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Artificial Intelligence

What about NLP today?

These days we all want machines to talk, and the only way a computer can talk is through Natural Language Processing (NLP). 

A clear example of this is Alexa, a popular Amazon product. A query is passed to Alexa by voice, and it can respond by the same medium, that is, by voice. It can also be used to ask anything, search for anything, play songs or even book a taxi.

However, Alexa is not the only example, and these talking machines that are popularly known as chatbots can even manage complicated interactions and streamlined business-related processes using only NLP. In the past, chatbots were used only for customer interaction with limited conversational capabilities because they were generally based on rules, but after the emergence of Natural Language Processing and its integration with Machine Learning and Deep Learning, now the chatbot can handle many different areas, such as Human Resources, health among others.

Now let’s take a brief look at some other NLP use cases via an  xenonstack  article :

-NLP in health care: in this case we will be able to make a prediction of different diseases by using pattern recognition methods and the patient’s speech and their electronic health record. An example of this is Amazon Comprehend Medical.

-Feeling Analysis using NLP:  The sentiment analysis is very relevant, since it has the ability to offer a wealth of knowledge about the behavior of the client and their choices, which can be considered an important decision factor.

-Cognitive Analytics and NLP: Using NLP, conversational frames have the ability to take commands by voice or by text. By using cognitive analysis, it is possible to automate different technical processes, such as the generation of a technical ticket related to a technical problem and also its handling in an automated or semi-automated way.

The collaboration of these techniques results in an automated process of handling technical problems within a company. It can also provide the solution of some technical problems to the customer also in an automated way.

-Spam detection: known to Google and Yahoo areuse NLP to classify and filter emails suspected of being spam. This process is known as Spam Detection and Filtering. This results in an automated process that can classify email as spam and stop it from entering the Inbox.

-NLP in Recruitment: NLP is also used in both the search and selection phases of the job recruitment, even, the chatbot can also be used to handle the job-related query at the initial level. This also includes identifying the skills required for a specific job and handling entry-level tests and exams.

Conversational Marcus: The NLP and related devices that are gaining huge popularity these days. Alexa is one of them, also Apple’s Siri and Google’s Ok Google, which are examples of the same kind of technology use cases.

Finally…We can’t leave without leaving you a gift! Download our ebook “The story behind Natural language processing”. There you can expand on this topic and learn more about NLP.

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Artificial Intelligence

AI: A Brief history on Natural Language Processing

Currently, Artificial Intelligence (AI) and everything that is derived from it, has made it possible for a  great change in companies, as it facilitates their way of operating in more effective and simpler ways.

One of the subset techniques of Artificial Intelligence known as Natural language processing (otherwise known as PLN in Spanish), has revolutionized the last few years. This is used to reduce the communication gap between the computer and the human being.

What is the NLP? 

NLP is a branch of artificial intelligence that helps computers understand, interpret, and manipulate human language.

First Steps 

It all started with the idea of ​​creating a translation machine (TM) which was born during World War II, in the 1940s. The original idea was to convert one language to another, but using the brain of computers. However, after that came the idea of ​​converting human language into computer language and vice versa.

Similarly, the original language was English and Russian. But the use of other languages ​​such as Chinese emerged in the early 1960s.

Later, a dark time came for MT / NLP during 1966. This fact was supported by a report by ALPAC (Automatic Language Processing Advisory Committee), according to which TM / NLP almost died because research in this area was lacking at the time. 

However, the conditions improved again in  the 1980s when the MT / NLP related product started to bring some results to customers. After reaching a near dying state in the 1960s, NLP / MT took on new life when the idea and need for Artificial Intelligence emerged.

In the 1980s, the area of ​​computational grammar became a very active field of research that was linked to the science of reasoning for the meaning and consideration of the user’s beliefs and intentions.

In the period of the 1990s, the growth rate of NLP / MT increased. Grammars, tools and practical resources related to this technique were available for a wide range of sectors.

