IBM CEO’s 5 Boldest Remarks On New Products, AI And Quantum Computing
IBM CEO Arvind Krishna’s boldest statements during his keynote at IBM Think 2024 Tuesday revolved around IBM’s new product launches, AI and his vision for quantum computing.
“Technology has always been used for productivity and automation. Think of it as helping your enterprise become lean, but I think there’s a shift that is happening,” said Krishna during his keynote at IBM Think 2024 in Boston.
“The role of technology is no longer about just being lean. The role has really shifted to how is technology powering the business to gain revenue, to gain scale, to get even more share in the marketplace—and that is a big shift,” IBM’s CEO said. “The goal for every one of you should be, ‘How do you embrace technology in the context of your business to help power that shift, not just to make yourself the leanest possible?’”
[Related: IBM Think: 10 Biggest AI, Red Hat, Nvidia And GenAI Launches]
IBM Think 2024
IBM Think 2024 is being held at the Boston Convention and Exhibition Center from May 20 to May 22.
Thousands of IBM partners, customers, employees and developers flocked to Boston to see IBM unveil a slew of new products and the $62 billion company’s vision.
Some key new products unveiled at the tech conference included AI-powered IBM Concert, a generative AI tool that provides insight across a customer’s portfolio of applications to identify, predict and fix issues. Another new product, InstructLab, was developed with Red Hat to advance true open-source innovation around large language models (LLMs).
“I really would like to thank all of you for your trust in IBM,” Krishna said on stage in front of a packed room at IBM Think. “Thank you also to all the partners who convey all of these capabilities to our clients.”
Here are the five boldest remarks Krishna made during his keynote around artificial intelligence, new innovation, quantum computing and expanded alliances with Amazon Web Services and Adobe.
Every business is using multiple public clouds, people have SaaS applications, people have their own in-house applications, etc. With generative AI, estimates say between 600 million and 1 billion new apps will be written by the end of this decade.
Just think about all of that sprawl, all of the connectivity and all of the infrastructure under all those. It means that it’s time to bring AI to IT operations and really help you get a sense of what’s going on.
So you know what is happening when something goes wrong. How can you adjust your response time? Can you take care of problems on your own as opposed to putting humans in the loop?
That is our vision behind IBM Concert.
The starting step: looking at all of the security implications on the environment. Where do you need to patch? Where are you getting something missing? What do you have to do to fix very quickly? The whole vision of what I described is what you’re going to see us rolling out over the next many months.
There is a topic that is dear to my heart that I just need to mention for a minute. We talked a lot about artificial intelligence and hybrid cloud. But another topic that is just coming around the corner is quantum computing.
I think quantum computing provides great potential for what we can all get done. First, a moment of pride.
We have built over 70 actual quantum computers. These systems have been deployed globally over the last half-dozen years or so. We have 250-plus organizations who participate very actively hands-on in our quantum network. Some of you are in the room.
People have run over 3 trillion individual experiments on our quantum systems over the last many years. I think that speaks to the reality of these technologies.
When you run 3 trillion of something, it’s no longer a science experiment. It is something that is becoming a real computer system.
As we look upon this, what’s the promise? There are problems that classical computers can never solve. I’m making a very strong statement there when I say ‘never.’
We look at problems around fertilizers, food supply, new materials, financial risk. These are issues that quantum computers will be able to solve in the next three to five years. That is something which really excites us.
Three Keys To Achieving ‘The Productivity That GenAI Promises’
To get the productivity that GenAI promises, we need three things.
One, we need to trust the underlying artificial intelligence. That is the reason why we [teamed] together with Meta to help form the Open AI Alliance, where more than 100 organizations have gotten together across industry, academia and government in order to help [create] standards and tests so that we can cross AI.
We also need AI to be flexible. How do we combine models? Models that come—whether from parties like us, from other commercial entities, from open source—you should all have the flexibility to deploy these models where you see fit. On a public cloud of your choice, on-premises, sovereignty, or in a large public environment. [IBM has] that flexibility.
Lastly, safety is critical. We fundamentally believe that safety comes when many eyes can look at something, also called open source. So how do you really get many, many more eyes on this, which brings you both safety and innovation? That’s one of the reasons why we’ve been pushing to bring a lot of capabilities around AI inferencing and deployment into Red Hat Linux.
IBM And Red Hat’s New InstructLab
Red Hat Linux has a lot of the capabilities [for] how you form a platform to run these AI models and run inferencing tasks across different hardware but are optimized to the hardware.
So that you can extract 10-times more performance than you might not be able to get otherwise.
We include the Granite models, but we also include any of the other open-source models.
Then you get the question of, ‘How do you begin to add skills?’ I’m using the word ‘skills’ purposefully. How do you infuse some of your own proprietary data and knowledge to add a skill? But a model that is built may not have something that is for that skill’s particular purpose. [For example], you could imagine cases around certain languages, around code and so on.
So you need to infuse it when you need to do it quickly at a much lower cost than other approaches. And that is why we’re bringing InstructLab to the market. It’s a first-of-its-kind model technique to bring open-source community contributions directly into LLMs.
With InstructLab, developers can build models specific to their business domains or industries with their own data so that they can see the direct value of AI rather than just the model providers seeing the value.
IBM Boosting Partnerships Is ‘Vital’
A vital piece of news is around our ecosystem. This is a place where we put a lot of investment, and we are really focused on both innovation and integration.
If you look at what we’re doing with OpenShift and watsonx with Adobe Experience platform—that’s a great integration.
When we look at AWS, we are integrating the watsonx family with AWS SageMaker, especially for governance. [That is] another great example of partnerships to help drive business I believe for both AWS and IBM.
Watsonx runs on Microsoft Azure, the whole platform. It is in the Azure Marketplace. That’s a great example of what’s there, also many other IBM products in the Azure Marketplace.
In the case of Meta, Llama 3 is now available through watsonx with indemnity back to our clients. That’s a great example again of us coming together.
Mistral AL, our partnership with large Mistral AI models can be purchased through watsonx. SAP, ServiceNow, Salesforce—integrations of our brand models with all three of these platforms, we’re announcing those.