AI open source, or free license software tools that support all the initiatives associated with the most innovative projects in the field of Artificial Intelligence. Open software is driving most of the projects but in some areas it is still preferred to use traditional commercial solutions.
AI open source the last frontier of a virtuous development that allows to bring new economies of scale to the world of software also in the field of Artificial Intelligence. Open source, in fact, allows the community of programmers around the world to coordinate and work on the same project regardless of where they reside. In this way, the programming activity can count on a number of resources with infinite capacity that every day contribute to improving the quality of the codes and the results.
The Evolution of the Open Source World
Over the past two decades, the use of open sources has been a pillar of the IT ecosystem. From the popularity of Linux in the corporate server environment to the Firefox browser, open source has found pride of place in the cyber pyramid. It is therefore not surprising that open source AI is enjoying significant popularity and use also in the field of machine learning and deep learning applications.
This is in contrast to other business technologies, such as operating systems and databases which traditionally had their roots in closed and licensed software.
AI Open Source
Considering the value and billions of dollars invested in the AI industry, the fact that the most popular and widely used AI tools are available in an open source key is a win for all concerned. The high quality of open source AI allows a wide range of users, including researchers and academics, start-ups and entrepreneurs, government organizations and large enterprises, to explore and experience AI without having to make significant upfront investments in terms of licenses and proprietary systems.
Large open source communities are giving organizations greater access to talent and skills, while allowing people to improve their knowledge without having to pay upfront investments in tools and technologies.
Examples of Open Source Machine Learning Tools and Toolkits
One of the main drivers of machine learning is the popularity of Python, an open source programming language widely adopted by the AI community. Through libraries like scikit-learn, the hugely popular Jupyter data science notebook, and Python-based open source projects, developers who are programming code for Artificial Intelligence have found all the tools they need to work. Members of the open source community often make pre-built toolkits and open source projects available via repositories such as GitHub to help all members of the community accelerate development.
The R programming language, with all its supporting ecosystem, is another hugely popular (and free) open source environment that supports a large number of machine learning and AI researchers.
Players such as Facebook, Google, IBM, Microsoft and many others have further enhanced the open source AI ecosystem by providing important contributions that have further enriched the open source tools. Google led the development of the TensorFlow machine learning platform as an open source project, while Facebook’s AI research group along with other teams helped develop PyTorch and Caffe.
Microsoft has released its Cognitive Toolkit as an open source package for enterprise-grade distributed deep learning. Likewise, Amazon and others helped lead the Apache MXNet project, which comes with the Gluon interface and provides quick and easy building blocks for machine learning development.
There has also been open source development at higher levels of the stack. Additionally, there is a wide range of open source natural language toolkits, including Natural Language Toolkit for Python, SpaCy, and PyTorch-NLP, as well as Java-based OpenNLP and many more.
Although not specific to AI, the Robot operating system is widespread, enabling the use of machine learning and artificial intelligence capabilities in the robotics community, favoring autonomous deployments without the use of proprietary software.
Likewise, there has been a major open source push for predictive analytics and data science. Open source tools include H2O, Apache Mahout, Apache Spark, and Hadoop for machine learning enabled big data analytics via Apache Spark MLlib. In addition to these popular systems, there are a couple of machine learning tools that are slowly gaining traction, including TPOT and AutoSklearn.
In the same way that Red Hat pioneered commercial enterprise support and add-ons for the open source Linux operating system, so too do artificial intelligence and big data companies like Cloudera, Databricks, and H2O.ai provide support. commercial and corporate and add-ons for open source AI software tools.
Pros and Cons of Commercial Software Alternatives
In the face of the fact that open source AI tools are extremely popular, there are many companies, researchers and government organizations that have invested and continue to invest in commercial solutions such as, for example, SAS and Matlab (used for analytics applications, data science and machine learning).
With the payment of one or more licenses, these tools are widely adopted by large communities of developers who have believed in and invested heavily, giving rise to a considerable ecosystem of supporting tools and applications.
It is true that the limit of commercial tools is represented by the restrictions in terms of costs and licenses, but it is also true that large organizations are rather cautious about using open source tools in critical environments or when the tools may need to be used in more contexts restrictive. In these cases, developers are conditioned by the lack of certain information regarding the legal conditions that authorize them to use open source tools. Conversely, commercial offers often have more explicit license terms. The vendors, on the other hand, promote their offer by emphasizing the quality of security in terms of updates and technical support.
However, there are more companies that offer similar benefits for open source tools as well. While there is no doubt that interest in AI and machine learning are driving the adoption of both open source and commercial offerings, providers have realized they can no longer rely on an established customer base to secure a future. The Artificial Intelligence market continues to grow. As open source AI tools pave the way for organizations to quickly and easily start intelligent automation projects by moving their systems to full-scale production, there is no doubt that open source AI software will continue to thrive.