Recent development in technology

Artificial intelligence

applied in various industries, such as

  • healthcare, finance, and transportation, to improve efficiency and accuracy of decision-making processes,
  • used in the development of autonomous vehicles, speech recognition, natural language processing.

Artificial intelligence (AI) refers to the development of computer systems that perform tasks that typically require human intelligence, such as

  • visual perception,
  • speech recognition,
  • decision-making, and
  • language translation.

Machine learning

Advancements in

  • Natural Language Processing (NLP): transformer models, like OpenAI’s GPT (including GPT-3), demonstrated remarkable performance in various NLP tasks like language translation, text generation, and sentiment analysis.
  • Reinforcement Learning Breakthroughs: in playing complex games like Go and chess; potential of reinforcement learning in solving complex problems.
  • Transfer Learning and Pretrained Models: pretrained on large-scale datasets and fine-tuned for specific tasks. Allowing for efficient transfer learning and improved performance on downstream tasks with limited data.
  • Federated Learning: enables the training of models across multiple devices or servers without sharing raw data, gained traction in applications like healthcare, finance, and Internet of Things (IoT).
  • Explainable AI (XAI): focus on developing methods to interpret and explain the decision-making processes of machine learning models. Aims to make AI more transparent, understandable, and trustworthy. Enabling users to understand why a particular decision was made.
  • Ethical Considerations: researchers and organizations actively working on mitigating biases, ensuring fairness, addressing ethical challenges associated with AI algorithms, such as privacy concerns and algorithmic accountability.
  • Deployment of AI in Various Industries: including healthcare, finance, autonomous vehicles, cybersecurity, and natural language processing applications. These industries are leveraging machine learning techniques to improve efficiency, accuracy, and decision-making processes.


Blockchain technology is the underlying technology of cryptocurrencies like Bitcoin. Applied in various industries to improve data security and transparency. It has the potential to revolutionize industries such as

  • finance,
  • supply chain management, and
  • healthcare.

Quantum computing

A rapidly advancing technology that has the potential to solve complex computational problems that are beyond the capabilities of traditional computers.

5G wireless networks

Provide faster and more reliable internet speeds, enabling new applications such as

  • self-driving cars, remote surgeries, and virtual reality experiences.

Augmented and virtual reality

Used in various industries, such as

  • entertainment, education, and healthcare.

They have the potential to revolutionize how we interact with digital content and to create immersive experiences that can enhance learning and understanding.

Internet of Things (IoT)

The Internet of Things (IoT) refers to the network of interconnected devices. They communicate with each other and share data. This technology used in

  • healthcare, agriculture, and transportation, to improve efficiency and decision-making processes.

Artificial Intelligence

designed to

  • learn
  • adapt over time, enabling to improve their own performance and accuracy as they process more data and encounter new scenarios.

Show the

Some common applications include

  • natural language processing,
  • computer vision,
  • robotics, and
  • machine learning.

This innovation has the potential to revolutionize many industries and change the way we live and work.

However, there are concerns around the ethical implications of AI and its impact on employment and the society.

The Page

In this page we write about

  • the technologies behind these systems,
  • the ethical view of those systems,
  • the social impact what those systems create,
  • recent news,

about the

  • applications,
  • softwares,
  • products which created by Artificial Intelligence.

We show how AI systems used and seen as sustainable and present e.g.:

  • how to use guides,
  • reviews of applications,
  • lists about good to knows,
  • technologies behind them, and
  • how AI can be part of sustainable development.

We show how AI can help raise social responsibility and be part of a more holistic view and help to create products and services which are part of the circular economy and sustainable for example the Go Green brand.

Why generative AI can be seen as a chameleon?

Generative AI

seen as a chameleon because it has the ability to

  • adapt and
  • generate content in a way that resembles different styles, genres, or even individual authors.

This achieved by neural networks, which trained on enormous amounts of data and can generate added content based on the patterns and structures they have learned.

In addition to this like a chameleon, generative AI can blend in with its surroundings by producing content that is similar like existing works in a particular genre or style. For example, a generative AI trained on a dataset of classic literature might be able to generate new works that mimic the style and tone of authors like

  • Jane Austen or
  • Charles Dickens.

Moreover these systems used to create content that is entirely new and unique, like a chameleon changing its colors to blend in with a new environment.

For example, a generative AI trained on a dataset of abstract art might able to generate new works that completely unlike anything that created before.

Generative AI’s ability to adapt and generate content in a way that

  • mimics existing works or
  • creates entirely new ones seen as similar to the chameleon’s ability to blend in with its surroundings or change its appearance to suit an unfamiliar environment.