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Top 7 ai trends in 2019

Artificial Intelligence is a method for making a system, a computer-controlled robot. AI uses information science and algorithms to mechanize, advance and discover worth escaped from the human eye. Most of us are pondering about “what’s next for AI in 2019 paving the way to 2020?” How about we explore the latest trends in AI in 2019.

AI-Enabled Chips

Companies over the globe are accommodating Artificial Intelligence in their frameworks however the procedure of cognification is a noteworthy concern they are confronting. Hypothetically, everything is getting more astute and cannier, yet the current PC chips are not good enough and are hindering the procedure.

In contrast to other programming technologies, AI vigorously depends on specific processors that supplement the CPU. Indeed, even the quickest and most progressive CPU may not be capable to improve the speed of training an AI model. The model would require additional equipment to perform scientific estimations for complex undertakings like identifying objects or items and facial recognition.

In 2019, Leading chip makers like Intel, NVidia, AMD, ARM, Qualcomm will make chips that will improve the execution speed of AI-based applications. Cutting edge applications from the social insurance and vehicle ventures will depend on these chips for conveying knowledge to end-users.

Augmented Reality

Augmented reality AI trend in 2019

Augmented reality (AR) is one of the greatest innovation patterns at this moment, and it’s just going to become greater as AR cell phones and different gadgets become increasingly available around the globe. The best examples could be Pokémon Go and Snapchat.

Objects generated from computers coexist together and communicate with this present reality in a solitary, vivid scene. This is made conceivable by melding information from numerous sensors such as cameras, gyroscopes, accelerometers, GPS, and so forth to shape a computerized portrayal of the world that can be overlaid over the physical one.

AR and AI are distinct advancements in the field of technology; however, they can be utilized together to make one of a kind encounters in 2019. Augmented reality (AR) and Artificial Intelligence (AI) advances are progressively relevant to organizations that desire to pick up a focused edge later on the work environment. In AR, a 3D portrayal of the world must be developed to enable computerized objects to exist close by physical ones. With companies such as Apple, Google, Facebook and so on offering devices and tools to make the advancement of AR-based applications simpler, 2019 will see an upsurge in the quantity of AR applications being discharged.

Neural Networks

A neural network is an arrangement of equipment as well as programming designed after the activity of neurons in the human cerebrum. Neural networks – most commonly called artificial neural networks are an assortment of profound learning innovation, which likewise falls under the umbrella of AI.

Neural networks can adjust to evolving input; so, the system produces the most ideal outcome without expecting to overhaul the yield criteria. The idea of neural networks, which has its foundations in AI, is quickly picking up prominence in the improvement of exchanging frameworks. ANN emulate the human brain. The current neural network advances will be enhanced in 2019. This would empower AI to turn out to be progressively modern as better preparing strategies and system models are created. Areas of artificial intelligence where the neural network was successfully applied include Image Recognition, Natural Language Processing, Chatbots, Sentiment Analysis, and Real-time Transcription.

The convergence of AI and IoT

IoT & AI trends in 2019

The most significant job AI will play in the business world is expanding client commitment, as indicated by an ongoing report issued by Microsoft. The Internet of Things is reshaping life as we probably are aware of it from the home to the workplace and past. IoT items award us expanded control over machines, lights, and door locks.

Organizational IoT applications would get higher exactness and expanded functionalities by the use of AI. In actuality, self-driving cars is certifiably not a commonsense plausibility without IoT working intimately with AI. The sensors utilized by a car to gather continuous information is empowered by the IoT.

Artificial intelligence and IoT will progressively combine at edge computing. Most Cloud-based models will be put at the edge layer. 2019 would see more instances of the intermingling of AI with IoT and AI with Blockchain. IoT is good to go to turn into the greatest driver of AI in the undertaking. Edge devices will be furnished with the unique AI chips dependent on FPGAs and ASICs.

