According to a report by IDC, worldwide spending on artificial intelligence systems is forecast to reach $35.8 billion in 2019, an increase of 44.0% over the amount spent in 2018. The report also predicts that the retail sector will lead the spending, followed by the banking sector. Artificial intelligence is well-positioned to impact various sectors like retail, healthcare, banking, finance, discrete manufacturing, transportation, etc. According to a Gartner survey, 37% of organizations have implemented AI in some way. In the early stages, AI was based on rule-based systems, in which, the AI system depended on a knowledge base of rules to deliver business value.
Just a few years ago, companies used innovation and digital transformation mostly to differentiate themselves and to stay competitive. The dramatic growth in digital technologies and cloud computing over the last couple of years has since changed this mindset. Today, organizations must be innovative and leverage the latest technologies simply to stay in business. Enterprises that implement online retail, banking, and other services aren't considering these channels as just another route to increase their revenue. They realize that online services are fast becoming their primary revenue channel.
Deep learning is a complicated process that's fairly simple to explain. A subset of machine learning, which is itself a subset of artificial intelligence, DL is one way of implementing machine learning (automated data analysis) via what are called artificial neural networks -- algorithms that effectively mimic the human brain's structure and function. And while it remains a work in progress, there is unfathomable potential. In this article, we'll briefly explain how deep learning works and introduce the best companies in 2020. Deep learning is a subcategory of machine learning methods powered by artificial intelligence technologies.
The Internet of things (IoT) has a significant potential to fall into the endless pit of a buzzword- vagueness, and it merely is an ecosystem of various kinds of objects that are connected through the Internet. These kinds of objects ranging from cell phones and wearables to machines, generate a constant and massive amount of data every day. The artificial intelligence (AI) also often falls into the same trap. The goal of artificial intelligence in the new IoT scenario is not only to use the humongous data to extract meaningful insights but also to help IoT integrated setups to derive higher value. It implies the machine's intelligence, where the device gains the capabilities of simulating a real human brain.
Today, Amazon Web Services (AWS) announced that Amazon Textract, a machine learning service that quickly and easily extracts text and data from forms and tables in scanned documents, is now eligible for healthcare and life science workloads that require HIPAA compliance. This launch builds upon the existing portfolio of AWS artificial intelligence services that are HIPAA-eligible, including Amazon Translate, Amazon Comprehend, Amazon Transcribe, Amazon Polly, Amazon SageMaker and Amazon Rekognition – that help customers retrieve data from documents more accurately to reach better healthcare decisions, operate more efficiently, and help identify medical and scientific trends. Critical healthcare information often lies within documents such as medical records and forms. Healthcare and life science organizations need to access data that is locked inside those documents in order to fulfil medical claims, streamline administrative processes, and process electronic health records. They routinely extract text and data from documents through manual data entry or simple optical character recognition (OCR) software.