Artificial intelligence (AI) is directly related to Data Science – the science about data that aims to extract value from a big mass of information. This value may reside in, for example, enhanced forecasting capabilities, discovering patterns, better decision-making. Basically, AI concerns with information processing algorithms and methodologies. Artificial intelligence operates on huge amounts of data, analyzes it, and uses gathered insight to develop solutions.Read More
We know that efficient supply chains can be a crucial part of differentiating an organization from its competitors. But how can a company truly leverage the advances in artificial intelligence and neural networks we’ve seen in just the last decade?
Particularly for Fast Moving Consumer Goods (FMCG), supply chain trends are emerging that must not be overlooked if a company wishes to become or remain a market leader. Supply chain agility, optimization, sustainability, and ethical considerations challenge established players and give disruptive newcomers opportunities to capture significant market share. This article covers the emerging role of AI in supply chain management. We discuss how it could help companies to more effectively analyze and improve the efficiency of their operations. There is an evolving role advanced technologies play in the FMCG industry. They have potential to dramatically impact supply chain optimization and supply chain productivity.Read More
These days, everyone has heard of artificial intelligence (AI). But not everyone understands what goes into making an AI algorithm work properly. At a high level, AI (also known as machine learning), works by ingesting a large set of data called training data. Then, the AI uses an algorithm to sort through this data and discover trends. This algorithm can be a bit of a black box, and there are many variations of machine learning algorithms. But the important part to remember is that AI “trains” on a dataset. This dataset is called training data. After an AI is fully trained on a dataset, it can be applied to “test” or “application” data. That is where you will typically see AI in action.
Of those three phases (the training data, algorithm, and test data), today we will focus on training data. Specifically, we will discuss how training data is collected today, introduce an alternative called synthetic data, and examine the several advantages of synthetic data.Read More
We hear a lot of buzz around some of the more popular emerging technologies like artificial intelligence and machine learning and for good reason. But, there are a few others that work in conjunction with these technologies that are on the verge of completely changing how we look at both macro and microeconomics.
Examining each one of these technologies individually is inspiring on their own. Perhaps even more awe-inspiring is when we consider in what ways they can work together and what that may mean for the world. One thing is for certain—the name of the game in the 2020s is disruption and the ways we will find to combine technologies this decade mean the world as we know it is about to fade into history.
A recent study has shown that a lack of skilled talent is the top hiring challenge, as 87% of HR professionals say that there were few qualified applicants for the positions they were trying to fill.
This is the reason why there’s currently a war for talent raging on, and companies have to go out of their way in order to attract and keep skilled people.
The screening and hiring processes are also complex and challenging. Companies receive avalanches of CVs for every vacant position, and it’s time-consuming to review every single one of them.
However, thanks to artificial intelligence (AI) and HR software, it’s much easier to find and hire the right person for the job.
Cracks on the surface are a major defect in concrete structures. Early crack detection allows preventing possible damage. There are various approaches to solving this problem. It can be manual inspection or automatic detection methods. But nowadays automatic detection methods include not only laser testing and radiographic testing. Progress in neural networks and computer vision allows us to use image processing for concrete surface crack detection.
In this article, we will share our approach to solving the problem mentioned above.
Implementing DialogFlow chatbots is cool and convenient if you have something trivial and easy to prototype: fancy UI – easily, extracting base entities like name, surname and phone number – here is a tool if don’t want to install and deploy – cloud solution is at your service.
But what if you need to go deeper:
– Do you have a Japanese tokenizer, dear DialogFlow?
– Transparent and customizable intent classification tool?
– Sorry, guys.
– Also I want to integrate my search index, knowledge graph and custom dialogue management policy.
– What are you talking about?
In a nutshell if you want fully controlled system, if you need custom advanced AI in your app, if you need natural language processing in your chatbot pipelines, if you want to scale your chatbot behaviour – on-premise solution is the way for a chatbot developer. And here it’s Rasa framework that really shines.
Artificial intelligence solutions are widely used in a variety of businesses. With opportunities they provide, it becomes possible to optimize processes and bring revenues to a new level.
E-commerce is not an exception. Lots of companies are now looking for ways to cross-sell and up-sell effectively. This is where an AI based recommender system can help.
As McKinsey reports have shown, 75% of content that Netflix users consume and 35% of products that Amazon users buy come from recommendations. After implementing a recommender system, Amazon reported a 29% increase in sales. Alibaba group managed to drive the conversion rates by 20% when it applied ML based recommendation algorithms to provide shoppers with personalized offers during the sales festival in 2016.
Actually, most online shoppers expect companies to provide them with personalized recommendations. According to Evergage, 56% of users will come back to the sites that offer recommendations again and again.
Wondering what kind of an intelligent recommendation engine to implement for your business? Or probably you are interacting with people who need to implement such a system? If any of these is the case, you definitely need to look through the possible use cases below.
In this article, I want to talk about the use of convolutional neural networks for the classification of images by style.
The goal of our project is to build software to identify whether an image is in the “BMW style”. In other words, we are faced with the task of classifying images. It is important to note here that images could be of any content, with and without cars. So, the main interest here is not to identify a car object, or identify a BMW car, rather identify a BMW look and feel – colors, composition and so on. But we can’t select these attributes of style manually. To solve this problem, it was proposed to use a neural network, in which such complex features will be found automatically in the learning process.
Greetings, dear readers! In this article, I’m going to share how I counted drops. Yes, you read that right. DROPS
It all began when our team started studying machine learning, or to be more precise, we studied Python and OpenCV. During the practice task, I had a chance to implement the algorithm that would count the falling drops in the given video or in real time. And now I’ll tell you how it all went.