MétaCan
Menu
Back to cohort
Record W6968614945 · doi:10.5281/zenodo.3628012

Future of Robotics: What Industries Will Use Robots the Most in 2020?

2020· article· en· W6968614945 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typearticle
Languageen
FieldEngineering
TopicDigital Transformation in Industry
Canadian institutionsnot available
Fundersnot available
KeywordsRobotWork (physics)Industrial robotSet (abstract data type)Automation

Abstract

fetched live from OpenAlex

Robotics has been the dream of humanity ever since the idea of an automatic helper appeared. Today, despite the fears that machines might rebel against humankind, robots are ubiquitous and well-integrated in our lives. There is a whole list of industries that would not exist in the way they do now without the help of robots. The most obvious of them is the automotive industry. It makes sense since the first-ever robot started working in this industry over half a century ago. Today, the industry employs not only heavy industrial and assembly line robots but also smaller collaborative robots (aka cobots) for more precise and delicate tasks. In 2020, the automotive industry will stay one of the biggest consumers of robots. Similarly, to the automotive industry, metalwork and heavy industries also employ robots in different tasks. For harsh conditions and rough handling, there are well-protected machines and smart power tools. For peripheral tasks, there are collaborative robots that allow humans to focus on more value-added job responsibilities. Certain industries are full of jobs that are not only complicated or dirty but also boring. Robots bring along the merits of automation, which means that boring tasks can be easily passed to them. Besides, robots can work around the clock and will never get tired. This makes them perfect for such industries as agriculture and food processing. <strong>Robots of the Future: Cobots</strong> Robots have been first introduced to relieve human workers from engaging in heavy, dangerous or dirty tasks. With time, the development of technologies and materials allowed them to complete more controlled and complicated assignments. For this reason, modern robots are not exclusively used in heavy industrial settings. Today, even mid-range and small businesses can automate their processes using cobots. This trend resulted in a growing demand for piece-picking robots able of delicate handling. These cobots are effective in such industries as packaging, warehousing, and logistics. Along with the robotics industry, engineering solutions are growing rapidly. The high technological robots need to proceed a lot of actions so the materials they are created from should be troubleshooted by manufacturers before the release stage and the motion control should be solid. The bright example of the company that is working for more than 10 years in motion technology innovations is Progressive Automations located in Canada and the USA. The company works for delivery linear actuators, the B2B website that represents complex industrial solutions is https://progressiveactuators.com/. Electronics is another traditional industry that engages robots for the most various tasks. They also are easy to program and are able to learn. Whether it is electronic assembly, inspection, or micro-manufacturing, robots are essential for this industry. Similarly, healthcare catches on with the benefits of employing robots for delicate or routine tasks. Today they are widely used for robot-assisted surgeries. Another job that requires high control and decreased risk of contamination, and so will likely benefit from using robots in 2020 is lab automation. While there is a number of industries that rely heavily on employing robots, it is also very plausible that all industries will see the rise of robot implementation in 2020. From pharmaceutical discovery to light manufacturing and fulfillment, robots will be used everywhere. Moreover, it is likely for the robots to move into the industries that provide services. And the best news? No robot uprising is planned for 2020 yet!

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.687
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.212
Teacher spread0.176 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicDigital Transformation in IndustryFrench-language works237,207