Below is my 20 minute speech I gave last week. The topic was "The Big Future of AI and Cognitive Technology". I had a lot of fun writing it and talking about this future I see. I would love your thoughts. Sorry it’s long! 20 minutes, you know? The slides were pretty much just titles with big pictures. Things like Wall-e and empty offices, and Hal 9000 at the end, of course. 🙂 My original speech is on my LinkedIn post, but LinkedIn is banned, so I copied and set it up here. Also, I posted this on r/futurology as well. Enjoy!
What is the future?
Take a second to imagine a world where you can tell a computer your new business idea and it will research the idea, start the business, operate the business, and optimize the business completely on its own, without any human interaction. When we have reached this point, the need for human critical and logical thinking will cease to exist.
The scary part about this is that many of you will live to see the day that world becomes a reality.
Now, that is a bold claim, but I make it because my company is one of many that is working to make it true. And today, I want to walk you through what we are doing so you can see the future like I do.
Why I’m even talking about this
My name is Nick Szabo, and I’m the COO of Swizzle Global. I’ve spoken around the world on the future of artificial intelligence and cognitive technology, mostly because of the work we do. At Swizzle, we use cognitive technology focused around natural language processing to analyze customer feedback, and we are currently using deep learning techniques to build an AI that will automate the whole process. We have helped companies around the world, from banks to game companies, from Korea to the US.
Recently I gave a speech at the Guiyang China Big Data Conference about the future of AI and cognitive technology, and I wanted to share my talk with you here. So this is my speech, modified a bit to be more easily read. Enjoy!
Cognitive Technology vs Artificial Intelligence
Before we start, we need to understand the difference between Cognitive Technology and Artificial Intelligence. Though these terms are often used interchangeably, they are very different.
On the path to true AI, the first step is to make people smarter using technology. We want to really optimize the way humans work, so we can make the best decisions possible, based on all of the variables.
Now, you are most likely aware that this is what we have been doing for decades with computers and big data. But in the past, computers were only able to help us with things that are quantitative in nature. Things we can assign numbers to. We can run really well made math equations (algorithms) on huge data sets and find incredibly valuable information, but when it came to finding important insights in things that are difficult to quantify, we were stuck. We still needed people to read words, look at pictures, listen to audio, watch videos, and learn from mistakes. And that is a problem, because we now have more words, pictures, audio, and video than we can deal with. And mistakes have never been costlier.
This is where a new wave of technology comes in. Cognitive Technology.
Basically a bunch of really smart people figured out how to make programs that can read, watch, see, listen, and more importantly, learn. This type of technology opens up a whole new set of data, and let’s us make incredibly smart decisions based on an awesome number of variables. And with the learning aspect, we can even leverage our past decisions.
Cognitive technology gives humans the ability to make decisions based on significantly more data than ever before, but the key thing is that the humans are still making decisions. This is the big difference between cognitive technology and AI.
An interesting side effect of cognitive technology is that, in order to create programs that can understand unstructured data, or data that isn’t numbers, we had to give programs the ability to actually learn. But now think about this. The whole point of this technology is to help us, humans, make smarter decisions. Now that we have programs that can learn and understand all sorts of inputs, we can tell the program what the decisions we have made based on the data the system gave us. And the program itself can start to learn why we made that decision, and can start to recommend future decisions, based on that knowledge. Eventually, the program will be able to recommend the same decisions a human would, except it will be able to calculate more variables, be able to make those decisions faster, and be able to optimize to a superhuman degree. This is when we hit Artificial Intelligence. A machine that does not help us make decisions. It makes them for us. When we reach this level, we will see a whole new level of job automation.
We’ve seen this kind of thing before. Over the last century, manual labor has been steadily going to computers and robots over humans. They are cheaper, faster, more reliable, and don’t need vacation. Any job that is repetitive in nature has been in danger of being automated.
But those of us in “thinking” jobs have felt safe. Those of us who make decisions, well, computers can’t take that! Right? AI is changing that. Actual artificial intelligence is coming that will automate thinking and decision making jobs. And when that happens, we are not ready.
