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Image creator program GauGAN

Created by NVIDIA Research GauGAN project is a program which allows you to turn simple paintings into photorealistic pictures using AI technology.

GauGAN gives you blank space to draw on, where each brush type is labeled with different type of environment like sand, grass, sky, ocean. Option of changing daytime is also available. Then AI converts the sketch filling into desired elements basing on online pictures database. Using GANs technology program creates credible outcome. In the process two neural networks are used where one is called generator and the latter one discriminator. Generator creates image that later is corrected by discriminator in terms of authenticity.

Gaugan is using GANs (Generative Adversarial Networks). These are neural networks that cooperate in the process of deep learning, by competing with eachother. E.g one is creating a fake cat picture, while the other one determines whether it is real or fake. This eliminates the need for human to correct the AI and automatizes the process. This way human effort in advancing neural networks is minimized.

In the future the application can help proffesions which require creating world representations like architects, urban planners or game developers. It would allow them to preview the outcome of their work with minimal effort and prevent from committing to bad ideas.

Current state of the project may be impressive but its very restrictive. The fact that you are allowed to use only certain labels which resemble specific parts of environment is disappointing and prevents from exploiting maximum potential of creativity. The most obvious way of solving that problem would be adding more labels but connecting elements from various types of environments could corrupt the final landscape. E.g covering a desert with a snow or placing a star on the dirt background would distort the picture, because AI would have no data to refer to. Project’s advancement is dependent purely on describing parts of the world with new algorithms and putting them to the GANs test.


EXAMPLES – Here are some masterpieces I’ve created.

REALISTIC EXAMPLE – easy to make, low chance of corruption

















ABSTRACT EXAMPLE – harder to make, high chance of corruption

















CORRUPTED EXAMPLE – elements added to habitat in which they not occure will distort the picture. Here is a house in the middle of the ocean with a bridge connected to a pile of mud above.













Here are few breathtaking possible outcomes.




My sketch using NVIDIA’s GauGAN – using machine learning to turn doodles into realistic landscapes from MediaSynthesis

AI learns to generate images from text and begins to better understand our world


OpenAI, co-founded by Elon Musk, has created the world’s most stunning AI model to date. GPT-3 (Generative Pre-trained Transformer 3) without any special prompts, can compose poems, short stories and songs, making one think that these are the work of a real person. But eloquence is just a gimmick, not to be confused with a human understanding of the environment. But what if the same technologies were trained simultaneously on text and images?

Researchers from the Paul Allen Institute for Artificial Intelligence have created a special, visual-linguistic model. It works with text and images and can generate pictures from text. The pictures look disturbing and strange, not at all like the hyperrealistic “deepfakes” created by generative adversarial networks (GANs). However, this capability has long been an important missing piece.

The aim of the study was to reveal whether neural networks can understand the visual world as humans.  For example a child who has learned a word for an object can not only name it, but also draw the object according to the hint, even if the object itself is absent from his point of view. So the AI2 project team suggested the models do the same: generate images from captions.

The final images created by the model are not entirely realistic upon close inspection. But it is not important. They contain the correct high-level visual concepts. AI simply draws the way a person who cannot draw would draw on paper.

This makes sense: converting text to an image is more difficult than doing the opposite.

“A caption doesn’t specify everything contained in an image,” says Ani Kembhavi, AI2’s computer vision team leader.

Creating an image from text is simply a transformation from smaller to larger. And it’s hard enough for the human mind, apart from programs.  If a model is asked to draw a “giraffe walking along a road,” then it needs to conclude that the road will be gray rather than bright pink, and will pass next to a field rather than the sea. Although all this is not obvious to AI.

Sample images generated by the AI2 model from captions. Source: AI2

This stage of the research shows that neural networks are capable of creating abstractions – a fundamental skill for understanding our world.

In the future, this technology will allow robots to see our world as well as humans, which will open up a huge scope of possibilities. The better the robot understands the environment and uses language to communicate, the more complex tasks it will be able to perform. In the current perspective, programmers can better understand the aspects of machine learning

“Image generation has really been a missing puzzle piece, By enabling this, we can make the model learn better representations to represent the world.”


