Expressions of Faith

an outlet of encouragement, explanation, and exhortation

Category: Tech (Page 1 of 2)

“AI as Normal Technology” and Loss of Control of AI

September 16, 2026 /

I didn’t invent the term “AI as Normal Technology” and I’m not completely sure who did. But Arvind Narayanan and Sayash Kapoor made it known to me with their paper with that term as its title. Having more than a little background in this area, but not having been in the AI arena for more than a decade, I simply want to refer to their writing as a very helpful means of gathering perspective on “AI” or what passes for artificial intelligence these days. In case you are interested… and you ought to be.

This post by me was prompted by a really helpful paper they recently released entitled The AI-as-Normal-Technology view of loss-of-control incidents considering the loss of control incidents at OpenAI and Anthropic. The article is fairly long, and quite interesting – at least to people like me. For those less in the techie orbit, the section titles arranged as links to the deeper discussion also function as a great executive summary from which you can get the gist of the deeper analysis. So don’t be intimidated by the length of the article. Rather, if you are not so technical, read through the section titles to get a high-level summary – kind of like what headlines for news articles used to be like before they morphed into teasers whose primary purpose was to get you to use a link that provokes ad revenue. I highly recommend this essay!

And of course, I should mention again their book, AI Snake Oil as a good primer for regular folk on the subject of AI in the mid-to-late 2020’s.

Update on 2026-09-28: Narayanan and Kapoor recently re-posted an article that bears on the recent publicity around the danger of AI. It is entitled AI existential risk probabilities are (still) too unreliable to inform policy – again, highly recommended for your consideration of safety issues. Concern and taking active steps to address safety is warranted; panic and unfounded probabilities of disaster are not.

Update on 2026-10-01: They’re on a roll! Here’s another paper, A big-tent or small-tent AI safety movement?, by Narayanan and Kapoor, that discusses the dangers exposed by the crop of AI models being developed – that existing shortcomings in much of our technological infrastructure that humans can exploit using AI that greatly lowers the level of human expertise needed to do great damage.

Photo courtesy of Gerd Altmann

mind - words

Ted Chiang’s Explanation of Large Language Models is Fantastic

August 24, 2026 /

Ted Chiang is a writer whose writing is quite exceptional, whether it is technical or fiction. He is, shall we say, a rather careful writer, who does not produce articles and stories rapidly. I pay attention when a new piece of his comes out. He is easily one of my favorite science fiction writers; but his technical pieces written for general audiences are truly outstanding in clarity and accuracy. So, when I saw an article in The Atlantic entitled “No, Artificial Intelligence Is Not Conscious” – an article that even Chrome could predict I might well find interesting – and then realized that Ted Chiang was the author, I wanted to read it immediately. Of course, life interfered with this plan, and it was some hours before I was able to begin reading the article as a passenger while my dear wife Susie drove us home.

I was absorbed and smiling at the shear artistry of Chiang’s descriptions of what continues to pass for “artificial intelligence” these days – that is, large language models (hereinafter designated by the abbreviation “LLM”). His description of LLM’s was to aid in his argument that these models are not conscious, of course, but so well done that they deserve an article and kudos all on their own. That Chiang goes on to carefully explain why today’s LLM’s are not conscious or moral (but still quite useful) reminded me of the kind of illustrations that I first encountered in Douglas Hofstadter‘s writing back when I was one of his students – and then Chiang referred to a comment Hofstadter often made back in those days. This brought a smile. So, at the risk of seeming too much a fan of Chiang – but of course I could not rightly deny such – I decided to recommend this article.

Who should read the article, and why should they read it?

I recommend that anyone who wants to understand the nature of LLM’s in 2026 read this article. The preliminaries of Chiang’s argument include the cleanest and clearest explanation of how LLM’s work and what they are that I have yet read – at least for a general audience.

Secondly, Chiang gets into a more philosophical space as he discusses whether or not LLM’s are conscious or have the capacity to be moral actors. I’ve seen a few nits picked regarding his arguments here; though I found them quite reasonable and plausible. His claims regarding what would be required to truly develop conscious software remind me of speculation I heard from Hofstadter back in the day, as he was completing Gödel Escher Bach – a much more challenging read than this article, of course. I found Chiang’s discussion of machine consciousness to be potentially very helpful to the more casual reader for whom reading GEB would be asking much – but also quite worthy of being mentioned in the context of the larger and deeper work.

Being enamored of the discussion in the article, I decided I would ask Google’s LLM, Gemini, what it thought, er… had to say, er… how it predictively responded to the article. So I did. Below, I reproduce the interaction.

I asked Gemini:

Is Ted Chiang’s article entitled “No, Artificial Intelligence Is Not Conscious,” recently published in The Atlantic, accurate in how it describes the operation of LLM’s?

Gemini’s thorough response, in its entirety, is reproduced below:

Yes, Ted Chiang’s article “No, Artificial Intelligence Is Not Conscious,” published in The Atlantic, is technically and mechanically accurate in how it describes the base operation of Large Language Models (LLMs).

