In September 1940, with the German air raids over Britain at their peak, M.I.T. mathematician and physicist Norbert Wiener wrote Vannevar Bush, in charge of American war research, and volunteered his services. Over the next few years, Wiener focused on making what he called the anti-aircraft predictor, a computational device designed to improve the accuracy of ground-based gunners by calculating more precisely the location of enemy aircraft at a given point just moments in the future. The success of the project was in part dependent on the type of enemy that Wiener imagined was piloting the airplane. \u00a0<\/p>\n
This was indeed clever, explained Peter Galison, a History of Science prof at Harvard:<\/p>\n
Wiener\u2019s ambition was to make a black box model of the enemy pilot and then use that to form an anti-airplane system that could characterize the pilot\u2019s movements and learn from past experience in order to predict where the anti-aircraft gun should aim. Some prediction was necessary because it took up to 20 seconds before anti-aircraft fire reached the airplane. So if you shoot at where the plane is now, you\u2019ll surely miss it. If you extrapolate to where it might be if it were to travel in a straight line, you\u2019ll also miss it if the pilot moves from side to side. So it was necessary to be able to predict where the pilot would go, and that\u2019s what Wiener\u2019s machine was designed to do. <\/p>\n
What Wiener did that was so unusual was that he characterized the motion of a particular plane using a primitive computer. The radar follows the plane, looks at its motion, makes a statistical characterization of what the pilot\u2019s been doing in the last several tens of seconds, and then uses that knowledge to predict where the pilot will be 20 seconds later. \u2026And then it could be blown out of the sky\u2026<\/p>\n
[H]e never got it to work more than a couple of seconds in advance; 20 seconds out or even 10 seconds out was too far. But the scientific administrators cleared to see Wiener\u2019s device found that its predictions for even those few seconds into the future were remarkable; more than remarkable, positively uncanny. The machine appeared to anticipate a person\u2019s intentions, to stand in for human intentionality in some fundamental way. It seemed astonishing to Wiener, astonishing enough to merit the foundation of a new science.<\/p>\n
Wiener dubbed his new science \u201ccybernetics,\u201d and through the war Wiener came to believe that cybernetics could one day predict human psychology more broadly. He had entire teams of engineers working under him, and was given significant budgets for research.<\/p>\n
After the shooting war and into the Cold War, Wiener owned the field of Cybernetics. He would show up uninvited and unannounced at cybernetic conferences to control the conversation, and therefore the science.<\/p>\n
![\"A](\"https:\/\/www.newsbeep.com\/ca\/wp-content\/uploads\/2025\/11\/AI-Plaque.webp\")
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Enter computer scientist\u00a0John McCarthy, then at Dartmouth College. Writes Amber Case<\/a>:<\/p>\n Simple: Come up with another term, and use\u00a0that\u00a0to advertise the conference.<\/p>\n \u201cOne reason for inventing the term [AI] was to escape association with cybernetics,\u201d as\u00a0McCarthy once bluntly explained. \u201cI wished to avoid having either to accept Norbert Wiener as a guru or having to argue with him.\u201d<\/p>\n So in a very real sense, AI started out as a term of\u00a0distraction,\u00a0not clarity. Created to position itself apart from cybernetics, it was coined so broadly that roughly\u00a0any\u00a0automated computer system can be called artificial intelligence.<\/p>\n What exactly does \u201cTo proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it\u201d actually mean?<\/p>\n \u2018Every aspect of learning\u2019? We have some inkling about some features of how some humans learn in some circumstances, but we\u2019re a long, long way from truly understanding learning. <\/p>\n And \u2018intelligence\u2019? Forget it, Jack, no one can agree on anything about it.<\/p>\n One thing I think we can agree on, however, is that there is nothing \u2018intelligent\u2019 at all in anything called \u2018artificial intelligence.