Computer Vision

Computer Vision is the procedure of systems and robots reacting to visual data sources — most normally pictures and recordings. To place it in a basic way, computer vision progresses the info (yield) steps by reading (revealing) data at a similar visual level as an individual and along these lines evacuating the requirement for interpretation into machine language (the other way around). Normally, computer vision methods have the potential for a more elevated amount of comprehension and application in the human world.

While computer vision systems have been around since the 1960s, it wasn’t until recently that they grabbed the pace to turn out to be useful assets. Advancements in Machine Learning, just as the progressively skilled capacity and computational devices have empowered the ascent in the stock of Computer Vision techniques. What follows is also an explanation of how Artificial Intelligence is born. Computer vision, as a region of AI examines, has entered a far cry in a previous couple of years.

Facial Recognition

Facial recognition AI trends in 2019

Facial recognition is a type of AI application that aides in recognizing an individual utilizing their digital picture or patterns of their facial highlights. A framework utilized to perform facial recognition utilizes biometrics to outline highlights from the photograph or video. It contrasts this data and a huge database of recorded countenances to find the right match. 2019 would see an expansion in the use of this innovation with higher exactness and dependability.

In spite of having a lot of negative press lately, facial recognition is viewed as the Artificial Intelligence applications future because of its gigantic prominence. It guarantees a gigantic development in 2019. The year 2019 will observe development in the utilization of facial recognition with greater unwavering quality and upgraded precision.

Open-Source AI

Open Source AI would be the following stage in the growth of AI. Most of the Cloud-based advancements that we use today have their beginning in open source ventures. Artificial intelligence is relied upon to pursue a similar direction as an ever-increasing number of organizations are taking a gander at a joint effort and information sharing.

Open Source AI would be the following stage in the advancement of AI. Numerous organizations would begin publicly releasing their AI stacks for structuring a more extensive encouraging group of people of AI communities. This would prompt the improvement of a definitive AI open source stack.

Conclusion

Numerous innovation specialists propose that the eventual fate of AI and ML is sure. It is the place where the world is headed. In 2019 and beyond these advancements are going to support as more organizations come to understand the advantages. However, the worries encompassing the dependability and cybersecurity will keep on being fervently discussed. The ML and AI trends for 2019 and beyond hold guarantees to enhance business development while definitely contracting the dangers.

Understanding the difference between AI, ML & NLP models

Technology has revolutionized our lives and is constantly changing and progressing. The most flourishing technologies include Artificial Intelligence, Machine Learning, Natural Language Processing, and Deep Learning. These are the most trending technologies growing at a fast pace and are today’s leading-edge technologies.

These terms are generally used together in some contexts but do not mean the same and are related to each other in some or the other way. ML is one of the leading areas of AI which allows computers to learn by themselves and NLP is a branch of AI.

What is Artificial Intelligence?

Artificial refers to something not real and Intelligence stands for the ability of understanding, thinking, creating and logically figuring out things. These two terms together can be used to define something which is not real yet intelligent.

AI is a field of computer science that emphasizes on making intelligent machines to perform tasks commonly associated with intelligent beings. It basically deals with intelligence exhibited by software and machines.

While we have only recently begun making meaningful strides in AI, its application has encompassed a wide spread of areas and impressive use-cases. AI finds application in very many fields, from assisting cameras, recognizing landscapes, and enhancing picture quality to use-cases as diverse and distinct as self-driving cars, autonomous robotics, virtual reality, surveillance, finance, and health industries.

History of AI

The first work towards AI was carried out in 1943 with the evolution of Artificial Neurons. In 1950, Turing test was conducted by Alan Turing that can check the machine’s ability to exhibit intelligence.

The first chatbot was developed in 1966 and was named ELIZA followed by the development of the first smart robot, WABOT-1. The first AI vacuum cleaner, ROOMBA was introduced in the year 2002. Finally, AI entered the world of business with companies like Facebook and Twitter using it.

