An overview of Natural Language Processing

By Natural Language Processing, or NLP, we mean the field of Computer Science and Artificial Intelligence concerned with the interaction between computers and human languages.

In brief, NLP is used to teach computers how to read, decipher, understand humans’ sentences. While nowadays many people daily rely on Natural Language Translation services, like Google Translate, or on personal assistants, like Google Assistant or Siri, it was not an easy way to get where we are now.

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So, we use Natural Language Processing to accomplish several tasks, such as:

· Language translation applications (we mentioned Google Translate)

· Word Processors, like Microsoft Word, grammar and tone of voice check applications, like Grammarly. …

Instead, they will earn customers’ trust and impact the employment landscape

From its humble beginnings as a collection of simple scripts designed to eliminate repetitive tasks, automation has evolved into advanced tech that provides sophisticated capabilities thus transforming the way IT teams work but also our lives. Let’s take a look where automation will take us in the near future.

Today, automation streamlines operations improves the customer experience and reduces costs by being efficient and effective.

Machine learning, coupled with the availability of massive computing power to manage big data, is providing increasingly deep insights. These developments in artificial intelligence technologies lead to automated decision-making that goes far beyond the traditional models driven by rules-based engines. …

The automation of everything from cars to combine harvesters or factories is just a fact of life these days and the hyperbole of the machines taking over the world in a Terminator-like scenario isn’t really hyperbole anymore (except maybe for the dystopian aspects…).

Since the IoT concept and pilot implementations explosion in the last couple of years, the technology’s expansion among industries is likely to continue in 2020, with connected devices and sensors, robotics, immersive reality, and AI taking over the physical world with an unprecedented level of technological sophistication. An overhaul of the existing IT infrastructures in order to enable intelligence for the next generation of technology will become more pressing than ever and a renewed focus on hardware needs, balanced with cloud and edge computing will be commanded by these technologies in order to harvest the rising opportunities. …

Autonomous cars, evolution and challenges

Start-up CEOs rising overnight as superstars of the Auto industry, AI becoming the base technology for every engineering department and consumers talking more about embedded technologies than about horsepower. Well, prepare yourself; the Automotive industry is changing, and it is changing at F1 speed rate.

Today’s vehicles are becoming computers on wheels, the high-end cars nowadays gathering up to 100 Electronic Control Units (ECUs) and running close to 100 million lines of code.

The development of Advanced Driver Assistance Systems (ADAS), autonomous driving and connectivity are fast enriching the features of modern vehicles and elevating the driving experience in terms of convenience, safety, comfort, and entertainment. …

Manual and automated testing and the role robots can play

As cars’ touch-screen infotainment systems are evolving and enhancing the driver’s experience with features such as voice recognition, seat controls and connectivity, the necessity of developing tests for both the software and the hardware makes more complex and time consuming the release process. Moreover, software testers must often be engaged in repetitive tasks.

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Automation — old challenges, new solutions

When the software is tested manually, the software tester performs an interaction with the device where the software is run, searching for errors and, when the process is complete, repeat the steps over and over again.

That’s when automation can step into the game. The work of automated testers consists in improving, adjusting and optimizing the process, being able to recycle the test that can be reiterated. Engineers are so liberated from repetitive tasks and can spend their time in more important activities involving the software they are working on. …

Promises and threats of a new technology

The Internet of Things (IoT) is one of the most talked-about technologies in the era of digital transformation. Some even call it the 3rd industrial revolution; it is the core technology behind smart homes, self-driving cars, and smart cities. Some of its first beneficiaries, the Automotive industry, healthcare, and logistics, have taken giant steps and led the manufacture of everyday “things” that connect with computers.

And it will be probably even bigger. According to Gartner, there’ll be around 26 billion devices on the IoT by 2020, a huge jump from the 2016’s 6 billion and IoT product and service suppliers will generate $300 billion+ in revenue. …

Re-thinking space with CV

As in the previous articles we discussed the history and the main concepts that characterize the functioning of Computer Vision, it’s time to dive in and get to the way technology impacts people’s life.

Computer Vision is currently employed in several industries, such as Automotive, to develop self-driving cars, Healthcare, Security and more.

In this article, we are going to talk about why computer vision matters in the Retail industry as well.

In-Store today

As many brick and mortar stores have already built and integrated online platforms, other digital-native brands who gained popularity online are opening physical shops, not with the purpose of directly selling their products, but simply providing retailtainment in the form of refreshments and pick-up services. …

What’s new and what’s next?

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A multidisciplinary field, computer vision was revolutionized in the last decade due to the availability and advances in hardware and cloud computing, that took deep learning and deep neural networks out of the sci-fi realm into the practical applications world. With a multitude of applications in real life, computer vision and hardware market is expected to reach $48.6 billion by 2022.

Considered a subfield of artificial intelligence and machine learning, computer vision has been able to take great leaps in recent years and even surpassed humans in some tasks related to detecting and labeling objects, thanks to advances in artificial intelligence and innovations in deep learning and neural networks. The amount of data we generate today is one of the driving factors behind the growth of computer vision. …

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RINF TECH announces the opening of its first Automotive delivery center in Timisoara, under the Senticle brand

Started locally and developed globally, RINF TECH is a Romanian entrepreneurial Software Engineering and Robotics organization, which expanded in Europe, featuring four development centers in Bucharest, Kyiv, Sofia and the newest one in Timisoara.

Senticle’s Timisoara team is expected to grow up to 30 people, with expertise in multiple technologies (mainly Embedded C for the moment), by the end of 2019, and 60 until the end of 2020, working on Automotive related projects focusing on Instrument Clusters, Infotainment Systems and Multimedia development.

Constantin Iftime, the CEO of RINF TECH commented on choosing the location: ‘The decision to expand in Timisoara was made easy by its proximity to our customers in the first place, and to the important talent pool available in the area. We always welcome great professionals to our company, as we want to stay in the frontline when it comes to technology innovation’. …

Introducing Computer Vision

If you are looking for a way to improve your resume, this is not the right article.

If you are curious about computer vision, read on!

Computer Vision, aka CV, is the field of study that pursues the enterprise of teaching computers how to see and extract information from digital visual content (photos, videos, etc.).

A little background

The history of out topic starts with first experiments in the 1950s, when some early models of neural networks were used to detect the edges of simple objects and categorize them into categories such as circles and squares.

We had to wait a few more years (1970s) before meeting the first commercial version of computer vision, with the creation of a software able to interpret typed or handwritten text using optical character recognition. …


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