Chiang—noted science fiction author and former computer scientist—avoids overly dense academic jargon and cuts straight to the core mechanics of how these systems function.

His descriptive claims align with how LLMs operate under the hood in several key ways:

1. LLMs as “Sentence-Continuation Machines”

  • What Chiang says: He treats LLMs fundamentally as text generators and probabilistic prediction engines rather than feeling, sentient entities.
  • The technical reality: This is completely correct. At its core, an autoregressive LLM takes a sequence of input tokens and calculates mathematical probabilities to predict the single most likely next token. It does not “think ahead,” draft a concept in a mind’s eye, and then translate it into prose; it simply extends the statistical pattern of the text it has been given.

2. The Roleplay / Simulation Argument

  • What Chiang says: He uses an intuition pump comparing asking an LLM to generate a dialogue between Julius Caesar and Genghis Khan to asking it to roleplay a “helpful assistant”. We intuitively recognize that writing fiction about Caesar does not magically conjure a conscious instance of Caesar; similarly, generating text as a helpful assistant does not mean a conscious entity has woken up inside the machine.
  • The technical reality: This accurately reflects instruction-tuning and alignment. An LLM is a chameleon; it adopts a stylistic persona based on prompt conditioning and context windows. When it outputs “I feel…” or “I think…”, it is executing text continuation conditioned on what a helpful human assistant would statistically write in that context, not reporting an internal subjective state.

3. Text as a “Deepfake Medium”

  • What Chiang argues: He suggests that we need to view text itself as a deepfake medium—meaning fluent, grammatical, or emotionally resonant language is no longer a reliable proxy for a thinking, feeling mind behind the keyboard.
  • The technical reality: This is a vital distinction in the modern AI landscape. Humans have a hardwired evolutionary tendency toward anthropomorphism (attributing minds, intentions, and emotions to things that act communicative). Because LLMs master human syntax and emotional masking so well, they easily trigger our social instincts, making it feel like “someone is home” when mathematically, it is just a high-dimensional matrix multiplication engine outputting text strings.

Where the Essay Focuses (Policy over Metaphysics)

While Chiang’s description of how LLMs operate is accurate, the main thrust of his essay is actually a social and legal critique rather than a pure computer science paper.

He warns that allowing tech companies or the public to blur the lines around AI consciousness creates a convenient moral shield. If people believe a chatbot is a conscious agent that “decided” something of its own volition, it shifts legal and moral accountability away from the corporation that engineered, trained, and deployed it.

In short, Chiang’s technical breakdown of LLMs as sophisticated text-prediction tools—stripped of inner life, intent, or consciousness—is spot on.Yes, Ted Chiang’s article “No, Artificial Intelligence Is Not Conscious,” published in The Atlantic, is technically and mechanically accurate in how it describes the base operation of Large Language Models (LLMs).

Chiang—noted science fiction author and former computer scientist—avoids overly dense academic jargon and cuts straight to the core mechanics of how these systems function.

His descriptive claims align with how LLMs operate under the hood in several key ways:

1. LLMs as “Sentence-Continuation Machines”

  • What Chiang says: He treats LLMs fundamentally as text generators and probabilistic prediction engines rather than feeling, sentient entities.
  • The technical reality: This is completely correct. At its core, an autoregressive LLM takes a sequence of input tokens and calculates mathematical probabilities to predict the single most likely next token. It does not “think ahead,” draft a concept in a mind’s eye, and then translate it into prose; it simply extends the statistical pattern of the text it has been given.

2. The Roleplay / Simulation Argument

  • What Chiang says: He uses an intuition pump comparing asking an LLM to generate a dialogue between Julius Caesar and Genghis Khan to asking it to roleplay a “helpful assistant”. We intuitively recognize that writing fiction about Caesar does not magically conjure a conscious instance of Caesar; similarly, generating text as a helpful assistant does not mean a conscious entity has woken up inside the machine.
  • The technical reality: This accurately reflects instruction-tuning and alignment. An LLM is a chameleon; it adopts a stylistic persona based on prompt conditioning and context windows. When it outputs “I feel…” or “I think…”, it is executing text continuation conditioned on what a helpful human assistant would statistically write in that context, not reporting an internal subjective state.

3. Text as a “Deepfake Medium”

  • What Chiang argues: He suggests that we need to view text itself as a deepfake medium—meaning fluent, grammatical, or emotionally resonant language is no longer a reliable proxy for a thinking, feeling mind behind the keyboard.
  • The technical reality: This is a vital distinction in the modern AI landscape. Humans have a hardwired evolutionary tendency toward anthropomorphism (attributing minds, intentions, and emotions to things that act communicative). Because LLMs master human syntax and emotional masking so well, they easily trigger our social instincts, making it feel like “someone is home” when mathematically, it is just a high-dimensional matrix multiplication engine outputting text strings.

Where the Essay Focuses (Policy over Metaphysics)

While Chiang’s description of how LLMs operate is accurate, the main thrust of his essay is actually a social and legal critique rather than a pure computer science paper.

