The drive is on to produce humanoid robots able to master a huge range of tasks. Much of our material universe will now become ubiquitous and available to be used. we are now in a general phase of design enhancement.
The ultimate reality is that everything needs robotic support. Understand that no robot will be a genius at driving a nail but will tirelessly drive a nail forever and anon. there is a difference. Once a robot is picking raspberries, i am happy to get lost and look for other issues.
picking bugs out of crops is useful. actually doing it is not. We have billions of trees needing to be groomed at our direction. this alone is hugely beneficial but we really cannot do it that well. We actually need a taller robot.
Yet the human mk i eyeball needs to be on the job to forsee issures.
Sanctuary AI Humanoid Bots Will Be Used in Magna Car Parts Factories on Path to Solving All Work
April 11, 2024 by Brian Wang
https://www.nextbigfuture.com/2024/04/sanctuary-ai-humanoid-bots-will-be-used-in-magna-car-parts-factories-on-path-to-solving-all-work.htm
Sanctuary AI, a company on a mission to create the world’s first human-like intelligence in general purpose robots, has announced a strategic partnership with global mobility technology company Magna.
Sanctuary’s CTO Suzanne Gildert explained their mind and hands first approach. Sanctuary AI views humanoid robots as a means to an end, where the end is human-like general intelligence.
They want to master the entire lengthy alphabet of actions and then compile them into full tasks and then jobs.
They are identifying who the customers are and the demand. They will be low order of numbers of robots for each trial.
They will simulate all of the robots to thousands of robots.
They think they will have millions of robots by 2030. Roughly scaling by tens each year.
They want to mimic the entire human form. Trying to take shortcuts will paint yourself into a corner where the system is no longer fully general.
This collaboration features:
Sanctuary AI’s development of general purpose AI robots for deployment in Magna’s manufacturing operations; a multi-disciplinary assessment of improving cost and scalability of robots using Magna’s automotive product portfolio, engineering and manufacturing capabilities; and a strategic equity investment by Magna.
Magna, one of the largest automotive suppliers in the world and a significant buyer of industrial robots, aims to leverage Sanctuary AI robots’ unique capabilities across multiple applications within automotive manufacturing processes. The partnership aims to catalyze the scaling of Sanctuary AI’s robots while maturing the technology for use in challenging manufacturing environments for Magna and other industrial and automotive customers.
“We founded Sanctuary AI with the goal to become the first organization in the world to create human-like AI,” said Geordie Rose, CEO and Co-founder of Sanctuary AI. “World-changing goals like these require world-changing partners. Magna’s position as a world leader in the use of robots today makes this partnership an essential advancement for our mission. We’re privileged to be working with Magna, and believe they will be a key element in the successful global deployment of our machines.”
Sanctuary AI has developed a number of cutting-edge technologies set to transform the manufacturing industry and beyond. This includes its industry-leading dexterous human-like hands, developed for its humanoid general purpose robot, Phoenix™, and their pioneering AI control system, Carbon™. With essential patents covering visual and tactile servoing, integration of language models, and AI training, Sanctuary AI paves the way for commercializing humanoid general purpose robots capable of performing hundreds of diverse tasks.
“Magna is excited to partner with Sanctuary AI in our shared mission to advance the future of manufacturing,” said Todd Deaville, Vice President, Advanced Manufacturing Innovation at Magna. “By integrating general purpose AI robots into our manufacturing facilities for specific tasks, we can enhance our capabilities to deliver high-quality products to our customers.”
Magna has been an investor in Sanctuary AI since 2021.
Solving All Human Labor
Machines with human-level intelligence should be able to do most economically valuable work. This aligns a major economic incentive with the scientific grand challenge of building a human-like mind. Here Sanctuary AIs CEO Geordie Rose and CTO Suzanne Gildert describe their approach to building and testing such a system. Their approach comprises a physical humanoid robotic system; a software based control system for robots of this type; a performance metric, which they call g+, designed to be a measure of human-like intelligence in humanoid robots; and an evolutionary algorithm for incrementally increasing scores on this performance metric. They introduce and describe the current status of each of these. They report on current and historical measurements of the g+ metric on the systems described here.
