IBM Shows Full-Stack Generative AI Products Might Be Considerable

IBM may not be the first name that comes to mind when you think of AI giants. IBM has so far not been caught in the consumer AI frenzy driven by OpenAI, Microsoft, Google and others. Over the years, however, IBM has made significant contributions to the field, such as with its cognitive computing platform, IBM Watson. For a detailed analysis of IBM’s prowess in this area, see our previous Moor Insights & Strategy blog.

At its recent annual Think conference, IBM was able to showcase hybrid cloud and AI innovations, especially enterprise-grade generative AI (GAI). This article details the main highlights of the event and my observations.

What is Generative AI?

Get your feet on the ground in the midst of all the hype. Generative AI (GAI) is a type of AI that employs deep learning models to generate content based on user input. GAI uses machine learning and deep learning algorithms to create different types of content such as text, images, videos, audio, music and code.

The now-famous ChatGPT, developed by OpenAI, is an example of a GAI that can provide detailed written responses to user queries and conduct ongoing conversations. Other companies such as Google and Facebook are also developing generative AI tools that produce text, images, and code that look real.

Generative AI works by training on large datasets. Researchers feed large amounts of data such as words, images, music, and other content into deep learning systems. For example, ChatGPT’s original training data, the OpenAI Codex, consists of over 700 GB of data collected from various sources such as books, websites, and technical manuals. By rewarding success and penalizing errors, the system learns to identify and understand complex relationships through supervised neural networks. Human oversight allows the system to generate new content over time.

ChatGPT is a tool frequently used by consumers. Enterprises should apply generative AI with a few key considerations in mind. Accuracy is very important because inaccurate answers from AI systems can be costly. Scalability is also a consideration, as enterprise-grade AI models must be deployed to hundreds or thousands of endpoints while maintaining accuracy. Strong governance is also essential to ensure transparency and accountability and to minimize bias in AI models.

In his keynote, IBM Chairman and CEO Arvind Krishna highlighted hybrid cloud and AI as two transformative technologies with the potential to deliver significant business value.

Hybrid cloud has become the leading choice for enterprises

Krishna referred to an IBM Business Value Institute (IBV) study that found that over 75% of IBM customers plan to leverage a hybrid model. This finding is consistent with my longtime prediction that hybrid cloud architectures will be the primary choice for most organizations.

The key to a successful hybrid cloud is a common platform across all clouds, on-premises and edge environments. This gives you a single set of skills that you can build once and manage from a single pane of glass. Central to IBM’s strategy is Red Hat OpenShift. IBM’s Red Hat OpenShift addresses this challenge by creating a logical container layer that enables the portability of data and workloads between clouds.

As hybrid cloud approaches gain momentum and organizations realize the value of leveraging both public and private clouds, Red Hat OpenShift delivers consistency across multiple clouds, driving IBM’s growth in the cloud market important in doing so.

AI played a leading role in the event

Hybrid cloud is critical to IBM’s strategy, and AI played a central role at the event. Krishna explained how AI can enhance various areas such as human resources, quote pricing, pricing, supply chains, and inventory management. AI can also have a big impact on his IT operations, improving code development and increasing his productivity by 40-80%. You can automate customer care and handle many queries for 24/7 availability and scalability. In some cases, up to 90% of customer care volume is handled by his AI agents.

AI is also important in cybersecurity, as it helps organizations triage and respond to the high volume of attacks they face. Other types of digital labor, such as promotions, employee transfers, onboarding, and procurement tasks, can also be automated with AI, allowing companies to efficiently scale their operations.

Krishna also highlighted IBM’s quantum computer and its potential to tackle complex problems in areas such as battery technology, carbon sequestration and traffic routing. He predicted that quantum computing would become mainstream within his three to five years.

Coming Out Party of Generative AI and Underlying Models

IBM’s hybrid cloud story is well established, but its focus on AI remains elusive. IBM Think 2023 was the launch party for IBM’s generative AI and underlying models. Learn more about the importance of the underlying model here.

Developed over the last three years, the watsonx platform combines AI capabilities such as machine learning, deep learning, and underlying models. Krishna touted its ability to enable businesses to harness the power of generative AI. The platform is built on Red Hat OpenShift, allowing for flexible deployment options.

watsonx has three main components.

1. watsonx.ai is a new studio that offers a comprehensive set of tools for creating new foundational models, generative AI and machine learning. It improves productivity compared to traditional approaches and allows users to work efficiently with AI models.
2. The watsonx.data lake house combines the flexibility of a data lake with the performance of a data warehouse. This component is a centralized repository that stores various data types such as structured, unstructured, semi-structured, and multimodal data.
3. The watsonx.governance toolkit enables responsible and transparent AI workflows. It helps users track data used to train models, understand model lineage, identify biases, and monitor model drift. By consolidating governance processes into a single platform, businesses can ensure a trusted and accountable AI adoption.

Companies need to adopt an AI-first approach

The era of generative AI and underlying models will force companies to approach AI differently. In fact, seeing AI as an add-on or a nice-to-have makes being an AI-first company critical to continued market success. All companies are now faced with a decision to go AI-first or let their competitors take the lead.

As an example of a different approach to AI, AI interfaces can be integrated into applications such as SAP, giving users easy access to information through AI-assisted natural language queries. I have already seen foundational models that leverage company data to generate accurate forecasts and improve over time by combining company-specific data with broad industry information.

Whether you are in a company with 500 employees or 50,000, you can take advantage of digital labor (work performed by robotic process automation (RPA) systems) and tasks that benefit from automation and increased efficiency. I think we need to focus on leveraging AI. .

summary

The overall sentiment emerging from Think 2023 is that IBM is renewed and rejuvenated. I have followed IBM for decades and the excitement surrounding the company’s recent developments is unprecedented.

As Arvind Krishna promised when he joined IBM, we look back at Think 2023 as a pivotal moment for IBM, with a focus on leadership in two major growth industries: hybrid cloud and AI.

The hybrid cloud part made more sense thanks to the Red Hat acquisition, but the AI ​​part was still questionable. Think 2023 sheds light on this issue by spotlighting generative AI and the foundational models of the enterprise.

IBM wants to be the go-to provider for enterprise-generated AI. The company provides the tools, data layers, governance and fabric needed to build the workflows needed, but speed to market remains a key issue.

In conclusion, this event was an important milestone for IBM. I will be closely monitoring progress, especially with regard to the company’s speed and ability to realize the productivity gains brought about by hybrid cloud and AI.