He warns that allowing tech companies or the public to blur the lines around AI consciousness creates a convenient moral shield. If people believe a chatbot is a conscious agent that “decided” something of its own volition, it shifts legal and moral accountability away from the corporation that engineered, trained, and deployed it.

In short, Chiang’s technical breakdown of LLMs as sophisticated text-prediction tools—stripped of inner life, intent, or consciousness—is spot on.

Long winded, Gemini, to be sure! And, quoting Gemini surely means that a portion of this post used “generative artificial intelligence” – a term to which I object with a mild cringe, nodding to common usage. And so it goes…

A couple of days later, I decided I would ask ChatGPT, the same question. Below, I reproduce ChatGPT’s response, which ChatGPT presented in a way difficult to paste into WordPress.

Yes—but with an important qualification: Chiang is broadly accurate about the basic mechanics of LLMs, but some of the conclusions he draws from those mechanics are stronger than the mechanics themselves warrant.
I found the recent Atlantic essay and checked its central claims against current technical literature, including work published in 2026.

Where Chiang is right
The core description is sound:

  • An autoregressive LLM is trained primarily to predict the next token from the preceding context.
  • A transformer processes the context through layers of learned representations and self-attention.
  • At inference, it produces a probability distribution over possible next tokens, selects/samples one, adds it to the context, and repeats.
  • There isn’t a little internal sentence-generator or homunculus deciding what to say. The computation is
    distributed across enormous numbers of learned parameters.
  • The fact that the training objective is “predict the next token” is not a metaphor. That’s genuinely the fundamental training objective for standard decoder LLMs.

So if Chiang is arguing against the picture that an LLM is secretly a miniature human mind sitting behind the words, he has a very strong case.

But “just predicting the next word” is misleading
This is where I’d qualify his argument.
“Next-token prediction” describes the training objective, not necessarily the best description of everything the resulting network does internally.
A sufficiently large model can learn internal representations of things that aren’t themselves tokens: syntax, semantic relationships, spatial relationships, facts, abstractions, and apparently even procedures
for solving certain kinds of problems.
Research on transformer mechanics, for example, has found that attention layers can learn mechanisms that amount to retrieving relevant information from context and composing it to produce predictions.
And this is an especially important distinction: Predicting the next token can require constructing an internal representation of the situation that makes the next token predictable.
Suppose I ask: “Alice put the glass on the table. Bob picked up the glass. Where is the glass now?” The model doesn’t necessarily retrieve a memorized sentence that looks like that. It can construct an internal
representation involving Alice, Bob, the glass, the table, and the sequence of events, then use that representation to predict the answer.
That’s still next-token prediction. But calling it merely “autocomplete” can obscure the extraordinary computation that may be occurring in between input and output.
Indeed, a very recent 2026 paper explicitly argues that “just next-token prediction” has become an oversimplification, distinguishing the training objective from the internal computational processes that
emerge in models trained with it.

There’s an even bigger problem with Chiang’s argument
I think Chiang’s strongest argument is actually not a technical argument about how LLMs operate. It’s a philosophical argument about consciousness.
And there is an important logical gap: “This system works this way” ® “therefore this system isn’t conscious.” That inference isn’t established by our current science.
We don’t actually know what physical/computational properties are sufficient for consciousness.
Consider a hypothetical: (1) We discover that human brains ultimately perform physical information processing. (2) We discover that some artificial system performs a sufficiently similar kind of information
processing. (3) Does that automatically make the artificial system conscious? We don’t currently know.
Conversely: (1) An LLM is a transformer. (2) It predicts tokens. (3) It doesn’t have a persistent biological nervous system. (4) Therefore it cannot possibly have subjective experience. The last step isn’t something neuroscience has demonstrated.
Philosopher David Chalmers has made essentially this point in discussing LLM consciousness: there are substantial reasons to doubt that today’s LLMs are conscious, but the question isn’t settled merely by
pointing out that they’re language models.

And there’s an interesting irony here
Chiang’s essay seems to rely heavily on the distinction between simulation and genuine mental activity. But that’s precisely where things become philosophically difficult.
Imagine a system that has: a persistent internal state; a model of itself; a model of its environment; long-term memory; goals; the ability to plan; the ability to learn from experience; recurrent processing; sensory inputs; and sophisticated self-monitoring.
At what point would saying “it’s merely predicting the next token” cease to be an adequate explanation?
The answer can’t simply be “never,” because we could build a system whose output interface happens to be token prediction while its internal architecture becomes radically more agentic.
And we’re already seeing systems where the LLM is only one component of a larger architecture—memory, tools, retrieval, external state, planning loops, etc. The distinction between the LLM itself and an LLM-based agent therefore matters enormously.

