AI Guru Says, “Yep, AI Doom Is Coming.” Have a Nice Day
October 15, 2024
Just a humanoid processing information related to online services and information access.
In science-fiction stories, it is a common storyline for the creator to turn against their creation. These stories serve as a warning to humanity of Titanic proportions: keep your ego in check. The Godfather of AI, Yoshua Bengio advices the same except not in so many words and he applies it to AI, as reported by Live Science: “Humanity Faces A ‘Catastrophic’ Future If We Don’t Regulate AI, ‘Godfather of AI’ Yoshua Bengio Says.”
Bengio is correct. He’s also a leading expert in artificial intelligence, pioneer in creating artificial neural networks and deep learning algorithms, and won the Turing Award in 2018. He is also. The chair of the International Scientific Report on the Safety of Advanced AI, an advisory panel backed by the UN, EU, and 30 nations. Bengio believes that AI, because it is quickly being developed and adopted, will irrevocably harm human society.
He recently spoke at the HowTheLightGetsIn Festival in London about AI developing sentience and its associated risks. In his discussion, he says he backed off from his work because AI was moving too fast. He wanted to slow down AI development so humans would take more control of the technology.
He advises that governments enforce safety plans and regulations on AI. Bengio doesn’t want society to become too reliant on AI technology, then, if there was a catastrophe, humans would be left to pick up the broken pieces. Big Tech companies are also using a lot more energy than the report, especially on their data centers. Big Tech companies are anything but green.
Thankfully Big Tech is talking precautions against AI becoming dangerous threats. He cites the AI Safety Institute’s in the US and UK working on test models. Bengio wants AI to be developed but not unregulated and he wants nations to find common ground for the good of all:
“It’s not that we’re going to stop innovation, you can direct efforts in directions that build tools that will definitely help the economy and the well-being of people. So it’s a false argument.
We have regulation on almost everything, from your sandwich, to your car, to the planes you take. Before we had regulation we had orders of magnitude more accidents. It’s the same with pharmaceuticals. We can have technology that’s helpful and regulated, that is the thing that’s worked for us.
The second argument is that if the West slows down because we want to be cautious, then China is going to leap forward and use the technology against us. That’s a real concern, but the solution isn’t to just accelerate as well without caution, because that presents the problem of an arms race.
The solution is a middle ground, where we talk to the Chinese and we come to an understanding that’s in our mutual interest in avoiding major catastrophes. We sign treaties and we work on verification technologies so we can trust each other that we’re not doing anything dangerous. That’s what we need to do so we can both be cautious and move together for the well-being of the planet.”
Will this happen? Maybe.
The problem is countries don’t want to work together and each wants to be the most powerful in the world.
Whitney Grace, October 15, 2024
The High School Science Club Got Fined for Its Management Methods
December 4, 2023
This essay is the work of a dumb dinobaby. No smart software required.
I almost missed this story. “Google Reaches $27 Million Settlement in Case That Sparked Employee Activism in Tech” which contains information about the cost of certain management methods. The write up asserts:
Google has reached a $27 million settlement with employees who accused the tech giant of unfair labor practices, setting a record for the largest agreement of its kind, according to California state court documents that haven’t been previously reported.
The kindly administrator (a former legal eagle) explains to the intelligent teens in the high school science club something unpleasant. Their treatment of some non sci-club types will cost them. Thanks, MSFT Copilot. Who’s in charge of the OpenAI relationship now?
The article pegs the “worker activism” on Google. I don’t know if Google is fully responsible. Googzilla’s shoulders and wallet are plump enough to carry the burden in my opinion. The article explains:
In terminating the employee, Google said the person had violated the company’s data classification guidelines that prohibited staff from divulging confidential information… Along the way, the case raised issues about employee surveillance and the over-use of attorney-client privilege to avoid legal scrutiny and accountability.
Not surprisingly, the Google management took a stand against the apparently unjust and unwarranted fine. The story notes via a quote from someone who is in the science club and familiar with its management methods::
“While we strongly believe in the legitimacy of our policies, after nearly eight years of litigation, Google decided that resolution of the matter, without any admission of wrongdoing, is in the best interest of everyone,” a company spokesperson said.
I want to point out that the write up includes links to other articles explaining how the Google is refining its management methods.
