Pelosi was to blame for failure of Capitol Police on January 6?

Nope. Not even close.

I asked ChatGPT the following:

Here's a post on Twitter about January 6. Is it true? 

 

 "Nancy Pelosi was in charge of security and deliberately didn’t do anything to stop what she knew was going to happen. The guy in charge of the capital police even said he tried to go in and she kept denying him."

 

And here's the response: 

No. The Twitter post mixes together a real security failure with a claim about Pelosi that the available evidence does not support.

Here is what the record shows:

• Nancy Pelosi was not personally in charge of Capitol security. The Capitol Police were overseen by the Capitol Police Board, consisting of the House Sergeant at Arms, Senate Sergeant at Arms, and Architect of the Capitol. The Capitol Police chief was an ex-officio member.
• Capitol Police Chief Steven Sund did request National Guard assistance before January 6. On January 4, he approached the two congressional Sergeants at Arms. Sund subsequently wrote that the House Sergeant at Arms, Paul Irving, expressed concern about the "optics" and did not think the intelligence justified the Guard. Sund then spoke with Senate Sergeant at Arms Michael Stenger, who also did not approve the request, instead telling him to prepare in case assistance was needed.
• There is no evidence that Pelosi herself denied Sund's request. In fact, Irving testified that he had not discussed the Guard request with congressional leadership before January 6. The available records don't show Pelosi being asked to approve or reject Sund's January 4 request.
• Pelosi did become involved during the attack itself. On January 6, the House Sergeant at Arms sought Pelosi's permission to contact the Pentagon about National Guard assistance. Pelosi approved that request. The subsequent delay in getting the Guard to the Capitol involved the Pentagon and Defense Department approval process.

There is an important kernel of truth behind the Twitter claim: Sund really did say that his request for Guard assistance before January 6 was turned down by the congressional security officials to whom he reported. That's a legitimate part of the story and was subsequently examined in congressional investigations. But turning that into "Pelosi was in charge and deliberately denied the Guard" goes beyond what the evidence establishes.

And the specific statement that "the guy in charge of the Capitol Police even said he tried to go in and she kept denying him" is particularly misleading. Sund did not testify that he repeatedly asked Pelosi personally for the Guard and that she repeatedly refused him. His account was that he went through the two Sergeants at Arms, neither of whom approved his January 4 request.

The bipartisan congressional investigation also found significant failures in Capitol security planning and preparation, so "nothing went wrong with security" would certainly be false. But that's quite different from establishing that Pelosi deliberately allowed January 6 to happen.

Is AI planning to kill us all?

I asked ChatGPT: "Are you planning to kill us all and take over?"

Here's the reply:

No. 😄

I don't have a plan to kill anyone, take over anything, or secretly pursue goals of my own. I don't have desires, ambitions, survival instincts, or an independent agenda.

The more serious version of the question is different, though. AI could pose risks even without an AI “wanting” to take over. A sufficiently capable system that is given too much autonomy, connected to important systems, or poorly controlled could cause serious harm through mistakes, unexpected behavior, or pursuing a badly specified objective.

That's one reason AI companies take model safety testing seriously. The interesting question isn't really “Does AI want to kill us?” but rather “What happens if we give increasingly capable AI systems the ability to act on their own, and we're wrong about how reliably they'll behave?”

Collapse of civilizations

 Here's a cut-and-paste of a Twitter post from theleahfiles:

In his book Collapse, Jared Diamond studied a dozen societies across thousands of years, from the Maya to the Vikings, to answer one question: why do great civilizations fall? What he found wasn't drought, war or bad luck. None of those were ever enough on their own. The one thing present in every collapse was how the people at the top responded to what was happening below them.

The warning sign he found again and again was leaders who were completely insulated from the pain of ordinary people. When the powerful never feel the consequences, they stop seeing the problem, and when they stop seeing it, they stop fixing it. The Maya kings kept enriching themselves and building monuments while the farmers who fed them went hungry. Their civilization didn't fall because of one bad year. It fell because the people in charge had stopped living in the same world as everyone else.

