Bernard Marr - Tech Trends in Practice

MIT researchers have developed an AI model that can predict the development of breast cancer up to five years in advance. 11Crucially, the system works as well for black and white patients, whereas similar projects in the past have often been based overwhelmingly on white patients.

Infervision’s image recognition technology uses AI to look for signs of lung cancer in patient scans. The technology is already in use in healthcare settings across China. 12

Content platforms like Netflix and Spotify are built on AI – they use AI to understand what viewers most want to watch or listen to, make personalized recommendations, and (in Netflix’s case) create new content based on what it knows users enjoy. Here are a few other examples of AI infiltrating our hobbies and downtime.

Chinese search engine Sogou has confirmed it’s creating an AI that can read novels aloud, simulating the voice of their authors 13(in a similar way to how deepfakes can create realistic audio and video content of people in the public eye). This could revolutionize audiobooks, particularly for self-published authors who perhaps don’t have the means to create their own audiobooks.

Sony has created an AI that can produce drumbeats for songs. Called AI DrumNet, the system was trained using hundreds of songs, and can now produce its own basic drumbeats to match other instruments on a track. 14

MIT researchers have taught AI how to reverse engineer pizza. After looking at a picture of a pizza, the AI can identify its toppings, and then tell you how to make it. 15Why do this, you might be wondering? In theory, this technology could be used to analyze any photo of food and produce a suitable recipe. So if you want to recreate an amazing restaurant meal at home, in a few years’ time there might be an app for that!

In 2019, Microsoft announced it was plowing $1 billion into AI research lab OpenAI– which was founded by, among others, Elon Musk. 16What’s behind such a big investment? OpenAI is dedicated to creating something called artificial general intelligence (AGI), widely considered to be the “holy grail” of AI.

While AI can do some incredible things when it comes to “general intelligence,” AI lags way behind the human brain. In other words, AI is great at learning to do specific things, but AI systems can’t just apply that knowledge to other tasks in the way humans can. This is the goal of AGI – to create an AI system that’s as generally intelligent and flexible as the human brain. It’s not been done yet – in fact, we don’t know if AGI is even possible – but Microsoft’s investment shows it’s certainly a serious goal.

I opened this chapter with a Stephen Hawking quote, “Success in creating AI would be the biggest event in human history.” Hawking immediately followed that up with, “Unfortunately, it might also be the last, unless we learn how to avoid the risks.”

AI isn’t without its challenges and risks. For one thing, there are potentially huge risks for society and human life as we know it (particularly when you consider some countries are racing to develop AI-enabled autonomous weapons). But let’s focus on the key challenges that everyday businesses will have to overcome if they’re to deploy AI successfully.

There will no doubt be regulatory hurdles to negotiate as regulators begin (quite rightly and belatedly) to take a greater interest in the application of AI. Until now, some of the early adopters of AI have played a bit fast and loose with the technology (Facebook, for example, is facing legal action over its use of facial recognition technology for auto-tagging photos, without gaining user consent). 17That sort of behavior can’t continue, and business leaders will have to take an ethical, responsible approach to AI.

Part of using AI ethically means making sure you respect individuals’ privacy, gain consent to use their data for AI applications, and make it clear how you are using their data. Again, this is where some big players have fallen short in the past. Amazon, for example, faced consumer outrage over the news that contractors were listening to people’s Alexa requests. Individuals could not be identified by their audio, and Amazon stressed the practice was necessary to help develop Alexa’s capabilities, but the fact remains that most users had no idea that anyone would ever hear their private audio. Amazon has since introduced a “no human review” option to its Alexa settings, which allows users to opt out of their audio being manually reviewed. 18

Remember I said that AI can now solve a Rubik’s Cube in just 1.2 seconds? Interestingly, the researchers who built the puzzle-solving AI can’t quite tell how the system did it. This is known as the “blackbox problem” – which means, to put it bluntly, we can’t always tell how very complex AI systems arrive at their decisions.

This raises some serious questions around accountability and trust. For example, if a doctor alters a patient’s treatment plan based on an AI prediction – when he or she has no idea how the system arrived at that prediction – then who is responsible if the AI turns out to be wrong? What’s more, under GDPR (the General Data Protection Regulation legislation brought in by the European Union), individuals have the right to obtain an explanation of how automated systems make decisions that affect them. 19But, with many AIs, we simply can’t explain how the system makes decisions.

New approaches and tools are currently being developed that help to better understand how AIs make decisions but many of these are still in their infancy.

Put simply, AI is only as good as the data it’s trained with. If that data is biased or unreliable, then the results will be biased or unreliable. For example, facial recognition technology was found to be generally better at identifying white males than women and people of color, because a leading data set used to train facial recognition systems was estimated to be more than 75% male and 80% white – something that programmers were able to correct by adding a more diverse range of faces to the training dataset. 20This means companies will need to ensure their data is as unbiased, inclusive, and representative as possible if they’re to get the best results from AI.

Finally, one area in which many companies will struggle is finding the right AI talent. There’s a shortage of people who can develop these complex AI systems – and what talent there is tends to be scooped up by the Googles and IBMs of this world. AI-as-a-service (AIaaS) could be part of the solution. AIaaS offerings from companies like IBM and Amazon allow companies to make use of AI tools, without having to invest in expensive infrastructure or new hires, which makes AI much more accessible to businesses of all shapes and sizes.

AI is going to revolutionize almost every facet of modern life, including business. Therefore, despite the challenges involved, businesses cannot afford to overlook the potential of AI. So how might you use AI in your business? Broadly speaking, companies are using AI to improve their business in three ways:

Developing smarter products (see Trends 2 and 3 for great examples of this).

Delivering smarter services (check out Trends 18 and 23 as examples of AI-driven services).

Making business process more intelligent (Trends 12, 13, and 17 for just a few examples of AI-enhanced business processes).