Now We’re Looking for Lepton Flavour Violation
21 July, 2015
Wouldn’t we all like to think that the world that we’re living in is more or less stable? Isn’t there a certain pleasure to be sure that our feet will be pulled to the ground as firmly tomorrow as they are today? Isn’t it reassuring to know that the cup of tea we’ve just put on our desk won’t disappear instantly and reappear on the bottom of the sea on the other side of the planet having traveled its diameter on a straight line? In classical physics, Newton’s laws give us this reassurance. These laws bestow predictability on objects or events as they exist or happen in our reality - on a macroscopic level. On a microscopic level - in particle physics - Fermi’s interaction theory, for instance, postulates that the laws of physics remain the same even after a particle undergoes substantial transformation.
In 1964, however, it became apparent that this isn’t always the case. James Cronin and Val Fitch showed, by examining the decay of subatomic particles called kaons, that a reaction run in reverse does not necessarily retrace the path of the original reaction. This discovery opened a pathway to the theory of electroweak interaction, which in turn gave rise to the theory we all now know as the Standard Model of particle physics.
Although the Standard Model is currently the most convenient paradigm to live with, it doesn’t explain a number of problems, including gravity or dark matter. Other theories compete very actively for the leading role in describing the laws of nature in the most accurate and comprehensive way. To succeed, they have to provide evidence of something that happens outside the limitations of the Standard Model. A promising area to look for this kind of evidence is the decay of a charged lepton (tau lepton) into three lighter leptons (muons), which happen to have a certain characteristic - flavour - that is different from the same characteristic of their ‘mother’ particle. According to the Standard Model, the probability of this decay is vanishingly low, but it can be much higher in other theories.
One experiment at CERN, LHCb, aims at finding this τ → 3μ decay. How are they going to find it? By searching for statistically significant anomalies in an unthinkably large amount of data. How can they find statistically significant anomalies in an unthinkably large amount of data? By using algorithms. These can be trained to separate signal (lepton decays) from background (anything else, really) better than humans. The problem here, however, is not only to find these lepton decays, but also find them in statistically significant numbers. If the Standard Model is correct, the τ → 3μ decays are so rare that their observations are below experimental sensitivity.
To come up with a more sensitive and scale-appropriate solution that would help physicists find evidence of the tau lepton decay into three muons at a statistically significant level, Yandex and CERN’s LHCb experiment have launched a contest for a perfect algorithm. The contest, called ‘Flavours of Physics’, starts on July 20th with the deadline for code submissions on October 12th. It is co-organised with an associated member of the LHCb collaboration, the Yandex School of Data Analysis, and Yandex Data Factory - a big data analytics division of Yandex - and is hosted on a website for predictive modeling and analytics competitions, Kaggle. The winning team or participant will claim a cash prize of $7,000, with $5,000 and $3,000 awarded to the first and the second runners-up. An additional prize in the form of an opportunity to participate in an LHCb workshop at the University of Zurich and $2,000 provided by Intel will be given to the creator of an algorithm that will prove to be the most useful to the LHCb experiment. The data used in this contest will consist both of simulated and real data, acquired in 2011 and 2012, that was used for the τ → 3μ decay analysis in the LHCb experiment.
Contest participants can build on the algorithm provided by the Yandex School of Data Analysis and Yandex Data Factory to make an algorithm of their own.
The metric for evaluation of the algorithms submitted for this contest is very similar to the one used by physicists to evaluate significance of their results, but is much more simple and robust thanks to the collective effort of the Yandex School of Data Analysis and LHCb specialists who have adapted procedures routinely used in the LHCb experiment specifically for this contest. Our expectation is that this metric will help scientists choose the algorithms that they could use on data that will be collected in the LHCb experiment in 2015, and in a wide range of other experiments.
Finding the tau lepton decay might take us out of the comfort zone of the Standard Model, but it just as well may open the door to extra dimensions, shed light on dark matter, and finally explain how gravity works on a quantum level.
