technology, and it has already reached its limit, the size of one transistor is about 10 sizes of atoms, that is, yes, 5 years and a further increase in this area is completely impossible, so state companies around the world are looking in the direction. so what is called alternatively some word very good looking computing systems. actually in order to understand what is a promising computing system. it can be, in fact , a completely arbitrary physical system. well here i fantasize, you can say, we have an aquarium with algae. we shine with light, a chemical reaction occurs as a result of a chemical reaction. we read the result, that is, the task of a modern scientist is to actually develop, pick up such a physical system and write a program for it. naturally, one of the most promising areas. here here is shown the famous schrödinger's cat is quantum computing, which is built on the principle of superposition. we don't know if the year is alive or not, yet the box we have not opened, but a quantum system in which 50 quantum bits are required on a classical computer. the fiftieth byte is one pit byte.

this is the memory of the most powerful supercomputer in the world in mighty, a prototype of a lump of a quantum computer has already been created, in which 256 quantum bits to describe it, all the atoms in the universe would be required, this is a very promising technology, the main problem with this promising technology, in addition to problems with iron, is that it is necessary to write algorithms for it. and these algorithms. need write now, but in fact, in what we do, we write algorithms for hardware that does not yet exist so that when this hardware is created, we do not end up in a box on which we do not know what to do. well, yesterday there was a sberbank award , in particular from the academy, holiva, received for quantum information the basis of quantum computing was laid by yuri manin holiva in the soviet union, by the way, not by physicists, but by mathematicians. this idea appeared in fact a key challenge. now in my opinion, vladimir vladimirovich seems to be a large-scale investment in the development of domestic software. but what is very important is the close connection with the developers of the hardware base throughout the spa. here are

these promising computing systems quantum computers photonic computational neuromorphic now we do not know which of them will be the next generation. but those who are the state, those companies that will be the first to receive algorithms and calculators, this bundle, they will receive a significant advantage in e-computing race - this will lead to the development science to the development of the economy. and in my opinion , they will obviously increase, uh, the quality of life of people. thank you i will be happy to answer your question. thank you so much. i'm just e at the end or i did not understand what you said or did not hear. you said quantum photons don't work. but no, this is a system that operates on the principle of the human brain, which means, er. well, so i heard and noted for myself, in principle, here, the thing is understandable, well, you need to invest in the program. liver

patriotic and we try to do it. how are you know, and we will continue to increase our efforts. hmm , i would be grateful to you if you said what exactly needs to be done in this regard, but as for, uh, quantum brass computing there, here we have two of our large state-owned companies. this is, uh, the russian railways rzd and rosatom, i think i talked about this in my speech. they, uh, have lent their shoulder to the state, and therefore they support us in the direction, uh, a line of communication has already been created between moscow and st. petersburg and we will continue to increase, uh, these efforts. if you said, specifically, you could now specify what you would specifically expect from the state. i was grateful to you. well, uh, i can say the main problem is that we have a separation of software developers and hardware developers. here we have personal experience, hardware developers consider software developers

personal enemies, because first we will make hardware, and then you will make it. write some software that provides, uh, which actually, might not work yet. and now, the key task in your task should be, roughly speaking, a consortium of a bundle, that the algorithms are here in parallel with the development of iron. they are separate physicists there, separate mathematicians there are specialists in machines. and now, if we manage to create just such a point of synergy, and this is simply difficult, because the mentality of people is different, that is, for the years of the nineties. when we had a hard time, everyone was very afraid that someone would come and take their money. it's a fact uh-huh well, it's just that fundamentally new system software is being made for new systems. i understand, but here's what i told you. you need to find such an assembly point that everyone would be interested in working together, so that all these participants would see the benefits. they saw this,

well, the danger for the fact that dmitry nikolayevich is a great specialist in organizing joint work. no, of course, we must think together. yes, vladimir we will definitely do everything with our colleagues , we work, we interact, we will do everything together. in anyway, we need to think to see what's going on there. thank you. thank you. thank you very much with your permission. uh, one speaker ivan thank you very much. thank you. well actually we are uh extremely grateful to the team of ivan and tycho because we are working on this project together and for us. that's a gigantic savings of just 15 percent. we save time when training a model with

our new model. we have been training now at supercomputing capacity for a year, which means that we will get 2 months of savings. of course, huge time savings, of course. a huge money saver in terms of electricity costs that this huge beast called a supercomputer eats up. uh, well, the task that the guys have to bring these parameters to 50%, if this can be done, it will be a completely unique experience, so thank you very much for this work and, uh, another speaker. i'd like to give the floor to, uh, the budding seed. ae semyon eh, is an employee of, uh, the institute of artificial intelligence, and uh, works just over uh there are more unique experts in artificial intelligence. they are working now. including over fusion, uh models, uh,

