A Xoogler May Question the Google about Responsible and Ethical Smart Software
December 2, 2021
Write a research paper. Get colleagues to provide input. Well, ask colleagues do that work and what do you get. How about “Looks good.” Or “Add more zing to that chart.” Or “I’m snowed under so it will be a while but I will review it…” Then the paper wends its way to publication and a senior manager type reads the paper on a flight from one whiz kid town to another whiz kid town and says, “This is bad. Really bad because the paper points out that we fiddle with the outputs. And what we set up is biased to generate the most money possible from clueless humans under our span of control.” Finally, the paper is blocked from publication and the offending PhD is fired or sent signals that your future lies elsewhere.
Will this be a classic arm wrestling match? The winner may control quite a bit of conceptual territory along with knobs and dials to shape information.
Could this happen? Oh, yeah.
“Ex Googler Timnit Gebru Starts Her Own AI Research Center” documents the next step, which may mean that some wizards undergarments will be sprayed with eau de poison oak for months, maybe years. Here’s one of the statements from the Wired article:
“Instead of fighting from the inside, I want to show a model for an independent institution with a different set of incentive structures,” says Gebru, who is founder and executive director of Distributed Artificial Intelligence Research (DAIR). The first part of the name is a reference to her aim to be more inclusive than most AI labs—which skew white, Western, and male—and to recruit people from parts of the world rarely represented in the tech industry. Gebru was ejected from Google after clashing with bosses over a research paper urging caution with new text-processing technology enthusiastically adopted by Google and other tech companies.
The main idea, which Wired and Dr. Gebru delicately sidestep, is that there are allegations of an artificial intelligence or machine learning cabal drifting around some conference hall chatter. On one side is the push for what I call the SAIL approach. The example I use to illustrate how this cost effective, speedy, and clever short cut approach works is illustrated in some of the work of Dr. Christopher Ré, the captain of the objective craft SAIL. Oh, is the acronym unfamiliar to you? SAIL is short version of Stanford Artificial Intelligence Laboratory. SAIL fits on the Snorkel content diving gear I think.
On the other side of the ocean, are Dr. Timnit Gebru’s fellow travelers. The difference is that Dr. Gebru believes that smart software should not reflect the wit, wisdom, biases, and general bro-ness of the high school science club culture. This culture, in my opinion, has contributed to the fraying of the social fabric in the US, caused harm, and erodes behaviors that are supposed to be subordinated to “just what people do to make a social system function smoothly.”
Does the Wired write up identify the alleged cabal? Nope.
Does the write up explain that the Ré / Snorkel methods sacrifice some precision in the rush to generate good enough outputs? (Good enough can be framed in terms of ad revenue, reduced costs, and faster time to market testing in my opinion.) Nope.
Does Dr. Gebru explain how insidious the short cut training of models is and how it will create systems which actively harm those outside the 60 percent threshold of certain statistical yardsticks? Heck, no.
Hopefully some bright researchers will explain what’s happening with a “deep dive”? Oh, right, Deep Dive is the name of a content access company which uses Dr. Ré’s methods. Ho, ho, ho. You didn’t know?
Beyond Search believes that Dr. Gebru has important contributions to make to applied smart software. Just hurry up already.
Stephen E Arnold, December 2, 2021
Xooglers Can Define Evil and Want a Judge to Validate Their Definition
December 2, 2021
I read “Google: Former Employees Sue Tech Giant for Allegedly Breaching Don’t Be Evil Pledge.” Nope, not a joke. When I first heard a real live Google spout this phrase to me at a search conference in 1999 in Boston, I thought the shy, perspiring billionaire to be was pulling my leg. I still think that the don’t be evil thing—alleged crafted by Paul Buchheit and Amit Patel — was a high school science club thing. Companies run by anyone but the Googlers had to be evil. The Googlers were a force for good. Right?
