Welcome to Happiness Isn't Brain Surgery with Doc Snipes. This podcast was created to provide you the information and tools Doc Snipes gives her clients so that you too can start living happier. Our website, docsknipes.com, has even more resources, videos and handouts and even interactive sessions with Doc Snipes to help you apply what you learn. Go to docsknipes.com to learn more. Hey there everybody and welcome to Happiness Isn't Brain Surgery with Doc Snipes. Today we're going to be talking about mindful eating. We're getting ready to go into that summer season where everybody's interested in putting on their bathing suits and going to the pool so people start dieting and dieting is one of those things that you know unfortunately you do it and you know a lot of us do it but generally dieting is not a eating pattern that you're willing to maintain for life. Mindful eating will help you become more aware of what your body needs and when your body needs it so you're not eating mindlessly and taking in unnecessary calories. Most people find when they start eating mindfully their weight starts to regulate and get down to where they're comfortable. So these are things that we want to look at and am I saying it's easy? No. You know mindful eating takes work. It takes practice to eat only when you're hungry and pay attention to what your body's telling you but it can reap some wonderful rewards. So we're going to start out talking about what mindfulness is, what mindful eating is and why it's important and then we're going to delve into a couple techniques that you can use to start eating more mindfully. Mindfulness is the practice of being present in the current moment. That means not worrying about the project you've got to do next week or something that you said to a colleague yesterday or you're not tying up your energy in past regrets or future worries. You're in the present moment aware of what's going on around you. Is there a butterfly? Is there a wasp? Is it hot? Is it cold? Are people being noisy? What is going on in your present moment?  What am I hearing, seeing, smelling, feeling, tasting and that can really get you into the present moment so you can focus. Mindfulness helps you be in tune with what you need to improve the next moment or continue on a positive path. Sometimes you check in with yourself and you're like, yeah, things are doing pretty good. I'm just going to keep going as I'm going. Other times you'll check in with yourself and you'll say, you know what, you know, I'm realizing that I'm really stressed out right now or I'm really tired. So it is what it is. What can I do to improve the next moment so I don't stay stuck here? Mindfulness requires radical acceptance. That is accepting life is as it is instead of fighting with it and saying I shouldn't be angry or I shouldn't be stressed or I should feel a certain way. You feel how you feel and that feeling is your body's alarm system or alert system telling you if it's happy, you know, do that again.   Mindfulness requires you to just accept your emotions as they are. If you're angry, okay, instead of fighting with it and whatever, figure out what you're angry about and how you're going to deal with it.  You need to figure out what's at the root of it and address that mindfulness means focusing on the things in the present moment that you have control over, you know, looking around, if you're in a cafeteria with a hundred people and it's noisy, you probably don't have control over that. What do you have control over? You can take your tray or your food and go outside and sit and eat if you want to be somewhere more quiet, if it's too stressful to be in around all that noise. So you want to focus on the things that you have control over and recognize that you don't have to be stuck in a certain feeling.  So I chose to call a friend and start talking about something else because there was nothing I could do to address that other situation and staying angry and dwelling on it until this morning when business hours opened again, wasn't going to do any good. So recognize that you don't have to stay stuck. Now mindful eating takes that whole, all those principles and applies it to, guess what, eating. Mindful eating means focusing on the here and now and turning all your senses to your meal and your body's response. In American society, we often eat on the run. I mean, think about going to drive-throughs and ordering food and eating in your car or going to the hot dog stand with your colleague and getting a hot dog and walking back to the office and gulping it down as you go. A lot of times we're eating while we're doing other things and we're not even focusing on the eating, which means we often eat too much. It takes about 20 minutes for your body to register I'm full.  Most people, it doesn't take them more than three or four minutes to eat a hot dog, you know, maybe 10 if they're talking. So before their body actually gets the signal that, hey, I'm full. They finished that hot dog.  Is that all there was? So they may go eat something else. And then those are calories they probably didn't need. If they would have waited about 20 minutes, the satiation chemicals would have probably kicked in. So it's important to slow down. We tend to have poor digestion. We tend to eat whenever we have a thought about food. I know I'll be watching TV in the evening and they'll have commercials on for various different kinds of fast food and, you know, processed foods and whatever. And I'm looking at it going, oh, that sounds really good right now. And my first thought is I want that. I need that. And then I think, you know what, that's just a thought. I don't have to have that right now because I'm really not hungry. It just looks really good. I can put that on the menu for lunch tomorrow or something. One phrase you can think of before you go into the kitchen or go to the fast food place is am I eating to live or living to eat? Now I love food. Don't get me wrong. I love food. So when I eat, I really enjoy my food. But it's not like I want to spend my entire day every day just eating. There's more to life than that. So what can we do to start becoming more mindful of our eating? So we reduce the number of unnecessary calories so we eat only until we're actually full instead of past that because we ate too fast and we improve our digestion so our body gets the nutrients that it needs. Well, the first thing is do a mindfulness scan before you eat. And I suggest this even if you're not trying to eat mindfully. Check in with yourself three times a day, breakfast, lunch and dinner. Ask yourself, how am I feeling emotionally? Am I feeling happy, content, scared, angry, stressed out? How are you feeling? And generally there's multiple different emotions there and that's okay.  Well, then you ask yourself, how am I feeling mentally? And that means am I concentrating well? Am I having difficulty problem solving? Do I have a foggy head? You know, that deals with how well and clearly you're thinking. Physically, what's going on with me? Do I feel good? Do I feel energetic? Am I dehydrated? What cravings am I having? That gives you a big clue as to what your body might need because your body tends to crave things when you are deficient in a mineral or a vitamin or something. I know when I get dehydrated, I crave salads. I crave iceberg lettuce, which is almost exclusively water. So that tells me I'm dehydrated. So paying attention to your body's cravings can help you feed your body what it needs in order to function optimally. The other thing you want to ask yourself is interpersonally, how am I feeling? And I know you're thinking, well, what does that have to do with eating? Well, a lot of times if we're feeling lonely or rejected, we may turn to food because a lot of times food represents times in our life where we've been accepted. It reminds us of happy times. It reminds us of celebrations. So a lot of times people will turn to food when they're feeling lonely as a way of self soothing, not because they're hungry, but because they feel bad. So if you're feeling that way, you really want to check in with yourself and say, am I hungry or am I lonely? And food is not going to fix lonely. It just kind of shoves it down for a few minutes. You need to do something else to address lonely. And finally, environmentally, how am I feeling? Am I feeling unsafe? You know, if you're in a big cafeteria and you don't like being around that many people, you may feel like you want to eat as fast as you can to get out of there. Or maybe you're in the cafeteria and you don't want to see a certain person that you work with, so you're trying to eat as quickly as possible so you don't make contact with them. Well, OK, you know, understanding that you're not comfortable in the situation that you're in tells you you either need to put yourself in a different situation or figure out how to deal with that to be cognizant of it because you're probably going to gulp your food. And are you feeling rushed? And that's what we do a lot of times when we're stopping by the fast food restaurant or whatever. We feel rushed. We don't feel like we should take time to eat. Eating food fuels your body. Your body makes the neurotransmitters that help you focus, learn, be happy, get good sleep, stay healthy. It makes all all those chemicals from the food you eat. But in order for it to do that, you have to not only eat the food, but digest it, which means you got to chew it. And, you know, a lot of times when we're gulping food, we're not chewing it very well. So tons of good nutrients that could have been absorbed, just go right out. Make sure you start practicing mindfulness. If you practice mindfulness before eating, and it doesn't take that long, it probably takes half the time that I've spent