To push the Applied AI much further, what are the missing links and opportunities between the electrical and computer engineering, & computer science?

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MGL tools

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Stam Nicolis

It’s not an issue of electrical engineering, but of software.

Eduard Babulak

Agree, to program yes, however to implement it on hardware it does get close to EE

Claus Brillowski

To push applied AI further, a connection to `logic' must be established. Today `logic' is identified with mathematical logic. But the sciences are rooted in practical everyday human life and reasoning. Practical reasoning follows different rules than mathematical reasoning. It must be effective and deal with inconsistencies (In mathematical logic, contradictions are simply excludes from further considerations via the principle of "ex contradictione quodlibet": `from a contradiction everything follows'. ) Practical reasoning was investigated first by Aristotle 2000 years ago. And today it is studied again in computer science. You can read more about the connection in my draft, `Universals, Types and Neighbourhoods'.

Saurabh Bhattacharya

The most critical part is going to be the application of "Design Thinking". The AI and Computer science will have to bridge the GAP and reapond according to "What the user wants "

Yazdani Hasan

There are several missing links and opportunities between electrical and computer engineering (ECE) and computer science (CS) that could be leveraged to push applied AI further. Here are a few examples:

Integration of hardware and software: One of the biggest opportunities is to integrate hardware and software to develop more efficient and powerful AI systems. ECE experts can develop specialized hardware, such as accelerators and specialized circuits, that can be used to speed up AI algorithms, while CS experts can develop software that can take advantage of these hardware innovations.

Development of new algorithms and architectures: ECE and CS experts can work together to develop new AI algorithms and architectures that can take advantage of the latest advances in both fields. For example, ECE experts can develop algorithms that are optimized for specific hardware architectures, while CS experts can develop new machine learning algorithms that can be used to improve the performance of these systems.

Optimization of AI systems: Another opportunity is to optimize AI systems for specific applications. ECE experts can work on optimizing the hardware and power consumption of these systems, while CS experts can work on optimizing the software and algorithms to improve performance and accuracy.

Data management and processing: AI systems rely heavily on data, and both ECE and CS experts can work together to develop more efficient and effective ways of managing and processing large datasets. For example, ECE experts can develop hardware accelerators for data processing, while CS experts can develop new algorithms for data compression and storage.

Interdisciplinary research: Finally, there is a need for more interdisciplinary research between ECE and CS experts to better understand the complex interactions between hardware and software in AI systems. This can involve collaboration on research projects, joint publications, and joint development of new technologies.

Overall, the integration of ECE and CS expertise can lead to significant advances in applied AI, by developing more efficient and powerful systems, optimizing them for specific applications, and better understanding the interactions between hardware and software in these systems.