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AI and COVID-19

Correctly diagnosing diseases takes years of medical training. Even then, diagnostics is often time and money consuming process. This puts doctors under strain and often delays life-saving patient diagnostics. In this worldwide health crisis, the medical industry is looking for new technologies to monitor and controls the spread of COVID-19. And on of technologies that can solve that issues finding the proper treatment, is Artificial Intelligence. For instance, tracking the spreading of disease, choosing the proper treatment .


Benefits of AI:

  1. Early detection and diagnosis of the infection. AI can quickly analyze irregular symptom and other ‘red flags’ and thus alarm the patients and the healthcare authorities
  2. Monitoring the treatment. AI can build an intelligent platform for automatic monitoring and prediction of the spread of this virus.
  3. Projection of cases and mortality. This technology can track and forecast the nature of the virus from the available data. Further, it can predict the number of positive cases and death in any region.
  4. Development of drugs and vaccines. Many of the analytical processes involved in drug development can be made more efficient with Machine Learning. This has the potential to shave off years of work and hundreds of millions in investments. What leads to faster and more efficient production of drugs.
  5. Reducing the workload of healthcare workers. It helps in early diagnosis and providing treatment at an early stage using digital approaches and decision science, offers the best training to students and doctors regarding this new disease. Furthermore, when AI lowers monotone workload of medical workers, they can provide more efficient treatment, based on individual approach.

AI machine learning models and algorithms can identify potential drug leads currently unknown in medical science and far faster than humans. While such systems will never replace scientists and clinicians, they can save both time and money.

The ability of machines to solve complex biological puzzles more rapidly than human experts has prompted increased investment in AI drug discovery by a growing number of large pharmaceutical companies.

To sum it all up, AI can significantly improve treatment consistency and decision making by developing useful algorithms.


Factory 56 — the future of cars manufacturing

Even in a difficult economic situation Mercedes-Benz continues to improve its manufacturing process and tries to set a higher bar in the car industry. Another proof of that is the opening of a new Mercedes-Benz’s factory named Factory 56 in the German city Sindelfingen. The company itself calls this factory “Flexible, digital, efficient and sustainable.” Factory 56 was opened at the beginning of September on a ground area corresponding to 30 soccer pitches. It took 2.5 years to finish the project. According to the company’s web-site, Mercedes-Benz is planning to invest about 2.1 billion euros at Factory 56.

But what makes this plant the future of car manufacturing?

Flexibility. At one factory’s assembly line is possible to assemble cars with different body types. The production can adapt to current demand on the market and allows to easily restructure process of assembling to switch from sedan to SUV, for instance.
Digitalization. The plant has no conventional assembly line. A car is manufactured on a remote platform, which allows a car to stay in one place most of the time when all required parts are being transported by remote carts connected to one intellectual network. The digital ecosystem is built on a powerful WLAN and 5G network, which allows workers to supervise all systems automatically. From the monitoring of parts in stock to the system of electronic workflow, which would save 10 tons of paper every year.
Environmental friendliness. Factory 56 became fully carbon-neutral with zero CO2 emissions and significantly low energy consumption. The plant was built using recycled concrete. 40% of the factory’s roof is covered with plants and trees, the roof is also filled with solar panels, which is a sign that Mercedes-Benz does care about the environment. 
Working conditions. More than 1500 employees are working at the factory in two shifts. While organizing the working space, Mercedes-Benz took into account employees’ preferences. For example, via an online survey, they were asked about personal opinions, preferable shifts to work, suggestions, and even people they want to work together with. More than 85% of the suggestions were successfully granted.

At Factory 56 Mercedes-Benz is manufacturing the new S-class W223 and EQS as start models to launch the site. It’s extremely crucial that the company approached this project with responsibility, taking care of the environment.

