AI is one of the key technologies set to transform both our personal and professional lives in unprecedented ways. According to a recent study from Stanford University, in the last 20 years there has been a 14-times increase in the number of AI startups, while in the UK, funding for AI developers from venture capital increased more than 200 per cent in 2018.

At the same time, the AI space has seen a growing number of technology giants – including Microsoft, Salesforce and Uber – open-sourcing their AI research.  Investing, or “giving back,” to the open source community has helped developers worldwide create and improve AI & Machine Learning (ML) algorithms faster. Open source software is now crucial to driving fast, reliable, and secure development in the AI space. But why did both industry giants and start-ups alike decide to embrace openness, and how will it affect technology and science moving forwards?

Why enterprises are turning to open source

Open source software started to play a significant role in IT development across industries following the launch of Netscape Navigator, the first open source program, in 1998. The strategy Netscape chose was to emphasise the business potential of sharing the software’s source code. As with science, if all researchers kept their methods secret, progress and innovation would take place much more slowly. With developers racing to deliver “the next big thing”, secure and easy-to-deploy software frameworks are essential to supporting this.

The high costs of AI and ML model development are usually driven by the computing power required, as well as having enough data in place to build and train an advanced model. Additionally, the skills gap is usually a big challenge for enterprises – according to recent reports, despite the availability of millions of AI-focused roles globally, there are only 300,000 professionals able to fill them. Open source software allows IT teams to access frameworks, data sets, workflows, and software models in the public domain and as such reduces  training costs. At the same time, the open source community is always monitoring the code for flaws and vulnerabilities – adding an extra layer of security and also making such concerns a common responsibility.

As an example, Kubernetes – the open source platform which automates the deployment and management of containerised applications, including complicated workloads like AI and machine learning – can be a facilitator, as it takes a large amount of ongoing effort required to keep cloud applications up to date.

Openness and trust are essential for AI

Open source disrupts the development of cutting edge technologies by fully democratising it, allowing any developer or IT team to facilitate cheaper, faster, more flexible and secure deployment. Developing in the open helps accelerate the adoption of numerous frameworks and software solutions through support from a large community of contributors. And, as mentioned, large technology companies are demonstrating a commitment to contributing to and supporting the open source community, making AI and ML frameworks accessible to everyone.

Google has been very active in opening their research to the public with Tensorflow, their popular machine learning framework, now used by companies including Airbnb, Uber, SAP, eBay and others. Following in Google’s step, Amazon has started opening up its internal machine learning courses to developers. The company sees this as an opportunity to hire more efficient people and accelerate machine learning growth. The latest addition to the AI/ML open-source club is Facebook, with its Deep Focus (the AI rendering systems data and code) becoming publicly available. With such established tech companies betting so heavily on the ‘openness‘ of AI, it is clear that AI development will continue to transform over the next few years.

An open source community working with AI and ML can accomplish its goals more easily and quickly by eradicating barriers, such as high licensing fees and limited talent, from getting in the way of delivering true AI workloads. In particular, knowledge sharing between companies allows developers to have access to trusted, secure and easy-to-deploy solutions, moving entire industries forward by making AI more accessible.

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