Author Archives: Piotrowska Maria

Air-cleaning vehicle

Reading Time: < 1 minute

Last week Chinese car manufacturer company IM Motors announced a launch of a new product – an air-cleaning electric vehicle. The startup is backed by tech-giant Alibaba. 

The new “Airo” concept is based on an idea to produce a car that would clean the air during riding. The company plans to sell this type of car in 2023. Moreover, the car was designed for self-driving, inside the seats are put infant each other with the table between. The space inside the car can also be used as a bedroom. 

When the designer was asked about the design of the car, he answered “designed to simultaneously address the global space shortage, Airo is also a multi-functional room with extra space for dining, working, gaming or even sleeping.”

The plan is ambitious. The company has to create a technology that would enable a full self-driving mode without the need for human supervising. Also, the cleaning-air technology might be challenging. The time will show whether it is only an ambitious, unrealistic project or whether it is the world-transforming idea that will shape the future. 


Facebook Silent Data Breach

Reading Time: 2 minutes

In today’s world, the topic of privacy and data protection is important as the number of internet users grows. The European Union passed legislation that provides people with rights to their data. The General Data Protection Regulation known as GDPR is one of the most famous examples of legislation about data protection and privacy, which was implemented in 2018.

According to GDPR law users which data had been stolen from the company, servers should be informed about it. Moreover, companies that do not respect the duty of keeping users data safe may be forced to pay a monetary penalty. 

Facebook owns data of over 2 billion users and is the largest social network globally. As a leader of social media platforms, it should be obligate to introduce a complex system of data protection tools, which would eliminate huge data leaks. Unfortunately, the data protection of Facebook users is flawed, in August 2019 more than 530 million accounts was hacked. As a consequence information such as telephone number, email, address full names had been stolen and recently these data were added to a public database. 

In this situation, Facebook should inform users who had been affected by this data breach. However, the Facebook representative informed that the company will not inform users individually about the data breach as the information was public, also no data connected to health or financial information had been stolen. 

In July 2019 Facebook has been punished by US Federal Trade Commission for not respecting the law which obligate companies to protect the privacy of their users. Facebook has to pay a $5 billion settlement for the neglect. 


Take a ride to sustainability with Google

Reading Time: 2 minutes

Tech giant Google is famous for its tools which help millions of users each day. One of the most commonly used apps is Google Maps. In last weeks it developed a new feature which is described in this article.

Google highlights the importance of reducing gas emissions. The company has purchased renewable energy through direct contracts to reduce its carbon footprint. Moreover, it invested to look for other sources of clean energy. The newest feature of Google Maps is consist of Google policy of taking responsibility for our planet and contributing to creating more sustainable solutions. 

The eco-friendly ride feature was launched last week in Google Maps. Google Maps was launched in 2005. During 16 years one of the first internet maps has grown into an advanced map with thousands of features such as Street View which gives a panoramic view, traffic monitoring, road planning and many more. While searching for a road between two places users see different route propositions – the shortest ones and the fastest ones. The new feature enables people to see which road is eco-friendly. The algorithm advises based on data about the fuel consumption, speed limits, traffics and many more information which route will emit the smallest gas quantity into the atmosphere.

This innovative approach to everyday life decision making of millions of users may seem irrelevant from the perspective of an individual but looking globally each month more than 150 million people use Google Maps (according to Statista 2018). If each of them would reduce its gas emission by 5-10% it creates a huge difference for the environment.


Who should mete out justice? The judge or an algorithm?

Reading Time: 3 minutes

An algorithm will decide whether a suspect should be detained during a legal case.

The new justice system will apply in California in October 2019. Cash bail [*] will be replaced by an algorithm, which qualifies people to three groups – low, medium and high risk of committing a crime. Based on a recommendation made by an algorithm the judge will decide whether a person should be detained during a legal case.

The aim of a new tool is to reduce a bias and a personal prejudice of judges, who pass judgements. The decision should be driven by data-driven recommendations rather than person’s gut feeling.

The second argument pro is that algorithms and databases can be helpful in allocating resources in the future. The government could decide which districts or groups of people should be under control of police to prevent future crimes.

How does it work?
An algorithm uses historical crime data and statistics in order to find a correlation and pattern. Based on machine learning it can predict a likelihood of committing a crime by every individual.