Collisions as seen within the LHCb experiment's detector (Image: LHCb/CERN)
Forgetting the right to search
15 June, 2015
The State Duma Committee on Information Policy and Communications has discussed a bill that requires search engine operators to delete hyperlinks to illegal or unreliable information, or even reliable information that refers to events that happened three years ago or more, from their search results on requests from individuals and without a court order.
Internet search is our core business. In more than 15 years in this market, we have put colossal human and financial investments into our search engine, first and foremost, to offer our users search results that are complete, unbiased and useful. If this bill is passed in its current form, a search engine based on these principles will be difficult or even impossible to develop. That is why we feel it is important for us to offer commentary on this bill.
According to its authors, this bill enables any individual to control distribution of unreliable or outdated personal information on the internet. In principle, this gives people a right, which is based on one of the most basic human rights – the right to privacy, including the right to control access to information about oneself. Unfortunately, the procedure offered in this bill does not stop information from being distributed online, but contradicts the basic principles of law and current legislation.
The current law does not permit limiting a person's right to access reliable information. The Constitution of the Russian Federation guarantees everyone the right to freely seek, obtain, transfer, produce and disseminate information by any lawful means (Article 29). The Federal Act ‘On Information, Information Technologies, and Information Protection’ also stipulates an individual’s or organization’s right to search and obtain any information in any form from any source (Article 8). This is exactly what a search engine does – searches for information available through any public source. This bill ignores the right to search for information.
The limitations introduced by this bill reflect imbalance between private and public interests. The need to seek and obtain information often falls within public interest and concerns public figures, whose actions can have an impact on the general public or private lives. This bill impedes people's access to important and reliable information, or makes it impossible to obtain such information. If this bill is passed, the information about a clinic or a doctor, a school or a teacher one is considering to choose, may be impossible to find.
In addition, the procedure for requesting a search engine to remove hyperlinks introduced in this bill opens the door to numerous opportunities for misuse, as it doesn't require any evidence or justification. A search engine, on the other hand, is required to delete an undefined number or hyperlinks to indeterminate web pages. This loophole can very conveniently be used by unscrupulous businesses to undermine their rivals, or by criminals to facilitate fraud.
But even if we assume that it is possible to equal adequate information with inadequate or illegal information in the right to be searched for, one question remains: who will study the information which is searched for, and decide whether it is legal, adequate, relevant or reliable? The bill assigns this role to search engines, while the functions of the court or law enforcement agencies are given to individual commercial organizations. Failure to comply with this role is punished with penalties and litigations.
This bill also ignores the basic principles of information technology and information search. It gives any person the right to request a search engine operator to stop providing hyperlinks to web pages that contain specific information, but it does not require this person to say which hyperlinks should be removed. All they have to do is provide the information, hyperlinks to which they want to be removed. Instead of deleting hyperlinks to specific web pages from search results, a search engine is expected to stop retrieving a piece of information on any search terms and regardless of its location on the internet. For this to become plausible, a search engine operator would have to find all pages containing this information that might appear in any place in search results triggered by any search term that a human mind can come up with. This step alone would take eternity. The next steps would require a search engine operator to make sure that these pages do contain the information hyperlinks to which were requested to be removed, and then confirm that this information is indeed inadequate or older than three years old. It is obvious that this is an impossible task.
Even though the list of flaws of this bill can go further, it doesn't make sense discussing them all at a point when the stipulated procedure itself contradicts the law and is technically impossible.
The current bill is much less well thought through than the Google Spain v ARPD, González (C-131/12) decision by the Court of Justice of the European Union, which has been widely criticized, and which the Russian bill has often been likened to.
The links to be removed from search results mandated by the ruling of the Court of Justice of the European Union are specific, lead to specific information and appear on a narrow class of search terms. Hyperlink erasure is also considered on a case-by-case basis to make sure it does not limit access to important information or alter the balance between private and public interests.
Tune in to Yandex.Radio
4 June, 2015
Today we’ve announced our newly hatched personal ‘jukebox’ media player Yandex.Radio. This service will be appreciated by anyone wishing to always hear ambient music matching their personal preferences, or serving as a backdrop to anything they do or how they feel at any time of their day or night. The user interface of Yandex.Radio is simple and intuitive – it just lets you play the music that you will enjoy.
