IBM, like other IT giants, is spending heavily on AI. For example, IBM Watsonx, a new platform for building AI models and using pre-trained models for generating code, language, and more, was introduced at its annual Think conference.
It’s a smack in the face to IBM’s back-office managers, who were recently informed the corporation would stop hiring for positions it expects AI may replace in the future.
IBM claims the launch was inspired by the problems many organizations face in integrating AI in the workplace. In an IBM poll, 30% of corporate leaders mention trust and transparency difficulties as impediments to AI adoption, while 42% cite privacy concerns concerning generative AI.
In a roundtable with reporters, IBM’s chief commercial officer Rob Thomas remarked, “AI may not replace managers, but the managers that use AI will replace the managers that don’t.” It transforms work.
IBM claims that Watsonx gives clients the toolset, infrastructure, and consulting resources to construct their AI models or fine-tune and adapt existing AI models to their data. Watsonx.ai, IBM’s “enterprise studio for AI builders,” allows customers to evaluate, deploy, and monitor models, simplifying their processes.
But don’t Google Amazon, and Microsoft provide this or something similar? Yes, briefly. Amazon has SageMaker Studio, while Google has Vertex AI. Azure AI Platform. IBM claims that Watsonx is the first AI toolset platform with various pre-trained, enterprise-developed models and “cost-effective infrastructure.”
“You still need a very large organization and team to bring [AI] innovation in a way that enterprises can consume,” IBM SVP Dario Gil told reporters during the discussion. IBM’s horizontal capabilities depend on that.
We’ll see. However, IBM Watsonx.ai offers seven pre-trained models, some of which are open source. It also includes thousands of Hugging Face models, datasets, and libraries. In addition, IBM will donate open-source AI development software to Hugging Face and make some in-house models available on its AI development platform.
At Think, the business is promoting fm.model.code, which creates code, FM.model.NLP, a collection of huge language models, and FM. Model. Geospatial, a NASA climate and remote sensing model. (Strange naming? You bet.
Fm. model generates code like GitHub Copilot. Code develops coding workflows from natural language commands. Fm.model.NLP includes organic chemistry text-generating models. And fm. model.GIS predicts changes in natural disaster patterns, biodiversity, land use, and geophysical processes.
These may seem familiar. IBM asserts that a training dataset with “multiple types of business data, including code, time-series data, tabular data and geospatial data and IT events data” distinguishes the models. Take its word.
“We allow an enterprise to use their own code to adapt [these] models to how they want to run their playbooks and their code,” IBM CEO Arvind Krishna said in the discussion. “It’s for use cases where people want their own private instance, whether on a public cloud or their own premises.”
IBM said its software products and services use the models like fm. Model.Watson Code Assistant, IBM’s Copilot, generates code using plain English instructions across tools like Red Hat’s Ansible. IBM claims that integrating fm. Model.NLP with AIOps Insights, Watson Assistant, and Watson Orchestrate, its AIOps toolkit, smart assistant, and workflow automation tech, will improve IT performance visibility, IT incident resolution, and customer service. FM.model.geospatial support IBM’s EIS Builder Edition, which helps enterprises handle environmental hazards.
IBM introduced Watsonx. data, a “fit-for-purpose” data storage for controlled data and AI workloads, together with Watsonx.ai. IBM says Watsonx. Data provides a single entry point, query engines, governance, automation, and interfaces with an organization’s databases and tools. According to IBM, governance protects client privacy, detects model bias and drift, and helps enterprises satisfy ethics requirements.
IBM announced a new GPU service in the IBM cloud for compute-intensive applications, including training and providing AI models with Watsonx.
The “AI-informed” IBM Cloud Carbon Calculator lets users measure, track, manage, and report cloud-generated carbon emissions. IBM claims it was created with Intel and based on IBM research to visualize greenhouse gas emissions across workloads and cloud services.
IBM is doubling in on AI with both products and the new Watsonx package. Vela, an AI-optimized cloud supercomputer, was constructed by the business. It also collaborates with Moderna and SAP Hana to scale generative AI.
The business forecasts AI will add $16 trillion to the global economy by 2030 and automate 30% of back-office jobs within five years.
“When I think of classic back-office processes, not just customer care—whether it’s doing procurement, whether it’s elements of supply chain [management], whether it’s elements of IT operations, or elements of cybersecurity—we see AI easily taking anywhere from 30% to 50% of that volume of tasks, and being able to do them with much better proficiency than even people can do them,” Gil said.
Wall Street has historically rewarded optimistic (or pessimistic, if you’re humanistic) predictions. In Q4 2022, IBM’s software segment’s automation solutions gained 9%. Data and AI solutions, which emphasize analytics, customer service, and supply chain management, increased revenues by 8%.
Seeking Alpha suggests lowering expectations. IBM lost Watson Health after technical issues ruined high-profile customer collaborations. IBM confronts competition in AI from major heavyweights like Microsoft and Google and well-funded startups like Cohere and Anthropic. IBM’s new applications, tools, and services: impactful? IBM hopes. We’ll see.