Google’s Android app “Google Now”, launched in the year 2012 was again an AI application. The most recent wonder of AI is “the Project Debater” from IBM. AI has currently reached a remarkable position

The areas of application of AI include

  • Chat-bots – An ever-present agent ready to listen to your needs complaints and thoughts and respond appropriately and automatically in a timely fashion is an asset that finds application in many places — virtual agents, friendly therapists, automated agents for companies, and more.
  • Self-Driving Cars: Computer Vision is the fundamental technology behind developing autonomous vehicles. Most leading car manufacturers in the world are reaping the benefits of investing in artificial intelligence for developing on-road versions of hands-free technology.
  • Computer Vision: Computer Vision is the process of computer systems and robots responding to visual inputs — most commonly images and videos.
  • Facial Recognition: AI helps you detect faces, identify faces by name, understand emotion, recognize complexion and that’s not the end of it.

What is Machine Learning?

One of the major applications of Artificial Intelligence is machine learning. ML is not a sub-domain of AI but can be generally termed as a sub-field of AI. The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.

Implementing an ML model requires a lot of data known as training data which is fed into the model and based on this data, the machine learns to perform several tasks. This data could be anything such as text, images, audio, etc…

 Machine learning draws on concepts and results from many fields, including statistics, artificial intelligence, philosophy, information theory, biology, cognitive science, computational complexity and control theory. ML itself is a self-learning algorithm. The different algorithms of ML include Decision Trees, Neural Networks, SEO, Candidate Elimination, Find-S, etc.

History of Machine Learning

The roots of ML lie way back in the 17th century with the introduction of Mechanical Adder and Mechanical System for Statistical Calculations. Turing Test conducted in 1950 was again a turning point in the field of ML.

The most important feature of ML is “Self-Learning”. The first computer learning program was written by Arthur Samuel for the game of checkers followed by the designing of perceptron (neural network). “The Nearest Neighbor” algorithm was written for pattern recognition.

Finally, the introduction of adaptive learning was introduced in the early 2000s which is currently progressing rapidly with Deep Learning is one of its best examples.

Different types of machine learning approaches are:

Supervised Learning uses training data which is correctly labeled to teach relationships between given input variables and the preferred output.

Unsupervised Learning doesn’t have a training data set but can be used to detect repetitive patterns and styles.

Reinforcement Learning encourages trial-and-error learning by rewarding and punishing respectively for preferred and undesired results.

ML has several applications in various fields such as

  • Customer Service: ML is revolutionizing customer service, catering to customers by providing tailored individual resolutions as well as enhancing the human service agent capability through profiling and suggesting proven solutions. 
  • HealthCare: The use of different sensors and devices use data to access a patient’s health status in real-time.
  • Financial Services: To get the key insights into financial data and to prevent financial frauds.
  • Sales and Marketing: This majorly includes digital marketing, which is currently an emerging field, uses several machine learning algorithms to enhance the purchases and to enhance the ideal buyer journey.

What is Natural Language Processing?

Natural Language Processing is an AI method of communicating with an intelligent system using a natural language.

Natural Language Processing (NLP) and its variants Natural Language Understanding (NLU) and Natural Language Generation (NLG) are processes which teach human language to computers. They can then use their understanding of our language to interact with us without the need for a machine language intermediary.

History of NLP

NLP was introduced mainly for machine translation. In the early 1950s attempts were made to automate language translation. The growth of NLP started during the early ’90s which involved the direct application of statistical methods to NLP itself. In 2006, more advancement took place with the launch of IBM’s Watson, an AI system which is capable of answering questions posed in natural language. The invention of Siri’s speech recognition in the field of NLP’s research and development is booming.

Few Applications of NLP include

  • Sentiment Analysis – Majorly helps in monitoring Social Media
  • Speech Recognition – The ability of a computer to listen to a human voice, analyze and respond.
  • Text Classification – Text classification is used to assign tags to text according to the content.
  • Grammar Correction – Used by software like MS-Word for spell-checking.

What is Deep Learning?

The term “Deep Learning” was first coined in 2006. Deep Learning is a field of machine learning where algorithms are motivated by artificial neural networks (ANN). It is an AI function that acts lie a human brain for processing large data-sets. A different set of patterns are created which are used for decision making.