Now three employees, assisted by a mini-flock of legal eagles, want to make the company pay big bucks for pitching the don’t be evil line for years. The phrase found its way into assorted company information outputs. I thought I saw it on a Google booth tchotchke shortly after my interaction with the Google billionaire to be in Boston.
How could “real” attorneys, hired by the ultimate science club, use the phrase don’t be evil in corporate outputs? Easy. Lawyers, once housed in trailers, a kilometer from the “real” office were nuisances to be tolerated. The “good” lawyers mostly did what they were supposed to do and rolled with the sci-club.
The write up reports:
The trio had raised concerns at town halls and other forums inside Google about the company potentially selling cloud technology to immigration authorities in the United States, which at the time were engaging in detention tactics considered inhumane by activists.
This appears to be an example of evil.
Perhaps there will be some existential moments in this matter. Google will have to offer an example of being good. Who will decide? A lawyer. Hopefully a member of the high school science club and a person who understands that saying something doesn’t mean anything when money is involved in Silicon Valley.
Stephen E Arnold, December 2, 2021
Want to Be a Consultant? No Problemo
December 2, 2021
Everyone claims to be an expert in a topic these days. People advertise themselves as experts in order to garner a clients, jobs, and a positive reputation. ReadWrite explores how the term “expert” has lost its meaning in: “Expertise Is Dead: How To Stand Out When Everyone’s An Expert.”
Claiming to be an expert is about marketing strategy. It is similar to how Google searches rank higher quality content ahead of lesser content. People need to post quality on top of quality to reach the top of searches. One way to do that is to claim to be an expert. After all, we prefer experts compared to novices. Not everyone can actually be an expert, so it diminishes the meaning of “expert.” It also creates an echo chamber:
“The nature of the internet incentivizes echo chambers and misinformation. The internet contains practically unlimited access to information and connective potential with everyone in the developed world. While this can be a tremendous strength, it also leads people to develop their own echo chambers – and makes it easy to find misinformation. Whatever your opinion is, you’re only one quick search away from finding a so-called “expert” who agrees with you, and a full community of people (along with intelligent bots) who will regurgitate your own opinions back to you.”
With everyone on jumping on the expert track, there are still ways to be noticed and be on top of the pile. To do that it is best to understand your competition, focus on your niche expertise, show and do not tell how you are great, and have the proof I the pudding.
This is great advice! It is nothing new however, because it has been repackaged and sold in job advancement books for decades. It still works, though.
Whitney Grace, December 2, 2021
Surveillance Made Easy: The Russian Way
December 2, 2021
US tech companies want a foothold in the Russian market and Putin wants them to have an edge to step on. There is a caveat, they must have an presence in Russia by the end of 2021 or else…er…face restrictions or bans. Rappler explains why Russia wants thirteen foreign technology companies to establish offices in: “Moscow Tells 13 Mostly US Tech Firms They Must Set Up In Russia By 2022.”
Communications regulator Roskomndazor released the demand on Monday, November 22 that explained what the companies needed to do and targeted ones that already have Russian offices:
“Foreign social media giants with more than 500,000 daily users have been obliged to open offices in Russia since a new law took effect on July 1. The list published on Monday names the companies for the first time. It lists Alphabet’s Google, Facebook, Twitter, TikTok, and messaging app Telegram, all of which Russia has fined this year for failing to delete content it deems illegal. Apple, which Russia has targeted for alleged abuse of its dominant position in the mobile applications market, was also on the list.”
If the companies do not follow the new demand, they will face restrictions of data collection, money transfers, and advertising or bans.
Russia wants to promote its own tech industry. The government is doing so by proposing more taxes on foreign companies, tax cuts for domestic tech, and any device to offer Russian software when brand new.
The demand is also viewed as a way for Russian to exert more control over the Internet and technology. It could hinder individual and corporate freedoms.
Rules are not clear about what and how tech companies should represent themselves in Russia. The Roskomndazor did say foreign entities are required to limit information that violates Russian legislation.
