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A few days ago, GOP senatorial nominee Dave McCormick released an ad that correctly noted that, if all goes as planned at the Democratic National Convention next month, she would be "the most liberal nominee in U.S. history." The ad basically went through a clip montage of why that is --...

Newly installed British Prime Minister Keir Starmer has vowed to ‘bring anti-globalists to their knees’ by cracking down on free speech and mass arresting citizens who oppose the WEF’s agenda. In the wake of the [...] The post UK PM Keir Starmer Vows To ‘Bring Anti-Globalists to Their Knees’ in Free Speech Crackdown appeared first on The People's Voice.

Court Stops NYC Mayor's Efforts to Block Migrant Buses

Flannan Isles Lighthouse located at the highest point on Eilean Mòr, one of the Flannan Isles in the Outer Hebrides off the west coast of Scotland. And it’s best known for its keepers’ mysterious disappearance in 1900. The first record that something was disturbing on the Flannan Isles was on 15 December 1900 when the steamer Archtor, on a passage from Philadelphia to Leith, noted in its log that the light was not operational in poor weather conditions. When the ship docked in Leith on 18 December 1900, the sighting was passed onto the Northern Lighthouse Board. The relief vessel, the lighthouse tender Hesperus, was unable to sail from Breasclete, Lewis as planned on 20 December due to adverse weather; it did not reach the island until noon on 26 December. From left to right: James Ducat, Thomas Marshall, Donald McArthur and Superintendent Robert Muirhead, c.1900. The lighthouse was manned by three men: Thomas Marshall, James Ducat, and Donald MacArthur, with a rotating fourth man spending time on shore. On arrival, the crew and relief keeper found that the flagstaff had no flag, none of the usual provision boxes had been left on the landing stage for re-stocking, and more ominously, none of the lighthouse keepers were there to welcome them ashore. Jim Harvie, the captain of the Hesperus, attempted to reach them by blowing the ship’s whistle and firing a flare but was unsuccessful. A boat was launched and Joseph Moore, the relief keeper, was put ashore alone. He found the entrance gate to the compound and main door both closed, the beds unmade, and the clock stopped. Returning to the landing stage with this grim news, he then went back up to the lighthouse with Hesperus’ second-mate and a seaman. A further search revealed that the lamps were cleaned and refilled. A set of oilskins was found, suggesting that one of the keepers had left the lighthouse without them, which was surprising considering the severity of the weather on the date of the last entry in the lighthouse log. The only sign of anything amiss in the lighthouse was an overturned chair by the kitchen table. There was no sign of any of the keepers, neither inside the lighthouse nor anywhere on the island. The Flannan Isles Lighthouse Moore and three volunteer seamen were left to attend the light and Hesperus returned to Lewis. Captain Harvie sent a telegram to the Northern Lighthouse Board dated 26 December 1900, stating: “A dreadful accident has happened at the Flannans. The three keepers, Ducat, Marshall and the Occasional have disappeared from the Island… The clocks were stopped and other signs indicated that the accident must have happened about a week ago.” On Eilean Mòr, the men scoured every corner of the island for clues as to the fate of the keepers. They found that everything was intact at the east landing but the west landing provided considerable evidence of damage caused by recent storms. A box at 33 metres (108 ft) above sea level had been broken and its contents strewn about; iron railings were bent over, the iron railway by the path was wrenched out of its concrete, and a rock weighing more than a ton had been displaced. On top of the cliff at more than 60 metres (200 ft) above sea level, turf had been ripped away as far as 10 metres (33 ft) from the cliff edge. The missing keepers had kept their log until 9 a.m. on 15 December. The entries made it clear that the damage had occurred before their disappearance. At that time many offered various theories in newspapers, grooming ideas of paranormal activity, ghost stories, pirate kidnappings, and spying affairs, all providing no evidence whatsoever to back their claims. It became difficult for the Northern Lighthouse Board to employ new keepers because of the widespread rumors about the mystery, as a dark shadow loomed over the Flannan Isles tower. Recent research by James Love discovered that Marshall was previously fined five shillings when his equipment was washed away during a huge gale. It is likely, in seeking to avoid another fine, that he and Ducat tried to secure their equipment during a storm and were swept away as a result. The fate of MacArthur, although required to stay behind to man the lighthouse, can be guessed to be the same. Love speculates that MacArthur probably tried to warn or help his colleagues and was swept away too. A further proposal is based on the psychology of the keepers. Allegedly MacArthur was a volatile character; this may have led to a fight breaking out near the cliff edge by the West Landing that caused all three men to fall to their deaths. Another theory is that one of the men went insane (perhaps MacArthur, as evidenced by him leaving the lighthouse without his rain gear and his strange behavior documented in the log book), murdered the other two, threw their bodies into the sea, and then jumped in to his own death. Among modern theories are those connected to paranormal activity, such as abduction by aliens In 2018, the film “The Vanishing” was released about this strange mystery. The post Unsolved Mystery Of The Disappearance Of Flannan Lightkeepers appeared first on Anomalien.com.

