AI: Big Hat, Some Cattle
March 17, 2020
Andreessen-Horowitz recently published the article: “The New Business Of AI (And How It’s Different From Traditional Software) that pulls back the curtain on AI startups. Locklin On Science delves further into AI startups with the aptly named post: “Andreessen-Horowitz Craps On ‘AI’ Startups From A Great Height.” AI startups are similar to other startups in that there is a lot of hype over a subpar product.
The biggest mistake people are making is that AI is really machine learning. Machine learning is the basis for AI and the terms should not be used interchangeably. Another problem is that AI can be treated like traditional software, however, this is far from the truth. AI software requires a cloud infrastructure which has mounds of hidden and associated costs. Also businesses believe once they launch an AI project, then humans are out of the equation. Nope!
“Everyone in the business knows about this. If you’re working with interesting models, even assuming the presence of infinite accurately labeled training data, the “human in the loop” problem doesn’t ever completely go away. A machine learning model is generally “man amplified.” If you need someone (or, more likely, several someone’s) making a half million bucks a year to keep your neural net producing reasonable results, you might reconsider your choices. If the thing makes human level decisions a few hundred times a year, it might be easier and cheaper for humans to make those decisions manually, using a better user interface.”
AI or machine learning startups also are SaaS companies disguised as a software business. They might appear to offer a one time out-of-the-box solution that only requires the occasional upgrade, but that is a giant fib. Machine learning can have a huge ROI, but all the factors need to be weighed before it is implemented. Machine learning and AI technology is the most advanced software on the market, thus the most expensive. It might be better to invest in better, experienced software and humans before trying to step foot into the future.
Whitney Grace, March 17, 2020
Click Money from Google: A Digital Dodo?
March 15, 2020
At the beginning of 2020, Google released its 2019 end of year financial report and some amazing surprises were revealed. ZDNet has the details in the article, “The Mysterious Disappearance Of Google’s Click Metric.” For the first time since acquiring YouTube, Google shared revenue for YouTube and its cloud IT business, but they removed information about how much money the company made from clicks or the Cost-per-Click (CPC) plus its growth.
What does this mean for Google? It is even more confusing that the Wall Street analysts did not question the lack of information. The truth is something that Google might not want to admit, but the key to their revenue is dying and they are not happy.
“Google has a rapidly deflating advertising product, sometimes 29% less revenue per click, every quarter, year-on-year, year after year…. Every three months Google has to find faster ways of expanding the total number of paid clicks by as much as 66%. How is this a sustainable business model? There is an upper limit to how much more expansion in paid links can be found especially with the shift to mobile platforms and the constraints of the display. And what does this say about the effectiveness of Google’s ads? They aren’t very good and their value is declining at an astounding and unstoppable pace.”
Google might start placing more ads on its search results and other services. It sounds like, however, Google will place more ineffective ads in more places. Google’s ads have eroded efficiency for years, plus there is the question of whether more bots, less humans are clicking these ads. Clicks do not create brands and most people ignore ads. Don’t you love ads?
Whitney Grace, March 15, 2015
Deep Learning Startups May Encounter a Gotcha
March 14, 2020
Though fragmented, the deep learning AI market is growing rapidly. Anyone wishing to launch (or invest in) such a firm may want to check out Analytics India Magazine’s article, “Common Pitfalls that the Deep Learning Startups Fail to Recognise.” Writer Sameer Balanganur describes prevalent missteps under these headings: Not Investing Enough in Data and Powerful Processors, Not Accounting for the Cloud Charges, Expensive Data Cleansing, The Edge Cases, and Hiring the Right People.
The part that struck me was this description under Expensive Data Cleansing, as it Illustrates something many fail to understand:
“Training the model nowadays to achieve the state-of-the-art results [still] involves a lot of manual cleaning and labelling of large datasets. And the process of manual cleaning and labelling is expensive and is one of the largest barriers the deep learning startups face. … Although as time passes, the AI systems are moving towards complete automation, which will significantly reduce the cost. However, these AI-based automation applications still need human intervention for years to come. Even if there is full automation achieved, it’s not clear how much the margin of cost and efficiency will improve, so this becomes a matter of whether one should invest towards processes like drift learning and active learning to enhance the ability.
We noted:
“Not only expensive, the human intervention sometimes hinders the system’s creativity, but they might also do it by selecting what is essential for an algorithm to process or not using deep learning for a problem it can easily solve. Many times, deep learning is seen as overkill for many problems. The costs incurred by human intervention and cloud are interdependent. Reducing one means an increase in another.”
AI investment could be quite profitable, if one considers carefully. As always, look before you leap. See the write-up for more details.
