Category Archives: AI

The world’s giants and their latest AI solutions

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AI is implemented almost everywhere and in everything, but global companies, not tall as this girl, but quite high, past social networks and online shopping where the recommendation system actively takes part as a main favorite, are always interested in additional areas to bring innovation to the whole world, not one segment of it.

Facebook AI recently introduced TextStyleBrush, a system that analyzes a banner ad, newspaper or any other medium of text information in real time, allowing it to replace what is written on the medium with text that the user creates.

The model, which uses the underlying StyleGAN2 framework, handles both handwriting input and typography and is able to analyze various subtleties of style and account for transformations and deformations such as rotations and twists. The authors note, however, that improvements still need to be made, particularly in the realism of replacements on metal supports and in the handling of reflections.

A little while ago, Facebook also launched GrokNet, a unified computer vision model with which they intend to create the world’s largest social media shopping platform. The model is currently running on Facebook Marketplace. The company soon plans to expand GrokNet to new apps on Facebook and Instagram.

GrokNet detects the products in the picture and predicts their categories. Unlike previous models, Facebook’s product recommendation system is a universal model that scales to billions of photos vertically, including fashion, auto and home decor.

If we talk about computer vision, it is impossible not to mention Google, which are big fans in the practice of this branch, especially in the practice for various areas such as in this case, medicine.

The University of Waterloo has been noted as using artificial intelligence for its researches, in particular now – the detection of skin cancer. It was noted by Google, which subsequently presented their solution to detect skin diseases by photos in real time.

Приложение Google Health инструмент искусственный интеллект фотоанализ кожные проблемы болезни глубокое обучение

The user takes three pictures of an area of skin, hair or nails that they think has a dermatological problem and answers a few questions about their skin type and the problem itself (other symptoms, pain and/or how long etc.).

And for the development of AI itself, in particularly the participation of ordinary users who are not programming gurus, Apple is creating a non-code AI platform.

The platform allows machine learning researchers and non-technical geospatial specialists to experiment with domain-specific signals and datasets to solve different problems. It adapts complex spatiotemporal datasets to standard deep learning models, in this case convolutional neural networks (CNNs), and formulates disparate problems in a standard way, such as semantic segmentation.

Perhaps to maintain this platform, gurus like people who have devoted years to learning certain languages will act merely as mentors, because IBM has announced Project CodeNet, a large dataset that aims to help teach AI how to understand and write code by itself.

CodeNet features 500 million lines of code, 14 million examples, and spans 55 programming languages including Python, C++, Java, Go, COBOL, Pascal, and more. Projects such as OpenAI’s GPT-3 are showing how AIs are becoming quite adept at penning the languages of us humans, but writing their own native code could be innovative for finding another different methods to solve the problems with code.



How artificial intelligence is shaping religion in the 21st century

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AI is fully embedded in everyday life and all areas of existence. Every day, our every choice about the ordered food or the post we liked in the social. the network affects the subsequent, progress has not spared religion either.

Muslims are delighted with apps like Muslim Pro, showing prayer times and sending notifications for sunset and sunrise. The Japanese have created a robot priest performing Buddhist rituals that cost 3 times less than human services.

Faith leaders are increasingly concerned with building humanoid machines, but the relationship between technology and religion has not always been smooth.

The fight against “machines” is divided into three stages: rejection, acceptance and adaptation. Due to the rapid development of technological progress, the initial negative reaction turns into support for the mainstream.

For example, a 400-year-old temple in Kyoto has a new priest named Mindar. Like other worshipers, he reads sermons and communicates with parishioners, although he has some features, such as a silicone metal case and the price of its exploitation (“services”) in a million dollars.

Mindar’s metal skeleton is naked and I think this is an interesting choice – its creator, Hiroshi Ishiguro, is not trying to make something that looks completely human.

Natasha Heller, assistant professor of Chinese religions at the University of

This statement provides a variety of technological influences on religious culture. Some believe that AI can interest the heavenly people, taking into account their interests and will become an object of worship itself, such as Anthony Lewandowski, who initiated a major lawsuit Uber / Waymo, which founded the first AI church called “Path of the Future.”

Others think that people tend to find their similarity in everything and the subsequent created machines will themselves determine their belonging to the culture, telling about their views and new possible religious visions.



Analysing material stress by images, future of physics and AI

Reading Time: 2 minutes

Engineering, physics – these fields of science can be named as BFF. Creators should even begin from the force of gravity law in order to make any mechanism work; each mech firstly has to fit some characteristics as form, consistency and its deformation capacity to proceed with. However, the equations solving can be computationally expensive, depends on material complexity.

MIT researchers decided to deeply focused on resolving and presented an Artificial Intelligence soft determining stress and strain of a material based on image recognition.

“This is always a difficult problem. It’s very expensive and it can take days, weeks or even months to run certain material simulations. So we thought, let’s teach an AI to solve this problem. […] From an image, the computer can predict all these forces: deformations, stresses, etc.”