Russia might be masking domestic technology development and economic recovery behind surveillance.
Whitney Grace, December 2, 2021
Right or Wrong to Be Forgotten?
December 2, 2021
While it is still possible to disappear, it is nearly impossible to forget some past mistakes. In 2014, the Court of Justice of the European Union recognized the “right to be forgotten.” The Irish Times reported that Google has something to say on that law, “Google Should Not Get A Say In What Is To Be Forgotten.”
The EU Court of Justice ruled in favor of the “right to be forgotten” against Google’s Spanish subsidiary by Spain’s data protection agency AEPD and a Spanish citizen. The right to be forgotten forces Google to delist information in searches, but the AEPD argued it was in the public’s benefit for information to remain listed.
The biggest issue in question is the current case of the Quinn family against the Irish Bank Resolution Corporation. Should information related to ongoing litigation and national economic concern be removed from the Internet? There is an even bigger question:
“The more fundamental issue which these delistings have drawn attention to, however, is the power of a private company to decide when, and whether, an individual’s right to be forgotten can be enforced. At present, right-to-be-forgotten claims (such as those made in the Quinn case) are considered and decided on by employees of the search-engine operators to whom the request is made. While these search engines publish annual transparency reports which include statistics about how many right-to-be-forgotten applications are made – and how many are successful – these reports do not detail the content of the decisions in right-to-be-forgotten cases – or the factors used in reaching those decisions. The result is that private companies have the power not only to delist articles but to do so based on their own assessment of whether a legitimate right-to-be-forgotten claim exists, what public interest, if any, would require the item to continue appearing in search results, and how to balance any public interest with the data-protection rights of the requesting party.”
There are very few guidelines about how “right to be forgotten” law is applied. Private companies determine who has the right, but how and why do they make that decision?
It sounds like another case of where the present is going to make the standard by which the future will abide.
Whitney Grace, November December 2, 2021
Amazon: The Online Bookstore Does FinTech
December 1, 2021
Several years ago, I did a series of reports about Amazon’s push into a data marketplace. That technology is chugging along, but it appeals to the back office crowd. “Goldman Sachs Unveils Amazon Backed Cloud Service for Wall Street Trading Firms” makes clear that the back office is an important part of the Bezos bulldozer’s post-Jeff itinerary. Instead of teaming with US government agencies, the online bookstore has connected with everyone’s favorite financial institution to create a fintech cloud.
The write up reports:
The bank is opening up access to its trove of market data and software tools to hedge funds and asset managers in an offering designed with Amazon’s cloud division.
Like other Amazon back office services, many Amazon watchers will yawn. The excite swirls around Black Friday deals and Amazon’s alleged manipulation of its product search results.
How long has the online bookstore been working with the estimable Wall Street eminence? The answer is more than a decade.
Worth watching because the back office in the world of finance is possibly more lucrative than selling Amazon Basics T shirts.
Stephen E Arnold, December xx, 2021
An Impossible Dream? Where Is the Windmill?
December 1, 2021
Cyberattacks are only growing in frequency, sophistication, and ROI for hackers. We know most companies need to do a better job at protecting themselves, but what will make the difference? Perhaps the problem lies in the gaps between departments. Network World suggests “3 Steps to Better Collaboration Between Networking and Security Pros.” IT Research firm Enterprise Management Associates finds many companies recognize the need for these departments to work more closely but are having trouble effectively bringing them together. The article identifies four key challenges: separate data silos, skill and knowledge differences between the teams, architectural complexity and, surprise, lack of funding. Writer Shamus McGillicuddy suggests three solutions. The first is to create common data repositories:
“The first priority is to establish a shared data repository that both teams can rely on for a common view of the network. In many companies, security teams are constantly requesting data from the network team when conducting investigations. If that’s the case, the network team should identify the data that security teams frequently request and establish repositories that are accessible to them. … network teams and security teams should centralize packet-capture infrastructure as much as possible so that both teams have a common record of raw traffic data.”