just talking about it to check in with yourself and say, you know, number one, am I really hungry? Do I need to eat or am I eating for some other reason? How am I feeling emotionally? If you're feeling sad and depressed, what can you do to improve the moment? The next technique is unhooking. A lot of times we say, I need to eat or I have to have that. And we may not say it out loud, but in your brain, you're going, oh, that looks really good. I need to have that right now. And in reality, you probably don't. In reality, you could probably have it for dinner. If it's two in the afternoon, you know, wait a couple of hours. Most of the time we are not so malnourished that we have to have food right now. But if you, when you have that thought, I need to eat. Change what you're saying to, I'm having the thought that I need to eat. So for me in the evening, I have a bad habit of grazing and that's very mindless eating where you're just kind of dinners over and you're still picking at stuff up until the time you go to bed. That's horrible for your metabolism. And it's a lot of empty calories because I got full at dinner. I'm just eating because I'm bored or, you know, stressed out or whatever it is. And that's not helpful. So after dinner, you know, we clean up the dinner dishes. And then when I start thinking I need to get something to eat, I need a snack. I replace that with, I'm having the thought that I need a snack. And thoughts come and go. Thoughts can come as, come in and go as quickly as they came in. So I think to myself, what could I do for 15 minutes?  And if I'm really hungry, then my stomach's going to kind of grumble and tell me that I'm hungry. Let me see what else I can do. Another thing you could ask yourself is, you know, I'm having the thought that I need to eat. Why is that? Is it because I just saw a commercial for something that looked really good? Is it because I'm stressed? Because I'm tired and I'm trying to find something to eat to help me stay awake. Why am I wanting to eat and address that? Other things you may tell yourself is I really want and fill in the blank a pizza, a hamburger, ice cream. And we can get stuck on that thinking I really want it.  Well, if you roll it over in your mind and start thinking about what it's going to taste like and what it's going to smell like and all that kind of stuff, then yeah, you're going to continue to think about it. And that urge is going to get stronger because you're feeding it. So instead, say to yourself, I'm having the thought that I really want this and then do something else. Thoughts will pass in a few minutes if that's all it is. Often thoughts of wanting to eat are triggered by a desire for dopamine and stress relief because when we eat, our body releases serotonin and dopamine to reward us for eating. You know, our body says, good, you're fueling the body. The body doesn't really know when it's had too much a lot of times unless you start getting, you know, uncomfortably full. So that's why mindful eating is so important because it's important to know whether you're eating for emotional reasons or you're eating for physical and health reasons. Another thing that can cause us to overeat or to have persistent thoughts of wanting to eat is if we're overtired. When you're asleep and your circadian rhythms are all in balance and everything, your hunger and satiation hormones are all in balance. But when people become overtired, their hunger and satiation hormones also get out of whack.  It's out of kilter. Just like you kind of probably feel out of kilter because you're too tired. So make sure you're getting enough sleep. Make sure you're not eating for emotional reasons.  And if that does the trick, great. If that doesn't do the trick, well, then revisit this. The third technique is keeping a nutrition diary. And this is really helpful because it helps you identify patterns, different types of foods that you crave at different times of day. When you have different moods, different seasons. There are a lot of different patterns you can look at. And if there are certain foods that you crave, like I told you earlier, I crave iceberg lettuce when I'm dehydrated. You can learn a lot about what your cravings mean by keeping a nutrition diary. And yeah, it's a pain in the butt to do. But the benefits are well worth it. So your nutrition diary, you want to have the date and the time, you know, that's kind of a no brainer. You can't do patterns unless you're, you know, what you're talking about. Identify how you're feeling emotionally and physically. Now this is, you know, we're not doing the whole mindfulness scan.  Am I tired? Am I in pain? Am I sad? How am I feeling? Am I happy? What are you craving? Salty, sweet, sour or spicy? What flavor are you craving? Because that can also indicate whether you're dehydrated or whether you're not getting enough, um, enough of certain minerals, et cetera. Then write down what you eat and how much, you know, obviously, that's going to be important.  And yes, green tea has caffeine in it unless you get decaffeinated green tea. So you want to make sure you put that in there because caffeine is a deretic, it will dehydrate you. So every, for every eight ounces of caffeinated drink you have, you have to have another eight ounces of water in order to kind of balance it out. Also identify whether or not you worked out my eating habits and my cravings are far different on days that I work out than on days that I don't. So that's important for me to know because it helps me figure out, you know, what am I really needing versus wanting? And when I work out, you know, what nutrients, what additional nutrients might my body need and how well you slept the night before. Cause remember on the last slide, I said, being overtired can keep you from knowing when you're hungry and when you're full. So in this nutrition diary, we're looking at, you know, possible reasons for eating, possible reasons underlying your cravings and generally what you're eating habits and patterns are your doctor, your nutritionist can also help you draw connections that there are certain foods that you crave a lot, especially if you just have this insatiable craving. You know, consider talking, asking your doctor about it. Is there, you know, some mineral in this that maybe I'm not getting? Do I need to take a multivitamin if you're not already taking one? These are all things that you can learn from your nutrition diary in order for your body to break down those foods and make the neurotransmitters and hormones it needs to keep your body machine running smoothly. Not only does it need protein and carbohydrates and fats and water, but it also needs all of the vitamins and minerals in order to act as catalysts and make those chemical reactions happen. So a good diet is really important if you're not eating a good diet. And I'll give you an example when I was in college. You know, I ate pizza a lot, like every night, probably for a year. The freshman year was, you know, a lot of bad eating, but that doesn't give you all of the nutrients that you need. So even though you're getting way more calories than you probably need, your body's probably still going to be craving certain foods. So you're going to be eating additional stuff on top of it because your body's going, no, I still need iron. So I'm going to make you crave red meat or I still need calcium. So I'm going to make you crave ice cream or whatever the case may be.  So you may be looking and eating additional foods that you could have worked into your diet a different way. So finally, eating mindfully, eat sitting at a table with no distractions. This is really hard for most people. That means don't be reading on your iPhone. That means don't be talking on the phone. That means don't be watching TV. And ideally when you're first learning to do this, don't be talking with somebody else. If you're engaged in a conversation, you're not going to be paying attention to what you're eating. Focus on what you're eating. What does it taste like? What's the texture like? What does it smell like? Put your fork down between bites. This is another hard one because the way we typically eat in American culture, we're kind of shoveling it in.   If you put your fork down, you're going to focus more on what you're tasting. See how small of a bite you can take and still get all of the flavors. That's just kind of a little game I play sometimes, which goes to the next two things of trying to notice the difference in flavors between foods and identifying the spices and foods used to make the dish. So like chili, chili has a lot of different things in it. So when I eat chili, I kind of savor it and I try to identify the garlic and the onion and what other tastes and seasonings that I'm identifying, obviously the chili powder. And that helps me know number one, be more mindful, but also know more what I like because certain chilies I really like and certain chilies. I'm like, no, it's just, it's not there. So knowing what you like, but eating mindfully gives you an opportunity to focus on those things and determine what you like to eat. It slows you down. So you appreciate what you're eating while you're eating it. And you don't feel like you need to have, you know, the cereal bowl that's a mixing bowl. My son does that sometimes, you know, teenage boy, he eats like crazy. He's always hungry though, and he'll get a big old mixing bowl and fill it with cereal and put half a gallon of milk on it. And I'm just like, Oh my gosh.  If I did that, I would just be eating until I was over full just because it tasted good, and then I wouldn't feel good afterwards. So that's the other thing with mindful eating. It helps you avoid that overstuffed over full feeling and allows you to sample more different types of things. If you're not having this huge plate of spaghetti, which has, you know, a thousand calories in it at lunch, if you have a small plate of spaghetti, then, you know, you have more calories left over that your body still needs to try different foods, to have different things at dinner and for a snack and things like that. So you actually get a lot more variety in your meals when you start eating mindfully. Alrighty, I hope that gave you some good tips on how to start eating mindfully and approach nutrition instead of looking at it as a diet on diet off sort of thing approach nutrition as a lifestyle. And if you start eating mindfully, you'll learn what you like, you'll only eat until you're full and you'll start feeling like you have more control over your thoughts about eating. The ability to launch docsnipes.com and the happiness podcast is in large part due to the sponsorship from our sister site, all ceuse.com, providing continuing education and pre certification training to addiction and mental health counselors around the globe since 2006. If you are a mental health counselor or addictions counselor needing CEUs, or if you want to become an addictions counselor, peer specialist, recovery residence administrator or certified recovery coach, please visit all ceuse.com unlimited ceuse starting at $59 and specialty certificate tracks starting at $89. Go to all ceuse.com to learn more.