Yandex.Radio shares a catalogue of more than 20 million tracks with Yandex.Music, Yandex’s recommendation-based music streaming service, which was first launched in 2010 and re-launched after a major overhaul in 2014. While Yandex.Music helps listeners discover new music based on their interests and preferences, Yandex.Radio offers them the music that matches their current mood or activity.
Out of 10 million people currently tuning in to Yandex.Music every month, one million do this on their mobile phones. This million of people just like to have some background music while they are working out in a gym, driving to work, or chatting at a party. Alternatively, people choose their background music to match how they feel at the moment – cheerful or vigorous, moody or relaxed. Yandex.Radio is a service for those who like to choose their music based on their current activity or state of mind. The new service taps into the catalogue of Yandex.Music to offer these users more than a hundred stations to choose from – not only depending on their mood or current activity, but also on a genre or time period of music.
Yandex.Radio is now available on desktop, as well as on iOS- and Android-based devices. The personal recommendation technology in use on the Yandex.Music service has also been implemented in Yandex.Radio to play music based not only on the listener’s settings, but also their personal preferences, streaming history, and previous likes or dislikes.
The new personal jukebox player is currently available free of charge only to users in Russia. The revenue from the service will come from audio advertising, which is a novel format for our business. Just like traditional radio broadcasting, Yandex.Radio will feature audio ads, which in the future will be complemented with a graphic image or text both in the app and on the desktop service. Advertisers on Yandex.Radio will be able to enjoy all the advertising and audience targeting possibilities available to them on Yandex.Direct, as well as banner advertising.
New Concept Yandex.Browser Boosts Privacy and Launches as Beta
21 May, 2015
The minimalist concept version of our Yandex.Browser launched at the end of last year to respond to the highly interactive nature of contemporary web browsing is now available as a beta version, which is designed to also address the rising demand for personal privacy.
To meet the expectations of those users who would like to have more control over their digital footprint, we’re now rolling out a much more private beta version of the experimental Yandex.Browser, available in 15 languages, including English, German, Portuguese, Spanish and French.
Unlike in most browsers, sending the information about users’ behaviour to the developer (i.e. Yandex) in the private version of Yandex.Browser is disabled by default. While sharing browsing history and web cache can in principle be disabled in other browsers, this opportunity isn’t normally offered as a default option. Sharing users’ information helps developers better understand their behaviour and offer them a better browsing experience. The problem is that the right to make a decision whether to share this information is effectively removed from the user – few can find a pathway to customised privacy settings in a browser.
In addition to data sharing disabled by default, Yandex.Browser provides the ‘Stealth Mode’ option, which blocks analytics cookies, sharing plugins etc. This mode is activated by clicking a button conveniently located right next to the browser’s ‘smartbox’, a combined search and address bar, at the top of the screen. The source code of the built-in blocking extension was developed by AdGuard and is available on Github for anyone to see.
Safe browsing, as well as search suggestions appearing in the browser’s smartbox, is the feature indispensible for contemporary browsing that relies on sharing user information, albeit in an anonymised form. The safe browsing technology allows us to warn the user about unsafe websites. Each fraudulent or potentially harmful website that we identify in the process of indexing more than 30 million webpages every day is logged in our proprietary cloud database. Every time a user is about to visit a website, the website’s address is automatically checked against this database to see if it might be there and whether a warning should be shown.
We have modified the safe browsing technology to use it in our privacy-conscious version of Yandex.Browser. Instead of sending the full address of a website the user is about to visit to Yandex in order to check this address against the database of potentially harmful websites, Yandex.Browser only uses a fraction of a ‘hash’ of this website, which is checked against a ‘hashed’ database of potentially harmful websites on the user’s own computer. The browser uploads a ‘shell’ of this database to the user’s computer at the first launch. This database is then ready to be ‘filled’ with fractions of ‘hashed’ website addresses the user intends to visit. To keep this database updated and the user safe, Yandex.Browser synchs the database on the user’s computer with Yandex’s cloud database every hour, using only fractions of each hash.
