Stanford experts concluded in the artificial intelligence report: "Increasingly powerful artificial intelligence applications are likely to have profound positive impacts on our society and economy that will appear from now on. By the time of 2030. "
The following open source artificial intelligence applications are at the forefront of artificial intelligence research.
1. Caffe
It was created by Jia Yangqing when he was a PhD student at the University of California, Berkeley. Caffe is a deep learning framework based on expression architecture and scalable code. What makes it famous is its speed, which makes it popular among researchers and enterprise users. According to its website, it can process more than 60 million images in a day with just a single NVIDIA K40 GPU. It is managed by the Berkeley Vision and Learning Center (BVLC) and has funding from companies such as NVIDIA and Amazon to support its development.
2. CNTK
It is the abbreviation of Computer Network Tookit (Computational Network Tookit). CNTK is an open source artificial intelligence tool from Microsoft. It has excellent performance whether it is on a single CPU, a single GPU, multiple GPUs or multiple machines with multiple GPUs. Microsoft mainly uses it for speech recognition research, but it has good applications in machine translation, image recognition, image subtitles, text processing, language understanding and language modeling.
3. Deeplearning4j
Deeplearning4j is an open source deep learning library for Java Virtual Machine (JVM). It runs in a distributed environment and is integrated with Hadoop and Apache Spark. This makes it possible to configure deep neural networks, and it is compatible with Java, Scala and other JVM languages.
4.DMTK
DMTK, the abbreviation of Distributed Machine Learning Toolkit, is Microsoft's open source artificial intelligence tool like CNTK. As an application designed for big data, its goal is to train artificial intelligence systems faster. It includes three main components: DMTK framework, LightLDA topic model algorithm and distributed (polysemy) word embedding algorithm. To demonstrate its speed, Microsoft claims that on an eight-cluster machine, it is able to "train a topic model with a vocabulary of 1 million topics and 10 million words (a total of 10 trillion parameters), on a single document Collect 100 billion symbols in ". This achievement is unmatched by other tools.
5.H20
Compared with scientific research, H2O focuses more on serving AI to enterprise users, so H2O has a large number of corporate customers, such as Capital One Financial Company, Cisco, and Nielsen Catalina, PayPal and Pan American are among its users. It claims that anyone can harness the power of machine learning and predictive analytics to solve business challenges. It can be used for predictive modeling, risk and fraud analytics, insurance analytics, ad tech, healthcare, and customer intelligence.
It has two open source versions: the standard version H2O and the Sparking Water version, which is integrated in Apache Spark. There is also paid enterprise user support.
6.Mahout
It is an Apache Foundation project. Mahout is an open source machine learning framework. According to its website, it has three main features: a programming environment for building scalable algorithms, pre-built algorithm tools like Spark and H2O, and a vector mathematics experimental environment called Samsara.
Companies using Mahout include Adobe, Accenture, Foursquare, Intel, LinkedIn, Twitter, Yahoo and many others. Its website lists third-party professional support.
7.MLlib
Due to its speed, Apache Spark has become one of the most popular big data processing tools. MLlib is Spark’s scalable machine learning library. It integrates with Hadoop and interoperates with NumPy and R. It includes many machine learning algorithms such as classification, regression, decision trees, recommendation, clustering, topic modeling, feature transformation, model evaluation, ML pipeline architecture, ML persistence, survival analysis, frequent itemsets and sequential pattern mining, distributed linear Algebra and statistics.
8. NuPIC
NuPIC, managed by Numenta, is an open source artificial intelligence project based on hierarchical temporary memory theory. Essentially, HTM attempts to create a computer system that mimics the human cerebral cortex. Their goal is to create a machine that "approaches or exceeds human cognitive abilities on many cognitive tasks."
In addition to open source licenses, Numenta also provides NuPic's commercial license agreement, and it also provides licenses for technology patents.
9. OpenNN
As an artificial intelligence with advanced understanding designed for developers and researchers, OpenNN is a C programming library that implements neural network algorithms. Its key features include deep architecture and fast performance. Rich documentation can be found on its website, including an introductory tutorial that explains the basics of neural networks
10. OpenCyc
OpenCyc, developed by Cycorp, provides knowledge of Cyc Library access and common sense reasoning engines. It has over 239,000 entries, approximately 2,093,000 triples and approximately 69,000 owl: a namespace similar to linking to an external semantic library. It has good applications in rich domain models, semantic data integration, text understanding, expert systems in special fields and game AI. The company also offers two other versions of Cyc: one that is free for research but not open source, and one that is available to businesses but requires a fee.
11. Oryx 2
Oryx 2, built on Apache Spark and Kafka, is an application development framework specifically targeted at large-scale machine learning. It uses a unique three-layer lambda architecture. Developers can use Orys 2 to create new applications, and it also has pre-built applications for common big data tasks such as collaborative filtering, classification, regression, and clustering. Big data tools provider Cloudera created the original Oryx 1 project and has been actively involved in its ongoing development.
12. PredictionIO
In February of this year, Salesforce acquired PredictionIO, and then in July, it donated the platform and trademark to the Apache Foundation, which listed it as for the incubation plan. So when Salesforce uses PredictionIO technology to improve its machine learning capabilities, the results will appear in the open source version simultaneously. It helps users create prediction engines with machine learning capabilities, which can be used to deploy web services capable of real-time dynamic queries.
13. SystemML
Originally developed by IBM, SystemML is now an Apache big data project. It provides a highly scalable platform for implementing advanced mathematical operations, and its algorithms are written in R or a Python-like syntax.
Businesses are already using it to track car repair customer service, plan airport transportation and connect social media data with banking customers. It can run on Spark or Hadoop.
14.TensorFlow
TensorFlow is an open source artificial intelligence tool from Google. It provides a library for numerical computation using data flow graphs. It can run on a variety of different systems with single or multiple CPUs and GPUs, and even on mobile devices. It offers deep flexibility, true portability, automatic differentiation, and support for Python and C. Its website has a very detailed list of tutorials to help developers and researchers immerse themselves in using or extending its functionality.
15.Torch
Torch describes itself as: "A scientific computing framework that prioritizes GPU use and has extensive support for machine learning algorithms." It is characterized by flexibility and speed. In addition, it can be easily used in machine learning, computer vision, signal processing, parallel processing, image, video, audio and network through software packages. It relies on a scripting language called LuaJIT, which is based on Lua.
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