I'm Shannon Kemp, and I'm the Executive Editor for DataVercity. We would like to thank you for joining the first DataVercity webinar on Cognitive Computing. Today we have a panel that will be discussing Understanding the New World of Cognitive Computing, operated by Steve Ardiri. Just a couple of points you just started due to the large number of people that attend these sessions. You will be muted during the webinar. For questions, we will be collecting them via the Q&A section in the bottom right-hand corner of your screen. Or if you'd like to tweet, we encourage you to share our highlights of questions via Twitter using hashtag DataVercity. As always, we will send a follow-up email within two business days containing links to the recording of this session and slides, any additional information requested throughout the webinar. With that, I will turn the webinar directly over to Steve to get us started. Hello and welcome. As Shannon said, we have one hour to go through a slide deck with excited about this panel, which I'll introduce shortly, or I'll introduce themselves. But essentially, what we're going to be doing is really kind of covering in a fluid network. Now, our highlights of Cognitive Computing, the analytics side, the machine learning, touch upon deep learning, all this subject matter. And we understand that this channel will be releasing this white paper that was kind of queued off of survey. And will it be published when, Shannon? At the end of the webinar, we'll see a copy of the research paper in the following email. We'll just fix on that. And so without further ado, let's just plunge right in. My name is Steve Ardiri. I'm an independent practitioner. I advise early state software startups, Cognitive Computing, AI, machine learning is a sweet spot. I've been doing it for 20-plus years. And today we have the following panel. And I'm going to introduce themselves. Tony, we'll start with you, please. My name is Tony Serres. And I'm an independent consultant in a boutique semantic technology consulting firm that I call Intusemantics. I also work as a technology evangelist for a company called Primal that develops semantic technologies. My background is originally in database technologies. I got into enterprise modeling as a result of that. And then the last wave of semantic technologies are in the late 1980s and early 90s. I got really involved in ontology and conceptual schema modeling. I went to work at UNICEF in metadata and repository technologies, and then have been very much trying to put this trend toward non-representation and taking advantage of knowledge for personal assistance and software agents. And that sort of thing, that's my primary of interest. Great. Graham, please. Yeah, I'm Jim Dillis. I'm with IBM, a company that's a little over a century old. I myself am considerably here, but getting up there in years. I'm an industry veteran as it says on the slide. I've been in IT for about 28 years now. I've been with IBM for just over two and a half years. And I was an industry analyst before joining IBM all around big data. And I'm IBM's as it says, big data evangelist. That means I speak on the power of big data analytics in business and in life in general. And I represent our huge brain trust in all things big data. I play a marketing role in product marketing of big data analytics solutions for IBM. And just as important, I am the editor-in-chief, a fairly new appointment. And as part of IBM Data Magazine, our thought leadership forum for us is to do with cognitive computing and big data and analytics. And we publish, basically it's our magazine. We have our forum now, very social. That brings together the best thinking from inside of IBM, but also from our partners and from industry influencers who would like to contribute goals to be published to our audience. So I'm happy to be on this webinar today. Thank you, Ryan, please. I'm sorry, I'm just laughing because that's a little list of tasks for James these days. I remember when you just had to be an industry analyst. I'm Ryan Bowles. I'm a market insurance firm called Storm Insights. And cognitive computing is one of the areas that we're covering. I do myself sometimes as a recovering academic. I used to be a serious computer science professor. And I was one of the areas that I worked in. And then I've taught in a number of schools. But for the last 15 or 20 years, I've primarily been an analyst and advisor to tech and to tech buyers. So I'm very excited to be participating in this, working with Steve. And I should get in a plug. I don't have a magazine to plug, but with my friends and colleagues, Judith Hurwitz and Marsha Kaufman, we'll have a book out on cognitive computing with John Wiley in January. Terrific, terrific. So we talked about the survey and what's going to be written. It's this white paper that Shannon mentioned is going to be published first that you get a copy of. But we'll share with you before we get into the flow and the presentation here some of the highlights from the survey. And so a couple of things that really stand out is over half the respondents in terms of the business perspective. A lot of the materials and analysts report and even articles are a little bit technical side. And a lot of the respondents felt that they really needed to provide a little more clarity in the business aspect of all the types of resources in terms of how do you implement these types of systems due to a lack of understanding of the business case. A couple of other things that stood out there was this notion of, well, we'll be in school with a lot of no-sequel big data projects, and now we're going to add on cognitive computing on top of that. And that's part of the end in terms of getting some of these projects kicked off. The plans, with that said, this is one of the organization's plans with respect to implementation. So you think, you know, and if it's a little hard to read here, roughly, you know, then 50% that still don't know whether it's how applicable it is because they don't have this clear understanding of the business case with the technical case. But it's encouraging that in the last two metrics there, there are, you know, there are tracking developments to begin with some type of roadmap, about 15%, and almost 15% or between 10% and 15% actually have active plans. So back to, you know, what can cognitive computing do to improve current methods of skill sets, not, you know, combination of data scientists, machine learning experts. Again, this comes up, this came up loud and clear with almost 50% stating that, hey, we have our hands full, you know, teasing in big data, no-sequel, and we haven't really constructed the cognitive computing business case, and a lot of them are already pegged with too much to do in a total of a time. So there are some things off here with addition. It's always good to have a definition of how much it came up, but in the, you know, as a, as a, as something to kind of frame to get people's back and, ultimately enough, if you take this as very good to print, it's a good, you know, healthy 70%, but interesting enough that you'll see when you get the white paper that there will be a lot of comments in terms of people's, you know, all-in-opinion. So I'm going to turn it for the first panel, you know, Ron Robbins, Jim, Adrian, Tony. We're going to get into deeper in this, but at first bluff, would it be, you know, what would you add to this or complement or what have you? I mean, I can start, Jim Cabela's here. I think that's a good start for a definition when I, when we conceptualize cognitive computing or of what's brand new here. You know, I often characterize it as AI for the 21st century or what I mean exactly. Well, un-selected data is at the core of what the applications of cognitive computing, and you've got that here, but it's really multi-structured, so this is correct. Machine learning is also very much machine learning models unsupervised, especially unsupervised, but also supervised learning too. We learn from fresh feeds of multi-structured data, very important. You've got that in here.  