Not a long time ago the Internet was all about pages where you could only read information. In contrast, now, people have an opportunity to create and share content online. So how did we get here?
Before we explore the definition of web3, it is worthwhile to break down the history of the Internet into three periods: web1, web2, and web3. Web1 is read-only, web2 is read-write and web3 is read-write-own. Let’s now figure out what it means.
Web1 was created in 1990 and featured mainly static websites, allowing users to only view information. Any online interactions were limited. The next generation of the Internet arrived in the form of web2. Instead of providing just content to users, companies also began to engage user-to-user interaction by utilizing platforms that allowed people to create content. However, big tech companies got control over the content and used it for monetary gains. For example, Amazon and Facebook collected personal information to facilitate better target marketing. Users became concerned about their data privacy and digital identity. Obviously, a decentralized version of the web with users in control of their data was a necessity. That is how web3 emerged.
It was aimed to create peer-to-peer network using blockchain technology that enabled users to take away the dominance of tech giants and get a complete ownership of their data. Users could connect with each other, share data and engage in transactions privately without depending on intermediaries. All concepts of web3 significantly contribute to the formation of a decentralized system. It means that no single centralized server can control the data, there is no central authority that governs decision-making and there is no central place to store information. Sounds nice, doesn’t it? But isn’t it too good to be true?
To answer this question, we can refer to the chart below:
It shows that the top 9% of accounts hold 80% of the whole market value of NFTs on the Ethereum blockchain. Moreover, the chart on the right depicts that only 2% of accounts own 95% of Bitcoin. Summarizing the given data, we can make a conclusion: “The advertised decentralization of power out of the hands of a few has been a re-centralization of power into the hands of fewer”.
Web3 is also accused of the existence of middlemen. A majority of decentralized applications, so-called dApps, rely on centralized services. Otherwise, it would be extremely time-consuming and capital-intensive for developers to run their own servers. Some platforms, such as Alchemy and Moralis, help build up dApps much faster.
Despite all contradictions, Web3 is still considered to be a novelty, a young and evolving system, so a lot of developers are continuing working on its improvements. We are on the way to creating a better web. Although web3 currently depends on centralized infrastructure, it takes time to build a high-quality and reliable one.
There are 17 rare earth minerals witch are vital to the production of every high tech piece of hardware in the world. The biggest manufacturer of those by far is China which in 2010 controlled 93% of world supply. As former Chineese peoples workers party first secretary., father of mnodern chineese capitalism Deng xiaoping famusly said: “Middle east have oil, china have rare earths”. But does the leverage on this market really make as influencial political weapon as oil does?
Rare earth minerals in contradiction to what the name suggests are not that rare. Average abundance in the earth’s crust of rarest thallium and most wiedly used in manucturing, Neudimium and cerium are: 0.52 ppm (parts per million), 41.5 ppm, and 66.5 ppm respectively. Comparing this to more widely used elements like silver 0.075 ppm and coper 60 ppm, we can see that scarcity is not an issue. Problem lays in the way taht REE’s are distribiuted troughout the earth’s crust, they dont occur in concentrated easly minable deposits like other minerals, in the result process of extracting those minerals requires extensive usage of strong chemicals severly polluting the enviroment around the mine. Beacuse of this reason China beacme the biggest fish in the market as western countries care about eviroment to much grater extent.
China Started extracting REE’s only in 1980’s when liberalisation of economy under already mentioned Deng xiaoping presented oportunity to dissrupt global supply chains by undercuting exsisting competition with low operationg cost due to lack of enviromental regulations and cheap labour. Up to this point most of world supply came from single mine in mountain pass california which is still exploited on and off to this day but struggles financialy and holds only fraction of previous market share.
As tensions between China and the West rises, and world is inevitably heading towards new cold war, it beacame clear that after decades of chineese dominance in this key area poses a serious problem to the wesatern nations. But quickly after first usage of this dominance by chineese rest of the world acknowleged issues posed by this situation and started implementing mesures to reduce their dependence on chinnese resourcess.
