Artificial Intelligence & Machine Learning
Power-Up Your Application
Artificial Intelligence – The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, and decision-making.
Machine Learning – A subset of artificial intelligence that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed to do so.
First off, we must define what AI & ML are and how they are different:
To simplify the discussion, think of AI as the broader goal of autonomous machine intelligence, and machine learning as the specific scientific methods currently in vogue for building AI. All machine learning is AI, but not all AI is machine learning.
Second, AI & ML may sound complex and fictional, as something only large corporations can afford or that is not yet being applied in daily life. Here are some examples of day-to-day AI & ML to bring a sense of reality and viability:
- Sentence prediction & word suggestions while typing on your smartphone
- Face recognition to unlock phones, tag people in a photo, or add filters such as in Snapchat
- Route suggestions on Maps and GPS apps
- Autopilot on Airplanes has existed for decades, pilots actually only manually fly a few minutes in the whole trip
- Spam filters analyses the content and metadata of emails to categorize them
- Image & writing recognition is what deciphers the checks you deposit via a smartphone photo
- Targeted advertising is used by Google & Facebook to present ads to you based on your preferences
This is only the tip of the iceberg. Applications of this technology can span any industry and demographic, it is not just for technology adopters. The specific needs of each product, their target user, and technology will dictate how AI will be shaped to improve the overall experience for all parties involved.
picture showing the components,
engines now run on the devices, not just the cloud.
AI & ML in Internet of Things (IoT)
One of the most valuable and relevant applications of AI & ML today is in IoT. As it is widely known, a key component of IoT systems is to collect huge amounts of data from several sources. Such influx of raw data needs to processing in order to be valuable and read by humans to be put in use towards business goals. AI & ML have evolved in this area to help reduce crunch time and keep up with the amounts of data generated. Predictive maintenance and analytics are the most clear example of AI & ML in IoT.
It is imperative to uncover all the 'aspects' that the system must perform in order to create architecture to support them. In the initial assessment phase, we make high-level assertions, along with a cursory assessment of the state of the technology available. Determining the real requirements of each, however, requires deeper interaction with developers and business stakeholders in your organization.
By carrying out these tasks with deliberate prioritization, the final architecture can be designed to accommodate new and future features and support any modules that the system provides as functionality to end users.
Reach out to us today to discuss your modernization needs, we can carry out a Technology Assessment to help you discover and decide your next steps.