My first job was to manage the website of a local furniture company. There were about 175 individual static HTML pages, and the first thing they needed me to do was update the company’s phone number on all 175 HTML pages.
Immediately I thought, “There has to be a better way.”
Soon after, I figured out that I could insert a little PHP snippet into the header of the website. I could make my edit once and then render the page without having to make the same change 175 times.
That experience has been a core lesson that has driven and pushed me – and now the GraphGrid team – for all of these years. We’re always looking for that better way to get the job done, for ourselves and for our customers.
We believe much of that “better way” for enterprise organizations is through knowledge graphs and artificial intelligence. At GraphGrid, we want to empower developers in the age of AI to more readily create the future in which we all live, work, and play. Here’s how we plan to get there in 2022 and beyond.
(This post is taken from my closing keynote at the GraphGrid 2021 conference. Watch the full video of the presentation here.)
Embracing the Power of Developers & Code
First and foremost, I believe the future of knowledge graphs and AI requires that we – as a society and as an industry – embrace the power of developers and their code.
Despite all the predictions to the contrary, it turns out that the low-code or no-code composable enterprise is not the future. I think that’s a good thing.
For organizations to succeed with AI systematically, developers must remain the hero of the story. At GraphGrid, we believe in letting humans do what humans are really good at and letting machines do what machines are really good at. For developers, this means being able to use code in all the right places. As organizations navigate towards and through the rise of AI, the developer has an even more important role to play in this epic tale – not less.
Every day, we build GraphGrid with this developer-first vision in mind. We want to give developers complete control over their code in all the critical places that ensure AI success.
To move at the pace of AI – to truly understand, adjust, and utilize it well – we need developers, and we need their code. This is the future.
Creating a Collaborative Environment for Humans & Machines
Second, the future of AI requires tooling that creates a collaborative environment for both humans and machines to work alongside one another.
AI is widely misunderstood. It’s often seen today as this magical concept you can just kind of sprinkle on to any organization’s system, but that’s not the case.
In order to understand AI today, we need to look back at its original definition: Artificial intelligence is whenever a computer can do as well or better than what a human can do. This broad definition has caused a lot of confusion in the marketplace, especially with non-tech stakeholders. After all, there are a lot of things that computers can do better than humans these days. That’s precisely my point.
As we build the future of tech, AI needs to reliably solve useful problems that are difficult, tedious, and time-consuming for humans to do. AIs need to be easily understood, bounded, measured, and refined by any and all stakeholders in the organization – not just the AI engineers. In addition, an AI should be aware of the context of its current organizational operating environment.
All of the above aspirations are more achievable than you might think. In some cases, they’ve already been realized.
At GraphGrid, we believe that AI augmentation is a core necessity to help you succeed in the future. This is why we’ve systematically introduced AI augmentation as a native component of GraphGrid.
Creating a Knowledge Graph-Driven AI System
Third, the future of AI will be intimately shaped by the future of knowledge graphs and vice versa. A knowledge graph is an organization’s flagship data asset that both humans and machines should use to inform their view of the world, especially as AIs augment more and more human processes. (It’s even better when your knowledge graph is built atop a native graph database.)
For example, human intelligence analysts often have to spend a great deal of time reading and skimming news articles every day to determine which trends and events are relevant to their organization, but in reality, humans can’t do this quickly at scale. When news data is automatically ingested into a knowledge graph, an AI can execute this process efficiently at scale better than any teams of humans could.
Your whole system – including your AI – needs to be able to understand and react to what’s in your knowledge graph. Also, as data changes in your knowledge graph, your system needs to proactively push those deltas to both the humans and AIs that work with that data. We’ve built this system-wide awareness into every aspect of GraphGrid.
All of the above reasons are why GraphGrid Connected Data Platform gives your developers the tools to create and manage a knowledge graph as the key collaboration point between humans and machines. We’re also building a continuous ML training pipeline that’s repeatable, measurable, and improvable, and it’s all driven from the knowledge graph.
Enabling Useful AI for Every Organization, Starting Today
Fourth and finally, the future of useful AI starts today at every organization – even yours.
According to Gartner, only 5% of organizations feel comfortable or ready to use artificial intelligence. I think that’s a shame. To test and see if you’re ready, I’ll ask you three questions:
- Do you have developers?
- Do you have data?
- Do you have GraphGrid?
If you answered yes to all three, then you’re ready to use AI.
We want GraphGrid to lower the barrier to entry for AI. We want to give organizations the confidence to quickly deploy AI and augment their processes with AI. Using our tools, they can do this quickly and easily.
In the forthcoming 2.0 release of GraphGrid, we want to take this AI mission to the next level via a new module with capabilities for Apache AirFlow, machine learning, and continuous AI training. It’s going to revolutionize the way you build your AI models and the way you curate your knowledge graph.
With every new release of GraphGrid Connected Data Platform, we’re going to further empower developers to wield the power of AI in a systematic way using a common knowledge graph that unifies both humans and machines. AI is not academic for us. It’s something that we need to make effective for knowledge workers that have real-world problems to solve in real mission and business use cases.
Every enterprise organization already has developers and already has data, so I challenge you to pick up and use GraphGrid, and you’ll be ready to take advantage of useful AI.
Conclusion
When I started creating software in the mid-90s, the internet was a symbol of freedom and possibility. If you could write code, you could reach the world. As an industry, it’s completely accessible to those who want to build with it. The same goes for AI.
Powered by your development team, I believe that knowledge graph-driven AI can be the brains of your organization that keeps both humans and machines operating with the same view of the world.
If you’re ready to create the future through AI, then I encourage you to try out GraphGrid. You’ll be amazed at how it can turn your existing data into AI-driven insights. I’m excited to see what you’ll build.
