
Find out how to apply AI
technologies to improve:
- sales;
- production;
- marketing;
- customer service.
Date is to be confirmed
Moscow
can help to increase operation efficiency of the company, predict customer behavior and demand for certain goods, decrease production costs, simplify the process of everyday tasks monitoring, choose the most beneficial development strategies, etc.
Artificial intelligence allows you to collect, store, classify and process massive amounts of structured and unstructured information.
Processing of big data
Face detection technology, machine learning and data collection and analysis tools can be used to predict customer behavior. The system collects data about actions and preferences of users by analyzing the demand for certain products. In addition, knowing the preferences, lifestyle and financial capabilities of the customers, the system offers the most suitable products and services within a certain period of time.
Predicting the demand and customers’ behavior
Chatbots and robotic consultants can carry out customer support 24 hours a day, 7 days a week. Service ceases to be intrusive, and becomes prudent and natural.
Improving the service quality
Advanced methods of large data analysis provide an opportunity to make the most effective and correct decision in an emergency situation. It improves safety greatly and eliminates most of the risks
Development of solutions in critical situations
AI tools are used to optimize the inner workings of the company, increasing the efficiency of each employee and the company in general. A thorough analysis of the processes allows you to select the most advantageous development strategy.
Company management
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Demo area
Leading international companies and developers will present the latest AI products and business solutions.
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Conference
Conference speakers – foreign and Russian experts – will tell you about ways of using AI technologies and big data for optimization of business processes and revenue increase.
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Investment panel
Conference will feature the meeting of investors and founders of startup projects.
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Startup Battle
Event will involve a contest between startup projects dedicated to AI developers.
MoreOleg Danilchenko
Head of competence center for applied data analysis
PwC, Russia
Eugene Kolesnikov
Head of the Big data and machine learning focus area
Jet Infosystems
All speakers
All speakersStanislav Kladov
Architect at IBM Client Center in Moscow
IBM
Kirill Petrov
Founder and Managing Director
Just AI
Mikhail Belyaev
Research Scientist, Head of Algorithm Development Group of CoBrain-Analytics project
Skoltech
Program
Expo zone
Conference
10:00-10:15
Oleg Danilchenko
Head of competence center for applied data analysis
PwC, Russia
Moderator's welcome speech
10:15-10:40
Eugene Kolesnikov
Head of the Big data and machine learning focus area
Jet Infosystems
"How Machine Learning predicts the company's needs for IT support. A case study"
How do e-commerce, banks and insurance companies get rid of crashes and fails caused by the high IT support load? Imagine a big e-commerce case: stable model predicts a change in the IT resources load depending on the growth/decline of business indicators and various factors (stocks, sales, etc.) and also increases business results.
10:40-11:00
Ilya Ukrainets
Co-founder of Addi
Head of user acquisition of Vezet Group
"When chatbot becomes a business assistant"
We’ll talk about how, chatbots have turned into smart assistants and learned to solve real business problems in recent years: from accepting applications to automating internal processes. We will discuss the integration of chatbots into the business environment: types of tasks for bots, ways to find business processes that require automation, as well as which bots will be in demand in the next 3 years.
11:00-11:25
Alexander Khanin
CEO
VisionLabs
"Face recognition and analysis for retail and financial sphere"
The presentation will provide real case studies in the sphere of biometric identification introduction as well as possible difficulties, needed resources and what to expect from the technology adoption in the future. We will also talk about the automation of identity identification in existing service channels as well as the inclusion of remote client identification in remote channels.
11:25-11:50
Stanislav Kladov
Architect at IBM Client Center in Moscow
IBM
"How IBM’s augmented intelligence helps businesses"
While many people are striving for the full replacement of human being in certain areas, let’s step back and examine the augmented intelligence concept. The speaker will reveal how IBM helps to create assistants to various specialists as well as what pitfalls one should expect, and what it can lead to.
11:50-12:10
Kirill Petrov
Founder and Managing Director
Just AI
"Chatbots in natural language: a tool for developers and businesses"
The following issues will be covered:
12:10-12:35
Dmitry Korobchenko
Deep Learning R&D Engineer
NVIDIA
"Deep learning: review of the technology and examples of application"
Deep neural networks are a core of today’s artificial intelligence. Nowadays, they allow us to successfully solve problems in such areas as computer vision, natural language processing, and mining of other data types. Moreover, the technology of deep neural networks learning proved feasible for creative tasks resolution: a synthesis of unique content from some concept or idea. The presentation will cover the mechanism of today’s neural networks working and learning, the nature of deep learning and its application spheres.