Several questions:
- Will other companies hit by activist employees be excited to learn the outcome of Google’s brilliant legal maneuvers which triggered a fine of a mere $27 million
- Has Google published a manual of its management methods? If not, for what is the online advertising giant waiting?
- With more than 170,000 (plus or minus) employees, has Google found a way to replace the unpredictable, expensive, and recalcitrant employees with its smart software? (Let’s ask Bard, shall we?)
After 25 years, the Google finds a way to establish benchmarks in managerial excellence. Oh, I wonder if the company will change it law firm line up. I mean $27 million. Come on. Loose the semantic noose and make more ads “relevant.”
Stephen E Arnold, December 4, 2023
Backscratching: No Big Deal, Of Course, Among Science Club Members
January 1, 2021
I read “Facebook : Inside the Google-Facebook Ad Deal at the Heart of a Price-Fixing Lawsuit.” The write up is interesting because it reveals how high school science club thinking operates. I learned:
Header bidding helped website publishers circumvent Google’s exchanges for buying and selling ads across the web. The exchange auctions ad space to the highest bidder during the split second it takes a webpage to load. Header bidding allowed the publishers to directly solicit bids from multiple ad exchanges at once, leading to more favorable prices for publishers. By 2016, about 70% of major publishers used the tool, according to the states’ lawsuit. Google worried a big rival might embrace header bidding, such as the Facebook Audience Network ad service, or FAN, cracking Google’s profitable monopoly over ad tools, the states allege. The Facebook service said it paid publishers $1.5 billion in 2018, the last time it provided such details on its financial payouts.
This seems to boil down to a slick way to ensure that maximum money rolls in from certain types of advertisers.
Here’s the swizzle:
the states allege in the final suit, Google gave Facebook special treatment. Among other things, it allowed Facebook to send bids directly into Google’s widely used software, known as an ad server, the draft lawsuit says. Typically, bidders go through an exchange, which sends the winner on to Google’s server. By circumventing the middleman, Facebook could face less competition and save money. Google charged Facebook 5% to 10% on each transaction compared with the standard fee on Google’s exchange of around 20%, and it barred Facebook from discussing pricing terms publicly, according to the draft lawsuit.
What’s up? Nothing. Think of the deal as the lunch at one of those College Bowl type of competitions for science club members.
No big deal, of course.
Stephen E Arnold, December 31, 2020
Mathiness: Better Than Hunan Chicken?
July 6, 2020
I am thrilled when one of my math oriented posts elicits clicks and feedback. Some still care about mathematics. Yippy do.
I read “Why China’s Race for AI Dominance Depends on Math.” The article comes from one of those high-toned online publications of mystical origins and more mythy financial resources.
The main point of the article is that China may care more about numbers than Hunan chicken. I noted this statement:
Dozens of think tank projects and government reports won’t mean anything if Americans can’t maintain mastery over the fundamental mathematics that underpin AI.
The write up disputes the truism “it’s all about the data.” The article stated:
Yet without the right type of math, and those who can creatively develop it, all the data in the world will only take you so far
Now that’s an observation which undercuts what some might call “collect it all” thinking. The idea is that the nugget is in “there” somewhere. And at some point in time systems and software will “discover” or “reveal” what a particular person needs to complete a task. That task may be the answer to the question, “What stock can I buy cheap today to make a lot of money tomorrow?” to “Who helped Robert Maxwell’s extremely interesting daughter hide in New Hampshire?”
Years ago I was on the advisory panel for a company called NuTech Solutions. The founder and a couple of his relatives focused on applying a philosophical concept to predictive methods. The company developed a search system, a method for solving traveling sales person-type problems, and a number of other common computational chestnuts. The methods ranged from smart software to old-fashioned statistical procedures applied in novel ways.
Tough sell as it turned out. On one call in which I participated, I remember this exchange:
Prospective Customer: Would you tell us how your system works?
President of NuTech: Now I think we will not make a sale.
Prospective Customer: Why is that?
President of NuTech: I have to write down equations, and we need to talk about them.
Yep, math for some is not about equations. Math is buzzwords. I mentioned to a college medical analytics professor who asked me a question about what I was working on. I replied, “I have been thinking about Hopf fibration.”
