In 2022, the Centre for Data Ethics and Innovation carried out a
survey to find out how the British public felt about artificial intelligence (AI).
It found that the majority of people were unable to define AI – and that respondents overwhelmingly thought of it as scary and futuristic.
The report implies a link between knowledge and optimism. Thirty-two percent of respondents were uncomfortable with AI being used to power internet search engines – something which around
90% of the population regularly takes advantage of. Those who were more digitally literate were correspondingly more optimistic about the uses of AI.
AI is here to stay – and it's used in sectors ranging from healthcare to telecoms, from manufacturing to finance. It often goes hand in hand with machine learning (ML) – a subtly different beast.
Before we take a look at how AI and ML are used in cloud technologies, let's define what we're talking about.
What's the difference between AI and ML?
In the aviary of digital transformation, AI is a parrot and machine learning is a pigeon.
Why? Because AI's task is to mimic human intelligence – a bit like how you can teach your parrot to speak or sing.
But machine learning is where a machine is taught how to perform a task. In the First World War, pigeons were trained to deliver messages on the battlefield. They learnt how to mimic human activity – but not human intelligence.
Google gives the following example of ML. If you ask your Google Nest or Alexa, "How long is my commute today?", you're getting the device to perform a task that you would otherwise have to do yourself.
The machine can learn to identify patterns that help it perform tasks – in this case, by analysing live traffic data. But it can also learn from the success or failure of its performance to inform its next attempt.
Although they're distinct, AI and ML often work together. Combining human intelligence and labour-saving machinery has been around for millennia – but throwing AI into the mix is relatively new.
Together, they can analyse a range of data sources far wider than they could manage alone. They can make decisions faster and in the process increase operational efficiency. And they can integrate predictive analytics into business reporting – which has all sorts of ramifications for how businesses operate.
How are businesses using AI and ML in the cloud?
The sheer quantity of data in the cloud is staggering. At the time of writing, nearly half of all corporate data is up there, or in there – however you like to visualise it.
If you really want to feel strange, contemplate the fact that if data continues to grow exponentially, there'll be 100 zettabytes of data in the cloud by 2025. One zettabyte is equal to 7.5 trillion MP3s.
Seven-and-a-half trillion! It's a far cry from the early days of home computing when you could get 2.8 megabytes on a floppy disc.
Businesses and organisations want to put this data to work. In the world of eCommerce, for instance, data is analysed to manage inventory, forecast demand and dole out personalised offers, experiences and recommendations to customers.
The argument for AI and ML in this context is that they reduce the margin of human error substantially. Humans are all too fallible. They miss things. They draw false conclusions. They act irrationally. None of this is good for business.
Harnessing AI and ML in cloud technologies means, in theory, that a company is less likely to over-stock, under-stock or sell out – and it can achieve more sales through personalised recommendations.
But it's not just retailers who are exploring the world of AI and ML. It's also being used in healthcare.
How health services are using AI
NHS England is already using AI-driven technologies –
in its own words, "to help improve the patient experience, give the workforce a helping hand and help the system run more efficiently".
AI is used by NHS England for diagnostics, service efficiency, P4 medicine, remote monitoring and therapeutic care. In 2022,
155 workforce groups were using AI technologies – the majority in clinical radiology settings.
Other healthcare providers use AI and ML to analyse patient records and clinical notes and model outcomes.
But no matter how sophisticated AI becomes, most people want there to be "a human in the loop". This was a finding of NHS England's survey into public perceptions of AI.
In order to trust AI, the survey suggests, people want health and care professionals to make decisions based on AI's findings – not the other way around.
Cybersecurity
If you start the day checking Gmail and end it watching Netflix, you're spending a day bookended by cloud technologies. They're everywhere – and while they have a number of benefits, they present one big challenge.
That challenge is security. Remember in 2014, when three billion Yahoo! users had their details hacked? If a corporation like Yahoo! is vulnerable, what chance do the rest of us stand?
Since 2014, zero-trust security and two-factor authentication have become the norm – and cybersecurity solutions are leveraging AI and ML.
To take a simple example, Gmail uses ML to filter emails. Chances are you don't want to manually sift through your inbox, popping each message into its relevant folder. Gmail's machine has learnt to do it for you.
Now apply that to security. Instead of emails dropping into your inbox, hackers are firing off a fusillade of malicious attacks.
AI and ML can identify patterns and learn from them, so they can make predictions about where your cloud-based operations are most vulnerable.
Like a data analyst and a research assistant rolled into one superhuman package, AI and ML can make your decisions more informed than ever before.

At Ascend, we provide expert
cloud solutions in Ireland and abroad. As VMware migration specialists, we've managed over 400 migrations and counting. Interested? Please don't hesitate to get in touch.