The catch—this change may require updates to data stores, which means spending some dough. Then there is the issue of training staff to better understand each other. McGillicuddy suggests it is up to management, not the teams themselves, to identify the necessary know-how:
“Leadership should recognize how skills gaps are undermine NetSecOps partnerships and lead from the top to close those gaps. Also, network infrastructure professionals are usually quite knowledgeable about network security concepts. They can bring that to bear as much as possible to find common ground with the security team.”
Again, companies must be willing to allocate funds to this endeavor. Finally, architecture should be simplified. The write-up stresses:
“If complexity is getting in the way, the network team should kill complexity and modernize legacy architecture as much as possible. One option is to adopt automation solutions that abstract complexity. And as they move into new environments like the cloud and work-from-anywhere, they should design for simplicity as much as possible.”
This step might be the most costly of the three, especially if legacy systems must be overhauled. All told, companies can be looking at a significant investment to establish harmony between their networking and security departments. The alternative, though, may be to risk a much more costly (and embarrassing) data breach in the future.
Cynthia Murrell, December 1, 2021
Forgotten IBM Watson? Despite Quantum Supremacy, IBM Loves Its AI Too
December 1, 2021
IBM continues to upgrade Watson; this time it is new natural language processing software. IBM’s Newsroom details the upgrade: “IBM To Add New Natural Language Processing Enhancements To Watson Discovery.” The enhancements will assist industries elevate customer care, accelerate business processes by discovering insights and synthesizing information.
Companies are using more NLP software to review their data and its varying formats. AI allows companies to discover insights, save research time, and help employees make more fact-driven decisions. Customization for different industries is a key determinant in the Watson Discovery upgrade:
“The new planned features that IBM announced today are designed to make it easier for Watson Discovery users to quickly customize the underlying NLP models on the unique language of their business. Stemming from NLP advancements developed by IBM Research, business users can train Watson Discovery to help read, understand and surface more precise insights from large sets of complex, industry-specific documents even if they don’t have significant data science skills.”
Among the new features are: advanced NLP customization capabilities, automatic text pattern detection, and pre-trained document structure understanding. NLP will change how the law, financial, insurance, and other industries conduct business.
While NLP upgrades are relatively new, but they will eventually become industry standards as the software becomes cheaper and more ingrained. NLP might actually become mandatory to combat inaccuracy and poor business practices.
Whitney Grace, December 1, 2021
Be a Machine Learning Whiz: Learn 10 Numerical Recipes
December 1, 2021
I like Cowboy Kent Rollins cooking videos on YouTube. His creations are presented in such a way that even the microwave-challenged chef can feed them thar hungry ranch hands. You can check out how to make a “real” tuna casserole at this link.
Now we have a similar approach to machine learning. Navigate to “All Machine Learning Algorithms You Should Know in 2022.” I love articles that assert the “all” thing; that is, the categorical affirmative. It’s like Milton’s definition of God’s power. Awe inspiring for sure.
First, these are the “popular” ones, which I think means commonly taught in university courses and endlessly recycled by the high school science club members who could remember the procedures, get an A, and go on to found an AI start up. Who documents popular? Why, silly goose, would you ask such a questions?
What are the 10 popular “all” algorithms? Herewith the listicle:
- Ensemble learning algorithms. There are a bunch of them, but let’s not quibble with the notion of 10.
- Explanatory algorithms. Again, there’s a pride of procedures here.
- Clustering algorithms. Yep, more than one to learn.
- Dimensionality algorithms. Yep, more than one to memorize.)
- Similarity algorithms. These will keep even a devoted math whiz busy. Why? Well, what is similarity in a particular case? Yeah, tricky.
Wait there are only five, not 10. What’s happened?
Yeah, counting may not be the core competency of a person who can identify “all” algorithms one needs to know for machine learning.
Stephen E Arnold, December 1, 2021
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