Will AI Data Scientists Become Street People?
November 4, 2024
Over at HackerNoon, all-around IT guy Dominic Ligot insists data scientists must get on board with AI or be left behind. In “AI Denialism,” he compares data analysts who insist AI can never replace them with 19th century painters who scoffed at photography as an art form. Many of them who specialized in realistic portraits soon found themselves out of work, despite their objections.
Like those painters, Ligot believes, some data scientists are in denial about how well this newfangled technology can do what they do. They hang on to a limited definition of creativity at their peril. In fact, he insists:
“The truth is, AI’s ability to model complex relationships, surface patterns, and even simulate multiple solutions to a problem means it’s already doing much of what data analysts claim as their domain. The fine-grained feature engineering, the subtle interpretations—AI is not just nibbling around the edges; it’s slowly encroaching into the core of what we’ve traditionally defined as ‘analytical creativity.’”
But we are told there is hope for those who are willing to adapt:
“I’m not saying that data scientists or analysts will be replaced overnight. But to assume that AI will never touch their domain simply because it doesn’t fit into an outdated view of what creativity means is shortsighted. This is a transformative era, one that calls for a redefinition of roles, responsibilities, and skill sets. Data analysts and scientists who refuse to keep an open mind risk finding themselves irrelevant in a world that is rapidly shifting beneath their feet. So, let’s not make the same mistake as those painters of the past. Denialism is a luxury we cannot afford.”
Is Ligot right? And, if so, what skill-set changes can preserve data scientists’ careers? That relevant question remains unanswered in this post. (There are good deals on big plastic mugs at Dollar Tree.)
Cynthia Murrell, November 04, 2024
Smart Software Project Road Blocks: An Up-to-the-Minute Report
October 1, 2024
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This essay is the work of a dumb dinobaby. No smart software required.
I worked through a 22-page report by SQREAM, a next-gen data services outfit with GPUs. (You can learn more about the company at this buzzword dense link.) The title of the report is:
The report is a marketing document, but it contains some thought provoking content. The “report” was “administered online by Global Surveyz [sic] Research, an independent global research firm.” The explanation of the methodology was brief, but I don’t want to drag anyone through the basics of Statistics 101. As I recall, few cared and were often good customers for my class notes.
Here are three highlights:
- Smart software and services cause sticker shock.
- Cloud spending by the survey sample is going up.
- And the killer statement: 98 percent of the machine learning projects fail.
Let’s take a closer look at the astounding assertion about the 98 percent failure rate.
The stage is set in the section “Top Challenges Pertaining to Machine Learning / Data Analytics.” The report says:
It is therefore no surprise that companies consider the high costs involved in ML experimentation to be the primary disadvantage of ML/data analytics today (41%), followed by the unsatisfactory speed of this process (32%), too much time required by teams (14%) and poor data quality (13%).
The conclusion the authors of the report draw is that companies should hire SQREAM. That’s okay, no surprise because SQREAM ginned up the study and hired a firm to create an objective report, of course.
So money is the Number One issue.
Why do machine learning projects fail? We know the answer: Resources or money. The write up presents as fact:
The top contributing factor to ML project failures in 2023 was insufficient budget (29%), which is consistent with previous findings – including the fact that “budget” is the top challenge in handling and analyzing data at scale, that more than two-thirds of companies experience “bill shock” around their data analytics processes at least quarterly if not more frequently, that that the total cost of analytics is the aspect companies are most dissatisfied with when it comes to their data stack (Figure 4), and that companies consider the high costs involved in ML experimentation to be the primary disadvantage of ML/data analytics today.
I appreciated the inclusion of the costs of data “transformation.” Glib smart software wizards push aside the hassle of normalizing data so the “real” work can get done. Unfortunately, the costs of fixing up source data are often another cause of “sticker shock.” The report says:
Data is typically inaccessible and not ‘workable’ unless it goes through a certain level of transformation. In fact, since different departments within an organization have different needs, it is not uncommon for the same data to be prepared in various ways. Data preparation pipelines are therefore the foundation of data analytics and ML….
In the final pages of the report a number of graphs appear. Here’s one that stopped me in my tracks:
The sample contained 62 percent user of Amazon Web Services. Number 2 was users of Google Cloud at 23 percent. And in third place, quite surprisingly, was Microsoft Azure at 14 percent, tied with Oracle. A question which occurred to me is: “Perhaps the focus on sticker shock is a reflection of Amazon’s pricing, not just people and overhead functions?”