multimodal, which you talked about in your speech. here, the guys said that these are the latest trances in the field of artificial intelligence. e, multimodal multilingual and multitasking e, those models that are as close as possible to a person. and, uh, the guys work there for thirty of this kind, uh, projects, and semyon develops hybrid models, a direction at the intersection of quantum chemistry and neural network architectures. if it is possible for you too. e. excuse me, semyon, there are no words on this topic, budyonny semyon mikhailovich. no, but a relative is simply no, no, no, no, i was deceived, they said it was not good, when the leader was elected, he deceives attentiveness. watch out for the possibility of losses. please, in short, i would like to talk about today's trends in artificial intelligence when creating new materials. and i will try to answer the last

three questions. first. ah, able or artificial intelligence to create fundamentally new technologies were previously inaccessible to man further. i want to talk about the existing backlog of the existing, so to speak, the results of the institutes of the artificial intelligence institute, some rudiments, and then i want to propose some specific measures on how these technological grains can be further at least activated when we talk about artificial intelligence in synthesis materials. i would like to note one important property of the property of invergence, by analogy with how living organisms formed complex groups turn from chaos into concise ones, so to say the flows of the group and possesses acquire new functions, for example functions, and resistance to a predator of a flock of a swarm of bees or a flock of birds. a or a means and so on then accordingly, artificial intelligence is like a complex system. it also acquires a new function and generates new technologies that were previously inaccessible and not

reproducible for humans. and the first question, which a we tried to answer here in principle. is it possible , ah, the existing life cycle of the production of new materials, to be reduced by orders of magnitude to that good an example of a well-known material is graphene, for the discovery of which our compatriots received the nobel prize, and this material has a number of unique properties and, by the way, the era institute together with the university and napolis on offer. a-a sberbank innovation research department, and set about creating the largest digital database of two-dimensional materials database using artificial intelligence. a and in the near future , make it available and this is very important for the development of data analysis in the application of the synthesis of new materials. and when we said promergency about the creation of something fundamentally new , previously non-existent in nature and not reproducible man. and the second factor is the question of time, or can we force artificial intelligence to create new technologies in a matter of months, the

airia institute partially answered this question when it predicted with the help of graph neural networks. calculated experimentally confirmed a new list of materials with exotic properties. the so-called quasi-crystals of these materials are practically non-existent. exists in nature. at the same time, they are unique in terms of properties, for example, they can potentially be applied in the field of electrical engineering, they have an anomalous behavior of conductivity with temperature change. they can also be used as simplifiers in metals alloys of metals, as known before man, but more trusted intuition and trial and error and said various receptors in order to achieve better results with material formulations further. having managed to generalize this experience, having formalized it, he appeared and received some levers for controlling these recipes, but now the state when artificial intelligence has its own intuition is able to do it in minutes and predict, for example, such a class

of materials that represents a binary metal here and this is what was calculated predicted and, accordingly, confirmed by experimental and these results have been published. in one of the world's most prestigious journals in the field, crystallography. there is another example from the field, for example, energy. our specialists are able were to calculate 650,000 different components for electrocatalysts and deceive, and calculation at first principles, replacing quantum chemical calculation and quantum chemistry calculations for complex compounds using artificial intelligence. we scaled the calculation down to the second and were able to quickly predict the properties for a new lattice with a new atomic composition. in this particular case. this is also not a drawn object. this is a lattice that represents the structure that we calculated. literally a second with artificial intelligence, we understand that the goal is to create fundamentally new technologies previously inaccessible to humans. we also should not forget about the improvement of existing technologies, if we talk about materials, one of the promising

technologies, so to speak, is photoaltaika, and we have shown and proved the potential for an increase in efficiency by 0.5%, let me remind you the efficiency of the share of energy that is converted due to the sun into electricity. it would seem that 0.5% is not much, but that recipe is that technological recipe for the production of solar panels through the art of intelligence, increasing the efficiency by 0.5% generates additional capacity, generating electricity 9 kw/h of electricity and in absolute terms. this is quite acceptable. this is comparable, according to our estimates, to about 2,500 households. this is quite a lot for itself, and a further increase in efficiency allows us to increase the capacity of electricity, and here we are with our partner, one of the largest manufacturers of sochi panels, already showing this potential. pragmatic approach in synthesis new materials require work in conditions of uncertainty in conditions of small resources and

