Year: 2019

Categories
Data Labeling

Data Labeling Approaches

Datalabeler_blog_data_labeling_approaches

Data labeling involves the process of tagging data with specific labels that helps to identify certain properties or classifications or characteristics of objects contained within an image or a video. The process comprises of tasks such as annotation, tagging, transcription or processing of data.

Labeled data highlights data features such as properties, classifications or characteristics that help to discover underlying patterns for identifying a target. This is particularly useful in building Computer Vision models. Data labeling helps to curate data for Machine Learning and Artificial Intelligence applications.

Data Labeler is one of the top data labeling companies in Philadelphia that offers the following services:

Bounding Boxes for Object Detection

Bounding box annotation mainly helps in the detection, localization, and classification of objects in images and videos. We at Data Labeler specialize in detecting objects through the 2D box and 3D cuboid annotations and help you in detecting objects of interest with high-quality and precision. By leveraging our data annotation services, you can build world-class Computer Vision models.

Use Cases

Object Detection

Detecting lanes, potholes, pedestrians, traffic, etc. for training autonomous vehicular models

E-commerce

Autonomously tag furniture, clothes & accessories for training visual search models in E-commerce.

Insurance Claims

Training ML models to detect the degree of damage like identifying roof or car damage during accidents for insurance claims. 

Polygons for Semantic & Instance Segmentation

For image analysis tasks, Data Labeler offers semantic segmentation and instance segmentation with polygons. With Semantic segmentation services, we will help you classify regions within an image into specific object categories. In the case of instance segmentation, we help you in producing pixel-level category annotations along with specific instances of particular object classes. With our polygonal data annotation services, we will help you achieve accurate segmentation.

Use Cases

Self-driving Cars

Assigning semantic labels like road, person, car or sky to train the autonomous driving models.

Drone Navigation

Teaching drones to easily navigate through trees and around birds & rooftops.

Training Robots

Equips robots to tackle new horizons like manufacturing and healthcare

Points for Facial Recognition and Body Pose Detection

Integrating facial recognition and body pose detection has never been as simple as it is today. Data Labeler offers key points for facial recognition that enables your ML models to analyze a series of face related attributes like age, gender, head pose, smile intensity, emotion, eye status, ethnicity, and others. Our body pose detection services help you to train your ML models to identify and track a person’s body position.

Use Cases

Self-detecting surveillances

Automatically spot a particular face from a crowded surveillance video

Hospitality

Improve functionality and security at hotel room entrance and automation of the check-in process.

Coaching Sportsperson

To create AI-powered coaches for the fitness and sports industry

Improving Airlines

Streamline the workflows at airports with improved speed for check-in & departures and reduced queues.

Texts for Image Captioning

Data Labeler helps to create datasets consisting of image caption annotations which are pulled across from millions of web pages. This ensures accuracy, a wider variety of image-caption styles and helps you to train your automatic image captioning models easily given the challenging nature of the task.

Use Cases

Helping the Visually Impaired

Describe images to people who have low vision or blind and rely on sounds & text for describing a scene

Enhancing Videos

Describes the happenings in a video in real-time

Making Web Content Accessible

Image description can be heard or seen when loading images is prohibited due to slow internet speeds.

Select for Image Classification

We provide datasets that empower your models to characterize an image and classify it efficiently and effectively. Data Labeler can categorize photos and images at large scale with accuracy.

Use Cases

Ecommerce Tagging

Train your models on how to categorize a product using its images.

Multi-Select for More Complex Image Classification

We classify images based on multi-level taxonomies. Data Labeler offers data in a structured format and this helps you train your models on complex image classification.

Use Cases

Landscape Classification

Classify landscape into the water, agricultural lands and forest.

About Data Labeler

At Data Labeler, we offer world-class video annotation services and specialize in producing best-in-class training datasets for building ML-based Computer Vision models. Reach out to us at sales@datalabeler.com for best-in-class data labeling services in New York.