As we know algorithms are not perfect and some of them make mistakes. In this case people spotted, that an algorithm is much more rigorous for a dark-skinned individuals. According to analysis made by reporter Julia Angwin “Blacks are almost twice as likely as whites to be labeled a higher risk but not actually re-offend,” and “{an algorithm} makes the opposite mistake among whites: They are much more likely than blacks to be labeled lower-risk but go on to commit other crimes”. In July more than 100 organisations (such as ACLU, NAACP) protested against using such tools during lawsuit and signed a statement against it.

“Distribution of defendants across risk categories by race. Black defendants reoffended at a higher rate than whites, and accordingly, a higher proportion of black defendants are deemed medium or high risk. As a result, blacks who do not reoffend are also more likely to be classified higher risk than whites who do not reoffend.”

However, the algorithm takes into consideration 100 factors (such as: age, sex and criminal history). It is worth mentioning that race factor is not used. Another factor which increases a probability of signing to higher risk group (almost as much as race factor) is being in low income bracket.

“If an algorithm found, for example, that low income was correlated with high recidivism, it would leave you none the wiser about whether low income actually caused crime. But this is precisely what risk assessment tools do: they turn correlative insights into causal scoring mechanisms.”

In my opinion a history of the USA have a huge impact on its citizens. Because of it they are very sensitive to race discrimination. A created algorithm works on data influenced by long-lasting unequal access to education, work and social system and all consequences of this inequality. A social inequality, lack of education and bad financial situation are factors which cause a higher rate of crimes all around the world. Summarising, I think that the result of an algorithm’s work is rather a consequence of the USA history rather than a mistake of an algorithm or current race discrimination in this country.

[*] cash bail – payment of money or pledge of property to the court which may be refunded if suspects return to court for their trial, bail practice vary in the USA from state to state (source: Wikipedia)


Would you like to be a Creator?

Reading Time: 2 minutes

With a help of a looking glass factory, you can create your own living form and start interaction with it.

The company “Looking Glass Factory” was established in 2015. Their first product was a volumetric display, a device which gives ability to see an object created on a computer in 3D. It simulates depth and other visual effects.

Currently they work on a new product. With a help of a Kickstarter they raised money (almost $850 000) for an interactive holographic display for 3D creators.

What is an interactive holographic display?

It is a device which gives its users ability not only to see graphic in 3D but also it gives them an opportunity to touch and interact with an object. The device is an ingenious invention for a game designers, architects, designers and 3D artists. It is available in two sizes 8.9” ($600) and 15.6” ($3 000).

A computer with a help of a program The Model Importer captures 45 simultaneous views of a designed scene at 60 frames per second. Next a signal is send to a holographic display via HDMI. Then the Looking Glass creates a full-coloured and three-dimensional scene on a screen. Depending on an angle of sight you see a different shape of an object. This is a method to see an objects in 3D without using VR, AR or 3D glasses.

The new product can be used simultaneously by a few people. It has different functions. One of them is leap motion controller. The object on a screen can be “touched” by a human. It can be hit or lift by user’s hand. Also if you would like to see something, which is bigger than your screen, you can connect two Looking Glasses and you will get one bigger screen.


Reading Time: 2 minutes

YOU decide who to share your identity with.

The year 2018 showed how difficult and important the issue of keeping users data safe is. Organisations such as Marriot and Facebook had been exposed to hackers attacks. The first one (3rd largest hotel chain in the world) lost control over the personal information of 500 million people.

Nowadays being in possess of personal data may give hackers a huge control over your identity. In digitalised world unwanted use of your ID, passport number or address can result in serious problems – for example becoming unexpectedly an owner of a massive loan!

The company which wants to fight with uncontrolled use of your personal information is Passbase. Startup founded in August 2018. The application gives its users a control over their data. As a user you scan your ID and the systems checks by taking short video of your face if you are a person from a given document. Then a digital identity is given not to a company but to the user. You have an access to an account in which you can decide with who to share your identity with.

Passbase uses blockchain, but their solution is not described in detail on their website. Their facial recognition system uses AI technology. Application is free for first 1000 uses, then you have to pay $1 for each check of your identity. Currently they produced MVP (minimal viable product) and they plan to launch full application in a year 2019.

