Storage platforms are becoming data platforms, capable of leveraging AI and data management technologies in a secure, accessible environment.

This was just one of several discussion points during theCUBE’s coverage of the IBM Storage Summit. The future of computing is being driven by data, and storage is being carried along in the wave. AI-powered solutions are transforming the storage industry into agile data platforms that must increasingly meet the demands of a distributed, hybrid infrastructure, where data processing is required wherever the information resides.

“There’s no company where all their data is in one platform or in one place,” said Rob Strechay, industry analyst for theCUBE, SiliconANGLE media’s livestreaming studio, during the event’s analyst discussion. “I think some companies would love that to be the case to make money off it, but I think what you need to look at is how can you look at and approach all of the data to bring it to where you need it? How do you really get to that next level of data usability with the security, with the performance so that it’s the right place, transformed the right way?”

Strechay was joined in the analyst segment by Sarbjeet Johal and Dave Vellante. (* Disclosure below.)

Here’s theCUBE’s complete analyst video interview:

Here are three key insights you may have missed:

1. Storage is adopting an entirely new persona.

Storage has evolved from a time when enterprises placed high value on how rapidly data could be exported or imported and in what amounts. Now, the focus has moved on to storage as a data-enabling commodity with vital security and analytics capabilities.

“I think the speeds and feeds conversations are over in storage,” said Daneyand Singley (pictured, right), executive director of enterprise architecture and system sales at Mapsys Inc., in an interview with theCUBE. “We’re now on what else storage can do for us. That’s where this whole cyber resiliency and cyber vault strategy comes from with IBM.”

Here’s theCUBE’s complete video interview with Daneyand Singley, who was joined by Karen Hsu (pictured, left), vice president of storage ecosystem at IBM Corp.

In a measure of how storage enables data protection, Sam Werner, VPof IBM Storage product management at IBM, noted how the intersection of AI and security has led to advancement in storage’s ability to provide timely data protection.

“We’re up to over 75% accuracy in detecting anomalies or potential problems within our storage, but now we’ve taken it even further,” said Werner, in an interview during the event. “We’re able to move into near real-time detection of anomalies in your I/O to potentially catch a ransomware attack before it spreads across your storage environment.”

Here’s theCUBE’s complete video interview with Sam Werner, who was joined by Scott Baker, chief marketing officer and VP of IBM Infrastructure Portfolio product marketing at IBM:

A central theme from the IBM Storage Summit involved consolidation. Distributed computing architectures have raised the complexity level for many organizations, and there is interest in solutions, such as IBM’s software-defined data management offering Storage Fusion, which combines technologies in one accessible platform.

“Fusion kind of brings things together, also more from the OpenShift or the container platform, and it’s actually built on similar technologies with IBM Storage Scale, as well as IBM Storage Ceph,” said David Wohlford, worldwide senior product marketing manager of IBM Storage for AI and cloudscale at IBM, during an appearance on theCUBE. “What Storage Scale and IBM Storage Ceph really bring is the platform and bringing it together. We offer basically the file and object, bringing these two protocols together onto a single platform.”

Here’s theCUBE’s complete video interview with David Wohlford, who was joined by John Zawistowski, global systems solutions executive at Sycomp, A Technology Company Inc.:

2. Security is becoming an integral part of storage array technologies.

IBM scientists and engineers have been working on techniques for scanning data for signs of disorder as it is delivered to a flash array, a process known as entropy. The goal is to assess how the data coming in could be changing and understand whether the degree of randomness or disorder represents a real attack. By dedicating processing power in the flash system controllers, IBM technology can evaluate entropy for each volume.

“Now, it’s not necessarily that it’s a ransomware attack. It may be somebody’s turned encryption on in the application and the storage administrator doesn’t know,” said Andy Walls, fellow, chief technology officer and chief architect of IBM FlashSystems at IBM, in an interview with theCUBE. “But the storage administrator needs to know because his compressibility is changing. He needs to know that he might need to allocate more storage. So we’re detecting anomalies, as well as looking for ransomware attacks.”

