The future speaks
Meet Graham and Marcelo, two innovators who are passionate about AI software and its potential for empowering progress
Graham MackIntosh
Graham MackIntosh is a pioneer in the field of advanced analytics, working as an AI consultant for NASA and the SETI Institute. Hear how NASA is using AI software to understand solar events, detect failures and expand our understanding of the universe. And discover lessons learned that you can apply to future work involving machine learning and big data. (1)
Marcelo Labre
Marcelo Labre is an executive director and AI evangelist at Morgan Stanley. He discusses the need to break through points of resistance to machine learning by demonstrating the advantages of applying AI solutions to real problems within the financial industry, healthcare, consumer analytics and more. Learn how you can get buy-in on AI and effectively implement AI models to your enterprise business as well. (1)
Make the shift to enterprise AI infrastructure
As AI capabilities rapidly evolve it’s vital to scale from experimentation to implementation. The artificial intelligence companies successfully achieving AI at scale are disproportionately financial outperformers. How do they accomplish this? By confronting data issues and bridging the AI skills gap. Find out how your business can do the same and become an AI innovator.
Your journey to AI starts with IBM
Organize data
A sharply defined data architecture, streamlined with an end-to-end storage solution, will help you establish proper data governance.
Test Your AI Applications
Use a sandbox computing environment to test run various AI applications drawing from the data you’ve gathered.
Implement Your AI Strategy
Move from a pilot program to a full AI strategy that you can scale up as needed.
What's in your AI future?
The impact of big data on your AI infrastructure
Applications like machine learning and deep neural networks require an AI infrastructure that can handle higher workloads and storage needs. Learn more about how you should be preparing for the big data of AI.
Three things to do in Q3 to get ready for AI
Your organization needs to start planning today for how you’re going to leverage AI in the future. Discover three tips for what you can start doing right now to prepare your business for AI.
Five steps to building a data strategy for AI
Once your AI infrastructure is prepared for big data and advanced analytics, you need a solid data strategy to help ensure the success of those analytics projects. Learn the five steps to take as you prepare that strategy.
Data housekeeping checklist
AI applications rely on one key element—data. If your data is a mess, you don't have the foundation on which to build AI success. Learn how to get your data organized and ready for AI.
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