The potential of AI – are we at a time where creativity should be passed to machines?

The potential of AI – are we at a time where creativity should be passed to machines?

Rachel Roumeliotis, Vice President of Data and AI at O’Reilly, discusses the true potential of AI and why, despite being a long way off from reaching the capabilities of truly autonomous AI, enterprises must keep investing to continue along the path to success.

It is fair to say that Artificial Intelligence (AI) is everywhere. Newspapers and magazines are littered with articles about the latest advancements and new projects being launched because of AI and Machine Learning (ML) technology. In the last year it seems like all of the necessary ingredients – powerful, affordable computer technologies, advanced algorithms and the huge amounts of data required – have come together. We’re even at the point of mutual acceptance for this technology from consumers, businesses and regulators alike. It’s been speculated that over the next few decades, AI could be the biggest commercial driver for companies and even entire nations.

However, with any new technology, the adoption must be thoughtful both in how it is designed and how it is used. Organisations also need to make sure they have the people to manage it, which can often be an afterthought in the rush to achieve the promised benefits.

What’s next?

It’s evident that Artificial Intelligence has graduated. It is now the student, teacher and special advisor to millions of people across the world. But, have we reached the inflexion point where AI is able to be more intelligent and creative than humans? The short answer is no, not yet.

We’re at the point when AI can truly learn and gradually get smarter. It can understand both labelled and unlabelled data and is starting to train itself, much like a young toddler learning to walk. However, it still requires significant human intervention when it first starts its development process and often times to course correct it.

Can AI be awarded patents?

In the UK, there have been a few cases of inventors putting forward patent applications that name AI as one of the co-inventors. However, although AI is certainly speeding up innovation and enabling greater human creativity, AI is not yet attributed with patents on its own. The UK’s Intellectual Property Office which set out UK patent law has stated that all applications must name a human as the inventor, or at least one of the inventors. The reason for this is down to the origins of an AI system and how it was created.

Fundamentally, any form of AI was created by an individual or more likely, a team of individuals, who set out its abilities, its development processes and its overall goal. Although these AI systems can be retrofitted to do different activities and thanks to reinforcement algorithms AI can digest new data and assimilate it. The fundamental limitations of an AI system are still set by the human. We don’t see autonomous cars starting up and driving off on their own just yet. Likewise, we don’t leave AI programs connected to power and come back after the weekend to find it has solved a problem it wasn’t first asked to contemplate.

What AI-assisted programs are enabling is for innovators to expedite research and problem-solve. With AI able to produce hundreds of lines of code in seconds, which can be tested, tweaked and put into production in mere minutes, innovators and creative thinkers can move at speeds never before seen. Likewise, thanks to low-code solutions, these highly creative individuals do not need degrees in data science and AI to make use of this technology. They’re able to plug and play using simple and clear instructions and still be assured of high-quality end products that can be integrated into established programs. This has been a game-changer in terms of the speed at which patents can be brought forward.

Tackling the remaining hurdles

Considerable hurdles that keep AI from reaching critical mass remain. To ensure that AI is represented by the masses and be used in a safe way, organisations need to adopt certain best practices.

One of these is making sure technologists who build AI models reflect the broader population. Both from a dataset and developer perspective, this can be difficult, especially in the technology’s infancy. This means it is vital developers are aware of the issues relevant to the diverse set of users expected to interact with these systems. If we want to create AI technologies that work for everyone – they need to be representative of all races and genders. It’s essential that organisations invest the necessary time and resources to get this right.

Maintaining AI investments

There has been hype around AI and the potential of the technology for what feels like decades. And yes, thanks to the proliferation of technology, what was once deemed Sci-Fi just five years ago, has now become reality in a matter of months. Just think of the Metaverse, which has gone from a concept and something that belonged in films like the Matrix to large enterprises buying virtual real estate in anticipation of the future. However, the percentage of organisations reporting AI applications in production, that is, those with revenue-bearing AI products in production, has remained constant over the last two years, at 26%. This would indicate AI has passed to the next stage of the hype cycle.

It is now time for AI to continue to develop at a slower pace and deliver products of real value – this could be significant cost savings, increased productivity for businesses, or building applications that generate real value for human lives. To achieve this, enterprises must not stop funding AI research.

Although we’re a long way from reaching the capabilities of truly autonomous AI – and likewise we’ve already spent a lot to get to where we are today – enterprises must keep investing to continue along the development bell curve AI is on. We’re a long way away from reaching the pinnacle. We are not going to see an AI winter, or a dark age of innovation for a long time, but we mustn’t lose sight of where AI has come from, not what its potential is.

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