Creating AI-based interactive
and smart search systems.
We can help
with the following
tasks1. Development
of Analytical
Systems
- Provide the first level of customer support
- Answer frequently asked questions
- Help customers find the right product
- Learn from real-life dialogues between consultants and customers
- Training on natural text
- Asking questions in arbitrary form
- Accounting for contextual synonyms
- Correcting spelling errors
- Classification of appeals by topic
- Automated selection of a suitable response template
- Highlighting key named entities - names, locations, dates
- Segmented case analytics
- Communicating with people on a wide range of topics
- Providing consultations on products or services
- Ability to define conversation goals
- Consideration of synonyms and paraphrases
- Provide the first level of customer support
- Answer frequently asked questions
- Help customers find the right product
- Learn from real-life dialogues between consultants and customers
- Training on natural text
- Asking questions in arbitrary form
- Accounting for contextual synonyms
- Correcting spelling errors
- Classification of appeals by topic
- Automated selection of a suitable response template
- Highlighting key named entities - names, locations, dates
- Segmented case analytics
- Communicating with people on a wide range of topics
- Providing consultations on products or services
- Ability to define conversation goals
- Consideration of synonyms and paraphrases
2. Data
Markup
3. Testing Scientific
or Business Hypotheses
Market Ready
Solutions
BRAIN2 is the first Russian neural network training platform, based on our own framework.
BRAIN2 allows developers to create predictive models on their own data, test the effectiveness of models, and then use them to make predictions. The platform greatly simplifies the process of neural network training by automatically selecting the optimal model architecture.
You can use our online platform with access through your personal account or purchase a license to install the system on your machine.BRAIN2NLP - is a Russian startup which develops artificial intelligence systems based on neural networks.
Main features of BRAIN2NLP include language detection, stemming, morphological and syntactic analysis, named entity recognition and determining the semantic vector of words.
This allows models trained with BRAIN2NLP to better understand the context and provide more relevant answers to user’s questions.Project
“Consciousness”
We have developed a theoretical basis for creating a complex system with an energy function and the ability to independently generate algorithms of its actions.
This will allow the creation of artificial intelligence, the main purpose of which will be to maintain its vital functions and compete with other models for survival. The capabilities of such artificial intelligence will significantly exceed the technologies used today.
Our
ProjectsWe have developed software for Promobot robots which "work" in government multifunctional centers.
It allows the visitor to obtain MFC service information upon the user’s request made in arbitrary form.
We have developed software for Promobot robots which "work" in government multifunctional centers.
It allows the visitor to obtain MFC service information upon the user’s request made in arbitrary form.
It is a robot assistant that “reads” the text of any volume and subject in a small amount of time and then answers the questions in natural language. The service is based on our own Semantic Vector Model, which is aimed at finding semantic links in the text and has the ability to take into account the context for each word.
It is a robot assistant that “reads” the text of any volume and subject in a small amount of time and then answers the questions in natural language. The service is based on our own Semantic Vector Model, which is aimed at finding semantic links in the text and has the ability to take into account the context for each word.
A question answering system for a robot, which allows it to answer visitors' questions in a arbitrary form and inform about interesting historical facts. Today the robot is already conducting tours for museum visitors.
A question answering system for a robot, which allows it to answer visitors' questions in a arbitrary form and inform about interesting historical facts. Today the robot is already conducting tours for museum visitors.
Scientific articles
and
Media
“The key concept here is meaning,” notes Artem Artemov. – The relationship between the cause (for example, the red light of a traffic light) and the effect (stopping the machines). The second component of “humanoid” intelligence is feelings …
We need them to create motivation and find meanings on our own. And who will appreciate these meanings if the AI cannot tell anyone about them? Therefore, we need a third element – the ability to communicate effectively ”
https://mipt.ru/upload/iblock/e65/2017_zanauku_1.pdf, pages 58-61
Valentin Malykh, Ksenia Ulanova
“We are sure that it is impossible to create artificial intelligence without teaching it to feel. We believe that artificial intelligence is only able to know when something will move it …
After all, our life is the path from suffering to pleasure. It would be nice to drive artificial intelligence there ”- Artem Artemov, Cognitive systems
https://rtvi.com/stories/znakomtes-vasha-novaya-personalnaya-pomoshchnitsa-na-chto-sposoben-iskusstvennyy-intellekt/
Sergey Morozov
Artem Artemov, CEO of Cognitive Systems, believes retailers should not have service personnel left in the near future, intelligent assistants will answer frequently asked questions.
“For the former analyst, part of the work will be done by the intellect, and the freed up time can be spent on something else. Do not be afraid that people will be left without work. Those who are afraid of changing with a changing world will be left without work. ” – claims Artem Artemov.
Искусственный интеллект в ритейле нужен для оптимизации работы
A. Artemov, A. Sergeev, I. Khasenevich
Intelligent computer programs come on the heels of representatives not only simple, but even creative professions such an interpreter and journalist, threatening in the near future to oust them from the market.
According to the UN report, robots will soon take 2/3 of the available jobs in developing countries. Let’s try to understand how justified science fiction films and robot-centric forecasts are, and whether it is possible to talk about the development of real artificial intelligence.
http://strf.ru/material.aspx?CatalogId=222&d_no=126692#.XCXck88zacZ
Report of the Kognitivnie Systemi CEO A.A. Artemov at OpenTalks.ai conference ” Nonrandom number generator and meaning understanding problems “. 2019
Report of the Kognitivnie Systemi CEO A.A. Artemov at Big Data & AI Conference 2019 ” Accounting for unknown features in the model on the example of the MFC case”. 2019
Artemov, B. Alekseev. The following paper presents a method of comparing two sets of vectors.
The method can be applied in all tasks, where it is necessary to measure the closeness of two objects presented as sets of vectors.
A. Artemov, B. Alekseev. The following paper presents a method of comparing two sets of vectors.
The method can be applied in all tasks, where it is necessary to measure the closeness of two objects presented as sets of vectors.
A. Artemov, A. Sergeev, I. Khasenevich, A. Uzhakov, M. Chugunov
Nowadays, the Internet represents a vast informational space, growing exponentially and the problem of search for relevant data becomes essential as never before. The algorithm proposed in the article allows to perform natural language queries on content of the document and get comprehensive meaningful answers.
The problem is partially solved for English as SQuAD contains enough data to learn on, but there is no such dataset in Russian, so the methods used by scientists now are not applicable to Russian.
Brain2 framework allows to cope with the problem – it stands out for its ability to be applied on small datasets and does not require impressive computing power. The algorithm is illustrated on Sberbank of Russia Strategy’s text and assumes the use of a neuromodel consisting of 65 mln synapses. The trained model is able to construct word-by-word answers to questions based on a given text. The existing limitations are its current inability to identify synonyms, pronoun relations and allegories. Nevertheless, the results of conducted experiments showed high capacity and generalisation ability of the suggested approach.
Artem Artemov, Eugeny Lutsenko, Edward Ayunts, Ivan Bolokhov
A study of the classification problem in context of information theory is presented in the paper. Current research in that field is focused on optimisation and bayesian approach.
Although that gives satisfying results, they require a vast amount of data and computations to train on.
Authors propose a new concept named Informational Neurobayesian Approach (INA), which allows to solve the same problems, but requires significantly less training data as well as computational power. Experiments were conducted to compare its performance with the traditional one and the results showed that capacity of the INA is quite promising.