Another function of this application is its offer for B2B. The companies which would like to buy an access to Passbase will have an ability to check all accounts of its users. In result they will have an opportunity to delete all fake accounts in their database.

Passbase helps you create a verified digital identity

Can AI eradicate Alzheimer’s disease?

Reading Time: 2 minutes

What if we are able to cure a disease 6 years before it will occur…

Currently in the USA approximately 5.7 Million people are living with Alzheimer’s disease (AD) and it is a 6th leading cause of death in this country. Based on Alzheimer’s association report number of affected by disease can raise to 14 Million by 2050.

A normal brain of a 70-year-old (left slice), compared with the brain of a 70-year-old with Alzheimer’s disease.Credit: Jessica Wilson/Science Photo Library

The team from University of California, Berkeley  and UCSF led by Benjamin Franc took data about 1,002 people living with AD from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). A deep learning algorithm used given brain images to learn how the brain looks 6 years before the AD activates.

The learning algorithm processed more than 2,100 FDG-PET brain images. On 90 percent of dataset it was trained by researchers to recognise the differences between the healthy brain and the one affected by AD. Then algorithm tested “his knowledge” on the remaining 10 percent. Furthermore the researchers made an independent test in which it was given 40 FDG-PET of 40 people which were unknown to him before.

For those uninitiated FDG-PET is “18-F-fluorodeoxyglucose positron emission tomography”. In practice it is a test which shows metabolic activity. The radioactive glucose compound is put into the blood, then the PET scan shows the uptake of FDG in brain cells. The disability of metabolic activity of a brain is one of the first damages caused by AD.

The results of the research showed that the algorithm had 95% confidence on predicting the final diagnosis also on average it could predict  AD 75.8 months before the final diagnosis. The algorithm focused on known areas of the brain but it also did pay attention to the rest of a brain.

In the future researchers would like to teach algorithm to recognise outcomes of other tests using different biomarkers specific to AD. Also the development of early diagnosis can help create preventative drugs.

What a painting! Is it Rembrandt, Rubens or AI…

Reading Time: 3 minutes

The story about GANs (Generative adversarial networks) creating paintings worth $432,500.

At the end of October the famous auction house Christie’s in New York sold a painting made by GANs for a surprising amount of money – $432,500. The average person may not know what generative adversarial networks really means. For this reason I will start with a short explanation of this class of artificial intelligence.

Let’s imagine two algorithms playing a game with each other. One of them uses machine learning to gain a knowledge about appearance of a certain object. In practice this algorithm may be given a 1000 photos of different kinds of apples and after that it should recognise shared features (shank, pip, encountered colours etc.). With this knowledge it starts to create new pictures representing the same object. During that the second algorithm (which also saw photos of apples) sees the works of the first algorithm and it is telling if objects in this drawings look similar to an apple or not.

Sounds simply? Let’s make it a bit more complicated! The first algorithm is called generator, the second is called discriminator. Both of them are neural networks. The generator creates random synthetic output (it can be an apple, face or an image) while the discriminator makes distinctions between green apple and the others or between painting representing abstract art and the others. After such interaction both algorithms are enhancing their skills.

In the picture we can see green line made of dots. It is a real sample. The second picture shows how those two algorithms read given data. The generator creates gradients while the discriminator makes predictions of sample. As the result we see a new line, which length do not vary in length and angle of incidence of the real sample. However an observer can easily tell, that two lines in two pictures are not the same, but have some common features.

Previously mentioned painting “Portrait of Edmond Belamy” which was sold at Christie’s was created by GENs. Creators of this algorithm (Hugo Caselles-Dupré, Pierre Fautrel and Gauthier Vernier) fed the system with a data of paintings from 14th century to the 20th. The GENs accumulate data of around 15 000 portraits and using the technic of “two neural networks playing a game” created a completely new portrait.


Such innovations generate new questions. Is it possible to create a piece of art without an artist? Where is the border between creating an art with AI and copying existing paintings? Who is the creator – GENs, authors of all paintings used by an algorithm or the developer of GENs?

In my opinion one of the most important is “What are the others applications of GANs?”. Nowadays GANs can be helpful to visualise different possibilities of designing shoes, clothes, interior designs or scenarios of computer games. It helps also to improve blurry images, such as old photos or destroyed paintings. Probably it is also capable to create things, which we can not imagine yet. It is a tool which can influence our next century.