Markus Buehler

An algorithm was developed by Zhenze Yang (lead author and PhD student in the Department of Materials Science and Engineering), Chi-Hua Yu (former MIT postdoc) and Markus J. Buehler (Director of the Atomic and Molecular Mechanics Laboratory and Professor of Engineering at McAfee), providing the possibility to implement connect computer vision and material in a real-time.

As data researches used different materials with various “from soft to hard” consistency. Main Machine Learning model was based on GAN (generative adversarial network) matching dozen of images to the future system in order to get the general “understanding” and as an addition be able to visualize micro details and singularities like cracks and other deformities.

In order to understand the pressure exerted with certain conditions objects were interpreted in random geometrical figures.


image strain

This visualization shows the deep-learning approach in predicting physical fields given different input geometries. The left figure shows a varying geometry of the composite in which the soft material is elongating, and the right figure shows the predicted mechanical field corresponding to the geometry in the left figure.

The recent innovation will open many doors in resolving estimating risk issues; a significant guarantee of constructions stability increase and revealing the potential of AI and computer vision in perspective.


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AI follows human gender biases

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Although a lot has changed in a recent years when it comes to empowering women in pursuing professional careers, there is still plenty of space for much needed improvements. Researchers form University of Melbourne, in their study “Ethical Implications of AI Bias as a Result of Workforce Gender Imbalance” commissioned by UniBank, investigated the problem of AI favouring male job applications over female ones.

Researchers observed hiring patterns for three specific roles chosen based on gender ratios:

  • Male-dominated – data analyst
  • Gender-balanced – finance officer
  • Female-dominated – recruitment officer.

Half of hiring panellists were given original CVs with genders of candidates showed, and the other half of panellists the exact same CVs but with genders changed (male to female and female to male, for example “Julia” was changed to “Peter” and “Mark” to “Mia”). The recruiters were asked to rank CVs, where 1 was being the best ranked candidate. Finally, the researchers created hiring algorithm based on the panellists decisions. 

The results showed that CVs of women were scored up to 4 places lower than men’s ones, even thought they had the same skills. The recruiters claimed that they were judging based on experience and education.

Male candidates were more often ranked in Top 3 for all jobs listed and female candidates were more often ranked in Bottom 3 for all jobs!

Male candidates were on average ranked higher for data analyst and finance officer position by both female and male recruiters! That proves that the bias was unconscious. However, in female-dominated role – recruitment officer, the bias worked also the other way round meaning female CVs on average ranked slightly better than male ones.

The researchers adopted a regression model of analysis which showed that candidate’s gender was one of the most critical factors in deciding who will get the job. 

Researchers warn that human bias can be adopted by AI on a bigger scale. Mike Lanzing, UniBank’s General Manager, points out that “As the use of artificial intelligence becomes more common, it’s important that we understand how our existing biases are feeding into supposedly impartial models”.

Dr Marc Cheong, report co-author and digital ethics researcher from the Centre for AI and Digital Ethics (CAIDE),  said that “Even when the names of the candidates were removed, AI assessed resumés based on historic hiring patterns where preferences leaned towards male candidates. For example, giving advantage to candidates with years of continuous service would automatically disadvantage women who’ve taken time off work for caring responsibilities”.

This study calls for immediate action to prevent AI from acquiring gender biases from people, which can be hard to eradicate later on, especially taking into account the constantly increasing use of AI in recruitment processes. The report suggests a number of measures that can be taken to reduce the bias, for example training programs for HR professionals. It is crucial to find the biases that are in our society before AI mimics them.


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OvuFriend – polish start-up using AI to help women understand their fertility

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History of OvuFriend started with a classic example of a pain point found. Joanna Fedorowicz, founder of OvuFriend, could not get pregnant. “Although my husband and I were only trying to get a baby for a few months, I have an “analytical nature”, and I didn’t just want to be lucky. It worried me that I was in a situation in which I had little influence and I knew so little about the processes taking place in my body” – Joanna recalls. This made Joanna came up with web and mobile application that analyses female fertility and help women get pregnant as soon as possible. OvuFriend is looking at the problem very comprehensively. It is not limited to counting the fertile and infertile days. It also determines, based on parameters of woman’s menstrual cycle, what are the obstacles that prevent from getting pregnant.

OvuFriend uses technology, science and advanced algorithms. It is based on health data that woman fills in during the whole menstrual cycle. The user keeps a diary of various symptoms that accompany the specific phases of a cycle. There is also a possibility to synchronize the app with data from fitness bands. 

AI analyses given data and gives woman ready-made analyses and reports about her health as well as expert advice for each day form gynaecologists, endocrinologists, nutritionists and other doctors needed. What is more, OvuFriend is able to notify if it detects any worrying symptoms not only about fertility, but also about hormonal health and diseases like hypothyroidism, endometriosis or polycystic ovary syndrome. AI will indicate what requires a medial consultation. The algorithms will also remind about important preventive examinations specific for certain age, period of life, health condition etc., which woman at all ages should never underestimate. 

