Video: AI Defined # 4 Unsupervised Learning

Hey there,

In this video I share the latest edition of the artificial intelligence defined series where I give definitions for common machine learning and artificial intelligence terms.

In video # 4 I define the 2nd type of machine learning: Unsupervised learning.

Check it out

Here

 

3 Problems Even Good AI and Machine Learning Content Suffers From

Every morning, I have an artificial intelligence and machine learning reading list and every morning I see the same 3 problems in AI and machine learning content.

Around 8 am each morning, I make my way to the office, usually listening to the Dan Le Batard show,  make my incredibly delicious Bali Blue Moon coffee in my Aeropress and I sit down to look at my aggregated AI and ML articles.

And here we come to problem # 1:

Problem # 1: Bad Headlines!

The first problem I notice is the headlines of some of the best and most interesting articles.

Here’s an example from this morning on Medium

Do you know what Numpy is as a new reader?

How about I/O?

I won’t even mention back propagation.

One of the Big Picture problems with marketing AI and Machine Learning is that the marketing materials are WAY too technical and complicated.

The Waymo article is REALLY close to having a good headline all they need to do is eliminate the term Google I/O re-cap which lowers the value of the content and is confusing if someone is looking for a more accessible easy to understand article which it is.

When it comes to writing headlines you want to take a cue from the high traffic sites like Buzzfeed.

An easy way to start creating more interesting headlines is to use this old Gawker trick: Begin with a question that would false if you removed the question mark.

You can also use Co-schedule’s awesome headline analyzer tool for free Here

Problem # 2: In Article Algorithms and terminology keys.

I come from the Ivy League world of academia, I understand the drilled into you process of showing your work in academic papers.

Guess what blogs and content marketing pieces aren’t

I’ll give you a second.

Did you guess academic papers?

Because academic papers make TERRIBLE content.

You don’t need to show your work in content pieces. All you need to do is show the BENEFITS of your product and those who are interested and so technically inclined will figure out how to get in touch with you and have their data scientists call your data scientists so to speak.

I know it can feel scary to strip out what you believe is the most important parts of your content but it’s not what the audience is trying to hear.

Your ideal clients want you to talk about what your AI or ML can do for them and their business.

Trying to show them HOW it works is like trying to sell hot stew during the Pawnee Summer

They don’t care about how brillant you are.

Sorry.

Problem # 3: There is no “Toaster” 

My friend the super smart marketer Tony Almeida has a concept he calls the “Toaster” for his content.

The concept of a toaster is that it is one easy to understand or implement idea that the person reading or watching your content can take away and use without even reading the whole article.

He calls it a toaster after the way that banks used to give out free toasters in order to get new clients to join the bank.

One of the things I’ve noticed from reading about an hour of AI and ML content a day for the past 2 years is that if the article or video does have a “toaster” it is usually in the form of some algorithmic breakdown that you need a CS degree to even begin to understand.

For every piece of content you create you want to have a toaster.

And you need to make that Toaster simple enough for anyone who might stumble upon it to understand like this short video on where AI uses sentiment analysis to determine that Ben Affleck is in fact displaying the emotions of sadness in the infamous “Sad Ben Affleck” video.

Now that is a GREAT toaster that anyone can understand.

Ok so let’s quickly re-cap the 3 big problems I see in even good AI and ML content

Having read a ton of AI and ML content over the last few years I noticed the following 3 big problems:

  1. Bad Headlines. Either Too technical or not focused enough on the benefits.
  2. In article algorithms and terminology keys. Just no.
  3. Not enough “Toasters” or overly technical toasters.

So now you know to spice up your headlines, remove the algorithms and terminology keys and include 1 simple easy to understand idea or take-away in each article.

If you do just those 3 things you will see a huge leap in the quality and reaction to your ai and ml content.

How AI Companies Can Find Their Voice In Their Content Marketing

I have a little bit of a rant for you today.

I have been doing a lot of work with Artificial Intelligence and Machine Learning, as I am sure you are all aware of.

If not here’s a link to my Medium Page

Anyway one of the things that is really interesting about the marketing in the AI world is that IT ALL SOUNDS THE SAME!

