Multimodal ML and the rise of robots with personality?
Machine learning (ML) describes computer systems' algorithms to learn and adapt without following direct human instructions. By drawing inferences and conclusions from patterns in data, this Internet of Things (IoT) technology is responsible for a number of impressive advances across virtually every segment of any industry.
Multimodal machine learning represents an evolution in how today's machines and artificial intelligence (AI) powered robots process information. By consolidating independent information from various AI devices into a single model, multimodal systems are able to process multiple datasets at once.
As opposed to separately analyzing data from a number of different devices in order to ascertain the relevance of particular data points, today's multimodal systems automatically do the work.
This article will examine what this augmented technology means for the future of the robotics and machine learning industries.
Draw your own conclusions
As outlined by Forbes, multimodal learning represents a significant advancement toward helping machines understand the world around them in new and different ways. By processing multiple forms of information, computers and AI-powered machines can view objects, items – and even humans – in a more robust fashion. By assimilating data from a variety of sources, each with their own unique perspectives on how to interpret this information, robots and ML platforms can essentially form a rudimentary version of individuality, or personality – building their own unique worldview.
Multimodal learning systems are able to comprise their perspectives by gathering information from speech, audio, written and illustrated sources – mimicking the complex sensory intelligence capabilities of the humans they are increasingly able to replicate.
It's a bright future
TechRepublic lists some of the emerging use cases for this burgeoning IoT technology. From Advanced Driver Assistance Systems (ADA) to In-Vehicle Human Machine Interface (HMI) assistants, applications of multimodal ML have already begun to permeate the automotive industry.
The robotics industry has quickly adopted multimodal ML to improve the movement and processing abilities of some advanced platforms, adding to the human mimicry of some advanced systems. Factor in consumer applications – Microsoft's Azure freeware cloud-computing technology is an early adopter – and it's clear why this technology is at the forefront of ML and AI-influenced evolution.
Given these exciting applications, ABI Research projects the total volume of devices shipped with multimodal learning technologies will grow from 3.9 million in 2017 to 514.1 million in 2023 – at a compound annual growth rate (CAGR) of 83%.
Perle powers innovation
Perle Systems is proudly offering IoT solutions for today's leading technology developers. Visit our industrial automation page to learn how Perle device connectivity hardware is helping factories run more efficiently. Perle process automation solutions enable virtually any device to connect to a corporate network. From industrial ethernet switches to fiber media converters, Perle has the solutions your business needs to increase operational productively.