Besides the health analysis of the data, the creators of OvuFriend managed to incorporate community building into the app. Women can publish charts of cycles and anonymously comment and compare their charts with the ones of other users. People with similar parameters can easily find each other and give each other support and the feeling of not being alone.

“There have been studies done at Harvard which show that a person who is treated for infertility experiences the same stress as someone who is diagnosed with cancer. When women are close to each other in this situation, it just gets easier for them.” – Joanna says. 

Problems with fertility affect tremendous number of people around the world – every 5th couple has difficulties with getting pregnant. Only in Poland, about 1,5 – 2 million couples may find it difficult to get pregnant! Large companies recognise the problem and support the projects with the potential to address this issue. OvuFriend was supported substantively and technologically by Google for Startups accelerator in Poland and accelerator program in Silicon Valley.

Lubię fakty i liczby | Girls Gone Tech

“Here at Campus Warsaw, I am surrounded by and have access to individuals who want to have an impact, solve tough problems, or challenge the status quo. I have never been more motivated and prepared to take OvuFriend to the next level.” – Joanna Fedorowicz, Founder and CEO of OvuFriend.

OvuFriend received 4,5 million PLN in funding from National Centre for Research and Development for data analysis and creation of AI algorithms.  

Joanna wants to go further and analyse more and more factors affecting fertility e.g., sleep quality, stress, physical activity. She also wants to enter international market in the near future. 

So far, OvuFriend have helped more than 40 thousand women to become pregnant!


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How AI help universities

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All universities/higher education across the world are transforming their back end processes using artificial intelligence. The impact of artificial intelligence in universities grown so rapidly and spread so wide.

The usefulness of artificial intelligence is undeniable and evident in the way departments nowadays teaches and also in the way the student learns. Furthermore, artificial intelligence, smart AI tech can now do a diverse set of varying tasks.

Artificial Intelligence has transform various sectors, and amid this the educational world cannot afford to still operate in the old-school ways. When it comes to the issue of universities, AI can produce a better personify, recommendation, and also automated administration for universities.

Not only does artificial intelligence help in modifying the study patterns and materials for the students according to their capabilities and ability, but artificial intelligence also allows intelligent automation for administration tasks in university. 

The technology can be friction to clarify the voluminous queries and application that are been received by the universities. Even for some of the educational institutions that collects intentional application along with the domestic ones, artificial intelligence algorithms can forecast the applicant most likely either to be accepted and enrolled as well.

Therefore, as we keep moving towards to more advanced world, we must keep focus on leveraging technologies like artificial intelligence to renovate the very place that initiates such education advancement.

Here are some of the ways in which Artificial intelligence has helped universities;

  1. Student Acquisition

Personify of enrollment process with the help of artificial intelligence tech in universities, permit universities to target students who are likely to perform well in their program.  Adaptive conversational assistants help universities to cater for student worldwide. Such incorporation of artificial intelligence in universities helps universities to boost the number of enrollment.

2. Operational Efficiency

Scientist have been flirting with the usefulness of artificial intelligence in universities for some years now, the outcome of this is the use of tools such as SOP grader, document recognition, and also chat-bots in universities. Artificial intelligence tech such as this will be able to use information from various campus system to guide the administrative decision and also channel the university syllabus towards the employer hiring needs.

3. Classroom Learning

Artificial intelligence mostly helps classroom learning by making available a comprehensive educational experience. It permits student to widen their imaginations with the use of tools and technologies such as virtual assistants and augmented reality. This classroom assistance tech for monitoring and evaluating helps the department to increase the educational experience.

Teachers using AI in education

Artificial Intelligence technology in universities makes classroom education easier and more efficient

4. Student Engagement

Artificial technologies such as the interactive assistants allows the student to be able to communicate their issues right when it pop up consecutively and increasing student retention rate. The engagement of student can also be advance by embracing unique artificial intelligence technologies such as the student success prediction model. Technologies like chat-bots in universities helps educators to improve their efficiency in the teaching based on the student opinions which is collected by the conversational assistants.

5. Reminders

With the help of artificial intelligence technologies in universities, institutions can help the student by sending them some useful text messages, push notification or emails. These will act as a reminder to them when there is a certain task needed to be completed, event coming up and also deadlines approaching messages. Such uses of artificial intelligence will help the student to get familiar with the AI tech and it will also increase student engagement.

Girl checking reminders from a chatbot in higher education

Conversational Artificial Intelligence chat-bots are used by universities to interact with students and send them relevant reminders via email and so on.


Successful AI Examples in Higher Education That Can Inspire Our Future

How Artificial Intelligence Can Change Higher Education