Artificial intelligence (AI), or more accurately Machine Learning (ML), was first developed in the 1950s and has been changing society ever since. Our daily lives are now influenced in numerous ways by algorithms that recognize speech, identify objects in photos, and recommend the next movie to watch.
Almost every product and industry can benefit from AI/ML, whether improving the technologies used or the development process itself. These solutions don’t always look the same, and it takes a breadth of knowledge to know which algorithms and technology stacks are right for a project.
While we can’t share the projects for which we’ve developed AI/ML, we can look generally at some interesting use cases for integration we hope might inspire you to incorporate machine learning or artificial intelligence technologies:
IoT cameras for security systems: neural nets can be used to do object detection on passing cars and determine the color, make and model of the passing vehicles, as well as create a text log for security personnel.
Event venue displays and audio equipment: natural language processing algorithms can be used to add captions to video of an event in real time. This helps make the event accessible to all.
Software project management automatic descriptions: when filing bugs or documenting changes to code, semantic analysis and natural language processing can summarize submitted code snippets automatically so that the code can be searched with plain English queries like “Where is the server instance configured?”.
Robotic surgery assistants: reinforcement learning can help “teach” robotic medical assistants what actions (clamp pressure, heating pads, etc.) are helpful to the patient and medical staff.
eCommerce stores: recommender systems can learn from customers’ actions on the platform and make recommendations for additional products or services the customer may want to purchase.
Pet home monitoring: neural nets can be trained to identify different pets in your home and dispense different amounts or types of treats depending on which animal is in front of the device.
Manufacturing facilities: anomaly detection can monitor data collected from sensors and tests on the production line and make predictions automatically when there is likely an issue that necessitates manual inspection.
This list is by no means exhaustive and there are many more industries and applications of AI/ML to products and product development. At Pensar, our Intelligent Computing team works closely with the software and hardware engineering teams to make sure that products are performing the best as they can right out of the gate. Our expertise with a wide variety of Machine Learning and AI algorithms, as well as our ability to integrating them with larger processes and hardware, enables us to keep schedules on track and make the best products possible for our clients.