AI News, Hanson Robotics: home artificial intelligence
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Hanson AI develops cognitive architecture and AI-based tools that enable our robots to simulate human personalities, have meaningful interactions with people and evolve from those interactions.
How 19 companies are using artificial intelligence to make smarter robots
It's a scenario movies and sci-fi stories have predicted for years: an intelligent fleet of human-serving robots become too smart and take over the world.
The company’s main product, an industrial autonomous cart, works in unstructured warehouse and factory environments — indoors or outdoors.
Drones can be operated manually or autonomously to aid fire services, emergency response teams, law enforcement and search- and-rescue operations.
A drone’s unique vantage point enables it to gather and report a wide array of information and data — like the source of a wildfire, the location of stolen property or missing people or the extent of disaster damage in a certain area.
Industry impact: Drones were used by a Texas power line company to assess tornado damage, saving time and keeping people off damaged towers.
Industry impact: Compared to the original model, the most recent Roomba is extremely smart and can determine room sizes, adjust to carpet or hardwood, choose optimal routes and remember where objects are in a room. The robots are steadily improving at learning and adapting to their surroundings.
Industry impact: Neurala solutions are designed to make drones smarter. Future uses include identifying early signs of corrosion in large equipment like wind turbines and curbing elephant poaching by deciphering between hunter and hunted.
Industry impact: Industrial robots typically are separated from human workers for safety reasons, but Veo's robots use 3D sensors to detect objects or people nearby and, if necessary, slow or stop.
The company utilizes multiple forms of artificial intelligence within its hardware including automated speech recognition and computer vision for tasks like facial and object recognition.
Industry impact: Engineered Arts partnered with researchers and experts at Leeds and Oxford Universities to create Ai-Da, a robotic artist who utilizes computer vision and AI algorithms to produce drawings and paintings.
The company uses AI throughout the planning, simulation and infrastructure of the car in order to ensure that the robots can see the world around them in real-time and react safely.
Location: Hong Kong, China How it’s using AI: Hanson Robotics is an AI and robotics company creating human-like robots that not only have human appearances but also characteristics like eye contact, facial recognition, speech and the ability to hold natural conversations.
Using a proprietary nanotechnology skin called Frubber, the robots can produce high-quality expressions that offers a less mechanical robotic experience. Industry impact: Hanson is probably most well-known for its highly conversational humanoid robot, Sophia, featured in countless news segments, discussion panels and technology conferences.
These robots are able to take items like groceries, food delivery orders or even retail store orders and deliver them to customers using AI to navigate their route along the way.
Location: Santa Clara, California How it's using AI: Because a humanoid robot brain would require enormous physical dimensions — much larger than those of an actual human brain — CloudMinds enables users to operate robots from the cloud, with mobile phones acting as cloud robot control units (RCU) and operating on secure networks.
She can animate over 60 expressions and has motion tracking with built-in cameras that coordinate with head and eye motions to track people’s eyes and faces to maintain eye contact.
I know that the design is primarily driven by your research, which is basically an architecture for robot and virtual embodied cognition to give rise to human-equivalent artificial general intelligence.
If achieved, this will make Sophia-like robots into remarkably valuable tools for home and commercial environments, and it will set the stage for humans and robots to learn an amazing amount from each other.
Machine learning also plays a role, training models to guide behaviors based on data regarding human behaviors that are identified as good training examples.
think we may be only a couple years from an AI system that can pass the Turing Test “artificially”—meaning that it will be able to imitate human dialogue, but without any real understanding of what it’s talking about.
Having a physical robot pass the Turing Test organically is probably many years off because, as realistic as Sophia is, she is certainly not hard to distinguish from a human if you’re really trying.
From a commercial perspective and a satisfying-human-needs perspective, this is a big plus, but some people have seen ethical issues here in terms of the possibility that people would form a deep emotional bond with an AI or robot on the false premise that this AI understands and empathizes with them more deeply than is really the case.
We’re seeing more and more results come out based on using AI to analyze clinical medicine or genomics data, creating new diagnostics, or suggesting new therapies.
A Narrow AGI system is one that displays powerful general intelligence, but is heavily biased in capability toward some particular domain, such as biomedical research or math theorem proving or urban planning.
Given the heavily commercial focus of the contemporary AI field, it seems likely that the path to full, human-level AGI is going to pass through a variety of Narrow AGI systems of progressively increasing generality and capability.
Large corporations have a valuable role to play in the modern technology ecosystem, but this role shouldn’t be one of hiring all the AI developers, ingesting all the data about everyone, and then controlling all the AI.
My SingularityNET colleagues and I believe that the creation of beneficial narrow AI applications and broadly beneficial AGI is more likely to happen if the underlying fabric of AI learning and reasoning algorithms is decentralized in ownership, control, and dynamics.
A decentralized control structure will allow a network of AI agents to address a greater variety of needs and problems, and to leverage contributions from a greater variety of people and human organizations.
SingularityNET is a completely open-source, decentralized AI platform, with the vision to democratize cutting-edge AI and datasets because we think AI is too powerful a technology to be kept within the silos of large organizations.
We are exploring ways in which we can bring core aspects of this technology to people who may not necessarily fully understand it today, such as non-profits, small and medium businesses, and even individuals who want to understand and trust how the black box behind the technology is working.
Currently we have about 100,000 people in the community and a bunch of contributors globally who are committed to the vision and that interact with us on a monthly basis in our groups and Telegram communities, and on chats and Twitter feeds.
The new for-profit company my colleague Cassio Pennachin and I have created, Singularity Studio, is engaged in creating enterprise AI software products to be licensed to large corporations—but with the twist that the core AI functionality behind the products is obtained by outsourcing to AI agents running in the decentralized SingularityNET platform.
SingularityNET provides a platform and infrastructure in which AI agents can cooperate, collaborate with each other, outsource work to each other, and provide services to customers for fairly negotiated prices—all in a purely decentralized way, without any centralized controller.
This decentralized AI platform can apply powerfully to every industry, but it has particular power in any domain where data from large numbers of ordinary product users plays a key role.
SingularityNET is perfectly architected to serve as the secure “robot mind cloud” behind a large, peaceful army of Sophia robots and other social robots of every kind.
The author of this article, or a firm that employs the author, is a holder of the following securities mentioned in this article : none
- On 24. september 2020
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