\u2019<\/p>\n But there we have it, the term \u2018artificial intelligence\u2019 was created as a PR phrase to muddy the waters, to confuse people, and so anything and everything to do with computers can be shoe-horned into it. As practiced, \u2018artificial intelligence\u2019 means \u2018the computer does something cool,\u201d which is not at all useful. <\/p>\n Computers have always done cool stuff, and still do.<\/p>\n I would like to differentiate two different kinds of \u2018cool stuff,\u2019 though. <\/p>\n First, there\u2019s a whole suite of truly useful computer tools that can be broadly described as \u2018machine learning.\u2019 Not to get too deep into the science of it, but machine learning programs are basically pattern recognition machines. They build a database of millions of items, and over time the machine recognizes a certain pattern as a particular thing.<\/p>\n Machine learning is what drives those apps that identify birds or plants or ticks. It\u2019s what\u2019s behind voice transcription software like Trint, which the Examiner staff uses to help transcribe interviews. It powers the grammar and spell-checking programs.<\/p>\n Impressively, it can quickly scan lots of X-rays to find potential cases of cancer.<\/p>\n One use of machine learning that I find especially cool is the translation of cuneiform writing, the stone tablets of ancient Mesopotamia. Learning to read cuneiforms can take decades, and so there are only a few dozen highly specialized people who can do it. And so there are hundreds of thousands of cuneiform tablets that have never been translated.<\/p>\n This 2023 academic article<\/a> explains how machine learning is translating cuneiforms. Note that the authors are careful about the limitations of the technology, and importantly they never use the term \u2018artificial intelligence.\u2019<\/p>\n But then Cornell University\u2019s PR department issued a press release<\/a> describing the research as \u201cartificial intelligence\u201d:<\/p>\n Deciphering some people\u2019s writing can be a major challenge \u2013 especially when that writing is cuneiform characters imprinted into 3,000-year-old tablets.<\/p>\n Now, Middle East scholars can use artificial intelligence (AI) to identify and copy over cuneiform characters from photos of tablets, letting them read complicated scripts with ease.<\/p>\n Why would a PR person use a term that wasn\u2019t in the original research? Quite plainly, because \u2018AI\u2019 has a societal buzz about it that the writer knew would draw more attention than the more accurate \u2018machine learning.\u2019<\/p>\n By 2023, ChatGPT had come out generating the enormous to-do about AI, but that wasn\u2019t the first AI hype bubble, explained Amber Case:<\/p>\n The explosive growth of ChatGPT since 2022 has generated such excitement, it\u2019s easy to forget another AI-related product enjoyed similar buzz less than a decade ago.<\/p>\n But it\u2019s true: back in 2014, AI hype orbited around voice-activated personal assistants, with Apple\u2019s Siri, Amazon\u2019s Alexa, and Microsoft\u2019s Cortana leading the charge. As with other\u00a0\u201ctruthy\u201d technology<\/a>, Hollywood turbo-charged this excitement, with Jarvis from the\u00a0Iron Man\u00a0movies and the Scarlet Johannsen-voiced AI depicted in\u00a0Her.\u00a0\u201cPersonal assistant AI is going to change everything!\u201d briefly became the grand pronouncement for that time (almost 10 years from the date of this op-ed).<\/p>\n \u2026<\/p>\n We do continue to use voice-driven assistants, of course, but in narrow contexts where it\u2019s genuinely useful \u2014 like map guides while we\u2019re driving. AI can be clever and witty in movies, but in real life we quickly realized it\u2019s an anemic representation of the human soul.<\/p>\n It\u2019s always the same pattern: every so often, new AI applications and play toys emerge from breakthroughs at research universities. These cause bursts of excitement among people at the periphery, particularly marketers and well-paid evangelists, who then create inflated expectations around them. These expectations blow way past what\u2019s technically possible for these systems, but company heads ask engineers to do the impossible anyway. And the AI applications continue doing only what they\u2019re good at: nerdy automation tasks. The public feels let-down again.<\/p>\n The voice trick was the first fuel to the broad AI-hype, but now it\u2019s been joined by sound, photo, and video generation.