No filed of study can advance significantly unless outsiders bring their knowledge and experience to that field of study.
Nowadays, disciplines are not as single as they used to be. The era of single is heading to the end, not only for the single subject, but the single team, and the underlying cause for such diversity is the increasingly common sense of inter-discipline, which requires experts to co-work. Accordingly, I strongly concede the speaker's claim that a filed of research, at present, could not receive a highly development without commingling powers attracting from other areas.
Admittedly, overdue extraction of knowledge coming from different subjects causes counterproductive effect. For example, Chaoxing laboratory, the most recent and prosperous research institution specializing on auto-driving co-founded by Xian Technological University and Xian Jiaotong University, foundered after only 3 years of operation. The causes had been discussed a lot in Xi'An District. One clue to its failure was suggested to be the short of funds of Xian Technological University, because another counterpart is the top ten university in China, understandably, such problem should not blame on it. But given the vacant clue of fiscal deficiency of the both schools, retrenchment is not likely a plausible reason. The fact that the leader hired a wide range of professionals majoring in disparate areas as math, computer engineer, artificial intelligent expert and even project leaders working in enterprises just in order to construct a multi-platform and the most advanced unmanned-driving laboratory in China, is more likely to be the culprit of its shutdown. Since too many experts, leader-role companions working in a sole environment without specific working contents, relationships between workers went to the ice point very quickly, finally lead to bankrupt.
Despite special examples I have mentioned, I still fundamentally agree with the assertation that outsiders' knowledge is the key for a field to increasingly progress. And I believe the prerequisites are clear work assignments and strong theoretical foundations. Examples from two study areas, machine learning and management science, could provide us how inter-disciplines work.
Machine learning, a deeper research point on the basis of computer science, requires knowledge far more wider than computer science major. Rigorously speaking, it is a huge cross-discipline containing mathematics, hardware engineering, physics and statistics. For the four subjects, the mathematics is the most important and the basic of machine learning. To introduce the substance of ML, we should understand the main task is to transforming usual things in daily lives to various mathematical formulas, and then to test these formulas on computers by using algorithms resembling the process of solving usual things(formulas), and in the end, to make computer systems to understand indicators of users. But single algorithm is not ML, but the byzantine and massive arrows of codes are. While outsiders may misconstrue business management subject as qualitative research subject, plethora mathematical and probability knowledge should be learned by pioneering researchers. Like other research areas requiring repeated experiments and accurate data, management science also needs to promise the objectivity and precision, though no labs and objective subjects for management researchers to use, how to design a perfect field-study wiping nearly all the interruptions and subjectivities is showing significance. Meanwhile, using quantitative methodology and coding skill to strengthen the objectivity of research and to streamline the collection of data is also necessary for them. Accordingly, at present, nearly every matured study area requires different abilities, creating an increasingly extroverted and inclusive academia.
In sum, I acknowledge and recognize that there sometimes have examples of the failure of bringing outside knowledge to certain areas, but we could not overlook the benefits inter-disciplines contribute to us. And I expect that, in the future, the global academia will connect more closer, requiring more versatile savants to change the world better. Moreover, outsiders like us--nonexperts--are playing roles making their contribution meaningful as well.
| Post date | Users | Rates | Link to Content |
|---|---|---|---|
| 2023-07-03 | zanzendegi | 83 | view |
| 2023-01-21 | carlossouza | 66 | view |
| 2022-07-30 | yyh123 | 83 | view |
| 2021-08-07 | rachnabehl43 | 66 | view |
| 2021-03-07 | Kay_1998 | 83 | view |
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Grammar and spelling errors:
Line 1, column 396, Rule ID: A_RB_NN[1]
Message: You used an adverb ('highly') instead an adjective, or a noun ('development') instead of another adjective.
...research, at present, could not receive a highly development without commingling powers attracting f...
^^^^^^^^^^^^^^^^^^^^
Line 5, column 98, Rule ID: POSSESIVE_APOSTROPHE[1]
Message: Possible typo: apostrophe is missing. Did you mean 'outsiders'' or 'outsider's'?
Suggestion: outsiders'; outsider's
...entally agree with the assertation that outsiders knowledge is the key for a field to inc...
^^^^^^^^^
Line 7, column 100, Rule ID: MOST_COMPARATIVE[2]
Message: Use only 'wider' (without 'more') when you use the comparative.