Here’s theCUBE’s complete video interview with Andy Walls:

While scanning for ransomware attacks may be effective, it does not guarantee that an attack won’t be successful. In the event of a breach, organizations need to be able to respond decisively, and storage resiliency becomes a critical element in data recovery. IBM’s FlashCore Modules offer gapless data resiliency to better recover from cyberattacks.

“We want to make sure that … you’re going to have copies of data you can come back from,” said Ian Shave, director of worldwide distributed storage and data resilience sales at IBM, in conversation with theCUBE. “The new thing we’ve added is that we can discover when the threats are actually getting in, and I think this is the great combination of both the software of the array and … the elements that we’ve got in our FlashCore Modules.”

Here’s theCUBE’s complete video interview with Ian Shave:

3. AI is driving key changes in storage architectures.

As AI has come to dominate the tech landscape, storage providers are adapting their portfolios to meet increasing data demand. Christopher Maestas, worldwide executive solutions architect at IBM, described how the company is providing multiple data interfacing methods and cross-platform integrations to handle larger workload categories.

“We’ve started to see changes in workloads from media and entertainment, healthcare, life sciences [and] financial services sectors,” said Maestas, during an interview with theCUBE. “AI really has changed it, because it picked the middle of the road — not the itty-bitty files that you see or the large streaming data that you’ve been doing. We’re really seeing that data size change and, again, having to adapt to a different data size that we’ve not traditionally handled in the past.”

Here’s theCUBE’s complete video interview with Christopher Maestas:

The burgeoning field of AI has also brought challenges in dealing with a growing pool of unstructured data. IBM’s watsonx.data solution allows customers to build governance into storage engines for managing increasingly diverse sets of information.

“IBM Storage has a capability to cross through all the unstructured data,” said Vincent Hsu, fellow, chief technology officer and VP of IBM Storage at IBM, in a discussion during the event. “For IBM technology, you can see a single pane of glass to see the distribution of all the data, and then you can apply the policy on those data to allow us to be able to perform some particular function — for example, remove the PIIs or the hate speech information from the raw data sources.”

Here’s theCUBE’s complete video interview with Vincent Hsu:

IBM’s storage approach also seeks to eliminate pain points for data scientists. One of these roadblocks includes having to slog through hours of searching for the right data, which the company has addressed through tagging and labeling for seamless queries.

“The number one problem for the data scientists today is not how long my inferencing takes or not how long it takes to do model training; it’s can I get to the right data quickly?” said Pete Brey, global product executive, IBM Storage Fusion, at IBM, during an interview on theCUBE. “Some of the estimates are like 80% to 90% of their time is spent just trying to find the right data, and that’s the problem that we solve.”

Here’s theCUBE’s complete video interview with Pete Brey:

To watch more of theCUBE’s coverage of the IBM Storage Summit event, here’s our complete event video playlist:

(* Disclosure: TheCUBE is a paid media partner for the IBM Storage Summit. Neither IBM Corp., the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE

When it comes to data storage, is there a solution that offers optimal performance, security, resilience and governance at minimal costs without trade-offs?

IBM Storage seeks to meet these objectives through its Fusion technology and a storage virtualization layer that eliminate the hassle of query engines having to deal with all kinds of diverse storage on-premises, in the cloud or at the edge, since consistent storage is provided, according to Vincent Hsu (pictured), fellow, chief technology officer and vice president of IBM Storage at IBM Corp.

“The IBM storage technology is going to leverage the hyper cloud scale object storage, be able to scale from edge to on-prem to the cloud and provide the most efficient store for the persistent storage,” he said. “What we do is provide a storage virtualization layer that is able to present a consistent storage to the query engines. But in the backend, we’ll be able to virtualize the very different type of storage HDFS files and objects.”

Hsu spoke with theCUBE industry analyst Dave Vellante at IBM Storage Summit, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how IBM offers consistent storage for optimal query engine performance. (* Disclosure below.)