conditions, so to speak, of contradictions, for example, the task of design research, that in actual engineering we solve the problem under the condition of the following contradictions. we want to have the material as light as possible, but at the same time as strong as possible. and for this, we created models that create a pattern of porous materials in such a way that during its 3d printing, it is asked to be maximum and here are specific samples that we literally recently synthesized with a 3d printer. and and all these calculations were carried out using generative models. we, as scientists, are often inspired by art, and it is worth noting that, for example, here, but according to the verse, and according to the poem further, there we are a generated image using the kandinsky model and trained on the christafar by sberbank specialists and based on technology based on multimodal technology , which synthesize such objects, just lie the same ones do not grow. the architecture we use to synthesize new materials i will end my report not with the thesis that we

have problems, but rather with the thesis that we have colossal opportunities. we want to move from the digital world to the physical world, so to speak, generative - the totality of all possible materials that artificial intelligence is able to synthesize exceeds the number of atoms in the universe, and this is the digital world when i talk about the digital world. it could be the formulation of alloys, like drawings of chemical formulas for a new one for new chemical elements. pharmaceutics. this the volume is huge, but the neck is narrow. in this story. this is a synthesis - this is equipment, if we expand it, then we will give new opportunities to our industries, we will create new technologies, new energetic technologies previously inaccessible to man. here is a specific proposal to focus on the creation of the so-called digital technology testing grounds with the possibility of rapid prototyping, the creation of experimental samples of materials based on the result. synthesis of artificial intelligence, if we create such a flow, expanding this funnel, we will give a colossal an opportunity for industry, and there

should be equal access to this site both from scientists and from the state, and from business. i'm sorry. that's all for me, i'm ready to answer questions. thank you so much. thank you , what you say is certainly exciting, as soon as you said that uh is created, materials that are not ee in nature, that is, not on earth. this, of course, is an immediate impression in itself, and certainly creates tremendous opportunities for all areas of human life. i i'm talking about the economy in general everywhere and in the same health care and industry everywhere. it's certainly, uh, very promising very interesting. so, if you say that for this it is necessary to create these polygons for e-equipment. equipment, then, of course, we are with colleagues in the government. understand. let's think for ourselves about what is possible with you. here,

you will tell me the steps to move along the path that you show us. thank you very much. thank you very much, simon e, thank you huge, vladimir vladimirovich indeed. you still have five people. i'm probably going to be impolite if we don't let you say anything at all, if possible, briefly, just on the problem, so that on the tasks that you see, uh, two more people you have there and guys. well, uh lucky then, if you can, let's not present a very problem. and here is what you would like to see and feel for all of us together as support, please then alexey naumov please. e, higher school of economics. good afternoon

let's not, let's go. good afternoon colleagues. ah, i do reinforcement learning. and this is actually very important. uh, if you are a small room, i will note a very important task, because it arises in the design of unmanned vehicles in production automation in the design of cooling systems for data centers and reinforcement learning generally underlies these systems and we probably already have a lot, there are a lot , as if examples of the fact that artificial intelligence, with the help of training this attachment, for example, beat world champion a year, and bonding training. it's not just like here's a conventional artificial intelligence model. she studies. but how does a person learn, yes, that is, a person gets up, walks, falls, and that is, he interacts with the environment here, too, if we consider, for example, an unmanned vehicle, yes, then he also interacts. uh, interaction with the environment with other road users and he receives rewards and penalties from interaction there and the goal is just to find

such an optimal strategy that would maximized rewards and minimized penalties, but here the biggest problem is the amount of experience gained. and when we, for example, teach. uh, self-driving car. yes, it is clear that it is very expensive to learn it in practice, and indeed we do not see these unmanned vehicles around us now and what they are doing. uh, many countries that she us too to develop. these are the so-called digital twins, in which unmanned vehicles can be put. and there, respectively, and train him. e. well, here is the biggest the problem arises and you already said in your speech about it that, and if we start using, here are the usual digital twin models in practice, then the price of an error. actually here, uh, just huge. yes, and that means you have to esk, huh? algorithms are reliable algorithms and moreover with mathematical guarantees of reliability, and that's what i did. uh, my team uh, we uh and very often there are actually u groups of researchers

who do the math, but their math is not applicable in practice, yes and there is a group of researchers who make very practical algorithms that are completely inapplicable in real stories. and that's what we did, uh, we made an algorithm that has mathematical guarantees of reliability and at the same time it can be transferred to real environments. this work was very highly appreciated by the scientific community, and today we in russia need to say that we need to develop this topic of reinforcement learning, because indeed it also arises in the automation of production and it is possible to reduce costs and make animal cars in the future. yes, and we really need to develop fundamental research and development. uh, respectively, an engineering school, because without engineers. for example, we will not be able to run our e, algorithms on the present. and now, within the framework of the federal project, there are, you have already mentioned them, the x6 artificial intelligence centers - these are support centers. this initiative is supported by the ministry of economic development until 2024

