Michael Abramov

The increasing use of AI in our systems brings up numerous questions that span across the whole fabric of our societies. We’ve already discussed the ethical and legal implications of artificial agents in a broader sense -  this time we’ll talk about specific issues and evolving frameworks designed

Did you know that AI tech in agriculture is expected to jump from $1.1 billion in 2019 to over $3.8 billion by 2024? The rise of artificial intelligence in farming needs to be mindful of ethical issues. Though AI could change farming for the better, it raises concerns

Despite AI's quick evolution, the laws guiding it lag behind. Compliance and oversight are tricky with it due to conflicting interests, tangled regulations, and legal gaps. To cope, most countries in the world are scrambling to propose principles and legal AI concept frameworks on how to handle legislating

The construction industry is worth over $10 trillion yearly. It is about 13% of the world's economy. Defect detection is a key area, where AI tools are spotting issues by analyzing huge data sets. They look for patterns that suggest problems to be solved. It's all

Data annotation for AI in e-commerce plays an important role in today's systems. Picking the best data tagging tools matters a lot for our models - it can lower mistake rates, ensuring top-notch tagged data. Labeling data for personalization empowers customer segmentation with ML, which allows e-commerce platforms

AI in finance relies on machine learning algorithms. They need large amounts of precise data to work well. Effective training lets AI systems make accurate predictions and offer valuable insights. High-quality training data is crucial for this. It allows AI to excel in the financial sector. Without it, these systems