Artificial Intelligence

There’s been quite the buzz around artificial intelligence since AI Art and Language models sprung into the public eye last year, and the last few years have seen several innovations and advancements, but AI has rooted itself in more areas of our lives than we may realise.

The Q&A through-out this post was done through OpenAI’s Chat-GPT3 language model.

What is Artificial Intelligence?

To fully appreciate the amazing advancements in AI as of late, we need to understand what Artificial Intelligence – and by extension Machine Learning, Deep Learning and a Neural Network – is.

Artificial Intelligence is an all-encompassing term for the theory and development of computer systems that are able to perform tasks requiring human intelligence – such as visual perception, speech recognition and decision-making.  AI is accomplished by studying the patterns of the human brain and analysing cognitive processes in order to develop intelligent software and systems.

Machine Learning, Deep Learning and Neural Networks are all lower-level implementations of AI.

Types of Machine Learning
Types of Machine Learning

Machine Learning gives the AI the ability to learn, using algorithms to discover patterns and generate insights from the data they process.

Deep learning, which is a subcategory of machine learning, provides AI with the ability to mimic a human brain’s neural network – making sense of patterns, noise and sources of confusion in data.

Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a further subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.

It’s the combination of the above concepts that allow for comprehensive and impressive AI art and language models to be developed, that in turn can be used within software applications and learning applications.

Applications of AI

AI is used everywhere – from search engines, to algorithms that feed you information on social media platforms, to high-impact domains like health and sustainability.

It’s impossible to note, in a single blog post, every area where AI proves to be valuable, so I’ll use Display Technologies as an example.

When asked how much time is reduced when AI is implemented in the research and development process at Samsung, Yong-jo Kim (Samsung Display’s head of CAE) said:

“Let’s take molecular design: If it originally took 15 days for an engineer to design 100 molecular structures of organic materials and derive characteristic values, now it takes only 30 seconds using AI. That’s an efficiency increase of approximately 43,000 times. The same phenomenon occurs in the field of panel circuit design: What AI can accomplish within a given period is unimaginable. If we previously struggled to get 100 design mappings validated, AI-powered by a 64-core CPU server produces more than 640,000 designs in a single day.”

It’s easy to see how AI’s impact on all industries’ output quality, growth and productivity can be massive.

AI Resolution Upscaling
An example of AI Resolution Upscaling

AI is being used to screen medical scans, high-resolution 8K displays are now incorporating artificial intelligence to enhance content resolution, and AI is used to greatly improve touch screen accuracy. All modern display technologies are being researched and designed with the help of AI.

A great example of AI improving quality of life is in the Health Sector. Recent improvements in X-Ray Technology saw massive jumps in scan resolution. The higher resolution X-Rays allow for a more expansive use of AI to screen scans. Some processes in this sector are already fully autonomous.

The Future of AI

In 2021, roughly 25 percent of patents submitted were AI-related and this number has grown last year (exact numbers not yet released).  In the same year Elon Musk donated $10 million to fund ongoing research at the non-profit research company OpenAI – where most of the recent advances in AI were accomplished. There is no doubt that AI will continue to see exponential growth in all major industries.

However – these advancements come with important social and ethical questions that can’t be ignored.  What limitations should be applied to AI applications and research, if any?  How do we protect ourselves from rogue AI or AI that believe the best course of action for their specific goal is the extinction of humanity? We don’t have comprehensive answers to these questions yet.

I'm sorry, Dave. I'm afraid I can't do that.
I’m sorry, Dave. I’m afraid I can’t do that

A question that is going to force itself into the forefront soon though, is how advances in AI will impact job availability?

AI expert Kai-Fu Lee touched on this idea during a lecture at Northwestern University in August last year:

“The bottom 90 percent, especially the bottom 50 percent of the world in terms of income or education, will be badly hurt with job displacement … The simple question to ask is, ‘How routine is a job?’ And that is how likely [it is] a job will be replaced by AI, because AI can – within the routine task – learn to optimise itself. And the more quantitative, the more objective the job is—separating things into bins, washing dishes, picking fruits and answering customer service calls—those are very much scripted tasks that are repetitive and routine in nature. In the matter of five, 10 or 15 years, they will be displaced by AI.”

In short, AI won’t be taking over the world soon, but we need to accept that AI will push society into different lines of work.  To not be caught off guard by this we need to keep asking these questions in order to adapt, and invest in education to expand our knowledge and skills for the future workplace.

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