<\/p>\n That brings me to the second kind of \u2018cool stuff\u2019 that computers do \u2014 generative AI. Distinct from machine learning, generative AI uses its database to predict the next in a series, and to simulate a creation of some sort \u2014 a text, a conversation, a photo, a video clip.<\/p>\n The predictions are based on what\u2019s happened before. So the giant AI companies (OpenAI, Anthropic, Google, and a handful of others) are basically trying to build databases of all human creations \u2014 all written works, all music, all photos, all videos, etc. \u2014 which can be used as the basis for a prediction.<\/p>\n This is what the giant data centres being proposed are all about: collecting all information ever created in order to make their models operate better. This is, in a word, theft on a global scale, the biggest heist possible, with all human created work and all its accompanying copyrights being stolen for private profit.<\/p>\n To make up an example, if you type in \u201cDonald Trump is\u2026\u201d the machine will look at its enormous database and see what is the most likely next word or phrase based on what other people have used in the past. Depending on the context of the rest of the \u2018conversation\u2019 and an analysis of what the mostly likely next thing said by people in the past in that context, the next word or words might be president or old or fat or a Republican or the saviour of America. There\u2019s no thought going into this, no creativity, no insight; it is not intelligent.<\/p>\n The distinctions between machine learning and generative AI are important, but both have been labelled \u2018AI,\u2019 and so it\u2019s difficult to have real conversations about them. The confusion is the point.<\/p>\n One distinction is economic. Machine learning programs can be extremely useful, but they cost money. Real money. Real money that people are willing to spend because the programs can bring important and useful results that make the world a better place.<\/p>\n In contrast, generative AI costs a tremendous amount of money, but no one is willing to pay real money for them, or at least not enough money to cover the full cost of them. ChatGPT, which is owned by OpenAI, loses money on every query, as does every other generative AI program. OpenAI, Anthropic, and the rest are losing enormous amounts of money, so enormous that it\u2019s hard to appreciate the scale of it. <\/p>\n This is not the dot com bubble of the 1990s; it\u2019s hundreds of times bigger than that. The big companies are collectively going to lose over a trillion dollars in coming years.<\/p>\n How is this possible? It only works so long as investors continue to pour money into the companies with the hope that one day they will actually turn a profit, and that hope is fuelled by a hype machine that is untethered to reality. Sooner or later, however, the financial reality will prevail; the question is only how much of the broader economy it will take down with it.<\/p>\n Another distinction between machine learning and generative AI is the environmental and social costs. We\u2019ll get way into this in future articles in this series, but the environmental and social costs of generative AI are on the same scale as their monetary costs, and just as unsustainable.<\/p>\n A third distinction is between intent and expected outcome. When machine learning is used to find cancer on X-rays or translate cuneiforms, we have a pretty narrow range of completely reasonable desired outcomes and expectations. But generative AI? If we listen to its promoters, we see a much more cynical and broader agenda, which I can only characterize as evil. Again, the subject of a future article.<\/p>\n So after the first couple of non-responses to meaningless questions, in the end I found the federal government survey about \u2018artificial intelligence\u2019 unanswerable.<\/p>\n If the government wants to underwrite research into machine learning, sure, there\u2019s always a case to be made for research that might make life better for people. But that\u2019s not how it is being framed. <\/p>\n Rather, a confusing mishmash of \u2018cool stuff computers can do\u2019 is being described as the undefined \u2018artificial intelligence\u2019 and then sold as a transformative bridge to a glorious future of increased productivity, \u2018innovation\u2019 (whatever that means), and competition (against companies that are losing money).