Future of Robotics: What Industries Will Use Robots the Most in 2020?
Bibliographic record
Abstract
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. Robots of the Future: Cobots 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!
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.024 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".