Suggestion: wider
...omputer science, requires knowledge far more wider than computer science major. Rigorously...
^^^^^^^^^^
Line 9, column 277, Rule ID: MOST_COMPARATIVE[2]
Message: Use only 'closer' (without 'more') when you use the comparative.
Suggestion: closer
...uture, the global academia will connect more closer, requiring more versatile savants to ch...
^^^^^^^^^^^
Transition Words or Phrases used:
accordingly, also, but, finally, if, look, may, moreover, so, still, then, well, while, for example
Attributes: Values AverageValues Percentages(Values/AverageValues)% => Comments
Performance on Part of Speech:
To be verbs : 22.0 19.5258426966 113% => OK
Auxiliary verbs: 8.0 12.4196629213 64% => OK
Conjunction : 28.0 14.8657303371 188% => OK
Relative clauses : 6.0 11.3162921348 53% => More relative clauses wanted.
Pronoun: 24.0 33.0505617978 73% => OK
Preposition: 78.0 58.6224719101 133% => OK
Nominalization: 19.0 12.9106741573 147% => OK
Performance on vocabulary words:
No of characters: 3609.0 2235.4752809 161% => OK
No of words: 619.0 442.535393258 140% => Less content wanted.
Chars per words: 5.83037156704 5.05705443957 115% => OK
Fourth root words length: 4.98795655647 4.55969084622 109% => OK
Word Length SD: 3.41093880209 2.79657885939 122% => OK
Unique words: 364.0 215.323595506 169% => OK
Unique words percentage: 0.588045234249 0.4932671777 119% => OK
syllable_count: 1121.4 704.065955056 159% => OK
avg_syllables_per_word: 1.8 1.59117977528 113% => OK
A sentence (or a clause, phrase) starts by:
Pronoun: 5.0 6.24550561798 80% => OK
Article: 7.0 4.99550561798 140% => OK
Subordination: 4.0 3.10617977528 129% => OK
Conjunction: 10.0 1.77640449438 563% => Less conjunction wanted as sentence beginning.
Preposition: 9.0 4.38483146067 205% => Less preposition wanted as sentence beginnings.
Performance on sentences:
How many sentences: 25.0 20.2370786517 124% => OK
Sentence length: 24.0 23.0359550562 104% => OK
Sentence length SD: 81.5581853648 60.3974514979 135% => OK
Chars per sentence: 144.36 118.986275619 121% => OK
Words per sentence: 24.76 23.4991977007 105% => OK
Discourse Markers: 3.96 5.21951772744 76% => OK
Paragraphs: 5.0 4.97078651685 101% => OK
Language errors: 4.0 7.80617977528 51% => OK
Sentences with positive sentiment : 12.0 10.2758426966 117% => OK
Sentences with negative sentiment : 4.0 5.13820224719 78% => OK
Sentences with neutral sentiment: 9.0 4.83258426966 186% => OK
What are sentences with positive/Negative/neutral sentiment?
Coherence and Cohesion:
Essay topic to essay body coherence: 0.123590492111 0.243740707755 51% => OK
Sentence topic coherence: 0.0277796357988 0.0831039109588 33% => Sentence topic similarity is low.
Sentence topic coherence SD: 0.0368198004341 0.0758088955206 49% => Sentences are similar to each other.
Paragraph topic coherence: 0.0654565593101 0.150359130593 44% => OK
Paragraph topic coherence SD: 0.0396990952472 0.0667264976115 59% => OK
Essay readability:
automated_readability_index: 18.4 14.1392134831 130% => OK
flesch_reading_ease: 30.2 48.8420337079 62% => OK
smog_index: 11.2 7.92365168539 141% => OK
flesch_kincaid_grade: 15.0 12.1743820225 123% => OK
coleman_liau_index: 16.83 12.1639044944 138% => OK
dale_chall_readability_score: 10.26 8.38706741573 122% => OK
difficult_words: 213.0 100.480337079 212% => Less difficult words wanted.
linsear_write_formula: 12.5 11.8971910112 105% => OK
gunning_fog: 11.6 11.2143820225 103% => OK
text_standard: 12.0 11.7820224719 102% => OK
What are above readability scores?
---------------------
Rates: 83.33 out of 100
Scores by essay e-grader: 5.0 Out of 6
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Note: the e-grader does NOT examine the meaning of words and ideas. VIP users will receive further evaluations by advanced module of e-grader and human graders.