At the end of 2016, Google DeepMind opened source their machine learning platform, DeepMind Lab. Google’s decision to open source its software to other developers is part of its efforts to further develop machine learning capabilities, despite warnings from experts such as Prof Hawking about ai technology. They’re not the only tech company doing this. Facebook opened source its deep learning software last year, and Elon Musk’s nonprofit OpenAI launched Universe, an open software platform that can be used to train AI systems. So why have Google, OpenAI, and others chosen to open source their platforms, and how will this affect the adoption of machine learning?

Why open source machine learning?

The examples mentioned above give us good hope, but if you look closely, you’ll notice that machine learning has always been open source, and that open research and development is the root cause of the current interest in machine learning.

Google has demonstrated its growing awareness of AI research by offering its own learning platform to the public. This has many advantages, such as finding new talent and capable start-ups for Alphabet. At the same time, access to DeepMind Lab will help developers address one of their key problems with machine learning — the lack of a training environment. OpenAI has launched a new virtual school for AI that uses games and websites to train AI systems.

Initiatives such as providing a machine learning platform to the public are sorely needed.

Advantages of 5 open source machine learning projects

  • Reproduce scientific results and fair comparison algorithmsIn machine learning, numerical simulation is often used to provide experimental verification and method comparison. The comparison between these methods is based on a rigorous theoretical analysis. Open source tools and technologies provide an opportunity to conduct thorough research using publicly available source code, independent of providers.
  • Find and fix bugs quickly: When you use open source software to perform machine learning projects, it is easy to detect and resolve bugs in the software.
  • Accelerate the development of scientific research with low cost and reuse methodsAs we all know, scientific progress is always based on existing methods and discoveries, and the field of machine learning is no exception. The availability of open source technologies in machine learning can well channel a large amount of existing resources into research and projects.
  • Long term availability and supportWhether individual researchers, developers, or data scientists, open source may serve as a vehicle to ensure that everyone can use his or her research or discoveries as they change jobs. Therefore, by releasing code under an open source license, you increase your chances of getting long-term support.
  • Industries are adopting machine learning faster: Open source software has a remarkable paradigm that has supported the creation of multi-billion dollar machine learning companies and industries. The main reason for researchers and developers to adopt machine learning is the availability of high-quality open source implementations for free.

Accelerate the adoption curve of open source machine learning

Advances in open source machine learning will make the adoption curve of AI steeper, spurring developers and startups to try to make AI smarter. The availability of software platforms is changing the way companies develop AI, prompting them to follow in the footsteps of Google, Facebook, and OpenAI for more thorough research.

The shift to open machine learning platforms is an important stage in ensuring that AI can be used by everyone, not just by a few technology giants.

Personally, there are three reasons why tech giants release open source machine learning projects:

  • Hire engineers who have already engaged with the open source community and built an understanding of machine learning through open source projects
  • Control a machine learning platform to make them work better for their broader SDK or cloud platform strategy
  • Grow the whole market because their market share has reached saturation point

When a startup releases an open source project, it gets noticed, some of which is translated into paying clients and hires. By their own definition, startups are trying to establish themselves in a particular market, not expand an existing one. Open source is frictionless, providing a service to another user and enabling the organization to solve real problems costs nothing, thus making the code more impactful.

Open source breaks down the restrictions on companies building proprietary technologies. One knock-on effect may be a shift in focus to where value lies, with the commercialisation of the entire AI technology shifting the focus from core machine learning techniques to building the best models, which require vast amounts of data and domain experts to create and train models. For this, large companies with a network presence have a natural advantage.

The best framework for open source machine learning

There are a number of open source machine learning frameworks that enable machine learning engineers to:

  • Build, implement, and maintain machine learning systems
  • Generate a new project
  • Create new impactful machine learning systems

Some important frameworks include:

  • Apache Singa is a general purpose, distributed, deep learning platform for training large deep learning models on large data sets. It is designed with an instinctive programming model based on hierarchical abstractions. Support a variety of popular deep learning models, including convolutional neural network (CNN), constrained Boltzmann machine (RBM), and cyclic neural network (RNN) and other energy models. There are many built-in layers for the user.
  • ShogunIs one of the oldest and most respected machine learning libraries. Shogun was created in 1999 and is written in C++, but is not limited to C++. Thank youSWIG library, Shogun is available in the following programming languages and environments:
    • Java
    • Python
    • C#
    • Ruby
    • R
    • Lua
    • Octave
    • Matlab

Shogun is designed for unified large-scale learning, such as classification, regression, dimensionality reduction, clustering, and so on, for a wide range of feature types and learning environments. It contains several unique state-of-the-art algorithms, such as rich and efficient SVM implementations, multi-kernel learning, kernel hypothesis testing, and Krylov methods.

  • TensorFlow is an open source software library that uses Data Flow Graphs for numerical calculations. TensorFlow uses data flow diagrams for numerical calculations, and the mathematical calculations are illustrated by directed diagrams of Nodes and lines. Nodes represent mathematical operations in the graph, and can also represent the beginning of a feed in/the end of a push out, or the end of a read/write in a persistent variable. The lines here represent arrays of dimensions that connect to each other, and they can transport dimensions that can be adjusted dynamically, tensor
  • Scikit-learn takes full advantage of Python’s breadth by building on several existing Python packages (NumPy, SciPy, and Matplotlib) for mathematical and scientific work. The resulting library can be used in interactive “workbench” applications or embedded and reused in other software. The suite is distributed under the BSD license, so it is completely open source and reusable. Scikit-learn includes a number of tools for standard machine learning tasks such as clustering, classification, regression, and so on. Because SciKit-Learn was developed by a large group of developers and machine learning experts, there is hope that new technologies will be introduced soon.
  • MLlib (Spark) is the Machine learning library of Apache Spark. The goal is to make practical machine learning more scalable and easy to use. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline apis. Spark MLlib is considered a distributed machine learning framework on top of Spark Core, mainly due to its distributed memory-based Spark architecture, which is nearly nine times the disk-based implementation used by Apache Mahout.
  • Amazon Machine Learning is a service that enables developers of any skill level to easily use Machine Learning techniques. Amazon Machine Learning provides visual tools and wizards to guide you through the process of creating Machine Learning (ML) models without Learning complex ML algorithms and techniques. It connects to data stored in Amazon S3, Redshift or RDS and can run binary classification, multi-class classification or regression on said data to create a model.
  • Apache Mahout is a free and open source project of the Apache Software Foundation. The goal is to develop free distributed or scalable machine learning algorithms for multiple domains such as collaborative filtering, clustering, and classification. Mahout provides Java libraries and Java collections for a variety of mathematical operations. Apache Mahout is implemented on top of Apache Hadoop using the MapReduce paradigm. If big data is stored in the Hadoop Distributed File System (HDFS), Mahout provides data science tools that automatically find meaningful patterns in these sets of big data to quickly and easily turn it into “big information.”

Last word

Machine learning can really solve real science and technology problems with the help of open source tools. If machine learning is to solve real science and technology problems, communities need to build on each other’s open source software tools. We believe there is an urgent need for open source machine learning software that will serve multiple roles, including:

  • Better ways to reproduce the results
  • A mechanism to provide academic recognition for quality software implementation
  • Speed up the research process by standing on the shoulders of others (not necessarily technology giants)