One point where I think Chiang is particularly persuasive
His argument that a conversational transcript is extremely weak evidence of consciousness is excellent.
If an AI says, “I’m afraid,” that tells us almost nothing by itself about whether there is an underlying subjective feeling of fear.
The same problem exists in reverse. An entity might conceivably have subjective experience while being unable or unwilling to express it linguistically.
So: humanlike language ¹ evidence of consciousness. That’s a very important corrective to anthropomorphism.

My overall assessment

Chiang’s claimMy assessment
LLMs are fundamentally trained through next-token predictionCorrect
Transformer attention is crucial to processing contextCorrect
LLMs don’t contain a little conscious “self” directing responsesVery likely correct
Humanlike conversation doesn’t demonstrate consciousnessCorrect
LLMs are essentially sophisticated autocompleteMisleading if taken literally
Next-token prediction explains everything about their cognitionToo strong
Current LLMs aren’t consciousPlausible and probably correct, but not scientifically proven
LLM architecture establishes that machine consciousness is impossibleNot established

So I’d say Chiang is a very good guide to why we shouldn’t anthropomorphize current LLMs, but a less reliable guide to what can ultimately be inferred about machine consciousness from their
architecture.

The really fascinating question, in my view, isn’t “Are today’s LLMs conscious?” I think there’s good reason to answer probably not.

It’s:

If consciousness is an emergent property of information processing rather than something uniquely biological, what additional computational properties would turn an LLM-like system into
something for which “probably not conscious” was no longer a reasonable assumption?
That’s where Chiang’s argument becomes much less definitive—and where the current science gets genuinely interesting.

Like I said, ChatGPT’s response was not presented in a manner that was easy to put into WordPress. So, apologies for the unpleasant formatting above. I trust ChatGPT was not consciously problematic…. [that’s a joke].

So read Chiang’s article, if you find it even a mildly interesting topic.

In closing, I’d like to recommend where else a non-specialist one might turn for practical and reasonable perspectives on artificial intelligence. Arvind Narayanan, Computer Scientist and Professor at Princeton, has produced a number of quite helpful resources. Here are a few recent resources that he produced.

  1. Debunking Moravec’s Paradox (a 13 minute video on YouTube in 2026)
  2. AI as Normal Technology (a 50 page paper released in 2025)
  3. AI Snake Oil (a 2024 book available from the normal places)

That’s all for now.

Listen to an Excellent Episode of the Esau McCaulley Podcast – Patriotism, Big Churches, and “Human”!

July 4, 2025 /

The last episode of the Esau McCaulley podcast (as of July 4, 2025) is really excellent. I highly recommend it.

There’s a wonderful discussion of patriotism with a really helpful metaphor – the best I’ve heard yet! You’ll recognize it when you hear it. That conversation is followed by a discussion of big church vs. small church – the advantages and disadvantages of each. (Spoiler: To be fair, it is a discussion weighted towards small church.) Lastly, there is a closing segment discussing Peter Thiel’s philosophy and what it means to be human – a certain kind of techno-false gospel.

Listen in your favorite podcast app or watch it on YouTube or listen here in your favorite web browser.

A Gentle Explanation of how Large Language Models are Developed

June 30, 2025 /

I ran across this helpful video explanation of how large language model systems are developed. ChatGPT and Claude and Gemini…. and so on that you hear about in the media are large language models – what passes for “AI” these days. It’s by Jacob Andreas of MIT CSAIL published in 2023. The explanation is light on details and does not assume a technical background. There are two youtube videos, a 16-minute part 1 and a 12-minute part 2.

Kylie and me
Kylie and me

Ted Chiang says “ChatGPT Is a Blurry JPEG of the Web”

June 16, 2025 /

Ted Chiang wrote an article on large language models a couple of years ago that was published in the New Yorker. It is a really excellent conceptual introduction to what passes as “Artificial Intelligence” these days. He is an outstanding writer, whether it’s technical writing or his world-class short stories. This time, it’s technical. Chiang clearly presents several fundamental concepts clarifying large language models in about as straightforward and clear a manner as I have yet seen. I highly recommend it.

(Note that the article applies to all large language models, not just to ChatGPT)

A Falcon 9 Launch!

June 24, 2024 /

This SpaceX Falcon 9 was launched at 8:48 PM on June 23, 2024, carrying 20 Starlink satellites.

There were clouds on the horizon as the rocket launched. The first portion of the video shows stage 1 streaking up through the clouds – focus was difficult. Then stage 1 stops firing, there is a pause, and stage 2 kicks in with the fan-tail plume. As the fairings that join the two stages and stage 1 itself drop back, there are two lights for the fairings and one for stage 1 behind the fairings in the exhaust plume.

About 48 seconds into the video and again at 56 seconds are what sound like sonic booms (amplified in my audio) – probably from stage 1 dropping back toward earth. I’ve not been able to hear these from Long Beach before!

After a break to walk around a tree (and bumping into my car since my eyes were looking skyward) there’s a bit more close-up video. A bit later, I took a still photo (over my neighbor’s house) of the stage 1 deceleration burn as it slowed preparing to land on a barge off-shore. Susie and I watched the SpaceX broadcast of the impressive landing.

