AI and Machine Learning: Enhancing Today’s Technology for Tomorrow’s Data Processing
The fourth era of computing is upon us. Mobile devices offer the equivalent data processing capability of the mainframe systems of decades ago. Virtual assistants and advisor bots are in our homes, offices, and pockets to make our lives easier no matter where we are.
Machine Learning vs. Artificial Intelligence
Machine learning (ML) extends business applications and transforms them from data storage and organization repositories into a processing engine, which learns by reasoning through data. In other words, ML algorithms go beyond the strict input/output/rinse and repeat computing model of previous computing generations. They remember the data they ingest so they can apply that learning when called upon.
Artificial intelligence (AI) applies code-based contextual bias to go a step further beyond machine learning. It identifies relationships and patterns based on what it's learned so it can become more accurate with every query. AI assists humans to make educated decisions. Potential use cases include:
- Predicting the trajectory of a hurricane
- Completing high volumes of tax returns
- Helping educators teach courses in multiple languages
- Identifying historical legal cases with precedents that exonerate a client
Not only does AI learn, classify, and remember data, but it lays out endless digital breadcrumb trails for when it needs the information next time. When a physician is working to diagnose a patient's symptoms, AI-augmented medical information systems can parse millions of patient cases across integrated systems. Then it can offer the doctor a far narrower set of possible diagnoses than the most experienced medical professional without help.
Data Is Everywhere
It could take many doctors their entire lifetimes to read, learn, and classify the same number of patient cases that ML engines can process in minutes or hours. AI doesn't get distracted or need breaks. It merely applies its programming to process any data that is introduced.
Robotic process automation (RPA) enables humans to focus on high value, strategic quality tasks. High quantity, low priority work like capturing metadata information from documents is better left to purpose-built technology.
Machine learning algorithms may not have a definitive, accurate answer one hundred percent of the time. They can retrieve a manageable subset of potential solutions with weighted degrees of probability far faster than the human mind can. With every query assignment, ML bots get smarter.
Advancing Document Management Applications
Similar to the legal and medical case application scenarios mentioned above, there are countless ways in which software developers can extend their applications with bots and virtual assistants. Not just for structured data in cloud applications like ERP or CRM, but for processing unstructured content like invoices and contracts.
Accusoft recognizes the opportunities which AI, ML, and RPA offer for application developers, whether they are in-house teams in financial services firms or building commercial solutions for businesses. Our long history of building extensible APIs and SDKs for document viewing, conversion, and markup help us to identify many practical uses for AI in this space. We look forward to bringing new, innovative solutions to the marketplace.
Do you have a document-centric requirement which you feel would be more effectively handled by algorithms? Send us a message and tell us about it, or stay tuned to our blog over the next few weeks and months to see what we have in store on our road map.