IEEE Computer Society (IEEE CS) tech experts unveiled their annual predictions for the future of tech, presenting what they believe will be the most widely adopted technology trends in 2020.
The tech future forecast by the world’s premier organization of computer professionals consistently ranks as one of its most anticipated announcements. “These predictions identify the top dozen technologies that have substantial potential to disrupt the market in the year 2020,” said Cecilia Metra, IEEE Computer Society President.
Artificial Intelligence (AI) and machine learning (ML) were prominent in the predictions, featuring in several technologies in the list.
With vastly increased instances of machine learning (ML) in daily interactions, the availability of massive crowd-sourced labelled data, more efficient and cheaper computing power, better algorithms, and synergy with 5G and IoT, the experts predict widespread deployment of ML in areas areas that will have a far greater impact on our daily lives, such as assisted driving, industrial automation, surveillance, and natural language processing.
“In 2020 we expect to see ever-increasing adoption of AI in various use cases such as AI@Edge, cognitive robots and drones, as well as with verticals that include cybersecurity, cyber-physical systems, and adversarial machine learning,” said Dejan Milojicic, Hewlett Packard Enterprise Distinguished Technologist and IEEE CS past president (2014).
AI is also predicted to feature increasingly in critical infrastructure systems, or “critical systems,” that directly affect the health, safety, and welfare of the public and in which failure could cause loss of life, serious injury, or significant loss of assets or privacy. Critical systems include power generation and distribution, telecommunications, road and rail transportation, healthcare, banking, and more.
AI and Cybersecurity
Another area where AI is predicted to feature prominently is cybersecurity, one of the key risks for any business today. The growing attack surface includes amateur threats, sophisticated distributed denial of service attacks, and skilled nation-state actors. Defense depends on security analysts who are rare, lack adequate training, and have high turnover rates.
Artificial Intelligence and Machine Learning (AI/ML) can help detect threats, offer recommendations to security analysts, reduce response time and scale analyst effectiveness. It can preserve corporate knowledge and use it to automate tasks and train new analysts. IEEE experts predict advancing the adoption of AI/ML applied to cybersecurity through a partnership among members of industry, academia, and government on a global scale.
Reliability and safety challenges for intelligent systems
With this increased deployment of more intelligent and autonomous systems, there will be stronger requirements in terms of reliability and safety for the intelligent systems’ operation in the field. Reliability is defined as the likelihood of correct operation for a given amount of time, while safety refers to the ability to avoid catastrophic consequences on the environment and users.
Guaranteeing the required high levels of reliability and safety that are mandated for highly autonomous intelligent systems will likely be one of the major technological challenges to be faced by 2020.
Another technology predicted to trend in 2020 is Digital Twins – digital replicas of real-world entities – which are already a reality in the manufacturing industry, with major IoT platforms, like Siemens MindSphere, supporting them.
They have also become a widespread tool in complex system operations; railways and power plants have been used in cities since Jan 1, 2019. The Singapore administration uses digital twins for planning, simulation and operations in Singapore. Cognitive digital twins are in the early stages of trial and experimentation.
Practical delivery drones
Parcel delivery is an industry of enormous economic impact, and yet has evolved relatively slowly over the decades. It can still be frustratingly slow, wasteful, labor-intensive, and expensive. These inefficiencies, combined with recent developments in drone technology, leave the field ripe for disruption. Several companies have recently worked to develop practical delivery drones, which may now be ready to completely transform this industry, and consequently society as a whole.
3D printing has existed since at least the early 1980’s but has largely been confined to part prototyping and small-scale production of special-purpose or exotic pieces. Currently, new processes, materials, hardware, software, and workflows are bringing 3D printing into the realm of manufacturing, especially for mass customization.
Unlike traditional manufacturing, additive manufacturing makes it economically viable to produce a high volume of parts where each one is different. For instance, companies like SmileDirect now use 3D printers to generate tens of thousands of molds each day, each customized to make an orthodontic aligner for an individual person.
Stronger and more robust materials, finer resolution, new finishing techniques, factory-level management software, and many other advances are increasing the adoption of 3D printing in industries such as healthcare, footwear, and automotive. In 2020, we expect to see this trend continue as other industries discover the benefits of mass customization and the opportunity to print parts that are not easy or affordable to produce using traditional means.
Legal related implications to reflect security and privacy
Data collection and leveraging capabilities are becoming more sophisticated and sensitive, often incorporating live feeds of information from sensors and various other technologies. These enhanced capabilities have yielded new streams of data and new types of content that raise policy and legal concerns over possible abuse: nefarious actors and governments may re-purpose these capabilities for reasons of social control.
Similarly, new technology capabilities also strain the abilities of average people to discern the difference between legitimate and fraudulent technology content, such as accepting an authentic video versus a “deep fake.” As such, the next year will prove critical to maintaining the fragile balance between preserving the social benefits of technology, on the one hand, and preventing undesirable re-purposing of these new technology capabilities for social control and liberty deprivation, on the other.
More aggressive legal and policy tools are needed for detecting fraud and preventing abuse of these enhanced technology capabilities.
Other technologies reviewed
The tech predictions analysis included a review of technologies that are considered very promising yet are not likely to reach broad adoption until after 2020. Such technologies include seamless assisted reality; virtual reality for business; distributed (cooperative) robotics; simulating whole world; autonomous vehicles; and printable bio-materials and tissue.
Technologies that were reviewed yet considered to have already reached broad adoption are photonic-based communication in data centers, facial recognition, 5G, multi-agent systems, security of IoT devices, disaggregated servers, and Blockchain.