AlphaFold Software Did Not Solve the Protein Folding Problem, and Is Not Great at Predicting Protein Structure

Materialists are always spreading unfounded socially constructed triumphal legends. Materialists have spread the groundless triumphal legend that Charles Darwin explained the origin of species. The more scientists discover about the oceanic depths of hierarchical organization and functional complexity in living organisms and the endless numbers of interdependent fine-tuned components in living organisms, the less credible such a boast seems. Materialists have also  spread the groundless triumphal legend that neuroscientists have explained minds and memory. The credibility of such a boast will dissolve once you have made an adequately deep study of topics such as the physical shortfalls in all brains, medical case histories defying "brains make minds" dogma (discussed here), and the defective research practices of today's neuroscientists. 

The latest example of a groundless socially constructed triumphal legend spread by materialists is the claim that some software called the AlphaFold software (existing in forms such as AlphaFold, AlphaFold2 and AlphaFold3) solved the long-standing problem of biology called the protein folding problem. 

The human body contains 20,000+ protein molecules, and most protein molecules have a sequence of hundreds or thousands of amino acids specified by some particular gene.  But a protein molecule is not a mere chain of amino acids. Protein molecules have folded three-dimensional shapes necessary for their functions. 

Simplifying things, you can think of it this way:

Gene --> Polypeptide sequence (amino acid chain) --> Protein

How is it that a protein molecule gets the three-dimensional folded shape needed for its function? Does it read instructions on how to make such a shape from a gene? No, it does not. Nowhere in DNA or its genes is there any such thing as a specification for how to make the three-dimensional shape of a protein molecule. 

Ignoring possible cases of shape duplication, you can say that within the human body there are very roughly 20,000+ different protein molecule structures assumed by very roughly 20,000+ different types of protein molecules. The problem of how these folded shapes arise is the unsolved problem called the protein folding problem. 

We should avoid getting confused between the protein folding problem and the protein folding prediction problem, which are two separate problems. The protein folding prediction problem is the problem of predicting the three-dimensional shape of a protein molecule using that protein's amino acid sequence. The protein folding problem is the very different problem of why proteins assume the three-dimensional shapes that they have. Some progress has been made on the protein folding prediction problem by some AlphaFold software using machine-learning and massive databases storing information on genes and the shapes that proteins have. But that progress is merely progress on the protein folding prediction problem, not progress in solving the protein folding problem. The protein folding problem is still unsolved. Scientists do not understand how proteins are able to form into the three-dimensional shapes that they have, the shapes needed for their function. A scientific paper notes that "AlphaFold2 (AF2) revolutionized protein structure prediction, yet it is often conflated with the protein folding problem," thereby noting the confusion that is occurring between a protein structure prediction problem and a separate and distinct protein folding problem. 

Below are some quotes by scientists confessing that the protein folding problem has not been solved.

•  "In real time how the chaperones fold the newly synthesized polypeptide sequences into a particular three-dimensional shape within a fraction of second is still a mystery for biologists as well as mathematicians."   -- Arun Upadhyay, "Structure of proteins: Evolution with unsolved mysteries," 2019.
• "The problem of protein folding is one of the most important problems of molecular biology. A central problem (the so called Levinthal's paradox) is that the protein is first synthesized as a linear molecule that must reach its native conformation in a short time (on the order of seconds or less). The protein can only perform its functions in this (often single) conformation. The problem, however, is that the number of possible conformational states is exponentially large for a long protein molecule. Despite almost 30 years of attempts to resolve this paradox, a solution has not yet been found." -- Two scientists, "On a generalized Levinthal's paradox," 2018. 
• "How proteins fold remains a central unsolved problem in biology. While the idea of a folding code embedded in the amino acid sequence was introduced more than 6 decades ago, this code remains undefined. While we now have powerful predictive tools to predict the final native structure of proteins, we still lack a predictive framework for how [amino acid] sequences dictate folding pathways....Almost seven decades of experimental and theoretical inquiry have not revealed a 'folding code' at the amino acid level, i.e., rules endowed with the generality and predictive power required to connect amino acid sequence to how the protein attains its structure....Machine learning made it possible to identify weak correlations to generate the structure most likely to correspond to a sequence. This tour-de-force effort has largely solved the problem of predicting protein structure from sequence...but with a key limitation: the algorithm that predicts the structure is a complex black box of pattern recognition that casts little light on the process of folding and that tells us nothing about why only some sequences fold, or how physics and evolution are coupled." -- Five scientists in the year 2025 (link). 
• "The real challenge—that remains unanswered after more than 50 years of research in the structural biology field—is understanding the mechanisms that lead proteins to fold into their native state. The reason for these difficulties is that the central question of the protein folding problem remains unresolved: specifically, how a sequence of amino acids encodes its folding pathways." -- Scientist Jorge A. Vila, 2025 (link). 
• "One of the most puzzling and unsolved challenges in molecular biology is understanding how proteins fold. " -- Scientist Jorge A. Vila, 2026 (link). 
• "The origin of functional proteins remains a fundamental biological enigma...The physical principles governing protein genesis itself, from prebiotic condensation to functional protein emergence, remain unresolved."  -- Nine scientists, 2026 (link). 

A year 2026 paper makes it clear that contrary to boasts in the press, the AlphaFold2 software does not actually solve the protein folding problem, the problem of how protein molecules almost instantly acquire very complicated 3D shapes needed for their function. The year 2026 paper states, "The explanatory scientific understanding of the protein folding problem is thus not directly advanced by AF2 [AlphaFold2]." Later the same paper says, "The protein folding problem remains unsolved."

A paper published in the year 2026 throws some cold water on triumphal boasts about the AlphaFold software, while reiterating that the protein folding prediction problem (a "what" problem) is very different from the protein folding problem (the "how" problem of how proteins fold into the shapes needed for the biological function):

"AlphaFold only works some of the time.... However, true single sequence structure prediction has remained elusive. The cautious old guard who initially responded to AlphaFold by highlighting the difference between protein structure prediction (the what) and protein folding (the how), correctly saying that the second remains an open problem, has surprised nobody by not going on to work on protein folding."

How well does the AlphaFold series of software predict the structure of a protein, given its amino acid sequence?  According to one recent paper, the "official" answer to this question is to be found in the 2026 paper "CASP16 Protein Monomer Structure Prediction Assessment" which you can read here. Below I'll call this the CASP16 wrap-up paper. Over many years, there have been a series of CASP competitions, in which competitors using different types of software have tried to predict the 3D shape of a protein molecule, given an input of its amino acid sequence. The latest of these competitions which has published results was the CASP16 competition held in 2024, and its results are described in the paper above. (A CASP17 competition has just recently completed, but it will be quite a few months before we have a scientific paper publishing its results.) 

