Artificial intelligence ethics is something everyone’s talking about now. Self-driving cars, face recognition and critical assessment systems are all cutting-edge solutions that raise a lot of ethical considerations. In this post, we are going to talk about the common problems that AI of today faces and how AI ethicists try to fix them.
I assume that if you’re reading this post, you have encountered the term ‘artificial intelligence’ before and know what it means. If not, please, check out our post about artificial intelligence, it will help.
It is also possible that the word ‘ethics’ is a bit unclear for…
Artificial intelligence promises great benefits to enterprises. According to research by Markets and Markets, AI will grow to a $190 billion industry by 2025. Gartner reports that AI use for enterprise applications has grown by 270% in only four years.
If you are considering an AI implementation, keep an eye on the ethical risks. More and more business owners are starting to think about how AI ethics can help them design better products and be a tool of innovation. Let me guide you through it.
The ethics of artificial intelligence is an academic field that appeared out of the necessity…
Early detection of diseases associated with genetic disorders is one of the biggest concerns for modern medicine. Recent research states that people diagnosed with lung cancer at an early stage have a 57% chance to survive the next 5 years compared to the 3% survival rate of patients with diagnosed IV stage cancer. Early-stage detection of another scourge of humanity, Alzheimer’s disease, allows patients to change their lifestyles, participate in clinical trials, and treat the brain-degrading symptoms in advance, effectively prolonging their lives. …
Usually, we think of a program as something that manipulates data to achieve some result.
But what is data?
Can we use the programs themselves as data? 🤔
In today’s article, we’ll go down the rabbit hole with the assistance of Elixir, a programming language that is permeated by metaprogramming.
Metaprogramming is just writing programs that manipulate programs. It’s quite a wide term that can include compilers, interpreters, and other kinds of programs.
In this article, we will focus on metaprogramming as it is done in Elixir, which involves macros and compile-time code generation.
To understand how metaprogramming works in…
In 2021, the focus on digitalization is as strong as ever before. Machine learning and AI have helped IT leaders and global enterprises come out of the global pandemic with minimal loss. And the demand for professionals that know how to apply data science and ML techniques continues to grow.
In this post, you will find some career options that definitely will be in demand for decades to come. And there is a twist — AI has stopped being an exclusively technical field. …
Parser combinators are one of the most useful tools for parsing. In contrast to regular expressions, they are much more readable and maintainable, making them an excellent choice for more complex tasks.
In this article, I'll explain how parser combinators work and what they are made of. We will try to build functional parser combinators from scratch. The combinators we’ll make will be low-level and worse than what you would get with simple regex. They are just there to illustrate the point.
We perceive and interpret visual information from the world around us automatically. So, implementing computer vision might seem like a trivial task. But is it really that easy to artificially model a process that took millions of years to evolve?
Read this post if you want to learn more about what is behind computer vision technology and how ML engineers teach machines to see things.
Computer vision is a field of artificial intelligence and machine learning that studies the technologies and tools that allow for training computers to perceive and interpret visual information from the real world.
‘Seeing’ the world…
Kaggle is the first place to go for anyone who studies machine learning. This interactive online platform provides hundreds of databases and tutorials that you can use to kickstart your ML career.
But what the website is the most famous for are its competitions. It can be hard for a newcomer to orient themselves in the interface and understand where to get started. So, in this post, we will get you started with your first Kaggle competition!
Kaggle competitions are machine learning tasks made by Kaggle or other companies like Google or WHO. …
Machine learning becomes more accessible to companies and individuals when there is less coding involved. If you are just starting your path in ML, feel free to check out these low-code and no-code platforms.
You have probably heard the terms ‘low-code’ and ‘no-code’ before.
Low-code simply stands for a reduced amount of coding. A lot of elements can be simply dragged and dropped from the library. However, it is also possible to customize them by writing your own code, which gives increased flexibility.
Today, it is almost impossible for some people to believe that such a field as software programming was once almost exclusively a female field. What started as an unprestigious tedious profession done by women is now the field where large amounts of money circulate. As soon as programming started to be used for rocket science and became more prestigious, women were squeezed out not only from their working places but also from the history of programming. Test yourself: how many great women in computer science can you remember?
Let’s try to fix this injustice. Feel free to share the names…