The motive of introducing Deep Learning is to move Machine Learning closer to its main aim. Cat Experiment conducted in 2012 figured out the difficulties of Unsupervised Learning. Deep learning uses “Supervised Learning” where a neural network is trained using “Unsupervised Learning”.

Taking inspiration from the latest research in human cognition and functioning of the brain, neural network algorithms were developed which used several ‘nodes’ that process information like how neurons do. These networks have multiple layers of nodes (deep nodes and surface nodes) for different complexities, hence the term deep learning. The different activation functions used in Deep Learning include linear, sigmoid, tanh, etc.…

History of Deep Learning

The history of Deep Learning includes the introduction of “The Back-Propagation” algorithm, which was introduced in 1974, used for enhancing prediction accuracy in ML.  Recurrent Neural Network was introduced in 1986 which takes a series of inputs with no predefined limit, followed by the introduction of Bidirectional Recurrent Neural Network in 1997.  In 2009 Salakhutdinov & Hinton introduced Deep Boltzmann Machines. In the year 2012, Geoffrey Hinton introduced Dropout, an efficient way of training neural networks

Applications of Deep Learning are

  • Text and Character generation – Natural Language Generation.
  • Automatic Machine Translation – Automatic translation of text and images.
  • Facial Recognition: Computer Vision helps you detect faces, identify faces by name, understand emotion, recognize complexion and that’s not the end of it.
  • Robotics: Deep learning has also been found to be effective at handling multi-modal data generated in robotic sensing applications.

Key Differences between AI, ML, and NLP

Artificial intelligence (AI) is closely related to making machines intelligent and make them perform human tasks. Any object turning smart for example, washing machine, cars, refrigerator, television becomes an artificially intelligent object. Machine Learning and Artificial Intelligence are the terms often used together but aren’t the same.

ML is an application of AI. Machine Learning is basically the ability of a system to learn by itself without being explicitly programmed. Deep Learning is a part of Machine Learning which is applied to larger data-sets and based on ANN (Artificial Neural Networks).

The main technology used in NLP (Natural Language Processing) which mainly focuses on teaching natural/human language to computers. NLP is again a part of AI and sometimes overlaps with ML to perform tasks. DL is the same as ML or an extended version of ML and both are fields of AI. NLP is a part of AI which overlaps with ML & DL.

The need for quality training data | Blog | Bridged.o

What is training data? Where to find it? And how much do you need?

Artificial Intelligence is created primarily from exposure and experience. In order to teach a computer system a certain thought-action process for executing a task, it is fed a large amount of relevant data which, simply put, is a collection of correct examples of the desired process and result. This data is called Training Data, and the entire exercise is part of Machine Learning.

Artificial Intelligence tasks are more than just computing and storage or doing them faster and more efficiently. We said thought-action process because that is precisely what the computer is trying to learn: given basic parameters and objectives, it can understand rules, establish relationships, detect patterns, evaluate consequences, and identify the best course of action. But the success of the AI model depends on the quality, accuracy, and quantity of the training data that it feeds on.

The training data itself needs to be tailored for the end-result desired. This is where Bridged excels in delivering the best training data. Not only do we provide highly accurate datasets, but we also curate it as per the requirements of the project.

Below are a few examples of training data labeling that we provide to train different types of machine learning models:

2D/3D Bounding Boxes

2D/3D bounding boxed | Blog | Bridged.co

Drawing rectangles or cuboids around objects in an image and labeling them to different classes.

Point Annotation

Point annotation | Blog | Bridged.co

Marking points of interest in an object to define its identifiable features.

Line Annotation

Line annotation | Blog | Bridged.co

Drawing lines over objects and assigning a class to them.

Polygonal Annotation

Polygonal annotation | Blog | Bridged.co

Drawing polygonal boundaries around objects and class-labeling them accordingly.

Semantic Segmentation

Semantic segmentation | Blog | Bridged.co

Labeling images at a pixel level for a greater understanding and classification of objects.

Video Annotation

Video annotation | Blog | Bridged.co

Object tracking through multiple frames to estimate both spatial and temporal quantities.