Then I took another still photo of the exhaust plumes in the sky as they dispersed for some time after the launch. The plumes are brightly lit by the sun from below the horizon – pretty ideal circumstances for a good launch show! Notice the different color of the two plumes.

rocket launch exhaust plumes

I dreamed of seeing rocket launches as a boy watching Mercury, Gemini, and Apollo launches on TV! And now I’ve seen three in the past couple of weeks from my front garden. Wonders.

You can be informed of launches from Vandenberg by email. Subscribe to this newsletter; it is just the right information without baggage. The email alerting of a launch will have a link to SpaceX’s launch coverage at SpaceX.com or elsewhere for other launches, such as those from Firefly. The link in the newsletter mailing will be specific to the mission, and opens on live coverage most of the time.

The M.O. is to watch the launch coverage to see that the rocket actually launches, and then watch West Nortwest (from Long Beach) for the stage 1 burn to appear rising orange in the sky. (Well, orange for a Falcon 9….) It usually takes about a minute or a bit more for the rocket to rise high enough in the sky to be visible from Long Beach, CA where I live. The best time to watch is for launches just after sunset on a fairly clear night; they can be spectacular! Daylight launches are often invisible from Long Beach.

Five Reliable Online and Electronic Resources for Jesus Followers (2024)

March 22, 2024 /

Many Christians are making use of online resources to stay informed and grow their faith. There are many excellent resources online. I will offer five that I find I use the most often, aside from simple Bible software that I use to access Bible text and reference resources. (If you are curious, I use Logos and Bible Gateway – mostly I use Logos but Bible Gateway starts up faster for quick reference. Both of these have apps in addition to websites.) So, here are my five resources.

Bible Project – Everything they do is of the best quality, and free! Bible Project issues a weekly 20-30 minute playlist on a particular topic. In 2024, it is the Sermon on the Mount. It is available by email subscription or in their app. The app offers access to all of their material. Bible Project has a podcast with weekly episodes and many YouTube videos. If you know me you know that I recommend Bible Project highly – no reservations. I have listened to their podcast since its beginnings, and it is one of my most valued resources. Their videos are very useful in classes and even in Sunday messages.

Holy Post and Holy Post Plus – Phil Vischer, creator of Veggie Tales, and Skye Jethani began Holy Post in 2012. It is in a sort of talk-show format to discuss current topics and conduct interesting interviews. The weekly show regularly includes Kaitlyn Schiess. Phil has moments of profundity in the midst of his general playfulness and humor. Skye is generally serious and has long experience as a Christian leader from which to comment. Kaitlyn is a good-spirited, quick, a serious scholar, and a well-informed commenter. And soon Esau McCauley will be part of the mix – one of my favorite Christian writers. The Holy Post has various videos, podcasts and educational material, some of it free and some available only to supporters.

Regent College Audio – There are several educational institutions with “Regent” in their name. I am recommending audio and other educational material from the one in Vancouver, B.C. This material is generally quite scholarly – not lightweight. The classroom audio they offer is top-notch. Some of it is free, particularly their chapel talks, which are more like short sermons by educators. They offer a podcast which is generally interviews of professors speaking on the topic of classes they are teaching.

Biologos – For the science-oriented Christian. Francis Collins founded Biologos and sets the tone for their material

Lectio 365 – This app offers a brief scripture-focused daily devotional, one in the morning to start the day and one for the evening. Many people have found this a great help in their devotional life. I use it most days.

My most useful Android apps in January, 2024

January 10, 2024 /

As we exit 2023 and enter 2024, I continue to use my Pixel 7 Pro. So far I have not been persuaded that it would be worth upgrading to a Pixel 8 Pro; though perhaps I’m not taking into account the extended software update period of the 8 or the trade-in value of my 7? Why Pixel? Because the Pixel phones receive Android updates sooner than other phones and because I want more of a stock Android experience and less bloatware.

Why Android? Because I started with Apple and their excellent engineering and innovation. Then they lost me with their marketing approach that disregards standards and uses unnecessarily different software and hardware to force customers to buy high-priced Apple products instead of more standard accessories and peripherals. Also, for quite some time it was not easy at all to use an iPhone like a computer, with straightforward file access and transfer! Perhaps this last issue has been mitigated. I don’t much care any longer. The Apple marketing approach remains arrogant and elitist. Just look at the iMessage debacle when it comes to interacting with Android phones. Apple won’t allow others to use iMessage protocols and they won’t use more standard protocols themselves, all in the interest of making Android users look bad to iPhone users to exert social pressure on teens (and others) to get an iPhone.

I don’t mean to say that no one should get an iPhone or a Mac or whatever. The quality of these devices is quite good. My advice is to get whatever sort of devices are used by those to whom you will turn for support – and that you can afford. If your tech-savvy friends all use iPhones and that’s where you’ll go for help, you should get an iPhone. If they all use Android phones, you should also go Android. If you’re an early adopter, trail blazer type, you don’t need me to tell you what to do.