An Interesting find

The history of the NLP cannot be fully complete without the mention of ELIZA, a chatbot program that was developed between 1964 and 1966 at the MIT Artificial Intelligence Laboratory. It was created by Joseph Weizenbaum.

This was a script-based program called DOCTOR that was arranged for the Rogerian psychotherapist and used rules to answer user questions that had a psychometric basis. It was one of the chatbots that were capable of taking the Turing test at the time.

Amazing, right?

Want to learn more? Click here or below to download our ebook titled “The story behind Natural language processing”. 

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Artificial Intelligence

The natural language processing: pt 1.

Today we can find a great variety of technologies and tools that offer thousands of benefits for the industries that apply them. 

To acquire and implement technology is not an easy task, that’s why if you want to implement them, you must look for professionals to support the process from the starting point.

In this article, we would like to tell you about one of the most innovative technologies available right now: the Natural language processing (NLP)

¡Here we go!

¿What is the NLP?

It is a field that covers different subjects of computational sciences: artificial and linguistic intelligence by studying interactions between computers and humans. Its objective is to provide the machine with the ability to interpret text simulating the human ability to understand language.

It origins goes to the idea of a translation machine that was born during the World War II. The main goal was to translate from one language to another, for example, from English to Russian using the power of computers. However, after that the idea of converting human languages into informatic language seemed natural.

¿What can we achieve with NLP?

With NLP it is possible to perform certain tasks such as automated speech and writing in less time. In that sense and due to important data (text) around, ¿Why don’t we use computers to perform algorithms and do tasks faster?

Over time more and more industries are getting into the tech wave and finding ways to implement Natural language processing into different matters between their processes and exploring new possibilities within their products.

¡In LISA Insurtech we love technology! that is why thanks to it we use techniques of NLP in the insurance industry, some of our cases of use are:

– Text qualification.

– Pattern recognition.

Feelings analysis.

-Search Engines.

-Chatbots.

Know… in our next article, we will tell you about the origins of Natural language processing and its evolution for you to keep getting to know more about this amazing subject. ¡Don’t miss it!

Categories
Artificial Intelligence

6 technology trends for 2022: pt 2

Today we share with you 6 other technology trends that are going to be very relevant for this coming 2022! As we mentioned in the previous article, technology is not only here to stay, but also to transform itself every day to be its best version and positively impact many companies.

Now lets start!

6. Software-defined networks

These are a set of techniques related to the area of computer networks, whose objective is to facilitate the implementation and deployment of network services in a dynamic and scalable way. The aim is to avoid the network administrator having to manage these services at a low level.

According to the vmware article, software-defined networking (SDN) represents an approach in which networks use software-based controllers, or application programming interfaces (APIs), to direct network traffic and communicate with the underlying hardware infrastructure.

OpenFlow and SDN will make networks more secure, transparent, flexible and functional.

7. The Cloud

According to the Eninetworks article, “What is the cloud and how is it used?“, it is a data storage service to servers located on the network. This allows programs and files to be uploaded, opened, modified or used through a connection without the need for them to be located on the storage of the device being used.

One of the key issues for the insurance industry is to transform insurance companies into data-driven organizations. Cloud computing adoption is increasingly being driven by the need for digital transformation and being perceived as a business enabler due to its large capacity to store information.

By 2022, the cloud will be more entrenched and more novel. However, the possibility of increasing capabilities and working in the cloud is still unlimited. 

8. Internet of things

The Internet of Things, or IoT for short, is a key factor and part of insurers’ transformation to the Digital Age, as the study and analysis of this field is what will define what is important to people.

It is a technology that connects to the Internet our household objects, those that are of daily use and that are ordinary. However, this connection to the Internet is not enough; the IoT is about connecting objects to each other.

This is how different objects will be able to interact with each other as part of a program. For example, the alarm clock can be synchronized with the coffee maker, and once the alarm is activated, the coffee maker can simultaneously start brewing coffee.

In this way, the relationship with our environment will change drastically. Thus, our clothes could give us an analysis of our biometric data or our homes could carry out the purchase of basic products in an automated way. In addition, the connection between the elements of our environment would ensure greater individual security. 

