Teaching a machine to think like a human

@IE University

There is an exciting future ahead for machine learning and with more breakthroughs in algorithms, we’ll soon begin to see it having an effect on our lives in a number of ways, including fine-tuned personalization, better search engine experiences, and no-code environments.

In recent years, machine learning mania has infiltrated our world at both a personal and professional level. We’re hearing the words “machine learning” and “artificial intelligence” more and more every day. So, it’s about time we understand what it’s all about and how we can best use it.

Learning about machine learning

In simple terms, artificial intelligence is the science behind making machines able to perform tasks just as a human would. Machine learning is an application of artificial intelligence that uses algorithms and statistical models in order to perform a specific task, rather than relying on explicit instructions.

Seems simple enough, but here comes the question: why are businesses so interested in using it?

Machine learning allows a program or system to identify templates. From these templates, it’s then able to make precise predictions. Companies of all different sizes can use machine learning across a number of functions including customer support, forecasting, and people management, among others.

Customer support

Both startups and large corporate organizations need to ensure they’re providing their customers with the best services they can offer. It’s not enough to simply acquire the customers, companies also need to find ways to hold onto them.

For this reason, they are now starting to use machine learning to improve the customer support experience. Ocado, the well-known Brazilian supermarket, is using Google’s natural language API to detect responses from customers and prioritize them in order of urgency, moving the more negative responses to the top of the queue. This has allowed them to respond to the most urgent messages four times faster than before. They are now able to reach out to customers who are at high risk of leaving and win back their trust in the company.


Predicting future sales, economic growth, and their place in the market allows companies to work more effectively. Organizations are increasingly using machine learning to build more accurate forecasting models. For example, Luxottica, a premium sports eyewear brand, uses machine learning from post launches to predict future sales performances. With this data, they are then able to tailor their future sales plans in order to succeed in the market.

Communication and Digital Media IE University

People management

Building a team of highly skilled employees is the key to success for businesses. But it can be difficult to find the best candidates in a sea of resumes and applications. This is where machine learning comes in. Using algorithms, it can filter resumes based on decisions that managers have made in the past. It’s also able to negate any biased language in job descriptions, highlighting applications who may have otherwise been overlooked by human recruiters.

It looks like machine learning brings many benefits to all levels of an organization. However, as with any hot topic nowadays, there’s a need to take into account the ethical side, risks, and consequences of this disruptive technology.

Is there a moral side to machine learning?

Today, it’s hard not to question the scope of technology, and with something so seemingly complicated as machine learning, it’s an issue that needs to be addressed.

Professor Stephen Hawking once argued that “once humans develop full AI, it will take off on its own and redesign itself at an ever-increasing rate.” But this view is not shared among all experts, and general consensus seems to be positive overall.

Teaching morality to machines

While humans are able to rely on their gut feeling in moral dilemmas, machines must have explicit instructions in order to function properly. For example, how do you program an algorithm to maximize fairness or overcome discrimination in the hiring process of employees? If the engineers don’t have an exact understanding of what fair is, then they are unable to create a machine system that can do so.

Some researchers are concerned that machines may in fact amplify discrimination and produce biases in their own data. They have suggested that if minority groups are underrepresented in data samples, then the model will be expected to have a bias. But instead of seeing this as a weakness of technology, we should consider it as highlighting the importance of humanity.

Data scientists are able to recognize their moral responsibility in ensuring that machine learning models are not infiltrated with biased data. They must carry out checks to review that the balance of data is fair and unbiased. Although we’re teaching computers to think like humans, they will never actually be humans. We will always have the moral responsibility to use the tools fairly and effectively.

A bright future

While many worry about the negative aspects of AI, the pros undoubtedly outweigh the cons. We must remember that machine learning can help us solve many pressing problems, such as, disease, poverty, and environmental destruction.

There is an exciting future ahead for machine learning and with more breakthroughs in algorithms, we’ll soon begin to see it having an effect on our lives in a number of ways, including fine-tuned personalization, better search engine experiences, and no-code environments.

Although there are many unanswered challenges, it is an exciting field to be involved in and the need for professionals who understand its scope is endless.