There was an experiment conducted in 2017 in London, with 100 lawyers participating in the event. The experiment involved finding accuracy in credit card irregularities in applications sent to the Finance Ombudsman. Where lawyers predicted an accuracy of 66.3%, a trained AI program achieved the accuracy of 86.6%.
Imagine a lawyer doing research for a case. Where humans may take weeks or even months to complete a particular research, AI will complete the research and provide results in a matter of seconds. Here, Artificial Intelligence will be able to perform better than experienced lawyers.
As Corona virus is spreading heavily in China and across the globe, hospitals are overcrowded and brave doctors are fighting round the clock to contain this pandemic.
Many healthcare institutions are looking forward to implement AI into their organizations to improve their operations and quality of patient care. Many such organizations have already implemented AI tools. With many articles being written on how AI is stealing human jobs, actually AI is re humanizing healthcare by helping healthcare professionals focus more on interacting with patients rather than spending time on routine tasks.
#MACHINE LEARNING 1. Skyl NLP now supports Bidirectional LSTM Algorithm
Amust try outon your dataset where text classification is dependent on context. With this form of generative deep learning, the output layer can get information from past (backwards) and future (forward) states simultaneously which leads to increase in the amount of input information available to the network and over.
Artificial intelligence (AI) and machine learning(ML) have helped optimize processes and workflows in many industries. In the healthcare sector, AI is increasingly helping in solving tough problems and uncovering hidden insights in the data generated by hospitals most of which isunstructured.
Artificial intelligence is finding applications in more and more business use cases across business verticals. The main reason for this popularity is due to the positive return on investment for early adopters of AI.
The early detection of diseases is crucial in the process of improving a patient’s health. However, this task can be time-consuming and complex, especially since many serious ailments can present themselves as the common cold or the flu in their early stages. The high demand for specialists that are able to discern illnesses causes many issues in early detection of diseases because they are often focused on more high profile cases. By using an ML system for predictions, we can drastically improve a patient’s chances of survival, in both pneumonia detection and other cases where radiology is necessary for early detection.
Image Classification
Computer visiontechnology is one of the many solutions to the issue of early diagnosis for serious diseases that may not present themselves as serious. A specific type of computer vision technology, called image classification technology, is the process that Skyl, a machine learning platform, used to automatically label whether or not an X-Ray image of a lung is one with pneumonia. Skyl has used it’s platform to create this project. It uses a deep learning methods, ensuring each prediction becomes more accurate as it receives more data. However, image classification technology is not limited to a single class, there could be multiple different categories that an image falls under. For example, a picture could contain a dog, cat, or fish. In this particular case, there were only two categories, pneumonia and not pneumonia.
The Skyl Solution
UsingSkyl,healthcare professionals can input data various from sources that have labeled data. For the lung project, Skyl used pre-labeled data from the National Institutes of Health Clinical Center. Using only this data and our platform, Skyl successfully created a project and we were able to train the algorithm, and deploy it on our website. However, this is only one example of how Skyl’s platform can be used. Image classification technology in the healthcare industry has been used to diagnose different forms of cancer, check for fractures in X-Rays, and process MRI scans. By automatically moving certain cases to a higher priority, doctors, nurses, and radiologists waste less time ignoring patients who may have serious ailments.
The uses of image classification technology are not limited to the healthcare industry and radiology. The same software that classifies images can classify any type of unstructured data which includes audio, video, and images. This technology has been used toanalyze satellite imagesand deforestation, find explicit words in music, and detect faces. We have only dipped our toes in the future of image classification technology and how it will affect the future of the way businesses, schools, hospitals, and our day-to-day lives are conducted. Certain companies, such as Skyl, are dedicated to making sure that every company on the planet can create a machine learning project, because we do not have all the ideas, other people do! We just hope to get enterprises to a point that their ML dreams can become a reality and not just a fun idea in the office.
Skyl.aiis a SaaS platform that allows any business to streamline a computer vision or natural language processing project. VisitSkyl.aito learn more about the future of computer vision technology and how it can impact your business!
Increasingly, digesting and reading news has become a standard in our day-to-day lives. Gone are the days when you get a single update in the evening after work while watching television before bed. People are now bombarded with a constant stream of news updates throughout the day, whether they want to be or not. Headlines are splattered over every form of social media, breaking news is sent to us in alerts, and if we ever need to seek out what is happening, it is a search away. However, it is possible to create your own “Google News” app using machine learning. By teaching an algorithm to automatically sort through headlines from the thousands of possible news sites there are, a business can filter through exactly which content it wants. There are multiple reasons a business would want to do this. Maybe, a veterinary clinic wants to automatically keep updating their website with feel-good stories about pets and babies and wild animals. On the flip-side of the coin, large corporations may want to save time by having an automated system that sends their employees the latest news real-time about finance, politics, or any other topic that the company may find relevant.