Categories
Artificial Intelligence

Inside a Neural Network’s Mind

Inside a Neural Network’s Mind

Neural Networks are a set of algorithms modeled after the human brain that recognizes the underlying patterns in a data set. Similar to a brain, the neural network learns all by itself without the need for explicit programming. What happens inside a neural network has intrigued many and research has been dedicated to seeing how the neural nets perform what they are intended to.

In this blog, we will explore the inner workings of a neural network that processes language. MIT in collaboration with Qatar Computing Research Institute has released several papers on an interpretive technique that analyzes neural networks trained for translation and speech recognition. Through the research, they could find some support for some of the common notions about how a neural network works.

Lower Level Vs Higher Level

Neural Networks concentrate on lower-level tasks before moving on to higher-level tasks. For instance, they seem to concentrate on sound recognition or a part of speech recognition before moving on to translation.

As per the researchers, the translation neural network considers a certain type of data which leads to the omission of some part of the data. By correcting the omission helps to improve the performance of the network which in turn helps to improve the accuracy of artificial intelligence systems. 

Neural Networks are typically arranged into layers with each layer consisting of nodes which are nothing but simple processing units. Each node is connected to several other nodes in the above and below layers. Different weights are assigned to the connections between the layers which determines how much a node’s output is considered for the calculation performed by the next node.

Since there are thousands to millions of nodes and connections involved, finding out what algorithm those weights give rise to highly impossible. The technique used by the MIT and QCRI researchers involved taking a trained network and using the output of each of its layers to corresponding individual training examples to train another neural network to perform the same task. This helps to understand what each layer is optimized to perform.

The researchers found through the technique that in translation networks, lower levels performed better at recognizing phones than higher ones and also were good at identifying the parts of speech and morphology. The higher levels were found to be good at semantic tagging.

About Data Labeler

At Data Labeler, we provide fully managed data labeling services and specialize in the production of high-volume and best-in-class training datasets for AI and ML initiatives. Reach out to us at sales@datalabeler.com for high-quality data labeling services.

Categories
Artificial Intelligence

Top 5 Business Applications of Artificial Intelligence

AI for business

As per research by Gartner in 2016, about 30% of companies will be using Artificial Intelligence in at least one part of their sales process. But if you are wondering how AI can benefit your business, then this blog is for you.

There is a myriad of possibilities by which your business can benefit from AI. It can make your operations more efficient and eliminate those mundane tasks that slow down your business.

Here are the top 5 business applications of AI;

  1. Cybersecurity

AI can play an essential part in your business’s cybersecurity defense strategy. Even though it cannot address all issues in your cybersecurity system for now, but still, it can play a major role in data protection.

AI continuously monitors and analyzes your system for normal behavior and reports any suspicious activity as and when it takes place. In business applications such as financial systems, it can detect any anomalies related to user behavior.

  • Healthcare

AI offers support to healthcare providers while taking decisions in case of critical medical events by preventing errors and documenting them in real-time for further analysis.

It can help doctors in diagnosis by letting them know when the patient’s condition is deteriorating or when the patient needs medical intervention.

Machine Learning especially can be used to detect cancer in the earlier stages thus helping to save lives.

  • Recruitment

Finding qualified employees is one of the difficult tasks of recruitment. Employers can harness the power of AI-powered recruitment tools to unearth the right candidates who may not have surfaced initially but are qualified for the role. Automating a few aspects of recruitment allows the recruiters to spend quality time with the potential hires which help to simplify the hiring process.

  • Infrastructure

Businesses can leverage AI to transform their security and network infrastructure and balance the workload of their systems. The ability of AI to function without human intervention helps to improve the efficiency of a company’s IT infrastructure.

In case of multiple failures of your business systems, AI can find out the root cause for it which otherwise is difficult to find due to human limitations to analyze information. This aids in preventive maintenance and prevents any such events from occurring in the future.

  • Chatbots

AI-driven intelligent chatbots and voice bots are being used to answer frequently asked questions, aid users in hotels with concierge services and even assist in shopping for products. This has been largely possible due to developments in deep learning and neural networks.

Chatbots help businesses in collecting data and deliver flexible and smart analytics through conversations with customers using voice-activated or standard messaging interfaces. This helps to take care of the increasing data needs of the businesses and streamlines the way analysts go about their work.