, and we believe that this is a very correct initiative and vladimir vladimirovich we ask you to extend this initiative until 2030, and we we will be very happy if new artificial intelligence centers appear in russia in a promising and only emerging area of ​​​​artificial intelligence. thanks a lot. thank you very much, alexey, regarding the extension of the program, of course, we will think it over. and it seems to me that we will do it, but also with regard to new centers. here poorer quality already hears. estimate, then we will consult through solutions thank you very much. good luck, yes, as far as drones are concerned, here we have very good developments and more than that, even we e our competitors in many ways, uh, we overtake, in the near future we are preparing an experiment on the use of drones on the high-speed moscow-petersburg please, thank you

very much, the last scientists. we have a girl teacher and scientists from st. petersburg from itmo university, she has extensive experience, including teaching, in addition to scientific achievements, and i ask anna to also present. e. your scientific achievements hello vladimir vladimirovich thank you very much. of course, i also prepared a presentation, but i will not show it, what i do. i am i am into machine learning automation. what is it? this is essentially artificial intelligence, which allows you to create all other artificial intelligence. why are we doing this ? because this year we see that the need for artificial intelligence specialists is seven times higher than the level that was literally 3 years ago, respectively, there are

more applied tasks, more need for implementation, more people are required to do this, and further it will only increase. and we, accordingly, will simply do not have time to graduate a sufficient number of specialists, so we are developing algorithms that make it possible to make the introduction of artificial intelligence much faster and more affordable, both in terms of time and in terms of computing resources, because objectively today in industrial companies not all there are significant computational resources using similar algorithms. we can reduce the time for the development and implementation of artificial intelligence solutions to just a few days. therefore, today we can say that we have algorithms, we know how to develop them, which is not enough to massively introduce artificial intelligence into the industry, firstly, this data is primarily industrial data

because, unlike the banking sector and trade areas in each industry completely different processes completely different equipment. it just doesn't generalize that easily. secondly, this is the lack of technological testing grounds that could exist at least for the main industries. industries on the main processes within these technological testing grounds. we could both create algorithms or show us what they are capable of as industry representatives. could assess what they are capable of and assess the potential risks from implementation. since today the implementation in the company is hampered also because after some algorithm has been developed and implemented, it often drags along. a whole, like a chain of additional actions. it is necessary to change entire business processes, and sometimes the whole system is a problem. and it seems to me, within the framework

of such technological testing grounds. it could be solved. i have all thanks. thank you so much. thank you so much. i have also noted this for myself and regarding technological polygons and data, especially in the field of industry. eh, this, of course, is such a complex task, but we need to work on the industries to understand, well, with your help. i hope you also understand how. is it possible to arrange this work, how to stimulate our colleagues, uh, in various industries with so that you and your colleagues who are engaged in this kind of these areas. uh, there was a necessary, uh, basis for generalization and work. we will definitely work. it is extremely important that i fully agree with your conclusions, and in my

speech i also spoke about the tasks in the field of personnel training. and now, if we can add all this sms, then without any doubt. we will get results. in all other areas of activity related to artificial intelligence. thank you very much. uh i want everyone thank you. uh, for your work, for today's participation in this work , i would like to thank the germans for your dedication to this. ah, serious attention. don't just don't leave this direction, on the contrary. you support it in every possible way, as well as all other participants in this process, uh, and uh people who are engaged in scientific creativity and those who organize this work, and in our companies in uh, government, uh and wish you success, because that this is absolutely no exaggeration

the success of the country as a whole will depend on your success. this is absolutely accurate and there is no doubt, and i want you to understand that we also understand this and are aware of our tasks in this area. well, of course, we can only achieve an effective solution by joining forces together with the people who work here in the industry. thank you very much and all the best. it was a broadcast from an international travel conference in the world of artificial intelligence. now, to the news, the heads of the russian and turkish defense ministries discussed the situation on northern syria and the grain deal sergei shoigu and khulasiakar talked on the phone, at the initiative of the turkish country, the ministers. we touched upon the issues of continuing the implementation of the black sea initiative to

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this is russia 24 we continue with the main facts of this day pridnestrovie appealed to the un and the osce because of the crisis in relations with the authorities in chisinau, the unrecognized republic accuses chisinau of pressure on tiraspol and the threat to humanitarian disasters in the region. now the head of the ministry of foreign affairs of transnistria, vitaly ignatiev, vitaly viktorovich, is directly connected with the facts. hello , tell us more about this appeal. what are you waiting for? what kind of reaction? well, most importantly, we are waiting for the end of social humanism.