The Right Data Annotation Solution - Kili Technology

1. The Right Data Annotation Solution - Kili Technology Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. In other words, Machine learning is something about how computers learn from data to make decisions or predictions. The computer must be able to learn patterns without being precisely programmed to. So, the data must be accurate, clean, complete, and well-labeled which needs to be trained to these machine learning systems so that the resulting ML Models are accurate. In machine learning, data labeling is the process of identifying raw data such as images, text files, videos, etc. and adding one or more informative labels to provide context so that a machine learning model can learn from it. This process can be manual but is usually performed by some software or tool. The process of classification and labeling the data available in various formats like text, video, or images is termed as Data Annotation. It is useful for Supervised Machine Learning so that machines can easily and clearly understand the input patterns. The purpose of data annotation is to add structure to raw data so that it could be comprehended by the artificial intelligence machines as part of its training process. This way, data annotation serves the function of building the training dataset for machine learning models to be able to achieve excellent and high quality performance - be it in automating customer email processes in banking, detecting cancers and tumors, speeding up manufacturing order process, detecting defects in assembly lines, and others. Kili Technology provides the complete solution to better manage your training data, image, video, text, pdf, voice: a software for fast annotation, simple collaboration, quality control, data management, and labeling workforce. Machine Learning Market Trends The machine learning market is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.

2. Across every industry vertical, ML is going to have a huge impact in the future. Machine learning is given so much significance since it helps in predicting the behavior and recognizing the patterns that humans with their restricted limit can not anticipate. The further developed types of AI, particularly deep learning neural networks, need large volumes of data to be able to design models with desired levels of precision. So every organization whether it builds its own machine learning model or uses a third-party model; the data needs to be complete, accurate, clean, and well-labeled in order to achieve the highest possible accuracy level. Importance of data labeling for machine learning A machine learning model needs to be instructed with the applicable data that has been accurately labeled. In Machine learning, data labeling is the process of adding contextual information to raw data available in various formats like texts, videos, or images. Adding structure to raw data is essential so that it could be understood by the AI models as part of its training process. This way, data labeling serves the purpose of building the training dataset for machine learning models to be able to attain excellent and high-quality performance. Data labeling helps to train machine learning models to learn and identify patterns for use case applications such as scaling up AI for breast cancer screening, customer order processing, customer email processing, and speeding up the mechanical processes, etc. In this Era, mostly all the information is recorded in digital format. For example, Facebook does it today when it questions you to comment on a picture or recognize a friend in a picture. Daily we upload more than a billion images to Facebook and label them. We produce a large learning database for Facebook so that they have been able to develop models that can recognize people from behind. But in most organizations, the data remains unstructured. Labeling data manually is the most time-consuming and expensive method, but to simplify and speed-up the process, a data annotation tool is used. Kili Technology Introduction

3. Kili Technology is a platform that provides a tool for labelling image, video, text & voice data and is specially crafted to aid companies use faster applications of machine learning. You can annotate your data within a few minutes through a catalogue of instinctive and configurable interfaces. Increase the speed of your labelling process by linking one of your models in order to pre label the data. Kili Technology eases the teamwork between technical and business teams, but also with outsourcing annotation firms. Kili Technology meets the requirements of small companies as well as those of large companies with huge stakes. Company Profile Company/Solution Profile Company Name Kili Technology Founded 2018 Employees 15 HQ Location Paris Compatible Browser Kili Technology is optimized for Google Chrome, up to version 67.0.3396.87. Kili Technology in Training Phase

4. Kili Technology in Production Phase Kili Speeding-up AI Across Industries Kili is here to help customers in building AI that matters across use cases. Banking In banking, there is an increase in demand to clean its data records for its machine learning automation to improve productivity.

5. ● Email processing ● Chatbot ● Banking contract processing Healthcare In healthcare, AI has enabled medical professionals to make faster, early, precise decisions that could save lives. ● Scaling up AI for breast cancer screening ● Enabling innovative bladder cancer detection ● Improve productivity with medical speech recognition Manufacturing In assembly lines, artificial intelligence automation is becoming more important to bring production efficiency in cost, speed, and quality. ● Defect detection ● Waste management ● Customer order processing Insurance Artificial intelligence in insurance business procedures saves time, productivity, and cost. ● Claim form processing

6. ● Car Damage Assessment Mobility The AI-powered features of self-driving cars are becoming the key aspects provided to customers. Autonomous driving - Road sign and cross mark detection Defense AI helps in defense by more efficiently monitoring border controls, target detection, defense communication, etc. ● National border surveillance ● Defense communication Government ● Natural disaster management ● Public health monitoring Agriculture Crop weed detection Retail

7. ● In-store cashierless transactions ● In-store security monitoring Utilities Solar network asset maintenance Social media Brand digital reputation management