To Monetize Is Not to Sell, Contends Google

April 7, 2020

Quite gradually, governments seem to be waking up to the problem of online privacy. The passage of the California Consumer Privacy Act, which went into effect on January first, is one example. The Electronic Frontier Foundation explains how Google is sidling around the law’s provisions in its article, “Google Says it Doesn’t ‘Sell’ Your Data. Here’s How the Company Shares, Monetizes, and Exploits it.”

Journalist Bennett Cyphers reminds us just how far Google has cast its data nets: Worldwide, the company commands 62% of mobile and 69% of desktop browsers; the operating systems on 71% of mobile devices; 94% of apps in the Play store; and 92% of internet searches. It runs code on about 85% of the sites on the Web while 73% of adults in the US employ YouTube. That is a mind-boggling amount of data on billions of people.

Though Google makes tens of billions of dollars each year off this data, it claims it is not technically selling it. The write-up explains two ways the company splits that hair. First, it builds user profiles filled with statistics and interests that it then sells to advertisers. Marketers then use those profiles to craft targeted campaigns. The second method is called real-time bidding; Cyphers explains:

“Real-time bidding is the process by which publishers auction off ad space in their apps or on their websites. In doing so, they share sensitive user data—including geolocation, device IDs, identifying cookies, and browsing history—with dozens or hundreds of different ad tech companies. Each RTB auction typically sees user data passing through three different layers of companies on its way from a device to an advertiser: supply-side platforms (or SSPs) collect user data to sell, ad exchanges organize auctions between them and advertisers, and demand-side platforms (or DSPs) ‘bid’ on behalf of advertisers to decide which ads to show to which people. These auctions take milliseconds, constantly churning away in the background of your browsing activity as companies at every level of the process share and collect more and more data to add to their existing profiles of users. … Real-time bidding is a convoluted, opaque system of data collection and sharing that enables profiling and surveillance by advertisers, data brokers, hedge funds, and ICE. It is at the center of everything that’s wrong with privacy in tech.”

The article describes how Google got this much power and elaborates on how it wields it, complete with

Illustrations and examples. Navigate there for those details. Not surprisingly, Cyphers concludes with a call for stronger laws, ones that make privacy the default setting. Is it too late to re-bottle that genie?

Cynthia Murrell, April 7, 2020

Acquisdata: High Value Intelligence for Financial and Intelligence Analysts

March 31, 2020

Are venture capitalist, investment analysts, and other financial professionals like intelligence officers? The answer, according to James Harker-Mortlock, is, “Yes.”

The reasons, as DarkCyber understands them, are:

  • Financial professionals to be successful have to be data omnivores; that is, masses of data, different types, and continuously flowing inputs
  • The need for near real time or real time data streams can make the difference between making a profit and losses
  • The impact of changing work patterns on the trading floor are forcing even boutique investment firms and global giants to rely upon smart software to provide a competitive edge. These smart systems require data for training machine learning modules.

James Harker-Mortlock, founder of Acquidata, told DarkCyber:

The need for high-value data from multiple sources in formats easily imported into analytic engines is growing rapidly. Our Acquisdata service provides what the financial analysts and their smart software require. We have numerous quant driven hedge funds downloading all our data every week to assist them in maintaining a comprehensive picture of their target companies and industries.”

According to the company’s Web site, Acquisdata:

Acquisdata is a fast growing digital financial publishing company. Established in 2010, we have quickly become a provider to the world’s leading financial news companies, including Thomson Reuters/Refinitiv, Bloomberg, Factset, IHS Markit, and Standard and Poor’s Capital IQ, part of McGraw Hill Financial, and ISI Emerging Markets. We also provide content to a range of global academic and business database providers, including EBSCO, ProQuest, OCLC, Research & Markets, CNKI and Thomson Reuters West. We know and understand the electronic publishing business well. Our management has experience in the electronic publishing industry going back 40 years. We aim to provide comprehensive and timely information for investors and others interested in the drivers of the global economy, primarily through our core products, the Industry SnapShot, Company SnapShot and Executive SnapShot products. Our units provide the annual and interim reports of public companies around the world and fundamental research on companies in emerging markets sectors, and aggregated data from third-party sources. In a world where electronic publishing is quickly changing the way we consume news and information, Acquisdata is at the very forefront of providing digital news and content solutions.