The rise of artificial intelligence in all things military, from intelligence gathering and autonomous air combat maneuvering to advanced loitering munitions, creates a big problem for the United States. While staying ahead of the Chinese both in terms of technological advancement and in fielding new and improved weapons systems is crucial, so is establishing a doctrine of artificial intelligence countermeasures to blunt Chinese AI systems. Such a doctrine should begin to take shape around four avenues: polluting large language models to create negative effects; using Conway’s law as guidance for exploitable flaws; exploiting bias among our adversaries’ leadership to degrade AI systems; and using radio-frequency weapons to disrupt AI-supporting computer hardware. Pollute large language models Generative AI can be expressed as the extraction of statistical patterns from an extremely large data set. A large language model developed from such an enormous data set using “transformer” technology allows a user to access it through prompts. A prompt, in turn, is a natural language text that describes the function the AI must perform. The result is a generative pre-trained large language model. Such an AI system might be degraded in at least two ways: Either pollute the data or attack the “prompt engineering.” Prompt engineering is a term of art within the AI community that describes the process of structuring instructions that can be understood by the generative AI system. A programming error would cause the AI large language model to “hallucinate.” An example from World War II illustrates the importance of countermeasures when an enemy can manifest speedy and exclusive information to the battlespace. Given that a website like Pornhub gets something in excess of 115 million hits per day, perhaps the Next Generation Air Dominance fighter should be renamed 'Stormy Daniels.' The development of radar (an acronym for radio detecting and ranging) was, in itself, a method of extracting patterns from an extremely large database: the vastness of the sky. An echo from a radio pulse gave an accurate range and bearing of an aircraft. To defeat enemy radar, the British intelligence genius R.V. Jones recounted in “Most Secret War,” it was necessary to put information into the German radar system that resulted in gross ambiguity. Jones turned to Joan Curran, a physicist at the Technical Research Establishment, who developed the optimum size and shape of aluminum foil strips, called “window” by the Brits and “chaff” by the Americans, used to create thousands of reflections that, in turn, overloaded and blinded the German radars. How can the U.S. military and intelligence communities introduce chaff to generative AI systems, especially when trying to deny access to new information about weapons and tactics? One way would be to assign names to those weapons and tactics that are at once ambiguous and non sequiturs. For example, such “naturally occurring” search ambiguities include the following: A search for “Flying Prostitute” will immediately reveal data about the B-26 Marauder medium bomber of World War II. A search for “Gilda” and “Atoll” will retrieve a photo of the Mark III nuclear bomb that was dropped on Bikini Atoll in 1946, upon which was pasted a photo of Rita Hayworth. A search of “Tonopah” and “Goatsucker” retrieves the F-117 stealth fighter. Since a contemporary computer search is easily fooled by such accidental ambiguities, it would be possible to grossly skew results of a large language model function by deliberately using nomenclature that occurs in very large iterations and is extremely ambiguous. Given that a website like Pornhub gets something in excess of 115 million hits per day, perhaps the Next Generation Air Dominance fighter should be renamed “Stormy Daniels.” For code names of secret projects, try “Jenna Jameson” instead of “Rapid Dragon.” Such an effort in sleight of hand would be useful for operations and communications security by confusing adversaries seeking open intelligence data. For example, one can easily imagine the consternation that Chinese officers and NCOs would experience when their young soldiers expended valuable time meticulously examining every single image of Stormy Daniels to ensure that she was not the newest U.S. fighter plane. Even “air gapped” systems like the ones being used by U.S. intelligence agencies can be affected when the system updates information from internet sources. Note that such an effort must actively and continuously pollute the data sets, like chaff confusing radar, by generating content that would populate the model and ensure that our adversaries consume it. A more sophisticated approach would use keywords like “eBay” or “Amazon” or “Alibaba” as a predicate and then very common words such as “tire” or “bicycle” or “shoe.” By then contracting with a commercial media agency to do lots of promotion of the “items” across traditional and social media, it would tend to clog the system. Use Conway’s law Melvin Conway is an American computer scientist who in the 1960s conceived the eponymous rule that states: “Organizations which design systems are constrained to produce designs which are copies of the communication structures of these organizations.” De Caro’s corollary says: “The more dogmatic the design team, the greater the opportunity to sabotage the whole design.” Consider the Google Gemini fiasco. The February launch of Gemini, Google’s would-be answer to ChatGPT, was an unmitigated disaster, which tanked Google’s share price and made the company a laughingstock. As the Gemini launch went forward, its image generator “hallucinated.” It created images of black Nazi soldiers and female Asian popes. In retrospect, the event was the most egregious example of what happens when Conway’s law collides with organizational dogma. The young, woke, and historically ignorant programmers myopically led their company into a debacle. But for those interested in confounding China’s AI systems, the Gemini disaster is an epiphany. Xi’s need for speed, especially in “informatization,” might be the bias that points to an exploitable weakness. If the extremely