Search suggestions in the browser’s smartbox give instant answers to users’ search queries without redirecting them to search results pages. The users of Yandex.Browser can at the first launch choose a default search provider from a selection of three, which varies depending on the user’s location.
To generate search suggestions, predict search terms and offer instant results without redirecting the user to the search results page, the browser has to share the search terms with the search provider as the user enters them into the browser’s smartbox. This option is enabled by default in Yandex.Browser. Although this type of data sharing can be disabled in settings, its benefits massively outweigh privacy risks. Also, web users have an opportunity to add any search provider they trust to the browser and set it as the default.
One of the flagship features of the new Yandex.Browser is rich search suggestions, which instantly take the user directly to the website, or even specific page on a specific website, via a widget and bypassing the search results page. Similarly, simple, straightforward searches in the smartbox of the new Yandex.Browser will retrieve simple and straightforward results right in the browser.
Other automated features essential for the contemporary web surfing, such as sending crash reports, resolving web navigation errors, or the autofill function, involve sharing users’ information in one form or another. These features remain enabled by default. The user has full control over this aspect of their browsing experience and can disable any or all of these features.
The beta version of experimental Yandex.Browser retains its minimalist look to offer the user unhindered experience interacting with the website. Browser tabs can now be toggled within groups, while tab groups can be moved within windows. Website information, the smartbox and favourite websites are hidden when not in use and can be summoned by clicking on the website’s header in Yandex.Browser.
Just like the alpha version of experimental Yandex.Browser, the beta version is available for Windows and OS X and can be downloaded at browser.yandex.com.
Yandex.Market Helps International Retailers Reach Russian Consumers
30 March, 2015
Russian online consumers are avid cross-border shoppers, and this trend is growing. According to a survey conducted by a market research firm GFK, about half of web shoppers in Russia made at least one purchase from an online retailer in EU, China or US last year, up from 36% in 2013.
Source: GfK, August-September 2014
While the most popular shopping destinations are traditionally the online market giants - AlibabaExpress and eBay - the niche for smaller businesses accepting payments from Russia and delivering shipments to this country is quickly expanding. Yandex.Market, a leading comparison shopping service, is in a position to accommodate the needs of international retailers wishing to sell their products to Russian customers by utilising all the benefits of a technologically advanced platform with over 16,000 stores, 68 million product offers and 22.3 million unique visitors per month (December 2014, comScore Media Metrix).
Now, international web stores have an opportunity to showcase their offers on Yandex.Market to target those customers who look to buy products specifically outside of Russia. Product search on Yandex.Market is designed to deliver the most relevant results with the best combination of customer service criteria, including time of delivery or specific payment options. This automatically gives domestic retailers an edge over international companies who are often limited in their customer service opportunities in Russia. Cross-border shoppers, however, are prepared to put up with longer delivery times or inconvenient payment methods for the sake of unique product selection or better price. They can now filter product offers from international stores from the millions of items available on Yandex.Market to instantly see them on top of other search results – including price in roubles, delivery options and a link to full product information.
According to GfK, product categories most popular with Russian consumers are clothing, makeup products, perfume, accessories and products for children and babies. International product offers in these categories are currently available on Yandex.Market and soon will be expanding to add new groups, which continuously grow in popularity, such as consumer electronics and gadgets from Chinese retailers, for instance.
Any online store anywhere in the world can join Yandex.Market by providing customers with a landing page in Russian, an opportunity to have their purchase delivered to a Russian address, as well as an opportunity to pay for purchases in Russia via a bankcard or electronic money. International retailers will soon be able to benefit from a universal payment solution provided by Yandex, which will allow web stores outside of Russia to receive payments from Russian customers regardless of their method.
Dozens of retailers, including UK's Asos.com, China's LightInTheBox, Germany's Kidsroom.de, a US website Shopbop.com, and Italian Yoox, are already offering their products to Russian consumers via Yandex.Market, and with the new functionality their number is expected to grow.