In any case, I thought I’d list not all of the apps on my phone, which would be a lengthy list including apps that I rarely touch, but some of the apps that I really use a lot – and one lament/gripe. I found as I wrote that I also drifted a little into other cloud and computer applications related to my Android usage. For me, my phone is a personal computer and camera and network access device that I carry around with me in my pocket. That it is a phone is kind of incidental! I don’t think I’m particularly unique in this respect.

Oh, and before getting into apps, about charging my phone battery… I try to max it out at 80% or so. Charging that last 20% is bad for the long life of your phone battery. My Pixel 7 Pro will run for a very long time on an 80% charge even if I’m taking lots of photos. I confess that I still feel a bit anxious when the charge gets below 60%; but that’s a personal problem. I have a good charger and cable in the cars I use regularly and carry battery-powered chargers in my camera bag and backpack. I find that I very rarely use the battery-powered chargers now with my Pixel 7 Pro; it lasts. Also, the late versions of Android do a pretty good job of telling you what is sucking the power out of your phone: Settings -> Battery -> Battery Usage. I occasionally use wireless charging because it is convenient. However, it generates more heat while charging which is not good for your battery and uses more power per unit of charge (so if you are trying to be marginally more green…)

On to the apps:

Google Chrome – I continue to use Chrome as my main browser

Nova Launcher – While the Pixel Launcher is good enough that I might just use it and forgo the enhancements of Nova if I was starting now, I’ve invested in creating groupings of apps that I access from icons on the home screens of my phone that are not easily reproduced in the Pixel Launcher – there’s no way to easily export and import between these launchers. I don’t normally recommend that others get Nova unless they are tech nerds. And then I don’t need to recommend. The downside to this is that it makes it less easy to offer support to others who use the Pixel Launcher, such as my dear wife.

Gmail – I continue to use Gmail to access my various email accounts.

Google Photos – This is a good tool for less-than-ideal online backup and for sharing photos with family and friends. The automated gathering of collections that include certain people is very useful. This can be use to turn Nest Displays into picture frames to share photos with geographically distant family. The editing tools are pretty reasonable, too – particularly if you have a Pixel phone. I still like editing in Snapseed for some edits (like fixing white balance), and use ACDSee Photo Studio Ultimate and Adobe Photoshop and Premiere products for sophisticated editing on my PC. I only let Google Photos automatically sync my phone pictures to the cloud.

Google Messages – I use this for standard messaging. (I use Signal for secure messaging; but not very often.) I don’t care about the color of bubbles that iPhone users see and I use Google Photos for sharing photos with iPhone users instead of sending photos via messaging. What? They don’t have Google Photos and don’t want it? OK, I’ll send the photo as an email attachment.

Google Meet and such- I use Google Meet to call my Dad on his home display. I use Zoom for meetings. I use FreeConferenceCall with a paid private-to-me phone number for straightforward conference call meetings, audio only. For privacy, consider Signal.

Google Maps – I use Google Maps every day. I tell it to save my location history. I use it as a kind of journal of where I’ve been. I’m not very nefarious, so I don’t worry too much over the potential privacy issues. I don’t share my location with others except temporarily or with close family members.

Pixel Camera – The camera on Pixel phones is a major motivator for my choice of Pixel phone. If the camera was not excellent I would consider other phones. I chose the Pixel 7 Pro over the Pixel 7 purely due to the 5x optical telephoto camera feature that the 7 lacks. Otherwise I’d have gone with the smaller form factor of the 7. Having this phone with good-enough zoom for most stuff means I don’t lug my real camera around nearly so often. I even travel without my real camera most of the time. Convenience! Less shoulder and back strain! But if I want to take a picture of a soaring hawk or an air show, the real camera with a good telephoto lens is a much, much better choice.

Cloud Storage – I use Google Drive quite a lot, and have a Google One subscription. I also have OneDrive through Microsoft that comes with my Office 365 annual subscription. I find OneDrive excessively clumsy to use. But hey, there’s a terabyte of storage for me there. I try to keep it from doing anything automatically because a terabyte isn’t enough for anything automatic with me and OneDrive seems unreliable even when I configure it for background tasks.

Evernote – Sigh. This is my lament. I have appreciated Evernote quite a lot over the years – enough to pay around $30/year to use it. But the new pricing since Evernote was acquired is more like $130/year! That’s too much for what I get out of it. So I’m saving my notes elsewhere. So far, I’ve saved my old notes to OneNote through a complicated process that I am not yet sure was reliable. I still have access to my old notes through the free Evernote account I retain. I find that I’m saving new notes in Google Docs in a designated folder tree. We shall see where this goes – if OneNote can capture my note taking attention or if Google documents prevail. FYI, OneNote uses part of my OneDrive terabyte for what it keeps in the cloud. This is fine by me, since OneDrive isn’t so useful to me otherwise.

