New Answers Need New Questions

‘Computers are useless. They only give you answers.’—Picasso

By Monica Livingston
An AWS/Intel thought leader on the mental shift required to get the most out of AI and ML, and the new questions you can ask with/of data.

Business leaders, even CIOs, are locked into old ways of thinking, shaped by what their systems are capable of. In this blog post, we’ll help readers ‘think the unthinkable’ to get the most out of AI-enabled data.

Unthinkable thoughts

I love The Beatles. And like any rock band you care to mention, they are a direct result of electricity: a second- or third-order effect that no-one could’ve imagined as we first harnessed electricity. They would, like so many unintended consequences, have been unimaginable to people at the time. But no electricity means no Sgt Pepper’s. And that would be unthinkable!
When I look at where we are with AI, I’m struck by how often the word ‘unthinkable’ comes up. From the second-order effects we can’t imagine, to the patterns and connections we can’t see (which are revealed to us by AI/ML), and even as far as innovations like object detection in physical implants might one day reliably let people see for the first time. The whole world of AI is (very excitingly, in my opinion) filled with things that are beyond our cognition—and yet they’re too important not to think about.
woman typing notes on laptop

Pivotal moment

People often ask me how big a deal AI is, really. I tell them we’re at a truly pivotal moment—as big as the car, electricity, or the internet.
Like electricity, we can see how AI can make existing tasks much easier: When electricity first entered our homes, everyone could see it meant a simpler, quicker way to light a room. But no-one envisaged social media. Or rock music. Or autonomous cars. And that’s where we are with AI. The second and third-order effects are mostly unimaginable, and they will be impactful.

Here, there and everywhere

The funny thing is that AI—supposedly a big new force in tech—is anything but new. When I was at college in [redacted], we had machine learning and neural networks—though admittedly a lot less complex than we have today. What has changed is availability of compute power, which now allows for models with millions of layers, and trillions of hyperparameters. Add on increasing global collaboration, free online learning (both turbo-charged by real-time AI translation, which has had a transformative effect in widening participation around the world) and open-source tools/code, and we find ourselves at a unique moment in time. The processing has caught up with the theory, and things are getting very exciting.

AI is already all around us. The biggest shock I see when talking to customers interested in exploring AI is just how much it already inhabits daily life. If you’re using Microsoft Office, Uber, an airline, or a bank you are already using AI-based capabilities..

Maybe that’s because we don’t call it ‘AI’ when we encounter it as end users. We call it ‘avoid traffic?’, or ‘background blur’, or ‘recommended for you’. But those the algorithms behind consumer AI power other uses. The shape recognition that enables background blurring on a smartphone photo app is the same tool that recognises a tumour during ML-enabled surgery.

That photo background isn’t the only thing that’s blurred. AI is increasingly part of our personal and professional lives.

Businesses built on AI

Some businesses are transformed by AI, while some are built on it. Uber, for example. Or StitchFix, a pioneer in online personal styling that has leveraged machine learning to disrupt fashion retail in several ways. StitchFix can recommend outfits based on stated preferences, as well as creating recommendations based on social media posts, previous purchases and returns, and buying patterns. A (human) personal stylist then makes the final selection: a great example of hybrid AI enhancing a human’s work, not replacing it.

But they have many other uses for AI: what to stock in each warehouse, which warehouses to associate with specific customers, and even which stylists to assign to who. All that means StitchFix now has a significant amount of data on style preferences and buying history, sliced by demographics and behaviours. Which means they can now mine that data, understand trends and become a source of insight for design houses and retailers. It's a data company as much as a fashion outlet.

Other industries and processes exist without AI, but can be hugely enhanced. Pharmaceutical drug discovery, for example. AIs can check compound interactions at a scale and consistency that humans can’t get near. You could put 1,000 scientists in a room, working in parallel, and they still wouldn’t get close to what AI can manage—we’re talking billions of checks in hours. But this doesn’t mean replacing scientists – this AI analysis means human researchers are investigating compounds already shown to have a high chance of effective and efficient outcomes.

Doing what we can’t

So what about AI and your business? I always tell people to avoid going in thinking ‘AI at all costs’, because maybe simple analytics are right for you. The best place to start is: ‘This is what my business does, this is the outcome we need, can AI help?

As well as automating mundane tasks (the AI equivalent of lighting rooms more easily), look for complexity: Where is it difficult to spot trends and patterns, and even harder to explain them? If they can’t be easily coded, or easily detected by people, then AI could be the answer.

So ask yourself, ‘what data do we have? Can we do something new with it?’ Perhaps you have massive amounts of customer data you could be slicing in new ways—across preferences, geographies and more—that could be informing everything from forecasting and logistics to product development and advertising.

Done right, AI can help you think previously unthinkable thoughts about your business. Efficiency and automation are great, but we’re talking about finding a new competitive edge and even, as in the case of StitchFix, a new business model.


There’s a lot of uncertainty and even fear about AI. And I totally get that. People worry about job losses—though I’m a firm believer in AI as the biggest assistive tool at our disposal, and so a net positive. Jobs will shift, but then they always have. People retrain, and new opportunities emerge.  There are no more job ads for streetlamp lighters, but there are plenty for electricians.

Beautiful designers working

Some uses of AI are so disruptive they jar us at first, especially when they involve human safety or care. Autonomous cars, or robo-surgeons, for example (even though we’re fine with autopilot when we fly. Watching your eldest child get their licence and drive off for the first time is terrifying—but a week later you’re asking them to give their siblings a lift. It’s like that with AI. As a rule of thumb, the more AI simplifies our life, the quicker we accept it.

Despite all that, something about robo-surgery still makes me uneasy. Even though a robotic surgeon can contain the expertise of 1,000 doctors, can’t get distracted, or come in tired after a bad night’s sleep. It still freaks me out a little.

And you know what? That’s fine. I’m human. Which means I’m impulsive, sometimes irrational and maybe a little superstitious at times. They way my brain works is (currently, anyway) ‘unthinkable’ to AI. AIs are rational, consistent, and definitely don’t love the Beatles like I do.

If we thought like AI, we wouldn’t have to invent it.

Monica Livingston

Sr Director, Artificial Intelligence and Graphics Sales

Monica Livingston leads AI and Graphics technical enablement at Intel Corporation driving implementation and deployment of AI solutions for end customers, as well enabling workloads for data center graphics.

She has previously held roles in large account management, field applications engineering and hardware design. She is passionate about tech, accessible STEM education and career development. Monica earned a Masters of Engineering in Electrical Engineering from the University of Florida and an MBA from the University of North Carolina at Chapel Hill.