I will have to wait until more data becomes available to me to determine if the AWS skew and the report findings are normal or outliers.
Stephen E Arnold, October 1, 2024
Agents Are Tracking: Single Web Site Version
August 6, 2024
This essay is the work of a dumb humanoid. No smart software required.
How many software robots are crawling (copying and indexing) a Web site you control now? This question can be answered by a cloud service available from DarkVisitors.com.
The Web site includes a useful list of these software robots (what many people call “agents” which sounds better, right?). You can find the list of about 800 bots as of July 30, 2024) on the DarkVisitors’ Web site at this link. There is a search function so you can look for a bot by name; for example, Omgili (the Israeli data broker Webz.io). Please, note, that the list contains categories of agents; for example, “AI Data Scrapers”, “AI Search Crawlers,” and “Developer Helpers,” among others.
The Web site also includes links to a service called “Set Up Your Robots.txt.” The idea is that one can link a Web site’s robots.txt file to DarkVisitors. Then DarkVisitors will update your Web site automatically to block crawlers, bots, and agents. The specific steps to make this service work are included on the DarkVisitors.com Web site.
The basic service is free. However, if you want analytics and a couple of additional features, the cost as of July 30, 2024, is $10 per month.
An API is also available. Instructions for implementing the service are available as well. Plus, a WordPress plug in is available. The cloud service is provided by Bit Flip LLC.
Stephen E Arnold, August 6, 2024
If Math Is Running Out of Problems, Will AI Help Out the Humans?
July 26, 2024
This essay is the work of a dinobaby. Unlike some folks, no smart software improved my native ineptness.
I read “Math Is Running Out of Problems.” The write up appeared in Medium and when I clicked I was not asked to join, pay, or turn a cartwheel. (Does Medium think 80-year-old dinobabies can turn cartwheels? The answer is, “Hey, doofus, if you want to read Medium articles pay up.)
Thanks, MSFT Copilot. Good enough, just like smart security software.
I worked through the free essay, which is a reprise of an earlier essay on the topic of running out of math problems. These reason that few cared about the topic is that most people cannot make change. Thinking about a world without math problems is an intellectual task which takes time from scamming the elderly, doom scrolling, generating synthetic information, or watching reruns of I Love Lucy.
The main point of the essay in my opinion is:
…take a look at any undergraduate text in mathematics. How many of them will mention recent research in mathematics from the last couple decades? I’ve never seen it.
New and math problems is an oxymoron.
I think the author is correct. As specialization becomes more desirable to a person, leaving the rest of the world behind is a consequence. But the issue arises in other disciplines. Consider artificial intelligence. That jazzy phrase embraces a number of mathematical premises, but it boils down to a few chestnuts, roasted, seasoned, and mixed with some interesting ethanols. (How about that wild and crazy Sir Thomas Bayes?)
My view is that as the apparent pace of information flow erodes social and cultural structures, the quest for “new” pushes a frantic individual to come up with a novelty. The problem with a novelty is that it takes one’s eye off the ball and ultimately the game itself. The present state of affairs in math was evident decades ago.
What’s interesting is that this issue is not new. In the early 1980s, Dialog Information Services hosted a mathematics database called xxx. The person representing the MATHFILE database (now called MathSciNet) told me in 1981:
We are having a difficult time finding people to review increasingly narrow and highly specialized papers about an almost unknown area of mathematics.
Flash forward to 2024. Now this problem is getting attention in 2024 and no one seems to care?
Several observations:
- Like smart software, maybe humans are running out of high-value information? Chasing ever smaller mathematical “insights” may be a reminder that humans and their vaunted creativity has limits, hard limits.
- If the premise of the paper is correct, the issue should be evident in other fields as well. I would suggest the creation of a “me too” index. The idea is that for a period of history, one can calculate how many knock off ideas grab the coat tails of an innovation. My hunch is that the state of most modern technical insight is high on the me too index. No, I am not counting “original” TikTok-type information objects.
- The fragmentation which seems apparent to me in mathematics and that interesting field of mathematical physics mirrors the fragmentation of certain cultural precepts; for example, ethical behavior. Why is everything “so bad”? The answer is, “Specialization.”
