According to neuroscientists, people make decisions based on their emotional response to an experience, not a logical response to data. That’s not to say data is meaningless. It can lead us in the right direction, helping us realize what’s best and most advantageous for us. But ultimately, data only plays a supporting role. The final decision to do something or buy something comes down to how we feel about it.

Machines, however, only respond to data – whether it’s received from other computers or from humans – with predictable, programmed responses.

And in the last few years, the number of those machines has skyrocketed. Today, we’re installing and embedding sensors and communication devices in almost every system, machine, and piece of equipment imaginable – at work and at home – many of them for the purpose of monitoring and orchestrating the mimgres.jpgachines we already had.

These sensors are creating, sharing, and receiving an enormous amount of data. And as more data is generated from more points, the signals and responses (inputs and outputs) that these machines produce are getting more complex, which has the accumulating effect of making our machines smarter and smarter as time goes by.  

This is the new world of machine-to-machine (M2M) communication, in which our machines are communicating with each other more often and more seamlessly than they’re communicating with us. But before you get overwhelmed by sci-fi fantasies of the malevolent machine takeover, it’s important to realize: THIS IS A VERY GOOD THING.

After all, we are the emotional beings. When it comes to data, machines are better at this than we are. They can process more of it (and more quickly), interpret it more accurately, and make better decisions than we’d be able to on our own. And as more machines talk to each other, the benefits of that will multiply, because instead of acting as siloed units, they’ll act more like teams, making them more efficient and effective.

But still, there’s that nagging question: how do machines and humans work with each other when our decision-making process is so fundamentally different?

As humans, we like to think we always make the logical call – that we’re smarter than computers, even when it comes to data interpretation. (Which in itself, is a decision driven by emotion and not the data.) But this belief in ourselves can lead us astray.

I’ve witnessed it firsthand, as engineers (some smart, some not-so-smart) will override automatic controls and put systems into “Hand” operation mode, sometimes because of safety concerns, but mostly because they didn’t know how to communicate with the machine to get it to agree on a desired result. This is like the pilot who doesn’t trust his own instruments and decides to fly by sight alone. Not always wise.

These situations raise a critical shortcoming in the M2M system. While we have seen enormous efficiency gains in how machines communicate with each other, we haven’t yet figured out a way to incorporate the human element. But if we can find a way to add a human knowledge base, intuition, and yes, even emotion into the machines, the gains will only increase.

In order to allow this human-machine communication to happen, we need a new interface. But what will it look like?

First, the interface needs to be secure. We cannot allow bad actors to prevent us from exploring the potential that these systems can deliver.

Second, the interface needs to be humanistic. People need to be comfortable with the technology if they are going to use it. It has to speak to their emotional response. It has to feel right.

Third, the data needs to be accessible and readable by people and machines. M2M communications will happen faster than any person can read and respond. People will need to be able to get the information they need in a way that can be read and understood.


Over time, our goal is for people and machines to work together more effectively. Hopefully, as people and machines learn from each other, we will establish enough trust that we’ll get to the point where we won’t feel the need to override the system. We’ll be like the pilot who trusts his instruments, even when things get hairy.  

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