Yandex Data Factory Predicts ‘Churn’ for World of Tanks
2 March, 2015
Customer loyalty and satisfaction is crucial in community-based gaming, where every single player matters, and devoted, experienced gamers are especially valuable for the game. Our big data unit,Yandex Data Factory, took game churn prediction – knowing how many gamers are likely to leave the game – to another level. Wargaming, an international MMOG developer, whose game World of Tanks, one of the world’s most financially successful games, with over 100 million registered players, can now determine more accurate which players are likely to stop playing soon and take measures to prevent that.
The challenge presented to the YDF team was to help increase WoT players’ loyalty and satisfaction with a minimal effort and at a minimal cost. To approach this challenge, a sample dataset of 100,000 random players who had 20 games or more in the past year was selected – this was done to exclude those who joined the game by accident or just to have a try. Based on a similar concept used in telecom and Wargaming’s own understanding, YDF analysts defined a ‘churner’ as a player who had zero games in the month following a gaming session. Next, the raw data for the ‘churners’, which included over 100 parameters – personal (obfuscated payment balances, purchase logs, etc.), as well as gaming (game logs, number of battles, battle types, number of destroyed tanks, clan battles data, free experience etc.) – was fed to our proprietary machine-learning algorithm, MatrixNet, to find similarities in gamers' behaviour and personal profiles. In result, a probability of churn was assigned to every gamer in the dataset.
WoT could then apply this churn prediction formula to the whole gaming community to spot top potential churners and target customer retention measures, such as special offers, new frictions, bonuses or community activities, specifically to them. The accuracy of YDF formula’s churn prediction measured at least 20-30% better than the current standard used in the gaming industry. Churn prevention – developing a formula for personalised retention measures – is the next challenge that YDF is ready to take on. Read more about YDF's churn prediction project for Wargaming.
Yandex’s School of Data Analysis Joins LHCb Collaboration
13 January, 2015
The Yandex School of Data Analysis has joined in collaboration with CERN’s Large Hadron Collider beauty (LHCb) experiment. The project is one of four large particle detector experiments at the Large Hadron Collider, and collects data to study the interactions of heavy particles, called b-hadrons.
As a result of this collaboration, the LHCb researchers will receive continuous support from existing applications (EventIndex, EventFilter) and the development of new services designed for the LHCb by the Yandex School of Data Analysis. YSDA will contribute its data processing skills and capabilities, and perform interdisciplinary research and development on the edge of physics and data science that will serve the aims and needs of the LHCb experiment.
LHCb experiment. Photo by Tim Parchikov.
The researchers at the LHCb experiment are seeking, among other things, to explain the imbalance of matter and antimatter in the observable universe. This programme requires collecting, processing and analysing a very large amount of data. Yandex has already been contributing its search technologies, computing capabilities and machine-learning methods to the LHCb experiment since 2011, helping the physicists gain quick access to the data they need. Since January 2013, Yandex has been providing its core machine-learning technology MatrixNet for the needs of particle physics as an associate member of CERN openlab, CERN’s collaboration with industrial partners.
The Yandex School of Data Analysis is now part of the game, with its exceptional talent, a strong tradition in hard-core mathematics, and proven experience of converting new theoretical knowledge into practical solutions. The YSDA is the only member of the LHCb collaboration that does not specialise in physics. Other collaborators in the project include such prestigious institutions as MIT (USA), EPFL (Switzerland), University of Oxford and Imperial College, London (UK).
The Yandex School of Data Analysis is a free Master’s-level program in computer science and data analysis, offered by Yandex since 2007 to graduates in engineering, mathematics, computer science or related fields. It trains specialists in data analysis and information retrieval. The school’s program includes courses in machine learning, data structures and algorithms, computational linguistics and other related subjects. It runs a number of joint programs, both at Master’s and PhD levels, with leading education and research institutions including the Moscow Institute of Physics and Technology, the National Research University Higher School of Economics (HSE), and the Department of Mechanics and Mathematics of Moscow State University. In seven years, the Yandex School of Data analysis has prepared more than 320 specialists.