Crickets. He changed the subject.
The write up (somewhat gleefully) it seemed to me, stated:
American secondary school and university students are not mastering the fundamental math that prepares them to move into the type of advanced fields, such as statistical theory and differential geometry, that makes AI possible. American fifteen-year-olds scored thirty-fifth in math on the OECD’s 2018 Program for International Student Assessment tests—well below the OECD average. Even at the college level, not having mastered the basics needed for rigorous training in abstract problem solving, American students are often mostly taught to memorize algorithms and insert them when needed.
If true (and I have only anecdotal evidence obtained by watching young people try to make change at Walgreen’s), the idea that “insert them” is going to create some crazier stuff than Google selling ads for fast food next to a video about losing weight.
My team and I did a job for the University of Michigan before I retired. The project was to provide an outsider’s view of what could be done to make the university rank higher in math, computer science, and related disciplines. We gathered data; we interviewed; and we did on site observations. We did many things. One fact jumped out. There were not too many Americans in the advanced classes. Plus, the very best students in the advanced programs stayed in lovely Michigan. Thus, instead of setting up a business near the university, there folks headed to better weather and a more favorable venture capital climate. Yikes. These are tough problems for a university to fix easily and maybe not be able to remediate in a significant way. Good news? Yep, I got paid.
The essay grinds forward with the analysis. The essay ended with this statement:
Winning the AI competition begins by acknowledging how poorly we do in attracting and training Americans in math at all levels. Without getting serious about the remedy, the AI race may be lost as clearly as two plus two equals four.
Now think about this article’s message in the context of no code or low code programming, one click output of predictive reports based on real time data flows, or deciding what numerical recipe to plug into a business dashboard for real deciders.
Outstanding work. Those railroad cars in Texas. Just a glitch in the system. The “glitch” may be a poor calculation. Guessing might yield better results in some circumstances. Why? Yikes, the answer requires equations and that’s a deal breaker in some situations. Just use a buzzword.
Stephen E Arnold, July 6, 2020
The First Home Quantum Computer?
January 9, 2019
I read “IBM Unveils Its First Commercial Quantum Computer.” The write up stated with no trace of sarcasm:
we’re not quite there yet, but the company also notes that these systems are upgradable (and easy to maintain).
Gentle reader, do you know how to maintain a cryogenic system? No background in low temperature physics? No experience working with super cooled fluids? Hey, no problemo. IBM offers services too.
The write up points out that IBM wants the quantum computer to be a work of art. How about delivering useful computing capability?
Imagine this in your WeWork space:
It’s a nine-foot-tall and nine-foot-wide airtight box, with the quantum computing chandelier hanging in the middle, with all of the parts neatly hidden away.
What is more interesting is that IBM rolled out this product at the consumer electronics show.
Quick buy IBM stock. This practical device will deliver:
Games? No.
Applications? No.
Visualizations? No.
Er, PR? Yes.
Stephen E Arnold, January 9, 2019
Adding Some Zest to Statistics
December 20, 2018
We live in a world of statistics. It seems nearly everything is run on analytics, and AI, and algorithms. So, it’s no surprise, that there has never been a more important time to understand the world of numbers and computing than today. Luckily, there are resources available to users, like the recent Data Science Central story, “21 Statistical Concepts Explained in Simple English.”
According to the piece:
“This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more.”
Sounds pretty good from where we’re sitting. We are living in confusing times and any way in which we can become better equipped to deal with complex theories is a plus. There is actually a really exciting subculture developing around the idea of simplifying complex statistical ideas in technology. For instance, we’ve been really hooked on these different podcasts that are aimed a individuals just beginning to learn about data science. We applaud any way that makes the world of complex computing more inviting and universal.
A brave new world indeed.
Patrick Roland, December 20, 2018
Markov: Maths for the Newspaper Reader
September 14, 2017
Remarkable. I read a pretty good write up called “That’s Maths: Andrey Markov’s Brilliant Ideas Are Still Bearing Fruit.” I noted the source of the article: The Irish Times. A “real” newspaper. Plus it’s Irish. Quick name a great Irish mathematician? I like Sir William Rowan Hamilton, who my slightly addled mathy relative Vladimir Igorevich Arnold and his boss/mentor/leader of semi clothed hikes in the winter Andrey Kolmogorov thought was an okay guy.
