The World Economic Forum (WEF) 2015 report "Technology Tipping Points and Societal Impact" predicts that by 2026 the first artificial intelligence system will take a seat on the corporate board of directors. The first official announcement about the artificial intelligence system in the board of directors was published in 2014 Position of a corporate director is one of the few that are required for execution by a "natural" person only. The main prerequisites for full automation of management decisions made at the level of the board of directors are formed in the field of corporate law, machine learning, and rules of non-discrimination, transparency, and accountability of decisions made and algorithms applied.

Radiologists disagree when they annotate medical images. There are many reasons why this could happen - human error, different skill level, bad instructions. Some of these factors can be mitigated and accounted for, but sometimes doctors just have different opinions. In my talk, I want to tell you about different ways of working with intra-observer variability, and to propose a novel method of learning annotator style.

"Test test test" - comprehensive testing of ML models is one of the mandatory steps according to the "Responsible AI" concept. We will discuss different machine learning tests that go far beyond evaluating the models' performance on the data subset, such as: pre-train tests, post-train tests (incl. behavioral testing, metamorphic testing, etc.) and data drift detection tests.
We will also introduce the NLP models' testing pipeline that is used in Gazprombank and talk about the ML testing tools (including a list of libraries).

Only 21 AI-based Medical devices are registered in Russia, and more than half of them are CV in radiology. Nevertheless there is a vast variety of other types of studies and clinical tasks in healthcare, so what are the main drivers and limitations of AI automation in healthcare? Discussion is based upon 13 research projects in various fields of healthcare (cardiology, colonoscopy, obstetrics, endocrinology) and types of studies (ultrasound, MRI, slide microscopy, ECG, endoscopy) for the leading Medical Research Centers.

Radiographic progression in rheumatoid arthritis gives an objective measure of anatomical damage that defines the course of the disease and the effects of treatment. In studies and clinical trials to assess radiological progression used a very time consuming method. We are developing a deep convolutional neural network (CNN) model to automatically evaluate hand radiographs in RA patients. Moreover, we present a prototype of a web application that can be used by radiologists to accelerate the formation of a study protocol.

How we deployed 22 algorithms from 12 vendors into EU27 hospitals

It is planned to discuss computer vision methods for working with medical research on images of different projections of the same area of interest. The talk will touch upon the following modalities: mammography, chest X-ray, etc.
There will be discussion of geometric methods of matching finds on different projections and neural network architectures that allow consideration of information from multiple projections at the same time.

The issue of core legal function automation though AI is a daunting, but, nevertheless, a feasible task. In his presentation Ignat Postny will talk about practical experience of developing a system for automatic analysis of legal documents (court documents), which is capable of:

- analyzing a set of incoming documents (court documents);

- drafting a report on all the shortcomings of the received set of documents;

- drafting, at the user's request, a set of necessary response documents.

Sometimes creating an ML-model requires significant resources, such a model can turn out to be unique. The ML developer's rights must be protected somehow, and there are legal remedies for this. In addition, during the working application of the ML-model, harm may be caused, therefore it is necessary to create a method for finding the tortfeasor when such harm is caused by artificial intelligence applications.

My talk will focus on quality control methods and how to build a data labelling pipeline within the company. We will discuss the main mistakes in the organization of the labeling process and will find out how to avoid them.
• Differences between Data-Centric and Model-Centric approaches
• Iterative approach to data labeling: pros and cons
• Building an effective learning process for data assessors
• Quality control methods
• Basic errors in data labelling management

We present AI service for synthetic data generation - SyntData. The service provides relevant, valid synthetic data, generated with deep insights from real data. Synthetic data guarantees safety of clients data, and providing DEVs, QA, DS opportunity to work with data similar to real data.

Доклад содержит в себе обзор результатов разработки и внедрения no-code платформы для создания поисковых микросервисов в компании ГАРАНТ.

