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Image: Allen Institute for AI

The Allen Institute for Artificial Intelligence (AI2) has released OLMo, an open large language model that aims to help better understand what happens in AI model processes, as well as contribute to developments in the field of language model science.

Allen’s collaboration with the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University, as well as partners including AMD, CSC-IT Center for Science (Finland), Paul G. Allen School of Computer Science & Engineering at the University of Washington, and Databricks are making the OLMo project a reality.

OLMo is being released along with pre-training data and training code that, as the institute’s announcement says, “is not available today in any open model of this scale.”

Among the development tools in the framework are pre-training data built on AI2’s Dolma set, which includes three trillion tokens, along with the code that creates the training data.

“Many language models today are published with limited transparency. Without access to training data, researchers cannot scientifically understand how a model works. This is akin to discovering drugs without clinical trials or studying the solar system without a telescope,” says Hannah Hajishirzi, OLMo project leader, senior director of NLP Research at AI2, and professor at UW’s Allen School.

He adds that thanks to OLMo, researchers “will finally be able to study the science of LLM, which is critical to building the next generation of safe and reliable artificial intelligence.”

The Allen Institute for Artificial Intelligence noted that OLMo provides researchers and developers with greater accuracy by offering insights into the training data behind the model, removing the need to rely on assumptions about how the model works. And because models and datasets are open, researchers can learn and build on previous models and work.

In the coming months, AI2 will continue to iterate on OLMo and will bring different model sizes, modalities, datasets, and capabilities into the OLMo family.

Today we’ve chosen to bring you the top 5 suitable programming languages for AI developers according to Cointelegraph.

Top 5 Programming Languages for AI Developers

Python
Python is the preferred choice for Artificial Intelligence (AI) development due to its simplicity, understandability and flexibility. It has a rich collection of libraries and frameworks for machine learning, natural language processing, and data analysis, including TensorFlow, Keras, PyTorch, Scikit-learn, and NLTK.

With these tools, you can create and train neural networks, work with huge data sets, interpret natural language, and much more. Python is a very well-liked language for research and training in artificial intelligence. Its ease of use and community support lead to the availability of many online tutorials and courses for people who want to get started in artificial intelligence development.

Java
Java is a general-purpose programming language. It is often used in the development of large-scale enterprise artificial intelligence applications. Because of Java’s reputation for security, reliability, and scalability, it is often used to create complex AI systems that need to manage massive amounts of data.

Deeplearning4j, Weka and Java-ML are some of the AI development libraries and frameworks available in Java. Using these tools, you can create and train neural networks, process data, and work with machine learning algorithms.

Java is the preferred alternative for creating AI applications that run on multiple devices or in a distributed context. This is due to the platform’s freedom and support for distributed computing. Because of Java’s adoption in enterprise development, a significant Java developer community and rich materials are available for those who wish to begin AI development in Java.

Lisp
Lisp is a programming language created in the late 1950s, making it one of the oldest programming languages still in use today. Lisp is known for its unique syntax and powerful support for functional programming.

Because it was used to create some of the earliest artificial intelligence systems, Lisp has traditionally had a significant impact on the field of artificial intelligence. Lisp is a good choice for AI research and development because it supports symbolic computation and can process code as data.

Although Lisp is not as commonly used as some of the other languages discussed previously in AI development, it still maintains a devoted following among AI experts. Many AI researchers and developers appreciate Lisp’s expressiveness and ability to handle complexity. Common Lisp Artificial Intelligence (CLAI) and Portable Standard Lisp (PSL) are two well-known AI frameworks and libraries that have been implemented in Lisp, for example.

C++
In artificial intelligence development, C++ is a high-performance programming language that is often used, especially when creating algorithms and models that need to be fast and efficient. Because of its well-known low-level hardware control, C++ is often used to create AI systems that need precise control over memory and CPU resources.

TensorFlow, Caffe and MXNet are just a few of the libraries and frameworks for AI development available in C++. With the help of these tools, you may create and train neural networks, process data, and work with machine learning algorithms.

R
R is widely used in the field of artificial intelligence development, especially for statistical modeling and data analysis. A popular choice for developing and exploring machine learning models due to its strong support for statistical analysis and visualization.

Caret, mlr, and h2o are just some of the libraries and frameworks available in R for AI development. Building and training neural networks, using machine learning methods, and processing data are all made possible by these technologies.

In addition, researchers who want to perform sophisticated data analysis or create forecasting models often use it for its user-friendly interface and strong statistical analysis capabilities.