In a previous post I did discussing the CASP14 competition, I noted that there are two different ways of calculating the accuracy of a protein shape prediction: a GDT_TS measure and a more stringent GDT_HA measure. Using the more stringent GDT_HA measure, Figure 2B of the CASP16 wrap-up paper gives us the graph below showing prediction accuracy of the latest versions of the AlphaFold software (AlphaFold2 and AlphaFold3). AF2 refers to AlphaFold2. AF3 refers to AlphaFold3. 

Each little dot represents a particular prediction (or maybe a set of predictions). The vertical position of the dot represents how accurate the prediction was. Dots near the the top of the graph (near the 100 mark) are very good predictions. Dots in the blue areas of the graph are not very good predictions, predictions only about 70% correct. Dots below the blue areas of the graph are poor predictions. 

Anyone who has read the hype about the AlphaFold series of software may be surprised by the result above. The graph seems to show an average prediction accuracy of only about 70%, with the accuracy ranging from only about 40% to as high as almost 98%.  But didn't we read again and again science news articles making it sound like the AlphaFold series of software had mastered the problem of predicting the structure of proteins from their amino acid sequences? Such articles were very misleading. 

We read this: "Overall, [predictive] performance on monomer targets in CASP16 showed minimal improvement compared to CASP15." The reference is to the CASP16 competition held in 2024 and the CASP15 competition held in 2022.  The statement I just quoted contradicts the impression that we have got from the press of skyrocketing progress in this area. 

A key issue in evaluating the predictive effectiveness of the AlphaFold series of software is the issue of target size.  The target size refers to the amino acid length of a protein which AlphaFold software attempted to describe using its structure prediction methods. The wrap-up paper mentioned above does a bad job of describing these target sizes. To find the target sizes, you must go to the page here  (the page for the CASP16 competition) and then click on the "Target List" link, which takes you to the page here. 

The page isn't well-designed in terms of presenting information in a way that the average reader will understand. Under the heading of "Multimers" we have a cryptic column heading of "Res" which gives numbers. Those numbers are the "residue length" of the proteins that were used as prediction targets, and these "residue lengths" were the number of amino acids in the proteins. If you click on the "Res" column, the column will sort by the lengths of the amino acid sequences in the proteins. When you see a number above about 450, you are looking at more difficult prediction targets.  When you see a number above 1000, you are looking at the most difficult prediction targets.  When you see a number below 400, you are looking at the easiest prediction targets.  

Scrolling down the page, I see that the selected prediction targets were mostly not very challenging. Very many types of human proteins have more than 1000 amino acids, and more than 500 types of human proteins have more than 3000 amino acids each.  But out of 150 prediction targets of the CASPR16 competition, only 29 had amino acid lengths greater than 1000. About 35 of the 150 or so prediction targets had an amino acid length between 1000 and 600. 38 of the prediction targets had an amino acid length between 600 and 450. About 49 of the 150 or so prediction targets had an amino acid length less than 450, which is less than the average amino acid length of a human protein.  

So given this not-very-challenging set of prediction targets that has less-complex-than-average types of proteins about as frequently as more-complex-than-average types of proteins, we should not be too impressed by Figure 2B showing a prediction accuracy of about 70%. It would seem that if the prediction targets had been proteins with above-average complexity as often as targets with average complexity, that the reported prediction accuracy of about 70% would have been something much smaller, such as maybe 60% or 50%. 

From the graph above, it is clear that the AlphaFold family of software does not very well predict the structure of protein molecules, using the amino acid sequence as an input.  Its performance accuracy should not be described as "very good" but merely as perhaps "fair-to-somewhat-good."

There is a site called the AlphaFold Protein Structure Database which you can reach here. The average person will be puzzled by how you can use this site to check the quality of predictions by the AlphaFold software.  I can describe one way.  You can get a list a protein names by going to the UnitProt database site here, and typing in a query like this:

(length:[1000 TO 50000]) AND (organism_name:"Homo sapiens")

This will give you a result of thousands of rows, showing human proteins that have more than 1000 amino acids. You can then type in some of the data from that result set into the search box of the page for the AlphaFold Protein Structure Database which you can reach here.

Doing that, I got some results from that page, and the results were not very impressive. For example, I typed in the phrase "Transcription factor TFIIIB component B'' homolog" using a phrase I had got from the UniProt result set using the query above. I got these results from a few queries using the AlphaFold Protein Structure Database:

Protein Name	Gene	UniProt ID	Amino Acid Length	Global Quality (How Well Alpha Fold Predicts the Struc- ture)
Transcription factor TFIII    B component B'' homolog	BDP1	A6H8Y1-4	1372	46.75 (Very Low)
AT-rich interactive domain-containing protein 1A	ARID1A	O14497-2	2068	48.75 (Very low)
Centrosomal protein of 290 kDa	CEP290	O15078	2479	60.53 (low)
Zinc finger protein 292	ZNF292	O14497-2	2578	46.91 (Very Low)
Photoreceptor cilium actin regulator	PCARE	A6NGG8	1288	43.78 (Very Low)
Transcription initiation factor TFIID subunit 4	TAF4	O00268	1085	52.78 (Low)

I did not have to do much work to find these examples of low-quality predictions by the AlphaFold software.  I had to only search through a random list of about 15 or 20 proteins with an amino acid length greater than 1000.  When I searched for proteins with an amino acid length between 500 and 700 (only somewhat more complex than an average human protein molecule), I quickly found some examples that the AlphaFold software performed poorly on when analyzing. For example, its prediction about the TANK-binding kinase 1-binding protein 1 of 611 amino acids was not very good, being rated as only "63.62 (Low)." And the AlphaFold software's prediction about a Nucleolar protein 4 of 536 amino acids was not very good, rated as only "60.91 (Low)."

I may note that the AlphaFold Protein Structure Database site which you can reach here uses quality-rating adjectives that are way too positive-sounding. The database routinely describes predictions that are only about 70% accurate as being "high" in quality. In few other fields would ratings be so charitable. For example, if a car assembly team assembled only 70% of the car's parts correctly, it would make no sense to claim its assembly skill was "high." And if someone predicting the outcome of mixing particular quantities of particular chemicals had an accuracy rating of only 70%,  people would not rate his accuracy as "high," but complain that he was poor at predictions.  You would hardly claim that your physician was "high" in proficiency if he prescribed the right medicine only 70% of the time. In field such as physics, any theory of gravitation would be subject to the most scornful derision if it made predictions that were only 90% accurate, and its proponents claimed that this was "high" accuracy.  In fields such as physics, a rating of "high" quality tends to go to only predictions that are at least 99% accurate. 

