Its definition, role and impact

Not long ago, artificial intelligence (AI) only existed in science fiction. Walking, talking robots that acted like us (albeit devoid of emotions), or super-powerful computers that may or may not have our best interests at heart.

Today, artificial intelligence is very much a reality and an increasingly integral part of everyday life. But how did we get here? Where are we going; And what exactly do we mean when we talk about the importance of artificial intelligence in 2024?

I will address some of these questions here as I consider how our understanding of artificial intelligence continues to evolve. This is crucial to understand as artificial intelligence touches more areas of our lives and affects society in new ways.

How did we get here?

The concept of artificial intelligence has been around for much longer than technology. The ancient Greek philosophers theorized about “thinking machines” and saw the human brain as a complex mechanism that we might one day be able to recreate or simulate.

However, the idea was largely confined to academia and entertainment until computers powerful enough for problem solving and statistical analysis appeared in the mid-twentieth century.

From the 1950s to the 1980s, progress continued in the field of machine learning, as it became apparent that teaching machines to find their own solutions using data was more effective than giving them clear instructions when it came to persuading them to perform complex tasks. Research often focused on the artificial neural network model, which attempted to mimic some of the learning mechanisms of the human brain.

Great leaps forward were made in the late 2000s and early 2010s with the development of deep learning and deep neural networks. As computers became more and more powerful, it became possible to build much larger neural networks, allowing computers to perform more complex logic and decision making. This has led to the emergence of technologies such as computer vision and natural language processing.

The 2010s were his era Big data – where, thanks to everyone and everything being online and connected, the amount of data in the world has skyrocketed. At this point, it became apparent that more data and more processing power lead to neural networks and algorithms that are getting better and better at doing their jobs.

And perhaps the last pivotal moment (so far) was the emergence of genetic artificial intelligence. This can be seen as both social and technological, with tools like ChatGPT and Dall-E enabling anyone to work with AI and use it in everyday life.

AI In 2024

AI in 2024 is at a similar stage of its evolution as the Internet was just when it was starting to become mainstream – say the mid to late 1990s. Most of us know what it is now and understand that in the future it will change almost everything.

What we have today is clearly the culmination of the technological leaps we covered in the previous section. But thanks to these leaps, we have reached the point where we are beginning to see deep and far-reaching social and cultural changes.

Today, artificial intelligence is becoming more accessible and user-friendly. But it’s more than that – it’s also making almost every other aspect of technology more accessible, breaking down barriers to communication between humans and machines.

Intuitive natural language interfaces and image recognition technology mean that almost everyone will find it easier to get machines to do what they want. Lack of technical know-how will no longer be a barrier for those with ideas about how technology can change the world for the better.

Today, this is often described as the “democratization” of the power of technology – a hugely important aspect of the role of AI in 2024.

We continue to see artificial intelligence integrated into more and more tools we use in our daily lives, from apps to our home appliances and the cars we drive.

Another key word today is “increase”. This is related to the trend of adopting AI technology in order to improve our efficiency routine tasksallowing us to devote more time and human skills to more creative, complex tasks.

And just as importantly, when we talk about AI today, the focus is likely to be on the ethical questions it forces us to face.

Hot topics of debate right now revolve around what is acceptable when it comes to issues such as privacy, AI bias, transparency and intellectual property rights. And the decisions we make about these issues today will likely have long-lasting consequences for how this world-changing technology is used tomorrow.

Here and now, in 2024, the conversation around artificial intelligence is just as likely to revolve around regulatory frameworks, liability and security as it is around algorithms, neural networks and transformer models.

Where do we go from here?

As we have explored, the immediate challenges surrounding artificial intelligence are likely to be ethical, regulatory, cultural and social. But there will inevitably be more technological breakthroughs down the line – and these will likely turn everything upside down once again!

Going back to the Internet analogy, I think AI 10 or 20 years from now will be as unrecognizable to us today as the early Internet of dial-up modems and Netscape Navigator is to a kid born in the 2000s who grew up on a smartphone and TikTok.

It will become smarter, faster and more integrated into our lives in almost every way imaginable. In part, this will be because computers will continue to get faster and more powerful (taking into account emerging technologies such as quantum computing).

But it will also be because solving today’s problems around social acceptance will lead to greater confidence in technology’s ability to improve our lives safely and ethically.

For many of those developing cutting-edge AI today, the goal is to bring us closer to achieving general (or “strong”) AI – capable of learning to perform any task as a human can.

In 2024, however, it becomes clear that to get there responsibly, we must first solve the problems we face today. And unlike the problems of the previous decade, these aren’t likely to be solved simply by throwing more processing power and data at them.