Chatbot Training

Chatbot training | Blog | Bridged.co

Building conversation sets, labeling different parts of speech, tone and syntax analysis.

Sentiment Analysis

Sentiment analysis | Blog | Bridged.co

Label user content to understand brand sentiment: positive, negative, neutral and the reasons why.

Data Management

Cleaning, structuring, and enriching data for increased efficiency in processing.

Image Tagging

Image tagging | Blog | Bridged.co

Identify scenes and emotions. Understand apparel and colours.

Content Moderation

Content moderation | Blog | Bridged.co

Label text, images, and videos to evaluate permissible and inappropriate material.

E-commerce Recommendations

Optimise product recommendations for up-sell and cross-sell.

Optical Character Recognition

Learn to convert text from images into machine-readable data.


How much training data does an AI model need?

The amount of training data one needs depends on several factors — the task you are trying to perform, the performance you want to achieve, the input features you have, the noise in the training data, the noise in your extracted features, the complexity of your model and so on. Although, as an unspoken rule, machine learning enthusiasts understand that larger the dataset, more fine-tuned the AI model will turn out to be.

Validation and Testing

After the model is fit using training data, it goes through evaluation steps to achieve the required accuracy.

Validation & testing of models | Blog | Bridged.co

Validation Dataset

This is the sample of data that is used to provide an unbiased evaluation of the model fit on the training dataset while tuning model hyper-parameters. The evaluation becomes more biased when the validation dataset is incorporated into the model configuration.

Test Dataset

In order to test the performance of models, they need to be challenged frequently. The test dataset provides an unbiased evaluation of the final model. The data in the test dataset is never used during training.

Importance of choosing the right training datasets

Considering the success or failure of the AI algorithm depends so much on the training data it learns from, building a quality dataset is of paramount importance. While there are public platforms for different sorts of training data, it is not prudent to use them for more than just generic purposes. With curated and carefully constructed training data, the likes of which are provided by Bridged, machine learning models can quickly and accurately scale toward their desired goals.

Reach out to us at www.bridgedai.com to build quality data catering to your unique requirements.


NLP in AI and the realization of futuristic robots

How a well-trained conversational AI can empower your business

When the most valuable asset in the world is data, the most powerful tool you can have is the ability to process exabytes of information that data has to offer, and productively so. As we begin to produce gigabytes of digital data every day, De Toekomst — The Future — is with those that can effectively utilize this space, or more appropriately, the cloud. And it is precisely here that Artificial Intelligence is making its mark.

While we have only recently begun making meaningful strides in AI, its application has encompassed a wide spread of areas and impressive use-cases. And the sphere where AI is making its presence felt like a real and tangible entity is when it has a voice of its own. Natural Language Processing (NLP) and its variants Natural Language Understanding (NLU) and Natural Language Generation (NLG) are processes which teach human language to computers. They can then use their understanding of our language to interact with us without the need for a machine language intermediary.

AI has grown to become our personal assistant helping us with tasks at our behest, literally. Apple’s Siri, Amazon’s Alexa, Microsoft’s Cortana and Google Voice Assistant are only a few examples of AI systems integrating themselves seamlessly into our daily lives and routine. They help us plan our schedules, carry out functions without us having to push a single button, inform us of the latest developments, all the while learning more about our preferences and customizing themselves for us just by listening. With our permission, AI can become our best help.

Leading voice assistants | Blog | Bridged.co

How businesses are leveraging the AI assistant

Equipped with the knowledge of human communication, AI bots can potentially be used in any field that involves language to derive fast, intelligent, and useful insights which can then be transformed into follow-up actions tailored for each customer. Companies have realized the benefits of this incredibly powerful service and have begun utilizing them to gain significant market advantages. We will now talk about a few major applications of the conversational AI, and how we at Bridged are helping companies realize their ambitions for the AI-driven future.

Voice Control and Assistance

Voice control and assistance | Blog | Bridged.co

Performing basic tasks — reading messages, checking notifications, news updates, changing settings, operating connected devices, speech-to-text services.

