Chapter 6 Artificial Intelligence

**Artificial Intelligence** in

*Nature Machine Intelligence*,](https://www.nature.com/natmachintell/),)

*Science Robototics*,](https://www.science.org/journal/scirobotics),) the

*IEEE Transactions on Pattern Analysis and Machine Intelligence*,](https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34),)

*IEEE Transactions on Neural Networks and Learning Systems*,](https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5962385),) the

*International Journal of Intelligent Systems*](https://onlinelibrary.wiley.com/journal/1098111x))

*Information Sciences*,](https://www.journals.elsevier.com/information-sciences),) the

*Physics of Life Review*,](https://www.sciencedirect.com/journal/physics-of-life-reviews),)

*Artificial Intelligence Review*,](https://www.springer.com/journal/10462),)

*Knowledge-Based Systems*,](https://www.journals.elsevier.com/knowledge-based-systems),)

*Neural Networks*,](https://www.journals.elsevier.com/neural-networks),)

*Neural Computing and Applications*,](https://www.springer.com/journal/521),) the

*International Journal of Computer Vision*,](https://www.springer.com/journal/11263),) and the journal

*Pattern Recognition*](https://www.sciencedirect.com/journal/pattern-recognition))

DeepMind](https://github.com/deepmind)) is an excellent gold-standard for the capability of deep-learning in the biological sciences, AlphaFold](https://github.com/deepmind/alphafold)) and the other amazing discoveries](https://github.com/deepmind/deepmind-research)) at DeepMind. The AlphaFold Colab](https://colab.research.google.com/github/deepmind/alphafold/blob/main/notebooks/AlphaFold.ipynb)) is also freely available as a simplified implementation.

RoseTTAfold](https://github.com/RosettaCommons/RoseTTAFold)) by Baek et al., 2021](https://www.science.org/doi/10.1126/science.abj8754)) and OmegaFold](https://twitter.com/peng_illinois/status/1538536909814874113))

I think AWS SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-training.html)) is best for its freedom of scalability; requires knowledge of AWS Data Science,](https://github.com/data-science-on-aws/data-science-on-aws),) and a review of the Sagemaker Workshop](https://github.com/awslabs/amazon-sagemaker-workshop)) and Examples](https://github.com/aws/amazon-sagemaker-examples))

The Microsoft DoWhy](https://github.com/py-why/dowhy)) library for causal inference](https://www.microsoft.com/en-us/research/blog/dowhy-a-library-for-causal-inference/)) recently popped on my radar

Further reading can be found at

Keras,](https://github.com/keras-team/keras),)

TensorFlow,](https://github.com/tensorflow/tensorflow),)

Scikit-Learn,](https://github.com/scikit-learn/scikit-learn),)

*‘An Intro to Statistical Learning’*](https://www.statlearning.com/))

A. Geron’s *Hands-On Machine-Learning*, 3rd ed.](https://github.com/ageron/handson-ml3))

F. Chollet’s *Deep-Learning with Python*](https://github.com/fchollet/deep-learning-with-python-notebooks))

Nvidia’s ‘Deep-Learning Examples’ in Python](https://github.com/NVIDIA/DeepLearningExamples))

*Machine Learning with R*](https://machinelearningmastery.com/machine-learning-with-r/))

*‘Deep learning with R’* 2nd ed.](https://livebook.manning.com/book/deep-learning-with-r-second-edition/welcome/v-1/1))