Net net: The pursuit of the ever more specialized insight hastens the erosion of larger ideas and cultural knowledge. We have come a long way in four decades. The direction is clear. It is not just a math problem. It is a now problem and it is pervasive. I want a hat that says, “I’m glad I’m old.”
Stephen E Arnold, July 26, 2024
Social Scoring Is a Thing and in Use in the US and EU Now
April 9, 2024
This essay is the work of a dumb dinobaby. No smart software required.
Social scoring is a thing.
The EU AI regulations are not too keen on slapping an acceptability number on people or a social score. That’s a quaint idea because the mechanisms for doing exactly that are available. Furthermore, these are not controlled by the EU, and they are not constrained in a meaningful way in the US. The availability of mechanisms for scoring a person’s behaviors chug along within the zippy world of marketing. For those who pay attention to policeware and intelware, many of the mechanisms are implemented in specialized software.
Will the two match up? Thanks, MSFT Copilot. Good enough.
There’s a good rundown of the social scoring tools in “The Role of Sentiment Analysis in Marketing.” The content is focused on uses “emotional” and behavioral signals to sell stuff. However, the software and data sets yield high value information for other purposes. For example, an individual with access to data about the video viewing and Web site browsing about a person or a cluster of persons can make some interesting observations about that person or group.
Let me highlight some of the software mentioned in the write up. There is an explanation of the discipline of “sentiment analysis.” A person engaged in business intelligence, investigations, or planning a disinformation campaign will have to mentally transcode the lingo into a more practical vocabulary, but that’s no big deal. The write up then explains how “sentiment analysis” makes it possible to push a person’s buttons. The information makes clear that a service with a TikTok-type recommendation system or feed of “you will probably like this” can exert control over an individual’s ideas, behavior, and perception of what’s true or false.
The guts of the write up is a series of brief profiles of eight applications available to a marketer, PR team, or intelligence agency’s software developers. The products described are:
- Sprout Social. Yep, it’s wonderful. The company wrote the essay I am writing about.
- Reputation. Hello, social scoring for “trust” or “influence”
- Monkeylearn. What’s the sentiment of content? Monkeylearn can tell you.
- Lexalytics. This is an old-timer in sentiment analysis.
- Talkwalker. A content scraper with analysis and filter tools. The company is not “into” over-the-transom inquiries
If you have been thinking about the EU’s AI regulations, you might formulate an idea that existing software may irritate some regulators. My team and I think that AI regulations may bump into companies and government groups already using these tools. Working out the regulatory interactions between AI regulations and what has been a reasonably robust software and data niche will be interesting.
In the meantime, ask yourself, “How many intelware and policeware systems implement either these tools or similar tools?” In my AI presentation at the April 2024 US National Cyber Crime Conference, I will provide a glimpse of the future by describing a European company which includes some of these functions. Regulations do not control technology nor innovation.
Stephen E Arnold, April 9, 2024
Why Humans Follow Techno Feudal Lords and Ladies
December 19, 2023
This essay is the work of a dumb dinobaby. No smart software required.
“Seduced By The Machine” is an interesting blend of human’s willingness to follow the leader and Silicon Valley revisionism. The article points out:
We’re so obsessed by the question of whether machines are rising to the level of humans that we fail to notice how the humans are becoming more like machines.
I agree. The write up offers an explanation — it’s arriving a little late because the Internet has been around for decades:
Increasingly we also have our goals defined for us by technology and by modern bureaucratic systems (governments, schools, corporations). But instead of providing us with something equally rich and well-fitted, they can only offer us pre-fabricated values, standardized for populations. Apps and employers issue instructions with quantifiable metrics. You want good health – you need to do this many steps, or achieve this BMI. You want expertise? You need to get these grades. You want a promotion? Hit these performance numbers. You want refreshing sleep? Better raise your average number of hours.
A modern high-tech pied piper leads many to a sanitized Burning Man? Sounds like fun. Look at the funny outfit. The music is a TikTok hit. The followers are looking forward to their next “experience.” Thanks, MSFT Copilot. One try for this cartoon. Good enough again.