<\/p>\n The confusion and misunderstanding that is purposefully built right into the term \u2018artificial intelligence\u2019 is a power grab. If we don\u2019t know what\u2019s being discussed, we can\u2019t oppose it. And if we find some kernels that we might sorta kinda think are good, then we have to support the entire mishmash of nonsense terms.<\/p>\n This is not how democracy works.<\/p>\n I hope that you\u2019ll find this month\u2019s exploration of \u2018artificial intelligence\u2019 interesting and helpful. We have some interviews with fascinating people lined up, and I\u2019ve asked the entire Examiner crew to bring their own insights and concerns to the project.<\/p>\n If you support this work, please support the Halifax Examiner with your subscription<\/a> or donation<\/a>. Financial support from readers is what makes this operation possible. <\/p>\n Thank you.<\/p>\n (To send or post this item, copy the website address at the top of this page.)<\/p>\n NOTICED<\/p>\n Time change<\/p>\n As I was researching the Daniel Sampson story and the general social and societal context of the 1930s, I stumbled upon the bizarre history of Daylight Saving Time in the Maritimes; I mentioned it in passing in a footnote<\/a> in Part 4:<\/p>\n In this era, there was much confusion around Daylight Saving Time (DST). Every city, town, and county, passed its own legislation on DST; the various jurisdictions started and stopped DST at different dates, or refused to adopt it at all. One year, the city of Halifax had two different time regimes for a period, as DST was applied in the populace generally but not in city offices. In practice, including in the documents cited in this article, no one is quite sure about whether they are using Standard Time or Daylight Saving Time. Which is to say, without further scrutiny, any time offered is uncertain.<\/p>\n As I recall, towns in the Annapolis Valley were very much opposed to DST, and refused to adopt it entirely, and one year there was a particularly nasty political dustup over it in Truro.<\/p>\n You could conceivably drive from Halifax to Truro to Pictou and travel through five or six different time regimes, and then a month later travel back through five or six different time regimes. I don\u2019t know how anyone made sense of it at the time. I don\u2019t know when the time change was made a provincial matter.<\/p>\n It wasn\u2019t the point of my research, but I probably should have better documented all the various decisions and time regimes. Perhaps it will be a future project, whenever I happen to find the time for such a distraction.<\/p>\n I was thinking of this when I woke up at 2am and couldn\u2019t fall back asleep for several hours. I know I need that last blast of REM or I\u2019ll be shit when I start writing, so I had to wait around to fall asleep again and then overslept.<\/p>\n Which is to say I\u2019m not a fan of the time change. Pick one time or the other, or something in between, I don\u2019t care, but whatever it is, stay with it. Twice a year I\u2019m off-kilter for a couple of weeks because of this craziness.<\/p>\n (Send this item: right click and copy this link)<\/a><\/p>\n THE LATEST FROM THE HALIFAX EXAMINER:<\/p>\n \u2018Accessibility is the first thing to go\u2019: Committee gives input on snow clearing in Halifax<\/p>\n Suzanne Rent reports:<\/p>\n Members of Halifax regional council\u2019s accessibility advisory committee gave their feedback on issues around winter operations in Halifax Regional Municipality (HRM) as part of a five-year review of service standards.\u00a0<\/p>\n Committee members expressed concerns about safety, communication, and expectations around snow clearing on municipal streets and sidewalks.<\/p>\n \u2026<\/p>\n As we\u00a0reported in June<\/a>, Halifax\u2019s auditor general Andrew Atherton found that the municipality is doing a snow job on winter clearing operations. Atherton\u2019s\u00a0report<\/a>\u00a0found that there was no monitoring of work done by outside contractors, and that improvements need to be made to in-house operations.<\/p>\n Click or tap here<\/a> to read \u201c\u2018Accessibility is the first thing to go\u2019: Committee gives input on snow clearing in Halifax.