I would say it should be an example for major companies that it’s possible to remain eco-friendly and efficient at the same time. And, of course, technologies. This is really astonishing to see real technological development, not the one Apple calls “revolution” each year. This is a huge step towards efficiency. For me, it’s really boldly for a company with a reputation and history to do this. It’s clear to see their struggles on this occasion, let alone the new S-class. Mercedes-Benz proved that even investing an enormous amount of money to implement modern technologies is worth it when it comes to technologies.



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AI detecting ripe fruits with human-level accuracy

See Tree is one of the leading companies in agricultural tech field. The number one priority of See Tree is to oversee their fruiting status of trees in order to increase their productivity. This calls for a device for automated detection of ripe fruits using machine learning techniques. To have better, faster results and to approach this task with all the tools that are needed, See Tree partnered with Ciklum, a company whose task is to help other companies in creating something new with their own specialists.
In this case Ciklum provided See Tree with a team of  skilled R&D experts that had to find a solution to these challenges:

  • Small dataset (~500 annotated photos). It was impossible to collect fresh data because oranges were not in season.
  • High level of noise in dataset annotation. Annotations to some images didn’t contain required data. Some bounding boxes were not precise enough.
  • Dataset contained photo duplicates with different labelling.
  • High occlusion level in some pictures, made it difficult to separate instances.

Data preparation 

To prepare data that is required for this task, Ciklum’s team duplicated photos and images of unlabled oranges or invalid polygons. Those images were filtered out of the dataset. The resulting dataset was divided into sets of train, validation and test,  afterwards, stratified by the wide variety of oranges located on the picture.






Model architecture 

The satisfactory end result became acquired with the Faster R-CNN architecture family.This pattern became a modern


method for resolving object detection task. ” Faster R-CNN is a two-stage object detection system in which the first

stage generates a sparse set of candidate object locations and the second stage classifies each candidate location as one of the foreground classes or as background using a convolutional neural network.”










Ciklum’s and See Tree’s prototype had these advantages:

  • able to detect ripe fruits with high accuracy, even if occluded;
  • although built to detect oranges, it can be easily re-trained to detect other kinds of fruits;
  • outperforms humans in most cases.






All references were taken from:

Radomyr Kostetskyi

Ghost Kitchens

42% of overall food orders are now placed online. The annual growth rate of online ordering and food delivery from restaurants is about 15-20% [1]. We value more convenience and oftentimes do not want to waste our valuable time on picking up meals ourselves. Therefore, we see less people visiting restaurants nowadays, but the costs of rent in shopping malls and expenditures to make their brand logos noticeable are not shrinking. Ghost kitchens come to the rescue!

What are they?

Shadow kitchens, dark kitchens, whatever you want to call them, they are the new booming trend in the Food & Beverage industry. These are food preparation facilities that stay hidden from the customers’ eyes. Set up out of sight, they cater mainly to delivery and do not serve customers directly. Their goal is to focus on making quality product cheaper and in a more efficient way.

What are the benefits?

Lower operating costs are possible thanks to a couple of factors. Choosing less obvious locations makes the rent less expensive. Neither dining area nor serving staff is needed in such a place. These elements combined save a lot of money, therefore the prices of meals can become more attractive to customers.

Not operating under a given brand and staying incognito enables them to change their menu and cook totally different food. Being so flexible is a very valuable opportunity not only for start-up restaurants that are not sure what products will sell best, but also for existing ones that try to experiment with their meal choices.

Are they flawless?

I think that they are close to perfection, but there is an issue.

If these places do not have their own app for delivery, this business model is dependent on the middlemen between the consumers and the kitchens. Being under somebody’s control may lead to toxic relationships.

Overall, I see a great potential for this trend, and I think it will successfully expand in the future.