We apply topological data analysis (TDA) to speech classification problems and to the introspection of a pretrained transformer models, namely, BERT and RoBERTa in NLP area, and HuBERT for Speech data. Our results demonstrate that TDA is a promising new approach for speech and language analysis, especially for tasks that require structural prediction. We also show that topological features are able to reveal functional roles of Transformer heads; e.g., we find the heads capable to distinguish between pairs of sample sources (natural/synthetic) or voices without any downstream fine-tuning.

The talk will cover one of the main topics in the international AI community - Creative Artificial Intelligence. First, I will speak about the task itself and its history, how we started with classic CV tasks and proceeded to text2image models. Further I will describe the main trends in multimedia data synthesis in 2022-2023 and observe current SoTA architectures, giving a brief description of our diffusion-based text2image model Kandinsky 2.0. After that we will speak about different applications of Creative AI today and in the nearest future in terms of my vision. And finally I will show how we proceed in Creative AI for high fidelity face swap on images and video, describe our current SoTA solution - the GHOST model, and show our marketing applications in movie production, advertising, etc.

The report presents our experience gained during the development of the apple picking robot. Particular attention is paid to the computer vision system for detecting apples. We will also talk about the positioning system relative to the camera and the robotic arm. This compares several stereo cameras, such as the Intel Real Sense Depth Camera D415/D455 and ZED2. What is the error in estimating the coordinates, why is the Internet of Things here and how did you manage to achieve recall at the level of 95%. It will be about problems, and about difficulties, as well as about the joy of the first picked apple.

This talk will explore the ways in which generative artificial intelligence (AI) is being used to augment and enhance the creative process in a variety of industries. The talk will cover the basics of generative AI, including some history, key concepts, and current state of the art. We will discuss specific applications of generative AI in fields such as music, film, and video games. I'll share some nuances of adapting conventional ML lifecycle to fit the requirements of creative industries, and how we overcame them at Deepcake. Overall, I'll try to provide a comprehensive understanding of the role of generative AI in the creative industries and its potential to shape the future of creativity and innovation from perspective of AI startup in the field.

The development of self-driving cars has been a major focus in the field of artificial intelligence. To achieve this goal, large amounts of data are required for training machine learning algorithms. However, collecting and labeling real-world data can be time-consuming and expensive. To overcome these challenges, this paper proposes using synthetic data for learning self-driving cars, including the ability to generate unlimited amounts of diverse and controllable data. We developed a solution for efficient and stable integration of RLLib with Carla simulator. We present end2end solution for learning self-driving cars in Carla simulation environment with GYM-interface. The results demonstrate the effectiveness of using synthetic data in training RL-agents for autonomous vehicles. The findings suggest that synthetic data has the potential to significantly accelerate the deployment of self-driving cars by providing a cost-effective and scalable solution for training machine learning models.

This presentation provides an overview of the current state of robotics and the latest developments in the application of large sequential models (such as GPT-3) to the field. The focus is on how these models can enhance the capabilities of robots and enable them to perform a wider range of tasks and interact with humans in new ways. The talk covers the latest trends in the field, including new models, such as SayCan, that are designed to enable more natural human-robot interaction, as well as the potential benefits and challenges of using large language models in robotics. The presentation concludes by exploring some of the future directions and opportunities in this rapidly evolving field.

Расскажу про реализацию системы восприятия на основе лидаров и камер в нашем беспилотном грузовике. Расскажу, как мы преодолели ограничения промышленного вычислителя для эксплуатации на объектах заказчиков.

Практическое применение генеративных нейронных сетей в практике работы компаний должно получать конкретные прикладные реализации. В своем докладе мы показываем на примере работы крупного Digital агентства, каким образом современные генеративные нейронные сети, будучи дообученными на исторических, маркетинговых, аналитических и финансовых данных компании, могут стать нативным инструментарием для самых различных ролей внутри компании, будучи интегрированным во внутреннюю ERP систему. Покажем реальный опыт внедрения и постараемся оценить результат и оказанный эффект на бизнес компании, порассуждаем о развитии инструментария.

