Artificially Intelligent Objects All Around You (And You Didn't Know!)

Artificial intelligence is blending into the products we use every day, bringing enhanced features and convenience. From the smartphones in our pockets to the appliances in our homes, there's hardly an everyday product that hasn't been imbued with a bit of artificial intelligence.

Behind the scenes, machine learning models, computer vision algorithms and other advanced AI technologies are powering "invisible smartness" built into the devices we interact with daily. Often we're unaware of the brilliant artificial brains making our products infinitely more useful. Here are the most common examples of AI in everyday products:

Smart Assistants

Voice assistants like Alexa, Siri and Google use machine learning and AI to get smarter over time:

• Complex speech recognition technology converts our voice commands into text. Larger datasets and recurrent neural networks have improved accuracy and allowed assistants to 'learn' from past interactions.

• Natural language processing and natural language understanding parse our requests and map them to pre-defined responses and actions. Advanced NLP techniques interpret intent and context.

• Machine learning algorithms fuel continuous improvement. As assistants process more inputs from users, their responses become more relevant and helpful.

Smart assistants can perform a wide range of tasks like responding to requests, playing media, controlling smart devices, setting reminders, and providing information.

Smartphones

Smartphones incorporate AI in several ways:

• Facial recognition technology uses AI algorithms like neural networks to analyze facial features for face unlock

• Face filters employ machine vision and computer vision algorithms to detect and track facial features in real time

• App recommendations analyze user behavior and app usage history to suggest new or relevant apps

• Intelligent text correction systems incorporate machine learning and large datasets to predict and correct spelling errors

• Automatic photo editing and organization uses computer vision to identify objects, scenes and faces in photos for easier management

AI is an integral part of the functionality and features we have come to expect from our smartphones.

Smart Cars

Self-driving car technologies rely heavily on AI:

• Machine vision systems use cameras, neural networks and computer vision algorithms to 'see' the road and detect objects

• Radar and LIDAR sensors detect obstacles and feed data into the AI planning systems

• Advanced planning and control algorithms use techniques like reinforcement learning and neural networks to navigate road environments and maneuver the vehicle

Driver assistance features like collision avoidance, lane keeping assist, automatic emergency braking and adaptive cruise control also use artificial intelligence and machine learning under the hood.

Internet of Things Devices

Many IoT devices use AI technologies to optimize operations:

• Smart home appliances like Nest thermostats incorporate machine learning models that are constantly retrained with new data from sensors. This allows the thermostats to optimize temperature settings based on historical patterns and user preferences.

• Smart security cameras employ computer vision algorithms like object detection and image classification neural networks to identify intruders, suspicious activity and even specific people.

• Smart speakers respond to voice commands using speech recognition, natural language processing and other AI technologies to perform requested tasks.

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Robot Vacuums

Robot vacuum cleaners rely extensively on AI for navigation and cleaning:

• SLAM (simultaneous localization and mapping) technology builds a constantly updated map of the environment using sensors, cameras and AI algorithms that allow the vacuum to navigate complex areas autonomously.

• Object detection algorithms allow robot vacuums to identify and avoid obstacles like furniture, cables and pet waste using computer vision and machine learning.

• Machine learning models are used to optimize routine cleaning by recording where dirt tends to accumulate and prioritizing those areas.

Recommender Systems

Platform recommendation algorithms, like those used by:

• Netflix

• Amazon

• YouTube

Employ AI techniques to generate product and content suggestions:

• Collaborative filtering analyses user behavior data from many users to find correlations and make predictions about a user's preferences.

• Content-based filtering algorithms examine the properties of items a user has interacted with in the past to suggest new similar items.

While powerful, these recommendation systems can also cause issues like filter bubbles where users receive a narrow range of suggestions that match their existing interests.

As AI steadily seeps into more and more of the objects we interact with on a daily basis, it's fundamentally changing what we expect from "dumb" devices and appliances. Tomorrow's everyday products will likely seem unthinkably "smart" in comparison to today's AI-enabled gadgets and gizmos.

Invisible AI promises to enrich the devices we use daily, enhancing efficiency, safety and convenience in subtle but profound ways. The rise of "everyday artificial intelligence" represents the next stage in AI's integration with our lives.

It's just that often we don't notice AI's invisible helpers until we start to look a bit more closely.