Business loyalty to artificial intelligence technologies is growing. This is driven by the increasing availability of solutions, accumulated experience of projects and real effects of implementation. The penetration of artificial intelligence (AI) technologies in business processes is growing worldwide. According to international company McKinsey, in 2021, 56% of global respondent companies used AI in at least one area of business, a figure that has increased by 6% since 2020. AI is predominantly used to optimize service operations (27% of companies), improve products (22%) and automate contact centers (22%).
The automotive, retail and FMCG sectors have the highest AI maturity – the degree to which they are using technology capabilities for high performance, according to international Accenture. According to the company, AI maturity reached 12% last year among the world’s largest companies, with an average of 30% of revenue generated by AI. In all, nearly 75% of the world’s major companies have integrated AI into their business strategies, according to Accenture. Forty-two percent of its respondents said the return on AI initiatives exceeded expectations.
In Russia, according to the National Research University Higher School of Economics (NRU HSE), almost one in three large businesses used AI in 2021 – primarily speech technologies (voice assistants, chatbots and other applications that work to automate the process of communication with the customer).
“Another powerful area is predictive analytics that aggregates large volumes of data,” notes Igor Pivovarov, chief analyst at the MIPT Research Center for Applied Artificial Intelligence Systems.
In some areas in large and medium-sized companies, AI is already integrated into 20% of the processes and can increase their efficiency six to seven times, said Deputy Prime Minister Dmitry Chernyshenko at the business breakfast “How to unite the business for mass implementation of AI in the industries” at the Artificial Intelligence Journey (AIJ) international conference. At the meeting, industry representatives discussed successful examples of implementing AI solutions in business.
In construction, AI helps to reduce downtime by five times, to reduce time by 48% and to reduce costs by 10-12%, said at the AIJ business breakfast Anton Elistratov, General Director of the development group of companies “Samolet”. “Gazpromneft is producing oil found by AI, says Oleg Tretiak, the company’s acting director of digital transformation. According to him, the company plans to double its investments in these technologies.
Anatoly Popov, deputy chairman of the board and head of Sberbank’s Corporate and Investment Business Block, presented at AIJ a service developed for the bank’s clients called Demand Forecasting in Manufacturing and Retail. “The accuracy of demand forecasting on the basis of AI models with details by region, time and other parameters reaches almost 100% and allows to increase profitability in trade and production,” said Anatoly Popov.
Barriers and opportunities
Artificial intelligence is becoming more accessible and efficient, say the authors of the Stanford Institute for Human-Centered AI Index 2022 report: since 2018, the cost of learning to classify images has dropped by 63.6%, and training time has fallen by 94%. Thanks to the democratization of technology, they are becoming more common in various industries – fintech, medicine, logistics, retail, industry, and marketing.
The main trend this year is customization or very simple application of industrial AI, available even to small companies, confirms Sberbank. At the bank itself, the financial impact of AI in 2021 was 205 billion rubles, the goal for this year is 230-250 billion rubles, said First Deputy Chairman of Sberbank Alexander Vedyakhin. More than 85% of client ways already contain artificial intelligence technologies, “smart” algorithms cover more than 65% of bank processes.
The services sector is the leader in implementing AI, says Konstantin Vishnevsky, director of the Institute of Statistical Studies and Knowledge Economy: the most intensive use of the technology is in the financial sector (13%) and trade (14.4%), while in the real economy (manufacturing, transport, etc.) the use of AI solutions is gradually increasing, but on average does not exceed 5%.
According to Sber forecasts, the greatest effect on gross added value by 2025 will bring the implementation of AI solutions in Russian construction (+2.1%), agriculture (+1.6%), manufacturing (+1.3%) and healthcare (+1%).
Unlike the financial, telecom and retail industries, capital-intensive industries with many complex physical assets (metallurgy, construction) implement AI technologies more slowly and the barriers are higher there, explains Alexey Masyutin, Head of the HSE AI Center.