The idea is that technology offers a short cut. Who doesn’t like a short cut? Do you want to write music in the manner of Herr Bach or do you want to do the loop and sample thing?
The article explores the impact of metrics; that is, the idea of letting Spotify make clear what a hit song requires. Now apply that malleability and success incentive to getting fit, getting start up funding, or any other friction-filled task. Let’s find some Teflon, folks.
The write up concludes with this:
Human beings tend to prefer certainty over doubt, comprehensibility to confusion. Quantified metrics are extremely good at offering certainty and comprehensibility. They seduce us with the promise of what Nguyen calls “value clarity”. Hard and fast numbers enable us to easily set goals, justify decisions, and communicate what we’ve done. But humans reach true fulfilment by way of doubt, error and confusion. We’re odd like that.
Hot button alert! Uncertainty means risk. Therefore, reduce risk. Rely on an “authority,” “data,” or “research.” What if the authority sells advertising? What if the data are intentionally poisoned (a somewhat trivial task according to watchers of disinformation outfits)? What if the research is made up? (I am thinking of the Stanford University president and the Harvard ethic whiz. Both allegedly invented data; both found themselves in hot water. But no one seems to have cared.
With smart software — despite its hyperbolic marketing and its role as the next really Big Thing — finding its way into a wide range of business and specialized systems, just trust the machine output. I went for a routine check up. One machine reported I was near death. The doctor was recommending a number of immediate remediation measures. I pointed out that the data came from a single somewhat older device. No one knew who verified its accuracy. No one knew if the device was repaired. I noted that I was indeed still alive and asked if the somewhat nervous looking medical professional would get a different device to gather the data. Hopefully that will happen.
Is it a positive when the new pied piper of Hamelin wants to have control in order to generate revenue? Is it a positive when education produces individuals who do not ask, “Is the output accurate?” Some day, dinobabies like me will indeed be dead. Will the willingness of humans to follow the pied piper be different?
Absolutely not. This dinobaby is alive and kicking, no matter what the aged diagnostic machine said. Gentle reader, can you identify fake, synthetic, or just plain wrong data? If you answer yes, you may be in the top tier of actual thinkers. Those who are gatekeepers of information will define reality and take your money whether you want to give it up or not.
Stephen E Arnold, December 19, 2023
Predictive Analytics and Law Enforcement: Some Questions Arise
October 17, 2023
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Note: This essay is the work of a real and still-alive dinobaby. No smart software involved, just a dumb humanoid.
We wish we could prevent crime before it happens. With AI and predictive analytics it seems possible but Wired shares that “Predictive Policing Software Terrible At Predicting Crimes.” Plainfield, NJ’s police department purchased Geolitica predictive software and it was not a wise use go tax payer money. The Markup, a nonprofit investigative organization that wants technology serve the common good, reported Geolitica’s accuracy:
“We examined 23,631 predictions generated by Geolitica between February 25 and December 18, 2018, for the Plainfield Police Department (PD). Each prediction we analyzed from the company’s algorithm indicated that one type of crime was likely to occur in a location not patrolled by Plainfield PD. In the end, the success rate was less than half a percent. Fewer than 100 of the predictions lined up with a crime in the predicted category, that was also later reported to police.”
The Markup also analyzed predictions for robberies and aggravated results that would occur in Plainfield and it was 0.6%. Burglary predictions were worse at 0.1%.
The police weren’t really interested in using Geolitica either. They wanted to be accurate in predicting and reducing crime. The Plainfield, NJ hardly used the software and discontinued the program. Geolitica charged $20,500 for a year subscription then $15,5000 for year renewals. Geolitica had inconsistencies with information. Police found training and experience to be as effective as the predictions the software offered.
Geolitica will go out off business at the end of 2023. The law enforcement technology company SoundThinking hired Geolitica’s engineering team and will acquire some of their IP too. Police software companies are changing their products and services to manage police department data.
Crime data are important. Where crimes and victimization occur should be recorded and analyzed. Newark, New Jersey, used risk terrain modeling (RTM) to identify areas where aggravated assaults would occur. They used land data and found that vacant lots were large crime locations.
Predictive methods have value, but they also have application to specific use cases. Math is not the answer to some challenges.
Whitney Grace, October 17, 2023
LLM Unreliable? Probably Absolutely No Big Deal Whatsoever For Sure
July 19, 2023
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Note: This essay is the work of a real and still-alive dinobaby. No smart software involved, just a dumb humanoid.