IBM watsonx.data is a game-changer

IBM’s new watsonx.data has incorporated more innovations and supports multiple engines. As a result, enterprises are provided with consistent performance, given that they can query any data anywhere at any given time, according to Hsu.

“Let me just focus on watsonx.data; I mean the data management piece of it,” he noted. “It has three salient points. Number one is it supports multi engines. The second one is IBM watsonx.data is supporting the Open Data format [which] allows different kinds of platforms to share data. The third one is the building metadata management and data governance to allow us to be able to truly harvest the insight of the data, be able to truly manage this very diverse data.”

When tackling data problems, a holistic approach is needed to deal with structured, semi-structured and unstructured data for various reasons, such as compliance and security. IBM comes in handy by offering governed data sharing, according to Hsu.

“IBM Storage has a capability to cross through all the unstructured data,” he stated. “For IBM technology, you can see a single pane of glass to see the distribution of all the data, and then you can apply the policy on those data to allow us to be able to perform some particular function — for example, remove the PIIs or the hate speech information from the raw data sources.”

IBM has mastered the art of improving query performance by caching and changing the data format from its original form to an open data format, which is more efficient, according to Hsu.

It’s important for storage technology to “be able to detect what is the right data and be able to cache those data from the remote data sources and cache the local NVMe drives to provide the very high performance,” he said. “In the lab, we have seen that seven to 10x performance improvement by doing this caching.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the IBM Storage Summit:

(* Disclosure: TheCUBE is a paid media partner for the IBM Storage Summit. Neither IBM Corp., the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE

Copyright © 2023 IDG Communications, Inc.

In a significant leap toward a more energy-efficient AI era, IBM has unveiled its prototype “brain-like” chip. IBM has drawn inspiration from the intricate network of connections in the human brain.

This innovation from the tech giant holds the potential to revolutionize AI by mitigating energy requirements.

Notably, high power consumption and emissions associated with sophisticated AI systems have been a matter of concern in accurate years.

Traditionally, concerns over the environmental impact of AI have stemmed from extensive warehouses with energy-consuming information systems. The prototype chip developed by IBM, however, enhances efficiency to reduce battery drainage.

Thanos Vasilopoulos, a scientist at the research laboratory of IBM in Zurich, Switzerland, stated that enhanced energy efficiency means “Large and more complex workloads could be executed in low power or battery-constrained environments.”

The human brain is able to achieve remarkable performance while consuming little power.Thanos Vasilopoulos

Besides, cloud service providers can capitalize on these chips to reduce energy bills as well as the carbon footprint. This marks a groundbreaking shift towards a more eco-friendly AI regime.

A Shift From Digital to Analogue

The integration of analog components, known as memristors, lies at the core of this innovation. This is different from the digital 0s and 1s storage approach of traditional chips.

These memristors are capable of storing a range of numbers, similar to the coordinated functioning of synapses in the human brain. Thus, the analog approach is dynamic, marking a departure from the binary nature of conventional digital chips.

Professor Ferrante Neri from the University of Surrey used the term ‘nature-inspired computing’ while talking about memristors, which mirrors the functioning of the human brain.

Besides, he noted that memristors have the capacity to “remember” their electric history, replicating the behavior of biological synapses. The professor also said, “Interconnected memristors can form a network resembling a biological brain.”

Challenges and Applications

Although IBM has developed a novel idea, the road to using this technology isn’t free from challenges. While the prototype chips are energy-efficient, it also involves digital elements to ease their integration into existing AI systems. Many contemporary phones already use AI chips for photo processing.

IBM is visualizing a future where its chips would enhance the efficiency of cars and smartphones, extending their battery life and reducing energy consumption.

This innovation also has broader implications. As AI continues to advance, the prototype chip promises a greener AI industry. These chips can eventually replace energy-intensive chips in data centers, which would go a long way in saving water for cooling and embracing energy efficiency.

While the innovation at IBM appears to be a significant milestone, experts are thinking about the path to its widespread adoption. James Davenport, Professor of IT at the University of Bath, stated that the chip wasn’t a straightforward solution but only the first step in a complex technical journey.

