Brent Gebhardt

Husband, father, engineer, Catholic.

[nicole ruiz @nwilliams030]

Do you enjoy:
- closing deals
- high stakes negotiations
- lean teams & wearing many hats
- translating technical concepts to nontechnical stakeholders
- competitive equity that vests over 18 years?

raising small children may be right for you!

[Brent Gebhardt @sbgebhardt]

@canonlawyered … and I’m happy!

[Brent Gebhardt @sbgebhardt]

@canonlawyered As a Blue Jays fan, stop it.

[Brent Gebhardt @sbgebhardt]

@oilersdailyy Can you please describe “polar opposite”? Is Babcock going to try to win games 1-0 (Sochi Olympics style)?

[B. A. S. @brentsasacerdos]

As promised:

I thank God for you who do so much to keep the parish friendly, fruitful and otherwise thriving. Our Pastor Father R______ has a great home here to return to.

I’ll say a few words about my 25th anniversary, and how I got to that day 25 years ago. I was born on a hot, summer’s day, surrounded by warriors and hippies. It was the era of the Vietnam War, and I was born on an air base from which young men were deployed to that war, and to which too many uniformed bodies returned in body bags. Outside the entrance of that air base were the protesters through which my grandparents had to pass before they could reach the base hospital. During the first years of my conscious life, I became aware of the moral dilemma created by our involvement in that war, and I was gaining sensitivity to its long-term effects upon those who survived it.

Then came Dracula. It was the beginning of summer after my 3rd grade year, and the film Dracula Has Risen from the Grave was on late night TV. That movie featured a priest fighting a vampire, a fiction revealing to me the fact that there is evil in the world and so somebody better fight it. I thought, perhaps one day I could be somebody who did just that.

By the time I was a teenager, I was eager to know everything there was to know about everything there was to know. In particular, I wanted to understand how we come to know how best to be human. Surely, we need to know why human beings exist. It seemed reasonable to me that there was a God who created us, and that he did so for some purpose, and that he provided some reasonable means for us to understand that purpose, along with the means of fulfilling our purpose.

This quest for meaning led me to, and eventually into, the Catholic Church. Not quite a year into my Catholic journey, but before I was Baptized, the local parish received a new pastor, who turned out to be rather predatory. I was rather autistic, which in my case meant I tended to trust everyone of a particular category or nobody of that category, every priest or no priest, etc. I was 17 when that priest arrived, and 19 when I finally reported him to his vicar general. I blame autism for my social vulnerabilities that got me into that violent situation, but I also praise autism for giving me the relentlessness to keep telling the truth about bureaucratically enabled predatory violence in the Church. So, my greatest liability is also my greatest asset, and I intend to both suffer with it and utilize it however God wills.

Although I spent a year in the seminary here in Texas, I concluded that I could not continue discerning the priesthood here, given the uncertain way my report about the predator was handled. So, thanks to the Glenmary Society, I ended up in Tennessee, ordained to the priesthood on this day in 2001. I had the opportunity to serve in seven wonderful parishes there for a total of 20 fruitful years. Recently, I became involved in a challenging leadership situation there that had parallels with the situation that prompted me to leave Texas. Hopefully, I followed God’s will in responding to that recent situation, and maybe some good will eventually come of my initiative and of the prophetic suffering endured by several other priests. It’s great to be a priest, but only if you’re first and foremost a prophet. I’m praying for truth, reconciliation and reparation, what social activists today call “restorative justice”.

As you know, I’ve asked that, rather than offering me any sort of gift on this occasion, you instead support the Sisters of the Little Way. You may do so through prayer or even donations, but most importantly please help me get the word out about who they are and what they mean to the Church. I’d go so far as to say theirs will turn out to be the most important new community of religious life in the Church this century.