12:35-13:00
Mikhail Belyaev
Research Scientist, Head of Algorithm Development Group of CoBrain-Analytics project
Skoltech
"Deep learning for neurovisualization data analysis"
The medical image analysis solves a whole range of machine learning tasks, including segmentation, classification, search and synthesis of medical images. Several key features of medical image data extract significantly limit the use of conventional deep learning methods developed to analyze two-dimensional images. First off, data high dimension is accompanied by a quite low volume of learning sample, while the application of data augmentation methods is pretty limited. Besides, a big size of 3D images imposes certain restrictions and leads to the development of specific convolution network architectures. Finally, data tool variability has a significant impact on the analysis outcome. During the presentation, we will discuss the use of deep learning methods to solve such tasks and provide several examples of neurovisualization data analysis projects.
13:00-14:00
Panel discussion "Application of AI technologies in banking"
moderator
Roman Davydov
Investment banker, founder
SKOLKOVO Private Banking & Wealth Management Club
speaker
Andrey Oberemok
Managing Director for data and development of Big Data and AI technologies at Corporate Business
PJSC Sberbank
speaker
Sergey Lukashkin
Digital Transformation Project Management Director
VTB Bank
14:00-14:50
Coffee break
14:50-15:00
Andrey Oberemok
Managing Director for data and development of Big Data and AI technologies at Corporate Business
PJSC Sberbank
Moderator's welcome speech
15:00-15:20
Rostislav Plankin
CEO
Marketingbot
"Mobile Messenger Marketing"
Mobile Messenger Marketing as a necessary part of companies’ digital strategies in 2018–2019. Messengers are going to become a primary entry point to the Internet, development prospects in Russia and threat for Yandex and Google search systems.
15:20-15:40
George Fomichev
Founder Endurance
"Business and chatbots. Successful business cases"
Why large companies have already noticed chatbots.
How do they help save money, increase sales and customer satisfaction index.
Successful business cases, metrics, development plans.
15:40-16:05
Dmitry Babaev
Researcher at Sberbank’s Artificial Intelligence Lab
Sberbank
"Problems of current-generation deep neural networks"
Deep learning of neural networks has been a breakthrough in the evolution of artificial intelligence systems. Many tasks lying beyond the reach of classic machine learning techniques have been solved at the level close to human abilities or even at a higher level. However, there are significant problems in teaching neural networks that seriously restrict their application. Examples of such problems include a large amount of marked data required to teach a network, difficulties of transferring the experience of a trained network on other tasks. The speaker will elaborate on these problems and possible ways of their solution.
16:05-16:25
Alexey Redozubov
Member of Presidium of the Support foundation of scientific research on mechanisms of brain function, therapy, neuromodelling named after Academician N.P. Bekhtereva.
"A new approach to solving the problem of strong generalization, based on modern ideas about brain function"
Modern neural networks are based on knowledge of the brain function of the early 20th century. There are fundamental limitations, including the training and stability of these algorithms. There have been many significant discoveries in understanding the brain over the past 20 years, which allowed us form a new concept of the brain function. This concept formed the basis of the development of a new type of neural networks that will work with the meaning of information, create strong generalizations and strong AI.
16:25-16:50
Dmitry Soshnikov
Senior Technical Evangelist
Microsoft
"Solving problems of computer vision based on Microsoft technologies: from simple to complex"
Computer vision is one of the most important components of AI, it is used to solve a wide range of practical problems: from collecting smart data from surveillance cameras video stream to automatic tag generation in photographs. Traditionally, the introduction of such technologies requires not only high qualification of developers, but also huge amounts of training data - hundreds of thousands of examples and more.
We will look at how computer vision tasks can be implemented using various solutions from Microsoft: starting with solving typical tasks based on cognitive services, using the custom vision service to create recognition models and also on the basis of neural networks using Cognitive Toolkit. We will take a look at the technologies themselves and give some examples of using image recognition in practice.
16:50-17:15
Aleksander Belousov
СЕО, founder
SOLUT
"Industrial IoT: movement recognition technology for efficiency improvement in production"
Since Henry Ford’s invention of assembly line, the efficiency of physical work has not changed crucially. SOLUT develops an industrial IoT project aimed at analyzing the efficiency of staff labor time in the manufacturing segment, construction sector, housing and public utilities, and other branches of economy.
The project goal is to increase the working efficiency of blue-collar job representatives by designing portable devices to determine down time, critical abnormalities of technological process, and violation of safety techniques.
Devices monitor workers’ activity during their shifts using acceleration indicators and gyroscopes as well as recognize types of activities typical for every certain specialty using patterns based on machine learning methods.
17:15-17:35
Alexander Berenov
CEO
Inspector Cloud
"Features of computer vision application in business processes of retail companies"
MOSCOW
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