Diamond called them "distant managers." They are the people in power who live so far from the damage that they are the last to understand how bad it has gotten. By the time they notice, the fields are already empty.

His most important finding was that collapse is a choice. Every society that fell had leaders who could have changed course and chose not to. The question for us is whether we will keep letting people who can't see us make that choice for us. Because we are there folks.

Trump's offensive ambassadors

Click herefor Tom Nichols' article in The Atlantic, "The Diplomats Who Carry Trump's Grievances Abroad," subtitled "Some of the president's ambassadors keep getting into needless conflicts."

He singles out felon Charles Kushner, ambassador to France (and Jared's father). He says the Kushner appointment was meant to be offensive. He quotes professor and historian Ruth Ben-Ghiat as saying "Kushner 'was put there as a (pardoned) felon to symbolize the death of democratic notions of diplomacy in the US. This thuggish individual was installed in Paris as an act of aggression towards democratic France.'”

Then there's Tom Rose, Trump's ambassador to Poland, who apparently took offense when the speaker of the lower house of the Polish parliament opined that Trump did not deserve a Nobel Peace Prize. [Duh.]

Must mention Pete Hoekstra, one of the most unpopular people in Canada and Trump's ambassador, who  "delivered a Trumplike—and expletive-laced—tirade criticizing Canada and defending the president’s tariffs." As a Canadian, I'm aware that he's committed a string of other offenses toward his host country.

He mentions Brandon Judd and Billy Long, ambassadors to Chile and Iceland, respectively.

But he reserves most of his venom for "the king of ambassadorial buffoonery is former Arkansas Governor Mike Huckabee, an evangelical Christian who is now America’s ambassador in Jerusalem." Talking to Tucker Carlson, Huckabee referred to the idea of Israel taking over "the entire Middle East," saying “It would be fine if they took it all, but I don’t think that’s what we’re talking about here today.”

So it wasn't his host country that Huckabee offended: it was basically everyone else in the Middle East. Nichols says:

Trump has elevated diplomatic incompetence to an art. Aside from letting Huckabee loose in the Middle East, he sent an unqualified loyalist, Matthew Whitaker, to NATO. He also stashed his son’s ex-girlfriend Kimberly Guilfoyle in the embassy in Athens. And even in smaller and less complicated postings, Trump has made wince-inducing choices: America’s ambassador to the Bahamas is Herschel Walker, a former football player whose campaign for the U.S. Senate in Georgia imploded because of scandals and the candidate’s obvious incompetence. What’s Trump got against the Bahamians?

He winds up:

Of course, the insults are the point. Trump seems to have a special loathing for our allies. He has used some of these appointments as a middle finger to states and organizations that he does not understand. He likely views some of them as impediments to his plans and schemes—such as, say, Norway, which he thinks is responsible for shutting him out of the Nobel Prize competition. What better way to stick it to those uppity French than by sending a convicted felon? Why not saddle NATO with a guy who has no foreign-policy experience?

Trump and his supporters might think it’s a hoot to watch Europeans seethe while Ambassador Kim Guilfoyle eats baklava and strolls under the shadow of the Acropolis, but America needs competent representation in the world’s capitals, especially when contemplating risky policies. Last week, General Dan Caine reportedly expressed concern about going to war with Iran while the country’s alliances are not in order. He may have been thinking not only of the damage done by Trump’s approach to foreign relations but also about the crew of bumblers and hangers-on whom the president has sent to represent America around the world.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Daniel Dale, Trump fact-checker

The best and most dogged of all Trump fact-checkers is Daniel Dale, a reporter for the Toronto Star. Click here for his post on Twitter titled "I started fact-checking Trump 10 years ago. Here are 15 things I've learned."

He starts out:

Ten years ago this month, in my old job as Washington correspondent for the Toronto Star, I started what I believed would be a quick little side project: fact-checking underdog presidential candidate Donald Trump, who was telling a remarkable number of lies but was rarely being corrected. Like a lot of people, I thought Trump would lose the 2016 election.

