Artificial Intelligence (AI) is infused into almost every aspect of the consumer’s life. From the time when consumers listened to the radio traffic report during their morning commute and changed routes accordingly, we have now moved into a world of AI-enabled personal assistants. These smart assistants present on our smartphones, inform us about the best route to reach a destination, and even reroute during delays. AI has enabled consumers to be more aware about their surroundings than ever before as today’s AI and Internet of Things (IoT) are converging to create “the intelligence of things.”
Technology is helping us to understand the human body better as well. Using the latest IoT devices, one can go beyond tracking heart rates, steps and calories burned to machine learning (ML) capabilities that can identify the early signs of sickness or stress, and accordingly, suggest the appropriate course of action. Empathetic AI has been designed to help with depression and could ultimately evolve to help consumers live a healthier and more fulfilling life.
Even though a number of AI-driven software have paved their way into our everyday world, the technology still has a long way to go and has only managed to scratch the surface when it comes to its true potential. Many technology enthusiasts believe that data is now alive and trying to communicate with us in what is driving IoT to become a world of intelligence.
The evolution of AI
AI has entered the market at a much faster speed than what was anticipated. The speed at which innovation is taking place and the resultant data is increasing day by day. Predictions by various analysts suggest that by the next decade, AI will be much further along than where it is today, so comprehending how it would impact the lives of consumers would be very difficult. Over tens of billions of connected devices are expected by 2020 and many of these devices will come infused with AI.
Utilizing Big Data applications, these devices are capable of tracking patterns, making predictions and suggesting actions based on insights generated before consumers even think about it. For instance, when passing a bank or an ATM, your smartphone or smart car might notify you to make a pending deposit, and in certain cases, may even do the transaction on its own – made possible through AI, ML and data — three pillars of “the intelligence of things.”
AI has unlocked the value of IoT and has created a disruption amongst existing industries, which has resulted in newer industries being created. Take for instance the disruption created by driver for hire apps in the transportation industry. As autonomous cars become the norm, hunting for a parking space in the future will not be an issue as AI will do it for us. Currently, we have only hit the tip of the iceberg as there are bigger societal impacts which can alter how we enter the future as constant developments in AI continue.
Intelligence of Things unlocked
The next step in AI is to use the large volumes of data collected and harvest it to generate value. For instance, surveillance cameras in retail stores, major intersections and banks record video for hours, days, weeks and years. Up until recently, this footage was saved on tape or hard drives for a certain period of time and then erased to store new recordings. When an incident occurs, these videos are reviewed for hours to identify relevant clues, as analytics are used infrequently.
AI, on the other hand, looks at context versus content. Surveillance videos use analysis of captured data to improve the aisle layout in stores or identify the effectiveness of promotions to maximise revenue. Traffic lights can be programmed by city planners to ensure smooth commutes. Databases can identify suspects and unusual activities by law enforcement. The content collected by these cameras can be tracked through AI and patterns can be identified using Big Data applications to create a dashboard of valuable insights. Addressing context versus content helps create the “intelligence of things.”
Today’s smart home security cameras come equipped with motion detectors that alert the owner if there is any activity in the surrounding area. As Fast Data applications evolve, soon there will be products with AI-enabled intelligent cameras that generate analysis and insights as the data is captured. For example, real-time facial recognition can be performed from a video feed comparing ‘the face’ to a database of facial signatures that trigger an action or generate valuable insights.
Extracting value from AI
The intelligence of an AI system depends on its design. The biggest challenge with AI in autonomous vehicles is capturing, extracting and learning from the data collected in real-time. Algorithms allow the self-driving vehicle to assess the road conditions and its surroundings with high confidence, however, these systems are not able to function in applications that they aren’t programmed to handle. It is at this point where flash storage at the edge plays a critical role, as well as ML.
In this scenario, the data is stored within the vehicle and later transferred to the cloud, where along with the other sensor data, is aggregated and analysed. Through this analysis, the algorithms are improved and downloaded to the car to improve its knowledge base. As such, communal learning and intelligence of the car’s AI systems and ML capabilities are continuously improved.
Embracing the Possibilities
Many innovative applications which are beyond imagination have been enabled by AI, and just the beginning of this transformation. AI will enable companies to identify correlations that have yet to be predicted as we have entered an era where technologies like AI and ML create more meaningful connections that enable environments to thrive.
Christopher Bergey is VP, Embedded Solutions Worldwide at Western Digital