"Capture and share the world's wonderful moments" is the slogan of Instagram, this states that images are the dominant point of communication in contemporary social media.
We present a technology for image generation for social media, which can help bloggers who have to produce huge amount of visual content daily to maintain high level of engagement rate for a blog.
Modern out of the box image generation technologies are mostly based on simple textual (and/or visual) "prompt", that is not able to take in consideration a lot of details, which determine blog style.
Our approach allows performing automatic detailed analysis of blog content and use all extracted details as a complex prompt to produce new content which is semantically close to the original and vary the proximity of the original and generated visual blog content style.

The process of creating a script for a voice robot operator involves a number of routine operations performed by trained specialists. Our experience in creating such scripts allow to confirm that almost the entire path of creating a robot script can be automated to the magic button "Create script", which will allow programming the robot without special knowledge to solve communication problems over the phone. Let's talk about experiments with AI generator to automate the creation of a script based on real dialogues of live operators with subscribers.

Text Style Transfer is an important task in NLP, which aims to control certain attributes in the generated text, and to generate or paraphrase text in a specific style. This talk concentrates on a specific style transfer approach known as controllable text style transfer, where one aims to generate a text in a specific style by controlling the generation of a language model so that the generated text is written in a desired style. The presentation gives the broad overview of the controllable text style transfer methods, covering such approaches as CTLR, GeDi, ParaGeDI, FUDGE, DExperts, and CIAF, highlighting possible ways of the developing of this area of research.

High-precision modeling of systems is one of the main areas of industrial data analysis today. Models of the systems, their digital twins, are used to predict their behavior under various conditions. We have developed a model of a data storage system using generative models of machine learning. The system consists of several types of components: HDD and SSD disks, disk pools with different RAID schemas, and cache. We represent each component by a probabilistic model that describes the probability distribution of component performance values depending on their configuration and external data load parameters. Machine learning helps to get a highly accurate digital twin of a particular system, spending less time and resources than other analogues. It quickly predicts the performance of the system, which significantly speeds up the development of new data storage systems. Also, comparing the forecasts of the model with the real performance helps to diagnose failures and anomalies in the system, increasing its reliability.

Nowadays, the relevance of information security of industrial automation systems is no longer in doubt. In our report, we will describe the developed method of detecting anomalies in such systems based on the analysis of a copy of network traffic. This approach is compatible with any industrial automation systems and doesn`t require information about the topology, network protocols and algorithms. An APRE algorithm is proposed, which allows to extract packet headers of unknown network protocols and determine their semantics without a priori knowledge of the protocol structure, based on changes in entropy and mutual information of packet bytes. A multi-agent modeling approach is used to detect anomalies in the automation system operation. For each component of the system, the creation of an agent capable to predict response on input signals extracted from a copy of the network traffic in the previous step is performed. Several ways of representing and training agents in the form of different types of automata are proposed.

Data-driven methods showed significant results in solving different tasks in many industrial applications. There are recent works that show NNs achieving state-of-the-art results in anomaly detection problems overperforming traditional algorithms and methods. In my speech, I will review some works and NN architectures related to the anomaly detection problem in industrial time-series data.

Despite being proven in the image processing field, neural networks still are a tricky tool for cartography. Can we trust the results? What should we do with the errors? Should we rely on selling ready models as a service, or stick to on-demand development?
In this talk I will share some practical cases: how do we derive the model for the particular task and area from the general off-the-shelf model, how do we collaborate with human cartographers and how to handle user's feedback.

Reinforcement Learning is a field of Machine Learning that solves interactive tasks via continuously learning an agent to pick actions that should be optimal in the long-term interaction horizon. This is a broad problem setup and many different tasks (both theoretical and practical) can be formulated in terms of RL. However, Reinforcement Learning algorithms (especially out-of-box solutions) often lose in terms of efficiency to the specialized ML or optimization methods. Nevertheless, there are also plenty of successful cases of applying RL to a variety of tasks. This talk is devoted to Reinforcement Learning and its applications in real-life tasks. We will briefly talk about common reinforcement learning approaches. Then, we will discuss some successful cases of RL applications both for real-life and digital twins tasks and also the challenges of developing robust solutions with RL.

