AI is the greatest threat to human creativity  
biggest hope for humanity’s evolving creativity

The dark side of AI

Algorithms have a hold on our dopamine responses. We’re slave to the algorithms. Artificial intelligence is taking jobs away from people. We’re headed for a socially divided, dystopian future where we’ll live in pods with our minds neurally linked to a metaverse that feeds off our serotonin. This doesn’t sound great for humanity, does it?

The pervasiveness of algorithms in our lives has led to concerns about how they are affecting our brains. Yes, we can become over-reliant on AI to help us make simple decisions, and yes, attention-addiction algorithms can be a big problem for mental wellbeing and productivity. There seems to be a mass anxiety about AI as an adversary to our human development, one that makes us dumber.

The light side of AI

Or… hear me out: AI is going to make us smarter – challenging our brains to think more creatively, deeply and intuitively. And this won’t happen in a survival-defence against AI, but rather through co-evolution. It’s already happening.

AI will have far greater processing capacity than the human brain, and already far exceeds human abilities in specialist tasks. In many ways, it can mimic activity that we see as intelligent and even creative.

Creativity has new inspiration

Take, for example, the various AI artworks created by Midjourney and DALL-E that are filling our LinkedIn feeds. Some people have signalled the end of the creative industry and the visual craft. Others see this as yet another industry evolution.

The process of creating professional-quality AI art requires a complex distillation of one’s imagination and a labyrinth of prompts to articulate a vision. It’s a hyper-version of Photoshop that will replace the need for the technical craft, while requiring a far greater conceptual articulation of the end visual direction.

Anyone who’s tried it will tell you that the process is an art form. What’s more, a new specialist career of AI artist is being created and universities are offering courses in AI art.

Another example is this very next paragraph. AI wrote it.

I’ve been spending my time thinking about how to keep your attention, while further explaining the points of my thinking. I wrote the original paragraph, but my sentences were partially constructed with grammar issues all over the place. So, I used our AI tool to condense my point, while saving my partially dyslexic brain from having to edit it a hundred times.

Rethinking our ways of thinking

If we consider that creativity and imagination is fundamentally about making connections between indirect ideas, AI is probably our best bet to show us more connections, reveal new patterns of thinking between those connections and help us arrive at ideas that may not be immediately obvious.

If you’re not convinced that AI is helping our grey matter, consider that machine learning can show us patterns we’ve never seen before, thereby teaching us new ways to think about problems. Just one example of this is precision medicine, the treatment of patients specifically based on their biomarkers, where hidden patterns in our DNA are being revealed to provide physicians with pathways to patient-specific treatment.

Company leaders will see new patterns in business performance, rewrite the old rules of enterprise and help us rethink work performance. Our thinking about people, culture and bottom-line performance will evolve.

Optimal being

AI can reveal the optimal point in a complex system, and thereby show us humans the benchmarks for our own performance. This will include AI helping to develop our thinking about what makes us happier, such as new ways to think about exercise, complex balances in our gut microbiome, and finding the ideal balance between stress and professional performance. It will help us see new patterns and embed the behaviours that make us happier.

And while we will have more time freed up from the tedium, AI will help us amplify our intelligence – if we choose to use it. The way people and machines interact will change – there will be many new types of human-machine symbiosis. Those who understand, learn and adapt their thinking to take advantage of this will enjoy success in their chosen field.

About the author: Brad Dessington was the Chief Strategist and Managing Director of September AI Labs, he lead product strategy, innovation and architecture.

Cancer research has taken a leap forward in quickly identifying the cancer cells that are responsible for the spread of cancer around the body. 

Our team at September AI Labs have joined forces with Edith Cowan University (ECU) Melanoma researchers to develop a way to use artificial intelligence to accurately identify cancer cells circulating in the blood.

Cancer spreads around the body when tumour cells shed from the primary tumour and travel through the blood to form secondary tumours (metastases) in other organs.

“By detecting and counting these circulating tumour cells (CTCs), clinicians and doctors can better understand what stage a cancer is at and predict the likelihood of a patient’s responsiveness to different treatments, thus improving patient outcomes” said ECU’s Associate Professor Elin Gray.

AI reduces detection time from hours to seconds

The CTCs are incredibly difficult to spot among thousands of other cells and matter in blood, they are very rare much like finding a needle in a haystack.

Within one millilitre of blood, there is often less than ten cancer cells amongst one billion red cells and one million white blood cells.

“Until now, it has taken a trained technician few hours per patient sample to manually filter different characteristics of cells using traditional imaging techniques,” Professor Gray said. “This AI technology has reduced this process down to a few minutes per patient.”

Trained in deep learning neural networks

Using more than 4,000 images from the Melanoma Research Group at ECU, the September AI team developed a machine learning model that was trained to identify circulating tumour cells with a 97 per cent accuracy.

September AI Labs Managing Director Brad Dessington said the detection of CTCs was a particularly complex challenge for machine learning to achieve such high accuracy.

“CTCs are organic biological shapes, no one is the same as each one is different in size, shape and presents in random positions among healthy cells in the blood,” Mr Dessington said.

“This was not just a matter of spotting the molecular signatures, the model had to be able to learn and understand complex images, to do it as well as a human, but far faster with robust convolutional neural networks and amped up computer power.” Says Fedja Hadzic, Chief Data Scientist at September AI.

ECU has entered into a partnership with September AI Labs to ramp up artificial intelligence and machine learning accessibility for research across the University.

Through this partnership, the CTC identification technology will also be broadened to investigate a range of other cancers including lung, breast, pancreatic and prostate.