9. Big Data 

This term describes the large volume of data, both structured and unstructured, that occupies large parts of the business. What really matters about big data is what companies can do with the data, because by analyzing it they can gain insights that lead to better decisions and strategic business moves.

10. Machine learning 

Machine Learning is one of the applications of AI that provides systems with the ability to learn and automatically improve the experience without having to be programmed. Thanks to this technology we can plan company resources, develop predictive systems on customer behavior and personalize communications, generating greater value for users. 

Machine learning plays an increasingly important role in our lives, whether to classify search results, recommend products or create better models of the environment.

11. Computer vision and pattern recognition

As the viso.ai article explains, it is a field of Artificial Intelligence (AI), which deals with computational methods to help computers understand and interpret the content of digital images. 

Thus, Computer vision aims to make computers see and understand visual data input from cameras or video sensors. This is to help computers automatically understand the visual world by simulating human vision using computational methods.

Finally…

It all boils down to the ability of companies to keep pace with technological innovations, to adopt them and take advantage of them.

Technology is going by leaps and bounds and does not stop, which is why it is so important to know it and take advantage of it. This is not limited to a single industry sector, but can even be taken to insurance companies.

How so?

It is no secret that the insurance industry is one of the oldest and most difficult industries to add technology to its processes. That is why LISA Claims was born with the aim of streamlining those processes and offering a better service to its policyholders.

LISA Claims is a platform that controls and manages all claims settlement processes through the use of technology, guaranteeing security, improved operational efficiency and greater policyholder satisfaction.

Thanks to technology, it is easy to innovate and transform tasks that previously depended on human labor into automated tasks with the help and fusion of artificial intelligence and an automaton.

What technologies does LISA Claims use?

  • Computer security.
  • Big data.
  • Cloud Computing.
  • Machine learning.
  • Computer vision.
  • Internet of things.
  • Interconnections and digital ecosystems.
Categories
Artificial Intelligence

Use cases of object detection

As we explained in our previous article, object detection is one of the star technologies of artificial intelligence. With it, we can detect visual objects of various kinds and take advantage of it.

Now, in this second part, we want to show you the use cases of Object detection and its influence on LISA Insurtech.

Object detection use cases + important

The use cases involving object detection are very diverse. There are almost limitless ways to make computers look like humans to automate manual tasks or create new AI-driven products and services.

Did you know that this technology has been implemented in computer vision programs that are used for a variety of applications, from sports production to productivity analysis?

Today, object recognition is at the core of most vision-based artificial intelligence programs and software. Object detection plays an important role in scene understanding, which is popular in security, transportation, medical, and other use cases.

According to the article “Object Detection in 2021: The Definitive Guide” some of the most relevant use cases are:

  • Autonomous driving: Autonomous vehicles rely on object detection to recognize pedestrians, traffic signs, other vehicles and more. For example, Tesla’s Autopilot AI makes extensive use of object detection to sense approaching vehicles or obstacles.
  • Vehicle detection with AI in transportation: Object recognition is used to detect and count vehicles for traffic analysis or to detect cars stopping in dangerous areas, e.g., at intersections or roadways.
  • Medical feature detection in the healthcare sector: Medical diagnoses rely heavily on the study of images, scans and photographs. In the same vein, object detection involving CT and MRI scans has become extremely useful for diagnosing diseases, for example, with ML algorithms for tumor detection.

Finally….

Object detection is one of the most fundamental and challenging technologies in computer vision. It has received a great deal of attention in recent years, especially with the success of deep learning methods that now dominate the latest detection methods.

Object detection is becoming increasingly important for computer vision applications in any industry.

Before concluding, we would like to open an important point since object detection within the insurance industry could be able to offer competitive services. For example, by detecting cars and evaluating their driving behaviour (and seeing the reasons why there are more or less traffic accidents).

It also serves as a support when evaluating an auto or home claim, but how?

At LISA Insurtech we specialize in streamlining all claims processes with cutting-edge technology, which is why we rely on artificial intelligence, machine learning and deep learning. From this, we can analyze photographic and video evidence to prevent fraud and make an accurate calculation of the value of the damage.

