What 5 new innovations will open source yield in the 2020s?
A look at what the past decade says about our future
When I look back to where technology was in 2010, it’s astounding to think about how much has changed — and how so many of those advancements were fueled by open source.
Ten years ago, AI was not a part of our everyday lives, most developers hadn’t even heard of containers or microservices, blockchain was little more than an idea, and serverless was a far-off dream. Now these technologies, built on open source projects and the communities that surround them, are shaping how developers do their jobs and how people interact with technology on a daily basis.
In this blog post, I talk about some of the trends that have shaped the past decade as we look forward to what 2020 — and the next decade — has in store for us.
Smaller, faster containers and microservices
Before 2010, the concepts of containers and microservices were merely ideas.
In 2013, Docker launched, planting the early seeds of the container industry. At the same time, microservices — and the technologies to make them possible — were borne in open source through the Netflix OSS project. Docker went on to become one of the most influential technologies of the 2010s, giving rise to a myriad of new open source projects, including Kubernetes, which launched in 2015.
Fast forward 10 years. Kubernetes is the largest open source project on the planet. At the recent KubeCon in San Diego, there were around 12,000 attendees — amazing! Companies are using the platform to transform their monolithic application architectures, embracing containerized microservices that are supported by service mesh capabilities of projects such as Istio. This rapid innovation is only happening because open source facilitates people building on each other’s ideas and successes.
In the next decade, we anticipate that open source projects such as Istio, Kubernetes, and OKD will focus on making containers and microservices smaller and faster to serve the needs of cloud-native development and to reduce the container’s attack surface. Keep an eye on unikernels (executable images that contain system libraries, a language runtime, and necessary applications), which may also gain traction thanks to the open source communities around them.
Instantaneous serverless workloads
AWS Lambda was released in 2014 and put all the PaaS services on notice. Lambda’s release was followed by IBM OpenWhisk® (which became Apache OpenWhisk), among others, in 2016. Both open source, distributed serverless platforms execute functions in response to events at any scale.
As Kubernetes gained prominence in the latter part of the decade, the desire to extend Kubernetes with capabilities that would enable serverless gave birth to Knative in 2018. Knative has since split into multiple open source projects including Tekton, each with their own set of innovations.
In the next few years, we expect to see these serverless projects continue to push the boundaries of how to make serverless platforms faster until we can spin up serverless workloads instantaneously. Given the energy and innovation around serverless open source projects, it will likely be before the next decade. Once we have that, where does that let us go from an app dev perspective? Will serverless be everywhere?
Trustworthy artificial intelligence
Though the use of AI has been around for a while, when IBM Watson® appeared on Jeopardy! in 2011, Americans were able to see the power of an AI-powered machine. Now, AI is part of our everyday lives — we interact with Siri and Alexa daily, talk with customer service chatbots regularly, use facial recognition to unlock our gadgets, and are nearing the advent of fully autonomous self-driving cars.
All of this technology is powered by AI and machine learning, and many of the AI advancements came about because of open source projects such as TensorFlow and PyTorch, which launched in 2015 and 2016, respectively.
In the next decade, it’s imperative that we turn our attention to not only making AI smarter and more accessible but to building trust into AI systems, ensuring that AI systems make decisions in a fair manner, aren’t vulnerable to tampering, and can be explained. Open source is the key for building this trust into AI. Projects like the Adversarial Robustness 360 Toolkit, AI Fairness 360 Open Source Toolkit, and AI Explainability 360 Open Source Toolkit were created to ensure that trust is built into these systems from the beginning.
We expect to see these projects and others from the Linux Foundation AI — such as the ONNX project — drive the significant innovation related to trusted AI in the future. The Linux Foundation AI provides a vendor-neutral interchange format for deep learning and machine learning.
- Learn more: Get up and running with artificial intelligence
New uses for blockchain’s tracking capabilities
In 2008, Satoshi Nakamoto published his now famous paper on bitcoin, introducing the concept of a blockchain network. The bitcoin network itself, while active, was rather obscure and had a single purpose: a decentralized cryptocurrency platform.
That innovation made people start to wonder about different ways that the blockchain concepts and technology might be applied in non-cryptocurrency use cases — in asset management, supply chains, healthcare, and identity, among others. In 2015, IBM contributed its Open Blockchain project to the newly established Hyperledger organization, which was to develop open source blockchain technology for the enterprise. That contribution launched what has arguably become one of the two or three most popular blockchain frameworks: Hyperledger Fabric.
While blockchain’s initial uses were confined to cryptocurrency, open source engagement around Hyperledger and Ethereum has expanded the possibilities for how this technology is used.
And the innovation is really just starting with blockchain. Innovation around privacy, including zero-knowledge proofs and quantum-resistant cryptographic algorithms will launch even more innovation — and nearly all of this is being done in open source.
Quantum processors available for developers
We’re sure you’ve heard about the promise of quantum computing – and the buzz about what they could be capable of in the coming years. And while an app with a “quantum advantage” hasn’t been developed yet, the ability for developers to start using quantum processors is growing — and will continue to evolve in the next decade.
Today, IBM’s open source Qiskit™ software gets developers coding in Python on real quantum hardware. Released in 2016, Qiskit is an open source quantum computing framework that developers can use to leverage IBM Q Experience systems for research, education, business, and even games.
The possibilities for how quantum computing will solve problems and interact with today’s technology seem endless, and getting quantum ready to code on today’s early systems is a great place to start since quantum computing could impact a wide range of domains, such as chemistry, finance, artificial intelligence, and others.
- Download Qiskit
- Watch this video series to learn how to get started
- Learn more in the Qiskit textbook
The trends of the past decade — the rise of containers, microservices, and serverless, the ubiquity of AI in our lives, the new uses for blockchain and quantum — were all driven by open source and the power of open source communities.
These advancements excite me because they highlight just how fast developers working together can change entire industries. Developers have the power to change the world, and open source is the best mechanism with which to bring about that change.
So, how are you going to get involved? Find a project where you can add value and figure out how to contribute. There’s no telling how your contributions will shape the next decade.