Artifical Intelligence & Machine Learning

Strategic has the ability to deliver advanced solutions in AI & ML through its worldwide network of trusted partners. We search the globe to find the developers and engineers that are the right fit for your project.  We can help your organization access insights from data, build models to identify patterns, create algorithms to derive actionable intelligence from data, and develop holistic software systems to manage it all.

 

There is no clear separation between Artificial Intelligence (AI) and Machine Learning (ML), because ML is part of AI. Artificial intelligence is a set of algorithms that is able to cope with unforeseen circumstances, and it differs from machine learning in that it can be fed unstructured data and still function. How data is defined as structured or unstructured is determined by the way it’s formatted and presented to the AI algorithm.  The reason AI is often used interchangeably with ML is because it’s not always clear as to whether the underlying data being ingested is structured or unstructured.

 

ML is a set of algorithms that is fed with structured data in order to complete a task without being programmed how to do so. A fraud detection algorithm is a good example of machine learning. The data in this type of algorithm receives structured data presented in a fixed format, where each transaction has fixed classifications of data such as source, date, time, and location. If the value for a variable deviates from what the algorithm usually sees, it will issue an alert notification and stop the process.

With regards to AI, a tradtional non-AI based computer management system might present current inventories levels of various products, while an AI based system can identify shortfalls, connect cause with effect within the inventory system and even among existing supplier relationships, and then make corrections as needed. Artificial intelligence can be allowed to make many of the decisions, or it can be used to improve upon a specific process. For many organizations this transformation is slowly taking place as more confidence is gained with this new technology. Perhaps the most transformational aspect of AI is that it allows code to rewrite itself as it adapts to its conditions and overall environment.

 

A good example of where AI differs from ML would be autonomous robots, which can physically navigate in real time to avoid obstacles, and learn as it goes. The robot doesn’t know what it will encounter, yet it can still function well without structured data. The unstructured data in this example is much more complex than in the fraud detection example, because the variables are unknown. Each time the algorithm is activated and encounters an entirely new situation, it continues to navigate terrain without any human interference. The key to AI is that it attempts to replace biological intelligence by enabling a software application, or system ,to operate with varying degrees of autonomy, thus eliminating manual intervention for many functions. The range of functions AI can handle on its own is growing every day. 

Common Artificial Intelligence Applications:

  • Speech Recognition, which allows intelligent systems to convert human speech into text or code

  • Natural Language Processing, a subset of speech recognition, which enables conversational interaction between humans and computers

  • Computer Vision, which allows a machine to scan an image and identify it using comparative analysis

  • Autonomous Navigation, where a robot or drone can learn about its environment and continue to navigate into unknown environments withoiut human intervention.

Unlike legacy enterprise software programs that can take many years to develop, and even newer more agile DevOps processes that push changes quickly with less disruption, AI goes a step further and allows a software program to optimize its code to address specialized use cases in real time.

Proper implementation of AI technology not only potentially lowers the cost of software development and support, it can also improve platform performance and the development of algorithms and processes that can provide significant commercial and competitive advantages.