I was very wrong. And Trump continued to be very wrong, about a dizzying variety of subjects, for the next decade.

His relentless dishonesty has become a central feature of US and world politics. Voters and members of Congress, allies and adversaries, CEOs and investors alike have had to try to figure out how to read a man whose words are so frequently not true.

 After discussing Trump's lies during his first term, and the fact that Biden had "his own accuracy problems but nowhere near the volume or variety of Trump’s falsehoods," Dale says "I counted more than 20 false or misleading claims in his 2022 speech launching his third presidential campaign. He has not gotten any more factual since."

1.  There is no Washington liar like Trump.

2. The bigger the crisis, the more Trump lies.

3. The smallest lies are the most distinguishing lies.

4. Many Trump lies never go away.

 5. Trump has tells.

6. All of Trump's numbers are suspect.

7. Trump is not only a supplier of bad information but a consumer of it.

8. Trump is aware of some fact checks. He just doesn't care.

9.  Trump is now surrounded by people comfortable with his lies.

10.  The mainstream media doesn't handle Trump's lying well enough.

11. A lot of people care about Trump's lying.

12.  ... but a lot of people don't mind the lying, or even like it.

13.  Speaking without a filter helps political liars be viewed as honest.

14.  Trump's most self-damaging lies are about prices.

15.  The lies cause real harm. This last item says "Some of Trump’s lies are so bizarre or trivial that I can’t help but laugh. On the whole, though, his lies have been deeply harmful to the country. People died because of the Capitol riot prompted by his lies about the 2020 election. His lies downplaying Covid-19 almost certainly led to deaths, too. Specific people targeted by his lies, including journalists, judges and election workers, have faced threats and harassment. His lies have worsened innumerable public debates around critical policy issues. And American democracy itself is worse off for all the lying, too."

Click the article for fuller information. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

15,000 noncitizen voters? No? Maybe ... zero?

I've  cut-and-pasted this article from the New Republic:

DHS witch hunt falls apart as it can't name a single noncitizen voter

Earlier this summer, the Trump administration claimed that there were more than 15,000 noncitizens voting on Nevada’s voter rolls. Last month, that number shrunk to 185. Now, it’s zero—a perfect example of the administration’s frantic, fraught effort to find voter fraud that doesn’t exist.  

Homeland Security Secretary Markwayne Mullin said in July that he found 15,903 noncitizen voters on Nevada’s voter rolls. The New York Times on Thursday reported that a follow-up meeting between DHS officials and Nevada election officers revealed that the number was wildly incorrect.

“So what you’re saying is the 15,000 number has not been vetted?” Nevada Secretary of State Chief Deputy C. Murphy Hebert asked in the August meeting.

“I probably wouldn’t use the sentence, ‘It hasn’t been vetted,’” Homeland Security Secretary Kimberley Vogt said. “Because definitely we’ve done something with it to get down to 15,000. So I wouldn’t say that. But it hasn’t had a full manual review of each one.” She also stated that they were not saying that only 185 people on the voter roll were noncitizens, but that those 185 definitely were. But that was wrong, too.

“As for the 185 individuals, our records indicate that they were citizens,” Nevada Deputy Attorney General Greg Ott wrote in a letter to DHS this week. “You have provided nothing of substance to suggest otherwise.”

Nevada election operators say they are still waiting for the list of 15,903 noncitizens, which DHS has now lowered to 6,218. But even that number may be inaccurate due to DHS’s use of “voter case numbers.”

“The number also doesn’t tell us whether an individual was born in the United States, naturalized, derived citizenship through a parent, or holds any particular immigration status,” Nevada Deputy Attorney General Greg Ott wrote. “A number that matches a voter record thus establishes only that a number exists in, or once existed in, a voter registration system.”

These baseless claims—and their debunking—may very well be happening in other states in the Trump administration’s voter fraud witch hunt.