AI projects can still be afforded mainly by major players due to high complexity of solutions, lack of dedicated staff and necessary datasets, the need to adapt AI solutions for specific tasks and radical restructuring of most business processes, commented Konstantin Vishnevsky.
One of the barriers is the cost of development and lack of ready, low-cost and convenient services that could be used “out of the box,” says Igor Pivovarov.
Growth points
Igor Pivovarov notes that in order to speed up the introduction of artificial intelligence technologies, it is impossible without investments from the state or provision of ready and available services by major players: “Support will be needed for small businesses that want to introduce AI technologies in their work, for example, by grants or tax reductions.
“If a company buys a boxed product based on AI, its implementation will require an already built IT infrastructure and data culture, and if a custom development is planned – the formation of internal competence of data researchers and ML-engineers,” adds Alexey Masyutin.
It is necessary to differentiate the processes of training of AI specialists – for example, to prepare the required number of AI engineers and AI scientists in a small number of universities – flagships in the development of breakthrough fundamental and applied AI solutions, says the head of the Center for Applied Artificial Intelligence “Skoltech” Evgeny Burnaev. At the same time it is necessary to stimulate the introduction of technologies based on AI in the real sector of the economy with scientifically and expertly proven effect from the expected implementation and its further replication.
The creation of an information resource platform that would combine both demand and supply for various solutions and developments based on AI could stimulate greater dynamics of projects using artificial intelligence in Russia, believes Alexey Masyutin: “We need our own analogue of profi.ru – profi AI.
We all love LEGO, a truly generation-free entertainment that loads of people are obsessed about despite age.Although LEGO sets have entertained generations of children and adults, the challenge of designing customized builds matching the complexity of real-world or imagined scenes remains too great for the average enthusiast.
Researchers at the Massachusetts Institute of Technology (MIT) have developed an Image2Lego neural network algorithm that builds instructions for building a 3D LEGO model from a 2D image.
The process goes as following:
1. The user uploads a regular image: for example, with an airplane.
2. The algorithm recognizes the plane in the photo and uploads it to the Image2Lego neural network.
3. Trained Image2Lego transforms a 2D picture into a 3D model of an airplane using neural networks and shows how it should look if assembled from LEGO bricks.
4. The algorithm creates instructions for assembling the model and tells you what parts will be needed for this.
Here is how the researches described the objective: “We design a novel solution to this problem that uses an octree-structured autoencoder trained on 3D voxelized models to obtain a feasible latent representation for model reconstruction, and a separate network trained to predict this latent representation from 2D images.”
In the past we’ve already seen implementations of AI in LEGO assembling. For example, BrickIt allows a user to lay out all their LEGO pieces flat and scan with a mobile. The image then is processed by computer vision to detect available bricks suggesting possible models for assembling, both genuine and custom. However, the MIT’s neural network is quite opposite to what the BrickIt app offers and allows more usage scenarios.
Assembling a simple model like a tower or a car is just too banal. Image2Lego is capable of processing a face image to create detailed instructions for assembling a detailed 3D face. Trying it out for building a model of your own face must be a unique thing.
Here is Image2Lego’s instructions for assembling actor Chris Pratt’s face:
Last time I showed you some really cool and practical device that helps in improving your skiing, so I would stay in this convention but with much simpler “device” if u can name it that. But first some data:
-5 in 10 young people listen to their music or other audio too loudly
-4 in 10 young people are around dangerously loud noises during events like concerts and sports games
I can not recall a moment when I saw someone studying without headphones. We are so attached to our headphones but not only because of music, often is just cause we want to cut ourselves from outer world, even listening to silence.