Yandex Data Factory Opens for Business
9 December, 2014
As far as the laws of mathematics refer to reality, they are not certain,
and as far as they are certain, they do not refer to reality.
A search engine is all about very big data and very advanced mathematics. What we have been doing here at Yandex for more than 17 years already, is develop and implement technologies and algorithms which from a billion of pages on the internet would pick the one that would offer an answer to a web user’s question or solve their problem.
The technologies that power our search are based on machine learning – an approach that allows automating the process of making a decision. Our core machine learning technology, MatrixNet, not only makes its own decisions about whether a certain piece of information is a good answer to a user’s question or not, based on previous experience, but it does so based on a relatively limited experience.
At this point in time, when we can feel that our technologies can be put to use in spheres other than internet search, we are prepared to offer what we’ve got for a larger range of applications.
Today, at the LeWeb innovation conference in Paris, we’re cutting the red ribbon for Yandex Data Factory, our new B2B-service for corporate and enterprise clients, who would like, using our machine-learning technologies, to turn large volumes of data they posses into hands-on business tools, and, by doing so, increase sales, cut costs, optimise processes, prevent losses, forecast demand, develop new or improve existing methods of audience targeting.
We first branched out of our natural realm with our collaboration with CERN on their Large Hadron Collider beauty (LHCb) experiment. For this project we trained our MatrixNet to search for specific types of particle collisions, or events, among thousands of terabytes of information about these events registered by the detector in the LHCb. Yandex provided the LHCb researchers with an instant access to the details of any specific event.
The success of this project gave us reasons to believe it can be repeated in other areas of application. Any industry producing large amounts of data and focused on business goals could benefit from our expertise and our MatrixNet-based technologies: personalisation of search suggestions, recommendations or search results, image or speech recognition, road traffic monitoring and prediction, word form prediction and ranking for machine translation, demographic profiling for audience targeting.
Prior to today's announcement we have run pilot projects for about a year designing experimental custom-made solutions for clients all over the world. Most of these projects involved using the data that already exist, which we used for training a MatrixNet-based model, which then was applied to new data – depending on the goal of a client, to generate suggestions for buying a specific product, or predict, with a high degree of accuracy, based on behaviour of thousands or millions of shoppers with similar behaviour patterns, which product exactly will be bought.
Using this machine-learning technique, we helped one of the leading European banks increase their sales by matching each of their products that needed upselling with the best communication channel for each customer. By applying MatrixNet to behavior data on a few million of the bank’s clients, we created a model that could predict net present value of communication of a product to a specific client via a specific channel. This model was then applied to the bank’s new data to generate personalised product recommendations for each client paired with communication channel and ranked by potential net profit value. Preliminary results of the first wave of the bank’s marketing campaign, which was run on three million of clients, were used to fine-tune the original model, which, in its turn, was used in the second wave on a much larger number of the bank’s customers. The resulting sales increase beat the increase forecasted by the bank’s own analysts by 13%.
The same machine-learning approach, together with our own data and expertise in geolocation, helped a road and traffic management agency boost their accident prediction accuracy making it 30 times more accurate. To enable the agency take measures to prevent road traffic accidents, we provided them with one-hour forecasts for traffic jams, as well as alerts for high-risk traffic conditions, in real time, and visualized potential congestion on interactive maps. Using MatrixNet, we first trained predictive formulas on our own UGC information about almost 40,000 road accidents and 5bn speed tracks minded over 2.5 years, complemented by the information provided by the agency: traffic information (i.e., number of cars passing through a given segment of the road in any given time), information about road conditions (type of surface, number of lanes, gradient etc.), weather information. These formulas were then applied to larger data sets and a predictive system for road traffic accidents was developed and deployed in the agency’s situation rooms.
Currently, we’re continuing to work on about 20 projects in various stages of completion across the globe. In essence, we're continuing to experiment, but this time, we know in which direction, or rather – in which directions – we are to move. While the majority of our potential partners, as well as data, come from finance, telecommunications, retail, logistics, utilities, and even the new-fangled 'smart cities', anyone who has data and a business goal can discover new opportunities brought about by mathematics. No matter what industry your business is in, mathematics will work for you. Despite what Einstein said.
