A lot of people seem to be thinking about how AI-driven bots could be the next buzzword of 2017.
There’s a lot of buzz around this, and I’m here to talk about what we think it could mean for the industry.
What is a bot?
A bot is a computer program that performs a task.
We can imagine a bot as a robot that looks like a human, but it’s a bot.
A bot can also be a person, but the bot’s job is to perform tasks for the person.
It has a job to do.
A person might be a teacher or a manager, but a bot could be anything.
We think of a bot in a lot more broad terms than just a computer, though.
We also think of an automated system that uses data to provide information.
It could be a restaurant that delivers meals to your house or a restaurant on your phone.
That information could be your location, your preferences, your shopping preferences, the food you order.
We might think of it as a grocery list.
There’s an argument to be made that bots could potentially be the future of advertising, too.
Robots are great for getting data about the behavior of your audience.
We’ve seen that with Facebook’s Messenger bot.
You can see when a person is interacting with a bot, and you can track their behavior to learn more about them.
If we think of these things in terms of information, it could be useful.
But there’s a catch: There are only so many of these systems that can be built.
So what can you do with a human to do the job that a robot is supposed to do?
That’s where the data comes in.
A lot of companies are thinking about what AI can do.
The best example I can think of is Google’s artificial intelligence engine, called DeepMind.
Google is developing a deep learning engine that learns about a particular topic, and it’s trained to think about it in terms you might understand.
It’s not perfect, but DeepMind has a lot going for it.
It knows how to learn, and that’s what we’re looking for.
The same is true for a lot, but not all, of the companies developing AI.
They’re not building a perfect AI system.
They want to build something that can learn and learn well.
And that’s a big challenge.
The data we need to know the right things to do is huge, and there are lots of ways we can do that.
Some of these companies are building machines that can solve problems, or build AI systems that are used to make decisions for a company.
But we need a way to understand what these systems can do and what they can’t do.
And that’s the real problem: They’re building machines and systems that don’t have enough data to understand how they work.
That’s why you need data to figure out what’s the right decision, and to make the right decisions.
The problem is that we need that data to build a better system.
The other problem is data storage.
Most companies will say that they store data in a way that can’t be altered.
That is, they’re storing information in a specific format that doesn’t change as long as it’s there.
A lot companies have these kinds of storage systems.
They might store data on servers, or they might store it in an encrypted format that only the company knows.
But those storage systems can be hacked, so you need to be able to break them.
If a company is building a robot, they might need to store a lot data, but they also need to build an intelligent system that can process data.
There are lots and lots of things you can do with data.
For example, we can store information about the things people like to eat, the things they want to drink, the stuff they’re most likely to do, and we can build systems to predict what people are going to do next.
And this is where the big challenge is to get that data in an easy format that can work with these types of systems.
I would argue that the biggest challenge is figuring out how to process data when it’s not easy to manipulate it.
A number of the big data companies are doing this right now, using the Hadoop language.
That means they use the Apache Hadoopy platform.
This is the technology that powers Google’s MapReduce system, which makes it possible to run large numbers of queries on an object-oriented data model.
The idea is that if you’re building a system that’s not built to handle this type of data, you can use something like MapReduces to get it working.
But when it comes to processing large amounts of data in this way, there’s still a lot to learn.
The big challenge for these big data firms is that there’s just not enough data.
They need to use lots of data to learn what’s going on. That data