6 AI and Machine Development Technologies to Follow

There is little to no doubt that the machine and artificial intelligence industry is changing the way we live and work. Advances in the past ten years have outstripped that of the past 50. With the ever-increasing pace of new developments, knowledge of the most innovative and game-changing technologies on the market quickly becomes out of date. 

Developments within the technology industry are vast and at times it can be difficult to keep track of the systems that will make the greatest impact on life as we know it. Below are the 6 fastest emerging machine and AI technologies in 2020 that will set the tone for the next decade of AI and machine development.

Digital Twin Modelling

“A digital twin is a virtual replica of a living or non-living physical object. The digital replica demonstrates the potential and physical assets of processes, things, people and places and can be used for various purposes,” explains Alice Willson, a tech journalist at OriginWritings. Digital twins utilise the Internet of Things, machine learning, software-network graphs, and artificial intelligence to create living digital simulation models that update and change as their physical counterpart changes, all in real-time. This could revolutionise how many industries work, from providing remote configuration services to customers or allowing trainee surgeons to practice on simulated bodies, instead of actual patients. 

Autonomous AI

Self-Driving cars, socially-enabled domestic robots and collaborative production assistants are examples of autonomous technology that is being developed at companies like Tesla and Bosch. Autonomous systems are designed to operate in complex and open-ended environments. The research is at an intersection between robotics and artificial intelligence, with a particular focus on the integration of particular methods to create complete cognition-enabled robotic systems, with software available in open-source libraries. Robots can complete tasks that transform lives and accessibility, such as assisting households with complex needs or assembling products in factories.

Conversational AI

Conversational AI is the technology behind familiar processes such as speech-enabled applications and automated messaging systems. Conversational AI is the gateway between human and computer interaction. The development of the software involves a strict discipline dedicated to designing conversational flow, context, relevance, understanding of intent and automated translation and speech recognition systems. The goal of conversational AI is to develop a system that interacts with the user in such a way that it is indistinguishable from human interaction. The technology will enable users to manage business tasks more efficiently by deploying powerful conversational AI interfaces, such as end-to-end bot hosting platforms.

AI Security

AI security is probably one of the fastest-growing areas of AI development currently on the market. AI security refers to the tools and cyber techniques used by artificial intelligence systems to identify potential threats based on similar and previous activity. AI shows the greatest potential for automated fraud identification, intrusion detection and risk probability for logins. Current AI security technology is designed to work harmoniously across businesses. The automated frameworks can identify and correlate threat patterns from huge amounts of activity datasets. Advanced cybersecurity can work seamlessly without disrupting business. Around 80% of telecom companies are relying on this technology to increase security on their systems and around $137 billion was spent on AI security and risk management in 2019.

Probabilistic Programming

Probabilistic Programming refers to the utilisation of algorithms to predict the likelihood of events and make informed decisions in times of uncertainty. The software is currently revolutionising areas of trade and advertising, being used to predict stock prices and recommend products to target consumers with greater accuracy. The technology is developed by borrowing methods from programming languages and incorporating them into statistical models. As computers become more efficient at dealing with probabilities at scale, it will enable us to evolve our current advanced computer aids into intelligent partners for decision-making and understanding.

Automated Machine Learning

Automated Machine Learning is a technological shift in the way companies and individuals utilise traditional machine learning methods and data science. Extracting relevant data from raw data sets using traditional machine learning is time-consuming and costly. Data scientists with computer programming skills are in short supply and high demand. Automated Machine Learning means that organisations can build and use machine learning models based on up-to-date data science expertise. Organisations can run systematic processes on raw data that automatically extract the most relevant material from huge datasets. Not only does this method save time, but it removes the risk of potential bias and human error from the results. This technology makes data processing available to industries such as healthcare, sports, public sector enterprises, and retail. These industries on average, do not have the resources at their disposal for accurate and detailed data processing. The technology also allows larger companies to outsource data processing to automated systems which frees up data scientists to concentrate on more complex problems.

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