Explainer Videos

What is AI? What is machine learning and how does it work? You’ve probably heard the buzz. The age of artificial intelligence has arrived. But that doesn’t mean it's easy to wrap your mind around. For the full story on the rise of artificial intelligence, check out The Robot Revolution: http://hubs.ly/H0630650

Let’s break down the basics of artificial intelligence, bots, and machine learning. Besides, there's nothing that will impact marketing more in the next five to ten years than artificial intelligence. Learn what the coming revolution means for your day-to-day work, your business, and ultimately, your customers.

Every day, a large portion of the population is at the mercy of a rising technology, yet few actually understand what it is.

Artificial intelligence. You know, HAL 9000 and Marvin the Paranoid Android?

Thanks to books and movies, each generation has formed its own fantasy of a world ruled -- or at least served -- by robots. We’ve been conditioned to expect flying cars that steer clear of traffic and robotic maids whipping up our weekday dinner.

But if the age of AI is here, why don’t our lives look more like the Jetsons?

Well, for starters, that’s a cartoon. And really, if you’ve ever browsed Netflix movie suggestions or told Alexa to order a pizza, you’re probably interacting with artificial intelligence more than you realize.

And that’s kind of the point. AI is designed so you don’t realize there’s a computer calling the shots. But that also makes understanding what AI is -- and what it’s not -- a little complicated.

In basic terms, AI is a broad area of computer science that makes machines seem like they have human intelligence.

So it’s not only programming a computer to drive a car by obeying traffic signals, but it’s when that program also learns to exhibit signs of human-like road rage.

As intimidating as it may seem, this technology isn’t new. Actually, for the past half-a-century, it’s been an idea ahead of its time.

The term “artificial intelligence” was first coined back in 1956 by Dartmouth professor John McCarthy. He called together a group of computer scientists and mathematicians to see if machines could learn like a young child does, using trial and error to develop formal reasoning. The project proposal says they’ll figure out how to make machines “use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”

That was more than 60 years ago.

Since then, AI has remained for the most part in university classrooms and super secret labs … But that’s changing.

Like all exponential curves, it’s hard to tell when a line that’s slowly ticking upwards is going to skyrocket.

But during the past few years, a couple of factors have led to AI becoming the next “big” thing: First, huge amounts of data are being created every minute. In fact, 90% of the world’s data has been generated in the past two years. And now thanks to advances in processing speeds, computers can actually make sense of all this information more quickly. Because of this, tech giants and venture capitalists have bought into AI and are infusing the market with cash and new applications.

Very soon, AI will become a little less artificial, and a lot more intelligent.

Now the question is: Should you brace yourself for yet another Terminator movie, live on your city streets?

Not exactly. In fact, stop thinking of robots. When it comes to AI, a robot is nothing more than the shell concealing what’s actually used to power the technology.

That means AI can manifest itself in many different ways. Let’s break down the options…

First, you have your bots. They’re text-based and incredibly powerful, but they have limitations.

Ask a weather bot for the forecast, and it will tell you it’s partly cloudy with a high of 57. But ask that same bot what time it is in Tokyo, and it’ll get a little confused. That’s because the bot’s creator only programmed it to give you the weather by pulling from a specific data source.

Natural language processing makes these bots a bit more sophisticated. When you ask Siri or Cortana where the closest gas station is, it’s really just translating your voice into text, feeding it to a search engine, and reading the answer back in human syntax. So in other words, you don’t have to speak in code.

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HubSpot | What is Artificial Intelligence (or Machine Learning)?

Artificial Intelligence will eliminate millions of jobs.

It’s inevitable.

While many of us are willing to accept that there are jobs that will be replaced by AI in our lifetime, we still believe in the lasting power of our own work.

This is a mistake.

Conventional wisdom would have us believe that only the most routine of jobs are at risk, but this just isn’t the case.
It’s not just trucking, assembly lines, and blue collar jobs.
In fact, AI will be replacing and drastically altering many jobs that few would consider “routine.”

Chris Orlob from Gong.io shared a glimpse into the future of sales coaching with AI-powered analysis of over 25 thousand sales calls.
Even if a sales coach listened to 45-minute sales calls for 8 hours a day, it would take them 9 years to analyze this data, assuming they take 0 vacation days and use their nights and weekends to actually deliver the feedback. It is impossible for any sales coach or manager to deliver this kind of insight.