The sense needs to be pulled out of that. It's not obvious to, a machine can't pull it out with the aid of some special, you know, algorithms, machine learning models. You've got a lot of the important things right in here, and you've tied the business value in as well in terms of better outcomes. It's really all about, you know, using the power of cognitive computing to find not-obvious insights within massive data sets and they're just all the time. Not-obvious insights that human beings, just using their eyeballs and their brains, unassisted, wouldn't be able to derive. So in many ways it's an extension of the human cognition apparatus, you know, our gray matter, in the cloud, leveraging all the advanced analytics that we can, quote-unquote, throw at it. So I think this is a good start. Great. Adrian, do you have anything else to add? Yes. Hi, Sarah. Hi. This is Sarah. Hi. I was just going to ask you, Adrian. What struck me about this is it's one of those cases of the elephant in the dark room, right? I think people are going to experience cognitive computing in different ways, coming at it from different perspectives. I think you've included in this definition those different ways that you can come to it. I think in a lot of cases today people see this language processing. They've experienced Siri or Google Now or one of the other tools. Maybe they work with machine learning. They're big data scientists. They experience it that way, but I think it's really the cause of all those things that is really going to ultimately get us where we need to be. And my personal bent is what do you do with it? You have that. You use it for decision-making to automate some aspects of what would otherwise be a human cognitive process to make decisions. In the role of a virtual personal assistant or an automated assistant. And for some business goal, making your job, your talent, doing more efficient or providing you more discovery into content that you wouldn't have got otherwise, recommendations, really just to help her to a human for better business outcomes. Right. Good. Good. And a number of conversations about this. I actually feel like this should have been a possible answer, which is this is pretty good, but I would take some out. I would add more. But just one more thing. My problem with the way the markets are evolving right now is, as we look at it from a perspective and everybody thinks what they're looking at is cognitive computing. So I don't have anything that has a learning from experience element to it as being kind of a baseline cognitive computing. And while natural language is important, and it's certainly part of the scheme of things for cognitive computing, I think that there's some stuff that's going on right now that doesn't use natural language. Things in neuro-magnitude or architectures, for example, or even, you know, Sean offered recently with Project Adam and Google Brent, you're dealing with a process that's learning natural language involved. So I think it's a good framework to work from. And just the fact that we can have a good conversation on it means it's good. Right. And we'll cover that downstream in the slides here. Just a quick question. I just wanted to quickly add to what Adrian said. A bit of a quick here. I liked his phrase, Learning from Experience. Yep. You know, cognition, the rational thought process are just one component of everybody's experience. There's affect. That's emotion. There's sensation. And there's some experience of abolition, I call it. There's cognitive computing, everything we're describing. There's affective computing, like sentiment analysis. Sensory computing, Internet of Things. And what I call volitional computing, things like decision automation, next best action, together we need to be incorporated into the notion of what do you need some total in terms of capabilities for a system to function from its 360 degree experience. I think cognitive computing itself is not enough to capture the entirety of what needs to go into an intelligent experience. That's a very good point. And we're going to pick up on that. One thing I want to focus right from the get-go is that there is a lot of similarities. In fact, there's more similarities in the big data and cognitive computing stack depicted by this generic. I kind of like, there's roughly, there's really, you know, it starts with your data sources, then it, you know, typically goes through some type of ETL with NLP. And there's literally the same, pretty much the same symmetry here where it starts to differentiate a little bit as the middle layer depending on what you're using. And it's really a combination of it.  What we're seeing over the last year is just a plethora of these Hadoop ecosystem, you know, Cloud support and work, platform, that are forming. And these are now transforming into enterprise data hub.  What I wanted to point out here and then turn this over for discussion is that your inference really starts to kick in on the other two layers, okay? So yes, you can do descriptive, predictive, and complex with big data. That's why I include the prescriptive and cognitive. If you go from left to right, then you're really going to see some of the, you know, and we're going to get into the differentiators, believe me. And then it's different types of applications, just like IBM and others. There's different UIs depending on what vertical and who you're addressing, whether it be clinicians, knowledge workers, consumers. So comments, Jim? We're about to start off. Yeah, Jim, again, I like this. You like the fact that at the very top, you put engagement. That's, you know, not a component of the big data because anybody conceives it, but it's fundamental to the cognitive computing stack.  It's all about engaging human beings in a conversation around the, you know, however conceptualized. So it's all about engagement, too, to do direct decision support and guidance to human beings trying to make decisions in various contexts. And then if you put engagement at the top layer here, I think is bang-on correct. You know, everybody knows this week that IBM and Apple entered into an alliance going forward. And everybody, you know, one of the shorthands of the journalists uses, you know, how will Watson and Siri play together? And, you know, that really in many ways is the things that we are grappling with in terms of both doing conversational computing on top of conceptual computing and on top of cognitive computing. The three C's here. It's exciting. You know, in other words, how can Watson or Siri or any other cognitive system, automated system, pass the Turing test? Yeah. We're going to talk lively about that. Right. I'm glad you mentioned that, Jim, because I actually have some slides downstream that really get into what it does on the upper two layers after we cover the symmetry and the similarities between big data and cognitive computing. Jim, comments? I want to say that I agree 100 percent that I think it's about what you do with the big data. And I think that's where the complementary relationship between the two comes in. I think we're so focused in calculating data and analyzing it with machine learning tools, but that is a domain that's very much left to data scientists or within the tools, maybe exposing it more to people in the business. But I think one of the things that we're hearing from people that want to know what the business value of collecting and analyzing all that data is, and I think that is adding it up to be exploited by cognitive technologies that can now take advantage of that knowledge that's been mined and the next step made explicit so that it can actually be used by predictive systems or by cognitive systems. And I think the whole U.S. and application API piece of that is out there at this point. Yeah, exactly. So the interesting question, and this is where we want to kick in, now we have to understand the symmetry and the similarities. What are the key differences? So I took a first chart with tongue-in-cheek taking gaping void here on the differences in short. So big data is more in the information and then cognitive computing where you're adding content associations really is more along knowledge. That's why there's a Google knowledge graph and there's Watson and so on and so forth. But more specifically, what I think is out in my opinion, here's the three key differences of cognitive computing. So I'd like to turn this open to the panel. Well, it's a question of taking advantage of all the knowledge that's sort of implicit in big data. It has to be made explicit. Patterns that are getting exposed, what do those patterns mean? How can we use them in a particular business context and why are they valuable to us? And I think that's the last layer where we're now tasked with adding on and making explicit to people so they can understand what it does and how they can use it. Right. I don't think I'm making some notes on this because I don't think you can take that slide in the past. I like it. You know, I look at learning and I say that learning is the fundamental thing for cognitive systems. The three words that generally come up are pattern relationships and context and they're all related. Going back a little bit, they're related in, like, I mentioned before, stack going from, you know, data to information to knowledge and if you really wanted to get people worked up, you'd talk about wisdom. But here where we're looking at is, you know, how do we take something, how to extract something of value out of a lot of data? First of all, finding the patterns, finding the relationships, putting them in context around it so that it can be used for business decisions or for research decisions, whatever the app is. I think when you go into inference, hypothesis, adapting and improving, that's the learning part that we're talking about. And so I really like to focus on your third bullet point there, that that's what I was looking at in terms of experience-based performance that doesn't require reprogramming. So, yeah, I think you got it here. Yeah, this is really nice