The Difference Between AI And Machine Learning And What It Means For The Future Of Work

With Artificial Intelligence (AI) poised to change the way we work, Facebook Director Workplace APAC Luke McNeal talks us through the differences between AI and its cousin machine learning.

Artificial Intelligence (AI) and machine learning (ML) are beginning to help Australians connect and build communities at home, using software agents such as voice-activated assistants to perform tasks. This is also seen on a larger scale in organisations, who are using machine learning-based tools or Artificial Intelligence to automate decision processes for example. But what really are the differences between them and how are we making work that little bit smarter?

The latest Genpact’s research series, AI 360: insights from the next frontier of business finds 43 per cent of Australians believe AI is improving their lives, slightly behind U.K. consumers (48 per cent) and 59 per cent in the United States. 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 to our lives, by saving us time, or helping us spend our time more wisely.

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 behaviour. 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 allows computers to learn from large amounts of data without the need to explicitly program them. Machine learning systems also learn from past behaviour to predict future behaviour

Putting this in context – 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? Here are three ways we’ve seen machines help us and our customers to do our jobs better.

Transforming companies into communities
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 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. Australian executives surveyed in the Genepact research said they plan to implement AI-related technologies over the next three years, citing greater inter-departmental collaboration (35 per cent), and more time afforded to employees to focus on important tasks (40 per cent) as the most commonly anticipated benefits. By learning what’s most important to someone throughout their working day, AI and machine learning can present the most relevant information to people at the right time. This helps make collaboration between people and teams faster.

Bringing companies closer with tailored communication
Machine learning means you won’t miss anything important when you return to work. Take our platform, Workplace by Facebook (Workplace), for example, which organisations use for internal communications and to create stronger culture within businesses. We use machine learning – our tool is called Work Graph – to asses who you work most closely with and which groups you’re most active in. So every time you log into Workplace, the algorithm brings you the most relevant posts and recommendations you’re most likely to find useful, while downgrading the ones you need less. This means you can go on holiday for several weeks, return to Workplace, and have the most relevant information at the top of your Feed – unlike email. We have found that this makes company-wide communication easier and more effective.

Using bots to make work more delightful

Artificial Intelligence is automating processes and making some of the most boring and repetitive tasks less painful. And by doing so, it gives 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 can significantly people’s everyday workflows. AI can assist your teams with time-consuming tasks likes scheduling meetings, creating IT help desk tickets, booking conference rooms and drip feeding on boarding material to new starters.

Our customers such as Stellar are using chat bots to help along its journey of digital transformation. Their chat bot ‘Ella’ has become Stellar’s very own virtual agent, with a focus on providing information to users quickly and accurately. Ella uses natural language access to access company information be it general, tax, HR, or specific information about leave accruals. They also use a survey Bot to gain insights on solving customer issues.

Breaking the language barrier using Auto-Translate

Workplace has also started to use real-time translation powered by machine learning. When Feed sees a post in a different language, it will offer to translate it there and then using Auto-Translate. So if you need to communicate with colleagues in other parts of the world you don’t need to take a language course first.

It’s an example of machine learning that’s leading to smarter business conversations in 46 languages including Spanish, German and Chinese. We’ve seen how customers like Starbucks and Nestle, benefit from this tool when connecting their disparate workforces.

Machine learning is helping people build community across time zones and geographies and helping make company culture stronger. Ultimately, it is helping make people and companies more connected.