A sales coach is not typically considered a routine job.
Marketing jobs are hardly any safer. Conde Nast is using Watson’s incredible processing power to locate influencers for brands based on data that would take a human years to analyze.
Graphic designers aren’t being put out of work quite yet, but AIs are actively working on the ability to understand and categorize images.

Last year, The Next Rembrandt project used AI to analyze the 17th century Master’s work and 3d printed a new portrait. Intuitive and independent image creation isn’t far off.

At the moment, we’re happy to outsource our scheduling to a virtual assistant who can work 24/7, but AI is on the verge of putting us all out of work, and there is very little we can do to stop it.

Is this really a bad thing?

When someone say, “People need jobs!” they are really saying, “People need a way to earn a livelihood.” Up until now, the two were inseparable: If people were not working, value was not being created in the economy, and society as a whole suffered-
But we’re entering a time when this equation holds less and less truth.
The future of AI job creation is one where we all could share in the value created by the machine laborers.
Maybe that means fewer jobs and the rise of job sharing. Maybe that means shorter work weeks or basic incomes.
Maybe it means no one ever having to do a job that they hate-
Yet this is a very uncomfortable thought exercise for many of us. We structure our lives around the work week, and jobs have become how we frame meaning and value.
Imagining a world of no jobs, or less work, feels, somehow… wrong.

But this can’t be an excuse for ignoring this possible future.
The question is this: will we be active participants in thinking about and creating this new world, or will we hold onto to a fading notion that somehow, magically our way of life will be safe?

Care about this conversation, it is the future, and it is being created right now.

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HubSpot | Artificial Intelligence + Machine Learning: Stop Pretending

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We are terrified of business failure.

And there's good reason for this fear is an evolutionary response that helps us stay live, happy, and safe.
But fear is also taught. Children are conditioned to succeed, not fail in school. Traditional grading systems reward doing the right thing that has expected outcomes. Risk and failure are rewarded with phrases like "try harder" or "you won't pass".
We take this lesson into our adulthood, and that means we are ashamed of doing poorly in any tasks that we are given. Failure induces feelings of shame and comes with churning thoughts, queasy stomachs, and lack of sleep.
This preconditioning then shows up in adulthood as we navigate personal and professional goals and even relationships. We want to hide mistakes. We second guess our abilities. And then we make more poor decisions because we aren’t sure how to rebound from failure. We judge ourselves not against attainable goals and a longterm vision, but with a hypercritical eye that tends to think everyone is succeeding but us. We stop being bold, taking action or pushing forward and we settle for safe planning or letting others lead.
There’s a success bias for others, but a failure bias for ourselves. We don’t know how many times that seemingly successful colleague failed on her way to the top. And we don’t hear about any successful person’s 100’s of failed attempts, we only hear about their wins.
The dictionary definition of failure is “lack of success” or “the omission of expected or required action”. But what if we changed the definition to include taking chances and thinking big. What if failure was not by how skilled you are at following the expected path but by how willing you are to find a new one.
Look at industries where failure is paired with an experimentation mindset. In science, you are expected and encouraged to fail. If scientists did not experiment, most of the large discoveries of the recent past would never have existed. Failure is how you eliminate the good from the great ideas. And with development in the digital world, shipping continually imperfect versions of something and letting people tell you how you failed is how you make a product that becomes very successful.
Ask yourself what you would choose to do if you knew that however long it took and whatever tries you had to make, “win” or “lose”, the business and your career would be better off because of it. Also let me be clear, we’re not talking feckless decisions, we’re talking thought through plans that contain inherent risk.
Ask yourself if you have ever made a good decision when you were afraid or full of doubt.
Feeling as if every failure is taking a step back in your goals is categorically untrue. Every failure is a disguised success if you keep going. Failure is transformative and acts as a crucible, where we have to ask ourselves “what can this be?” If you are doing whatever you can to succeed, you will fail. But it’s going back at it, rethinking your plans, and doing it again differently, and then repeating this process, that turns failure into success.
A mentor told us we need to be more WWYDIYWA what would you do if you weren’t afraid? So What would you do if you weren’t afraid?

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HubSpot | Why Only Those Who Fail Can Succeed

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