I’d like to dedicate my 25th anniversary to all the prophetic victims of priestly predatory violence who courageously come forward to warn Church leaders about wolves in the fold. Our appreciation for them helps correct the way Church bureaucracy treats them.
I’d also like to take this opportunity to recognize Fr. S____ P______, whose (1.) service to his country in the armed forces, (2.) protection of his community as a peace officer, (3.) willingness to lose everything in telling the truth about corruption in our seminaries, and (4.) ministry now as a priest, serve as a examples of integrity for us all to follow. If Christ is the standard, then Fr. S____ is the standard-bearer. Christ warned us, “a hired man, who is not a shepherd and whose sheep are not his own, sees a wolf coming and leaves the sheep and runs away, and the wolf catches and scatters them”. But “a good shepherd lays down his life for the sheep”. Few things on this planet make more sense to me than that. Even the worst they can do to a shepherd only makes him even more a shepherd.

On the whole, I am, at best, a mediocre priest, but I’ve found that God compensates for that by sending many able laymen to his aid. In some cases, it’s the whole parish working together that keeps things on track. Thank you for being what God needs you to be.

While I am, perhaps, closer now to my death than to my ordination, I hope to see you all again in 25 years, whether in this life or in the next!

[Pascal @KnowsPascal]

A bunch of little old Catholic ladies got talked into investing in a wildcat well out in the middle of nowhere. They got worried about the investment, and consulted their priest, who suggested invoking St. Rita, patron saint of impossible causes.

The deal was that the oilman had to take a rose blessed by the priest and sprinkle the petals from the top of the derrick.

That well became the discovery that opened up the largest oil field in the United States.

The wells name was St. Rita.

[Brent Gebhardt @sbgebhardt]

@joansrome I love this story!

[Brent Gebhardt @sbgebhardt]

@TomlinsonCJ @scottmhayward I rapped with a nun around a campfire. Does that count?

[Brent Gebhardt @sbgebhardt]

@MrCasey62 My patron Saint for this year! St. Augustine, pray for us.

[Brent Gebhardt @sbgebhardt]

@Oilersnation_de @JasonGregor Ich auch!

[Brent Gebhardt @sbgebhardt]

@HoCStaffer I’ve visited: very powerful.

[Brent Gebhardt @sbgebhardt]

@KatiePMcGrady @NASA @NASAArtemis @SIRIUSXM @CatholicChannel And if @frdarryl is unavailable, I volunteer to be present as your Catholic Engineer.

[Brent Gebhardt @sbgebhardt]

@MattWalshBlog Totally agree.

[Brent Gebhardt @sbgebhardt]

Starting on my Christmas book list! @JohannKurtz

[rightnowhq @RightNowHQ]

TikTok user "Frances k", who has a significant following online, put out a video on social media filled with so many errors about our undercover investigation and abortion in Canada in general, we had to respond.

[Brent Gebhardt @sbgebhardt]

@Woodguy55 @mike_wintrs 35 years ago, I tried to use that to score two points in Scattergories. The people I was playing with hadn’t heard of that fine show and did not allow it.
I mean, what were they doing on Saturday morning?

[Brent Gebhardt @sbgebhardt]

@HettrickMichael @GmorganJr And it’s only 16 pages long- no escuses for not reading it!

[C2C Journal @c2cjournal]

The Emptiness Inside: Why Large Language Models Can’t Think – and Never Will

By Gleb Lisikh

Early attempts at artificial intelligence (AI) were ridiculed for giving answers that were confident, wrong and often surreal – the intellectual equivalent of asking a drunken parrot to explain Kant. But modern AIs based on large language models (LLMs) are so polished, articulate and eerily competent at generating answers that many people assume they can know and, even better, can independently reason their way to knowing.

This confidence is misplaced. LLMs like ChatGPT or Grok don’t think. They are supercharged autocomplete engines. You type a prompt; they predict the next word, then the next, based only on patterns in the trillions of words they were trained on. No rules, no logic – just statistical guessing dressed up in conversation. As a result, LLMs have no idea whether a sentence is true or false or even sane; they only “know” whether it sounds like sentences they’ve seen before. That’s why they often confidently make things up: court cases, historical events, or physics explanations that are pure fiction. The AI world calls such outputs “hallucinations”.

