Blog

The integration of Artificial Intelligence (AI) in retail is revolutionizing shopping experiences, offering unprecedented levels of personalization. By analyzing vast amounts of data, AI enables retailers to understand and predict consumer preferences with remarkable accuracy, leading to highly customized shopping journeys.

The journey towards integrating AI into the medical field is fraught with challenges that must be navigated with precision and care. These challenges not only test the resilience of AI systems but also underscore the importance of meticulous preparation and the need for advanced solutions.

‍

Meet LinkedAI’s Magic Tool, a state-of-the-art automatic segmentation tool that promises not just efficiency, but unparalleled accuracy. But does it live up to the hype? Let’s dive in.

‍

Artificial Intelligence (AI) has made great strides in recent years, thanks to advances in deep learning, which have enabled the creation of increasingly complex models capable of identifying objects in images and performing tasks that were previously deemed impossible.

‍

With the Magic Tool 2.0, machine learning teams can now easily generate automated mask predictions for multiple objects in their images, across various real-world computer vision applications like the detection of different plant patterns and classification for smart agriculture, fast and precise medical diagnosis of various pathologies and diseases, detection and classification of different products in retail solutions and much more.

‍

‍

There’s certainly a lot to be said about how artificial intelligence and machine learning algorithms are transforming the way things are being done in just about every industry. The use of machine learning in the healthcare sector however, is life-changing and, to an extent, life-saving.

‍

Automation in production and manufacturing is nothing new. For years now it has been making transformative changes on factory floors, helping manufacturers exercise greater control over their operations and costs. However, with the advancements in the fields of robotics, artificial intelligence, and machine learning, there is so much more that automation can do.

‍

On a daily basis, the world is generating a staggering 2.5 quintillion bytes of data — yes, that’s a real number with eighteen zeros. On one hand, having that much data (or a fraction of that) can be very challenging in terms of collection, storage, cleaning, preparation, and all other processes that go into analyzing data.

There was once a time when the retail experience consisted mostly of consumers going to a store and picking out something that they need or like. The process has slowly evolved over the years, thanks largely to technology, and now, artificial intelligence in retail.

‍

Synthetic images are all those images created by computer processes, from the company logo to the representation of an imaginary city. Currently, a large part of the images found on the Internet are created artificially, that is, they are not photographs or paintings but are created entirely on a computer.

With the global population expected to reach 8 billion by the end of 2022, it is imperative that more efficient ways of farming are discovered to fulfill man’s basic need — food. Farmers are thus put under a lot of pressure to adopt better methods and minimize risks in order to meet the growing demand. When there is a need to move beyond traditional farming, the answer lies in modern agriculture.

‍

Artificial Intelligence (AI) can only advance as well and as quickly as the data that it trains with. This makes the process of data labeling one of the more crucial parts of the development of machine learning and deep learning algorithms. With high quality data and accurate labels, an AI model has a greater chance of learning and accomplishing the goal it’s been set out to do.

‍

With the introduction of potentially disruptive technologies, such as virtual reality, genomic illness prediction, data analytics, personalized medicine, stem cell therapy, 3-D printing, and nanorobotics, medicine progress is at its peak and making a breakthrough.

‍

Las imágenes sintéticas son todas aquellas imágenes creadas por procesos informáticos, que van desde el logo de una empresa hasta la representación de una ciudad imaginaria. Actualmente gran parte de las imágenes que se encuentran en internet son creadas de manera artificial, es decir, no son fotografías o dibujos, sino que son creadas completamente en un computador.

‍

In large-scale agriculture, the quantification of products becomes an arduous task that ends up being replaced by the counting of packages, however, in crops such as grapes which a large variable is the number of bunches that come out of each grape plant that is planted, it is necessary to count these to predict the final product obtained.

‍

Today, AI is impacting different aspects of our lives but did we anticipate the same for art — one of the most expressive forms of human emotion? AI generated art and many such applications have been possible due to the high quality synthetic data that’s been generated as well, using AI systems.

‍

Artificial intelligence (AI) subfields such as machine learning (ML) and deep learning rely heavily on data — massive amounts of it, in fact. But while there is no shortage of data available from the web, transactions, machines, and other traditional sources, the huge challenge lies in making sense of all that data. This is where data labeling can prove to be very valuable.

‍

En la agricultura a gran escala, la cuantificación de productos se convierte en una ardua tarea que acaba siendo sustituida por el conteo de bultos, sin embargo, en cultivos como la uva en los que una gran variable es el número de racimos que salen de cada planta de uva que se siembra, es necesario contar estos para predecir el producto final obtenido.

‍

The value of AI is being recognized now more than ever, with applications of artificial intelligence being utilized in almost every industry. From powering autonomous cars, influencing consumers’ spending habits, cleaning offices and large equipment, determining nutrient deficiencies in the soil, and more, it’s clear that AI-driven intelligence systems are making a significant impact in people’s lives.

‍

Nowadays, we are in the Big Data era where thanks to the internet, it is possible to have thousands of free accessible data, nonetheless, this data usually doesn’t come with its respective annotations. Making the process of training Deep Learning or Machine Learning models very difficult, and even harder when the data is a customized group of images, where there are few possibilities to obtain large amounts of data.

‍

Data is the lifeblood of the deep algorithms of Artificial Intelligence (AI) and Machine Learning (ML), but the truth is that only a few big players have the most power over this resource, leaving other companies at a disadvantage. In this scenario, synthetic data is a great tool to minimize the gap when little data is available and allows organizations of all levels and sizes to be part of the development of these algorithms. In other words, democratize machine learning.

‍

Son los datos la base de los algoritmos profundos de la Inteligencia Artificial (IA) y Machine Learning (ML), esto pone en ventaja a las empresas que los poseen. En este escenario los datos sintéticos son una gran herramienta para minimizar la brecha cuando se cuenta con pocos datos y permite a organizaciones de todos los niveles y tamaños entrar en el desarrollo de estos algoritmos. En otras palabras, democratizar el aprendizaje automático profundo.

‍

Abstract

As students of Machine Learning, we were given the opportunity to implement an idea of our choosing. We partnered with mentors Divait Parra and Paula Villamarin from LinkedAI and Cristian Garcia a Machine Learning Engineer who has contributed to many open-source projects; to create an Anomaly Detection model to automate the process of anomaly detection on the production line.

‍

In today’s world data is considered an asset and one of the most valuable resources, and truth be told only a few big players have the strongest hold on that currency. The biggest companies around the world are even so generous of giving machine learning algorithms for free, because in the end, these algorithms are not that valuable without the data that feeds them.

‍

Annotation in Machine Learning is the process of labeling the data, which could be in the form of text, video, images or audio. Image annotation helps to make images readable for computer vision, computers use the annotated data to learn to recognize similar patterns when presented with new data.

‍