‘I Like to Think that the Solutions We Find Can Help People in the Future’
Innopolis University has announced the results of Global Al Challenge, an international AI industry online hackathon in which teams of developers compete to create new materials using artificial intelligence. The DrugANNs team, which included students from the HSE University Faculty of Computer Science, took third place.
90 teams from 15 countries competed in the online competition for the prize fund of 1 million roubles. Their task was to build the structure-activity relationship for COVID-19 targets. The teams had to assess whether the molecule was active against the protein, build a model that could predict this activity, and make predictions for the test dataset.
‘The description of the problem seemed interesting to us,’ says German Magaj, doctoral student in ‘Theoretical Foundations of Computer Science’ of the HSE Faculty of Computer Science, member of the DrugANNs team. ‘We decided to put a team together. For two weeks, we called each other regularly to share progress and problems. We managed to take the prize because everyone in the team made their contribution, each of us worked as an expert in their field, and our efforts paid off.’
Maxim Beketov, 2nd-year doctoral student of the HSE University Department of Higher Mathematics, explains: ‘There is little data on such activity actually obtained through laboratory methods or computational chemistry. In addition, a single molecule, if it’s large, can have exponentially many configurations of its components in the environment. Some of them may be active against a protein, and some may not. The spatial structure is very important here: the protein takes the form of a certain code describing its 3D model, and this 3D model may have several points where the molecule can ‘attach’—and work—or ‘not attach’.
Maxim adds that the use of machine learning in biological and medical problems motivates him to participate in such competitions: ‘I like to think that the solutions we find can help people in the future. In addition, the methods applied in this area involve beautiful mathematics such as equivariant graph neural networks, neural networks on simplicial complexes as generalisations of graphs, and so on.’
Dmitrii Kiselev, 3rd-year doctoral student of the programme ‘Computer and Information Sciences’, member of the DrugANNs team, agrees with Maxim, who noted that the use of graph neural networks (GNN) is a relevant and rapidly developing field.
‘Recently, GNN has been actively used to solve problems in the natural sciences,’ says Dmitrii. ‘In particular, it is used in chemistry to predict the properties of molecules, model them, and more. I wanted to try my hand at this field for a long time. Discoveries in this area can be important for the whole of society and bring benefits.’ He adds that predicting the activity of molecules is a well-known task, and similar competitions are held regularly. ‘I tried many repositories, updated different ideas, tried to combine different approaches, but failed to achieve a good-quality result. At some point, I even got upset and decided that I needed to dig deeper,’ he explains. ‘But later, our colleagues (a chemist and a specialist in bioinformatics) helped me to correctly preprocess the data, and everything worked.’
Team members from other universities—a chemist, a specialist in bioinformatics, machine learning specialists, experts in particular graph neural networks—also actively worked on the problem. This helped DrugANNs to find the right solution and win. ‘We’ve stayed in touch since the hackathon ended’ says Maxim. ‘We also discuss the topic of the hackathon problem: it’s interesting to all of us, we would like to develop it, participate in other hackathons or see how we do with other formats.’
Over three days, more than 300 conference participants attended workshops, seminars, sections and a poster session. During panel discussions, experts deliberated on the regulation of artificial intelligence (AI) technologies and considered collaborative initiatives between academic institutions and industry to advance AI development through megaprojects.
A team from HSE University’s Faculty of Computer Science took first place in a Rosneft hackathon held in October among the country’s universities. The hackathon was organised by the research institute RN-BashNIPIneft LLC. The competition participants had the chance to try their hand at solving real production problems.
Top development teams around the world are trying to create a neural network similar to a curious but bored three-year-old kid. IQ.HSE shares why this approach is necessary and how such methods can bring us closer to creating strong artificial intelligence.
Seungmin Jin, from South Korea, is researching the field of Explainable AI and planning to defend his PhD on ‘A Visual Analytics System for Explaining and Improving Attention-Based Traffic Forecasting Models’ at HSE University this year. In September, he passed the pre-defence procedure at the HSE Faculty of Computer Science School of Data Analysis and Artificial Intelligence. In his interview for the HSE News Service, he talks about his academic path and plans for the future.
Machine Learning (ML) is a field of AI that examines methods and algorithms that enable computers to learn based on experience and data and without explicit programming. With its help, AI can analyse data, recall information, build forecasts, and give recommendations. Machine learning algorithms have applications in medicine, stock trading, robotics, drone control and other fields.
At the end of September, the HSE University Faculty of Computer Science launched the laboratory ‘Artificial Intelligence in Mathematical Finance’, which brought together more than 70 participants. Representatives of the laboratory told students and staff about the key tasks and goals of the new research department.
'Intelligence is ten million rules,' said Douglas Lenat, one of the creators of artificial intelligence (AI). For nearly four decades, he worked to instil 'common sense' in computers, painstakingly describing hundreds of thousands of concepts and millions of relationships between them.
Today, neural networks can easily identify emotions in texts, photos and videos. The next step is modelling them—an essential component of full-fledged intelligence in people and machines alike.
HSE AI Research Centre works on achieving applied results, striving not only to develop innovative algorithms and models, but also to put them into practice to solve real-world problems and tasks. Alexey Masyutin, Head of the AI Research Centre, spoke about some of the the specifics of applying AI technologies in various fields.
A hackathon was held in Nizhny Novgorod for students in grades 9–11 as part of the Data Analysis National Olympiad (DANO). More than 90 school students in grades 9–11 from Moscow, Nizhny Novgorod and the surrounding region, St Petersburg, Samara, Cheboksary, and Ufa—a total of 15 Russian regions—took part in the hackathon.