Cities are so biased and loud that sometimes it is hard to focus on your thoughts and basically it overwhelms us. Too loud noises can be really dangerous. In the US one in eight people has hearing loss in both ears so nearly 13% of their society is going deaf. Sometimes music isn’t the best tool to focus because it is extremely hard to find one without lyrics or sounds that disturb you even more. In my case only few jazz artists will be helpful. So why we are paying hundreds for device we don’t even use?
Interesting solution was introduced by Belgian company named Loops. The founders suffered for chronic ringing in their ears. Their vision was to release soundproof plugs to concerts, learning or for people with noisy lifestyle. What is interesting its not only an earplug but it is a kind of jewellery due to its aesthetic and unusual design.
I think earplugs can be necessary in sooner future, because cities are getting bigger and big festivals gaining popularity among young people. Headphones are vicious circle as they are damaging your ear fibres and cause loss of hearing, so you start to volume up your music with cause it even faster.
Let me know in the comments what are yours views about focus and loud noises. Maybe you noticed some changes in yours hearing lately?
The number of spheres where Artificial Intelligence successfully substitutes humans’ actions and work is considerably growing and is projected to grow at the same pace in the foreseeable future. I am genuinely impressed by the diversity of these fields. But have you ever thought whether AI could substitute judges in courts and that instead of a person you will see a robot in a courtroom?
To begin with, the application of Artificial Intelligence in the judicial systems is already sufficiently developed in some countries. Automatic information and evidence extraction, prediction of crime occurrence and optimisation in data acquisition are one of the most common AI techniques integrated into judicial policies. However, whether AI application in courts could evolve from assistance into the replacement of humanoid judges is still an open question.
Let’s figure out how the idea of AI-driven adjudication has arisen. There are 2 main issues that have led to the consideration of humanoid judges’ inefficiency. The presence of human bias and discrimination is the first obstacle on the road to the judicial system’s perfection. It is a common situation that personal preconceptions of judges regarding the gender, race or age of the defendant could incline them to a negative court resolution. Another common problem that is widespread among judges is the correlation of personal mood and mental condition with their decision. Very often the final argument of the jury is considerably influenced by his feelings or emotions, which could be the reason for the unfair imprisonment of an innocent person or ,on the contrary, the relief of a dangerous maniac. Of course, a robotic system is not capable of being affected by a bad mood because of a broken nail or a dramatic break-up and neither is it programmed to be dependent on certain prejudices. Even though, now there is an ongoing debate among society’s members whether a robot could take the role of the judge. Although AI systems guarantee greater fairness and the elimination of bias and discrimination in the courts, numerous issues still have to be considered and fixed. The lack of emotional intelligence and accountability and moreover the inability to make unprecedented decisions set significant barriers to the transition of the judicial system to a fully technological one. Many professional data analysts are convinced, that since AI is trained by being programmed to synthesise and analyse the immense inflow of previous cases, it unintentionally processes the bias and prejudice of numerous lawyers who dealt with these cases in the past. Hence, there is no real possibility to get rid of biased decisions in courts since AI is constantly digesting bias and discrimination patterns of humanoid judges.
So, in my opinion, the application of AI in the judicial environment should be emphasised on help and assistance rather than on the total substitution of human beings at the head of the court. Artificial Intelligence could automate and alleviate the work of judges to a large extent by saving them a lot of time and providing them with a greater possibility to concentrate more on the final decision rather than on case details and documentary work. Of course, we could expect the introduction of AI to the judge’s duties, as the presence of bias, discrimination and prejudice are at least minimised there, but before this implementation, bias and discrimination tendencies should be removed from people’s mindsets thus bringing AI judicial systems to perfection.
AI can do a lot of good for those involved in music – and many operations that take humans hours to complete, AI can execute in a matter of seconds, without engaging us in the entire event. One of the most recent solutions that has interested me is LALAL.AI, which allows us to completely isolate background music from vocals.
LALAL.AI is an AI-based music source separation solution built using artificial intelligence and machine learning. As an outcome, you can obtain smooth vocal and instrumental files in the audio format of your choice.