because I think you hit the nail on the head. What's differentiating now is that there's so much data and there are different patterns that might be potentially findable within the data but there aren't enough human beings on Earth who can be trained to be data scientists. Even if somebody on Earth was given the most powerful data science tools available and everybody went and got a PhD in computer science or, you know, STAT or whatever, you still wouldn't have enough resources to find all the valuable insights. So what I'm getting at is that automation is absolutely essential. The models themselves will learn from fresh data. The models will find the sense the models will adapt, machine learning models and so forth. Well, that's the need for direct reprogramming here, really direct modeling and simulation. Automation is absolutely essential for the human race not to get swamped by all the data coming in. But also to drive not only to find the insights in an automated, it is close to an automated fashion as we can make, but to find those insights in a fairly automated fashion downstream into all the applications or decision points where they may be needed without the need to go and write any code by any human being. It just happens mathematically or mathematically in the infrastructure. Well said. Well said. And with that said, we're into some of the building blocks that has led, and this is you again, Jim, regarding how Pog's computing can take the semantic web to the next level. This was the post you did earlier this year. Can you elaborate a bit of that? Sure. I'm charging a nickel every time somebody cites me. You know, like, at least my Indian, but yeah, so the semantic way, no, actually, you know, a big part of my job at IBM is I'm, you know, I publish blogs every week and various channels including data diversity. So, in January, I was thinking about the very topic of AI for the 21st century, and I was thinking, okay, what's missing from general discussions or specific discussions of computing to make this reality, AI, and how do we normally perceive the branches of AI? And clearly, semantic web and semantic analysis related to natural language processing and so much more has been discussion for a long time. And when you look at, you know, finding the sense in the unstructured content, when I say unstructured in this context, I'm referring not just unstructured text, but also especially to media, you know, audio and video and so forth. What's absolutely essential is that as you extract the patterns, you're able then to tag patterns, the data, the streams, you know, deepen the metadata that gets associated with that content and ensure that metadata is downstream to all the consuming applications so they can fully interpret all of the content of those objects in their full, whatever the relevant context is, I thought, gosh, the semantic web people have had those standards and those technologies for a long time in terms of all and RDF and ontologies and taxonomies that need to be brought into the overall cognitive computing discussion. It's a key part of what I've elsewhere called thick metadata to enable semantic computing as an integral component of cognitive computing because when you think about cognitive computing, many people who aren't really experts in this area, they think of structured data, they think of just, okay, just more of the decision automation and unstructured data, like standard core enterprise business applications, but most of the new applications of cognitive computing are completely unstructured sources, but where the semantics is not defined in an art structure or whatever in advance, it needs to be extracted from the content and then mapped into a static ability of one sort or another and managed in repositories and so forth. So far as cognitive computing to achieve, it's promise. We're going to need that thick metadata layer that incorporates semantic tagging formula for us. That was the germ of my thought there. That's what I've said. And a couple of instantiations of this is, of course, Google's acquisition of MetaWhip a few years ago that is forming the base of their knowledge graph. And there's a lot of graphs, but that's essentially a strong example of that. I'm going to give everybody a shout out here after perusing your Twitter stream. I kind of like this depiction, Adrienne, of this tweet you did in June regarding how IBM Watson, you had a lot of interaction with them, how the functions layer maps to the cognitive computing. Can you just give us some of your thoughts on this? So what distinguishes cognitive computing from data mining and machine learning? We're going to cover machine learning in a lot of detail right after these series of slides here. But on data, did you want to take the data mining question, Jim? Repeat the question. Yeah, we're going to cover machine learning. But the analogy between cognitive computing and data mining? Well, data mining, of course, is the process of finding sense in the field of computing one of its core applications. Not the only one is defined sense in multi-structured. So there's a direct analogy. So I'm going to talk about cognitive computing in a broader context. It's finding sense in, really, it's geared towards finding sense in the unstructured sources, the multi-structured sources. So in many ways, you can look at it as there's data mining, there's text mining, there's sense mining or semantic mining. You can use the word mining. You could, you know, qualify a number of ways. But the thing is, you know, when you're talking about cognition here, you know, what you're finding it for is senses that you can then graph out. You can graph out a model of one sort or another. You know, as opposed to say you can graph out the relationships in an affective context as well, in terms of sentiment or patterns of influence based on, you know, people's feelings about that topic. So in many ways, it's just the broader concept of mining for meaning. Meaning of meaning. So do we have Adrian back? I'm here. Okay. I have a couple of elaborations on your diagram here as far as the foundation maps to cognitive computing or some of your permutations on that. Okay. In fact, I just, so I just pushed the button and the updated version of this will appear with the hashtag of cognitive computing. This is the first stage to talk about ACE for cognitive computing, you know, with the foundation technologies of data and analytics. And of course, you have blowed out a lot of things with hardware and work loads, workflow and architecture. But what I wanted to get at was the idea that learning is kind of the central thing without which none of that really matters in terms of context. But for those of you that are looking at the slide on the webinar, actually kind of made an overlap between perception and learning to account for the learning that goes on outside of natural language processing and outside of the time that we're talking about with Watson when you get to Google Brain and Project Atom because that's what I said earlier, learning based on experience, but there's no language involved. What I think that I've been putting or promoting lately is the connection between structured and unstructured has bothered me for a long time because my feeling is that if it's really, if there's no structure, then it's noise. It's the fact that we haven't identified the structure yet or able to extract out the patterns within that structure. So if we know that it's natural language, if we know that something's written in English, and we have reasonable that it's actually valid English structure there, it's just not something that the surface level like you would have with data. That's an excellent point and we're going to pick up on that in two slides here. Before we do that, Tony, a couple things that stood out on a couple of your tweets, and I kind of like this, this whole notion of non-concentration and design serendipity, which is another fundamental part of the differentiating between computing and just being able to do that. Can you elaborate on that? In the blog post and the emphasis on the fact that it's very long on the A, the artificial part, and short on the I, the intelligence part, I was really trying to say there that I think for the people who declared victory in AI, basically because we are getting quite sophisticated in machine learning techniques, we've got more and more of this big data everywhere. It's premature to declare a victory and I worry about the same sort of hype cycle that happened back in the late 80s and early 90s around the last wave of AI and it didn't come to fruition. So I think we're closer now. We've got better technology, but I was trying to make the point that exploiting this representation isn't really the same as just doing text processing or natural language processing, and it's even not the same as doing machine learning where you expose patterns and you try to make predictions based on those patterns. That's a step that can enable her, but I think you really have to take the knowledge and encode it in some way and make it explicit and readable. I think there's a difference if people are familiar with the notion of tacit knowledge versus explicit knowledge. So I'm a cook. I can make a recipe and just kind of do it without even thinking or reflecting on it. But if somebody asks me to describe how I make something, it's very difficult for me sometimes to describe that process and all the little things you just do about how much you have been able to put in and when you know it's right. That's what we have to get to to be able to have these cognitive systems and systems that can really act at least semi-autonomously. And that's what you mean about designing serendipity, to be able to kind of meld implicit or tacit