Начиная с 2019 г. на Международной космической станции работает российско-итальянский эксперимент "УФ атмосфера" (Mini-EUSO), основным инструментом которого является широкоугольный телескоп, направленный в надир. Главной целью эксперимента является получение карты излучения ночной атмосферы Земли в ультрафиолетовом (УФ) диапазоне, что является необходимым элементом подготовки крупномасштабного эксперимента по изучению космических лучей предельно высоких энергий с помощью орбитального телескопа. Как и более ранний эксперимент ТУС, прибор "УФ атмосфера" регистрирует сигналы разнообразных процессов, происходящих в атмосфере в УФ диапазоне, и среди них - свечение метеоров. Мы описываем две простые нейронные сети, которые позволяют эффективно выделять сигналы метеоров в общем потоке данных. Реализованный подход может быть применён для поиска трекоподобных сигналов различной природы в данных флуоресцентных и черенковских телескопов.

Neural networks penetrate deeper into business and hold more of the information space. But despite the fact that about ten years have passed since neural networks boom in computer vision, there are still not so many products that are really able to make money with such technologies. Much more often the topic of neural networks sounds for PR or attracting investments. The main problem is the high cost of development and the cost of hardware. I will tell you about our product, which, despite the large and complex CV engine, broke in the long-established advertising market, managed to compete with classic solutions in price and be cost-effective.

Exploring principles of multi-agent simulation, collaborative decision making and experiential learning to active portfolio management relying on crypto-finance as a reference.

FusionBrain — это исследовательский проект, основными задачами которого являются разработка эффективных мультизадачных и мультимодальных моделей и применение их для решения широкого круга практических задач. Общая цель и идея проекта — научиться создавать модели, которые смогут как можно более эффективно извлекать дополнительные важные знания из большого количества модальностей и задач при обучении и за счет этого лучше решать другие задачи. Исследования проводятся во многих модальностях: тексты, изображения, аудио, видео, языки программирования, графы (например, молекулярные структуры), временные ряды. Список решаемых задач очень большой: от классических задач CV и NLP до задач, вовлекающих разные модальности: VideoQA, Visual Commonsense Reasoning, IQ tests (эти задачи сложны даже для человека). Изучается также способность моделей решать задачи, сформулированные на естественном языке (в частности, в формате инструктивной генерации с применением методов RLHF), и даже справляться со скрытыми задачами (для которых в обучающей выборке отсутствовали примеры). Исследования сосредоточены в том числе на сокращении данных, человеческих и вычислительных ресурсов, необходимых для обучения и инференса различных моделей. В рамках доклада будут рассказаны результаты исследований: в частности, речь пойдет о некоторых разработанных архитектурах, таких как ruCLIP, ruDALL-E (Kandinsky), Kandinsky 2.0, RUDOLPH, а также о проведенных соревнованиях, таких как FusionBrain Challenge и FusionBrain Challenge 2.0, и о разработке мультимодального бенчмарка

Описан метод интерпретируемого обнаружения аномалий с использованием сетей глубокого обучения автокодировщиков, RBM. Рассматривается вопрос обеспечения интерпретируемости показаний модели с использованием анализа значений нейронов скрытого слоя автокодировщика. Приводятся результаты применения модели на синтетических данных и на открытых датасетах.

Разработан программный продукт для прогнозирования потребления электроэнергии на каждый час следующих суток. На основе метода машинного обучения Huber regressor разработана новая, полезная и качественная математическая модель, связывающая потребление электроэнергии с выявленными факторами. Регрессионная модель позволяет получать прогнозные оценки на каждый час следующих суток с ошибкой 3,03% на тестовой выборке данных и прогнозировать на каждый до трех суток c относительной ошибкой в 4,82%.

