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Author: Tronserve admin

Monday 2nd August 2021 10:17 AM

AI Hold for Engineering Education, What Does the Rise?


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On paper, new disciplines in computer science and electrical engineering such as deep learning, facial recognition, and advanced graphics processing, look easy to exploit for universities wishing to update their STEM curricula. After all, the business press is awash with gushing propaganda on vertical applications for neural networks and pattern-recognizers exploiting big data sets. A broad-based academic institution could pick domains in which they excel, such as medicine or industrial automation, and apply emerging chip and subsystem architectures to the writing of dedicated applications for those vertical domains.

Easy, right? The model has worked before in communications and embedded processing.

But academia will hit fundamental speed bumps in coming years that could stymie efforts to develop effective criteria. The most obvious problem is in simple expansion of high-level software languages in vertical domains. We’ve already seen the colloquial wonderland of “coding,” which has been the magical mantra for institutions wishing to excel in software, as well as for parents of students wanting their offspring to get a good job as a code jockey. However, as AI penetrates further, all that touted training in coding will become no longer that important.

Already, fields in gaming and embedded processing have turned from well-defined environments written in C++, JavaScript, or Perl, to self-generating code modules in which the talents of software programmers come close to being marginalized. In the next generation, leading platforms in deep learning and pattern recognition will dispense with the need for human programmers, because such hardware platforms are not programmed in the traditional sense. They are trained from large data sets at the machine level, so the need for high-level language virtually disappears. AI developers have been warning about this shift for more than a decade, but few CS/EE schools have moved to prepare for this change in their curricula.

This trend to generative modules of software does not mean the death of traditional programming languages, but rather the relegation of such code to an embedded-like status. In fact, the danger in relying on high-level modules and non-programmed training platforms is that the collective knowledge of writing in compiled or object-oriented languages could fade, just as the knowledge of BASIC or FORTRAN has disappeared.

Hence, educators must strike a balance between preserving an institutional knowledge of high-level programming, while disabusing students and families of the idea that a lucrative career can be built around C++ or Perl expertise.


ENGINEERING EDUCATION



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Posted on : Monday 2nd August 2021 10:17 AM

AI Hold for Engineering Education, What Does the Rise?


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Posted by  Tronserve admin
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On paper, new disciplines in computer science and electrical engineering such as deep learning, facial recognition, and advanced graphics processing, look easy to exploit for universities wishing to update their STEM curricula. After all, the business press is awash with gushing propaganda on vertical applications for neural networks and pattern-recognizers exploiting big data sets. A broad-based academic institution could pick domains in which they excel, such as medicine or industrial automation, and apply emerging chip and subsystem architectures to the writing of dedicated applications for those vertical domains.

Easy, right? The model has worked before in communications and embedded processing.

But academia will hit fundamental speed bumps in coming years that could stymie efforts to develop effective criteria. The most obvious problem is in simple expansion of high-level software languages in vertical domains. We’ve already seen the colloquial wonderland of “coding,” which has been the magical mantra for institutions wishing to excel in software, as well as for parents of students wanting their offspring to get a good job as a code jockey. However, as AI penetrates further, all that touted training in coding will become no longer that important.

Already, fields in gaming and embedded processing have turned from well-defined environments written in C++, JavaScript, or Perl, to self-generating code modules in which the talents of software programmers come close to being marginalized. In the next generation, leading platforms in deep learning and pattern recognition will dispense with the need for human programmers, because such hardware platforms are not programmed in the traditional sense. They are trained from large data sets at the machine level, so the need for high-level language virtually disappears. AI developers have been warning about this shift for more than a decade, but few CS/EE schools have moved to prepare for this change in their curricula.

This trend to generative modules of software does not mean the death of traditional programming languages, but rather the relegation of such code to an embedded-like status. In fact, the danger in relying on high-level modules and non-programmed training platforms is that the collective knowledge of writing in compiled or object-oriented languages could fade, just as the knowledge of BASIC or FORTRAN has disappeared.

Hence, educators must strike a balance between preserving an institutional knowledge of high-level programming, while disabusing students and families of the idea that a lucrative career can be built around C++ or Perl expertise.


ENGINEERING EDUCATION


Tags:
engineering education coding programming programmers