Planning and Scheduling — setting up meetings, calendar events, automated replies, navigation, online assistance, payments.

Personalization and Security — compiling playlists, product suggestions, mood-based ambiance control, surveillance, and security.

Bridged.co Services: Voice Recognition, Speech Synthesis, Search Relevance.

Chat-bots

Chatbots training | Blog | Bridged.co

An ever-present agent ready to listen to your needs complaints and thoughts, and respond appropriately and automatically in a timely fashion is an asset that finds application in many places — virtual agents, friendly therapists, automated agents for companies, and more.

Bridged.co Services: Chat-bot Training, Virtual Assistant Training, NLP.

Sentiment Analysis

Sentiment analysis | Blog | Bridged.co

The ability to monitor end-user opinions of a brand or product and gain an understanding of the same on a large scale is clutch in any competitive scenario. Customer retention has become a zero-sum game and sentiment analysis stands at the center of this marketing field. Armed with NLP and machine learning, AI can listen to the scores of available user opinions across multiple platforms be it social media or community forums or even personal blogs. Accurate analyses of brand value at scale provided by accurate AI are invaluable to businesses.

Bridged.co Services: Brand Sentiment Analysis, E-commerce Recommendations, User Content Support.

Customer Service

Customer service | Blog | Bridged.co

AI is revolutionizing customer service, catering to customers by providing tailored individual resolutions as well as enhancing the human service agent capability through profiling and suggesting proven solutions. AI can be put up to a) responding to common queries, b) as a first layer of gathering service request info and routine troubleshooting, c) integrating with the resolution system, learning from successful cases, and suggesting or implementing final calls. AI makes the whole system faster and more efficient.

Bridged.co Services: Chat-bot Training, Sentiment Analysis, User Content Support.

Translate languages as you speak

The need for a multi-language translation book or for a local guide to communicating your need in a tongue you don’t speak is reduced with the advent of live translation by conversation bots that speak your message out loud, as and when you call on them right from your phones and smart devices.

Bridged.co Services: NLP, Voice Recognition, Speech Analysis.

Real-time Transcription

You can count on AI to take down notes for when you are in meetings or need to parse audio or video clips, or just want to pen down your thoughts. Transcription of speech to text is a very common application and finds use in several business tasks.

Bridged.co Services: Audio/Video Transcription, NLP, Voice Recognition.

We are at a very exciting juncture in the development of AI technology. New machine learning techniques including deep learning applied to NLP processes have made it possible to stretch the boundaries of what can be built using AI bots.

Computer vision and image annotation | Blog | Bridged

Understanding the Machine Learning technology that is propelling the future

Any computing system fundamentally works on the basic concepts of input and output. Whether it is a rudimentary calculator, our all-requirements-met smartphone, a NASA supercomputer predicting the effects of events occurring thousands of light-years away, or a robot-like J.A.R.V.I.S. helping us defend the planet, it’s always a response to a stimulus — much like how we humans operate — and the algorithms which we create teach the process for the same. The specifications of the processing tools determine how accurate, quick, and advanced the output information can be.

Computer Vision is the process of computer systems and robots responding to visual inputs — most commonly images and videos. To put it in a very simple manner, computer vision advances the input (output) steps by reading (reporting) information at the same visual level as a person and therefore removing the need for translation into machine language (vice versa). Naturally, computer vision techniques have the potential for a higher level of understanding and application in the human world.

While computer vision techniques have been around since the 1960s, it wasn’t till recently that they picked up the pace to become very powerful tools. Advancements in Machine Learning, as well as increasingly capable storage and computational tools, have enabled the rise in the stock of Computer Vision methods.

What follows is also an explanation of how Artificial Intelligence is born.

Understanding Images

Machines interpret images as a collection of individual pixels, with each colored pixel being a combination of three different numbers. The total number of pixels is called the image resolution, and higher resolutions become bigger sizes (storage size). Any algorithm which tries to process images needs to be capable of crunching large numbers, which is why the progress in this field is tangential to advancement in computational ability.

