The difference between AI and machine learning and what it means for the future of work

by Rossa Brown

AI and machine learning are already helping people connect and build communities at home. We take a look at the differences between them and how they're now making work that little bit smarter...

Only 9% of people in the UK have heard of machine learning. But according to the Royal Society, almost everyone has used at least one of its applications. Whether that's using a virtual assistant, pinging an instant message, or clicking on a recommendation when visiting an online store - smarter machines are already making a difference.

The terms are often used interchangeably, and both result in smarter applications that boost productivity and help automate tasks. But what are they exactly and how do they differ? Here's a quick definition:

  • Artificial intelligence (AI) is an umbrella term for a branch of advanced computer science that attempts to build machines capable of intelligent behavior. It replicates human attempts to carry out tasks and solve problems – but is much, much faster
  • 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 behavior to predict future behavior

Think of it this way – AI is 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? How can smarter machines and quicker queries make the lives of billions of workers better?

Transforming companies into communities

Making work faster with AI and machine learning

As organizations 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 people don’t suffer information overload.

AI and machine learning are increasingly helping to power 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, systems like those you find in Workplace can present the most relevant information to people at the right time.

And that helps make collaboration between people and teams faster.

Bringing companies closer with tailored communication

You’ve been on holiday for a week so you haven’t used Workplace. Machine learning means you won't miss anything important when you return to work. News Feed makes sure you see the most relevant information first rather than in chronological order.

It does this by assessing who you work most closely with and which Workplace groups you’re most active in. So every time you log into Workplace, the algorithm brings you the posts and recommendations you’re most likely to find useful while downgrading the ones you need less. It also allows comms professionals to highlight 'must-see' messages using tools like pinned posts and Mark as Important.

Which makes company-wide communication easier and more effective.

Using bots to make work more delightful

You can also use Artificial Intelligence 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.

Bot integrations within platforms such as Workplace can significantly people's everyday workflows. AI can assist your teams with time-consuming tasks like:

  • Scheduling meetings
  • Creating IT help desk tickets
  • Booking conference rooms
  • Drip feeding onboarding material to new starters

Which enables organizations to make the everyday and routine tasks more familiar, more user friendly, and more delightful.

Breaking the language barrier using Auto-Translate

Workplace delivers real-time translation powered by machine learning. So if you need to communicate with colleagues in other parts of the world you don’t need to take a language course first.

When News Feed sees a post in a different language, it will offer to translate it there and then using Auto-Translate.

It's an example of machine learning that's leading to smarter business conversations in 46 languages including Spanish, German and Chinese.

Machine learning that's helping people build community across geographies. That's helping make company culture stronger. And that ultimately, is helping to make people and companies more connected.

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