasn’t.)
At dinner someone asked me, “Where did you get your PhD?” I replied with my standard line: “I don’t have a PhD. I quit to take a job at Halliburton Nuclear.” As you might imagine, the others at the dinner were not impressed.
I gave my lecture and no one — absolutely none of the 100 people in the room — asked a question. No big deal. I am familiar with the impact some of my work has elicited. One investment banker big wheel threw an empty Diet Pepsi can at me after I explained how the technology of CrossZ (a non US analytics company) preceded in invention the outfit the banked just pumped millions into. Ignorance is bliss. Same at Yale during and after my lecture.
Has Yale changed? Seems to be remarkably consistent: Detached from the actions of mere humans, convinced of a particular world view, and into the zeitgeist of being of Yale.
But $40 million?
An ethical wake up call? Nope, hit the snooze button.
Stephen E Arnold, April 5, 2022
Vimeo: A Case Study in Management Desperation?
March 19, 2022
Video is expensive. Bandwidth is a killer. Even storage is a problem at scale. Then there is marketing, customer acquisition, customer retention, and paying those who deserve the big bucks. Vimeo wants to generate revenue, and it has been struggling to be upfront about its predicament: Money.
A couple of years ago, I put my DarkCyber videos on Vimeo. I was curious about the platform. I think I had a dozen or so 12 minute programs on the service. I received an email explaining that because I was a commercial customer, I had to pay a lot of money. I liked that angle crafted by 20 somethings sitting in a cramped, uncomfortable conference room figuring out who was commercial and who was not.
My criteria were:
- I was retired
- My videos contained zero advertising
- I made the programs available to those attending my lectures at FLETC, the ISS Telestrategies’ conferences, and the National Cyber Crime Conferences, among others
- I don’t sell anything any more.
The Vimeo automated system informed me that I had to pay up or have my videos deleted. I cancelled my account and deleted the videos. I mentally noted that Vimeo was floundering. Where is that life preserver? Ooops. Not near me.
I read “Vimeo Is Sorry, and Here’s How It’s Changing.” The write up dances around the central problem of Vimeo: Making money. There’s hand waving from Vimeo management. There’s information about Vimeo’s contradictory statements about “policies.” There’s information about exceptions for special people.
Enough. Vimeo is stuck. Vimeo’s management is apparently rudderless. And most important, I find the firm’s splashing around in the pool mildly amusing. Will it gulp water and drown? Will it become the new Rumble or BitChute? Will the firm’s decisive management team knock YouTube for a loop.
Splish. Splash. Vimeo is taking a bath and there is no party going on.
Stephen E Arnold, March 25, 2022
Online Gambling in Brazil: Pinga and Soccer Fun
March 8, 2022
In the 1950s, my family lived in Brazil. Our city was Campinas. At that time, it was an okay, sort of an out-of-the-way place. I recall a couple of things from my childhood. Mr. Ricci, a family friend, pointed out individuals who drank pinga at a tiny bar, took a couple of staggering steps, and leaned against a wall until the shock wave subsided. Pinga (now called cachaça or caninha) was cheap and packed an alcohol content around 38 to 48 percent. I also recall street vendors with stands papered with lottery tickets. The idea was that Brazilians really believed that a big pay day awaited the lucky gambler. Mr. Ricci, as I recall, said, “Own the lottery. Don’t play the lottery.” After watching the pinga lovers and the lottery ticket buyers, I carried away a life long aversion to alcohol and gambling. Pretty silly, right?
If a young child about 11 years old could figure out that many Brazilians liked gambling and distilled sugar cane, one would think others would too. Nope. Just do a couple of carnivals or check out the action outside the stadium when Palmeiras plays Fluminense.
I thought about my memories of Campinas as I read “Brazil’s Move to Legalize Sports Gambling Is Fueling a Digital Gold Rush.” The article states:
With the help of Eccles, the Brazilian startup followed a game plan similar to FanDuel’s and convinced regulators that fantasy gaming should be considered a game of skill, rather than luck. Now, armed with 1.6 million users in Brazil, Rei do Pitaco is ready to move into traditional sports gambling when it becomes fully regulated. [Emphasis added]
Yep, skill. Just like card counting or being James Bond at the baccarat table.