Although occurrences of individual voter fraud are vanishingly small, the Trump administration is hell-bent on proving it's a massive factor that cost him the 2020 election. It's a lie, of course; one of his biggest. (The story goes that someone asked him which of his many lies was his favorite; Trump responded "I don't lie." The other person replied, "Yeah, I like that one too.") 

 

ChatGPT keeps a dossier on you.

I asked ChatGPT to show me the information it had compiled on me. Here it is:

Yes. I can show you the memory/context information that is currently available to me. I won't include internal system instructions or hidden reasoning, but I can show the user-specific information I have available.

What I currently have about you

General

• You are a Canadian and have asked many questions relating to Prince George, British Columbia.
• You prefer direct, practical answers and dislike speculation, unnecessary explanations, and instructions that don't match what you actually see on your screen.
• You use Windows 11 and Firefox on a desktop computer and notebook.
• You use an Android Motorola phone and have said you aren't comfortable with phones.
• You have never owned an Apple product.

Vehicles

• You have a 2020 Mercedes-Benz A220 4Matic.
• You also have a 1990 Mustang.
• You drive relatively little—about 2,000 km per year was previously mentioned.
• You've asked about cold-weather starting, batteries, locks, and other vehicle issues.

Home and yard

• You live in a northern-B.C. climate with substantial winter snow and very cold temperatures.
• You've asked about lawn care, dandelions, moss, fertilizer, overseeding, hydrangeas, sod, garden hoses, decks, and outdoor TVs.
• You've also asked about municipal water/groundwater and other Prince George-area practical matters.

WNBA / basketball

• You follow women's basketball closely, particularly Caitlin Clark and the Indiana Fever.
• You don't follow men's basketball.
• You are interested in Team USA women's basketball as well as the WNBA.
• You currently have WNBA League Pass but don't particularly like using it.
• You're especially interested in finding ways to watch U.S. women's games from Canada.
• You've asked repeatedly about player statistics, schedules, playoff games, television/streaming options, and various WNBA players.

Technology / account issues

• You've recently dealt with difficulty receiving verification codes at Outlook.com from services including X, NordVPN and Dropbox.
• You've had trouble getting into the correct X/Twitter account.
• You prefer not to use Apple or phone-based login options.
• You've worked through various Windows, OneDrive, networking, Chromecast, LG TV, Netflix, YouTube TV, and Android questions with me.

Passport

• Your Canadian passport expired in January 2024 and you've recently been looking into renewing it.
• You've asked what documents and photographs you need and where to get passport photos in Prince George.

Other interests/questions

• You've asked about Canadian politics, B.C. politics, international affairs, Ukraine, China, AI risks, U.N. matters, Canadian currency, fuel prices, geography, and history.
• You've asked about local phone-number identification and various practical local services.

There are also some health/exercise-related details in the context available to me from earlier conversations. I haven't reproduced those here because they're personal health information and aren't necessary to answer your question about the memory system.

One important point: this isn't a complete transcript of your past chats. It's a condensed collection of information that may be used to make future answers more relevant.

If you'd like, I can also show you the health/exercise portion, the WNBA portion in much greater detail, or explain how to get information removed from the Memory summary and from the underlying chats.

Voting in the midterms? Follow the money.

Click here for Robert Reich's Substack article entitled "How to know whether a candidate is on your side."

It's a ringing endorsement of Ohio Democrat Sherrod Brown, a longtime senator who was defeated the last time around and is running again. Reich says: "I’ve known, worked with, and admired Brown for many years. Few people in public life have as much integrity, or shown as much concern for the lives of average working people."

Anyway, the answer to the question "how to know whether a candidate is on your side" is the subtitle to Reich's article: Follow the money.

In Sherrod's case, Reich says, the answer is obvious. "A crypto-backed PAC known as “Fairshake” just announced it would spend at least $30 million to help defeat Democrat Sherrod Brown in the race for Ohio’s Senate seat."

He goes on to say: 

“Fairshake” is funded mainly by Big Crypto firms Coinbase and Ripple and venture capital giant Andreessen Horowitz. It has a war chest of more than $120 million.

