Sanctuary Cognitive Systems Corporation (Sanctuary AI), a company on a mission to create the world’s-first human-like intelligence in general-purpose robots, today announced that it has successfully completed the first ever deployment of a unique system designed to deliver the world’s first human-like intelligence in general-purpose robots at a customer commercial facility through its partnership with Canadian Tire Corporation (CTC).
The first deployment of its kind in a commercial environment took place at a Mark’s retail store in Langley, BC, Canada. The week-long pilot at the CTC-owned store successfully tested the general-purpose robot in a ‘real-life’ store environment with 110 retail-related tasks completed correctly, including front and back-of-store activities such as picking and packing merchandise, cleaning, tagging, labelling, folding, and more. The tested tasks had previously only been performed in Sanctuary AI’s labs, which accurately mirrored the commercial setting to ensure a seamless technology implementation.
A human-like AI controlling Sanctuary AI general-purpose robots should be able to perform physical labor across virtually every industry. Sanctuary AI has created a strong ecosystem in a brand new category that, according to Goldman Sachs, has the potential to be a market of up to US$154 billion by 2035.
Sanctuary AI is backed by a number of investors that add even greater strength to their ecosystem. The Sanctuary AI investor list includes: Bell, Canadian Tire Corporation, Evok Innovations, Export Development Canada, Magna, SE Health, Verizon Ventures, and Workday Ventures. Sanctuary AI has now received more than C$100 million in funding since its founding in Vancouver, Canada in 2018, and is active in its next funding round to fuel its mission.
Ecosystem for Humanoid Robot Ecosystem and General AI
Sanctuary.ai is creating a humanoid robot ecosystem. One of their first partners is Cycorp. Cycorp is the longest running AI organization in the world. Their Cyc technology traces its roots back to 1984. Cycorp’s leader, Doug Lenat, is a legend in the field of artificial intelligence. He has steadfastly championed symbolic reasoning and logic as a path to human-level AI. You can learn more about him and Cycorp on Lex Fridman’s podcast here. The Cycorp team under Doug’s leadership have built what they believe is one of the most important parts of this puzzle — the part that uses logic and reasoning on symbols that a mind uses to understand the world.
Cycorp’s people have thoughtfully engineered tens of millions of logical rules intended to model what we think of as common sense. Cyc reasons using common sense both deeply and quickly, using over a thousand specialized reasoners which attack each problem as a team.
What is common sense, exactly? What they mean by this needs to be written down somewhere. This is excruciatingly difficult outside of the scope of statistical approaches such as Large Language Models (LLMs). Geordi Rose does not think scaling LLMs will produce common sense.
One of the things Doug said that stuck with me is that Cyc encodes all the things that never get written down that we all take for granted. They are betting that Cyc and LLMs working together will be much, much greater than the sum of their parts. And since Cyc operates by logical pro- and con- argumentation, unlike LLMs, it can fully explain every step in its reasoning. This is important for any AI to be trustworthy. This is very important when AI controls machines. Doing this right is very important to us.
Sanctuary and Cycorp have worked closely together for several years. They have deeply integrated Cyc into Sanctuary’s technology stack. Their business relationship includes an equity position and an exclusive license to Cyc for humanoids.
They have seen remarkable success and validation of our intuition on Cyc’s central role in delivering human-like AI. Here’s Doug on the partnership: “Cycorp’s mission is to deliver on the founding promise of the field of AI — the creation of technologies that model and understand how the world works, as deeply and broadly as people do, enabling them to responsibly, sanely and ethically solve problems like they do.
Apptronik is one of the few organizations in the world that could realistically produce robots that can move with the function, mobility, grace, and fluidity of the human body. Apptronik and Sanctuary are dramatically accelerated progress towards a humanoid robot future.
Santuary is working with Common Sense Machines. CSM is building state of the art systems for producing 3D assets for world models.
The human hand is a marvel. Robot hands that mimic human hands are very hard both to build and to control. To help with this, Sanctuary acquired the entire intellectual property portfolio of Giant AI. The team at Giant pioneered a set of techniques for using vision to help robots grasp and manipulate objects as humans do.
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
Known for identifying cutting edge technologies, he is currently a Co-Founder of a startup and fundraiser for high potential early-stage companies. He is the Head of Research for Allocations for deep technology investments and an Angel Investor at Space Angels.
A frequent speaker at corporations, he has been a TEDx speaker, a Singularity University speaker and guest at numerous interviews for radio and podcasts. He is open to public speaking and advising engagements.
I remember reading about Cyc ages ago. Nice to see they kept going with the project. Not that I’m qualified to judge the effort’s approach compared to others.
I’m sceptical about the real world tests. I’ve seen some pretty severely autistic student placements with varying levels of independence. Some of the least capable can sort of be productive if they have an assistant constantly helping and reminding them of things it’s easy to lose track of what abilities are coming from the assistant as opposed to the student.
Then there is the issue of implementing the tech, assuming a perfect humanoid worker could be developed. There is currently electronic shelf labeling technology in existence but I’ve only ever seen it in a store dedicated to selling high tech consumer goods. Employees in stores still put up thousands of sale price tags every week. There are Romba type robot floor cleaners in existence but most stores don’t have them. Those that do often seem to be trying to impress investors with their innovative initiatives rather than actually trying to save labour costs. Stores employ human floor cleaner services, once a week, have employees sweep after closing and you will still see a store manager grab the mop bucket if a spill happens when short staffed. I won’t let myself get started about the DOS based legacy spaghetti code running massive operations (mostly in government spaces but who knows).
I think many of these new technologies will at best be partially implemented in some fields before I reach nature’s mandatory retirement.
Figure.ai also came out of stealth mode as another humanoid robot manufacturer, similar to to Apptronik, though Apptronik appears to have some connections with the University of Texas in Austin and RoMeLA (RoMeLa is allegedly heavily linked to the Optimus Teslabot).
Another recent humanoid robot manufacturer is Clone Robotics, which is using a high biomimicry penumatic tendon actuator based system built on a human human skeleton analog. Their robot hand actuator systems at first glance seem interesting, but they aren’t yet up to a full humanoid.
The interesting question is, is a heavily integrated software/hardware approach going to win, or a more general software stack like ROS that can run on multiple different hardware? For a ROS style system based on open source cores, where are the business models for the higher level functions? I don’t think you can entirely divorce navigation from lower level skills, plus with learning systems, at what points do a skill app ecosystem fall over?