New Yandex.Browser Paves Way to Future
27 November, 2014
At a point in time when web pages have stopped merely hosting content and now look more like fully-fledged applications interacting with their users in more ways than one; when websites no longer redirect their visitors to other places on the internet to give them what they need – web browsers cannot remain the same square windows through which to look at 'carved-in-stone' content.
Facebook.com users, for instance, can now watch a video right in their timeline, play music and talk to their friends. Soundcloud.com isn't just a music hosting website, it offers their visitors nearly professional music recording, streaming and sharing experience, complete with advanced search functions and equaliser settings. In the existing environment, when most web-based resources – from social networking websites to newspapers to shopping platforms – are expected to have their mobile reincarnation as an app, the role of the desktop browser, as well as its look and feel, cannot remain the same.
In response to and accordance with the evolutionary change of the web, Yandex releases a new alpha version of its browser. The new streamlined Yandex.Browser is a new step in its evolution. It reflects the current trend in web user experience, which puts an emphasis on interaction and personalisation. The new Yandex.Browser lets users experience the web directly, while offering secure protection from the darker side of the internet. It is designed to respond to all the current needs of a web user, which aren’t limited to mere browsing, but now also include shopping, reading websites in a foreign language, booking flights, trains, taxies or hotel rooms and restaurant tables.
By bringing all these changes to our browser, we're hoping to make the internet more user-friendly for everyone. The new Yandex.Browser is a weighty contribution to our goal of creating a smart and transparent environment for a happy and comfortable internet experience. Instant page view, 'pages as apps', see-through user interface, rich search results, personalisation, integrated Yandex products and services and many more – are all implemented in the new Yandex.Browser, a trailblazer for the future of internet experience. With user feedback, we're hoping to understand how well we're faring on this path
The new alpha version of Yandex.Browser is currently available for download for Windows and OS X devices.
In Memory of Alexey Yakovlevich Chervonenkis
29 September, 2014
Alexey Yakovlevich Chervonenkis tragically died on September 22. A professor of the Moscow Institute of Physics and Technology as well as Royal Holloway, University of London, and a lecturer at the Yandex School of Data Analysis, he made a huge contribution to the theory of machine learning.
So far there have been three periods in the science of machine learning: pre-computer, computational, and the contemporary period of big data.
The first great work of Chervonenkis and Vapnik was this article from 1971. The theory of the uniform convergence of the frequencies of occurrence of events to their probabilities set the course for the development of this field of science for several decades ahead.
This was the period of the “theoretical” development of machine learning. At that time, only some kind of M-200 or, at best, a BESM was available for computing so there was not yet even any talk of widespread 'practical application in the nation's industry'. But even then it could already be used to find targets in the air, for example, or to help detect abnormalities in echocardiograms.
Then came the second period in the history of machine learning – the computational stage. In the 1990s people learned, for example, how to quite effectively recognise and digitise texts (including handwritten documents) and keep e-mail free of spam. Half of these methods worked on the renowned SVM (Support Vector Machine) method conceived in the early 1990s by Chervonenkis and Vapnik (Vapnik-Chervonenkis dimension). In the mid-2000s all the well-known companies worked on SVMs – including us, and Yahoo!, and Google, and Amazon. SVM is described in any textbook on the subject.
And then came the third era in the development of machine learning, with the appearance of big data and methods for working with it. Now it appears that everything around us, all objects and services, will become a bit smarter and learn to help us in every detail, anticipating our desires to some extent. This is similar to how various mechanical and chemical inventions have changed our lives, only now in a slightly different sphere.
In this third era, Chervonenkis taught at the Yandex School of Data Analysis, and presented the development of his fundamental 1971 work at our conference.
Alexey Chervonenkis loved to walk. He would walk 20 kilometres a day – around Moscow, or London, or forests – that’s how he thought. In summer he had an operation and he couldn’t walk for three weeks. Then one day he started walking again – first a kilometre, then two, then three. And last week he set off on a 20-kilometre walk along a familiar route through Losiny Ostrov National Park.
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