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Five Primary Technologies of the Decade: Choice of Venture Investors

28.12.2019
Source: Forbes.ru

Forbes asked venture investors and high-tech entrepreneurs to name the leading technologies that changed the economy in the 2010s and give their forecast for the next decade.

To name the primary technologies that determined the main directions of progress in the 2010s, Forbes editors turned to Russian specialists in the field of venture investments and entrepreneurs in the area of high technologies.

The survey was attended by Alexey Basov, RVC Investment Director, Alexander Belov, Director of the business incubator MIPT Fiztekh. Start, Timur Bergaliev, Head of the laboratory of applied cybernetic systems at MIPT, Igor Vasiliev, Senior Analyst at Sberbank, Anton Gopka, the founder of ATEM fund Capital, Denis Efremov, Investment Director of Da Vinci Capital, Alexander Ivanov, Founder of Waves, Arthur Isaev, Founder and Chairman of the Board of Directors of the Institute for Human Stem Cells, Evgeny Kuznetsov, Ambassador of SingularityU Moscow, General Director of Orbit Capital Partners, Dmitry Pimkin, Deputy Chairman of the Board, Head of Investment Division K, RUSNANO Management Company, Andrey Prokhorovich, General Director of Eurasia Development, Timur Tavberidze, General Director of MIPT Engineering Centre, Alexey Fedorov, Head of the Group of Quantum Information Technologies of the Russian Quantum Centre.

The technology long list, compiled by experts with the participation of the editors, included:

  • 3D bioprinting, organ printing
  • 5G networks
  • Big Data
  • Autonomous driving, drones
  • Alternative energy
  • Blockchain
  • Virtual and Augmented Reality
  • Shale Oil Production
  • Immunotherapy, including Checkpoint therapy for cancer
  • Artificial Intelligence (machine learning, speech and image recognition)
  • Quantum Communication
  • Quantum Computing
  • Cyber-physical systems ("robots build robots")
  • Neurobiology, optogenetics
  • Cloud Technologies
  • Regenerative Medicine
  • Gene Editing and Gene Therapy
  • Robotics
  • Synthetic Biology
  • Synthetic Food
  • Power Accumulation Technologies
  • Photonics
  • Digital Medicine
  • Electroceuticals

Based on the results of the vote, five technologies were selected that deserve to be called "technologies of the Decade," according to the majority of experts. We present them in increasing order of the number of votes cast.

5. Gene Editing and Gene Therapy

Explains Konstantin Severinov, professor at the Skolkovo Institute of Science and Technology and Rutgers University (New Jersey, USA):

The idea of genomic editing therapy is as follows: if you can cut a DNA molecule in a chromosome, for example, a human cell in a precisely specified place, then the cell itself will sew up the introduced gap in the way you need — this will, for example, correct any mutation or create a change in the genetic material that leads to the appearance of the sign you need. Clinical applications — for example, to fight cancer or muscle dystrophy — can be based on different editing technologies. In 2010, CRISPR technology did not exist: although the phenomenon itself was already known, it had no relation to practice. Then there were two approaches with which scientists tried to solve the problems of genomic editing — "molecular scissors" based on "zinc fingers" and talen. These technologies work well, but they are complex: in order to edit any specific place in the genome, literally, years of work of highly qualified specialists were required.

In 2012, the first articles were published, showing that CRISPR nucleases can also be used for editing. Editing with CRISPR nucleases is an order of magnitude easier than with previous editors. This led to a revolution: people began to say that now gene editing can be done in a garage (although this is an exaggeration). Speed and simplicity are two factors that distinguish CRISPR 2010 technology from the technology of the 2000s.

However, “simple” is not necessarily good: “Zinc fingers” are often more accurate editors than CRISPR. There are very few clinical studies using genomic editing for the treatment of human diseases, and many of them are based on zinc fingers. At the same time, it should be understood: all these technologies are so new that no real patients have been treating them yet, apart from the scandalous experiences of He Jiankui, who also didn’t treat anyone, but simply created girls with targeted mutations caused by CRISPR nucleases. Press publications sometimes leave a feeling that tomorrow we will cure everyone. Still, it will not be tomorrow, but at best in five to ten years, and only for a limited circle of rare diseases. Clinical studies are in their early stages. On the other hand, these methods are now widely used to obtain "edited" farm animals and plants, products of which are already on sale in some countries. You just need to understand that such animals and plants are GMOs.

4. Big Data

David Rafalovsky, Executive Vice President of Sberbank, Head of Technology, speaks out:

We are on the verge of changes that can compare with the birth of life on Earth. In the 2010s, we moved from the information age to the digital one. Today is a data economy.

The main difference between the digital age and the information one is that people have learned how to work with raw, unstructured data. Over the past 15 years, the number of machines connected to the network, the number of people using the Internet, and computer performance have grown exponentially. At the turn of the 2010s, Big Data technologies became available to a large business. At the same time, we enter an exponentially developing world with linear thinking and a linear idea of the future — this is the fundamental contradiction of the new era. We called this new world, 'transit' and 'turbulent'.

It is characterized by a massive increase in the data flow. Every two days, we now generate as much information as it has appeared since the creation of the world until 2003. According to forecasts, in 2025, we will generate information by a hundred times more: 62 GB of data.