My team and I are working on an interesting project. Part of that work requires that we grind through papers, journal articles, and self-published (and essentially unverifiable) comments about smart software.
“What do you mean the outputs from the smart software I have been using for my homework delivers the wrong answer?” says this disappointed user of a browser and word processor with artificial intelligence baked in. Is she damning recursion? MidJourney created this emotion-packed image of a person who has learned that she has been accursed of plagiarism by her Sociology 215 professor.
Not surprisingly, we come across some wild and crazy information. On rare occasions we come across a paper, mostly ignored, which presents information that confirms many of our tests of smart software. When we do tests, we arrive with specific queries in mind. These relate to the behaviors of bad actors; for example, online services which front for cyber criminals, systems which are purpose built to make it time consuming to unmask a bad actor, and determine what person owns a particular domain engaged in the sale of fullz.
You can probably guess that most of the smart and dumb online finding services are of little or no help. We have to check these, however, simply because we want to be thorough. At a meeting last week, one of my team members who has a degree in library science, pointed out that the outputs from the services we use were becoming less useful than they were several months ago. I don’t spend too much time testing these services because I am a dinobaby and I run projects. My doing days are over. But I do listen to informed feedback. Her comment was one I had not seen in the Google PR onslaught about its method, the utterances of Sam AI-Man at OpenAI, or from the assorted LinkedIn gurus who post about smart software.
Then I spotted “How Is ChatGPT’s Behavior Changing over Time?”
I think the authors of the paper have documented what my team member articulated to me and others working on a smart software project. The paper states is polite academic prose:
Our findings demonstrate that the behavior of GPT-3.5 and GPT-4 has varied significantly over a relatively short amount of time.
The authors provide some data, a few diagrams, and some footnotes.
What is fascinating is that the most significant item in the journal article, in my opinion, is the use of the word “drifts.” Here’s the specific line:
Monitoring reveals substantial LLM drifts.
Yep, drifts.
What exactly is a drift in a numerical mélange like a large language model, its algorithms, and its probabilistic pulsing? In a nutshell, LLMs are formed by humans and use information to some degree created by humans. The idea is that sharp corners are created from decisions and data which may have rounded corners or be the equivalent of wad of Play-Doh after a kindergartener manipulates the stuff. The idea is that layers of numerical recipes are hooked together to output information useful to a human or system.
Those who worked with early versions of the Autonomy Neuro Linguistic black box know about the Play-Doh effect. Train the system on a crafted set of documents (information). Run test queries. Adjust a few knobs and dials afforded by the Autonomy system. Turn it loose on the Word documents and other content for which filters were installed. Then let users run queries.
To be upfront, using the early version of Autonomy in 1999 or 2000 was pretty darned good. However, Autonomy recommended that the system be retrained every few months.
Why?
The answer, as I recall, is that as new data were encountered by the Autonomy Neuro Linguistic engine, the engine had to cope with new words, names of companies, and phrases. Without retraining, the system would use what it had from its initial set up and tuning. Without retraining or recalibration, the Autonomy system would return results which were less useful in some situations. Operate a system without retraining, the results would degrade over time.
Math types labor to make inference-hooked and probabilistic systems stay on course. The systems today use tricks that make a controlled vocabulary look like the tool of a dinobaby like me. Without getting into the weeds, the Autonomy system would drift.
And what does the cited paper say, “LLM drift too.”
What does this mean? Here’s my dinobaby list of items to keep in mind:
- Smart software, if left to its own devices, will degrade over time; that is, outputs will drift from what the user wants. Feedback from users accelerates the drift because some feedback is from the smart software’s point of view is spot on even if it is crazy or off the wall. Do this over a period of time and you get what the paper’s authors and my team member pointed out: Degradation.
- Users who know how to look at a system’s outputs and validate or identify off the mark results can take corrective action; that is, ignore the outputs or fix them up. This is not common, and it requires specialized knowledge, time, and mental sharpness. Those who depend on TikTok or a smart system may not have these qualities in equal amounts.