This is a scary future that most people on this planet do not actually think will happen. My team and I, though, are actively working on bringing this reality to life, and we are not the only ones. Today, I am going to walk you through what we are doing, so you can see the future like I do, and see how quickly it is coming for you.
Use Machines to make People Smarter
As I said, the first step towards getting rid of all of our jobs, is to make us better at our jobs using technology.
At Swizzle, we focus on cognitive technology built around text analytics and natural language processing; Teaching computers how to read. The problem I solve is actually pretty simple. Companies today get TOO much customer feedback. Between emails, reviews, comments, forums, telephone conversations, blogs, and so much more, companies are buried in what their customers are saying. Inside of this feedback is incredibly valuable information, but there is way too much to read, so they are stuck. For example, we just helped a large bank. Their mobile app is ranked as one of the top 3 apps in the world, so you can imagine they get an insane number of app store reviews every day. Inside those reviews are bug reports, potential new product suggestions, marketing insights, PR problems, and so much more, but you can only read a small fraction of them. Using traditional text analytics solutions, like keyword analysis and sentiment analysis, they are able to see that some words have negative sentiment, but what does that really mean? Of course, a human could read the reviews and tell you what you should know and what you should do, but this kind of technology doesn’t dive deep like that, and a human can only read so much.
Our solution is to use cognitive technology. Specifically, we created a system, called SIMON, that can learn what we are looking for, read for us, and tell us what we should know. The way it works is we pull all of the bank’s app store reviews and plug them into our Swizzle SIMON system. The system then identifies which reviews are the most important using algorithms we have made, and gives these reviews to a human, our data strategist.
The data strategist then teaches the system why those reviews are important and what they mean. SIMON can now run calculations to learn why those reviews mean that, and take that knowledge and apply it to the rest of the reviews. When it finds new problems it doesn’t understand, it can work with the human who teaches it more. In the end, Swizzle’s system identifies, categorizes, and summarizes all of the important information that is inside the app store reviews, and gives it to the data strategist.
It is really like a human working with a computer as a team.
Now with this system, the data strategist knows everything he needs to know inside the reviews, and he has trained Swizzle SIMON to understand the reviews. For the bank, our data strategist understood exactly what they could do to make their app even better. He knew found who the bank’s customers think the biggest competitors are, and why. He uncovered buried testimonials where clients ranted and raved about how amazing the bank was. He knew what users really wanted to use the app for, and how the bank could help them. He knew every piece of important information inside the whole history of the app’s reviews as if he read and remembered every word. With this information our data strategist was able to talk to the bank, help them improve their product, and put together a report that summarized just what they needed to know. Using Swizzle’s SIMON, it was as if he downloaded all the reviews into his brain. Pretty cool, huh?
Use people to make computers smarter
So now the bank has a report and understands everything they need to know about their past reviews, but they still need a way to keep up to date on what people are saying in reviews every day. Just doing a report every few months is a quick way to stay out of touch with your customer.
Thankfully, a side effect of using our system as a tool to help the data strategist is that the data strategist taught Swizzle how to truly understand what that bank’s customers are saying in app store reviews. And remember, the amazing thing about cognitive technology is that it can learn. We showed Swizzle’s SIMON what is important, why it is important, and what that particular bank’s customers mean when they leave a review. Now all we have to do is connect to the app store’s API and feed reviews into our system as they come in. Swizzle’s SIMON is now constantly reading reviews, analyzing them, and organizing them into what is important or not.
Then, every week, SIMON creates a report of what is important to know, how previously identified problems are doing, and new information. This report is sent to our data strategist, who makes a decision as to what the bank needs to know, and trains Swizzle’s system further. To the bank, it is as if the data strategist is constantly just reading and remembering every review. It really is amazing, and keep in mind, this is not theoretical. This is what is happening right now. Today. The next step is where things get fun.
The next step on the path of us all losing our jobs is to move from Cognitive Technology to Artificial Intelligence.