Would you like to learn more about our flagship product LISA Claims?

Categories
Artificial Intelligence

Let’s talk about object detection

Object detection is a key field in artificial intelligence (AI), which allows computer systems to “see” their environments by detecting objects in visual images or videos.

It is used to detect visual objects of various kinds (humans, animals, cars or buildings), in digital images such as photos or video frames. Its goal is to develop computational models that provide the most fundamental information needed by computer vision applications (where the objects are and where).

Detection of persons

This type of detection is a variant of object detection used to detect a primary class “person” in images or video frames. This is an important task in modern video surveillance systems.

Recent deep learning algorithms, provide robust person detection results. Most modern person detection techniques are trained on frontal and asymmetric views.

Why is object detection important?

Object detection is one of the cornerstones of computer vision and forms the basis for many other subsequent computer vision tasks. For example, instance segmentation, image captioning, object tracking and more. 

Specific object detection applications include pedestrian detection, people counting, face detection, text detection, among others

Object detection + deep learning

Rapid advances in deep learning techniques have greatly accelerated the momentum of object detection. With deep learning networks and the computing power of GPUs, the performance of object detectors and trackers has improved tremendously, achieving significant advances in object detection.

Machine learning (ML) is a branch of artificial intelligence (AI), and essentially involves learning patterns from examples or sample data as the machine accesses these and has the ability to learn from them (supervised learning on annotated images).

How does object detection work?

Thanks to an article from viso.ai, we can learn that object detection can be performed using traditional image processing techniques or modern deep learning networks:

  1. Traditional image processing: Image processing techniques generally do not require historical data for training and are unsupervised in nature.
  • Advantages: Therefore, these tasks do not require annotated images, where humans manually label the data (for supervised training).
  • Disadvantages: these techniques are restricted to multiple factors, such as complex scenarios (no single-color background), occlusion (partially hidden objects), illumination and shadows, and clutter effect.
  1. Modern deep learning networks: Deep learning methods generally rely on supervised training. Performance is limited by the computational power of GPUs, which is rapidly increasing year after year.
  • Advantages: deep learning object detection is significantly more resilient to occlusion, complex scenes and challenging lighting.
  • Disadvantages: large amount of training data is required (plus the image annotation process is laborious and expensive).

Finally…

Object detection is one of the most fundamental and challenging technologies in computer vision. It has received a great deal of attention in recent years, especially with the success of deep learning methods that now dominate the latest detection methods.

Object detection is becoming increasingly important for computer vision applications in any industry.

Stay tuned! In our next article we will tell you about the use cases and how we employ it at LISA Insurtech.

You can’t miss it!

Categories
Artificial Intelligence

Edge computing: What is it and what are its advantages?

What is Edge Intelligence and Edge AI?

The combination of Edge Computing and AI has given rise to a new area of research called “Edge Intelligence” or “Edge AI”. Edge Intelligence makes use of pervasive edge resources to power artificial intelligence applications without relying entirely on the cloud.

While the term Edge AI or Edge Intelligence is completely new, practices have started early.

However, despite the early start of exploration, there is still no formal definition of edge intelligence.

Currently, most organizations and printers refer to Edge Intelligence as “the paradigm of running AI algorithms locally on an end device, with data (sensor or signal data) being created on the device.”

Here’s a curious fact…

Several major companies and technology leaders, including Google, Microsoft, IBM and Intel, demonstrated the benefits of edge computing. Their efforts include a wide range of artificial intelligence applications:

  • Real-time video analysis
  • Cognitive assistance
  • Precision agriculture
  • Smart home
  • Industrial IoT.

Advantages of pushing deep learning to the edge

Thanks to the viso.ai article we were able to learn that the merger of artificial intelligence and edge computing is natural, as there is a clear intersection between them. Data generated at the edge of the network relies on artificial intelligence to fully unlock its potential and edge computing can thrive with richer application and data scenarios.