well-paid, DEI-obsessed computer programmers at the Googleplex campus in Mountain View, California, can screw up so immensely, what kind of swirling vortex of programming snafu is being created by the highly regimented, ill-paid, constantly indoctrinated, young members of the People’s Liberation Army who work on AI? A solution to beating China’s AI systems may be an epistemologist who specializes in the cultural communication of the PLA. By using de Caro’s Corollary, such an expert could lead a team of computer scientists to replicate the Chinese communication norms and find the weaknesses in their system — leaving it open to spoofing or outright collapse. When a technology creates an existential threat, the individual developers of that technology become strategic targets. For example, in 1943, Operation Hydra, which employed the entirety of the RAF British Bomber Command — 596 bombers — had the stated mission of killing all the German rocket scientists at Peenemunde. The RAF had marginal success and was followed by three U.S. 8th Air Force raids in July and August 1944. In 1944, the Office of Strategic Services dispatched multilingual agent and polymath Moe Berg to assassinate German scientist Werner Heisenberg, if Heisenberg seemed to be on the right path to building an atomic bomb. Berg decided (correctly) that the German was off track. Letting him live actually kept the Nazis from success. It is no secret that five Iranian nuclear scientists have been assassinated (allegedly) by the Israelis in the last decade. Advances in AI that could become existential threats could be dealt with in similar fashion. Bullets are cheap. So is C-4. Exploit biases to degrade AI systems Often, the people and organizations funding research and development skew the results because of their bias. For example, Heisenberg was limited in the paths he might follow toward developing a Nazi atomic bomb because of Hitler’s perverse hatred of “Jewish physics.” This attitude was abetted by two prominent and anti-Semitic German scientists, Philipp Lenard and Johannes Stark, both Nobel Prize winners who reinforced the myth of “Aryan science.” The result effectively prevented a successful German nuclear program. Returning to the Google Gemini disaster, one only needs to look at the attitude of the Google leadership to see the roots of the debacle. Google CEO Sundar Pichai is a naturalized U.S. citizen whose undergraduate college education was in India before he came to the Unites States. His ties to India remain close, as he was awarded the Padma Bhushan, India’s third-highest civilian award, in 2022. In congressional hearings in 2018, Pichai seemed to dance around giving direct answers to explicit questions, a trait he demonstrated again in 2020 and in an antitrust court case in 2023. His internal memo after this year’s Gemini disaster mentioned nothing about who selected the people in charge of the prompt engineering, who supervised those people, or who, if anyone, got fired in the aftermath. More importantly, Pichai made no mention of the internal communications functions that allowed the Gemini train wreck to occur in the first place. Again, there is epiphany here. Bias from the top affects outcomes. As Xi Jinping continues his move toward autocratic authoritarian rule, he brings his own biases with him. This will eventually affect, or more precisely infect, Chinese military power. In 2023, Xi detailed the need for China to meet world-class military standards by 2027, the 100th anniversary of the People’s Liberation Army. Xi also spoke of “informatization” (read: AI) to accelerate building “a strong system of strong strategic forces, raise the presence of combat forces in new domains and of new qualities, and promote combat-oriented military training.” It seems that Xi’s need for speed, especially in “informatization,” might be the bias that points to an exploitable weakness. Target chips with energy weapons Artificial intelligence depends on extremely fast computer chips whose capacities are approaching their physical limits. They are more and more vulnerable to lack of cooling — and to an electromagnetic pulse. In the case of large cloud-based data centers, cooling is essential. Water cooling is cheapest, but pumps and backup pumps are usually not hardened, nor are the inlet valves. No water, no cooling. No cooling, no cloud. The same goes for primary and secondary electrical power. No power, no cloud. No generators, no cloud. No fuel, no cloud. Obviously, without functioning chips, AI doesn’t work. AI robots in the form of autonomous airborne drones, or ground mobile vehicles, are moving targets — small and hard to hit. But their chips are vulnerable to an electromagnetic pulse. We’ve learned in recent times that a lightning bolt with gigawatts of power isn’t the only way to knock out an AI robot. High-power microwave systems such as Epirus, Leonidas, and Thor can burn out AI systems at a range of about three miles. Another interesting technology, not yet fielded, is the gyrotron, a Soviet-developed, high-power microwave source that is halfway between a klystron tube and a free electron laser. It creates a cyclotron resonance in a strong magnetic field that can produce a customized energy bolt with a specific pulse width and specific amplitude. It could therefore reach out and disable a specific kind of chip, in theory, at greater ranges than a “you fly ’em, we fry ’em” high-power microwave weapon, now in the early test stages. Obviously, without functioning chips, AI doesn’t work. The headlong Chinese AI development initiative could provide the PLA with an extraordinary military advantage in terms of the speed and sophistication of a future attack on the United States. Thus, the need to develop AI countermeasures now is paramount. In World War I, the great Italian progenitor of air power, General Giulio Douhet, very wisely observed: “Victory smiles upon those who anticipate the changes in the character of war, not upon those who wait to adapt themselves after the changes occur.” In terms of the threat posed by artificial intelligence as it applies to warfare, Douhet’s words could not be truer today.