An example of the grotesque misinformation being stated in the popular press about the AlphaFold software is this claim in an article:

"DeepMind started developing AlphaFold in 2018. In 2020, it was recognized as a solution to humanity's 50-year-old 'protein folding problem,' which sought to answer how amino acids automatically fold into complex 3D shapes. "

The AlphaFold software did nothing to solve the "50-year-old 'protein folding problem,' which sought to answer how amino acids automatically fold into complex 3D shapes." All that the AlphaFold software did was to make some progress on a different problem, the protein folding prediction problem. And the progress that the AlphaFold software made was very limited. The graph above with the blue areas shows that it is still impossible to accurately predict the structure of most proteins from their amino acid sequences. 

It wasn't just the writer quoted above who misinformed us on this topic. It was the organization that runs the CASP competition, which issued a highly misleading press release in 2020. One of endless science press releases with unfounded boasts, the press release quoted a Professor Dame Janet Thornton as saying the following: 

" One of biology s biggest mysteries is how proteins fold to create exquisitely unique three-dimensional structures. Every living thing   from the smallest bacteria to plants, animals and humans   is defined and powered by the proteins that help it function at the molecular level.  So far, this mystery remained unsolved, and determining a single protein structure often required years of experimental effort. It s tremendous to see the triumph of human curiosity, endeavour and intelligence in solving this problem. A better understanding of protein structures and the ability to predict them using a computer means a better understanding of life, evolution and, of course, human health and disease." 

Here Thornton bungled badly by confusing two different problems: the protein folding prediction problem (the problem of predicting the structure of proteins from their amino acid sequence) and the protein folding problem (the problem of how proteins are able to form their three-dimensional structures from amino acid sequences that do not specify such structures). It was a goof as bad as someone getting all mixed up and conflating and confusing the problem of how to play the card game called bridge, and the problem of how to construct a bridge. At the time this quote was made, and even so today, nothing had occurred to justify this boast about "solving this problem," as nothing had been done to solve the protein folding problem; and no software existed that very accurately and reliably predicted protein structure. And no such software exists even today. 

Referring to the AlphaFold3 software using the phrase AF3, a year 2026 paper states this: "We conclude that AF3 is a poor predictor of D-peptide chirality, fold, and binding pose."

We are reminded here that materialism is a giant myth-making machine.  Materialism grinds out example after example of unfounded socially constructed triumphal legends, particularly when such legends serve the ideological needs of materialists. The protein folding problem is a great thorn in the side of materialists. Somehow proteins in our bodies are constantly forming into the three-dimensional shapes needed for their biological function, just as if some purposeful intelligence of vast power was acting to achieve such effects.  This reality is troubling to materialists, and we can understand why they would wish to construct an analgesic legend to ease their discomfort over this matter.  But the claim that AlphaFold family of software programs solved the protein folding problem is as unfounded as the claim that Darwin explained the origin of species. 

At about this time (September, 2026) the latest in the CASP series of competitions has completed, a competition called CASP17. It will be quite a few months before there is published a paper that details the predictive accuracy of competitors in this competition. In December 2026 there will be a conference announcing preliminary results of the CASP17 competition. Given the misleading content in the November, 2020 CASP press release, we should be suspicious of boasts of any press release announcing results of the CASP17 competition, and subject such a press release to critical scrutiny. 

A major contributor to the social construction of unfounded triumphal legends is the Nobel Prize organization, which sometimes gives undeserved Nobel prizes, or publishes unfounded or misleading claims when awarding Nobel prizes, as I document in my post here. When it awarded a Nobel prize in chemistry to two people who had worked on protein structure prediction, the claim was modest. Officially the award is merely going "for protein structure prediction."  But the announcement page links to biographies of the two scientists awarded, and in both cases the pages have remarkably misleading statements. On the pages here and here, we have this very misleading statement:

"In 2020, Demis Hassabis and John Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all known proteins."

No such accurate prediction occurred, as my text above documents; and AlphaFold2's accuracy as measured in the graph above is only about 70%. Someone trying to defend this statement quoted above would sound pretty duplicitous. He might say, "We just said predict, we didn't say predict accurately." 

AI May Soon Replace Pushover Science Journalists, But Not Analysts of Scientist Truth Claims

 So-called artificial intelligence (AI) is making progress, and quite a few workers are worried that their jobs may be replaced by AI. One type of worker who might reasonably have such worries is the typical person calling himself a science journalist. In recent years the work of those calling themselves science journalists has been largely a kind of formulaic work that AI programs might be able to reproduce. Typical science journalists act like cheerleaders to promote the latest scientific papers.  

To understand the current role of the science journalist, you need to understand the economic motivations behind misleading exaggerations about scientific papers and the uncritical regurgitation and amplification of scientist boasts. In my post "Why the Academia Cyberspace Profit Complex Keeps Giving Misleading Brain Research Reports" I discussed the economic reasons why we keep getting misleading research about brains, and misleading headlines about brain research. The analysis in that post holds true not just for brain research, but for scientific research in general. We live in an economy in which misleading stories about scientific research and groundless but interesting-sounding scientific speculation are highly incentivized, so that there are strong motives for such things. To give a short synopsis of what I discussed at much greater length in that post, the economic motivations are something like this:

(1) Scientists are judged by how many papers they publish and how many citations such papers get.

(2) Because of publication bias (in which papers reporting positive results and particularly interesting-sounding positive results are more likely to be published), scientists are strongly motivated to publish papers claiming positive results and also claiming interesting-sounding results.

(3) Wishing to make themselves appear like sources of important research breakthroughs to help justify their exorbitant tuition, universities are motivated to produce press releases exaggerating the importance of research papers published by their professors.

(4) Since science news is published on web pages with ads that generate revenue for the people running or funding the web pages, with revenue proportional to how interesting-sounding a story is, those running science news web sites or science analysis web sites have an enormous economic motivation to create clickbait headlines that generate higher numbers of page views, and more advertising revenue. Science news sites these days are almost always built in the form of headlines that you must click to read the story; and each time this causes a web page with ads to appear, the people running or funding the site get money from views of the ads displayed on the page you opened up.

The result of all of this is a very wacky world we might call the world of scitainment, to use a word that combines the words "science" and "entertainment." Scitainment is a part of the internet that blends science and entertainment. Very much of what we read in this strange world of scitainment is true, and very much of it is false. The world of scitainment blends fact and fantasy, always trying its best to produce entertaining stories and clickbait headlines. It's all about luring you in to click on the headlines that lead to articles, so that you go to pages that generate ad revenue for the people running the web sites. 

Scitainment is very big business these days.  A vital part of scitainment is what we can call the pushover science journalist.  The pushover science journalist is typically someone ever-ready to promote and hype and sing the praises of low-quality science research.  