Несмотря на высокий объем работ и высокую долю ВВП, производительность в строительной отрасли росла медленнее, чем в других сферах (в среднем, 1% ежегодно за последние 20 лет). За счет цифровизации, Самолету уже удалось увеличить производительность на 60% и впереди еще много работы в этом направлении

Neural networks trained using the backprop are prone to catastrophic forgetting. If we first teach the network to recognize cats and then start teaching it to recognize dogs, then it will forget some amount of information about cats. This problem is especially evident when new data, that needs to be learned, appears continuously during the work of the neural network. This sub-area of machine learning is called Continual Learning. There is a wide variety of approaches to this problem, ranging from the simplest ones, such as remembering all previous data, to sophisticated weight updates that reduce the forgetting of learned knowledge. We will talk about these and other methods in detail in this report.

The implementation of artificial intelligence (AI) and machine learning in the mining industry provide many economic benefits for the mining industry through cost reduction, efficiency, and improving productivity, reducing exposure of workers to hazardous conditions, continuous production, and improved safety. However, the implementation of these technologies has faced economic, financial, technological, workforce, and social challenges. This report discusses the current status of AI, machine learning implementation in the mining industry and highlights potential areas of future application. The report also presents some cases of implementation these technologies and what are some of the steps needed for successful implementation of these technologies in this sector.

Chain coneveyor monitoring is one of technically complex CV tasks solved at Severstal. We will describe what challenges did we have, how we dealt with lack of data, what ML pipeline did we create and how it is deployed and works on 39 cameras throughout 3 factory shops.

One of the most effective continuous deep reinforcement learning algorithms is normalized advantage functions (NAF). The main idea of NAF consists in the approximation of the Q-function by functions quadratic with respect to the action variable. This idea allows to apply the algorithm to continuous reinforcement learning problems, but on the other hand, it brings up the question of classes of problems in which this approximation is acceptable. The presented paper describes one such class. We consider reinforcement learning problems obtained by the time-discretization of certain optimal control problems. Based on the idea of NAF, we present a new family of quadratic functions and prove its suitable approximation properties. Taking these properties into account, we provide several ways to improve NAF. The experimental results confirm the efficiency of our improvements.

Training acceleration is one of the prominent research directions in the field of deep learning. Among other directions in this field, Hebbian learning is considered to be a highly prospective approach. Although Hebbian learning does not produce models of accuracy comparable to training with a traditional backpropagation approach, there is an emerging trend of applying Hebbian learning as a part of mixed training strategies that might include various backpropagation methods. Also, Hebbian learning is plausible for neuromorphic hardware due to its locality and highly parallel nature. In this paper, we overview existing approaches of applying Hebbian learning to training one of the largest and most demanded classes of deep neural networks - Convolutional Neural Networks. We analyze the availability of existing software solutions for Hebbian learning. More importantly, we investigate various approaches to the implementation of Hebbian learning to convolutional and linear layers as they are foundational for modern deep neural networks. This paper will be interesting both for researchers who want to accelerate training and for engineering practitioners who might be interested in exploring new ways of training Convolutional Neural Networks on new types of hardware.

We will provide derivation and description of diffusion models which are now one of the most promising techniques for generative modeling. Diffusion model will be considered from different angles that highlight its advantages over analogues. We will discuss several facts from the theory of stochastic differential equations that allow better understanding the logic of diffusion models and its attractive properties. In the last we will present a generalisation of diffusion model that may deal with non-gaussian noise and can be especially useful when there are additional manifold constraints on data.

