IEEE Computer Society's Top 12 Technology Trends for 2020

 

IEEE Computer Society's Top 12 Technology Trends for 2020

Quantum Computing

The quest for realistic quantum computing will move ahead in 2020, yet remain incomplete. At the start of 2020, experimental quantum computer demonstrations devour about 1/10,000 the energy of the world's biggest supercomputers whilst outperforming them by means of 1,000x or extra--but the tested applications appear to be quantum laptop self-assessments. If quantum computer systems are destined to be successful, they will come approximately by growing relevance and generality, having already confirmed a computational advantage. We task demonstrations to become greater compelling in the next year. For example, a quantum computer would possibly carry out a chemical simulation not possible by means of any fashionable supercomputer, leading to a greater nuanced debate about whether or not the chemical that may be observed may be beneficial to society. 

The tech predictions evaluation protected a evaluation of technology which are considered very promising yet aren't likely to reach wide adoption until after 2020. Such technology consist of seamless assisted truth; virtual truth for enterprise; distributed (cooperative) robotics; simulating complete international; self sustaining motors; and printable bio-substances and tissue.

Technologies that had been reviewed but taken into consideration to have already reached wide adoption are photonic-based conversation in data facilities, facial popularity, 5G, multi-agent structures, safety of IoT devices, disaggregated servers, and Blockchain. 

The IEEE CS group of leading era specialists consists of Mary Baker, HP Inc.; Tom Coughlin, Coughlin Associates; Erik DeBenedictis, entrepreneur; Paolo Faraboschi, Hewlett Packard Enterprise VP and Fellow; Eitan Frachtenberg, information scientist; Danny Lange, VP of AI at Unity; Phil Laplante, professor, Penn State; Andrea Matwyshyn, Professor and Assoc. Dean of Innovation, Penn State Law - University Park, and professor, Penn State Engineering; Avi Mendelson, professor, Technion and NTU Singapore; Cecilia Metra, professor, Bologna University and IEEE CS President; Dejan Milojicic, Hewlett Packard Enterprise Distinguished Technologist and IEEE Computer Society president 2014; Roberto Saracco, Chair of the Symbiotic Autonomous Systems Initiative of IEEE-

Cognitive skills for robots

preading more and more from the manufacturing floors into spaces occupied by humans. There is a need for robots in such environments to be able to adapt to new tasks through capabilities such as increased comprehension of the environments within which they are situated. We predict that recent breakthroughs in large-scale simulations, deep reinforcement learning, and computer vision, collectively will bring forth a basic level of cognitive abilities to robots that will lead to significant improvements in robotic applications over the next few years.

Adversarial Machine Learning

  ML commonly assumes that the environment isn't maliciously manipulated at some point of the training and assessment of models. In different words, most ML models have inadequately considered the approaches wherein an adversary can assault and manage the version's functionality.  Yet, protection researchers have already demonstrated that hostile, malicious inputs can trick machine studying fashions into favored consequences, even with out complete facts about a target version's parameters. As ML becomes included into other systems, the frequency of malicious assaults on ML will rise.  As such, protection studies into antagonistic device gaining knowledge of and countermeasures aimed at detecting manipulation of machine studying structures becomes seriously crucial. Similarly, recognition of the fallibility and manipulability of ML structures will start to tell policymaking and felony paradigms.