Several observations:
- Digitizing gambling puts Teflon on exploiting some people who bet on many things
- Pinga lubricates decision making for some people
- Organized operators can put a finger on the scales in some athletic contests
Net net: Digitizing lowest common denominator activities is a way for some to demonstrate skill. Sure enough.
Stephen E Arnold, March 7, 2022
Switzerland: Clean Cows and Clean Money Mostly
March 1, 2022
Here is yet another inventory of the rich and infamous. The Irish Times reports, “Vast Leak Exposes How Credit Suisse Served Strongmen and Spies.” This latest financial data leak lists 18,000 Credit Suisse accounts from the 1940s into the 2010s, though contains no data on current accounts. In keeping with the alliterative tradition established with 2016’s Panama Papers and continuing through 2017’s Paradise Papers and last year’s Pandora Papers, this roster has been dubbed Suisse Secrets. We learn:
“Among the people listed as holding amounts worth millions of dollars in Credit Suisse accounts were King Abdullah II of Jordan and the two sons of former Egyptian strongman Hosni Mubarak. Other account holders included sons of a Pakistani intelligence chief who helped funnel billions of dollars from the United States and other countries to the mujahedeen in Afghanistan in the 1980s and Venezuelan officials ensnared in a long-running corruption scandal. The leak shows that Credit Suisse opened accounts for and continued to serve not only the ultra-wealthy but also people whose problematic backgrounds would have been obvious to anyone who ran their names through a search engine. Swiss banks have long faced legal prohibitions on taking money linked to criminal activity, said Daniel Thelesklaf, the former head of Switzerland’s anti-money laundering agency. But, he said, the law generally hasn’t been enforced.”
You don’t say. Of course, Swiss banks are famous for their high security, so this leak was quite the feat. A whistleblower sent the data to German newspaper Süddeutsche Zeitung over a year ago. That paper has since then shared the list with the Organized Crime and Corruption Reporting Project and 46 other news organizations around the world. None of those outlets were based in Switzerland, however, since a 2015 Swiss law prohibits the publication of articles based on internal bank data. The article also notes:
“Among the biggest revelations is that Credit Suisse continued to do business with customers even after bank officials flagged suspicious activity involving their finances. One account holder was Venezuela’s former vice minister of energy, Nervis Villalobos. Employees in Credit Suisse’s compliance department had reason to be wary of doing business with him.”
See the write-up for more on Villalobos and other noteworthy examples, including several Middle East officials.
A Credit Suisse spokesperson notes many of the accounts date back to “a time where laws, practices and expectations of financial institutions were very different from where they are now.” Indeed. Since its founding in 1856 until fairly recently, the institution was largely untouchable. As the public’s tolerance for shady dealings has waned, however, the bank has faced more scrutiny. We are reminded that, in 2014, it pled guilty to helping Americans file false tax returns; in 2016, it forked over $5.3 billion to settle allegations about its mortgage-backed securities marketing; and last year it agreed to pay $475 million to authorities in the US and the UK over a Mozambique kickback and bribery scheme. Of course, those are small prices to pay compared to managing more than $100 billion in questionable funds. Currently, an ongoing trial in Switzerland sees Credit Suisse accused of helping drug traffickers launder millions of euros and the US Justice Department and Senate finance committee are investigating whether US citizens are still hiding millions within its hallowed vaults. What are the odds of that?
Cynthia Murrell, March 1, 2022
Misunderstanding a Zuck Move
February 4, 2022
I read some posts — for instance, “Facebook Just Had Its Most Disappointing Quarter Ever. Mark Zuckerberg’s Response Is the 1 Thing No Leader Should Ever Do” — suggesting that Mark Zuckerberg is at fault for his company’s contrarian financial performance. The Zucker move is a standard operating procedure in a high school science club. When caught with evidence of misbehavior, in my high school science club in 1958, we blamed people in the band. We knew that blaming a mere athlete would result in a difficult situation in the boys’ locker room.
Thus it is obvious that the declining growth, the rise of the Chinese surveillance video machine, and the unfriended Tim Apple are responsible for which might be termed a zuck up. If this reminds you of a phrase used to characterize other snarls like the IRS pickle, you are not thinking the way I am. A “zuck up” is a management action which enables the world to join together. Think of the disparate groups who can find fellow travelers; for example, insecure teens who need mature guidance.