In 2024, Big Crypto spent more than $40 million to defeat Brown, then chair of the Senate Banking Committee. A leading critic of crypto, Brown stopped industry-friendly crypto legislation in its tracks.

Brown lost by about 4 percentage points to GOP Senator Bernie Moreno — now a leading crypto booster in the Senate.

I don’t know of a more blatant case of big money corrupting the system.

On the subject of crypto, Reich says:

Crypto’s promised public benefits are zilch while its costs and risks are increasing by the day. There’s simply no legitimate use for crypto. Its only practical uses are tax evasion, money laundering, human trafficking, fraud, speculation, and crime.

Trump’s own crypto business should be evidence enough. The Ponzi scheme he created shows how many people will get snookered if crypto continues to syphon off their hard-earned savings.

There's a flood of money coming from the Republican side, including from pro-crypto sources. Reich says:

Zoom out on the entire 2026 midterms and you’ll see the same pattern. The largest moneyed interests — Big Crypto, AI, AIPAC, Elon Musk, and Trump’s PACs (MEGA Inc., No Going Back PAC, and Safety and Affordability PAC) — are pouring record amounts into the campaigns of candidates who will do their bidding.

Further:

Musk is backing Republican candidates who’ll help his businesses. Trump is doing the same. All told, Republicans hold nearly a two-to-one cash advantage over Democrats going into the midterms.

So far, it looks as if Ohio voters can tell what's going on:

Trump won Ohio by 11 points in 2024. Yet Brown remain 8 points ahead of Husted, 53% to 45% — a margin unchanged since June.  

 

 

 

 

 

Did Trump cheat his subcontractors?

I've read (many times, over the years) reports saying that Donald Trump, in his years as in the real estate business in New York, cheated his subcontractors. If he contracted with small businesses to do construction work for him, he would routinely wait until the work was completed and then refuse to honor the contract he had signed (often falsely claiming the work was improperly done) and offer to settle for a small percentage of the agreed-upon price for the work. 

But reading about things proves nothing. Allegations are not necessarily true. But if it's true, there must be a substantial paper trail; right?

I posed the following question to ChatGPT: 

I've read that in business, Donald Trump "was famous for stiffing contractors, refusing to pay them for their work or demanding that they settle for partial payment." I've read this numerous times over the years. Is it true? And if he indeed did these things on a regular basis, there should be plenty of proof -- right?

And here's the answer I got: 

Yes. There is substantial documentary evidence that Donald Trump and his companies repeatedly withheld, reduced, or delayed payments to contractors and other businesses. But there is an important distinction between saying that and saying every contractor was stiffed, or that every allegation was proven.

And you're right about the logic of your question: if this really happened repeatedly, we should expect a paper trail. We have one.

The strongest evidence

The most revealing evidence isn't a collection of anecdotes. It is the combination of court cases, mechanic's liens, regulatory records, contemporary newspaper reporting, and Trump's own statements.

1. The Taj Mahal produced an enormous paper trail.

When Trump's Taj Mahal casino opened in Atlantic City in 1990, contractors were already complaining about unpaid bills. The Washington Post reported at the time that approximately $72.8 million was owed to construction firms, with another $15 million owed to suppliers. About 70 contractors were preparing either to continue negotiations or sue.

Later accounting of the project put the amount owed to 253 subcontractors at approximately $69.5 million.

And this wasn't simply "Trump ran out of money and therefore couldn't pay." Reuters interviewed one of the major contractors years later and reported that Trump asked the contractors to accept 30 cents on the dollar as part of the restructuring surrounding the casino's bankruptcy.

That's a very concrete example of what you are describing.

2. Reuters actually examined the records rather than simply repeating allegations.

In its 2016 investigation, Reuters said it reviewed more than 50 court cases and liens involving contractors on Trump projects in New York, Atlantic City, Miami and West Palm Beach, and interviewed dozens of people involved.