The economic ecosystem that incentivizes unfounded boasts about weak-quality scientific research shows no signs of going away any time soon. 

Part of the reason such an ecosystem persists is that researchers have a "wink and a nod" toleration for the lies and exaggerations of university press releases, knowing that such hype increases the likelihood that researchers will get an increase in their cherished goal of higher citation counts. 

But while the work role of "pom-pom" pushover science journalists shows no sign of disappearing, there is a reason to think that trying to build a long-term career working in such a role may be a hazardous bet. The reason is that it may be likely that the role of pushover science journalist can be replaced by AI programs (so-called artificial intelligence). 

Nowadays one of the main features of so-called artificial intelligence programs (AI) are what are called large-language models (LLM).  Such programs are trained on very large bodies of text such as billions of pages that can be accessed by programs roaming the internet. Such programs are able to detect certain types of patterns, and mimic such patterns. 

So, for example, suppose you give a large-language model 10,000 cases of science-related press releases appearing online. Such press releases will typically have a link to a scientific paper being promoted. A large language model can train using some algorithm in which the scientific papers are regarded as inputs and the press releases are regarded as outputs.  From such training, a LLM may be able to pick up outputs that tend to be produced when scientific papers are being promoted. This does not involve any real understanding of the content. The large language model simply learns that certain types of scientific papers become associated with certain types of promotional documents that use language in a particular way. 

We can imagine a day not very far in the future in which the role of pushover science journalists is largely taken over by AI programs such as large-language models.  A few years from now, it might be that a university wishing to promote its latest science study may simply provide the study to some AI program that produces a promotional press release. We can also imagine that in a few years AI programs may largely take over the job of writing clickbait science news stories that are based on a particular press release and a particular scientific study. 

Part of the reason why the work of pushover science journalists may be achievable by AI programs is that such work is so formulaic and tends to repeat the same old catchphrases, the same old boasts, and the same old recitations of the speech customs of materialists. The more formulaic some type of writing is, the easier it is for an AI program to duplicate it.  An example of a formulaic statement constantly appearing in puff piece press releases are statements that sound like:  "This new research sheds light on the long-standing mystery of ____________________."

But there's a type of writer very different from the pushover science journalist.  Using an acronym, you might call this type of writer an ASCOT. The acronym ASCOT stands for Analyst of Scientist Claims of Truth. 

An ASCOT has no goal of providing boastful accounts of some scientific paper. An ASCOT has no goal of writing online stories that provide clickbait headlines.  An ASCOT has the goal of analyzing the truth claims made by scientists, and to judge whether such claims are well-founded or poorly supported, and whether such claims are contradicted by other things that humans have observed.  Part of the job of being a good ASCOT is the job of being a reality detective, one willing to ponder and classify all of the clues that reality gives us, without throwing away clues after giving lame justifications such as "professors don't believe in that." 

There is no way to automate most of the work of an ASCOT, because such work requires analyzing the claims of scientists from a perspective that considers all observations that humans have made and all facts that humans have learned, with a large degree of understanding and insight (something so-called AI systems do not actually have).  Any good ASCOT needs to be a diligent scholar of many different topics and many different subjects.  A good ASCOT needs to have a deep understanding of many broad topics, an understanding that is beyond the reach of AI systems, which do not really understand anything. 

Let us consider a hypothetical example in which some scientific study claims to have determined that Little Neural Effect X offers a solution to what the authors call "the problem of consciousness." Analyzing whether such a claim is a groundless boast first requires some understanding of whether the matter of explaining human minds is equivalent to solving a "problem of consciousness." But an AI system could not have such an understanding.  To decently understand such a thing, you would need to understand how human mentality and human mental experiences and human mental capabilities are something a thousand times bigger or a million times bigger than mere "consciousness." The diagram below illustrates a bit of the understanding you might need. 

If a person has studied all of the items listed above, and the person has some understanding of such topics, he may have done quite a bit of the work needed to be a good ASCOT. But no AI system is likely any time soon to have such understanding.  

A study of all the topics mentioned would produce only some of the knowledge and understanding needed to skillfully analyze scientist truth claims. Very much more study would be needed, including a study of the topics that are discussed on this blog and my blog here. 

AI systems using large language models (LLM) work by web crawling and frequency-of-assertion ranking. This works well when the AI system is called on to answer factual questions such as how did Abraham Lincoln die.  Web crawling and frequency-of-assertion ranking can also produce good results when it comes to opinions that are widely held and true. But web crawling and frequency-of-assertion ranking can fail very badly when it comes to assertions that are frequently made but are untrue or not well justified. Very many of the claims in today's science literature are such untrue claims, claims that are massively made but not justified by observations, and often contradicted by other observations that have received insufficient attention. When unjustified claims are being massively made, AI systems and large language models can aid, abet and amplify the "echo chamber" effect by which some erroneous idea "went viral." 

How can you sort out which commonly made claims in science literature are well-justified, and which are mainly just speech customs and social constructs of a conformist belief community with headquarters in academia? That requires understanding, insight and the most careful and thoughtful study of many very deep subject matters.  That is the type of work that cannot be done by AI large-language models that work based on web crawling to tally up which assertions are most commonly made. To properly sort out which commonly made claims in science literature are well-justified and which are not, it requires insight and philosophical judgment beyond the reach of AI systems. 

An ASCOT will often be someone taking the long, hard, winding path that leads to a destination of philosophical insight after the most laborious study and pondering of many deep topics and many strange and surprising observational reports. There's no way to get to such a destination by unleashing some horde of web-crawling software programs that engage in programmatic screen-scraping and frequency-of-assertion ranking. 

Naked-Eye Sightings of Mysterious Orbs (Part 11)

 Below are some posts I have published about people reporting they saw mysterious orbs with the naked eye:

Naked-Eye Sightings of Mysterious Orbs (Part 1 of 4)

Naked-Eye Sightings of Mysterious Orbs (Part 2 of 4)

Naked-Eye Sightings of Mysterious Orbs (Part 3 of 4)

Naked-Eye Sightings of Mysterious Orbs (Part 4 of 4)

Naked Eye Sightings of Mysterious Orbs (Part 5)

Naked Eye Sightings of Mysterious Orbs (Part 6)

Naked Eye Sightings of Mysterious Orbs (Part 7)

Naked-Eye Sightings of Mysterious Orbs (Part 8)

Naked-Eye Sightings of Mysterious Orbs (Part 9)

Naked-Eye Sightings of Mysterious Orbs (Part 10)

Let us look at some more cases of this type.

Below is an account from page 10 of the November 1962 Psychic Observer which you can read here.