AI helps to transform every aspect of the business. Hence as a business leader, if you have to withstand the competitive market and stay ahead of your competitors, it is best to leverage this cutting-edge technology to the fullest.

About Data Labeler

At Data Labeler, we provide fully managed data labeling services and specialize in the production of high-volume and best-in-class training datasets for AI and ML initiatives. Reach out to us at sales@datalabeler.com for high-quality data labeling services.

Categories
Artificial Intelligence

7 Artificial Intelligence Predictions for 2019

AI Predictions for 2019

Artificial Intelligence (AI) hogged the limelight in the year 2018 by successfully automating more functionalities. The general public has been receptive to interacting with the machines daily and more companies have started exploring the various AI applications. In 2019, the stage is set for AI to transform the society, add spark to discussions and give rise to innovative business models.

Here are the top 7 AI Predictions for 2019;

  1. Machine Learning as a Service (MLaaS) will gain prominence

Tech giants like Google, Amazon, and Microsoft have taken giant strides in MLaaS as a technology by offering prebuilt Machine Learning Solutions. Amazon’s SageMaker helps developers build, train, test and deploy Machine Learning models whereas Google Cloud’s ML engine helps them by building large and complicated algorithms for various applications. Microsoft Azure is a developer-friendly platform that lets them easily build ML models with drag-and-drop options.

This trend is going to pick up in the coming years. Smaller companies without the necessary resources or in-house talent can benefit from prebuilt solutions. Companies having technical experience and know-how can benefit from selling and deploying packaged solutions as they become attractive in the market. As per Transparency Market predictions, MLaaS is estimated to reach US $20 Billion with an alarming growth rate at 40% CAGR.

  • AI will become more transparent

The democratization of AI has started taking place which has to lead to the rise of various open-source libraries and tools such as TensorFlow, PyTorch, Scikit Learn, etc. This gives rise to expectations that the open-source community will take the charge in building transparent AI in 2019. Explainable AI will help to tackle the black box problem which is posing limitations in situations where organizations are implementing AI without a thorough understanding of how it works.

  • Creation of more jobs than eliminations

AI-driven automation will eliminate most of the manual and repetitive jobs in the long run. But, in 2019, AI is going to create more jobs than it eliminates. As per a prediction from Gartner, AI is going to create about 2.3 million jobs in the coming two years while eliminating only about 1.8 million jobs.

AI models currently augment the existing processes and systems. This has given rise to management jobs that require humans to work alongside AI where they will provide support for the implementation of AI and oversee its working.

  • AI Machines will become more thoughtful

Most of the AI discussions are based on physical actions that the machines can manage. Recently, the trend has shifted to the development of thoughtful and insightful machines. In the customer service environment, AI can be used to identify the sentiment in a customer’s voice and give suggestions as a human agent would do to keep the person happy thereby boosting customer engagement. In 2019, more importance will be given to the development of smart and predictive AI that can help by giving insightful tips.

  • Greater impact on healthcare

The deployment of AI-based systems in the healthcare industry will help to deliver personalized medical care to patients. New business models will be developed that will provide improved and accurate diagnoses by monitoring patient lifestyle data and history.

  • AI will change the political landscape globally

AI will have a major impact on the global political landscape with superpowers like the US and China heavily investing in the technology. Countries that focus on fostering AI talent will experience alarming growth thereby widening the global technology gap. Discussions will take place on the ethical use of AI and a different approach to this topic by countries will affect the political relationships.

  • Rise of pervasive and useful AI assistants

Already we have seen the rise of AI assistants like Amazon’s Alexa and Apple’s Siri. Google also had ventured into this space with Duplex that can call and book appointments on behalf of the users. In 2019, more sophisticated AI assistants with advanced capabilities will be developed. The collection of more behavioral data and advances in speech recognition and natural language processing will lead to smoother and useful interactions between humans and machines.

About Data Labeler

By leveraging innovative technologies and complex tools, Data Labeler offers best-in-class image labeling services that help you to build ML models with high accuracy. Reach out to us at sales@datalabeler.com for high-quality training datasets.