DarkCyber was able to obtain one of the firm’s proprietary Acquisdata Industry Snapshots. “United States Armaments, 16 March 2020” provides a digest of information about the US weapons industry. the contents of the 66 page report include news and commentary, selected news releases, research data, industry sector data, and company-specific information.

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Obtaining these types of information from many commercial sources poses a problem for a financial professional. Some reports are in Word files; some are in Excel; some are in Adobe PDF image format; and some are in formats proprietary to a data aggregator. We provide data in XML which can be easily imported into an analytic system; for example, Palantir’s Metropolitan or similar analytical tool. PDF versions of the more than 100 weekly reports are available.

DarkCyber’s reaction to these intelligence “briefs” was positive. The approach is similar to the briefing documents prepared for the White House.

Net net: The service is of high value and warrants a close look for professionals who need current, multi-type data about a range of company and industry investment opportunities.

You can get more information about Acquisdata at www.acquidata.com.

Stephen E Arnold, March 31, 2020

MiningLamp Technology: Another Palantir?

March 30, 2020

DarkCyber found “China’s Palantir MiningLamp Raises US$300 Million in Funding Round Co-Led by Temasek, Tencent” intriguing. Palantir Technologies, a company providing commercial and government services, has obtained about $2 billion in funding since it was founded in 2003. Furthermore, Palantir in the past 17 years has worked to become the Analyst Notebook and BAE NetReveal for some of its clients. Note that Analyst Notebook was founded in the early 1990s and BAE’s initial intelware products date from a few years later. In short, MiningLamp wants to become:

  1. A company that requires decades to gain momentum
  2. A company that requires billions in funding or the support of a giant industrialized services firm like BAE to survive
  3. Expert lobbying to spark and obtain government contracts
  4. Remain out of the public spotlight while endeavoring to displace products that are long in the tooth.

Does this make sense? Of course, the MiningLamp operation wants to be a global software and services company. The backers of MiningLamp want to have a seat at the table when certain types of projects are planned and executed.

The write up does not point out these rather obvious facts. DarkCyber learned:

Founded in 2014, MiningLamp gained initial success by offering online ad performance evaluations and fraud detection services for advertisers, before expanding the business to industries such as public security, smart cities, finance, logistics, entertainment, retail and manufacturing.

What’s MiningLamp’s technology deliver?

Although not as well known as US equivalent Palantir Technologies, which reportedly contributed to America’s success in hunting down Osama bin Laden, MiningLamp’s data mining software is used to spot crime patterns, track drug dealers and prevent human trafficking.

Plus, the write up points out:

The company’s software enables users to search huge volumes of heterogeneous data – information with a great variety of types and formats – and process that into actionable knowledge and insight using a combination of proprietary data management tools.

The interesting point is that advertising technology leads to a Palantir metaphor. The second fact is that the funding is anchored in Singapore and the allegedly independent company Tencent. There’s no reference to any other funding, including funding from Chinese government entities or fellow travelers. Finally, Singapore has become a hub for many companies engaged in Palantir-like activities. Need a bagel? Singapore has them because there are quite a few foreign nationals who crave this food essential.

Now how much revenue can specialized software companies generate. Analyst Notebook, BAE NetReveal, Recorded Future, and similar firms do generate revenues, but none of these companies bang into glass ceilings and walls. For example, how many government agencies are there that can pay hundreds of thousands of dollars and dedicate personnel to using these intelware systems? Are there other benefits to companies in the intelware business? The market for intelware is tough to move laterally. Talk about intelware methods and customers in non-government sectors, and many of the prospects get really nervous. There are good reasons.

Is MiningLamp another Palantir? Sure, it will require large amounts of cash, lobbyist support, and funding the peculiar and costly intelware marketing puzzle.