\u201d<\/p>\n (Send this item: right click and copy this link)<\/a><\/p>\n Government<\/p>\n City<\/p>\n Monday<\/p>\n No meetings<\/p>\n Tuesday<\/p>\n Halifax and West Community Council\u00a0(Tuesday, 6pm, City Hall and online) \u2014\u00a0agenda<\/a><\/p>\n Province<\/p>\n Monday<\/p>\n No meetings<\/p>\n Tuesday<\/p>\n Community Services\u00a0(Tuesday, 10am, One Government Place and online) \u2014\u00a0Supporting<\/a>\u00a0Healthy Families; with representatives from the Department of Opportunities and Social Development and Maggie\u2019s Place: A Resource Centre for Families<\/p>\n Human Resources\u00a0(Tuesday, 1pm, Province House and online) \u2014\u00a0Capital Plan Updates on School Development and Maintenance Initiatives & Appointments to Agencies, Boards and Commissions;\u00a0details<\/a><\/p>\n On campus<\/p>\n Dalhousie<\/p>\n Biochemistry & Molecular Biology Seminar\u00a0(Monday, 11am, Theatre A, Tupper Building) \u2014\u00a0Jena Barter\u00a0will present<\/a>\u00a0\u201cInvestigating the Role of the Cholesterol Transporter, STARD3 in Estrogen Receptor-Positive Breast Cancer Cells;\u201d Abby Edison will present \u201cAssessing the role of BAF subunit genetic variants implicated in neurodevelopmental disorders using Drosophila-based functional assays\u201d<\/p>\n Noon Hour Free Live Music Series: Woodwinds\u00a0(Monday, 11:45am, Strug Concert Hall) \u2014\u00a0details<\/a><\/p>\n King\u2019s<\/p>\n So You Wanna Write a Book\u00a0(Monday, 7pm, online) \u2014\u00a0free webinar<\/a><\/p>\n NSCAD<\/p>\n Reception\u00a0(Monday, 5:30pm, Anna Leonowens Gallery) \u2014\u00a0new exhibitions<\/a><\/p>\n Literary Events<\/p>\n Monday<\/p>\n Author reading\u00a0(Monday, 7pm, R.P. Bell Library, Mount Allison University, Sackville N.B.) \u2014\u00a0and Q&A<\/a>\u00a0with\u00a0Ren\u00e9e Belliveau,\u00a0A Sense of Things Beyond<\/p>\n Tuesday<\/p>\n Readings at the Woodside\u00a0(Tuesday, 7pm, Woodside Tavern, Dartmouth) \u2014\u00a0details<\/a><\/p>\n In the harbour<\/p>\n Halifax Cape Breton Footnotes<\/p>\n Now that sportsball is over we can all get back to hating on Toronto again.<\/p>\n","protected":false},"excerpt":{"rendered":"I always have a hard time planning out the Halifax Examiner\u2019s subscription drives. Sometimes we land on some…\n","protected":false},"author":2,"featured_media":259141,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[62,276,277,49,48,61],"class_list":["post-259140","post","type-post","status-publish","format-standard","has-post-thumbnail","category-artificial-intelligence","tag-ai","tag-artificial-intelligence","tag-artificialintelligence","tag-ca","tag-canada","tag-technology"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts\/259140","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/comments?post=259140"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts\/259140\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/media\/259141"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/media?parent=259140"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/categories?post=259140"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/tags?post=259140"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"Photos](\"https:\/\/www.newsbeep.com\/ca\/wp-content\/uploads\/2025\/11\/cuneiforms.png\")
An overview of the coding needed to read cuneiforms from the paper \u201cCuneiML: A Cuneiform Dataset for Machine Learning.\u201d<\/p>\n![\"The](\"https:\/\/www.newsbeep.com\/ca\/wp-content\/uploads\/2025\/11\/ZW-2021-06-11-0775.jpg\")
The clock tower on Citadel Hill in June 2021. Credit: Zane Woodford<\/p>\n![\"On](\"https:\/\/www.newsbeep.com\/ca\/wp-content\/uploads\/2025\/11\/ZW-2023-02-11-118-copy.jpg\")
A plow operator works in Downtown Halifax on Tuesday, Feb. 14, 2023. Credit: Zane Woodford<\/p>\n
05:30: MSC Kilimanjaro IV<\/a>, container ship, arrives at Pier 41 from Montr\u00e9al
06:00: Tropic Hope<\/a>, container ship, arrives at Pier 42 from Philipsburg, St. Croix
13:00: IT Intrepid<\/a>, cable layer, sails from Pier 9 for sea
15:30: Zim Atlantic<\/a>, container ship, arrives at Fairview Cove from Valencia, Spain
20:00: MSC Kilimanjaro IV<\/a> sails for sea
21:30: One Falcon<\/a>, container ship (146,287 tonnes), arrives at Pier 41 from New York <\/p>\n
05:00: CSL Kajika<\/a>, bulker, sails from Aulds Cove quarry for Cape Canaveral, Florida
13:00: Radcliffe R. Latimer<\/a>, bulker, moves from Mulgrave to Aulds Cove quarry
13:00: John J. Carrick<\/a>, barge, with Leo A. McArthur<\/a>, tug, transits through the causeway en route from Montr\u00e9al to Halifax
21:00: CSL Tacoma<\/a>, bulker, arrives at Coal Pier (Point Tupper) from Baltimore<\/p>\n