Lifeboat Foundation

Ford Motor Company recently filed a patent for a combustion engine that will run on hydrogen, Motor1 reports. The patent isn’t like your normal hydrogen-powered vehicle which uses a propulsion system that converts energy stored as hydrogen to electricity via a fuel cell. The patent Ford filed is for a turbocharged combustion engine that runs on hydrogen.

Muscle Cars and Truck, which initially discovered and reported on the patent, noted that on paper, Ford’s engine should be capable of operating across a wide range of air/fuel lambda, which is the Greek letter used to represent a fuel’s stoichiometric value as 1.00, with values depending on torque demands. MCT also noted that internal exhaust gas recirculation and valve timing will be used to control combustion.

MCT quickly touched upon the importance of the stoichiometric value of a fuel, which is the ratio by which all of the fuel is mixed with all of the oxygen to produce a competitive burn. Notably, Ford’s new method of turbocharged hydrogen will explore lambda values in excess of 2.00. This means that the new engine would be able to operate in an extremely lean state and use more than double the amount of air required for the stoichiometric combustion of hydrogen. You can read more about this here.

Limited mileage range and rare mineral mining have been sticking points for electric vehicle batteries, but Tesla is working on a design to last 100 years.

(2022). Journal of Experimental & Theoretical Artificial Intelligence. Ahead of Print.

AI has for decades attempted to code commonsense concepts, e.g., in knowledge bases, but struggled to generalise the coded concepts to all the situations a human would naturally generalise them to, and struggled to understand the natural and obvious consequences of what it has been told. This led to brittle systems that did not cope well with situations beyond what their designers envisaged. John McCarthy (1968) said ‘a program has common sense if it automatically deduces for itself a sufficiently wide class of immediate consequences of anything it is told and what it already knows’; that is a problem that has still not been solved. Dreifus (1998) estimated that ‘Common sense is knowing maybe 30 or 50 million things about the world and having them represented so that when something happens, you can make analogies with others’. Minsky presciently noted that common sense would require the capability to make analogical matches between knowledge and events in the world, and furthermore that a special representation of knowledge would be required to facilitate those analogies. We can see the importance of analogies for common sense in the way that basic concepts are borrowed, e.g., the tail of an animal, or the tail of a capital ‘Q’, or the tail-end of a temporally extended event (see also examples of ‘contain’, ‘on’, in Sec. 5.3.1). More than this, for known facts, such as ‘a string can pull but not push an object’, an AI system needs to automatically deduce (by analogy) that a cloth, sheet, or ribbon, can behave analogously to the string. For the fact ‘a stone can break a window’, the system must deduce that any similarly heavy and hard object is likely to break any similarly fragile material. Using the language of Sec. 5.2.1, each of these known facts needs to be treated as a schema,¹⁴ and then applied by analogy to new cases.

Projection is a mechanism that can find analogies (see Sec. 5.3.1) and hence could bridge the gap between models of commonsense concepts (i.e., not the entangled knowledge in word embeddings learnt from language corpora) and text or visual or sensorimotor input. To facilitate this, concepts should be represented by hierarchical compositional models, with higher levels describing relations among elements in the lower-level components (for reasons discussed in Sec. 6.1). There needs to be an explicit symbolic handle on these subcomponents; i.e., they cannot be entangled in a complex network. For visual object recognition, a concept can simply be a set of spatial relations among component features, but higher concepts require a complex model involving multiple types of relations, partial physics theories, and causality. Secs. 5.2 and 5.3 give a hint of what these concepts may look like, but a full example requires a further paper.

Continue reading “Projection: a mechanism for human-like reasoning in Artificial Intelligence” »

The mainstream approach to driverless cars is slow and difficult. These startups think going all-in on AI will get there faster.

All I can say is that I hope his self indulgence for his favorite ☆HOBBY☆ — Twitter itself — doesn’t sabotage the interplanetary future he’s defined and actually begun to to successfully realize, doing so against all odds in so many fields, cas diverse as science, engineering, economics, politics, and the recent history and the seeming decline in public enthusiasm, funding, and any sort of clear direction. He didn’t just subvert those roadblocks, he OBLITERATED them. SPECTACULARLY.

All that progress and innovation can and WILL be undone in seconds if he makes himself into an allie of a republican party that has abandoned truth, abandoned science, and abandoned every semblance of honor, loyalty, and reason.

A republican party that has abandoned Democracy ITSELF.

Continue reading “Twitter shareholders sue Musk, claim he sought to drive down stock price” »

Is this true?

The Perseverance rover has been on Mars for two weeks and has now spun its wheels and began its maiden trek over the red planet’s surface. According to new images transmitted to Earth by the one-ton robot on Friday, the voyage was a quick one.

Engineers have worked tirelessly to get the vehicle and its numerous equipment up and operating, including instruments and a robotic arm. Perseverance’s mission is to look for indications of alien life in the Jezero crater, which is located near the equator. This will take roughly 15 kilometers throughout the following Martian year (approximately two Earth years).

Continue reading “The Perseverance Rover of NASA captured a flying object on Mars” »

Millions of lasers shot from a helicopter flying over the Amazon basin have revealed evidence of unknown settlements built by a “lost” pre-Hispanic civilization, resolving a long-standing scientific debate about whether the region could sustain a large population, a new study finds.

The findings indicate the mysterious Casarabe people — who lived in the Llanos de Mojos region of the Amazon basin between A.D. 500 and 1,400 — were much more numerous than previously thought, and that they had developed an extensive civilization that was finely adapted to the unique environment they lived in, according to the study, published online Wednesday (May 25) in the journal Nature (opens in new tab).