"Another frightening UFO encounter—a thirty mile chase—was reported by Mr. and Mrs. Richard DuBois, 13481 Shirley Street, Westminister, California. On the night of October 21, returning from a vacation, they were driving along Route 60 in New Mexico. Except for their car, the highway was deserted. It was about 2 a.m., when a 'brilliant ball of white light' flashed down across the car, slowed, then turned back and followed alongside. 'At first, I thought it was an airplane making an emergency landing,' said Mrs. DuBois. But the strange 'ball ’ streaked back into the sky. DuBois’ conjecture that it might be an odd moonlight reflection was quickly disproved. For the bright object again flashed down, raced ahead of the car, then flowed to let them catch up.  'We were really frightened by this time.' Mrs. DuBois reported. And their alarm increased when the strange device pacing them suddenly separated into four smaller, glowing objects. Flying alongside, the formation kept pace with the car until a service station appeared ahead. When DuBois slowed down, the four UFO's flashed upward and went out of sight. 'We were so terrified.' said Mrs. DuBois, 'That my husband drove at 100 miles an hour to the next town. We asked if there had been any reports of flying objects, but people only laughed. They thought was it was funny, but we were plenty scared.' "

The account includes this artist's drawing:

On page 121 of the document here, we read an account by J. Ricardo Lavicka, telling what he saw at about  age 14. He says that he saw forming above the head of a dying sister a translucent sphere about as big as a basketball. We read this: 

In a book on page 43 the author Jas Robertson reports his first attendance at what apparently was a seance of David Duguid (the wording is a little unclear about which medium was involved):

"At my first sitting I witnessed phenomena which I have never forgotten, and I can recollect saying to myself, when I saw a globe of light travelling from the end of the room, and assuming the form of a human hand when it reached the table, 'There never can come to me in all the future years anything which will weaken or make me deny what I am now witnessing.' "

Telekinesis refers to some paranormal ability to move matter only by mind or the power of spirit. You could use the term for any inexplicable movement seen at a seance, such as the table levitations that were so widely reported. On page 294 of the document here, we have a letter by Arthur Conan Doyle, dated July 19, 1922. He records observations at a seance of the Austrian medium Frau Silbert:

"Yesterday, July 18th, I was one of six who sat with Frau Silbert, the Austrian medium, at the British College of Psychic Science....I was seated on the left of the medium. Behind her was the 'cabinet,' a curtained enclosure, partially open in front. This I examined and found empty. The red light was kept fairly high—so high that we could easily see every movement of the medium or of each other. We laid our watches upon the ground under the table, as it was said that the medium’s control was able sometimes to scratch his name, Ivel, upon them. I may say at once that this phenomenon did not take place....

Presently the medium sank into trance, and made curious movements with her hands as if she were drawing some substance from her mouth and nose. If anyone were choking with cobwebs and was trying to clear oneself, it is the movement they would have made. She then stood up, gave a sharp cry, and picked apparently out of the air behind her one of the watches which had been on the ground. We were all agreed that neither she nor any member of the company had stooped down from the moment that the watches had been placed there. It was a clear case of movement without contact or telekinesis.  

The trance now deepened and the woman gave little whining cries, exactly like those which are the prelude to childbirth. Eva gives the same cries when she is about to emit ectoplasm. The actual emission is, I fancy, a relief. Presently I saw on the floor, about a foot behind the medium, and between her chair and mine, a luminous ball, like a phosphorescent sponge, rather larger than my fist. It was quite clear but it vanished in a few seconds. Further luminous patches then appeared protruding from under the curtains. The ball was exactly such as has been described by Miss Scatcherd in her observations upon Eva.....Several times the curtain of the cabinet was blown out as in a high wind, and twice I was touched by some solid body when I was quite clear of the medium. The sensation was that of a dog rubbing against my leg."

The mysterious fluttering of a curtain (as if blown by a wind) was an effect frequently reported in the seances of Eusapia Palladino.  From a quality-of-evidence standpoint, a dated account like this (stating what someone saw on the same day or the previous day) is relatively good evidence, much better than someone just recalling what he saw months ago or years ago. I advise people to always immediately write down a dated signed account of any thing very unusual that they witnessed, whether it be something paranormal-seeming or something that may be a matter of investigation by a scientist or a court. 

Below is an account from a paper dated September 10, 1959:

Brothers, Neighbor Report UFO Here Bright As Moon (From The Peoria Journal Star) 

"A Peoria drug salesman reported seeing an unidentified flying object 'just about as bright as the moon but not as high' at 10:30 p m. Saturday as be went out to move his car. J. H. Baker, 2725 Renwood Ave., Hamilton Park, said he at first thought the object was an airplane, but it then appeared as 'a round large ball of white light.' The phenomenon, said Baker, could be seen moving rapidly from southwest to northeast at a height of 10,000 to 12,000 feet. Baker said the object also was seen by his brother, R. J. Baker, visiting here from Chicago, and by a neighbor, Jack Rhodes, 2724 Renwood. The brother trained a powerful pair of binoculars on it. The strange object stayed in sight from 4 to 6 minutes. 'It was moving much faster than a jet plane,' Baker reported." 

The length of time the sight was observed seems to rule out a meteor (something that appears visible in the sky for less than 15 seconds).  A check of a moon phase calendar suggests that on the Saturday in question (September 6, 1959) the moon looked like a mere thin crescent. 

A very distinguished Earl (called under British customs the Master of Lindsay or Lord Lindsay) had many meetings with the famous medium Daniel Dunglas Home. Lord Lindsay kept careful records of paranormal events occurring at such meetings. Lord Lindsay was interviewed by the investigators of the famous Dialectical Society of London, when they did their long formal investigation finding resoundingly in favor of paranormal phenomena, which resulted in a long report that can be read in full using the link here or here. Lord Lindsay described viewing various dramatic types of paranormal phenomena, and his testimony is summarized in my post here. 

Lord Lindsay had printed up a small number of copies of a privately printed document discussing his observations of the paranormal around 1870. For many years the document was available only to a small, select group of people who had one of the rare copies. Finally in the 1920's the Society for Psychical Research published the document, which appeared in Volume 35 of the Proceedings of the Society for Psychical Research (1925), which you can read here.  We have a book-length document describing some of the most amazing marvels humans have ever seen. 

On page 60 and the following pages we have a description of a Seance #6 at the house of Mr. Jencken, the husband of the medium Kate Fox, by then Kate Fox Jencken. (Kate was herself someone associated with many reports of the paranormal, as discussed here.) First we read that a servant at the house had been "seeing phosphorescent balls of light in her room at night."