What problems would the world present to a humanoid robot faced with the problems humans might be inclined to relegate to sufficiently intelligent robots of this kind?
Most of the tasks that humans perform for pay could be automated… Machines exhibiting true human-level intelligence should be able to do many of the things humans are able to do. Among these activities are the tasks or “jobs” at which people are employed. We can replace the Turing test by something the “employment test.” To pass the employment test, AI programs must be able to perform the jobs ordinarily performed by humans. Progress toward human-level AI could then be measured by the fraction of these jobs that can be acceptably performed by machines.
The Employment Test aligns a major economic incentive (automating labor is by definition the largest market possible) with the scientific grand challenge of understanding the human mind.
An Employment Test for Robots
In 2019 Sanctuary AI began developing an approach to measuring the capability of a robot to do work derived from the O*NET (Occupational Information Network) system (Gildert, 2019). O*NET is a source of information about work and workers developed and maintained by the U.S. Department of Labor/Employment and Training Administration (USDOL/ETA) (ONET, 2023).
This approach is predicated on the notion that economic activity forms a hierarchy of goals that overlaps significantly with the set of all human goals. In their framing of the problem of building a human-like mind, thye make the non-trivial assumption that a machine that can do anything we value enough to pay for must possess a mind with many, perhaps all, of the properties of out own minds. For example, Marcus provides a threshold for a system to be considered AGI, which would easily be passed by a system that could do all work (Marcus, 2022).
Currently there are 19,265 identified tasks (ONET, 2023b). Each task in O*NET is labeled by an integer, ranging from 1 (“Resolve customer complaints regarding sales and service”) to 23,955 (“Dismount, mount, and repair or replace tires”). Some integers in this range are not associated with tasks.
Occupations typically require the performance of 20-30 different tasks in order to do everything the job requires. Selecting for example the Retail Salespersons occupation, we obtain 24 tasks (see Table 1) (ONET, 2023c). O*NET contains similar breakdowns for every occupation tracked.
In the O*NET taxonomy, there are 33 skill types, 52 abilities, and 35 categories of knowledge; all of these are scored on a scale from 0 (none) to 7 (near the top of human performance). Lists of O*NET skills, abilities, and knowledge are included in the O*NET Skills, O*NET Abilities, and O*NET Knowledge tabs.
The g+ Score
A work fingerprint is a 120-dimensional vector of integers, each in the range 0 to 7. While this is the fundamental vector we track, we additionally define a scalar that is useful for tracking progress over time. They call this scalar g+ (pronounced g plus).
To compute g+, first note that as we have subtask fingerprints for all occupations in O*NET, it is straightforward to first compute the sum over all 120 dimensions for each of these (giving a number between 0 and 120*7 = 840) and then take the average over all 1,015 occupations (doing so results in a mean value of 267.3).
They then define g+ to be the sum of all 120 scores in either a work or subtask fingerprint multiplied by 100/267.3. This normalization makes it so that a score of 100 is equal to the average value across all occupations. g+ ranges from 0 to 314.3. The standard deviation of minimum g+ scores required for occupations is 15.4 (see O*NET Occupations Ranked by g+ in (Data, 2023)). This makes the distribution of g+ required to perform occupations very similar to IQ, which has a defined mean of 100 and standard deviation of 15.
The subtask g+ scores for Producers and Directors; Poets, Lyricists and Creative Writers; Cooks, Restaurant; Computer Programmers; Mathematicians; and Models, are 102.0, 84.5, 91.3, 91.7, 100.9, and 44.7 respectively. The Model g+ score of 44.7 is the lowest across all O*NET occupations. This means that a person or humanoid robot with a g+ score of less than 44.7 is not capable of performing any full occupation, regardless of distribution of strengths and weaknesses.
A person who successfully performs all five of the Producers and Directors, Poets, Lyricists and Creative Writers, Cooks, Restaurant, Computer Programmers, and Mathematicians subtasks would have a g+ score of 143.9.
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