Skip to main content

Remembering When Alan Turing Developed a Portable Voice Encryption Device

2 weeks 3 days ago
Long-time Slashdot reader smooth wombat writes: Alan Turing, one of the more famous people who worked at Bletchley Park to decipher the German Enigma coding machine, was also working on a separate project. His private papers, known as the Bayley papers for his assistant Donald Bayley who held onto the papers until his death in 2020, reveal Turning had produced a working model of a portable voice encryption device. He even demonstrated it by using a Winston Churchill speech recording. "Weighing just 39 kg, including its power pack," Jack Copeland wrote in an article for IEEE Spectrum, "Delilah would be at home in a truck, a trench, or a large backpack." More from Popular Mechanics: Turingâ(TM)s work at Bletchley Park actually informed the Delilah experimentation he was doing at Hanslope Park, and not just because he used Red Forms, the Army-issue sheets Hanslope staffers were meant to use to alert Bletchley staffers to enemy signals, as his personal scrap paper for Delilah experiments. He drew inspiration from one of the German cipher machines they had decoded at Bletchley; not the famed Enigma machine, but rather the SZ42. While the former relied on Morse Code, the latter utilized a 5-bit telegraph code, which Copeland notes âoewas a forerunner of ASCII and Unicode and is still used by some ham radio operators.â The SZ42 produced an obscuring key of telegraph characters, with an identical key produced to both the sender and receiver. If it could be done for text, Turing reasoned it could be done for sound as well... [T]he reason Delilah fell to the wayside of history isnâ(TM)t because it was a failure, but rather because it simply wasnâ(TM)t needed anymore. By the time Turing had built and demonstrated his device, the war was over. What good was a portable voice encryptor if you had no major enemies trying to intercept your calls, the government reasoned. So funding for the project stopped, and Turingâ(TM)s two-year experiment ended with a whimper. Turingâ(TM)s time as an electrical engineer at Hanslope Park became a footnote in his story, if even that.

Read more of this story at Slashdot.

EditorDavid

Tech Pundit Cringely Co-Founds Startup '2Brains Inc' to Solve LLM Hallucinations

2 weeks 4 days ago
Long-time tech pundit Robert Cringely started his career at the Stanford Artificial Intelligence Lab back in 1978. Last month 73-year-old Cringely explained why his site went on a two-year hiatus — and it's not just because of a heart attack and a stroke last July: Just like everyone else, I've been busy all this time on Artificial Intelligence, founding with two partners a company called 2Brains... The work we were doing together is unfinished, but it's not stopped. The patents are filed, the architecture is documented, and the small team continuing the work includes me. Cringely's first piece made the cast that "the trillion-dollar bet the AI industry is making right now may be wrong, and that there's an architectural alternative we've patented and built." In Machines of Loving Grace, Amodei made the case that scaling compute would eventually solve essentially every hard problem in artificial intelligence. Buried in that optimism — or maybe not buried, maybe right out in the open — was a quiet absolution. Hallucinations, the embarrassing tendency of these systems to state falsehoods with total confidence, would take care of themselves. Make the models big enough, train them long enough, and the problem dissolves. You don't have to solve it. You just have to wait, and spend. And so the entire AI industry breathed a sigh of relief. I have spent forty years watching this industry, and I know a permission slip when I see one. Because that is what the essay became, whatever Amodei intended. It gave every other person writing nine- and ten-figure checks a reason not to worry about the one thing that should worry them most. The hallucination problem is the difference between a clever toy and a system a hospital or a bank or a court can actually rely on. It is the whole ballgame for enterprise AI. And the prevailing wisdom, blessed from the top, is that you needn't address it directly. Scale will provide... A small company I helped start, 2Brains Inc., set out in 2022 to solve hallucinations — before ChatGPT, before the scaling consensus hardened into received truth, back when the polite assumption was that the problem was simply insurmountable. We did not solve it by waiting for bigger models. We solved it architecturally, by separating the part of the system that generates language from the part that retrieves and verifies facts, and reconciling the two before anything reaches the user. It runs on ordinary processors. It is cheap. And on the industry's own benchmark for this kind of faithfulness, it more than doubles the published baseline, with no fabricated facts in the verified case at all. The article asks whether scaling will, at tremendous cost, eventually reduce hallucinations — or even worse, if the largest companies in the world "are spending a fortune chasing a cure that is not coming." And last week Cringely pitched more advantages for their solution, noting that most prompts aren't even chatbot-level creative prompts — but just requests to retrieve simple data: The reason 2Brains doesn't lie and the reason it's cheap are the same reason. It looks the fact up instead of guessing it — so it cannot fabricate, and the lookup runs on a processor that sips power instead of a chip that gulps it. Trust and thrift are not a trade-off you balance against each other. They fall out of a single design decision. You do not pay extra for the honest version. The honest version is the cheap version. That sentence is the whole company.

Read more of this story at Slashdot.

EditorDavid