DeepMind to Test AI's IQ

Most models answered questions with an accuracy of 75%
12 July 2018   1395

DeepMind, a subsidiary of Google, talked about the experiment with testing artificial intelligence models on generalization skills and abstract thinking. Specialists have developed a generator that formulates questions based on the notion of progression, color properties, shapes or sizes and their interrelationships. Similar tasks are encountered in IQ tests for people.

Most models answered questions with an accuracy of 75%. At the same time, researchers found a strict correlation between the effectiveness of tasks and the ability to identify the underlying abstractions. They managed to increase efficiency by training algorithms to explain their answers, to show what interrelations and properties should be considered in one or another issue.

However, in some models it is difficult to "transfer" the studied relationships to new properties, for example, if it trained to identify logical sequences relative to the color of objects, and in the task it is required to establish the dependence on their form.

The team found out that if the neural network correctly extrapolated its knowledge of the relationship to a new combination of values, then the accuracy of the tasks was increased to 87%. In the case of incorrect extrapolation, it fell to 32%.

TensorFlow 2.0 to be Released

New major release of the machine learning platform brought a lot of updates and changes, some stuff even got cut
01 October 2019   199

A significant release of the TensorFlow 2.0 machine learning platform is presented, which provides ready-made implementations of various deep machine learning algorithms, a simple programming interface for building models in Python, and a low-level interface for C ++ that allows you to control the construction and execution of computational graphs. The system code is written in C ++ and Python and is distributed under the Apache license.

The platform was originally developed by the Google Brain team and is used in Google services for speech recognition, facial recognition in photographs, determining the similarity of images, filtering spam in Gmail, selecting news in Google News and organizing the translation taking into account the meaning. Distributed machine learning systems can be created on standard equipment, thanks to the built-in support in TensorFlow for spreading computing to multiple CPUs or GPUs.

TensorFlow provides a library of off-the-shelf numerical computation algorithms implemented through data flow graphs. The nodes in such graphs implement mathematical operations or entry / exit points, while the edges of the graph represent multidimensional data arrays (tensors) that flow between the nodes. The nodes can be assigned to computing devices and run asynchronously, simultaneously processing all the suitable tensors at the same time, which allows you to organize the simultaneous operation of nodes in the neural network by analogy with the simultaneous activation of neurons in the brain.

Get more info about the update at official website.