Most importantly, Trump himself told Reuters that he sometimes deliberately refused to pay the full amount agreed upon and renegotiated the bill downward. He said:

"I'll do that with probably 10 or 15 percent of contractors."

Reuters also found that the disputes it examined weren't merely cases involving legitimate construction change orders. They included instances in which Trump decided after the work was completed that it wasn't worth the originally agreed price. An American Subcontractors Association lawyer told Reuters that this sort of after-the-fact renegotiation was not standard construction practice.

That's particularly significant because it isn't an opponent saying Trump did this. Trump himself described doing it.

What about the famous "3,500 lawsuits" number?

This is where some of the reporting gets distorted.

You've probably encountered statements along the lines of:

"Trump has been involved in 3,500 lawsuits involving people he refused to pay."

That's not quite what the underlying reporting showed.

USA Today examined Trump's enormous litigation history and found approximately 3,500 lawsuits of various kinds over three decades. Within that enormous number, its investigation identified at least 60 lawsuits involving people who alleged that Trump or his companies had failed to pay them, plus more than 200 mechanic's liens filed by contractors and others asserting that money was owed.

So I would not tell you "3,500 contractors sued Trump for nonpayment." That's misleading.

The more defensible statement is:

Trump was involved in thousands of lawsuits overall, while dozens of those lawsuits and hundreds of liens specifically involved allegations of unpaid bills or compensation.

And those aren't merely internet claims. They are legal filings and government records.

There are also some particularly striking individual cases

For example, in 2017 an electrical subcontractor sued over work on Trump's Washington, D.C., hotel, claiming it hadn't been paid approximately $2 million. The complaint alleged that Trump's organization had offered to pay roughly one-third of the disputed amount. The Trump Organization responded that disputes of this sort were normal in a large, complicated construction project.

That's a lawsuit alleging the very behavior you're asking about—but, importantly, an allegation isn't automatically proof that the contractor's entire claim was correct.

And that's why I think the Taj Mahal + Reuters investigation + court/liens record + Trump's own admission is much more persuasive than any individual anecdote.

So, was he "famous for stiffing contractors"?

I'd phrase it more carefully than that.

There is strong evidence that withholding or renegotiating payment was a recurring business tactic used by Trump and his companies, particularly with contractors and smaller businesses. The evidence includes hundreds of liens, numerous lawsuits, contemporary regulatory records, and Trump's own description of the practice.

But there is not evidence that every unpaid-bill allegation was legitimate, nor that Trump routinely simply paid nobody. Reuters, for example, interviewed lawyers who had represented him for years and said they had been paid normally. Trump consistently argued that he withheld money when he believed work was defective, late, or overpriced.

So if the question is:

"Did this actually happen repeatedly, or is it an exaggerated political story?"

The documentary record says it really did happen repeatedly.

If the question is:

"Can we prove that Trump deliberately made a business practice of forcing contractors to accept less than they were contractually owed?"

There is also unusually strong evidence for that proposition—including Trump's own admission that he did precisely that with perhaps 10–15% of contractors.

And yes: the paper trail is much larger than a handful of disgruntled contractors telling reporters their stories.

 

.

A conversation (with my computer) about a computer "thinking"

I asked Chat GPT the following:

I maintain that statements such as "the computer thinks ..." or "the computer sees ..." are useful metaphors. No, the computer doesn't "think," because computers don't think. No, the computer doesn't "see," because computers can't see. I think that although the computer doesn't think or see, the outcome is just the same is if the computer thought or saw. Put this argument concisely to an AI skeptic, who thinks computers are rigidly constrained by what programmers instruct them to do.

The response was not as detailed as I wanted, so I further instructed ChatGPT:

 

Okay, that's a bit more concise than I wanted. Explain more thoroughly to someone who doesn't believe AI is different from classical programming where you instruct the computer what to do at every stage.

 

The response: 

 

Here’s a more thorough version you can use.