Understanding images | Blog | Bridged.co

The building blocks of Computer Vision are the following two:

Object Detection

Object Identification

As is evident from the names, they stand for figuring out distinct objects in images (Detection) and recognizing objects with specific names (Identification).

These techniques are implemented through several methods, with algorithms of increasing complexity providing increasingly advanced results.

Training Data

The previous section explains the architecture behind a computer’s understanding of images. Before a computer can perform the required output function, it is trained to predict such results based on data that is known to be relevant and at the same time accurate — this is called Training Data. An algorithm is a set of guidelines that defines the process by which a computer achieves the output — the closer the output is to the expected result, the better the algorithm. This training forms what is called Machine Learning.

This article is not going to delve into the details of Machine Learning (or Deep Learning, Neural Networks, etc.) algorithms and tools — basically, they are the programming techniques that work through the Training Data. Rather, we will proceed now to elaborate on the tools that are used to prepare the Training Data required for such an algorithm to feed on — this is where Bridged’s expertise comes into the picture.

Image Annotation

For a computer to understand images, the training data needs to be labeled and presented in a language that the computer would eventually learn and implement by itself — thus becoming artificially intelligent.

The labeling methods used to generate usable training data are called Annotation techniques, or for Computer Vision, Image Annotation. Each of these methods uses a different type of labeling, usable for various end-goals.

At Bridged AI, as reliable players for artificial intelligence and machine learning training data, we offer a range of image annotation services, few of which are listed below:

2D/3D Bounding Boxes

2D and 3d bounding boxes | Blog | Bridged.co

Drawing rectangles or cuboids around objects in an image and labeling them to different classes.

Point Annotation

Point annotation | Blog | Bridged.co

Marking points of interest in an object to define its identifiable features.

Line Annotation

Line annotation | Blog | Bridged.co

Drawing lines over objects and assigning a class to them.

Polygonal Annotation

Polygonal annotation | Blog | Bridged.co

Drawing polygonal boundaries around objects and class-labeling them accordingly.

Semantic Segmentation

Semantic segmentation | blog | Bridged.co

Labeling images at a pixel level for a greater understanding and classification of objects.

Video Annotation

Video annotation | blog | Bridged.co

Object tracking through multiple frames to estimate both spatial and temporal quantities.

Applications of Computer Vision

It would not be an exaggeration to say computer vision is driving modern technology like no other. It finds application in very many fields — from assisting cameras, recognizing landscapes, and enhancing picture quality to use-cases as diverse and distinct as self-driving cars, autonomous robotics, virtual reality, surveillance, finance, and health industries — and they are increasing by the day.

Facial Recognition

Facial recognition | Blog | Bridged.co

Computer Vision helps you detect faces, identify faces by name, understand emotion, recognize complexion and that’s not the end of it.

The use of this powerful tool is not limited to just fancying photos. You can implement it to quickly sift through customer databases, or even for surveillance and security by identifying fraudsters.

Self-driving Cars

Self-driving cars | Blog | Bridged.co

Computer Vision is the fundamental technology behind developing autonomous vehicles. Most leading car manufacturers in the world are reaping the benefits of investing in artificial intelligence for developing on-road versions of hands-free technology.

Augmented & Virtual Reality

Augmented and virtual reality | Blog | Bridged.co

Again, Computer Vision is central to creating limitless fantasy worlds within physical boundaries and augmenting our senses.

Optical Character Recognition

An AI system can be trained through Computer Vision to identify and read text from images and images of documents and use it for faster processing, filtering, and on-boarding.

Artificial Intelligence is the leading technology of the 21st century. While doomsday conspirators cry themselves hoarse about the potential destruction of the human race at the hands of AI robots, Bridged.co firmly believes that the various applications of AI that we see around us today are just like any other technological advancement, only better. Artificial Intelligence has only helped us in improving the quality of life while achieving unprecedented levels of automation and leaving us amazed at our own achievements at the same time. The Computer Vision mission has only just begun.