Data from:

  1. The Lavu Team. “The Growth of Online Ordering and Food Delivery Is Transforming the Food Business Industry.” IPad Point of Sale System for Restaurants, Bars & More, 24 Feb. 2020,


Article I base on and present different view from them:

Hanet, Isabelle. “What Is a Dark Kitchen?” Deliverect,


External articles:

“Ghost Kitchen.” Wikipedia, Wikimedia Foundation, 13 Oct. 2020,

Bromwich, Jonah Engel. “Farm to Table? More Like Ghost Kitchen to Sofa.” The New York Times, The New York Times, 24 Dec. 2019,

The future of financial system

Recently, investors have shown considerable interest in DeFi projects. This can be understood from the chart of total value locked (USD) in DeFi projects on the website called DeFiPulse. At the moment, the total value locked in DeFi projects almost 4 times as high as it was 90 days ago. Furthermore, the costs of some DeFi projects’ tokens on exchanges have sometimes increased even more than 10 times. For the comparison, the price of gold for the same period of time increased only 1.2 times.

As I’ve shown above, DeFi is quite popular at the moment among investors, however what is DeFi? According to the website DeFiPulse, DeFi (decentralized finance) is a complex of services based on smart contracts and decentralized applications (Dapps) that make up a decentralized financial ecosystem and provide users with access to a variety of financial services such as investing, lending, trading on exchanges, etc.

The main aim of DeFi is to change traditional financial system on new trustless blockchain-based one. At the moment, there are accessible such services as borrowing & lending, using decentralized exchanges and wallets, making payments, holding crypto-assets linked to real life assets and last, but not least, insurance.

Like everything in this world, DeFi has its pros and cons.

As regards advantages, firstly, anyone can get one or another financial service avoiding banks and their high fees. Secondly, all transactions in the blockchain are open-source for everyone. Finally, there are a plenty of ways to combine DeFi applications with existing services using unique smart contract-based protocols.

Speaking about drawbacks, firstly, since DeFi applications are still a new domain there is a chance to lose your money due to hacking of some unreliable smart contracts. Secondly, because of the little speed of transactions’ confirmation of Ethereum, on which at the moment based the vast majority of DeFi apps, the fees for confirmation sometimes really high.

In conclusion, there is no doubt that DeFi will replace the traditional financial system in the future, however, it needs some time to overcome those obstacles that hinder the development of the system now.


What is DeFi?

Is the PS5 Digital Edition Really Worth it?

Finally after all the hype, speculation and leaks. The successor to the PS4 has arrived, sony’s long-awaited Playstation 5 is set to hit the stores in select countries such as the US and Japan markets from the 12th of November 2020 and the rest of the world will be able to get their hands on the console from the 19th. Something more unusual about the launch of the PS5 is that there’s going to be more than one console releasing this year, a digital edition of the PS5 which is essentially a streamlined digital-only console without a disc drive.



The price you may ask? Well, The PS5 and PS5 digital edition costs 499$ and 399$ respectively. Honestly if you ask me, it’s probably wise to save up and spend the extra 100$ on the standard edition with a disc drive because I think buying the digital edition is a huge commitment to Sony. Let me elaborate, so for example you buy the digital edition and you save the additional 100$ for buying your favourite game, so the only way you can purchase or gain access to a game is through the Playstation Store, there’s no other way. So you’re basically falling into “Sony’s trap” here because in case you didn’t know the Playstation Store is notorious when it comes to overpricing video games to the point where it doesn’t make sense for the consumer to even consider it. In my opinion, the most important benefit of having a disc drive in your console is the ability to swap, borrow, loan and trade physical game discs with friends and exchange stores for a fraction of the original price. Also, there are a lot of video game collectors out there who love collecting physical copies of a game to add to their exquisite collection so I’m pretty sure they won’t be the target audience for the Digital Edition.

Now for the juicy bit, the specifications. Well we’re looking at 8 cores/16 threads x86-64-AMD Ryzen Zen 2 processor which has a variable frquency upto 3.5 GHz with an AMD Radeon RDNA 2-based graphics engine which is capable of a whopping 10.3 teraflops. Both the editions of the PS5 come with an Ultra HD Blu-ray opitical drive with 3D audio, GDDR6 16GB/448GB/s bandwidth. Historically, this is the first ever Playstation console to have an inbuilt SSD(825GB).


So what does the future of gaming look like?