Cynthia Murrell, March 14, 2020
Amazon Versus Microsoft: A Jedi Fight Development
March 13, 2020
DarkCyber spotted this story on the BBC Web site: “Pentagon to Reconsider Jedi $10bn Cloud Contract.” Since we are in rural Kentucky, the intrepid team does not know if the information in the Beeb’s write up is accurate. The factoids are definitely interesting. The story asserts:
The US Department of Defense is to “reconsider” its decision to award a multi-billion dollar cloud contract to Microsoft over Amazon.
The story points out that Microsoft is confident that its Azure system will prevail. Amazon, on the other hand, is allegedly pleased.
What’s at stake?
- Money
- A hunting license for other government contracts
- Implicit endorsement of either AWS or Azure
- Happy resellers, integrators, and consultants
- Ego (maybe?)
When will JEDI be resolved? Possibly in the summer of 2020.
Stephen E Arnold, March 13, 2020
Google Stadia: Google Wood or Just Recycled Cardboard?
March 12, 2020
DarkCyber does not play games. Sure, there are some young-at-heart DarkCyber games, but I ignore them. One of these hard-working individuals spotted “Google Stadia Hits an All-Time Low With This Embarrassing Tweet.” I am not much of a tweeter.
Apparently someone at Google does read tweets and noted one that contained this high school cheer / acrostic thing:
Note that there is no game for I.
A Googler replied, with a tweet, of course: “Why would you bring attention to this?”
I assume the answer is one of these choices:
a. It’s millennial or Gen X, Y, or Z humor
b. Stadia is not performing
c. Someone actually cares about Stadia to try to spell a word using the first letter of games on the service
d. There is a game on Stadia which uses the “what’s up” emoji instead of words.
The write up states:
Clearly, whoever is in charge of the Google Stadia Twitter account has stopped caring. It’s probably for the best since everyone else stopped caring about it months ago.
Google Stadia seemed doomed from the start, and things haven’t gotten much better. It lacks games, has a terrible monetization system, and generally isn’t all that convenient. It even pales in comparison to other similar systems like GeForce Now and Project xCloud. If the state of their social media is anything to go by, Google is already well on its way to just checking out and letting the system die. It’s hard to blame them. So far, Google Stadia seems like it was just a horrible idea.
DarkCyber has little insight to how things work at Google. I would surmise that whoever worked on Stadia has made an effort to catch on with a hot project team. No, not solving Death. Solving Stadia, however, may be a comparable challenge.
Stephen E Arnold, March 12, 2020
Amazon Versus Microsoft: JEDI in Play?
March 7, 2020
DarkCyber spotted a story in Stars and Stripes titled “Judge Says Amazon Likely to Succeed on Key Argument in Pentagon Cloud Lawsuit.” The source appears to be the Bezos-owned Washington Post. That fact may provide some context for the story.
The main point in the write up seems to be:
A federal judge has concluded that a bid protest lawsuit brought by Amazon over President Donald Trump’s intervention in an important Pentagon cloud computing contract “is likely to succeed on the merits” of one of its central arguments, according to a court document made public Friday [March 6, 2020].
The article states:
In an opinion explaining her reasoning, Campbell-Smith sided with Amazon’s contention that the Pentagon had made a mistake in how it evaluated prices for competing proposals from Amazon and Microsoft. She also concluded that the mistake is likely to materially harm Amazon, an important qualifier for government contract bid protests.
What’s missing from this story? Detail for one thing.
Several observations:
- Planners for the JEDI program are likely to experience uncertainty
- Regardless of the ultimate decision, time to implement newer systems is being lost
- The cost of the procurement process for JEDI will climb and, at some point, may become larger than the program itself.
Net net: Government procurement remains an interesting and impactful process. Procurement just keeps grinding its procedural mechanisms, delivering “efficiency.”
Stephen E Arnold, March 7, 2020
Honeywell: The Quantum Computing Thermostat Company
March 5, 2020
Yeah, that’s a bit of rural Kentucky humor. Honeywell is in four businesses and a fifth apparently has been added: Quantum computing. If you think Honeywell and recall the user friendly thermostat in your home, you are not thinking about the future, government contracts, breaking computing barriers, and putting technology pretenders like IBM, Google, and dozens of other companies in their place.
The Honeywell he CommercialPRO 7000 Programmable Thermostat is fantastic, according to Honeywell. For an entertaining experience, ask a friend to set the temperature for 4 pm today. This is a TikTok viral video DarkCyber believes.