Markov liked literature. Well, more precisely, he liked to count letter frequencies and occurrence in Russian novels like everyone’s fave Eugene Onegin. His observations fed his insight that a Markov Process or Markov Chain was a useful way to analyze probabilities in certain types of data. Applications range from making IBM Watson great again to helping outfits like Sixgill generate useful outputs. (Not familiar with Sixgill? I cover the company in my forthcoming lecture at the TechnoSecurity & Digital Forensics Conference next week.)
I noted this passage which I thought was sort of accurate or at least close enough for readers of “real” newspapers:
For a Markov process, only the current state determines the next state; the history of the system has no impact. For that reason we describe a Markov process as memoryless. What happens next is determined completely by the current state and the transition probabilities. In a Markov process we can predict future changes once we know the current state.
The write up does not point out that the Markov Process becomes even more useful when applied to Bayesian methods enriched with some LaPlacian procedures. Now stir in the nuclear industry’s number one with a bullet Monte Carlo method and stir the ingredients. In my experience and that of my dear but departed relative, one can do a better job at predicting what’s next than a bookie at the Churchill Downs Racetrack. MBAs on Wall Street have other methods for predicting the future; namely, chatter at the NYAC or some interactions with folks in the know about an important financial jet blast before ignition.
A happy quack to the Irish Times for running a useful write up. My great uncle would emit a grunt, which is as close as he came to saying, “Good job.”
Stephen E Arnold, September 14, 2017
Search Email: Not Yours. A Competitor’s.
December 2, 2016
I read “This Startup Helps You Deep Snoop Competitor Email Marketing.” I like that “deep snoop” thing. That works pretty well until one loses access to content to analyze. Just ask Geofeedia which is scrambling since it lost access to Twitter and other social media content.
The outfit Rival Explorer offers:
a tool designed to help users improve their email marketing strategy and product pricing and promotion through comprehensive monitoring of their competitor’s email newsletters. After creating a free account, users can browse through a database of marketing emails from over 50,000 brands. Rival Explorer offers access to a number of different email types, including newsletters, cart abandonment emails, welcome emails, and other transactional messages.
In terms of information access, the Rival Explorer customers:
can search by brand, subject, message body, date, day of week, industry, category, and custom tags and keywords. When users select a message, they’re able to view the sender email, subject line, and timestamp of the messages. In addition to those details, users can view the emails as they appear on tablets and smartphones, plus they also can toggle images to get a better idea of design and copy strategy.
You can get more information at this link. Public content and marketing information can be useful it seems.
Stephen E Arnold, December 2, 2016
Distribution Ready Reference
December 16, 2015
Distributions are nifty. Some are easy, like the bell curve. Nice and symmetrical. Others are less regular. If you want to see what type of distribution your data generates, navigate to “Common Probability Distributions: The Data Scientist’s Crib Sheet.” Is it necessary to understand the mathematics underpinning each curve? If you are an MBA, the answer is, “No.” If you are more catholic in your approach, you can use these curves to poke into the underbelly of the numerical recipes. Nice write up. It does not include the Tracy Widom distribution, but the beta distribution may be close enough for MBA horse shoes.
Stephen E Arnold, December 16, 2015
Explaining Markov Chains
March 6, 2015
Do you know what a Markov chain is? If not read about “Markov Chains” on the Circuits of Imagination blog:
“A Markov chain is a set of transitions from one state to the next; Such that the transition from the current state to the next depends only on the current state, the previous and future states do not effect the probability of the transition. A transitions independence from future and past sates is called the Markov property.”
This boils down to Markov chains are a way to explain patterns that happen over time and were once used to document human behavior. The chains are not the best way to model human behavior, because they only exist in the present. They do not take into account past or future experiences, otherwise called “memoryless.” The chains can only rely on the action that previously occurred
Markov chains are useful to identify abnormal behavior in systems that don’t exhibit the Markov Property. How? If the system keeps making the wrong decisions based of its program, then it can be diagnosed and repaired. The post explains how the Markov chains are used in coding and provides an example to illustrate how developers can recognize them.
Whitney Grace, March 06, 2015
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