To train, LALAL.AI utilizes a neural network and feeds it with thousands of studio-quality songs over the period of several months. To achieve high accuracy, its engineers used 20TB of training data. Its advanced artificial intelligence is also becoming wiser everyday, executing more procedures to produce more exact outcomes. Furthermore, its machine learning algorithms can recognize songs quickly and reliably isolate background tracks and voices from audio files like films, videos, songs, or podcasts.
Although LALAL.AI may seem complicated when you look into the details of the technology, its utility is the total opposite. Anyone with minimal computer knowledge can use this program. It offers an easy-to-use interface and straightforward techniques for removing vocals or instrument sounds from any record.
Simply upload the audio file of your choosing, wait a few moments, and you’ll have the raw background music or vocals themselves. Instrumentals are ideal for performers who want to cover a song or simply can round out a playlist of pure beats.
LALAL.AI supports a variety of file formats such as MP3, OGG, FLAC, WAV, AVI, MKV, MP4, AAC and AIFF. The best part is that it supports both audio and video files. You can also download your track in the fast processing queue. Another important thing is that LALAL.AI extracts audio tracks in the same file format and quality as the input file. This is important because many other video or audio editor codecs do not recognise different file types, causing problems.
There is no problem, though, with modifying such a track. You don’t have to bother duplicating the entire thing in a program or searching for it on the internet. LALAL. AI allows you to delete specific components from the background music, such as percussion, bass, piano sounds, electric or acoustic guitars, with a single click, while also allowing you to add your own accents.
The same AI technique can ‘clean up’ an audio file, and it works well enough to transform a music recorded with a smartphone pre-release at a concert into a nearly finished track suitable for streaming platforms. The whole thing is quite impressive, and it is reasonable to say that LALAL.AI is the audio industry’s counterpart of DALL-E.
The acquisition of Twitter by Elon Musk began on April 14 and concluded on October 27. In the first month of his saga, he became the largest shareholder with a 9.1 percent ownership stake. He wrote that he wants to create “a common digital town square”, where everyone can speak freely.
Elon’s actions
In the first two days, an eccentric billionaire took over the company he fired half of the employees including the CEO, CFO, and head of legal policy, trust, and safety. A week after he set an ultimatum for the rest of the employees: either work in a “hardcore environment” or leave. Probably he didn’t expect that the next 1200 employees will resign. It led to the closing of whole departments of the company. The funny thing, in Californian law there is a record that says all formalities regarding employee resignation must be done within 72 hours after it. For Twitter, it was hard to do, because the department which was responsible for this action was closed. The CEO of Tesla scrapped the company’s work-from-home policy, but after those events, he had to temporarily close offices. Everything led to lots of bad press on Elon Musk, the creation of #RIPTwitter, and 4 million dollars lost per day.
Funny paradox
Everyone wants to see Twitter dead and it makes this media source as alive as it never was. The platform now is doing very well, besides the internal problems of the company, they are making all-time highs almost every day. Last week there were 1.6 million daily active users. Moreover, a lot of Elon’s fans are ready to work for free just to help their “hero”. I think that Twitter’s mission is the thing that makes it special. The platform is elevating citizen journalism and by that, they are competing with mainstream media oligopoly, forcing them to be more accurate. There is freedom of speech, but as Elon Musk said, not freedom of reach. It means that negative/ hate tweets will be deboosted. You won’t be able to find them unless you specifically seek them out.
What do you think about Elon’s recent moves? Is Twitter going to die or rather thrive?
In recent years the market of voice assistants had significantly grown as some leading tech companies were striving to developing their voice assistants’ technologies. The market is being led my three technological giants: Google, Amazon and Apple, having respectively 81,5mln, 77,6mln and 71,6mln users[1]. Although those technologies are developing and gaining users, all of those companies are struggling to monetize their assistants. In this post I would like to focus and analyze the case of Amazons’ Alexa.
