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The future of artificial intelligence — explained by a robot

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ChatGPT quickly garnered attention for its detailed responses and articulate answers across many domains of knowledge (Screenshot via YouTube)

While Paul Budde is an expert in the field of technology, this week he's letting artificial intelligence speak for itself to discuss the current state of AI.

*Also listen to the audio version of this article on Spotify HERE.

WE HAVE TALKED about artificial intelligence (AI) in previous articles. Recently, a new AI service was launched called ChatGPT. I have used it now several times and I am amazed at the accuracy of this AI tool.

ChatGPT is a large language model developed by OpenAI. It is a variant of the Generative Pre-trained Transformer (GPT) model, which uses deep learning techniques to generate human-like text. The model is trained on a massive dataset of text from the internet, allowing it to generate a wide range of text on various topics.

ChatGPT is designed to generate conversational text and is commonly used in applications such as chatbots, language translation and text summarisation. It is also used to generate creative writing and even used in programming to generate code.

The following article is not written by me.

I asked ChatGPT the following question:

‘Write a 1,000-word essay on where the development of AI stands at this moment in time.’

Here is its response, of which I have not changed a single word:

Artificial intelligence (AI) is a rapidly evolving field that has seen significant advancements in recent years. At present, the development of AI stands at a point where the technology is being integrated into a wide range of industries and applications, with the potential to greatly improve efficiency and productivity.

One of the most significant areas of development in AI is in the field of machine learning (ML). This technology allows machines to learn and improve their performance without being explicitly programmed. ML algorithms are used in a variety of applications, including image and speech recognition, natural language processing (NLP) and predictive analytics. These algorithms have seen significant improvements in recent years, thanks to the availability of large amounts of data and advances in computing power.

Deep learning, a subfield of ML, has been particularly successful in recent years. This technology uses artificial neural networks (ANNs) to model complex patterns in data. ANNs are made up of layers of interconnected nodes, which allow them to learn and improve their performance through training. Deep learning has been used to achieve state-of-the-art performance in image and speech recognition, as well as NLP tasks such as machine translation and question-answering.

Another area of development in AI is in the field of robotics and autonomous systems. These systems are designed to perform tasks without human intervention and are being used in a variety of industries, including manufacturing, transportation and healthcare. Advancements in sensor technology, computer vision and machine learning have enabled robots to better understand and interact with their environment, leading to improvements in their performance and reliability.

In recent years, there has also been a significant increase in the use of AI in the healthcare industry. AI-powered systems are being used to analyse medical images and improve diagnostic accuracy, as well as to assist in the development of new drugs. AI is also being used to improve the efficiency of clinical trials and to analyse large amounts of patient data to identify patterns and improve treatment outcomes.

Despite the significant advancements that have been made in AI, there are still many challenges that need to be addressed. One of the biggest challenges is the lack of interpretability of many AI models, which makes it difficult to understand how they make decisions. This is particularly important in applications where safety is a concern, such as in healthcare and autonomous systems. Another challenge is the lack of diversity in the data used to train AI models, which can lead to biased or inaccurate results.

In conclusion, the development of AI is at a point where the technology is being integrated into a wide range of industries and applications, with the potential to greatly improve efficiency and productivity. Advancements in machine learning, particularly deep learning, robotics and autonomous systems have been particularly successful. However, there are still many challenges that need to be addressed, including the lack of interpretability and the lack of diversity in the data used to train AI models. It is important for ongoing research and development to address these challenges to ensure the continued success and responsible use of AI.

Back to me.

AI is unstoppable but there are serious issues with its use. Google and Microsoft have already mentioned they have similar products ready, but they seem to be hesitant because of its unpredictable implications. With the launch and success of ChatGPT, I am sure they won’t wait long to introduce their services, purely driven by commercial incentives.

It is just mindboggling to think about five or ten years from now, how and what can we trust. If we are not careful, it can easily further undermine our societies. As societies (governments, regulators) we will have to start some serious discussions about AI and not leave it to the market to sort things out, but by then, it might be too late.

*This article is also available on audio here:

Paul Budde is an Independent Australia columnist and managing director of Paul Budde Consulting, an independent telecommunications research and consultancy organisation. You can follow Paul on Twitter @PaulBudde.

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