This is something I have noticed in very technical or academic types of businesses. These sorts of businesses get very concerned with sounding scholarly or academic and end up sounding out of touch.

One of the big reasons for this is something called “The Curse Of Knowledge.”

The curse of knowledge is a cognitive bias which states that the more you know about a subject the more likely you are to explain it in a way that assumes a high level understanding in the listener.

So for example an AI company might do text analysis and text analysis is kind of like the swiss army knife of Artificial Intelligence it works for a variety of industries from law to human resources which means there are a lot of companies that this AI firm could do business with.

Not only that, but the guy(or girl though it’s rare) who founded the company did his/her thesis paper at some super fancy tech school on text analysis. They have been working on this stuff since he/she was literally a teenager. We’re talking 15-20 years of daily obsessive focus on text analysis. So when he’s thinking about marketing, he REALLY wants to show off all of the high level cutting edge functions of this technology.

And he/she forgets that their audience is not nearly as advanced.

If you look at the AI marketing that is currently being done, the voice is academic, and technical.

It’s writing that is designed for academia not sales.

So, how can AI companies find their voice?

I have a few suggestions:

  1. Don’t let your engineers or founders create marketing materials. One of the most frustrating things about working with AI companies in 2018 is that they have marketing departments that are staffed with engineers and technical writers not marketers. There is a strange distrust and quite frankly and obvious lack of respect for the marketing profession at most AI companies as if somehow marketing is a “soft” science as opposed to the more rigourous computer and data science needed for AI. Right now having technical content is not hurting these companies but it will VERY soon. 85% of executives surveyed by the Harvard Business Review predicted that AI is going to change their companies by 2021. This means that in order to grow AI companies are going to need to stop focusing on Fortune 1000 companies and start focusing on SMBs who are not going to have anyone capable of doing some of the equations in these white papers.
  2. Find the fun. One of the best examples of AI marketing done right is Soundhound which showcases it’s unique Houndify voice enabled platform in everything from cars to break dancing robots at CES in 2018. Instead of focusing on the science behind their platform, Soundhound put the focus on all of the fun and entertaining ways their platform could be used. People gravitate to content that is enjoyable and fun. As Bomani Jones once said ” No one can regulate how much fun you have at work.” AI companies should find the fun both in the office and in their marketing.
  3. Identify and Write To Your Evangelists. One of the interesting things about technology and media is that they need evangelists to spread. I personally learned about Spotify, Uber, S-Town, and Serial from friends and media personalities I trust. When it comes to technology because the learning curve is usually so steep the early focus has to be on finding and creating evangelists both in the press and in the business world. Quick question, right now who is the most trusted writer in artificial intelligence? Chances are you couldn’t think of one because there really isn’t one. Furthermore there isn’t even a group of gurus who you can trust like there is in marketing or other industries. The group of experts are a bunch of data science PHDs who have trouble explaining these concepts without using words that the audience has to look up. Right now when you google AI talks or videos there’s an 89.9% chance you’re going to get a PHD up on stage. And who do academics talk speak to? Other academics. At some point the cycle has to be broken and companies have to identify who the AI evangelists are in the media and who knows how to market AI to SMBs and other companies that didn’t even realize they were the ideal audience.
  4. Pick a niche and produce the best AI content in it. Right now another problem for AI companies is that their products can be used in too many different ways which means that the same company might have a white paper on using AI in sports and a video on using AI in medicine. Right now there is an incredible opportunity to own parts of the AI world. Companies need to focus their content and the voice in their writing and videos on being the best company at a certain niche whether that’s being the best drug discovery AI firm or being the AI firm that handles legal briefs. Once you define this niche it helps you to define what your content is actually about and frame things appropriately.

I’ve tried really hard to avoid saying HIRE CONTENT PROFESSIONALS because I understand that right now the online sales market for AI is not really there and all sales are made at the C level face to face or through long arduous government contracts. But the point stands. Before these companies know it, the time to sell to SMBs and focus on widespread adoption will be here and the companies that take their time in finding their voice, dominating a niche, and continue to let academics create marketing materials the further behind they will fall.