Meering the end of our show, but not before we tackle a very important topic and a set change as you can see So to help me out I like to bring Lisa London VP of marketing for productivity products to the stage and joining her Please welcome Chris Benson chief scientist on artificial intelligence and machine learning at Honeywell All right as you may have guessed Lisa and Chris are here to talk about the fifth and final theme of our show AI and machine learning Well Chris AI and machine learning are here and it's not just a futuristic idea right so can you give us your perspective as the chief Scientist on this evolution sure so machine a machine learning artificial intelligence They've been predicting that the air revolution would occur for the last 60 years and there's a reason it's finally come into being and that's Three key ingredients the first of which is cloud computing and IoT Edge devices because together They give us essentially unlimited computing power and then now we have modern algorithms They're actually better at solving specific problems than any human could before any other computer approach could before and then finally We're in the age of big data and big data is a core ingredient for being able to train a modern AI model and so when you put those three together It means that practical AI has finally arrived. Wow So before we get too deep what is the difference between artificial intelligence and machine learning just so you can Give us some perspective what does machine learning mean and how is it different from AI sure So machine learning consists of algorithms that are able to learn without explicit programming by human beings But there's a wide variety of machine learning algorithms out there and they span from traditional data science algorithms all the way up to what's called deep learning and these days kind of the the gold standard within machine learning would be AI and AI takes the form of deep learning so deep learning is the technology That's really powering the AI revolution and it is the type of AI that is working today for us Okay, so how can businesses apply practical eye AI in the real world today? So you need to start with an AI strategy and that means that you need to look for problems that lend themselves to AI and That usually is or surrounding up pattern recognition for really complex relationships in your data So that really means that you need a data strategy as a prerequisite for your AI strategy So people often say well, what do I collect and what I answer is you want to collect the data that's relevant to the business problem that you're trying to solve and if you're not sure what that is today Go ahead and kind of collect everything I joke be like the NSA collect everything and that way Tomorrow when you know what problem you're trying to solve you'll have a body of data to apply to that. Okay Now Chris you know I want to wrap up with a very loaded question here. What's the future of AI in the workplace? So I get that's that a lot and one of the things that I really believe is that AI will not replace humans in the workplace Rather it will supercharge humans in the workplace So let me give you a scenario Imagine that you walk on site and you're about to start your job for the day and the environment uses sensors like machine vision wearables and you're able to interact with that environment with natural speech process and natural language processing and speech recognition And it basically comes alive and so the human is right at center of that their job is to kind of Orchestrate a symphony of automation that is centered around that worker to get the job done and enable them to be more productive than they ever could before and so When I think about the future I have a five-year-old daughter named Athena and I imagine What might she live in the world when she's my age even working in the space? I find it unimaginable. I think it's a pretty amazing time Mind blowing. So you're both given us so much to think about and a lot to look forward to so thank you, Lisa and Chris All right