The advantages of implementing deep learning at the perimeter include:

  1. Low latency: Deep learning services are deployed close to the requesting users. This significantly reduces latency and the cost of sending data to the cloud for processing.
  2. Privacy preservation: privacy is enhanced as the raw data needed for deep learning services is stored locally rather than in the cloud.
  3. Increased reliability: decentralized, hierarchical computing architecture provides more reliable deep learning computing.
  4. Scalable deep learning: with richer application and data scenarios, edge computing can promote the application of deep learning and drive the adoption of artificial intelligence.
  5. Commercialization: diversified and valuable deep learning services extend the commercial value of edge computing and accelerate its deployment and growth.
Finally…

Technology has always advanced by leaps and bounds. With the emergence of both Artificial Intelligence and IoT, the need arises to push the frontier of the former from the cloud to the Edge Computing device.

Edge computing has been a widely recognized solution to support compute-intensive machine vision and artificial intelligence applications in resource-constrained environments.

LISA Insurtech

We believe that keeping up with technology will not only provide us with many benefits. This favors the top of mind of traditional sectors such as insurance, which has been one of the last to acquire technology.

Therefore, our promise has always been to pursue the technology of insurers with artificial intelligence, machine learning, deep learning, big data, the cloud, among others.

Categories
Artificial Intelligence

How can you take advantage of the Edge computing?

With the advances in technology, we have witnessed in recent years a boom in artificial intelligence (AI) applications and services such as edge computing. All this thanks to the momentum of advances in mobile computing and the Internet of Things (IoT), where billions of mobile and IoT devices are connected to the Internet, generating trillions of bytes of data.

Undoubtedly, there is an urgent need to bring AI to the edge of the network to fully unlock the potential of Big Data at the edge. To realize this trend, Edge Computing is a promising solution to support compute-intensive AI applications on edge devices.

Edge Intelligence or Edge AI is a combination of AI and Edge Computing, which allows the implementation of machine learning algorithms in the edge device where the data is generated.

What is Edge Computing?

Edge computing is the concept of capturing, storing, processing and analyzing data closer to the location where it is needed, in order to improve response times and save bandwidth.

In that sense, edge computing is a distributed computing framework that brings applications closer to data sources, such as IoT devices, local end devices or edge servers.

As noted in NetworkWorld, Edge Computing “allows data produced by Internet of Things devices to be processed closer to where it was created rather than being sent over long distances to reach data centers and compute clouds.”

Why do we need Edge Computing?

As a key driver pushing the development of AI, Big data has recently undergone a radical shift of data sources from large-scale cloud data centers to increasingly pervasive end devices such as mobile and IoT devices.

Traditionally, big data, such as online shopping records, social media content and enterprise computing, was born and stored primarily in large-scale data centers. However, with the emergence of mobile computing and IoT, the trend is now reversing.

Today, a plethora of sensors and smart devices generate massive amounts of data, and ever-increasing computing power is driving core computation and services from the cloud to the network edge. 

Did you know?

Today, more than 50 billion IoT devices are connected to the Internet, and it is predicted that by 2025, 80 billion IoT devices and sensors will be online.

What is edge intelligence and what are its advantages? Don’t miss the topic in our next article – expect it this week!

Categories
Artificial Intelligence

Use cases of image recognition

Image recognition technology with AI is becoming more and more indispensable in any company you can imagine. Its applications bring economic value in sectors such as healthcare, retail, security, agriculture and many others..

In this article you will learn more about Image recognition use cases.

Face identification and analysis

It is a leading image recognition application. Modern ML methods make it possible to use the video feed from any digital camera or webcam..

In these applications, image recognition software employs AI algorithms for simultaneous face detection, face pose estimation, gender and age recognition using a deep convolutional neural network.

Facial analysis with computer vision enables systems to recognize identity, intentions, emotional and health states, age or ethnicity. Some photo recognition tools seek to quantify perceived attractiveness levels with a score.

Medical image analysis

Visual recognition technology is widely used in the medical industry to enable computers to understand images that are routinely acquired throughout the course of treatment.