with the explicit. That's an excellent question. Yeah, I worry in that case about we talked a long time ago about the filter bubble that was coming up in search, right? And the fact that people would get down deeply into seeing the same sorts of things that they found before for the topics that they put into search engines that they would never discover other things going on around them. Well, I worry about the same thing with machine learning that we're dealing with all the time that we're just going to reinforce our existing knowledge and existing biases. So I do think we have to regain things in different ways. We have to be able to move up a graph if there's a up in a hierarchy and yes, so we have to be able to kind of explore their path through the graph and then to play other aspects of knowledge that might not necessarily be obvious, but that's where real sort of creativity comes in and where we begin to get to things that are more unique versus purely machine processing. But I think we're ways to wait for that. You have one more point, Adrian? I'll make the point later on. As I mentioned, one of the key layers in the previous diagram, the five-stack layer, we're going to concentrate on the machine learning, the reasoning, and then the user engagement. So machine learning is a branch of AI. So the algorithms are processing the data. They're drawing the conclusion. It's pretty much broken down to two buckets with different methods, you know, supervised and supervised. And this is not meant to be thorough. This is the typical, you know, algorithms and what the goal is in terms of doing this. And we'll get to deep learning in a bit, but the recent, the quoted part of them was when you really think about it, you know, you say, well, you might have thought about it, but when Matthew Zeller made this, you know, in this Wilder article, Google is really a machine learning company. And a lot of companies are turning it in. You know, you can make the same thing with Facebook. And in Watson, there's a lot of machine learning. And it's really interesting to, to, it's really pretty much the new black machine learning. On the, on the, on the, on the spot, is, is basically taking, you know, replacing map reduce. So getting back to the symmetry between machine and cog-tube shooting, the Hadoop distributor has made Spark part of their distribution. And it's, when Google says we're not using MapReduce anymore, well, no foolin'. No one else really is either. It's moving to Spark. And it's also being applied to other data stores. Cassandra, MangaDB. And, and, and, and, and, and, and, and, and, and, and, and, and, and, and, you know, and the E.T.L.  And you're starting to see it to applied for use cases and churn, you know, fraud detection, analytics, and so forth. I think there was a conference this week, this is our conference. One, this was a pretty interesting post, a characterization of this depiction here from GraphLabs. Actually, it was the GraphLabs conference this week. Kind of just takes you through this automation process of the data sources to the ETL and then, you know, a batch of static dynamic models to be able to make these predictions. One of the things that stood out to me is, right now, you know, my curve is better than your curve demo. They think, you know, this metric was pretty interesting. 80% to 90% of the use cases can be in the cycle without requiring centralized deep engagement. Or, and we're going to get into this notion of, you know, if you can automate a lot of this, then you can, you know, I guess get closer to the goal of democratization machine learning. Comments from the panelists. It will be democratized in the future to the extent that, literally, it's available everywhere, ubiquitously at lower, no cost. And they'll have a machine cloud computing on the back and it'll demand mobile and really, you know, any form factor client on the front end have access to machine, the outputs of machine learning models and so forth. But also democratization of machine learning means the tools for building and tuning machine learning models need to be so, I'm going to use the word foolproof, need to be so embedded in the experience of developing apps or, you know, knowledge or whatever that we don't even realize that we're doing it. Everybody's doing it. Everybody's sharing their knowledge. And to, you know, to tune, fundamentally, machine learning is all about, machine cognitive computing is all about machine learning, assistant learning and human learning, the judgments of experts and just regular people. The crowdsourcing of intelligence improves machine learning. You know, you continue to adapt the models to the results. So democratization will have been, will be a reality. When all of that has happened, and we're nowhere near as an industry, you know, ubiquitous machine learning on the back end, ubiquitous on the front end, contemporary versus Watson, you know, spectrum, as well as, you know, real-time user-friendly, interactive, visual development tools. If for a reality today, we're using it to develop, you know, Hadoop, MapReduce models and so forth, but even though Hadoop, you know, has, even though it's the biggest of the new approaches to big data, has achieved nowhere near, anywhere near democratization in that sense in terms of broad applicability yet. Yep. Yep. And it's just a time I'm going to move on. What I wanted to point out from this slide is machine learning is in new black. There's a lot of dollars flowing. This is a partial list. There's a lot more. But this gives you an idea of the, you know, very old start-ups that are getting, you know, 20, 30, 40 million, 50 million. But it's the money starting to flow big time. And there's a combination of different methods. Some of these are deep learning plays like vicarious and others are using some of the methods. The newest, the newest big player to the group was the announcement of Microsoft as your machine learning. And that's a significant effort as well. You know how hot machine learning is? Even an old, a grave like Bill Gates, somebody of my generation who has nothing to prove, has built, he's the richest man on earth. He's this great philanthropist. Even he, I can, you can read interviews with him recently. It's like, God, if he was going to get back in the game, he says machine learning would be it. You know, he'd focus on all of that. It's like, even if somebody like Bill Gates is getting re-energized by machine learning, you know it's hot. You know it's actually hot. That's a valid point. That's a valid point. So, getting back to what we just touched on and, you know, this whole notion of automating the data science. This is a provocative post by Louis Derrug talking about, and this reinforces what you just stated in previously Jim regarding, you know, making up a data scientist. And his, he was really emphasizing the, they really need to know machine learning because that's really the fundamental key of what we've been talking about in previous slides. Comments? I love something else. I've been on a run again to get out of data science to see what people are doing. Yeah. Right. So, this really boils down, so this really kind of segues into this other kind of interesting post regarding algorithms. I mean, again, you're always going to have teams of data scientists, and there was a question about moving forward with, in the white paper that you'll get at, will you need for more data scientists for cognitive computing equal number, or less than I forget how the metric came out, but basically, this also has the sophistication of the algorithms, you know, can, if you're embedded into semantic models to do what they're doing, you can not have, you know, the algorithms, more sophisticated algorithms becoming, you know, the power designers. That's the whole purpose of that slide there. The interesting thing is that, you know, I think automated algorithms are really good at generating prototype after prototype, or iteration after iteration. You'll still need, and that's the notion of explicit knowledge. It could be automated, the generation of it, because of the patterns. You still need the tacit knowledge of human beings, either subject matter experts, or outsourcing just the average person to then feedback, no, that model doesn't, that pattern doesn't make sense. Yes, that does. That's closer. To feedback, are human judgments continually into this automated system that's producing one pattern after another to help refine the models to make sure they're still on track with what? The overall consumer or stakeholder, human beings experience it. So, you know, like I said, machine learning and human learning need to be really co-dependent. It's a co-dependent process of continuing to iterate or refine those. I totally agree with that. I totally agree with that. It is a co-dependent process, but this is an interesting observation by Vinod Koshla, Three Predictions for Future of Health. So, he's very much, you know, maybe it's a little exaggerated, but it's a pretty high number of 80% of what doctors do being placed by machines. You know, the notion of, to your point, still tell or made, that whole human-to-machine interaction, and that whole consumer type.   