Machine learning is helping companies transform their business. Machine learning is allowing companies to improve their operations, provide better customer experiences, and increase security. Machine learning has become a significant investment as companies strive to outperform the rest and achieve more with the same resources. As ML for GTS becomes more complex, it can be hard to generate quality ground truth or training information for these applications. GeospatialWorld featured Kyle Miller as a guest host. He discussed geospatial apps for both the public and private sectors, as well the future prospects for geospatial data solutions.
Here are 5 key messages from this article:
- Geospatial applications can be used to support a wide variety of private and public sector activities.
- Geospatial information is obtained from aerial sources. This captures everything within a given geographic area. The data annotation required depends on the final purpose. For instance, in the case insurance companies, GTS uses picture classification along with 2D polygon annotation to capture building features for assessing the premium rate.
- The amount of data currently available and being collected is on the rise. This has led to a higher demand for Audio Datasets of better quality and resolution. There are many kinds of data available. One example is synthetic aperture data (SAR), which has been around for a while.
- We are now seeing different methods of collecting data. The most transformative of all is LIDAR. With it, you can create 3D models or representations of different aspects of our world.
- GTS/machine learning will continue evolving, bringing with them unique use cases as well as complexities in data consumption. This dynamism will require data annotators training to develop their skills to seamlessly supply high-quality data for Video Transcription that can be used to feed machine learning-led artificial intelligence systems.
Here are five machinelearning use cases that demonstrate how machine learning has changed the way business is done.
Use Case 1 Personalization Websites, apps, and mobile apps now have advanced algorithms that can detect customer preferences for food and electronics. Machine learning can be used to help businesses match customers with the best products. Machine learning algorithms are able to use everything from past purchases and brand loyalty to refine results so customers can find what they need . They also discover new products that they will enjoy.
Use Case 2 Customer support AI has transformed customer support. Although the empathy of human customer service might never disappear, it is evident that intelligent automation allows enterprises to increase service levels and customer satisfaction. IBM says that chatbots can answer 80% or more of routine customer questions. They also help to reduce operating expenses.
Use Case #3: Search Engines Deliver Better Results Search engines used keywords and algorithms in the past to produce results. Search engines can now use artificial intelligence to better understand user's intent, and provide relevant information. Google uses deep neural networks AI technology, which can analyze large quantities of digital content to recognize images, recognize spoken commands, respond to, and even predict Internet-search queries.
Use Case 4 Enhance Data Security Hackers continuously create new malware to attack data security, steal valuable information and compromise it. Machine learning uses patterns to identify malware and tighten data security, so users can use digital content more confidently without fear.
Use Case 5. Automated Management McKinsey says that companies are automating their back-office like never before. This is thanks to machinelearning. Computers with the correct machine learning algorithms are capable of performing tasks such as data extraction and processing. Some companies believe machine learning will automate 85% of their operations. KPMG also estimates that this automation could lower operating costs by up to 75%. Machine learning is helping companies create better experiences. It also enhances security and efficiency. However, machine-learning needs high-quality data to realize its full potential. Appen offers high-quality training dataset to support machine learning on a large scale. We partner with leaders in the industry to help them build, enhance, and utilize products that rely heavily on machine learning or AI.
As ML and GTS become more complex, it becomes increasingly difficult to produce high-quality groundtruth or training data. Although it is well-known that training datasets should be large, it may not be known that these data must be labeled in an increasingly detailed manner by subject matter experts. Foundational to the creation of datasets is feature extraction. This refers to the process by which similar spectral or spatial attributes are extracted from geospatial imaging.
GTS training data can now be created with data labelers, GTS Technicians and other data tools that are closely managed. Because of the breadth of the applications in this field, you need to be able to understand the type of data for Speech Transcription that is needed for each use case, as well as subject expertise, or a mixture of both.
We offer a full range of services from data gathering annotation to search evaluation and relevance to linguistic consulting. Call us for a discussion about your business goals. We've helped many businesses like yours improve their machine intelligence efforts.