Таксономия методов Federation Learning, обзор существующих платформ и основных игроков, существующих вызовов и трендов развития индустрии

A key enabling factor in the innovative AI work you see from organizations such as DeepMind, FAIR, OpenAI is powerful computing infrastructure available to their DL researchers to train large scale neural networks. We believe that this kind of computing infrastructure should be not restricted only to a few privileged companies. Rather, such infrastructure should be available to startups, researchers, universities and non-profits at low costs and without the system engineering chops required. Scaletorch speeds up your deep learning training between 10x-1000x by leveraging GPU capacity across multiple clouds in a fault-tolerant manner. With Scaletorch accelerate your AI training by 100x and at a 98% lower cost with ZERO CODE CHANGES.

Доклад посвящен актуальным и перспективным разработкам в области реализации алгоритмов машинного зрения и нейронных сетей в медицине, промышленности и других сферах человеческой деятельности, построенных на базе отечественных процессоров НТЦ Модуль.

Доклад о кейсе разработки голосового ассистента для кофейных автоматов компании Unicum. Ассистент отвечает на вопросы пользователей, позволяет принимать заказ и оплачивать напитки с использованием голоса.

DeepPavlov Dream is an open-source multiskill AI assistant platform emerged after the DeepPavlov's DREAM team participation in Amazon Alexa Prize Socialbot Grand Challenges 3 & 4 in 2019-2021. While we used various large language models at DeepPavlov, like DialoGPT, GPT-2, BlenderBot etc., recent developments like ChatGPT and GPT3.5 (davinci-003) made us re-think our design approach to the development of the AI assistants. In this talk you will learn how you can tame the wild power of the generative AI and build your own generative assistants with large language models and DeepPavlov Dream.

Traditionally, the variety of lexical analysis and thematization tools for speech analytics cases has been limited to searching manually created lists of key words or phrases. Meanwhile, the growing volume and pace of data creation, as well as the increasing complexity of the cases being resolved by contact center, is placing greater demands on both the performance of algorithms and the sophistication of their decisions. When simple full-text search is no longer sufficient for most tasks, advanced ML and DL thematization algorithms come to the analysts' aid.

The report describes which new algorithms have appeared in modern speech analytics systems and for which tasks they can be used.

Many NLP tasks benefit from using large language models (LLMs) that often have more than 100 billion parameters. With the release of BLOOM-176B and OPT-175B, everyone can download pretrained models of this scale. Still, using these models requires high-end hardware unavailable to many researchers. In some cases, LLMs can be used more affordably via RAM offloading or hosted APIs. However, these techniques have innate limitations: offloading is too slow for interactive inference, while APIs are not flexible enough for research. In this work, we propose Petals − a system for inference and fine-tuning of large models collaboratively by joining the resources of multiple parties trusted to process client's data. We demonstrate that this strategy is more than 10x faster than offloading for 100B+ models, running inference of BLOOM-176B on consumer GPUs with ≈ 1 step per second. Unlike most inference APIs, Petals also natively exposes the hidden states of served models, allowing its users to train and share custom model extensions based on efficient fine-tuning methods.

Throughout recent decades, natural language processing (NLP) tasks have uncovered many sophisticated problems in the machine learning (ML) domain, leading to the rapid development of NLP techniques. The object of research in the NLP domain is a spoken or, more commonly, written text in natural language. In contrast, the research objectives vary depending on the specific NLP tasks: text classification, text generation, question answering (QA), summarization, and many others. However, these models often fail to incorporate specific linguistic knowledge about the texts. In such cases structural approaches for language processing can come to the fore. In this work, we overview modern NLP domain and the ways to combine them with structural techniques in order to boost the models' performance.

We will provide derivation and description of diffusion models which are now one of the most promising techniques for generative modeling. Diffusion model will be considered from different angles that highlight its advantages over analogues. We will discuss several facts from the theory of stochastic differential equations that allow better understanding the logic of diffusion models and its attractive properties. In the last we will present a generalisation of diffusion model that may deal with non-gaussian noise and can be especially useful when there are additional manifold constraints on data.

