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More and more modern cars are equipped with new safe and comfort technologies. Nowadays, it is possible to enter a car without a key, cars can recognize oncoming obstacles and stop. Moreover, we do not realize that the ease of use for modern cars is owed to thousands of information systems, and that creates problems we would never connect to motorization.

It is true that the mentioned systems make our lives easier and make using the cars safer. Due to the complexity of these systems and the fact that various components come from different companies, modern cars are one of the most complicated IT systems in the environment. While nuclear power plants’ code lines can be counted in hundreds, the lines in modern cars sum up to millions. Each of the modules is provided by a different producer that is why the possibility of verifying that the car is working correctly is diminishing.

The most common concern related to intelligent cars is the possibility of theft. The no-key systems allow not only the owner to get into the car, but also adequately equipped thief. Not so long ago an information on loopholes in Volkswagen key security systems came up. It is estimated that over a hundred million cars throughout the whole world are vulnerable to such intrusions.

Another concern is the fact that the producer can remotely control the functions of the car systems. In this case, it is possible that someone other than the producer can interfere in the systems. For example, with the right access one could turn off the communications on the board computer, so the driver will not recognize the coming threats. The criminal could also turn off airbags or use the brakes during the car’s movement. The possibilities growing in size. There is also a problem with autonomous cars, which can drive almost by themselves. The car producer Tesla had a situation where an error in the car’s software cause a few collisions, which has ended the promotion of Tesla S as a car that driver itself.

In conclusion, the information systems in modern cars come with great cost to the producers and a great risk to the consumers. In case of cars it is not possible to have a hundred percent assurance of safety. Furthermore, it appears that the more expensive a car is the greater the risk, because it has more technology implemented and it encourages the criminals more to break the security systems.

 

References:

http://www.techtimes.com/articles/67253/20150728/driverless-cars-safe.htm

http://www.informationweek.com/mobile/mobile-devices/smart-cars-vulnerable-to-security-hacks-report-finds/a/d-id/1319031

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From its launch Google Translate did not cope with accurate sentence translations. It has been an object of ridicule, as the transcriptions were usually unhelpful and produced humorous results. However, Google recently announced a game-changing project called Google Neural Machine Translation (GNMT), which supposedly uses AI algorithms in its translations.

Currently, Google Translate uses Phrase-Based Machine Translation (PBMT), which breaks sentences into words and phrases to be translated independently. The new GNMT does not split sentences, but instead treats them as a unit for translation. The system can analyse a sentence and remember the beginning as it gets to the end, which mimics the way humans comprehend sentences. The company is boasting that the new service reduces translation errors by more than 55-85% on several major language pairs.

This technique was considered by researchers for years, however, there were many problems with adapting it to production systems, such as Google Translate. The main challenge was getting the system to work sufficiently fast and accurate on very large data sets. Google overcame this problem and the GNMT has been released on the company’s web apps for Chinese to English translations. The company also provided examples of translations in few languages, as well as, comparisons with the previous PBMT system and human translation.

More examples of translations can be seen here.

Google’s achievement certainly is a milestone step in machine translation. The sentences converted by GNMT are much more accurate and seem closer to human translated ones. However, the examples given by Google are rather trivial, the fragments of randomly picked news from business or political portals are understandable, but what if the sentences would aim at implying something more than pure facts?

In order to understand the limits of machine translation one has to comprehend the underlying approach humans take, while translating from one language to another. The translator does not convert the text word-for-word or sentence-by-sentence, but actually understands it, then through the use of intuition and language imagination he or she writes the content in another language. It can be said that the translator builds a bridge between two different cultures, over which words of natural, cross-cultural language flow. Therefore, humans can translate humour, poetry, or even complexities of diplomacy, which are not simply written in words, but implied in the text.

The new GNMT project can surely be helpful in matters, such as tourism, web surfing, or even simple business proceedings. However, when it comes to more serious translations it does not matter how accurate the system is, or how big a database can it use. Without understanding the content of the text, machines cannot translate on the same level as humans do.

 

References:

https://research.googleblog.com/2016/09/a-neural-network-for-machine.html

www.wired.com/2016/09/google-claims-ai-breakthrough-machine-translation

http://www.dobreprogramy.pl/Tlumacz-Google-z-neuronowym-silnikiem-by-chinszczyzne-bylo-latwiej-pojac,News,76543.html