Artificial Intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry, as well as many other industries. The research associated with AI is highly technical and specialized. The core problems of AI include programming machines for certain traits such as knowledge, reasoning, problem solving, perception, learning, planning, and the ability to move and manipulate objects. AI is being used for a multitude of things, including to speech recognition, learning, planning, problem solving, and much more. Most of us are familiar with AI, but are you familiar with Hybrid AI?
Issues Within a Traditional AI Model
Hybrid AI is taking us to a new era in automation. There can be drawbacks to a system that is entirely autonomous with no human oversight. Automation can take longer and mistakes can be made.
Some of the issues with AI are:
- Artificial Intelligence is susceptible to trainer bias. This means that when the system is initially trained, its logic is influenced by the bias of the people that trained this. Since most AIs are operating at the intelligence level of a fifth grader, this bias will cause the AI to make many mistakes, particularly if the AI is being used as a general AI (meaning it is not specialized)
- Self-training AIs rely on trial and error to resolve a problem that they were not specifically trained to handle. (This is also subject to trainer biases). Trial and error can often take extended periods of time, and does not use human interaction to half-step or skip to more relevant potential solutions.
- The AI is limited by its initial training. Often this logic is static and the AI cannot learn new capabilities.
Hybrid AI fixes these issues with AI by ensuring that the AI learning is accelerated by constant interaction and in the case of advanced hybrid AI's like Layer8, a diverse mix of data scientist and end-users. This is called supervised learning and greatly increases the accuracy and reduces the learning time dramatically. In basic terms, the hybrid AI model integrates humans throughout the automation process to overcome the shortcomings of pre-trained existing AI systems. This often includes using technology such as deep learning and natural language processing to make the automation systems faster and smarter.
Hybrid AI Model
In a Hybrid AI Model, the end users drives the deep learning cycle and natural language processing, a static pre-trained AI in itself, allows the end-user to interact with the AI.
AI technology is not yet ready to completely replace humans, especially if it is a mission critical application. AI can be disastrous on its own, especially for larger organizations. Even though AI is capable of running without human assistance, AI still needs humans to train it and make sure the mission stays on track. While AI is becoming more and more impressive, it is still artificial. Making sure humans are in the mix ensures that communication is present and the outputs are accurate and relevant to the mission. Humans can train the AI to help it learn faster rather than the AI taking the time to learn on its own. It can speed up the learning and automation process, making the AI faster and smarter in the process.
AI needs humans, but humans also need AI. Over 37% of organizations have already integrated AI in some way or another. For example, data mining can be handled much faster by an AI. Not only is it done faster, but in massive volumes compared to a human. Organizations can effectively turn data into insights that can then be used to assist humans in decision making. This drives innovation and gives a competitive advantage. Including humans in the machine learning process safeguards against the risks of gaps in learning and helps organizations get the most out of AI and new solutions at each step. It is up to each organization to determine the right mixture of AI and human interaction that works best for your mission.
Hybrid AI Strategy
The big challenge for for most organizations is determining when and how to implement and start using hybrid AI.
A great strategy is to limit the automated functions: For example, allowing an AI to make unrestricted changes to a firewall without human intervention could quickly wind up isolating all users or the wrong users from accessing information protected by the firewall. In a hybrid model, we can have the AI make suggestions and the user actually approve or implement the change Another example would be to allow the AI to analyze and aggregate data and present this curated data as information to an end user, allowing the end user to act on the information, allowing the AI to learn from the users actions so it can suggest those actions to the users in the future.
The cost of training AI can be much higher when a hybrid model is not used. Along with the long learning cycles, other expensive systems can offer:
- Long deep learning cycles uses lots of CPU
- Programmers and data scientists are costly to keep on staff
- Because of long training times and compute resources scheduling, back-logs can occur that reduce the time to market or result in costly mistakes
- Costly integration with cloud AI resources
The good news is, there are simple strategies to manage these costs! Ensure that your AI provides a good, flexible, hybrid AI model that effectively leverages specialized AI as needed. Your AI needs these components:
Good mixtures of specialized AIs
- Natural Language Speaking Engines (NLS)
- Ability to interact with external AIs
- You need to be able to easily have end-users training the AI
- Ability to easily import data
- Adapters that allow you to connect or subscribe to data sources with little or no programming
- Tools to view and alter the AI logic
Layer-8 - Vigilant's Intelligent Workspace
Vigilant’s “Layer-8” software, also known as the Vigilant Intelligent Workspace, is a patented AI-enabled data fusion and user-defined information management platform. It is designed to save information workers 30 to 60% of the time otherwise spent in research, data aggregation and workflow creation for tasks requiring a large amount of data. Using API plugins, the platform gathers relevant data from BI components, outside AIs, public and private data sources, collaborative tools and document management systems, and integrates that data into the end-user’s workspace. The system then suggests workflows and grooms data for further processing by end-users. Vigilant’s Intelligent Workspace technology is the subject of a USAF SBIR Phase II grant awarded in November 2019, and is under review by the USAF for multiple applications in the area of Space Situational Awareness. It is also in place supporting airline and airport operations in an operational Sabre Task Connect project. With Layer8, you can turn your data into action information within 30 minutes, without hiring a data scientist!
Want to know more about our work with AI and our patented Layer-8 solution? Let's talk!