The difference between AI and machine learning and what it means for the future of work
Artificial intelligence (AI) and machine learning are different – but they're both making work smarter. Here's how.
AI is currently in the spotlight, being used across sectors from healthcare and education to finance and e-commerce. And it's the same outside the workplace. Whether we're using virtual assistants or unlocking our phones with facial recognition, it's no secret that AI is everywhere.
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The terms "machine learning" and "AI" are often used interchangeably, but while they both result in smarter applications that help automate tasks and boost productivity, there is actually a difference between them.
What is AI?
AI is computer software that replicates human attempts to carry out tasks and solve problems – but it's much, much faster than any human could be.
It's an umbrella term that covers a variety of other sub-branches, including machine learning, deep learning, natural language processing and robotics.
We use AI in our daily lives, whether we're aware of it or not. Voice assistants use AI to answer our questions, while AI works behind the scenes to personalise our social media feeds.
What is machine learning?
Machine learning is a sub-branch of AI. It enables computers to learn from large amounts of data without the need to explicitly program them. And machine learning systems also learn from past behaviour to predict future behaviour.
Machine learning is the most common way in which we interact with AI. Examples include everything from predictive text to online chatbots which direct us to the content we need, to seeing personalised video or music recommendations on streaming platforms.
Generative AI can be categorised as machine learning. It uses algorithms to actually create new content, whether that's written text, videos, images or simulations.
So, AI is essentially the broader scientific concept and machine learning focuses more on the algorithms that make machines smarter. But it's one thing to talk about algorithms and data. What does it mean for the future of work?
Using AI and machine learning in business
AI and machine learning are being widely used in business – 56% of respondents to a McKinsey survey said they were using AI in at least one function. Here are some of the ways AI and machine learning are being put to work.
As organisations collect more data about how they work, it's important that technology has the intelligence to strip away the noise and leave only what's important so that people don't suffer information overload.
AI and machine learning are increasingly helping to power team collaboration platforms. It means they get smarter and more relevant the more that people use them. By learning what's most important to someone throughout their working day, these platforms can present the most relevant information to people at the right time. And that helps make collaboration between people and teams faster.
Improving customer service
Chatbots, which use machine learning to understand customers' queries, are increasingly being used to answer straightforward questions about products and services. This gives employees more time to deal with more complex issues.
Making work more interesting
Use AI to automate processes and make some of the most boring and repetitive tasks less painful. And by doing so, give people the time and space to focus on the more meaningful and creative pursuits you hired them for in the first place.
Finding out what customers think of you
Using AI and machine learning to scan reviews and social media posts provides vital insights into the way that people see your brand. This sentiment analysis can give you a window on what's working well – and what you could be doing better.
In the financial sector in particular, machine learning is being used to spot potentially fraudulent transactions. If the algorithm picks up something suspicious, the transaction is stopped and alerts are sent out.
Working with text
AI can be used to extract information from text to create summaries. It can save hours of time by scanning documents for key phrases. And, with the arrival of generative AI such as ChatGPT, AI can create documents which humans can then check and edit.
Improving HR processes
According to Gartner, 81% of HR leaders have explored or are using AI to improve processes. AI can be used for anything from automating payroll and employee benefits to creating job descriptions and analysing resumes.
Breaking down language barriers
Real-time translation resources powered by machine learning are now available. So if you need to communicate with coworkers or clients in other parts of the world you don't need to take a language course first.
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