Therefore, the “brain-like” chip marks the outset of yet another research that would push the boundaries of energy efficiency. It remains to be seen how the researchers develop the chip to make it compatible with a variety of solutions.

Disclaimer Statement: This content is authored by a 3rd party. The views expressed here are that of the respective authors/ entities and do not represent the views of Economic Times (ET). ET does not guarantee, vouch for or endorse any of its contents nor is responsible for them in any manner whatsoever. Please take all steps necessary to ascertain that any information and content provided is correct, updated, and verified. ET hereby disclaims any and all warranties, express or implied, relating to the report and any content therein.

ARMONK, N.Y., Aug. 17, 2023 — IBM announced today that it 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.

Credit: Laborant/Shutterstock

With today’s news, 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.

In addition to the new offering, IBM and Microsoft have been collaborating around AI, leveraging IBM Consulting skills and Azure OpenAI Service to create potential solutions and address specific use cases, including:

• Procurement and source to pay: Together the companies are offering a solution that combines Microsoft Power Platform and Azure OpenAI Service to help businesses automate the highly manual and fragmented sourcing and procurement process as well as drive new insights about their supply chain. The solution is designed to Improve operational efficiency, save time, and generate new actionable insights for users.
• Summarization and content generation: Financial institutions and banks are exploring how generative AI can accelerate the development of personalized content for their customers through summarization. For example, IBM Consulting and Microsoft worked on a use case in a hackathon with Julius Baer Group to efficiently process and summarize financial reports while automatically creating an audio version of the report.
• Streamline healthcare processes: IBM Consulting is leveraging Azure OpenAI Service to offer a solution that is designed to automatically ingest and analyze complex medical records and policy documents to help automate the prior authorization process. In addition, it is built to provide nurses and doctors with a virtual assistant to help collect information from patient records. The solution aims to help decrease the time needed to process prior authorization requests, reducing administrative burdens and improving the clinician experience.
• Enterprise search and knowledge base: For many organizations the information employees need to do their jobs is dispersed and siloed. Working together, IBM Consulting and Microsoft helped Wintershall Dea implement a knowledge extraction tool designed for information retrieval within vast knowledge bases. By integrating OCR and Microsoft Azure OpenAI, a user-friendly tool is created that eliminates the need for manual browsing, allowing users to effortlessly search for valuable insights.

“Businesses are looking for responsible ways to adopt and integrate multi-model generative AI solutions that augment the work their teams are doing in areas such as creative content and code creation, content summarization and search,” said Francesco Brenna, Global VP & Senior Partner, Microsoft Practice at IBM Consulting. “Our work with Microsoft is another example of IBM’s open ecosystem model designed to bring value to clients while helping them responsibly build and scale generative AI across their businesses.”

As part of the new solution, enterprise customers will also have access to IBM Consulting experts, including 21,000 data, AI and experience consultants, who can help them effectively implement generative AI models to advance their business transformation.

An Open Ecosystem Approach to AI

IBM Consulting takes an open and collaborative approach to plan, build, implement and operate generative AI solutions that embrace multiple models on multiple clouds from industry leaders. An open ecosystem approach helps clients define the right models and the right architecture to deliver the desired outcomes. As part of this open approach, IBM Consulting works with clients across industries to assess their generative AI readiness, define the right strategies for their business and help them implement and responsibly govern generative AI in production.

Getting to enterprise AI at scale requires a human-centric, principled approach, and IBM Consulting helps clients establish guardrails that align with the organization’s values and standards, mitigates bias and manages data security, lineage and provenance.

Proven Work, Expertise and Partnership Momentum

To help clients prepare data to fuel their generative AI models, select IBM AI technology is currently available on the Azure Marketplace and can be deployed on Azure. Together we’re enabling clients to accelerate the impact of generative AI using their trusted data.