For example, there are multiple papers on the identification of melanoma, a deadly skin cancer. Deep learning image recognition software enables tumor tracking over time.

Animal monitoring

Agricultural visual AI systems use novel techniques that have been trained to detect the type of animal and its actions. AI image recognition software used for remote monitoring of animals in agriculture for the detection of diseases, anomalies, compliance with animal welfare guidelines, etc.

Pattern and object detection

AI photo and video recognition technologies are useful for identifying people, patterns, logos, objects, places, colors and shapes. The customizability of image recognition allows it to be used in conjunction with multiple software programs.. 

For example, after an image recognition program specializes in people detection, it can be used for people counting, a popular computer vision application in retail stores.

Automated plant image identification

Thanks to an article from viso.ai, we can learn that in a July 2021 research analyzed the accuracy of image identification. This in order to determine plant family, growth forms, life forms, etc.

The tool works by using the photo of a plant with an image comparison software to query the results with an online database.

The results indicated a high recognition accuracy, as 79.6% of the 542 species from about 1,500 photos were correctly identified. While the plant family was correctly identified for 95% of the species.

Image recognition is very favorable and relevant for any company since it speeds up many processes, favors the collection of data and also the work without human hands.

Making way for technology and all that image recognition entails is not an easy task, but with knowledge, a good organization and a specialized team, it will be possible.

At LISA Insurtech we stand out for streamlining insurance settlement processes with cutting-edge technology. One of our star performers is our Artificial Intelligence.l.Thanks to it, we are able to recognize images, documents, videos and photographs in order to avoid fraud and avoid so many frictions during the claim settlement.

Categories
Artificial Intelligence

Basic concepts of Image Recognition

Image recognition with deep learning is a key application of AI vision and is used to drive a wide range of real-world use cases today.

Image recognition with deep learning is a key application of AI vision and is used to drive a wide range of real-world use cases today.

What is image recognition?

Simply put, it is the task of identifying objects of interest within an image and recognizing to which category they belong. Photo recognition and image recognition are terms that are used interchangeably.

When we visually detect an object or scene, we automatically identify objects as distinct instances and associate them with individual definitions. However, visual recognition is a very complex task for machines.

​​Image recognition using artificial intelligence is a long-standing research topic in the field of computer vision. Although different methods have evolved over time, the common goal of image recognition is the classification of detected objects into different categories (also referred to as object recognition).

In recent years, machine learning, in particular deep learning technology, achieved great successes in many computer vision and image understanding tasks.

What is image recognition used for?

Across all industries, AI image recognition technology is becoming increasingly indispensable. Its applications bring economic value in sectors such as healthcare, retail, security, agriculture and many more.

Three most popular image recognition machine learning models

Thanks to the viso.ai article we were able to learn about these three most popular types of models:

Support vector machines

SVMs work by making histograms from images that contain the target objects and also from images that do not. The algorithm then takes the test image and compares the trained histogram values with those of various parts of the image to check for matches.

Feature bag models

These models, such as scale invariant feature transform (SIFT) and maximally stable extreme regions (MSER), work by taking as a reference the image to be scanned and a sample photo of the object to be found. It then attempts to match features in the sample photo to various parts of the target image to see if matches are found.

Viola-Jones Algorithm

A face recognition algorithm widely used in the era before convolutional neural networks, it works by scanning faces and extracting features that are then passed through a boosting classifier. This, in turn, generates a series of boosted classifiers that are used to check test images.

To find a successful match, a test image must generate a positive result from each of these classifiers.

Deep learning image recognition models

The most popular deep learning models, such as YOLO, SSD and RCNN, use convolution layers to analyze an image or photograph. During training, each convolution layer acts as a filter that learns to recognize some aspect of the image before moving on to the next.

One layer processes the colors, another the shapes, and so on. At the end, a composite result of all these layers is taken into account to determine if a match has been found.

This topic is quite extensive! But we have made for you a series of articles with compressed information that will teach you everything you need to know about image recognition.

Stay tuned and don’t miss it!