See, this whole time, the data strategist is not only training Swizzle’s SIMON to understand how to read these app store reviews, he is also training it how to interpret those reviews into important insights. Because this technology can learn, we can feed our client facing insights and recommendations into our system. SIMON can take this, and learn how a human interprets the data, and it tries to make those insights automatic. Feeding insights to our data strategist.
Of course, these aren’t perfect yet, but soon, we will hit a point where the data strategist will not need to give our system feedback. Swizzle will run automatically and provide actionable insights of the same quality our data strategist does now.
What’s more, you have to remember that this system is constantly reading reviews. It will be able to see the results of its recommendations, and learn how effective they are. Swizzle’s SIMON will be able to constantly find better and better recommendations as it continues.
We can even train this system to know what teams need what information. If it finds a bug, it can tell the development team what they need to know to fix it. If it sees a PR problem, it can tell the PR team how to get ahead of it. If it notices a UX problem, a security alert, a product improvement idea, or anything, it can notify the right team, and give advice to take action on.
Think about what this means. When we achieve this, there will be no need for anyone to read any app store reviews at that bank. There will be no need for anyone to look at any graphs, or analysis of keywords or sentiment. There won’t be a need for someone to interpret the results into something actionable. To communicate the customer’s will into actions. There won’t even be a need for customer surveys. The bank will have a system that does all of this, figures out what needs to be done, and then notifies the correct person on what they need to do.
And remember, this is the step we are working on right now. This is happening today, not some distant future.
Now let’s go even further and see how many jobs one AI can take.
Instead of looking at just app store reviews, remember our product at Swizzle is to analyze all customer feedback. Let’s take emails, marketing engagements, comments, forums, blogs, survey data, telephone transcripts and more. Let’s put every means of communication our bank’s customers have through the same system we put app store reviews through.
Swizzle’s artificial intelligence now takes in every piece of customer feedback available. Analyzes it. Categorizes it. Understands it, and builds actionable informative data from it. Swizzle uses this information to provide detailed recommendations on how to optimize your offerings, your messaging, your communications, and more. It can be asked detailed questions about your customers. Who they are, what they like, what they hate, what they want. Will they like this product or this? How should we approach them? Who are my competitors, etc.
It can learn to identify PR disasters, before they happen, and help you get ahead of them. It can learn to identify new competitors or threats, recommend new products, or help you perfect your current products. It can do everything a human could, if that human was able to ingest EVERYTHING your customer says about you. Except it can do it for real, it can do it faster, it can do it better, and it can do it cheaper.
When Swizzle’s AI can take in all customer feedback, it will have the capacity to replace human decision making in marketing research, customer analytics, product development research, parts of customer service, and more. You will now have a singular, intelligent entity that will take over listening to your customers, and recommend what you should do.
The next step is team Swizzle up with all the other AI’s.
Who else is building AI?
Because, remember, this path I took you down is the path of one company. It is what we are working on, and it started with us just trying to make app store reviews more useful.
You know we are not the only one solving business problems with cognitive technology. Other companies around the world are working on technology to help businesses with business intelligence, marketing automation, product development, financials, and so, so much more. Every aspect of running a business is currently being taught to a computer somewhere in the world in hopes of making things easier and more efficient. And every one of those companies are pushing toward automation. And that is happening right now!
An intelligent company They are all building AI’s that will perfectly optimize their section of business. And when they all succeed, how long will it take for someone (or something) to piece it all together? To take all of the different AI’s, make them communicate with one another, and make one, incredible company that does not need humans. It will be able to research new ideas, start new businesses, operate those businesses, and optimize them without any human.
Every aspect of running a business is currently being taught to a computer somewhere in the world in hopes So you ask me, what is the future of AI and cognitive technology? It is beautiful, amazing, optimized to a whole new degree. And it has little place for humans.
Does this scare you a bit? My advice? Find all the different companies out there that are automating various aspects of your business. Go out of your way to engage with them. Take some risks and try to find what parts of your business you can automate. Keep an open mind and try to trust technology more. If you get ahead of this, you’ll lead the way. If you try to follow, you might never catch up…
from Artificial Intelligence http://ift.tt/2roYtck