We have been witnessing significant changes in some supermarket shelf products. Aiming not to raise prices for the customer, the industry is reducing the size of products while keeping the price close to the previous one or with some minor reduction. This practice is called “SHRINKFLATION.” Some cases have started to become the butt of
Neural networks, an artificial intelligence method inspired by the human brain’s architecture, teach computers how to process data and acquire knowledge through experience. This technique finds application in various tasks, including image recognition, voice and facial recognition, natural language processing, recommendation systems, data analysis, phenomena predictions, and more. Training a neural network can be complex
Use cases of artificial intelligence are exceptionally increasing, and machine learning models are everywhere, in the banking sector, education, health, and our homes. One of the major challenges we face in these projects is enhancing the model, and a key issue is its performance, as deployment makes little sense if the model is not accurate
Artificial Intelligence is changing the way companies work with data and automation, and one of the most effective ways to implement AI projects is through cloud computing. This enables companies to take advantage of AI advancements without the need to heavily invest in their own infrastructure. According to research by Gartner, global cloud services revenue
They say the world spins fast, faster and faster, right? Likewise, the business strategies that were thought for 10, 20 years ahead are no longer viable today, or at least are very risky. Of course, there are long-term strategies, but the trend in recent years, reinforced by the startup model, the Lean, agile vision, is
Machine Learning is one of the most promising areas of artificial intelligence and has revolutionized the way we handle data and make decisions. However, as with any field of research, there are challenges to be faced, and in this post, we will explore some of them. Insufficient Training Data This is a common problem in
For years, studies on strategies have aimed at a sustainable competitive advantage as the ideal strategy for growth and business perpetuation. Innovation has become essential. Recently, with the advent of AI transforming into a differentiated “workforce”, organizations have begun to reap significant results with its application. Engineering is not just about numbers and calculations, but
Artificial intelligence (AI), being a comprehensive application technology, impacts various areas and transforms social interactions, changing the way people relate to both technology and each other, whether through automation, virtual assistants, or data analysis. Ethics, Privacy, and Security in AI That’s why ethics, privacy, and security are fundamental factors for the development and use of
The modernization of energy infrastructure is a process that involves updating the systems and technologies used in the production, distribution, and use of energy. The goal of this modernization is to make the energy infrastructure more efficient, secure, and sustainable, as well as to improve the quality of service provided to end users. Challenges and
When it comes to artificial intelligence, one of the concerns that arises is about the impact this technology will have on people’s work. Research points to various predictions indicating low or high risk of job replacements. One of the most well-known studies was conducted by Carl Frey and Michael Osbourne in 2013, predicting that nearly