But there it is, Project Adam. So, it's a beach slide to a number of things. The beach is very big. So, you're seeing a move from, you know, Microsoft has it, Google has it, Apple is doing it right now. We talked about the images and we're seeing the improvements already. You know, the voice condition in Android, Skype transit will be coming out later for real translations. Pretty impressive, if you've seen the data. And you can just see in the images, these are pretty interesting metrics. Humans can match 97.53, but Facebook beat it, or worked close to it, but then the Chinese University of Hong Kong has a 99 with a classifier neural network. So, the whole point, and now that I'm in here, we've got the latest models. This is work that other companies are doing, like Dumeta. They said after nine years of research, they can mimic the way the brain works and that plays into continuing iteration of cognitive computing. Now, what we need to get in the interest of time is just 16 minutes left. I want to talk about the top level, the UI, the application engagement. So what I came up on here is more about having, you know, the typical BI, you know, user interfaces. With cognitive computing, my contention is that it's contingent upon the vertical use case and who's the target of user, whether it be a clinician or knowledge worker or a consumer. And I just did, okay, for licenses, you can, you know, when you're doing topological graphs of different cancer types and shapes and meetings, these are cut out from a yasty. It's that way for collaboration, something, or if it's customer service, maybe it's that intelligent personal agent. Comments from the panelists. Yes? This is Chris. I do think back to one of the, I think, cognitive computing that was raised, you know, we make a business case around it. I think this sort of thing really does begin to get to the real use cases, whether it's augmentative in a decision support sort of way, helping a doctor with diagnoses, content discovery, you know, personal assistance, product recommendations, all sorts of things. And I wanted to raise the point, we spent quite a bit of time there on machine learning. I don't equate cognitive computing with machine learning exclusively. I'd like to think of four approaches and you can sort of slice and dice these different ways to getting to cognitive computing for semantic technologies. And I think constructive ontologies, constructive knowledge modeling, like whether it's done, you know, by groups or individuals or crowdsourced in the semantic web. The linked open data models are very valuable ontologies that can be used for cognitive computing today in practical terms. And that comes from that. Machine learning, if we move from just using the patterns to really analyzing the patterns and building explicit knowledge models and doing that iteration that I think Jim mentioned where you have to have that feedback loop and you begin to really develop the data that you're producing to create explicit knowledge models. There's what I like to call generative or probabilistic models, I think as well, that can be for particularly everyday sorts of tasks where you don't have to have high accuracy but you want to look for long, disruptive sorts of things. So for content discovery, for personal assistance, for calendars and just everyday sorts of tasks. So I think we have to bring in to bear a lot of different approaches and based on the use case that will dictate the approach and how much we want to invest in that and what the business return is on it. Yeah. I like the slide because you've got the top of the map of 14 cancer and oncologist, a doctor who focuses on cancer and diagnosis and treatment would use these kind of these kinds of maps. What I'm getting at is that if you look at targeted users, like for example, oncologist trying to diagnose those cancer or whether it's likely to spread or whether it already has spread. What they're doing is that they're saying if they should be doing this, they should be a scientist. They should have a scientific approach, meaning they're gathering more evidence. They're weighing it against their prior hypothesis and they're saying what do I know and not know with what degree of probability, very probabilistic, hopefully the cognitive computing application will give them guidance on going throughout the investigation, gather this data, try this treatment, see if they respond so forth that the backend system, whether it be Watson or something else is irrelevant, should then allow that scientist essentially, the oncologist to get closer to a confirmation of some hypothesis potentially that gets deep down to the factors that they're looking for so they can treat it effectively. So that's very much, you always have to provide the guidance geared to the specific decision points being confronted by somebody who hopefully is using a scientific, critical data-driven framework for decision-making. It doesn't have to be an oncologist or somebody with a high degree. It could be a consumer. There were consumers there. It could be a consumer who is just looking for the best buy, to borrow whatever in the marketplace. So there are many different kinds of products that might meet their needs and the advisor would help them work through the decision tree in terms of finding the best product of what sort to meet to do with whatever outcome they're trying to achieve. Hopefully doing it in a more of a scientific manner so they can have great confidence that the answer they arrive at is pretty best or close enough. Exactly. So an interest of time and a move on. We do have a question from the attendees. Here really quick before we get too much further. Do cognitive computing and semantic computing still need a soul behind it to make decisions? Which in semantic computing deals with the storage of decision paths for automation? I'll answer it and then I'll let the other panelists answer the second. Yeah, cognitive computing needs and semantic computing needs soul, meaning you need human judgment to do the modeling, but also to evaluate the outputs of the models to make sure that they are on track with whatever humans happen to be experiencing, whether it be edge humans or just average humans. So yes, they definitely need a quote-unquote soul, human judgment somewhere in many places in the overall process of building and tuning and using the output models. In terms of this one, what branch of semantic computing deals with the storage of decision paths for automation? I don't know. Yeah, that's a better clue to me. It may be in the follow-up. One thing I wanted to get across is the UI for mobile. You know, when you're bringing, and by the way, there's another Watson. It's called AT&T Watson. It's a speech platform. That pans out regarding, you know, trademark. Yeah. Yeah. Absolutely. I can't wait to ask without throwing out an alternative view and a lot of what we're doing with computing, I would say we don't need a soul. I don't think that in the diagnostic area in some of these things, there's nothing to do with what we think of in terms of humanity and the nature that views present because there's a lot of stuff going on now in terms of the nature that these robots take over. Right. So the reason I teased it again, I just used them.  There's Nuance. Each one of the big companies.  There's a lot of other parameters regarding the diagnostic management in addition to the natural language processing. So the point of that, a lot of this is being rolled into Cortana Bing. See, that's Watson. And then of course with Google, with Google now and the Knowledge Graphics, there's a lot of other types of, you know, parameters that have to be factored into the scheme, right? Which really, in big data, this is again the different, you know, one of the differentiators, is there's all these other modalities. Okay. And speaking of it, just the news like with, what's kind of interesting here is now Watson and, well not just Watson, but IBM and Apple. So this is sort of like, really interesting to where you can actually have intelligence at the interface, combined with intelligence on the back end. And you can see here, really, that I teased that article, is that there's, you know, very specific interfaces depending on what the use case is. And then you have a general assist, and I couldn't resist when I saw this news, of bringing in the old concept piece, the Apple Knowledge Navigator from 1987, which really is a marvelous piece, by the way, in terms of, and I remember this, the deforestation in the Amazon versus deforestation, that's why Africa's over there, where you're really, you know, it goes way beyond the capabilities, what we see today in Cortana, Google, now in Siri. Well, Watson, of course, was just adapted to speaking to Alex Trebek in a game show setting, you know. So we've gone well beyond that. Yeah. Yeah. I think you're, you know, I'll go ahead. You know, we're really, in terms of IBM Watts, and we very much build applications for specific applications, like healthcare and so forth, that use very specific interfaces to those decision points, and so forth. So, yes, that's great. In some of these areas, more of like, you indicate you're a general assistant, you know, going forward, IBM's going to tune Siri and other Apple and IBM technologies to the specific conversational computing requirements of different decision points. So I think there'll be a blending going forward. I'm not speaking on behalf of IBM. This is just my feeling personally. There'll be a blending of sort of the general capabilities that a Siri provides in almost any decision environment with the more vertical capabilities of Watson for particular applications and so forth. It's extremely likely to happen. Let me just say it that way. Okay. So, again, the competition is intensifying. Again, it's kind of a pull-through, but basically, it ends in, you know, there's start-ups as well. We talked about, you know, machine learning start-ups, but it's interesting how we're, the later IBM also acquired the start-up cognia to get personality to virtual assistants. Cortana is in the game, and that quote from the manager is, we decided to infuse with a personality for better use of attachment. The good news, you know, Google is talking about full reasoning outside, but, okay, it's still a few years away yet. But we're starting to see quite a clamoring and an aggregation of movement towards adding other types of, you know, amplitude and other elements to, you know, combine with more functional AI along with personalities. Do you have a comment? This is Tony. I was going to say, I don't really care so much if the guy or the cognitive computing technology personal assistant has its own personality, but I do care that it understands my personality. So I think that the text will convene peace that needs to be around here, that it knows where I'm at, what time it is, what I've been doing recently, what I'm likely to be doing next, sort of my objective and goal, what my interests are, what my social network is, and it has all that context. So it knows that me and it knows what I'm doing, whether it's personality or not, I don't really care. Right. It's very likely to happen in the industry as a whole, as wearables, because I'm adopted more widely. I bet that the individual wearables within your personal area and network will have their own distinct personalities. It'll play together as almost, okay, your own personal game to some extent.  