International Business Machines (IBM)
Software : Cloud Computing : General
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Computing With Chemicals Makes Faster, Leaner AI
A device that draws inspiration from batteries now appears surprisingly well suited to run artificial neural networks. Called electrochemical RAM (ECRAM), it is giving traditional transistor-based AI an unexpected run for its money—and is quickly moving toward the head of the pack in the race to develop the perfect artificial synapse. Researchers recently reported a string of advances at this week’s IEEE International Electron Device Meeting (IEDM 2022) and elsewhere, including ECRAM devices that use less energy, hold memory longer, and take up less space.
A commercial ECRAM chip that accelerates AI training is still some distance away. The devices can now be made of foundry-friendly materials, but that’s only part of the story, says John Rozen, program director at the IBM Research AI Hardware Center. “A critical focus of the community should be to address integration issues to enable ECRAM devices to be coupled with front-end transistor logic monolithically on the same wafer, so that we can build demonstrators at scale and establish if it is indeed a viable technology.”
A.P. Moller - Maersk and IBM to discontinue TradeLens, a blockchain-enabled global trade platform
TradeLens was founded on the bold vision to make a leap in global supply chain digitization as an open and neutral industry platform. Unfortunately, while we successfully developed a viable platform, the need for full global industry collaboration has not been achieved. As a result, TradeLens has not reached the level of commercial viability necessary to continue work and meet the financial expectations as an independent business.
The TradeLens platform was announced in 2018 and jointly developed by IBM and GTD Solution, a division of Maersk, as a blockchain-enabled shipping solution designed to promote more efficient and secure global trade. Maersk will continue its efforts to digitise the supply chain and increase industry innovation through other solutions to reduce trade friction and promote more global trade.
TradeLens: Transportation Transformation or Quixotic Quagmire?
TradeLens is the highly publicized blockchain global trade network launched over four years ago by Danish shipping giant Maersk. Beyond the initial hype about eliminating duplicate invoices and digitizing paper workflows, little has been said about it. What was TradeLens all about then, where is it now, and what can we take away from its progress (or lack thereof) to date?
It appears that while TradeLens has made substantial technical and practical progress, success is not a foregone conclusion. The system is far from ubiquitous adaptation, and even industries with high market participation, such as shipping, are not able to utilize the network to digitize trade at scale. In fact, only a miniscule percentage of transactions are conducted on a fully digitized basis.
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.