Michael Abramov

When it comes to identifying imminent cybersecurity threats, machine learning seems like the ideal tool for the job. Machine learning can evaluate endpoints and detect malicious activity faster than we can. What is machine learning in security? Why do organizations like Google rely on machine learning to keep their endpoints

Computer vision models have exploded in recent years. From Google’s self-driving cars and Tesla’s autopilot mode to Amazon’s Virtual Mirror, computer vision has transformed the face of modern technology. But machine learning image processing isn’t as easy as it may seem. Training and validating computer vision

Artificial intelligence has transformed industries. From retail to agriculture, high-precision algorithms drive productivity and better decision-making. But how does it work? What enables deep learning algorithms to understand our day-to-day business operations better than we do? To answer this question, we need to talk about a technique known as data

The ultimate goal of technology has always been to mimic the human brain. The rapidly expanding field of artificial intelligence aims to do just that. But what factors allow AI to make sense of the world accurately? To answer that question, we need to discuss machine learning model performance. Machine

In the world of machine learning, data is king. But data in its original form is unusable. That’s why more than 80% of each AI project involves the collection, organization, and annotation of data. The “race to usable data” is a reality for every AI team — and, for many,

Artificial intelligence has revolutionized industries. But while most people have heard of self-driving cars and facial recognition software, many of us are unaware of the enormous impact AI has had for the waste management and recycling sectors. Imagine a waste management facility. Are you picturing rows of machines? That’s