This work builds on accurate momentum with IBM and Microsoft to help clients transform their businesses. IBM Consulting, which has a dedicated global practice focused on Azure Data and AI, has focused on training its consultants, who now have over 40,000 Azure certifications. Additionally, IBM Consulting brings expertise and capabilities to help Microsoft clients through its acquisition of Neudesic, which specializes primarily in Microsoft Azure.

IBM Consulting and Neudesic together were also recognized with Microsoft’s 2023 Partner of the Year Award in 13 categories. IBM Consulting is this year’s U.S. Partner of the Year Winner for GSI Growth Champion, which distinguishes IBM as the partner that’s demonstrated the most significant growth – a partner that best offers solutions aligned with Microsoft’s in driving digital innovation and cloud transformation for our joint customers in the U.S.

“Together, Microsoft and IBM are collaborating to deliver innovative solutions, that will help customers responsibly accelerate deployment of generative AI,” said Dinis Couto, GM Global Partner Solutions, Microsoft. “As a leader in the delivery of generative AI and data solutions, we believe that partners like IBM are critical to enabling customers successful use of generative AI to advance business transformation.”

IBM Consulting’s AI Capabilities

IBM Consulting recently announced its Center of Excellence for generative AI, which includes more than 1,000 consultants with specialized generative AI expertise ready to help accelerate its clients’ business transformations with enterprise-grade AI, including technology from Microsoft, IBM and other ecosystem partners.

The Center of Excellence stands alongside IBM Consulting’s existing global AI and Automation practice and leverages proven methods like the IBM Garage for Generative AI, where IBM consultants apply a comprehensive, collaborative method to help clients fast-track innovation in the emerging category of foundation models for generative AI. That includes rapid use case ideation and prioritization, an open, multi-model approach to selecting architectures and training, as well as fine tuning and scaling models to unique business needs.

IBM Consulting accelerates business transformation for our clients through hybrid cloud and AI technologies, leveraging our open ecosystem of partners. With deep industry expertise spanning strategy, experience design, technology, and operations, we have become the trusted partner to many of the world’s most innovative and valuable companies, helping modernize and secure their most complex systems. Our 160,000 consultants embrace an open way of working and apply our proven co-creation method, IBM Garage, to scale ideas into outcomes.

To learn more about our partnership with Microsoft, click here.

To find out more about how Julius Baer and Wintershall Dea are using AI, click here and also here.

About IBM

IBM is a leading provider of global hybrid cloud and AI, and consulting expertise. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs, and gain the competitive edge in their industries. More than 4,000 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM’s hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently and securely. IBM’s breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and consulting deliver open and flexible options to our clients. All of this is backed by IBM’s legendary commitment to trust, transparency, responsibility, inclusivity and service.

Source: IBM

IBM claims to have cooked up a new mixed-signal part-analogue chip using a combination of phase-change memory and digital circuits that it's claiming will match GPU performance when it comes to AI inferencing but do so at much greater efficiency.

No, we don't entirely understand that either. But the implications are easy enough to grasp. If this chip takes off, it could put a cap on the skyrocketing demand for GPUs used in AI processing and save them for, you know, gaming.

According to El Reg, this isn't the first such chip IBM has produced. But it's on a much larger scale and is claimed to demonstrate many of the building blocks that will be needed to deliver a viable low-power analogue AI inference accelerator chip.

One of the main existing bottlenecks for AI inferencing involves shunting data between the memory and processing units, which slows processing and costs power.  As IBM explains in a accurate paper, its chip does it differently, using phase-change memory (PCM) cells to store inferencing weights as an analogue value and also perform computations.

It's an approach known as analogue in-memory computing and it basically means that you do the compute and memory storage in the same place and so—hey presto—no more data shunting, less power consumption and more performance.

Things get more complex when you start describing the scale and scope of the weighting matrices that the chip can natively support. So, we won't go there for fear of instantly butting up against the exceedingly compact limits of our competence in such matters.

But one thing is fore sure. AI-processing power consumption is getting out of hand. An AI inferencing rack reportedly sucks up approaching 10 times the power of a "normal" server rack. So, a more efficient solution would surely gain rapid traction in the market.

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