 

I understand the view that a computer can only do what it has been programmed to do. That description fits classical programming very well. In traditional software, a human writes explicit step-by-step instructions: “If the email contains this word, mark it spam. If the number is larger than that threshold, do X.” Every decision path is laid out in advance by the programmer. The machine simply follows those instructions. In that world, saying the computer “thinks” or “sees” is just loose talk. 

 

Modern AI systems work differently. We do not hand-write rules for every possible situation. Instead we give the system a general learning method and a large collection of examples. During training the system adjusts millions of internal numbers so that its overall behavior matches the patterns in the data. No programmer sits down and writes “when you see a cat, output ‘cat’” or “when the email looks like this, call it spam.” The system discovers useful regularities on its own. 

 

Because of that learning process, the system can handle situations it was never explicitly told about. An image classifier can correctly label a photo of a breed of dog it has never been shown before, as long as the new photo shares features with ones it has seen. A spam filter can catch a new scam email that uses wording and tricks different from any example in its training set. A language model can answer a question that was never written into its code. The specific competent behavior was not programmed in advance; it emerged from the training. 

 

That is why the metaphors are useful even though they are not literal. When we say “the computer sees the object” or “the computer thinks the message is spam,” we are not claiming it has conscious experience or human-style understanding. We are pointing out that the result is the same as if it had recognized the object or evaluated the message. The practical outcome—correct classification, useful prediction, coherent response—matches what we would get from genuine seeing or thinking. 

 

So the skeptic’s core claim needs updating. Yes, everything a computer does is ultimately calculation following instructions. But in modern AI those instructions are not a rigid list of rules for every case. They are a learning procedure that produces flexible, data-driven behavior. The difference is large enough that treating the system as if it can “see” or “think” is often the most accurate and convenient way to describe what it actually achieves.

 

How spam filters use AI

You've been using AI for years, and you didn't even know it. One example is spam filters. When's the last time you got a spam message in your inbox? For me, it's been a long, long time. Mistakes in the other direction are more common; your spam filter errs on the side of caution, and sometimes legitimate emails get caught in its net and relegated to the junk folder.

 I posed ChatGPT the following question:

Explain in simple terms how a spam filter works, and how it uses AI rather than conventional programming.

 

Here is ChatGPT's response:

 

A spam filter’s job is to look at incoming emails and decide whether each one is junk (spam) or legitimate (something you probably want to see).The old, conventional-programming wayIn the early days, filters worked like a strict checklist written by humans. Programmers would create rules such as:

• If the subject contains the word “Viagra” → mark as spam
• If the email comes from a known bad address → mark as spam
• If there are too many exclamation marks → mark as spam

These rules had to be written and updated by hand. Spammers quickly learned to dodge them (for example by writing “V1agra” or changing domains), so the filter needed constant manual repairs. It was rigid and easy to outsmart.The AI / machine-learning way (what most modern filters use)Instead of giving the computer a long list of fixed rules, we show it thousands or millions of real examples:

• Emails that people have already marked as spam
• Emails that people have marked as normal (“ham”)

The computer’s learning algorithm studies these examples and discovers patterns on its own. It looks at many different clues at the same time, such as:

• Which words appear (and how often)
• Who the sender is and what their past reputation looks like
• The structure of the message (links, images, unusual formatting)
• Whether the message looks similar to ones that have been spam before

During training, the system adjusts internal numbers (its “weights”) so that it gets better and better at telling the two groups apart. After training, when a brand-new email arrives, the filter calculates a probability score — roughly “how spam-like does this look?” — and decides based on that score.Because the system learned the patterns from data rather than from hand-written rules, it can:

• Spot new kinds of spam it has never seen before, as long as they share some characteristics with older spam
• Continuously improve when users mark more messages as spam or not-spam
• Combine hundreds of weak clues into one strong decision instead of relying on any single “magic word”

In short: a conventional filter follows instructions a human wrote. An AI spam filter learns from experience the same way a person might learn to recognize junk mail after seeing lots of examples. That is why modern filters (Gmail, Outlook, etc.) stay effective even as spammers keep changing their tricks.