Well, I think the future is bright and colourful as manufacturers have finally realised the importance of implementing an inbuilt SSD which can significantly reduce game load times and provides a much smoother gaming experience. It’s definitely a step in the right direction and I see this as a permanent addition to future consoles as well. To sum up, if you’re an avid gamer without the finances to make the big jump to the top-tier PS5, then by all means go for the digital edition, it’s still a next generation console after all. But always keep in mind that there is a gray area in the digital ownership, be it music, films or games because you don’t actually own a title in perpetuity. Yes, online elements of modern games means that patches come regularly and can sometimes be required before even a game will work. But if Sony’s servers ever get taken down due to unpredictable circumstances, its not quite as reassuring to have a solely digital game collection compared to a physical one.



Shopping without barcodes

An Australian tech start-up Tiliter has a thing to brag about as it has raised $7.5 million in funding led by Investec Emerging Companies. One of the investors was also Eleanor Venture and New York’s Cornell University.

Tiliter, started in 2017, now advertises themselves as the developer of the most accurate identification software that by the use of Artificial Intelligence identify products without the need for a barcode. They are already able to recognize products with 99% accuracy in less than a second. What is impressive is that the software is able to distinguish organic from non-organic products, even though there is a little appearance difference between them. There are more advantages to barcode-less product recognition. First, there is a potential of reducing checkout time. Retailers could also limit the number of workers and therefore make cost cuts. Incorrect selections (intentional or accidental) will be removed. Also, there will be no need for packaging on which barcodes are located. I believe that user-experience will also be improved.

The software they are using is pre-trained so the is no need for a learning period. Additionally, the software has self-learning abilities and can add a new product to a database within 20 minutes. The technology is also easy to install and use.

Tiliter has also developed a Scan&Go Scale, which works on a similar principle. It is a scale that automatically recognizes items without the need of entering the name of the product manually. Afterward, a client is supposed to scan the barcode which will add products to a virtual cart. This scale is already used in a number of stores in Australia, New Zealand, and the USA.

Tiliter claims to have a price advantage over Amazon which also offers autonomous store solutions. Amazon’s cashier-less technology is called “Just Walk Out” and just started being available to other retailers. Amazon’s technology setup and installation last up to a few weeks, so there is pressure on Tiliter to develop a “plug and play” checkout technology that will be installed quickly.

As the Covid-19 pandemic is not about to leave us any sooner I believe that the founders made a good call backing Tiliter.



AMD Radeon RX 6000 series – the new player on a top-level GPUs field

In case new reference AMD graphics cards are going to be presented on the 28-th of October and unreferenced ones will be in the middle of November (including Sapphire Nitro and Asus Strix), there is some inside information about the new Radeon RX 6000 series. Will it finally be a worthy competition between Nvidia and AMD or it’s just a new view at old graphic cards?

There was a leak of a code for macOS Big Sur that includes the characteristics of the future flagman AMD graphic cards, and now it is possible to know the power of three new GPUs. Their code-names are Navi 21, Navi 22 and Navi 23. According to the data, the number of counting units will be 80 for Navi 21, 40 for Navi 22 and 32 for Navi 23. Also, programmers told the approximate number of threads for each processor, it’s 5120 for the most powerful variant, 2560 for the middle one and 2048 for the lowest. The frequency of every GPU will be hesitating from 2GHz to 2.5GHz, when the factory new Nvidia RTX3000 series GPU frequency is about 2 times lower (from 1,4GHz to 1.7GHz).

However, the quantity of power the “red” cards need is even lower than for their “green” colleagues. The code says that the XT version of Navi 21, will need around 320W for reference graphic card and around 355W for unreferenced models while it is almost 290W for the XL version in comparison with 370W for RTX3080. However, there is no information about the memory of the cards in the code, but some programmers assume that it will get about 16Gb of memory that will work at 16GHz speed, while, for example, RTX3080 is working with 10Gb of memory and 19GHz speed.

Taking into account all the characteristics of the Radeon RX 6000 series, we can predict that it might be almost of the same power as the RTX3000 series. But knowing the AMD company, the price will definitely be cheaper and this is what will attract customers more.

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