A socially oriented non-financial development institution and a major organizer of international conventions, congress, exhibitions, business, social and sporting, public, and cultural events.

The Roscongress Foundation is a socially oriented non-financial development institution and a major organizer of international conventions; exhibitions; and business, public, sporting, and cultural events. It was established in pursuance of a decision by the President of the Russian Federation.

The Foundation was established in 2007 with the aim of facilitating the development of Russia’s economic potential, promoting its national interests, and strengthening the country’s image. One of the roles of the Foundation is to comprehensively evaluate, analyse, and cover issues on the Russian and global economic agendas. It also offers administrative services, provides promotional support for business projects and attracting investment, and helps foster social entrepreneurship and charitable initiatives.

Each year, the Foundation’s events draw participants from 208 countries and territories, with more than 15,000 media representatives working on-site at Roscongress’ various venues. The Foundation benefits from analytical and professional expertise provided by 5000 people working in Russia and abroad. In addition, it works in close cooperation with 160 economic partners; industrialists’ and entrepreneurs’ unions; and financial, trade, and business associations from 75 countries worldwide.

The Roscongress Foundation has Telegram channels in Russian (t.me/Roscongress), English (t.me/RoscongressDirect), and Spanish (t.me/RoscongressEsp). Official website and Information and Analytical System of the Roscongress Foundation: roscongress.org.

RC personal account
Восстановление пароля
Введите адрес электронной почты или телефон, указанные при регистрации. Вам будет отправлена инструкция по восстановлению пароля.
Некорректный формат электронной почты или телефона
22 July 2019
Vitaliy Milke

Vitaliy Milke on Pre-trained Neural Networks in Medicine and Genetics

The first algorithm for training neural networks was formulated by the Canadian neuropsychologist Donald O. Hebb, who studied neuron interaction and was interested in the principle governing their combination into ensembles. Hebb’s ideas were initially criticized, though some years later a group of American scientists were able to model an artificial neural network that could distinguish squares from other geometric shapes.

In 2014, a revolutionary event took place: Deep Artificial Neural Networks were able to recognize objects in pictures with an accuracy of more than 95%, exceeding human capabilities. From this moment, neural networks began their triumphal procession across the globe.

Neural networks of this kind are quite large (composed of several dozen to several hundred layers), and large computational resources are required for training. Every picture, however, consists of approximately the same set of elementary objects: points, sloping lines, fine detail resolutions, and so forth. As a result, one need not begin training the neural network from scratch every time, working instead from the last point.

For example, you can take a database of X-ray images of lung cancer and retrain only the last few layers of an existing neural network. And more importantly, additional training does not require powerful computing resources.

Each day brings with it more examples of the successful application of artificial intelligence in medicine: neural networks can already recognize malignant tumours, including skin neoplasms, blood clots, and visual impairments, and they are able to diagnose the condition of internal organs on the basis of ultrasound, x-rays, MRIs, etc.

Weak artificial intelligence technology has been an extremely popular topic lately, though some aspects continue to fly under the radar. In the next issue, we will take look at these issues together in more detail. Stay tuned for more!

Material prepared by:
Vitaly Milke
Advisor to the President for Economy and Finance, JSC Business Alliance
PhD reseacher in Computer Science & Machine Learning

Articles on the topic
Analytics on the topic