We then read this:

"Home stood up and said, 'He is very strong and tall.'  and standing there beside me. Home grew, I should say, at least, six inches. Mr. Jencken, who is a taller man than Home, stood beside him, so there could be no mistake about it. Home's natural height is, I believe, 5 feet 10 inches. I should say he grew to 6 feet 4 inches or 6 feet 6 inches. I placed my hands on his feet, and felt that they were fairly level on the ground. .... He appeared to grow also in breadth and size all over, but there was no way of testing that. He diminished down to his natural size, and said, 'Daniel will grow tall again ' ; he did so, and said, 'Daniel's feet are on the ground,' he walked about, and stamped his feet. He returned to his natural size, and sitting down, he said, 'Daniel is coming back now, sit down, and do not tell Daniel at once what he has said.' In a few seconds he awoke."

This was only one of many times in which a paranormal elongation of the body of Daniel Dunglas Home was reported. We then read that the seance ended in this way

"We all went into the drawing room ; it was quite dark. Home sat at the piano, and played a few notes. Mrs. Hennings sat near him ; Mrs. Jencken a little way off ; Mr. Jencken and I stood near the piano. Soon we observed the light that we had been told we should see. A small luminous ball flitting about, sometimes very brilliant ; the chords of the piano were swept, but the keys were not touched. The piano was lifted off the ground about 2 inches. I had my hand underneath, and it was again lifted about 2 inches, and then without any effort, I should say 8 inches higher. It was not tilted, but lifted bodily. We now heard loud raps, the alphabet was called, and ' Good night ' spelled out. Nothing more occurred.."

Effects such as these in the presence of Daniel Dunglas Home were reported by many witnesses. As discussed in my post here, Home was investigated by the leading physicist Sir William Crookes, who reported dramatic paranormal effects occurring in Home's presence, including the levitation of Home himself (as you can read about in Crookes' publications here and here). 

On page 187 of the March 21, 1935 edition of the periodical Light, which can be read here, we get two astonishing accounts. First, we hear of the sighting of an apparition:

"In the Revue Spirite Beige for February (Liège), Dr. Lucien Graux tells the following story. One night, M. Albert Lambert, of the Comédie Française, was silently going over his part in ' Ruy Bias'  in his private room, when there entered a man in a long cloak, who was the very image of the Ruy Bias of the title part of the play. The apparition commenced to recite Victor Hugo’s verses. Albert Lambert listened with every sense alert; it was as though revelation and enlightenment were coming to him from the other world. When he was afterwards questioned about this vision, the famous actor replied: ' You wish to know whether it was an illusion, whether I really saw, or only thought I saw? My emphatic reply is this : I both saw and I heard.' ”

Then we have an account of a mysterious blue orb appearing at a seance:

"An eye-witness has sent to the Neues Wiener Journal (Vienna, January 7th), an interesting account of a sitting with the young French Medium, Mile. Remy. The sitters were the guests of Professor Lyonel, and included two well-known University Professors, two medical men, and an engineer, who was in charge of an intricate set of control apparatus, including a camera for infra-red film photography, as well as one that registered even the faintest manifestation of light. The delicate-looking young girl sat down in an armchair and was then put into slight hypnotic sleep. After an interval of only five minutes, Professor Lyonel was touched by a hand and the correspondent by another; then the table on which the sitters’ hands were resting was raised by powerful pressure from below and levitated into the air. The wish was expressed that the hitherto sceptical investigators might be enabled to see what was taking place, and a second later an iridescent ball of blue light appeared close under the ceiling; and the ticking of the light-registering apparatus gave independent evidence of this fact. By the light of this ball of blue, the sitters saw that the table was raised six to seven feet above the floor. The light then sank down over the Medium’s chair, revealing the sleeping figure of the young girl; and so profound was this sleep that it was not broken when, next moment, with a loud report, the ball of light burst and disappeared.

The account ends: 'There followed ten minutes of expectant waiting, when suddenly the room was filled with the most marvellous but infinitely sad music of a violin. We had seen before the room was darkened that, amongst the instruments on its walls, there was a violin, with its bow beside it. But whose was the masterhand that was now playing it? Certainly not the Medium’s, for, under the controlling eyes of the apparatus, she could not leave her seat even for one second without being detected; and the strip of film which I was allowed to examine later on proved that throughout the seance Mile. Remy had never left her chair. But the photograph also showed a film-like hand hovering in the air, with the delicate tapering fingers of a woman—ethereally delicate, as if from another world than ours.' ”

On page 375 of the June 22, 1934 edition of the periodical Light, which you can read here, we read the following account of observing mysterious orbs in a seance:

"MME. NIJINSKY, who recently returned to London, tells us of a strange experience of Rudi Schneider and his wife. Rudi said he had never sat with a Medium. Mme. Nijinsky took him and his wife to Miss Frances Campbell. During the sitting—which Rudi, we are told, hugely enjoyed—balls of light sprang up and skipped from Rudi to Mme. Nijinsky’s lap. Miss Campbell gave accurate descriptions of the family affairs of her sitters ; then she turned to Mrs. Schneider and said : 'I feel I must prepare you for a blow. Your brother has been shot to-day in Linz.'  Mrs. Schneider protested that it could not be true. Next day, however, a telegram arrived stating that in the riot which cost so many lives in Austria, Mrs. Schneider’s brother had been killed."

On page 13 of the January 1897 edition of the Journal of the Society for Psychical Research, which you can read here, we read this:

"On the evening of May 21st, 1881, I was seated by a window at A. B. Rolfs in Council Bluffs, Iowa. Saw two large balls of light pass the window followed by a long crimson scarf that [it] seemed would never pass, its length was so great. I rose from my seat and went to the dining-room where some of the family were at tea ; told them what I saw, and stated my convictions that it was some national calamity. The balls meant death and the long scarf blood that would enwrap the whole nation. I then saw a stout, dark young man who handed me a paper. I then said, 'We will hear of this by such a young man as I describe.'  Time passed and the vision had gone from memory, when one day in July, same year, we all sat sewing and a man passed the window. I recognised the young man I had seen in my May vision ; sprang to meet him and he handed me a bulletin announcing Garfield's assassination, or rather the shooting that cost his life—the first intimation we had had of such a thing."

President Garfield was shot on July 2, 1881, and never fully recovered. He died on September 19,1881. The shooting was about six weeks after the strange sight of moving balls of light. 

Below is a passage from page 110 of the book The Other World  by Frederick George Lee, which you can read here: 

"The husband was often absent, but he and his wife occupied the room in which Mrs. Webb had died, while· their daughter, a girl about ten years of age, slept in a small bed in the comer. ...

