International Business Machines (IBM)
Software : Cloud Computing : General
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IBM Consulting Collaborates with Microsoft to Help Companies Accelerate Adoption of Generative AI
IBM (NYSE: IBM) is expanding its collaboration with Microsoft to help joint clients accelerate the deployment of generative AI - and deliver a new offering that will provide clients with the expertise and technology they need to innovate their business processes and scale generative AI effectively. IBM Consulting, in collaboration with Microsoft, will focus on helping clients to implement and scale Azure OpenAI Service. The new IBM Consulting Azure OpenAI Service offering, which is available on Azure Marketplace, is a fully managed AI service that allows developers and data scientists to apply powerful large language models, including their GPT and Codex series. It aims to help businesses define an adoption strategy and an initial set of specific and value-add generative AI use cases.
IBM and NASA Open Source Largest Geospatial AI Foundation Model on Hugging Face
IBM (NYSE: IBM) and open-source AI platform Hugging Face today announced that IBM’s watsonx.ai geospatial foundation model – built from NASA’s satellite data – will now be openly available on Hugging Face. It will be the largest geospatial foundation model on Hugging Face and the first-ever open-source AI foundation model built in collaboration with NASA.
The model leverages IBM foundation model technology and is part of IBM’s larger effort to create and train AI models that can be used for different tasks and apply information from one situation to another. In June, IBM announced the availability of watsonx, an AI and data platform that allows enterprises to scale and accelerate impact of the most advanced AI with trusted data. A commercial version of the geospatial model, which is part of IBM watsonx, will be available through the IBM Environmental Intelligence Suite (EIS) later this year.
IBM and AWS partnering to transform industrial welding with AI and machine learning
IBM Smart Edge for Welding on AWS utilizes audio and visual capturing technology developed in collaboration with IBM Research. Using visual and audio recordings taken at the time of the weld, state-of-the-art artificial intelligence and machine learning models analyze the quality of the weld. If the quality does not meet standards, alerts are sent, and remediation action can take place without delay.
The solution substantially reduces the time between detection and remediation of defects, as well as the number of defects on the manufacturing line. By leveraging a combination of optical, thermal, and acoustic insights during the weld inspection process, two key manufacturing personas can better determine whether a welding discontinuity may result in a defect that will cost time and money: weld technician and process engineer.
Microsoft Cloud for Manufacturing: Tackling data accessibility in manufacturing alongside partners
I’m very excited about all the updates being shared at Microsoft Inspire 2023, particularly about the announcement of the new AI Cloud Partner Program (MACPP) and the additional offerings and benefits this brings for partners. Under the MACPP, I’m thrilled to announce that we will be including manufacturing partner solutions through new independent software vendor (ISV) designations.
This designation represents our commitment to bringing the best partner solutions to our customers and provides a way for customers to identify proven partner solutions aligned with the Microsoft Cloud and our industry clouds. The designation validates that our partners’ solutions meet the high standards of data accessibility specific to the manufacturing industry.
Redefining the consumer experience: Diageo partners with SAP and IBM on global digital transformation
Today, we are thrilled to announce an exciting collaboration between Diageo, IBM and SAP that aims to modernize Diageo’s operations on a global scale and set new benchmarks for the industry. Diageo will implement RISE with SAP S/4HANA Cloud, streamlining their IT infrastructure and providing unified support across the 180 countries in which they operate.
IBM Consulting will bring our decades of expertise in helping consumer product organizations successfully navigate digital transformations. We are bringing together a diverse team of consultants to lead the strategy and help implement RISE with SAP S/4HANA Cloud across Diageo’s global business. This collaboration underscores IBM’s unwavering dedication to delivering exceptional value and driving transformative change for consumer goods companies across the globe.
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.”
IBM Unveils World's First 2 Nanometer Chip Technology, Opening a New Frontier for Semiconductors
IBM (NYSE: IBM) today unveiled a breakthrough in semiconductor design and process with the development of the world’s first chip announced with 2 nanometer (nm) nanosheet technology. Semiconductors play critical roles in everything from computing, to appliances, to communication devices, transportation systems, and critical infrastructure.
The potential benefits of these advanced 2 nm chips could include:
- Quadrupling cell phone battery life, only requiring users to charge their devices every four days.
- Slashing the carbon footprint of data centers, which account for one percent of global energy use. Changing all of their servers to 2 nm-based processors could potentially reduce that number significantly.
- Drastically speeding up a laptop’s functions, ranging from quicker processing in applications, to assisting in language translation more easily, to faster internet access.
- Contributing to faster object detection and reaction time in autonomous vehicles like self-driving cars.
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.