To refresh your memory, DarkCyber wants to point out that Honeywell was once based in Wabash, Indiana. The firm generates about $40 billion a year from:
- Aerospace
- Building technologies
- Materials
- Safety productivity systems.
Now Honeywell is in the quantum computing business, according to the Wall Street Journal, March 4, 2020, edition. You may be able to locate the story behind a paywall at this link.
Honeywell has enjoyed a number of government contracts, and the firm is one of the leaders in smart controls and weapons management technology. In 1955, Honeywell teamed with Raytheon in order to compete with IBM. By the mid 1960s, Honeywell was one of the Snow White and the Seven Dwarfs of Computing. (Unfamiliar with this bit of digital history, Bing or Google may turn up some relevant hits, but I would recommend microfilm of the Minneapolis newspapers from this era. Don’t let your Bermuda shorts get in a bunch as you explore the innovations of Burroughs, Control Data Corp., GE, NCR, RCA, and my personal fave Univac.
Honeywell does a significant amount of computing and software/systems development. The firms owns a number of high technology business; for example, a radiation detection firm and has a stake in Zapata Computing.
Zapata says here:
We are the deepest bench of quantum scientists in the industry. Our founders helped create the field of near-term quantum algorithms including the invention of VQE, the progenitor of variational quantum algorithms.
The company’s approach relies on quantum charge coupled device (QCCD) architecture. The approach uses a technology called “trapped ions.” The idea is that useful work can be done due to leveraging mid circuit measurement. The idea is to insert a dynamic “if” based on the state of the calculation at a point in time. IonQ and Alpine Quantum Technologies also use the method. For some details, do a patent search for “trapped ion”. The background of US5793091A (assigned to IBM) provides some helpful information.
What business opportunities does Honeywell envision for its quantum computer? Here’s a selection gleaned from the PR blitz Honeywell launched a short time ago:
- Landing more customers like JPMorgan, Chase, and Company
- Speeding up financial calculations
- Creating new trading strategies (high speed trading?)
- Materials science applications (heat shields, stealth coatings?)
- Run Monte Carlo simulations (nuclear fuel analyses, risk and fraud analyses?)
The Honeywell quantum computer will be bigger than IBM’s quantum computer.
Interesting business play because Honeywell has a deal with Microsoft to plug the Honeywell technology into the Azure cloud.
The coverage of Honeywell’s announcement reveals the hyperbole associated with quantum computing. DarkCyber interprets the assertions as the equivalent of an athlete’s pre-season exercise routine. Progress may be made, but the effort can only be judged when the “star” is on the field and in the game.
Until then, the buzzword sells expectations, not a solution to a here-and-now problem. One has to admire Honeywell’s PR generating capability.
Stephen E Arnold, March 5, 2020
Import.io and Connotate: One Year Later
March 3, 2020
There has been an interesting shift in search and content processing. Import.io, founded in 2012, purchased Connotate. Before you ask, “Connotate what?”, let me say that Connotate was a content scraping and analysis firm. I paid some attention to Connotate when it acquired Fetch, an outfit with an honest-to-goodness Xoogler on its team. Fetch processed structure data and Connotate was mostly an unstructured data outfit. I asked a Connotate professional when the company would process Dark Web content, only to be told, “We can’t comment on that.” Secretive, right.
Connotate was founded in 2000 and required about $25 million in funding. The amount Import.io paid was not revealed in a source to which DarkCyber has access. Import.io, which has ingested about $38 million. DarkCyber assumes that the stakeholders are confident that 1 + 1 will equal 3 or more.
Import.io says:
We are funded by some of the greatest minds in technology.
The great minds include AME Cloud Ventures, Open Ocean, IP Group, and several others.
The company explains:
Starting from a simple web data extractor and evolving to an enterprise level solution for concurrently getting data that drives business, industry, and goodness.
What’s the company provide? The answer is Web data integration: Identify, extract, prepare, integrate, and consume content from a user-provided list of urls. To illustrate the depth of the company’s capabilities, Import.io defines “prepare” this way:
Integrate prepared data with a library of APIs to support seamless integration with internal business systems and workflows or deliver it to any data repository to develop robust data sets for advanced analytics capabilities.
The firm’s Web site makes it clear that it serves the online travel, retail, manufacturing, hedge fund, advisory services, data scientists, analysts, journalists, marketing and product, hospitality, and media producers. These are a mix of sectors and industries, and DarkCyber did not create the grammatically inconsistent listing.
Import.io offers videos which provide some information about one of its important innovations “interactive extractors.” The idea is to convert script editing to point-and-click choices.
The company is growing. About a year ago, Import.io said that it experienced record sales growth. The company provided a link to its Help Center, but a number of panels contained neither information nor links to content.
