Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals. In computer science AI research is defined as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.

How graphs enhance AI

A Neo4j view on how large graphs and knowledge graphs enhance machine learning and AI in general.

Learning to add with LSTM

Using long-short term layers to learn to add.


It is a common misconception that AI is absolutely objective, since AI is objective only in the sense of learning what human teaches.

Text classification with Tensorflow 2.0

The classic IMDB classification based on Tensoflow 2.0

Fashion MNIST using Temsorflow 2.0

The MNIST fashion set consists of images of clothing, like sneakers and shirts. It's somewhat more complex than the the classic MNIST dataset.

Serving TensorFlow models

Restify TF networks and other ways to serve intelligence.

The Keras Functional API

The Keras API makes creating deep learning models fast and easy.

GPT2 using Mathematica and MXNet

A large-scale unsupervised language model which generates coherent paragraphs of text, achieves state-of-the-art performance on many language modeling benchmarks, and performs rudimentary reading comprehension, machine translation, question answering, and summarization—all without task-specific training.

Advanced topics in deep reinforcement learning

Part of a large set of lectures on deep reinforcement learning by DeepMind.

Apache Jena disaster

Jena and Fuseki are not reliable.