But because the LLM’s speech is fluent, users instinctively project self-understanding onto the model, triggered by the same human “trust circuits” we use for spotting intelligence. But it is fallacious reasoning, a bit like hearing someone speak perfect French and assuming they must also be an excellent judge of wine, fashion and philosophy. We confuse style for substance and we anthropomorphize the speaker. That in turn tempts us into two mythical narratives:

Myth 1: “If we just scale up the models and give them more ‘juice’ then true reasoning will eventually emerge.”

Bigger LLMs do get smoother and more impressive. But their core trick – word prediction – never changes. It’s still mimicry, not understanding. One assumes intelligence will magically emerge from quantity, as though making tires bigger and spinning them faster will eventually make a car fly. But the obstacle is architectural, not scalar: you can make the mimicry more convincing (make a car jump off a ramp), but you don’t convert a pattern predictor into a truth-seeker by scaling it up. You merely get better camouflage and, studies have shown, even lessfidelity to fact.

Myth 2: “Who cares how AI does it? If it yields truth, that’s all that matters. The ultimate arbiter of truth is reality – so cope!”

This one is especially dangerous as it stomps on epistemology wearing concrete boots. It effectively claims that the seeming reliability of LLM’s mundane knowledge should be extended to trusting the opaque methods through which it is obtained. But truth has rules. For example, a conclusion only becomes epistemically trustworthy when reached through either: 1) deductive reasoning (conclusions that must be true if the premises are true); or 2) empirical verification (observations of the real world that confirm or disconfirm claims).

LLMs do neither of these. They cannot deduce because their architecture doesn’t implement logical inference. They don’t manipulate premises and reach conclusions, and they are clueless about causality. They also cannot empirically verify anything because they have no access to reality: they can’t check weather or observe social interactions.

Attempting to overcome these structural obstacles, AI developers bolt external tools like calculators, databases and retrieval systems onto an LLM system. Such ostensible truth-seeking mechanisms improve outputs but do not fix the underlying architecture.

The “flying car” salesmen, peddling various accomplishments like IQ test scores, claim that today’s LLMs show superhuman intelligence. In reality, LLM IQ tests violate every rule for conducting intelligence tests, making them a human-prompt engineering skills competition rather than a valid assessment of machine smartness.

Efforts to make LLMs “truth-seeking” by brainwashing them to align with their trainer’s preferences through mechanisms like RLHF miss the point. Those attempts to fix bias only make waves in a structure that cannot support genuine reasoning. This regularly reveals itself through flops like xAI Grok’s MechaHitler bravado or Google Gemini’s representing America’s Founding Fathers as a lineup of “racialized” gentlemen.

Other approaches exist, though, that strive to create an AI architecture enabling authentic thinking:
· Symbolic AI: uses explicit logical rules; strong on defined problems, weak on ambiguity;
· Causal AI: learns cause-and-effect relationships and can answer “what if” questions;
· Neuro-symbolic AI: combines neural prediction with logical reasoning; and
· Agentic AI: acts with the goal in mind, receives feedback and improves through trial-and-error.

Unfortunately, the current progress in AI relies almost entirely on scaling LLMs. And the alternative approaches receive far less funding and attention – the good old “follow the money” principle. Meanwhile, the loudest “AI” in the room is just a very expensive parrot.

LLMs, nevertheless, are astonishing achievements of engineering and wonderful tools useful for many tasks. I will have far more on their uses in my next column. The crucial thing for users to remember, though, is that all LLMs are and will always remain linguistic pattern engines, not epistemic agents.

The hype that LLMs are on the brink of “true intelligence” mistakes fluency for thought. Real thinking requires understanding the physical world, persistent memory, reasoning and planning that LLMs handle only primitively or not all – a design fact that is non-controversial among AI insiders. Treat LLMs as useful thought-provoking tools, never as trustworthy sources. And stop waiting for the parrot to start doing philosophy. It never will.

The original, full-length version of this article was recently published as Part I of a two-part series in C2C Journal. Part II can be read here. https://t.co/3lTZyaWb6c

Gleb Lisikh is a researcher and IT management professional, and a father of three children, who lives in Vaughan, Ontario and grew up in various parts of the Soviet Union.

[Brent Gebhardt @sbgebhardt]

@Woodguy55 @Bar_Qu Ditto