IBM to Train AI to Follow Code of Ethics

Tech giant made one more step in artificial intelligence research
19 July 2018   1453

Developers from IBM have created an artificial intelligence that dynamically forms norms of ethics, expressed in a set of rules when creating content recommendation algorithms. This is reported by Venture Beat.

The IBM Neural Network is trained in two phases. Initially, the AI ​​receives a set of constrained examples that the system of recommendations should adhere to. By processing the received data, the neural network forms the required norms of ethics. The more a set of examples, the more accurate the limitations will be.

At the second stage the AI ​​works directly with the user, studying his preferences and reaction to various content. Based on their received information, the system forms a list of recommended videos for viewing. In this case, the neural network adheres to the previously obtained ethical restrictions. In the system settings, you can set the priority relationship between the user's interests and ethical standards.

The problem with this approach is the need to compile examples for the AI. For a child, an older generation has to set up the limits. However, in the overwhelming majority of cases, the system remains with the user tete-a-tete. The user can also determine some ethical norms, however, in the same way, he can change them. On this issue, a group of IBM researchers continues to work together with MIT Media Lab. While the developers offer to choose as a mentor, responsible for ethical issues, a friend or a family member.

In 2017, hundreds of researchers and IT experts, including Elon Mask and Stephen Hawking, compiled a list of 23 basic principles that should be followed when developing AI. In the same year, the Institute of Electrical and Electronics Engineers defined ethical standards for artificial intelligence. Now AI learns to form ethics for people. And on June 17, 2018, IBM announced the creation of a neural network capable of arguably arguing with live opponents.

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   217

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.