Software : Cloud Computing : General
IBMers believe in progress—that the application of intelligence, reason and science can improve business, society and the human condition.
Can AI help create less carbon-intensive concrete?
Cement is a popular binding and fortifying agent with a high production cost (and we’re not talking about $$): For every ton of cement produced, at least one ton of CO2 is released into the atmosphere—adding up to at least 8% of annual global emissions. The researchers trained a generative AI model on environmental impact data and a small public dataset. Using semi-supervised learning, the model sought out concrete formulas that checked all of the researchers’ boxes: 1) lower carbon footprint, 2) significant compressive strength, and 3) similar durability and other qualities.
Neuro-symbolic AI could provide machines with common sense
Among the solutions being explored to overcome the barriers of AI is the idea of neuro-symbolic systems that bring together the best of different branches of computer science. In a talk at the IBM Neuro-Symbolic AI Workshop, Joshua Tenenbaum, professor of computational cognitive science at the Massachusetts Institute of Technology, explained how neuro-symbolic systems can help to address some of the key problems of current AI systems.
“We’re trying to bring together the power of symbolic languages for knowledge representation and reasoning as well as neural networks and the things that they’re good at, but also with the idea of probabilistic inference, especially Bayesian inference or inverse inference in a causal model for reasoning backwards from the things we can observe to the things we want to infer, like the underlying physics of the world, or the mental states of agents,” Tenenbaum says.
There are several attempts to use pure deep learning for object position and pose detection, but their accuracy is low. In a joint project, MIT and IBM created “3D Scene Perception via Probabilistic Programming” (3DP3), a system that resolves many of the errors that pure deep learning systems fall into.
Industry 4.0 and the pursuit of resiliency
There are two parts to the Zero D story. Visual inspection and asset performance management (APM). Visual inspection uses computer vision models focused on quality inspection. APM uses machine learning models based on time series data to determine health of assets and probable failures in the future. Toyota is using Maximo Visual Inspection, and now they are also using the Maximo Asset Performance Management (APM) suite. They tested Maximo APM on some of their machinery that does liquid cooling and found that was another problem area for them. By implementing the software into this pilot, they are now able to monitor the asset health 24×7 and predict probability of failure in the future.
Ford presents its prestigious IT Innovation Award to IBM
The Maximo Visual Inspection platform can help reduce defects and downtime, as well as enable quick action and issue resolution. Ford deployed the solution at several plants and embedded it into multiple inspection points per plant. The goal was to help detect and correct automobile body defects during the production process. These defects are often hard to spot and represent risks to customer satisfaction.
Although computer vision for quality has been around for 30 years, the lightweight and portable nature of our solution — which is based on a standard iPhone and makes use of readily available hardware — really got Ford’s attention. Any of their employees can use the solution, anywhere, even while objects are in motion.
Ford found the system easy to train and deploy, without needing data scientists. The system learned quickly from images of acceptable and defective work, so it was up and running within weeks, and the implementation costs were lower than most alternatives. The ability to deliver AI-enabled automation using an intuitive process, in their plants, with approachable technology, will allow Ford to scale out rapidly to other facilities. Ford immediately saw measurable success in the reduction of defects.
IBM’s vision of the connected factory
As far as our software is concerned, we are providing solutions for specific use cases that can deliver the quick wins that manufacturers are looking for. We have a solution called Maximo Application Suite which can monitor equipment effectiveness, asset health, asset performance, and visual inspection. And these kind of quick wins can already be delivered as part of a standard product. We are also working with customers in the field on things which are not necessarily already coded in the software. Something else which IBM brings to the table is that we are open source.
Evolution of Machine Autonomy in Factory Transactions
So while we’ve not completely entered the age of the machine economy, defined as a network of smart, connected, and self-sufficient machines that are economically independent and can autonomously execute transactions within a market with little to no human intervention, we are getting close.
The building blocks to create the factory of the future are here, including the Internet of Things (IoT), artificial intelligence (AI), and blockchain. This trifecta of technology has the potential to disrupt the industrial space, but it needs to be connected with a few more things, such as digital twin technology, mobile robots, a standardized way for machines to communicate, and smart services, like sharing machine capacity in a distributed ecosystem.
“The biggest obstacle is culture,” said IIC’s Mellor. “The average age of the industrial plant is 19 years. These are huge investments that last for decades. The organizations that run these facilities are very cautious. Even a 0.5% chance of failure can cost millions of dollars.”
Using AI to Find Essential Battery Materials
KoBold’s AI-driven approach begins with its data platform, which stores all available forms of information about a particular area, including soil samples, satellite-based hyperspectral imaging, and century-old handwritten drilling reports. The company then applies machine learning methods to make predictions about the location of compositional anomalies—that is, unusually high concentrations of ore bodies in the Earth’s subsurface.