The presentation highlights the main technological aspects of developing next- generation artificial intelligence (AI) systems based on the neuromorphic approach. The strengths of these AI systems are energy efficiency, high speed, and adaptability. As part of the neuromorphic AI ecosystem, we cover Kaspersky Neuromorphic Platform – an open source software and hardware platform for spiking neural networks (SNN) development. Also, we propose the concept of a neuromorphic machine vision system for energy-efficient analysis of fast processes. The system includes a neuromorphic video sensor (DVS camera), a neuromorphic chip AltAI, and software that implements SNN on the AltAI chip. Finally, we demonstrate our neuromorphic pipeline working on some practical cases.

Neuromorphic systems – a field of AI that appeared as a result of the mutual influence of such areas as spiking neural networks and non von Neumann computers. This field is now considered promising for creating a variety of autonomous intelligent devices and building large computing systems for implementing AGI. Like neural ensembles of the brain, neuromorphic systems operate with information presented not in the form of numbers, but as a sequence of atomic events – spikes. However, there are still obstacles on the way to a technological breakthrough of neuromorphic systems. The most significant of them is the lack of a clear understanding of how to build computational processes not on the basis of numbers, but on the basis of spikes. This talk is about this issue - calculations, learning, prediction, memorization, implemented as operations with spikes.

ExplainitAll library is intended for interpretation of outputs of transformer neural networks. The main advantage of the approach implemented in the library is that the interpretation will work both for Embedding Networks, and for generative tasks in QA (Question-Answering Systems) setting. The result of the work can be grouped and generalized into semantic clusters. Also, ExplainitAll developers and users will be able to use the ready-made transformer response reliability metrics as well as create their own, with attention visualization.

The report presents a project to create the first AltAI neuromorphic processor in Russia, designed for highly efficient execution of impulse neural networks. The technical and market aspects of the project are considered, information on current results and plans for further development is given.

Modern artificial intelligence (AI) systems, based on von Neumann
architecture and classical neural networks, have a number of
fundamental limitations in comparison with the mammalian brain. In
this presentation we discuss these limitations and ways to mitigate
them. Next, we discuss an overview of currently available neuromorphic
AI projects in which these limitations are overcome by bringing some
brain features into the functioning and organization of computing
systems (TrueNorth, Loihi, Tianjic, SpiNNaker, BrainScaleS,
NeuronFlow, DYNAP, Akida, Mythic). Also, we discuss the principle of
classifying neuromorphic AI systems by the brain features they use:
connectionism, parallelism, asynchrony, impulse nature of information
transfer, on-device-learning, local learning, sparsity, analog, and
in-memory computing.

Since multimodality became popular, lots of engineers are trying to make a domain-universal search. Search engines which can find an image by textual query, the HTML file by a piece of audio, and so on. So here is our (Unum) approach with a bias towards multilingual models, GPU accelerating inference (for underlying models), and passion to distribute everything.

In the talk, we will cover the changing landscape of the language models and their applications in 2023: the technical approaches and problems, tasks that are now possible and the economic value of the LLMs.

In the report we will talk about the development of our new ML platform, namely, how we organized the management of NLP services and models, standardized the usage of our own and open-source solutions, overcame the problems of low intents detection quality on complex data and trained controllable paraphrase model for assistance to users.

My talk is about open-source tool to experiment with SOTA NLP models and algorithms. You can easily construct your own model or use pre-train checkpoints on many languages. Also, there are data processing pipeline toll and fast C++ server for inference available.

Как автоматизировать и сократить время создания скрипта для робота на основе исходных записей живых операторов и абонентов?

Real-world data has form, and form "makes a difference". However, standard machine learning methods often do not take into account the shape of the data. In turn, modern topological data analysis methods precisely analyze the form of the data as its most important property. In the presentation, we will discuss how topological data analysis works. We will show how topological features make it possible to describe the shape of data and significantly increase the efficiency of machine learning models. Based on a new topological measure of similarity, an approach will be described for constructing a low-dimensional description of a data shape that has the property of "disentanglement", i.e., when various parameters of this description are automatically responsible for different properties of the data, which makes it possible to increase the interpretability of machine learning models.