I found this comment out of step with the brilliance of the lean in behavior of Mr. Zuckerberg:
Ultimately, you don’t become more relevant by pointing to your competitors and blaming them for your performance. That’s the one thing no company–or leader–should ever do.
My reasoning is that Mr. Zuckerberg is a manipulator, a helmsman, if you will. Via programmatic methods, he achieved a remarkable blend of human pliability and cash extraction. He achieved success by clever disintermediation of some of his allegedly essential aides de camp. He achieved success by acquiring competitors and hooking these third party mechanisms into the Facebook engine room. He dominated because he understood the hot buttons of Wall Street.
I expect the Zuck, like the mythical phoenix (not the wonderful city in Arizona) to rise from the ashes of what others perceive as a failure. What the Zuck will do is use the brilliant techniques of the adolescent wizards in a high school science club to show “them” who is really smart.
Not a zuck up.
Stephen E Arnold, February 4, 2022
Cloud Computing: Is There a Free Lunch Paid for by Amazon, Google, and Microsoft?
February 3, 2022
What have those cloud computing giants done for organizations lately? Here are some thought starters:
- Argued that it is better, faster, and cheaper to outsource anything involving computing to the fluffy clouds?
- Pushed for low code and no code solutions so that anyone can create applications and not get involved with expensive, unreliable, and uncooperative technical employees who don’t understand what an MBA or art history major needs now?
- Offered free or low cost entry fees which bedazzle the customer who is unable to see the telecommunications-style pricing methods like “cross a threshold and pay twice the cost stated on Schedule C, subparagraph 4”?
- A false narrative about ease of use, the reliability of pre-packaged data sets, and off-the-shelf components anyone can configure while waiting for a latte at a Starbuck’s in Normal, Illinois?
Enough questions. Read “More Than a Third of Firms Hit by Unexpected Cloud Costs.” Here’s a sentence from the write up which I found interesting:
Lack of transparency among providers is consistently cited as a problem.
Is this a nice way of saying, “These cloud people are doing the Theranos thing?”
Do you answer yes or no? Companies have difficulty hiring, retaining, and managing technology people. Costs for technology are often uncontrollable by traditional mechanisms crafted in less-illuminated times. Sales are easy because some organizations are believing what the cloud vendors say.
I am in the yes camp.
Stephen E Arnold, February 2, 2022
Synthetic Data: The Future Because Real Data Is Too Inefficient
January 28, 2022
One of the biggest problems with AI advancement is the lack of comprehensive datasets. AI algorithms use datasets to learn how to interpret and understand information. The lack of datasets has resulted in biased aka faulty algorithms. The most notorious examples are “racist” photo recognition or light sensitivity algorithms that are unable to distinguish dark skin tones. VentureBeat shares that a new niche market has sprung up: “Synthetic Data Platform Mostly AI Lands $25M.”
Mostly AI is an Austria startup that specializes in synthetic data for AI model testing and training. The company recently acquired $25 million in funding from Molten Ventures with plans to invest the funds to accelerate the industry. Mostly AI plans to hire more employees, create unbiased algorithms, and increase their presence in Europe and North America.
It is difficult for AI developers to roundup comprehensive datasets, because of privacy concerns. There is tons of data available for AI got learn from, but it might not be anonymous and it could be biased from the get go.
Mostly AI simulates real datasets by replicating the information for data value chains but removing the personal data points. The synthetic data is described as “good as the real thing” without violating privacy laws. The synthetic data algorithm works like other algorithms:
“The solution works by leveraging a state-of-the-art generative deep neural network with an in-built privacy mechanism. It learns valuable statistical patterns, structures, and variations from the original data and recreates these patterns using a population of fictional characters to give out a synthetic copy that is privacy compliant, de-biased, and just as useful as the original dataset – reflecting behaviors and patterns with up to 99% accuracy.”
Mostly AI states that their platform also accelerates the time it takes to access the datasets. They claim their technology reduces the wait time by 90%.
Demands for synthetic data are growing as the AI industry burgeons and there is a need for information to advance the technology. Efficient, acceptable error rates, objective methods: What could go wrong?
Whitney Grace, January 27, 2022
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