Mrs. Accleton, during her husband's absence, having engaged her mother to sleep with her one night, was suddenly aroused at the same hour of two by a strange and unusual light in her room. Looking up she saw quite plainly the spirit of Mrs. Webb, which moved towards her with a gentle appealing manner, as though it would have said, ' Speak, speak ! ' 

" This spectre appeared likewise to a Mrs. Radboune, a Mrs. Griffiths, and a Mrs. Holding. They assert that luminous balls of light hovered about the room during the presence of the spirit, and that streams of light seemed to go up towards a trapdoor in the ceiling, which led to the roof of the cottage."

Page 231 of the May 19, 1883 edition of the periodical Light, which you can read here,  has an article by Stella Dunbar about experiences at a supposedly haunted house. We read this:

"Just at this time a young lady was crossing a passage, and looking from the window to the garden saw a woman in a light dress crossing the lawn. She called her companion, and they both watched the figure disappear. The same evening two others, knowing nothing of what these had seen, met the same figure coming from my room, and even noticed that it had very large bare feet."

We later read this:

"A visitor saw a woman’s figure, in everyday attire, sitting at the foot of her bed ; and my mother, entering the same room, saw what—until she spoke to it—she imagined to be the servant. In this room are frequently seen large balls of bright light."

The account has some other mentions of spooky figures making fleeting appearances at the house. We are told, "The figure of a child has been seen by the drawing-room fire-place, and at the top of the stairs."

Not long ago there was a news article on the CBS News web site entitled "Pentagon solves 1 UFO mystery but still probing cases of 'large orange orb,' 'large metallic cylinder.' "

In the article we read this strange description of an orb encounter:

"One case was reported by a law enforcement officer 'out West' who observed 'a large orange orb floating several hundred feet above the ground,' according to Kosloski. 

As the officer approached where he thought the orb would be, he saw 'a blacker than black object' that was about the size of a Prius. When he reached a distance of 40-60 meters away from the object, it tilted 45 degrees and shot up vertically, traveling faster than any drone he had seen before.

Just as it left his field of view, it emitted bright red and blue lights that lit up the inside of his vehicle 'as brightly as if someone had set off fireworks just outside of his vehicle,' Kosloski told Congress."

Becker Skillfully Critiques Grandiose Schemes of Hi-Tech Visionaries

 In the series of posts of here I have made many critical comments about the work of science journalists, who all too often these days act like pushovers who treat with kid gloves groundlessly boasting scientists, while failing to apply critical scrutiny to the dubious claims of such scientists. So I was rather surprised to read a recent book by a science journalist who uses an impressive amount of critical scrutiny when discussing claims in the technoscientific sphere.  The very readable and informative book is called "More Everything Forever," and its author is Adam Becker. The subtitle is "AI Overlords, Space Empires and Silicon Valley's Crusade to Control the Fate of Humanity." 

Becker is someone with a PhD in astrophysics, but the book's dust jacket describes him as a science journalist. Some of his astrophysics background comes in handy when he gets around to criticizing some of the schemes and dreams of hi-tech titans that he examines. 

We read about hi-tech titans such as Sam Altman who are predicting there will soon be "AGI" or "Artificial General Intelligence." The term "AI" stands for artificial intelligence. Strictly speaking, there does not really exist any such thing as artificial intelligence. So-called AI is mainly merely computer programming and data processing. There has been very much progress in computer programming and data processing. But no such progress ever justified the use of either the term AI (artificial intelligence) or the term AGI (artificial general intelligence). 

What happened is a massive abuse of language similar to the massive abuse of language that occurred around so-called "natural selection" (which is not really any such thing as selection, because "selection" means a choice by a mind, and blind unguided unnatural processes do not make choices). Back around 1988, the term "artificial intelligence" was just a kind of aspirational term used for software toolkits that could be used to try to make progress at producing artificial intelligence. In the following years more and companies realized that it was very profitable to just stop talking in aspirational terms, and to start pitching whatever product they had as an example of "artificial intelligence." It was a deceitful escalation of hype, and the public fell for it. 

By now we have examples of computer programming and data processing that are much more impressive than anything existing around 1988. But no such examples qualify as "artificial intelligence," using a good definition of "intelligence." The Merriam Webster dictionary defines "intelligence" as "the ability to learn or understand things or to deal with new or difficult situations : reason." The American Heritage Dictionary defines "intelligence" as "the ability to acquire, understand, and use knowledge." The Oxford English Dictionary defines intelligence as "The faculty of understanding; intellect." Computer systems do not understand anything, so they are not intelligent. So-called AI systems such as OpenAI and Google Gemini are not minds and not intellects. They understand nothing. 

You may start to realize what a swindle the term Artificial Intelligence is when you realize that you cannot have an intellect and you cannot have intelligence until you first have experience, which means a mind that is living a life. Given a mind that is living a life, there may be some understanding of some concept by that mind. But where there is no experience and no life, there can be no understanding. Computer systems do not have experiences, do not have lives, and do not understand anything. 

How do you infer that other people are living a life, that other people have experiences, and that other people understand things? Well, it works rather like this. You know for certain that you have a life, that you have experience, and that you understand things. And you know what kind of being you are, from viewing your body and seeing yourself in the mirror; and you know that other people around you look and move pretty much the same as you look and move. So it is entirely logical to infer that other people you see have real lives like you do, that they have experiences like you do, and that they understand like you do. But no such inference would ever be justified when thinking about a computer. 

If you were a robot having experiences and living a life and understanding things, and you looked down to see a robot metal body below your eyes, and you saw other metal robots looking like yourself, then you would be justified in inferring: robots have lives, robots have experiences, and robots understand things. But you would never, ever be justified in making such a conclusion if you had only lived as a person of flesh and blood. 

Around page 14 Becker starts to discuss longtermism. It's a philosophical approach in which various extravagant proposals are justified by means of dubious calculations in which someone tries to quantify the goodness of supposedly good things that can happen over extremely long time spans. So, for example, someone engaging in longtermism might justify spending billions on an interstellar expedition, because that expedition might be the beginning of a colonization of the galaxy that might result in 1,000,000,000,000,000 humans existing over the span of a million years. The longtermism reasoner will say things like this: "You must support my plan to launch the mission, because if it is not launched, that will be like killing 1,000,000,000,000,000 future inhabitants of the galaxy!"

As I point out in my post here, entitled "Longtermism Is Fueled by a Goofy Belief in Computer-Generated Lives," longtermism is centered upon the unreasonable cheat of assuming that there can exist computer-generated lives. In an article describing longtermism, we read this:

"Longtermism is a quasi-religious worldview, influenced by transhumanism and utilitarian ethics, which asserts that there could be so many digital people living in vast computer simulations millions or billions of years in the future that one of our most important moral obligations today is to take actions that ensure as many of these digital people come into existence as possible. In practical terms, that means we must do whatever it takes to survive long enough to colonize space, convert planets into giant computer simulations and create unfathomable numbers of simulated beings. How many simulated beings could there be? According to Nick Bostrom —the Father of longtermism and director of the Future of Humanity Institute — there could be at least 10⁵⁸ digital people in the future, or a 1 followed by 58 zeros."