The company offers a free version and a premium version. Price quotes are provided by the company.
Like Amplyfi and maybe ServiceMaster, Import.io is a company providing search and content processing with a 21st century business positioning. A new buzzword is needed to convey what Import.io, Amplyfi, and Service Master are providing. DarkCyber believes that these companies are examples of where search and content processing has begun to coalesce.
The question is, “Is acquiring, indexing, and analyzing OSINT content a truck stop or a destination like Miami Beach?”
Worth monitoring the trajectory of the company.
Stephen E Arnold, March 3, 2020
Quantum Computing Dust Up: Is the Spirit of Jeffrey Influencing Some Academics?
March 2, 2020
If you are into quantum computing and the magic it will deliver… any minute now, you won’t bother reading the MIT Technology Review article “Inside the Race to Build the Best Quantum Computer on Earth.” Please, keep in mind that MIT allegedly accepted funds from the science loving Jeffrey Epstein and then seemed to forget about that money.
Here’s the key sentence in the write up:
None of these devices—or any other quantum computer in the world, except for Google’s Sycamore—has yet shown it can beat a classical machine at anything.
One minor point: MIT’s experts appear to have overlooked China, Israel, and Russia Is it really ignoring quantum computing?), to name three nation states with reasonably competent researchers.
The focus on IBM and Google is understandable. Did DarkCyber mention that IBM is contributing to MIT’s funding; for example, the IBM Watson Lab?
What’s the point of the MIT Magazine research? Let’s try to see if there are quantum-sized clues?
First, Google asserted in 2019 that the fun loving folks in Mountain View had achieved “quantum supremacy.” IBM responded, “Nope.” This write up expands on IBM’s viewpoint; specifically, Google’s quantum magic was meaningless. Okay, maybe from IBM’s point of view, but from Google’s, the announcement was super duper click bait.
Second, IBM is doing research and business development in parallel. Google sells ads; IBM sells … what? Consulting, mainframes, managed facilities, and Watson? Google sells ads. Ads generate money for Google moon shots and quantum PR. IBM spends its money on ads. Okay, that’s a heck of a point.
Third, IBM wants to build a quantum business that does business things. Google wants to build a cloud computer to [a] sell ads, [b] beat Amazon, IBM, and Microsoft in the cloud, [c] accomplish a goal like climbing a mountain, [d] it is just Googley, [e] two of the four choices.
Net net: The write up walks a fine line. On one side is IBM and its checkbook and on the other is the Google. Is the write up objective? From DarkCyber’s point of view, like artificial intelligence, quantum computing is just around the corner.
DarkCyber is checking to make sure that when NewEgg.com offers quantum components, the team can buy one. For now, we will stick with the Ryzen 3900x: It works, is stable, and does jobs without too much fiddling.
Quantum computers require a bit more work. But when deciding between funding and ads, maybe fancy dancing around quantum computing is the tune the MIT band is playing?
Stephen E Arnold, March 2, 2020
After Decades of Marketing Chaff, Data Silos Thrive
March 2, 2020
Here’s another round of data silo baloney—“Top 4 Ways to Eliminate Data Fragmentation Within Your Organization” from IT Brief. Surveys have found that many businesses are not making the most of all that data they’ve been collecting, and it has become common to blame data silos. It is true that some organizations could store and access their data more efficiently. There’s just one problem, and it is one we have mentioned before—there are some very good reasons to keep some data fragmented. Silos exist because of things like government requirements, legal processes, sensitive medical data, experts protecting their turf, and basic common sense.
The article asserts:
“Many organizations are finding it difficult to extract meaningful value from their data due to one endemic problem: mass data fragmentation. With mass data fragmentation, data volumes continue to rise exponentially, but companies struggle to manage that data because it’s scattered across locations and infrastructure silos, both in on-premises data centers and in the cloud. Organizations often don’t know what data exists, where it is and whether it’s being stored securely and in compliance with regulations.”
Of course, entities must ensure data is stored securely and that they comply with regulations. Also, the write-up’s advice to keep redundancies to a minimum and to understand how one’s data is being stored and accessed in the cloud are good ones. However, the exhortation to eliminate silos entirely is off the mark; trying to do so can be a fruitless exercise in expense and frustration.
Why?
- A person wants to hoard his or her information
- Rules or regulations prevent sharing to those “not in the fox hole”
- Lawyers and HR professionals don’t want legal documents available and “people” managers definitely do not want employee health and salary data flying around like particles motivated by Brownian motion.
Net net: Reality has silos. Accept it. Omit the marketing silliness.
Stephen E Arnold, March 2, 2020
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