Ecosystem for developing
and financing projects
and financing projects
in the artificial intelligence space and blockchain technologies
En
Venture Fund
Technology Company
Invests in technologies, teams and projects in
the AI space;
Supplements and takes advantage of the technology company's data
Supplements and takes advantage of the technology company's data
Develops AI technologies;
Creates and compiles data for AI technologies;
Operates the data management platform (DMP), DigiU's key innovation.
Creates and compiles data for AI technologies;
Operates the data management platform (DMP), DigiU's key innovation.
Specializing in project development and
financing.
Working towards creating a digital copy of the human persona and a habitat for its existence, integrating the person into the digital world.
Working towards creating a digital copy of the human persona and a habitat for its existence, integrating the person into the digital world.
DigiU
Венчурный
фонд
фонд
The technological base of the company
The first stage
of development is laying a foundation for an environment in which data
gathering, analysis, and learning can take place based on the data prepared for DigiU's
artificial intelligence.
BIG DATA
Data Managment
Platform
Platform
Big Data and Data
Science are the two fields of inquiry that underpin the processes of data collection
and the subsequent application of machine learning models to those datasets. The key tools used
in these processes are the Data Management Platform (DMP) and the Machine Learning Framework
(MLF).
Core system
Big Data (ETL, DWH, BI)
Data Science (ML/AI)
DATA SCIENCE
Machine Learning
Framework
Framework
Overview of Big Data technologies
System level storage:
HDFS
App level storage: Hive, HBase, GreenPlum, Cassandra
ETL: Kafka + M-R apps + NiFi processors
Processing: Hadoop stack (M-R, Spark, YARN)
Containerization: Docker + Helm + Kubernetes
Monitoring: Grafana, Kibana, Prometheus
BI with analytic: Qlick, Tableau, Power BI
App level storage: Hive, HBase, GreenPlum, Cassandra
ETL: Kafka + M-R apps + NiFi processors
Processing: Hadoop stack (M-R, Spark, YARN)
Containerization: Docker + Helm + Kubernetes
Monitoring: Grafana, Kibana, Prometheus
BI with analytic: Qlick, Tableau, Power BI
Overview of Data Science technologies
Frameworks: KubeFlow +
AutoML, TensorFlow, Keras, Deeplearning4j, Caffe, XGBoost
Ready-made network architectures and pretrained networks based on them: MobileNetV2, Yolov3, EfficientNet, and others
Ready-made network architectures and pretrained networks based on them: MobileNetV2, Yolov3, EfficientNet, and others
Overview of existing data sources
Open sources (examples):
Mnist
ImageNet
CIFAR-10/100
COCO (Microsoft)
FashionMNIST
(Zalando)
Google audioset
Visual genome
Billion words
2000 HUB5 English
Evaluation Transcripts
Collections:
Kaggle
Kdnuggets
MLData
Sealed sources:
aidata.me
amberdata.ru
audience segments,
advertising campaigns
on various bases.
Mnist
ImageNet
CIFAR-10/100
COCO (Microsoft)
FashionMNIST
(Zalando)
Google audioset
Visual genome
Billion words
2000 HUB5 English
Evaluation Transcripts
Collections:
Kaggle
Kdnuggets
MLData
Sealed sources:
aidata.me
amberdata.ru
audience segments,
advertising campaigns
on various bases.
The ecosystem encompasses fields of technology
that factor into the development of the digital persona. Each field contains an array of
subsystems that function as autonomous services and apps which have the potential for rapid
growth in user base and investment. The Ecosystem.
The Ecosystem
DigiU monetizes technologies in the following
fields:
Computer vision
Natural language processing
Biometrics
Predictive analytics
Speech technologies
Speech technologies
Example of a technology and path to
monetization
Telephone customer service conducted by AI
Intelligent voice assistant
Increasing productivity through voice commands
Predictive analytics
Example of a technology and path to
monetization
Consumer demand forecasting
Client recommendation systems
AI for trading on exchanges
Biometrics
Example of a technology and path to
monetization
Finding perpetrators through facial recognition
Remote proof of identity
Identifying human silhouettes and behaviors
Natural language processing
Example of a technology and path to
monetization
Understanding meaning for conversations with
humans
Extracting specific information from data
streams
Creating social media content
Detection and diagnosis of medical conditions
Example of a technology and path to
monetization
Facial recognition time clock for employees
Computer vision
Product identification
Обработка ествественного языка
Пример разработки и монетизации
Понимание смысла информации
для ведения диалога с человеком
для ведения диалога с человеком
Извлечение конкретной информации
из потока данных
из потока данных
Создание контента для соц. сетей
The annual growth of the Artificial Intelligence
market
31% per year
$52.5 billion
The total size of the AI technologies market,
according to the Frost & Sullivan estimate for 2022
2030
Widespread adoption of Artificial Intelligence
technologies
$15.7 trillion
Growth of the global market of goods and services
by 2030
Advantages of the AI market:
Through the accumulation of data and the implementation of the multiple DigiU ecosystem services.
In just 1.5 years of operation, over 50,000 participants from 180 countries have joined the project.
Fast-growing capitalisation
Rapid growth
Why choose DigiU?
Our aim is to educate people about the possibilities and prospects of IT and AI, to teach relevant professions and to help people adapt to the new digital world.
DigiU is engaged in research and development in innovative technologies such as blockchain and artificial intelligence.
Aim
High tech
2019
Q1 - Project launch.
Crowdinvesting begins.
Q1 - Project launch.
Crowdinvesting begins.
2020
Q1. Q2 - Establishing a material base and bringing together the team.
Q3.Q4 - DMP and MLF development begins. In-house platform testing.
Q1. Q2 - Establishing a material base and bringing together the team.
Q3.Q4 - DMP and MLF development begins. In-house platform testing.
2021
Q1.Q3 - Launch of pre-trained networks and other models.
Q4 - DMP and MLF prototypes ready. Technologies released to pre-production.
Q1.Q3 - Launch of pre-trained networks and other models.
Q4 - DMP and MLF prototypes ready. Technologies released to pre-production.
2022
Q1 - DPM and MLF put to use.
Q2 - Release of generative, predictive, recommendation, and segmentation services and tools based on DPM and MLF
Q3 - Automated search and testing of models and services for use.
Q4 - DPM and MLF platform ready.
Q1 - DPM and MLF put to use.
Q2 - Release of generative, predictive, recommendation, and segmentation services and tools based on DPM and MLF
Q3 - Automated search and testing of models and services for use.
Q4 - DPM and MLF platform ready.
2023
Q1.Q2 - Bringing to market DMP and MLF-based products and services. First dividends paid out.
Q1.Q2 - Bringing to market DMP and MLF-based products and services. First dividends paid out.
Project roadmap