There are interesting facets to the MiningLamp effort, but DarkCyber does not think the answer will be found in providing Bluedot-type services or morphing into an outfit like Palantir Technologies. Palantir, DarkCyber recalls, has experienced employee protests, litigation with Analyst Notebook related to reverse engineering the ANB file format, and bureaucratic scuffles with procurement professionals.

Another Palantir? Maybe, maybe not. Those writing checks for $300 million may be surprised at the intelware market’s behavior. Will the Five Eyes sign up for MiningLamp licenses? Maybe, maybe not.

Stephen E Arnold, March 30, 2020

NR2 Search

March 23, 2020

DarkCyber noted that NR2 made a public version of its innovation search engine available. This information and investment startup was founded about a year ago. NR2 won the K Startup Grand Challenge in 2019, beating 176 other startups from 95 countries.

The company says:

Powered by big data and artificial intelligence, we deliver insights that predict trends in innovation before they sweep the globe. We sit at the heart of China and Europe’s innovation hubs, enabling us to seamlessly link global investment to local innovation, accelerating growth and returns for both.

The company’s headquarters are in Paris. The founders are Maxim Parr, a graduate of HEC Paris (a business school with an eight percent acceptance rate),and Jordan Monnet, a PhD who is proficient in Mandarin.

The founders told the HEC publication:

We can learn so much about how innovation thrives by understanding data on start-ups. Our algorithms have found many characteristics present in disruptive companies and we use them to help our users identify the next generation of revolutionary companies. We started with Chinese start ups, but we are building out our search engine to capture innovation everywhere.

A query for analytics returned a list of companies shown below:

image

The companies listed were Chinese.

Clicking on the company Hesaitech returned a useful summary of the firm:

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The icon allows the user to copy the link to the NR2.io entry.

Observations:

  1. Extremely useful resource in its present form. DarkCyber anticipates enhancements
  2. There are a number of monetization options available to this company
  3. Content valuable to investment firms and organizations looking for companies to explore deals or partnerships
  4. Data appear to be of high value because obtaining current information about venture funded firms in China and elsewhere can be time consuming.

We ran our standard queries for surveillance technology, facial recognition, and investigative software and did not immediately locate useful information. We think that this is a result of the search terms we tested.

DarkCyber will add this resource to our list of useful resources. Worth a look.

Stephen E Arnold, March 23, 2020

Artificial Intelligence: Delivering But Not Yet Arrived

March 22, 2020

Artificial intelligence is the basis for most technology currently in development, because it is capable of handling complex actions and is designed to learn. AI is the most advanced algorithms invented to this day, but what does that mean in reference to business and industry? Forbes published the, “Levels And Limits Of AI” to provide perspective on AI’s capabilities, while Andreessen Horowitz takes a similar yet differing approach in, “The New Business Of AI (And How It’s Different From Traditional Software).”

One thing both articles agree on is that AI is here to stay until something more advanced is designed. Andreessen Horowitz agrees humans will be involved with AI beyond creating the algorithms, but there is more required to get AI working correctly for a company. AI, according to Forbes, is designed to do three things:

“There are several “levels” of artificial intelligence. A few years ago my friends John Frank and Jason Briggs, who run Diffeo, suggested breaking artificial intelligence into 3 levels of service: Acceleration, Augmentation, and Automation. Acceleration is taking an existing human process and helping humans do it faster. For example, the current versions of textual auto-complete that Google offers are acceleration AI. They offer a completed version of what the user might already say. The next level, augmentation, takes what a human is doing and augments it. In addition to speeding up what the human is doing (like acceleration), it makes the human’s product better. An example of this is what Grammarly does with improving the grammar of text. The final level is automation. In the previous two levels there are still “humans in the loop.” Automation achieves a task with no human in the loop. The aspiration here is Level 5 autonomous driving like Aurora and Waymo are pursuing.”

