Is the ChatGPT the new calculator

The image above is produced with another OpenAI tool DALL-E https://openai.com/product/dall-e-2 

OpenAI's chatGPT is a conversational piece of software that can also produce answers (or tries to) based on what you ask. So if you asked it to "Write a descriptive text on the role of chaptGPT in the workplaces", it will. An example of this, using, at the time of writing the latest version using GPT4 via https://poe.com/gpt-4 is shown below 

It is good and so has rapidly produced debate on its impact. But is this debate new?

When calculators became affordable enough that it become reasonable to factor into assessed work at school and universities the was debate that it might be cheating to use a calculator. We are perhaps at a similar stage now with chatGPT is it cheating to use it. There are perhaps lessons to be learnt from introducing calculators to assessments and in the workplace.

It is cheating?

Is it, or is it cheating for what is currently being done? As an example if you are testing someones ability to do mental arithmetic then a calculator would appropriately be seen as cheating and so you wouldn't allow a calculator. The first question though has to be what are you assessing or in the workplace needing the person to do though, if they are applying techniques to solve a problem then maybe it is not cheating - the calculator is an aid. Is that how we should look at chatGPT as an aid to us, possibly enabling us to focus on the task? Maybe. This view has been by a number of people.

In the Bloomsberg article (Aldrick, 2023) this argument was put forward and it aligns with the view of others (e.g. Feretzakis et al, 2020) that Artificial Intelligence techniques can be used to complement 'human' work. The jury is out and will be for a very long-time on this.

Are there opportunities here?

So let us take the lesson learned from calculators in education, what happened, the assessments changed with arguably deeper consideration of what was need to be assessed and why. Could the same happen with chatGPT I think yes, these chatbots can produce some very good starting points for an assessment on an essay and chatGPT can produce simple programs. So perhaps assessments need to change be more personalised or make use of it with assignments? Though not specifically focussed on chatGPT but looks at authentic assessments might this help?

What about work. This is always a controversial one but some jobs may change but new ones arise and other opportunities. Might it change further how articles are produced?

In the image below some of the text from this blog was put through the more widely used chatGPT (using GPT3). I think it lost a few of the points I wanted.

References

Aldrick, P. (2023) ‘ChatGPT Will Be the Calculator for Writing , Top Economist Says’, Bloomberg, pp. 1–2. Available at: https://www.bloomberg.com/news/articles/2023-01-18/chatgpt-will-be-the-calculator-for-writing-top-economist-says.

Feretzakis, Georgios, Evangelos Loupelis, Aikaterini Sakagianni, Dimitris Kalles, Maria Martsoukou, Malvina Lada, Nikoletta Skarmoutsou, Constantinos Christopoulos, Konstantinos Valakis, Aikaterini Velentza, Stavroula Petropoulou, Sophia Michelidou, and Konstantinos Alexiou. 2020. "Using Machine Learning Techniques to Aid Empirical Antibiotic Therapy Decisions in the Intensive Care Unit of a General Hospital in Greece" Antibiotics 9, no. 2: 50. https://doi.org/10.3390/antibiotics9020050

All opinions in this blog are the Author's and should not in any way be seen as reflecting the views of any organisation the Author has any association with. Twitter @scottturneruon

Optimizing artificial neural networks using LevyChaotic mapping on Wolf Pack optimization algorithm for detect driving sleepiness

 

Optimizing artificial neural networks using LevyChaotic mapping on Wolf Pack optimization algorithm for detect driving sleepiness

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 22, 3, 2022, 128-136. doi: https://doi.org/10.33103/uot.ijccce.22.3.12

Abstract

Artificial Neural Networks (ANNs) are utilized to solve a variety of problems in many domains. In this type of network, training and selecting parameters that define networks architecture play an important role in enhancing the accuracy of the network's output; Therefore, Prior to training, those parameters must be optimized. Grey Wolf Optimizer (GWO) has been considered one of the efficient developed approaches in the Swarm Intelligence area that is used to solve real-world optimization problems. However, GWO still faces a problem of the slump in local optimums in some places due to insufficient diversity.

This paper proposes a novel algorithm Levy Flight- Chaotic Chen mapping on Wolf Pack Algorithm in Neural Network. It efficiently exploits the search regions to detect driving sleepiness and balance the exploration and exploitation operators, which are considered implied features of any stochastic search algorithm. Due to the lack of dataset availability, a dataset of 15 participants has been collected from scratch to evaluate the proposed algorithm's performance. The results show that the proposed algorithm achieves an accuracy of 99.3%.

Cited as 

Sarah Saadoon Jasim; Alia Karim Abdul Hassan; Scott Turner. "Optimizing Artificial Neural Networks Using Levy- Chaotic Mapping on Wolf Pack Optimization Algorithm for Detect Driving Sleepiness". IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 22, 3, 2022, 128-136. doi: https://doi.org/10.33103/uot.ijccce.22.3.12

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An intelligent routing approach for multimedia traffic transmission over SDN

 

An intelligent routing approach for multimedia traffic transmission over SDN

Turner, S., Al Jameel, M., Kanakis, T., Al-Sherbaz, A. and Bhaya, W.

Abstract

Nowadays, multimedia applications such as video streaming services have become significantly popular, especially with the rapid growth of users, various devices, and the increased availability and diversity of these services over the internet. In this case, service providers and network administrators have difficulties ensuring end-user satisfaction because the traffic generated by such services is more exposed to multiple network quality of service impairments, including bandwidth, delay, jitter, and loss ratio. This paper proposes an intelligent-based multimedia traffic routing framework that exploits the integration of a reinforcement learning technique with software-defined networking to explore, learn and find potential routes for video streaming traffic. Simulation results through a realistic network and under various traffic loads demonstrate the proposed scheme's effectiveness in providing a better end-user viewing quality, higher throughput and lower video quality switches when compared to the existing techniques.

Conference

15th International Conference on the Developments in eSystems Engineering (DeSE2022)

Official URL

https://dese.org.uk/dese-2022/

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