Such calculations are nonsense, largely because of the impossibility of using computers to generate a life. Computers do not and can never produce experience or lives.  What happened is that people got interested in Nick Bostrom's senseless theory that we could be living in a computer simulation created by extraterrestrials, a theory predicated on the utterly irrational and groundless idea that computers can generate lives like the lives we are living. The same people who had got interested in Bostrom's nonsensical theory then kind of reasoned: "Well, if the extraterrestrials can generate computer-generated lives, then we can too!" So they started estimating that future humans with far more powerful computers will be able to generate far more lives than the flesh-and-blood lives that now exist on planet Earth -- maybe billions or trillions or quadrillions of times as many lives. 

This was all the worst kind of nonsense, nonsense as bad as Bostrom's original speculation that we are living in a computer simulation. Computers cannot generate experience. Computers cannot generate lives. So we cannot be living in a simulation created by extraterrestrials. And we will not be able to create lives by having computer systems generate lives.  And you will not be able to obtain immortality by downloading your mind into a computer. But that's no reason to be sad. The very reasons for concluding the impossibility of mind uploading into computers are part of the very many reasons for having confidence that your mind is something that cannot be produced by a brain, and something that will survive the destruction of your body. 

Believing that progress in computer systems will one day lead to computers being able to generate lives is a kind of error similar to the error of thinking that progress in improving the resolution of TV sets justifies the claim that one day we will have TV sets that cause animals and people to literally jump out of the TV screen, allowing you to hold them or touch them. 

Around page 39 to 47 Becker discusses the Singularity claims of Ray Kurzweil. It starts out on page 39 with a creepy discussion by Kurzweil about hi-tech means to "resurrect" his dead father. On page 39 the book quotes him as saying this about his father:

"We can find some of his DNA around his grave site...AI will send down some nanobots and get some bone or teeth and extract some DNA and put it all together. Then they'll get some information from my brain and anyone else who still remembers him...[They'll] just send nanobots into my brain and reconstruct my recollections and memories." 

This is creepy nonsense, both because of the impracticality of nanobots doing such a task, and also because brains do not actually store memories. Many brains of the recently deceased have been examined with very powerful microscopes, as has recently extracted tissue from living people. But such microscopic examination of brain tissue has never discovered the slightest trace of anything a human ever learned -- not a single word, not a single letter, not a single image and not a single pixel. No scientist has a credible account of how a brain could learn or recall anything, given the known physical shortfalls of all brains (which neuroscientists senselessly ignore). 

Becker's critique of Kurzweil's claims is not as strong as it could have been. He fails to make a list of incorrect predictions that Kurzweil previously made. A list of some of those predictions can be found in my post here. Becker also fails to criticize the incorrect assumptions about minds and brains that are a foundation of Kurzweil's claims about a Singularity. But Becker does make a good criticism of the flaws in Kuzweil's claim about a law of accelerating returns, the claim that progress is increasing exponentially. He points out that a pillar of such an idea (Moore's Law, that the number of transistors on a chip doubles every two years) no longer holds true. 

Today's user of the Internet should laugh hard at the notion that progress is increasing exponentially.  More and more times that I go to some web site I used without any hassle in the year 2021, I find that the website is no longer functional for anything other than viewing headlines. Upon clicking on one of these headlines, I so often get a screen telling me I cannot view that article without paying for a subscription. The web pages that I go to are very often very badly cluttered up with ads, both on the sides and in the main text. So reading is rather like this:

4 or 5 sentences of the article. 

An ad. 

6 or 7 more sentences of the article. 

An ad. 

4 or 5 more sentences of the article. 

An ad. 

A typical user of the Internet should laugh hard at claims of a law of accelerating progress, and maybe complain that the Internet worked better in the year 2000. Ask some so-called AI system some of the most important questions, and you will often get some very unwise or very false answers. What often happens is that materialist misconceptions and dubious socially-constructed triumphal legends of academia are being sucked up by web-crawling AI systems, with the errors and dubious dogmas being repackaged and regurgitated as AI slop. You might call the resulting answers "Artificial Stupidity." 

Around page 70, Becker criticizes Kurzweil's claims that there would be exponential progress in brain scanning that would help allow computers to become as smart as people. He points out that such progress in brain scanning has not occurred, saying that "the maximum resolution of noninvasive human brain scanning in 2024 is not appreciably better than in 2000." 

Becker's argumentation around this point misses the mark. He argues along the lines of "it's hard to exactly reproduce the brain electronically." He should have studied brain physical shortfalls, and argued "it's futile to try to reproduce the brain electronically." The real problem here is that no amount of brain scanning or brain emulation will do anything to help build superintelligent computers. Brains do not produce minds, and brains bear no real resemblance to computers or devices for storing or retrieving memories. No neuroscientist can give any credible explanation of how a brain could produce a thought or imagine anything or learn anything or recall anything. When neuroscientists talk about such things, they give us only the emptiest of hand-waving, or suggest ideas that cannot possibly be true given what we have learned about the physical shortfalls of brains and the rapid turnover and short lifetimes of their components such as protein molecules, dendritic spines and synapses. 

Thus far the study of human brains has done absolutely nothing to help produce advances in computing and software. There has been progress in various types of software that are wrongly called "neural nets." But such software does not bear any close resemblance to anything going on in the brain.  In so-called neural nets, information is passed along with 100% reliability. This bears no resemblance to the physical reality of the brain, in which electrical signals have to pass through endless numbers of synapses, each of which transmit a signal with a reliability of less than 50%.

Around page 223 Becker criticizes aspirations to colonize space by building the type of space colonies imagined by Gerard O'Neill. He says the scheme depended on the existence of a "mass driver" that would eject raw materials from the moon, but that "the technological advances that O'Neill was hoping would enable the mass driver simply didn't work out."

O'Neill's original proposal was for massive rotating cylinders which would give the inhabitants a magnificent view of a large landscape. His later proposal was for space colonies offering no such view, and we might ask, "Who would want to live in such a colony?"

Around page 226 Becker's background in astrophysics comes in handy, as he criticizes the impracticality of schemes such as Elon Musk's proposed scheme to colonize Mars. He criticizes the "Techno-Optimist Manifesto" of Marc Andreesssen, something I criticized in my post here. 

Although he misses some opportunities to make a stronger critique, Becker has shown lots of ability to effectively criticize unwarranted ideas of powerful figures in the hi-tech field. It would be great to see Becker write another book making a critique of grandiose but unwarranted claims, with the focus being on the grandiose but unwarranted claims of today's biologists.