IBM extends its goals for AI and quantum computing, shows off roadmap

Forward-looking: While no one doubts the heritage of tech advancements that IBM has made over recent decades, there are certainly those who’ve started to wonder if the company is able to sustain those types of efforts into the future. At a recent analyst day held at the historic Thomas J. Watson Research Center, IBM made a convincing argument that they are up to the task, especially in the fields of AI as well as quantum computing.

What stood out was IBM’s demonstration of a much tighter connection between its research work on advanced technologies and the rapid “productization” of this work into commercial products. In both prepared remarks and in response to questions, it was clear that there’s a renewed focus to ensure that the two groups are in lockstep with regards to their future outlook and development priorities.

Historically, not all of IBM’s research initiatives have reached the market. However, under the clear direction of CEO Arvind Krishna, formerly head of IBM Research, the company is now concentrating on key areas such as hybrid cloud, AI, and quantum computing. Current research director Dario Gil confirmed that collaboration between the research and commercial products teams is now stronger than ever. This enhanced coordination is leading to the rapid development of innovative capabilities that are swiftly integrated into commercial products.

One real-world outcome of this strategy is IBM’s rapid development of its AI suite, dubbed ‘watsonx.’ First introduced at this year’s Think conference (see “IBM Unleashes Generative AI Strategy With watsonx” for more), watsonx is evolving rapidly, driven in large part by new capabilities first developed by the IBM research group.

At the recent analyst event, IBM showcased numerous real-world applications and customer cases using watsonx. Despite many organizations still being in the exploratory phase with Generative AI, IBM shared a variety of successful real-world implementations. Furthermore, IBM detailed an extensive range of applications for watsonx and generative AI, highlighting their increasing relevance in various business sectors.

On the application front, IBM identified three primary areas where companies are increasingly deploying Generative AI: Digital Labor or HR-related activities, Customer Care or customer support, and App Modernization or code creation. Within those categories the company discussed content creation, summarization, classification, and coding applications. Given the long history of older mainframe-related software that run on IBM mainframes, IBM noted particular interest in companies who want to move from old COBOL code to modern programming languages with the help of GenAI-powered tools.

IBM also discussed several technological initiatives within its research group aimed at enhancing watsonx. These include efforts in Performance and Scale, Model Customization, Governance, and Application Enablement. For performance, IBM is exploring new methods to enhance the efficiency of large foundation models through techniques such as model size reduction via quantization and improved resource sharing with GPU fractioning.

Emphasizing its commitment to open-source, IBM elaborated on its collaboration with the AI application framework Pytorch, originally made open source by Meta in 2017. By leveraging both the open-source community and its internal resources, IBM is making strides in optimizing model performance and facilitating the deployment of Pytorch-built models on diverse computing architectures. Adding a hardware abstraction layer like Pytorch opens up the potential for a much wider range of programmers to build or customize GenAI models. The reason is that models can be created with these tools using languages such as JavaScript that are much more widely known than the chip-specific tools and their lower-level language requirements.

At the same time, these hardware abstraction layers often end up adding fairly significant performance penalties because of their high-level nature (an issue that Nvidia’s Cuda software tools don’t suffer from). With the new Pytorch 2.0, however, IBM said they and others are making concerted efforts to reduce that impact by better organizing where various types of optimization layers need to be and, as a result, are getting closer to “on the metal” performance.

On the Model Customization front, IBM acknowledged the trend of companies primarily customizing or fine-tuning existing models rather than building new ones. Techniques like LoRA (Low Rank Adaptation) and multi-task prompt tuning are being refined for commercialization in watsonx. IBM also emphasized the importance of providing educational guidance to developers for choosing appropriate models and datasets. While this may sound simplistic, it’s an absolutely essential requirement as even basic knowledge about how GenAI models are built and function is much more limited than people realize (or are willing to admit!).

To read more about that development and some of its potential industry implications, check out my recent column “The Rapidly Evolving State of Generative AI“.

In Governance, IBM is focusing on the tracking and reporting of model creation and evolution details, an area of critical importance, especially in regulated industries. The company is working on implementing safeguards against biases, social stigmas, obscene content, and personally identifiable information in datasets, as well as on risk assessment and prevention. IBM’s indemnification offer for customers using their foundation models against IP-related lawsuits is a testament to their leadership in addressing concerns about the trust and reliability of GenAI technology.

In the area of Application Enablement, IBM talked a great deal about the work it’s doing around Retrieval Augmented Generation (RAG). RAG is a relatively new technique that supercharges the inferencing process, makes it significantly easier and more cost-efficient for companies to leverage their own data, and eases the process of fine-tune existing foundation models so that organizations don’t have to worry about creating models of their own. IBM says it has already seen a number of its customers start to experiment with and/or adopt RAG techniques so it’s working on refining its capabilities there to make the creation of more useful GenAI applications much easier for its customers.

The company showed off a detailed technology roadmap that extends all the way out to 2030

In the world of quantum computing, IBM continues to lead, showcasing a detailed technology roadmap extending to 2030. While some tech companies are willing to share their plans a few years out, it’s virtually unheard of for a company to provide this much information so far in advance. In part, IBM needs to do it because quantum computing is such a dramatic and forward-looking technology that many potential customers feel the need to know how they can plan for it. To put it simply, they want to understand what’s coming in order to bet on the roadmap.

Full details of the IBM quantum computing developments will be unveiled at a December event. Suffice it to say, the company continues to be at the cutting-edge of this technology and is growing increasingly confident about its ability to eventually make it into mainstream enterprise computing.

Given the long and sad history of early technology companies who no longer exist, it’s understandable why some harbor doubts about the 112-year-old IBM’s ability to continue innovating. As shown, however, not only is that spirit of invention still alive, it looks to be gaining some serious steam.

Bob O’Donnell is the founder and chief analyst of TECHnalysis Research, LLC a technology consulting firm that provides strategic consulting and market research services to the technology industry and professional financial community. You can follow him on Twitter @bobodtech

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