Artificial intelligence is revolutionizing all industries, and academia is no exception. Keep in mind that the field of artificial intelligence evolves rapidly, and new tools emerge every day. The popularity of these tools can vary depending on the specific research area within AI. Here are 13 tools that you might consider:
Elicit AI:
One AI tool for research is Elicit AI. Elicit AI developed by Ought, is a versatile tool with various applications. It is used for evidence synthesis and text extraction in research, particularly in fields with empirical research such as biomedicine (Kung, 2023). The tool has also been utilized in leadership training programs to teach about organizational structures.
Connected Papers:
Connected Papers is a unique, visual tool to help researchers and applied scientists find and explore papers relevant to their field of work. It is also useful for building graphs.
Symmetry AI:
Using GPT-4 and the idea of semantic search, this project aims to create a search engine for scientific research. Since the present iteration of the project is built upon the ArXiv data, it is somewhat constrained by the range of topics it covers in addition to certain other variables.
TensorFlow:
Developed by Google, TensorFlow is an open-source machine learning library widely used in various AI applications. It supports both deep learning and traditional machine learning.
PyTorch:
Developed by Facebook, PyTorch is another popular open-source machine-learning library. It is known for its dynamic computational graph, making it more intuitive for researchers and developers.
Scikit-learn:
Scikit-learn is a simple and efficient tool for data analysis and machine learning. It provides simple and efficient tools for data mining and data analysis and is built on NumPy, SciPy, and Matplotlib.
Keras
Keras is an open-source deep-learning API written in Python that allows for easy and fast prototyping. It can run on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit.
Caffe:
Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC). It is known for its speed and efficiency, especially in image classification tasks.
MXNet:
MXNet is an open-source deep learning framework designed for both efficiency and flexibility. It supports multiple languages, including Python, Scala, and Julia.
Theano:
Theano is a numerical computation library for Python that allows researchers to efficiently define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays.
Microsoft Cognitive Toolkit (CNTK):
Developed by Microsoft, CNTK is an open-source deep learning framework designed for efficient training of deep neural networks.
IBM Watson Studio:
Watson Studio is an integrated environment designed to make it easy to develop, train, manage models, and deploy AI-powered applications.
H2O.ai:
H2O.ai provides an open-source machine-learning platform that is used for building machine-learning models. It is designed to be easy to use for both data scientists and non-technical users.
Go ahead and check the respective websites for each tool to ensure you have the latest information on their features and capabilities. Additionally, it’s always a good idea to explore new tools and frameworks that emerge daily.
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