One of the recurring barriers for businesses to adopt AI technologies is the lack of a clear understanding of the specific value and application cases of AI. Here we will discuss some specific value-cases from AI industry leaders and how they're applying AI to support their business goals.
Understanding AI and How To Apply it to Business
This list is by no means exhaustive, it's just the tip of the iceberg! If you'd like to work with V-Soft AI Practice Lead Konrad Konarski on a discovery call to show discuss the ways in which AI can improve your business, sign up here.
Below are cutting-edge value cases that leverage recent advancements in artificial intelligence in the form of image processing, natural language processing, and deep-learning.
Image Processing for Quality Assurance
There are some major leaps in this field that have allowed new and compelling value-cases to surface. Ongoing advancements in object detection algorithms (such as You Only Look Once [YOLO]) have increased processing speed and capacity in which video or static images can have objects detected in them with greater accuracy and at a faster rate. In addition image capture technologies have improved with 100+ MegaPixel and 3D cameras allowing for high resolution and depth perception.
With this evolution, one of the compelling value-cases for Artificial Intelligence in image processing is for quality control and inspection. Processes that are normally done using human visual inspection can be automated through image processing. The higher resolution imagery and the faster image processing capability driven by both increased processing power and better object detection algorithms make the possibilities endless for businesses especially in manufacturing. Adding Image Processing capabilities does require the collaborative support of subject matter experts that can provide a learning baseline for the AI algorithms but can be rolled out to the organization in a non-disruptive way by phasing the training, proof-of-concept, and production level systems into the workflow.
There are of course other technologies such as laser imaging that have some established footprints in the space but they are limited in terms of their scalability and do not have inherently self-learning capabilities that could be extended with an AI based image platform. Technologies like laser imaging are often much slower in their ability to resolve inspection information.
Natural Language Processing for Smart Appliances
For natural language processing, although some of the more traditional platforms tend to still be the highest performing in this space, there are various performance enhancing mechanisms (i.e. word injection) that prove to, with certain corpuses, provide improved precision and recall (metric of performance for NLP). One of the more compelling examples of how technology evolution is opening new opportunities is in the ecosystem of smart home devices. Of course we know of the Alexas and Echos of the world that are providing an enhanced in-home experience through voice to text interaction grounded in natural language processing. These features are now extending into smart appliances that are able to collect and process complex natural language instructions such as cooking instructions written in a natural language form within a cookbook and can adjust appliance settings according to the cookbook instructions.
Deep Learning for Pharmaceuticals
There are a variety of neural network model techniques in deep-learning (ex: deep reinforced learning) that have evolved more recently as tangible value-cases surface. One of the more compelling applications of deep reinforced learning is in the pharmaceutical and bio-pharmaceutical industry where this modeling technique can be used to optimize chemical reactions. This allows for scientists to reduce the trial-and-error related with research and development activities. AI platforms can optimize the reagent quantities and compositions based on a positive feedback method after analyzing the reaction outcome. Such platforms are ultimately providing not just granular enhancements in time savings but are leading to faster product-to-market efforts by enhancing various R&D and logistical processes for the pharma and biopharma industries.
This is just a small taste of value-cases that are breaking down the barriers to adoption of Artificial Intelligence across the industrial manufacturing, pharmaceutical, aerospace, and oil & gas industries.
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About Author
Konrad Kona
rski is V-Soft's Practice Lead for AI & IoT where his cumulative expertise is used to commercialize and deliver industry-changing solutions for businesses. Konrad has worked with emerging technologies for more than 15 years. As a successful entrepreneur, engineer, business professional, and thought leader, he has a holistic perspective on delivering AI solutions that bring tangible value to customers.