The technologies of big data analysis, computer vision, speech analytics, speech, and voice recognition allow businesses and governments to "reinvent" themselves. Producers of goods and services understand that a "figure" comes into their lives. And if they do not engage in digitalisation, then competitors will do it, which will eventually drive them out of the market since digitalization will radically change the business model, customer satisfaction, cost, and quality of any goods and services. Big data has a radical impact on society. To be on the wave of progress, decisions are needed at the level of each person, and business. It is necessary to provide opportunities for retraining personnel, introduce new business models, and reform the management and service delivery system.

3. Cloud Technologies

Comment by Mikhail Lobotsky, Managing Director of SberCloud:

In the second decade of the 21st century, cloud technology has become a multi-billion dollar business, which will continue to grow in the next decade. According to analysts, the IDC cloud market will grow by 22.3% per year and will reach $500 billion by 2023. The cloud divisions of giants such as Amazon and Microsoft have become key divisions of these companies. The reason for such an impressive triumph of clouds is their high demand in business and versatility. Any company can get its competitive advantages from the use of cloud infrastructure and services, regardless of the size and direction of its activities. These advantages can be described in two words: speed and efficiency.

Migration to the cloud makes it possible for businesses to quickly scale their computing power and rapidly launch new products and solutions on them. You can get additional servers or storage systems from your cloud provider within minutes. If you do this on your own IT infrastructure, then the process of purchasing and setting up equipment, hiring IT professionals can take months. With the same speed, in the cloud, you can reduce the consumption of computing power and, accordingly, save money.

Separately, I would like to note that the clouds have become a complementary technology for what is now called “artificial intelligence”. This fact further contributes to the growing popularity of cloud solutions. Now, for training neural network models, you don’t need to buy much expensive hardware. Even a small company or an independent developer can use cloud power to train their software models.

2 Alternative Energy

Evgeny Terukov, doctor of technical sciences, professor at the Physicotechnical Institute named after А. F. Ioffe in St. Petersburg:

Alternative energy sources (AES) usually mean solar energy, as well as wind, tidal, and bioenergy energy. In terms of the pace of development, the wind is in the first place today. Still, there are some comments regarding the environmental consequences of wind generation. As for solar energy, over this century, its total capacity has grown from about 1 GW in 2001 to 500 GW now.

The technology itself has not fundamentally changed over this time: The physical phenomenon on which solar generation is based was discovered at the beginning of the last century, and in the 1950s, the first silicon solar cells for space programs were developed. Only later, technologies developed for space were transferred to ground-based energy.

The only problem was that the solar cells that were produced for space were expensive. Therefore, the large-scale introduction of solar energy has been delayed. Terrestrial energy gained global proportions only in the 21st century. An active increase in the volume of solar stations has begun. Until 2011, 40 GW of capacities were built. Then every year 30, 40, 60 GW were added — every year; there was an increase in the commissioning of new solar stations. Similar dynamics and wind farms.

Of course, this growth was influenced by both the problems that nuclear energy (safety) faced and the problem of CO2 emissions associated with the use of traditional sources of raw materials in the energy sector (e.g., coal, gas, oil, etc.). However, it must be understood that the capital costs of introducing the capacities of the atomic power station and traditional generation are incommensurable: a 5 MW solar station is built for 2–3 months, and a large-scale facility of conventional energy — nuclear, thermal or hydro — take years to make.

Concerning profitability, it is difficult to compare, since it is impossible to calculate, for example, the long-term damage from the disposal of structural materials from a spent nuclear reactor. However, now in the USA and Germany, the cost of solar electricity has become equal to. For some regions of these countries, it has become lower than the energy received from traditional sources. The most powerful solar energy today is being developed by China, the USA, Japan, Germany, and India.

According to experts, by the end of the century, 80–90% of energy will be produced using renewable energy technologies.

1. Artificial Intelligence

Told by Igor Pivovarov, Chief Analyst at the NTI Competence Centre at the Moscow Institute of Physics and Technology, in the direction of Artificial Intelligence:

Developments in the field of artificial intelligence have been in progress for a long time: the beginning was decided to count from the famous conference in Dartmus, the USA in 1956. Since then, there have been several different generations of AI technologies — question and answer systems, expert systems, many other approaches and, finally, machine learning technologies, primarily deep neural networks, which today are mainly associated with the concept of "artificial intelligence". A neural network is a simple mathematical model of the human cerebral cortex, capable of learning to solve a specific problem on a large amount of data. In such a network, there can be several million neurons connected by billions of connections (by the way, in the human brain ~ 86 billion neurons and trillions of contacts).

The revolution in this area took place around the 2010s and was associated with three factors:

  1. There are cheap computing power that made it possible to train large networks
  2. Vast amounts of tagged data have appeared — for example, photos on Instagram, which users themselves sign with hashtags
  3. Open-source has appeared — open-source code that companies upload to the network, and all programmers can use it.

All these factors significantly accelerated the development of new AI models and their training, and today we have trained networks that can translate from language to language no worse than a person or recognize objects in a picture even better than a person. However, real intellect is still far away. All these technologies are referred to as "weak AI," that is, capable of performing one small task. And any person can perform many different tasks, quickly switching between them. Machines cannot do these so far. Neither can they set goals and plan their implementation. These developments are still underway, so there are many exciting discoveries ahead of us!

By: Alexey Aleksenko.



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