Probably most of you know what Alexa is, however if you do not, let me quickly explain. Alexa is an intelligent voice assistant which is connected to a cloud-based service with voice commands. According to Wikipedia[2], “the technology is largely based on a Polish speech synthesizer named Ivona, bought by Amazon in 2013”. Customers may use it through Amazons’ Echo device, a hands-free speaker. In order to use the device, you need to “wake it up” simply by saying “Alexa”. Moreover, the device is known for the ability to amuse customers with short stories and having a great sense of humor.
According to Financial Times[3], Amazon is planning some huge layoffs, and Alexa unit is probably an area which will get hit hardest. Alexa was launched in 2014 and through all of those years was being strongly developed, what resulted in creation of one of the best voice assistants in the world. Alexa is able to perform more than 100000 skills globally, with the biggest skillset of 77000 in US[4]. The functionality ranges from voice interaction, making to-do lists, playing music and providing weather and news to controlling several smart devices, however the list is much longer.
As we may look at the data from previous years, Alexa had the biggest impact and a remarkable advantage on the voice assistant industry according to Voicebot[5] in 2019 and 2020. Moreover, in 2021 the shipments of Alexa reached 21.9mln of devices[6] and Amazon had a 44% market share (according to the same research, in 2020 the shipments reached 45mln devices and a 64% market share, however due to pandemic it is being treated as an outlier).
All of those data might have looked optimistic, however in 2021 sales dropped to 4mln which means 78.9% decrease year to year[7] and currently Amazon is planning layoffs. The biggest problem is that, through all these years they were struggling to monetize it, but unfortunately failed as the devices are being sold at cost[8]. The idea was to sell people a device which they can use not only to check weather and news but also to purchase products from Amazon. It seems to be a good idea, as the purchase was supposed to be done by voice, albeit people are wary of buying things without seeing them. The next idea was to give a possibility of ordering food or drinks from restaurants, unfortunately it also failed. In 2021 only 17.5% of consumers used voice shipping, which definitely isn’t enough to satisfy the creators of voice assistants.
As a voice assistant, Alexa has to listen all of the time, and that is what concerns over 33% of consumers, as Voicebot mentions. “Amazon’s Alexa collects more of your data than any other smart assistant”[9], to be precise 37 out of 48 possible parameters, and e.g., Google collects 28. That information also isn’t encouraging for consumers, specifically for those who are vulnerable about their privacy.
Other companies are also struggling with creating a profitable business model for their devices and I think that the nearest future will determine the existence of voice assistants. Let me know your thoughts about those devices, and whether you have any ideas to monetize them!
However, at the moment we can see that it is just beginning to develop. Even though nicknames appeared gradually (at alphabetical intervals), which should have stirred up public interest, due to insufficient marketing (an ordinary user could only find out about this from an automatic Telegram message after the update, which many people do not pay attention to), many ordinary users and companies are not aware of this feature. Therefore, we see that now many valuable usernames are being sold at a very cheap price. For example @honda (3.150 TON) @clubhouse (3.302 TON) @kaspersky (3.360 TON) and so on. This suggests that for now, many large companies are in no hurry to buy usernames with the names of their brands, and later when the website becomes more popular, they will have to buy them at an inflated price, since at the moment most usernames are bought by resellers who rely on future sell out more expensive. For example, TON Wallet Ef-exuKIGuFDFVB0ldQzCJxVV6U-YT4B3nrg1VE8Mj1yOEp0 has already bought more than 50 expensive usernames, including the three most expensive ones. (Picture 2)
Picture 3. The most expensive usernames sold on fragment.com.
Telegram partially solves this problem with the help of a verification system that gives a special checkbox to official channels and groups, however, in one of the latest updates, it became possible for premium users to put custom stickers in the status next to the name, among which there is also a Telegram verification checkbox. (Picture 3)
Picture 4. Premium emoji status examples.
To conclude, Telegram users now can securely sell and buy usernames. However, at the moment we see that it is more like a beta version because many nuances need to be finalized.