Andreessen Horowitz breaks down the obstacles businesses will encounter with AI deployment. In short, there will be some, most of which are SOP when implementing any new technology. The only difference is that AI is smarter, but there is this consolation for humans:

“The need for human intervention will likely decline as the performance of AI models improves. It’s unlikely, though, that humans will be cut out of the loop entirely. Many problems – like self-driving cars – are too complex to be fully automated with current-generation AI techniques. Issues of safety, fairness, and trust also demand meaningful human oversight – a fact likely to be enshrined in AI regulations currently under development in the US, EU, and elsewhere.”

AI is a tool meant to simplify and advance society, essentially it is not meant to replace humanity. Common sense weighs in on deploying AI-based technology. The ROI is a big factor, but also consider the phrase, “if it is not broken, do not fix it.”

Whitney Grace, March 22, 2020

China and AI: Activity but Cash Friction

March 18, 2020

Despite being a communist country, China loves money. One way that China loves making money is through new technology startups, especially with AI startups. The South China Morning Post examines the current market of Chinese AI startups in the article, “China’s AI Start-Ups Are Closing More Funding Deals, Yet They’re Still Attracting Less Money Than The US.”

According to the article, Chinese AI startups have attracted more funding deals than their US counterparts, but they are not bringing in as much money as the past. The trade war with the US is a big determiner. The US continues to dominate in the AI market, but that has steadily been dropping from 71% in 2014 to 39% in 2019. The US also has more AI fundraisers than China. The US has a 64% of AI startup fundraising, up four points since the last year, while China only had 11%. Overall AI generated 2019 $26.6 billion across the globe in 2019.

China only had $2.9 billion from that total.

The big problem is that China lacks creativity in their AI startups:

“‘Investors are much more cautious now, especially after the second half of 2018 as we sensed a bubble – most AI projects haven’t produced strong performance,’ Chris Lai, a partner at Beijing-based Shunwei Capital, said. ‘We haven’t seen many AI applications that are beyond imagination, most are used in surveillance cameras.’”

There are some startups that add some spice to the Chinese AI market and bolster the hope that the country will be an AI world leader by 2030. Some of these startups include Horizon Robotics and facial recognition startup Face+++. The market is predicted to shift to vision and hearing applications with smart home appliances.

China might be moving towards more smart home appliances, but it is China for goodness sake! China has an authoritarian government, so it wants more AI security technology to track its citizens.

Whitney Grace, March 18, 2020

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

In the UK, Brexit Leads to Taxit for Techs

March 13, 2020

US technology companies are likely to face a rocky 2020. The Coronavirus is creating some problems. If the information in “US Tech Companies Will Be Hit with New UK Tax in Just Three Weeks” is accurate, those juicy margins may be trimmed. The write up states:

The UK government said Wednesday [March 11, 2020] that it’s moving ahead with a 2% tax on revenue from digital services such as search and advertising starting on April 1. The levy will apply to firms with global sales of more than £500 million ($648 million), with at least £25 million ($32.4 million) coming from UK users.

Is the tax discriminatory? Yep.

What happens if the US technology companies pay up?

That’s easy. There are a number of European entities eager to implement a taxation model that generates revenue.

What happens if the US retaliates?

There will be collateral damage.

How likely will countries be to escalate if the tax fails? Some may implement a simple but Draconian solution: Throttling or blocking maybe?

Monopolies are good for those who obtain money from the firms in the cat-bird seat. Some European countries may not share the same view.

Stephen E Arnold, February 13, 2020

India Finance: Sharia Issue

March 9, 2020

DarkCyber found the information in “Sharia Fintech”: Startups Race to Tap Indonesia Growth by Aligning with Islam” suggestive. Is the information spot on? Possibly, the source makes a great effort to explain trust. The main point of the write up strikes DarkCyber as:

Winning over conservative Muslims like Iswara in the world’s most populous Muslim-majority country is both a challenge and multi-billion dollar opportunity for fintech firms that are riding its mobile internet boom and aim to sell financial services. Of Indonesia’s 270 million population, half lacks bank accounts but most now have mobile phones.

Implications? A handful.

Stephen E Arnold, March 9, 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

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