- Entrepreneurs want money, power, or a new Tesla. Bringing up issues about smart software growing increasingly crazy like the dinobaby down the street is not valued. Hence, substantive problems with smart systems will require time, money, and expertise to remediate. Who wants that? Smart software is designed to improve efficiency, reduce costs, and make money. The result is a group of individuals who do PR, not up-to-snuff software.
Will anyone pay attention to this cited journal article? Sure, a few interns and maybe a graduate student or two. But at this time, the trend is that AI works and AI applied to something delivers a solution. Is that solution reliable or is it just good enough? What if the outputs deteriorate in a subtle way over time? What’s the fix? Who is responsible? The engineer who fiddled with thresholds? The VP of product development who dismissed objections about inherent bias in outputs?
I think you may have an answer to these questions. As a dinobaby, I can say, “Folks, I don’t have a clue about fixing up the smart software juggernaut.” I am skeptical of those who say, “Hey, it just works.” Okay, I hope you are correct.
Stephen E Arnold, July 19, 2023
Researchers Break New Ground with a Turkey Baster and Zoom
April 4, 2023
Note: This essay is the work of a real and still-alive dinobaby. No smart software involved, just a dumb humanoid.
I do not recall much about my pre-school days. I do recall dropping off at different times my two children at their pre-schools. My recollections are fuzzy. I recall horrible finger paintings carried to the automobile and several times a month, mashed pieces of cake. I recall quite a bit of laughing, shouting, and jabbering about classmates whom I did not know. Truth be told I did not want to meet these progeny of highly educated, upwardly mobile parents who wore clothes with exposed logos and drove Volvo station wagons. I did not want to meet the classmates. The idea of interviewing pre-kindergarten children struck me as a waste of time and an opportunity to get chocolate Baskin & Robbins cake smeared on my suit. (I am a dinobaby, remember. Dress for success. White shirt. Conservative tie. Yada yada._
I thought (briefly, very briefly) about the essay in Science Daily titled “Preschoolers Prefer to Learn from a Competent Robot Than an Incompetent Human.” The “real news” article reported without one hint of sarcastic ironical skepticism:
We can see that by age five, children are choosing to learn from a competent teacher over someone who is more familiar to them — even if the competent teacher is a robot…
Okay. How were these data gathered? I absolutely loved the use of Zoom, a turkey baster, and nonsense terms like “fep.”
Fascinating. First, the idea of using Zoom and a turkey baster would never roamed across this dinobaby’s mind. Second, the intuitive leap by the researchers that pre-schoolers who finger-paint would like to undertake this deeply intellectual task with a robot, not a human. The human, from my experience, is necessary to prevent the delightful sprouts from eating the paint. Third, I wonder if the research team’s first year statistics professor explained the concept of a valid sample.
One thing is clear from the research. Teachers, your days are numbered unless you participate in the Singularity with Ray Kurzweil or are part of the school systems’ administrative group riding the nepotism bus.
“Fep.” A good word to describe certain types of research.
Stephen E Arnold, April 4, 2023
More Impressive Than Winning at Go: WolframAlpha and ChatGPT
March 27, 2023
For a rocket scientist, Stephen Wolfram and his team are reasonably practical. I want to call your attention to this article on Dr. Wolfram’s Web site: “ChatGPT Gets Its Wolfram Superpowers.” In the essay, Dr. Wolfram explains how the computational system behind WolframAlpha can be augmented with ChatGPT. The examples in his write up are interesting and instructive. What can the WolframAlpha ChatGPT plug in do? Quite a bit.
I found this passage interesting:
ChatGPT + Wolfram can be thought of as the first truly large-scale statistical + symbolic “AI” system…. in ChatGPT + Wolfram we’re now able to leverage the whole stack: from the pure “statistical neural net” of ChatGPT, through the “computationally anchored” natural language understanding of Wolfram|Alpha, to the whole computational language and computational knowledge of Wolfram Language.
The WolframAlpha module works with these information needs:
The Wolfram Language modules does some numerical cartwheels, and I was impressed. I am not sure how high school calculus teachers will respond to the WolframAlpha – ChatGPT mash up, however. Here’s a snapshot of what Wolfram Language can do at this time:
One helpful aspect of Dr. Wolfram’s essay is that he notes differences between an earlier version of ChatGPT and the one used for the mash up. Navigate to this link and sign up for the plug in.
Stephen E Arnold, March 27, 2023
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