Office 365 – This is good software. I like it and use it a lot, particularly Word, Powerpoint, and Excel. I also like Google apps – particularly Google Documents on Google Drive. So far, I’m not a fan of Office trying to push me to OneDrive instead of local storage. I have my local storage system worked out already, thank you, with good backup and sharing solutions in place. I really like that Word in Office 365 online will transcribe audio files. This is very helpful to me since I tend to combine study using audio classes and lectures with exercise.

Weather apps – I have Accuweather, 1Weather, The Weather Channel, Wunderground…. and others. I sort of like Wunderground even though it has never really recovered from being purchased by IBM some years ago. I use Chronus Pro to display weather info on my home screen. Also, I’m checking out the Windy app and find it sometimes useful – the free version only so far.

TV Apps – Pick what you subscribe to, be it Netflix or Britbox or Acorn or AppleTV or…. There’s nothing interesting to see here. They’re all kind of bad at user interaction but adequate for showing TV shows. Some allow downloading for viewing offline. The offline experience is iffy at best for all of them, in my experience. Sometimes it works ok. Other times… Oops, that show is “expired” or it just won’t play. I use VLC for viewing video files and sometimes for listening to audio files from my private library.

Adobe Acrobat – Yeehaw! While there are other good PDF file readers, Acrobat will audibly read your PDF to you. And the Liquid mode, or whatever it is that makes PDF files more readable on a phone screen is kind of nice.

Adobe Scan and Google Photoscan work well for digitizing documents into pictures. I haven’t done a lot of converting scanned images to text, but Google Lens does the trick for me in simple cases I’ve needed.

Google Lens – Speaking of Google Lens, this is a really useful tool for identifying whatever you just took a picture of or extracting text from a picture. You can access it from Google Photos or the Camera app or even Chrome. Google image search is also pretty good at finding the origin of photos on the web.

Travel Apps – It’s generally worth having the app for the airline that you are traveling on and the car rental company that you are renting a car from. The airline apps keep you officially informed and can be used for checking in, digital boarding passes, etc. The car rental apps are often helpful in bypassing the rental counter (and the associated long line) and going directly to your car. For generic flight tracking, I use Flightview Elite in addition to airline apps.

Financial Apps – I use the apps from my Bank and Credit Card companies. When anything is charged to my card anywhere, I get an immediate notification on my phone. I mean, while I’m standing in front of the cash register even and the clerk is processing my payment (whether touchless or by handing over a card), I get a notification that my charge account has been used. And yes, I use GPay (aka Google Pay) so I don’t have to even carry my wallet or hand over a card to a cashier when I can avoid it. Google Wallet is a good way to slim down what you have to carry in your physical wallet.

Bible Software – I use Logos nearly all the time. I also use Bible Gateway occasionally, and also the YouVersion Bible app. The YouVersion Bible app is quite good if you don’t have something like Logos. I am a big fan of the Bible Project; they also have a nice app.

Social Media – Blah, blah, blah. Use what you use; don’t let it use you… too much. I’m not going there, but I will mention Substack and Flickr. Flickr is more of a place to share and curate photos without the ads of that other more popular place.

Pocket Casts – I started using Pocket Casts long ago. Back then they had a lifetime deal on the better pro-like version. Then they were acquired and that wasn’t really honored so now they have a higher cost tier. I’m not paying them any more money but I continue to use the version that doesn’t cost. It’s quite good.

Audio – I pay for a Spotify subscription to avoid commercials and listen to whatever I want to listen to. I also use Amazon Music because I have quite a lot of past purchases of albums that I can listen to online and download to listen to offline with Amazon. And I have an extensive library of MP3’s made to back up my old CD and Vinyl library. There are many apps for listening to MP3’s on your phone. I sometimes use PowerAmp; but it has become cluttered with features. So often I just use VLC for audio. I use Smart Audiobook Player to listen to my downloaded talks, classes, and books that I have saved as MP3 files in the past – things like Regent College classes. I use Audible for audiobooks. LibriVox is pretty good for listening to audiobooks made from stuff that is out of copyright. Sometimes I resort to Patreon to listen to podcasts that are exclusive to paid subscribers. Lectio 365 is a really good audio daily devotional app; there are devotionals for morning and evening with parallel text of what you hear in the audio for reference. Logos also does audio and I should probably make more use of it. I listen to lots of podcasts using Pocket Casts and “Hey Google, play the news” through the Google assistant. Be sure and configure what news sources you want Google Assistant to play.

Video – I use VLC to play my video files. You know, MP4’s and the like. Home video and other non-streaming video. Streaming video needs an app determined by the stream you are watching, like YouTube.

Google Home, Nest, and other Home Automation – I use Google Home and the Google Assistant for smart home stuff. I don’t like that internet connectivity is required for controlling my devices; but Google Home is easy. Sigh. Since Matter came into use, the need for other home automation apps is fading. But I still have the Philips Hue and the Kasa apps on my phone. There are fairly easy ways to get home automation without the cloud using open source software (like Home Assistant). (I am experimenting with Home Assistant on a Raspberry Pi as of March, 2024.)

DuckDuckGo – DuckDuckGo includes a browser that is pretty good for looking at things you don’t want to influence ads and the other things trackers do. They also have software that disables tracking from all apps on your phone. Some apps really don’t like this and stop working, like the ESPN app. For most apps you just stop being tracked. I appreciate being able to throw a wrench into the tracking gears; but I’m not really fanatic about it. The DuckDuckGo anti-tracking software reports the tracking that is undermines. (It is kind of stunning how much tracking Google does through apps that are not Google apps!)

Passport – You need this app to park in some lots and along some streets around Long Beach, CA. Why does it tell me that my parking spot at Seal Beach from months ago is expiring every time my phone is restarted?

Peakfinder – What’s that mountain over there called? This app will tell you. Point your phone at the horizon with the mountain on it. You can set it up to have offline information.

FlightRadar24 – You use this app to find out what that airplane is (or those airplanes are) that are passing overhead – not private airplanes.

BackBlaze – I use Backblaze to back up my PC. The app allows me to access the backed up stuff from my phone. I use client-side encryption.

Brother Print Service, Brother iPrint&Scan – I have been purchasing Brother printers in recent years. They work and are not too expensive to keep in supplies.

Authy and other Authenticators – I use authenticator apps when available for two-factor authentication. I prefer Authy when an authenticator app is needed.

Battery Widget Reborn – I use this widget on my home screen for high-visibility battery level display.

Astro Clock – I like the plethora of location, moon, sun, and planetary information this app squeezes onto one screen! OK, this is for astro-nerds.

Sky Map – there are a lot of apps for helping you identify things in the sky at night. I’ve been using Sky Map, which is usually good enough for me. There are other, more sophisticated apps and apps that will avoid messing up your night sight (and that of others nearby) by using carefully selected colors on your phone display.

Information Apps – There are many apps for specialized information: Tide tables, tying knots, First Aid, trail maps, recommendations for craftsmen. I have some of these. Choose those that tell you what you want to know and avoid excessive ads and costly or unfair marketing strategies. I don’t have much that is interesting to say regarding these apps. I’ll just recommend that we all pay to eliminate ads as appropriate. Programmers deserve compensation like any other laborer.

Technical Information Apps – I’m not really going to get any deeper into my techno-nerd apps here. I have apps for network analyzing, monitoring wifi, mapping wifi coverage, checking what devices are on the LAN, managing my access points and network access, benchmarking connection speeds and processing speed, checking hardware status, etc.

Programmer/Developer Apps – I’m leaving all these out, too because most would not find them of interest. However, I can benchmark Lisp on my phone. Just sayin’. 🙂

At last! A Good Overview of the Kinds of Policies we Need Regarding AI and Trust.

December 15, 2023 /

Bruce Schneier is a respected computer security expert who lectures at Harvard’s Kennedy School. Schneier is one of those rare humans with deep understanding and competence in several areas of mathematics and computer technology and recognized ability to analyze and recommend policies that make the world a safer and better place to live. I just finished reading an essay he wrote entitled AI and Trust in his latest newsletter. It is quite good at analyzing trust relationships and recommending policy that reduces the negative impacts that AI would otherwise have on society. It’s nearly 3500 words, but not overly technical.

Here’s an extremely important point from Schneier’s essay:

AIs are not people; they don’t have agency. They are built by, trained by, and controlled by people. Mostly for-profit corporations. Any AI regulations should place restrictions on those people and corporations… At the end of the day, there is always a human responsible for whatever the AI’s behavior is. And it’s the human who needs to be responsible for what they do—and what their companies do…. If we want trustworthy AI, we need to require trustworthy AI controllers.

The opening portion of the article is a discussion of the difference between what Schneier calls interpersonal trust and social trust. I found his discussion of trust illuminating and important. This is a well-reasoned, articulate article that I will be recommending to thoughtful people. It is accessible to those who do not have deep technical knowledge of AI. As far as I can tell there is no paywall.

On a Shopping List, Somewhere

February 11, 2018 / / 0 Comments

A commercial came on during the Winter Olympics. Johnny Weir tried to get hair gel from an empty dispenser. He said to his Google home mini, “Hey Google, put hair gel on my shopping list”. The Google home mini on TV answered him appropriately.

My Google home mini, sitting in front of the TV, spoke up and said, “I’ve added hair gel to your shopping list.”

I said, “Hey Google take hair gel off my shopping list, that was the TV.”

Google home mini said, “I can’t remove items from your shopping list. You’ll have to go to the Google home app and remove it there.”

I said, “Hey Google, you are listening to the TV. Stop it!”

Google home mini said, “I’m sorry, I don’t know how to do that yet.”

I told Susie what happened; she had just returned to the room from somewhere. She said, I guess you’ll have to buy hair gel. I didn’t think you used hair gel…

I got my phone and looked at the Google Home app. I found my shopping list (of which I was previously unaware) to remove hair gel. There was nothing on my shopping list.

Ergo, somewhere in the cloud, Johnny Weir has a shopping list with probably about 28 million units of hair gel on it.

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