Yeah. Yeah, I've written a comment in the interest of time, so we're down to four minutes. Oh, yeah. One more question here from the audience, Steve, and we actually have just a couple of minutes left in. For a knowledge system to be termed as cognitive, what are the mandatory characteristics and features? As you guys were discussing, you guys... Well, actually, the slide upstream, you know, talk about those three, you know, basically what I would say, we covered that in the previous slide, so without belaboring the point, and let's finish up the next three slides in a minute here. So the whole notion of, of, emotional ordained to two is my point in this slide. So there's work going on. This is a laboratory for animal technologies in Auckland, where, you know, you can learn and interact in real time. So you've got some of the models, you know, baked in, to where you can actually, you know, create much more like, you know, personalities that, that can, you know, that can also emote and learn about you, giving to some of the previous points. Now, we're seeing this in robotics. Now, this is kind of interesting. I was, you know, I just picked this up. Edmonton Airport is using robots. Somebody said, well, it looks like an iPad and wheels, but the whole notion is there they are. Rather than going to the information booth, you can go to these robots and they'll be able to help you. And this company, Jibo, and Aldeo, this is Cynthia Brazil from MIT. She just, she just goes five million from the Indiegogo campaign, you know, and putting things out. It's actually quite interesting in terms of being that, that personal assistant. The north time to cover, which we'll just touch upon here, is bringing cognitive capability and noromorphic nor syntactic chips right into the chip set. Okay. So, you know, this is not meant to replace traditional CPUs or GPUs, but it's going to be complementing players that are mentioned here or working on that. We're not going to have time. We talked about whereabouts, but the whole notion of the industrial internet, this consortium that, that with major players is kicked out for the Internet of Things. That is a lot, you know, that's going to be 10 times in terms of machine learning. And we don't have time to cover cognitive computing potpourri here. But it's being applied to, you know, to data centers to be able to look at photos to diagnose, you know, diseases in, you know, things like that. And the observations and the close here, let's just talk about this, is, we did talk about it. It's not about replacing humans. It's the collaboration, the combined strength and common final comments from age panels to really close. Make a move. Turn around and put it all into one hour. Yeah. Coming forum in San Jose next month. I'll be there. I'm sure the other guys will as well. Yeah, sure it is. Yeah. We have some of you at, at this forum, which is August 20, 2021 in San Jose. Wouldn't miss it. If you want to close, close the meeting, San Jose. Thank you. Thank you. Thank you. Generally, it was a fantastic discussion. It was really, just really impactful of a lot of information as you said, you know, there's, it's so hard to get to everything in an hour, but you guys really covered a lot. Very, some very good points in there. And thank you to the attendees as always for attending and for asking your questions and joining in on the conversation. Always very appreciated. And as Steve already said, we hope you will join us at the Cognitive Community Forum, August 20th and 21st in San Jose. Hope everyone has a great day. And thank you so much for participating in today's webinar.


Hi guys, what is up? Welcome to my channel. It has been a hot minute since I've come in with a Sephora haul. So I'm going to show you the items that I picked out for the Sephora Summer Proma event. So if you want all the details of that event to see what ended up in my basket then just keep watching. This video is sponsored by Sephora. This is the second time I am working with them for this kind of event. So I want to say it thank you to you guys for giving me this opportunity. It really is so surreal. You have no idea how incredibly grateful I am for the audience that I have. That does mean that the items in this haul were gifted to me but I picked them out. I picked out items that I thought you guys would be interested in hearing about. So let's talk about the event. This one kind of crept up on us. So this is starting Friday August 14th and it's running until Sunday August 23rd. The offer is you get $20 off of $100 or $15 off of $75 when you shop in store and online. Now there is no limit in stores to this event however you get one use online. It is also a beauty insider exclusive. So if you are not a beauty insider it's completely free to join and trust me it's worth it with the point systems and everything you want to be a beauty insider. And my Canada gals this offer is available in both the US and Canada. You were not forgotten and all you have to do at checkout is use the code welcome back. All right you guys let's get into the goodies. I'm wearing just about everything on my face right now. So I do have a little bit of insight onto my first impressions of the products. So the first one that we have is from Urban Decay and this is the All Nighter Ultra Glow face primer. I cannot tell you the last time that I purchased something from Urban Decay but something about this called my name. I am a pretty big fan of the All Nighter spray and something about this interested me because it's the ultra glow face primer and it is a dewy foundation grip. So I do have drier skin so I do prefer a little bit of a more dewy foundation primer. It's a squeezy tube and this actually comes out completely like a clear gel. Upon applying it it reminds me a lot of the milk makeup grip primer in that it does have that grip and it's clear and it's gel. I want to do a side-by-side comparison but I think I prefer this a little bit more because it's a little bit less sticky and I feel like it feels a little bit more moisturizing. I've only had the primer on for maybe about an hour. I liked it. The milk one caused me to break out so I hope that I don't break out with this guy but so far so good. If you like the milk you might want to try this one out. This is very similar. I'm excited that I have a new Urban Decay product in my collection. I have been yearning for a new foundation. It's been a while since I've done a foundation wear test. I decided to go with the It Cosmetics Your Skin But Better Foundation and Skincare. This is supposed to improve your bare skin in two weeks. I love to hear that. It's WaterLight Medium Coverage with a natural radiant finish. I got the shade Medium Neutral which I think is a very good shade for me. I love wearing It Cosmetics foundations. I think a lot of people are curious about them so I think I'm going to film a wear test review tomorrow of this but upon first application WaterLight is how they describe it and that's really true. It's a very liquidy lightweight foundation. You don't get a ton of coverage from it. I would say light medium type of coverage. I don't notice it necessarily having a natural radiant finish. I actually thought it was a little bit mattifying. Not super matte but it leaned more matte than it did it do we? Like I said I've only had it on for an hour but my skin looks very good. It looks very natural and I think as the day goes on and my oils come through my skin is going to look progressively better. I will definitely do a review for this if you guys are interested because this is their newest one and I just love It Foundations. I love reviewing them. They're so interesting to me. So far so good. I'm liking this. I would say this is good for people with oily skin who just want a lighter coverage but I will have to update you on that. I also picked up a Charlotte Tilbury Airbrush Flawless Finish Skin Perfecting Micro Powder. Now I have this already. I love it but I wanted to pick it up in the lightest shade which is number one fair.  It just kind of darkened the area and I did find that it did make my under eyes crease a little bit but I think with this new shade I'm going to like it a lot more. I did set my under eyes with it. It brightened it. The color was better and I'm not noticing any creasing with it so far. I love this because it's one of the most finely milled powders on the market. My absolute favorite powder to set with in the middle of the day. It's not going to cake up at all. It just sucks in all of your oils and pretends they were never there. So this is one of the most amazing setting powders if you ask me. These lights make my lips sweat so I'm just gonna put some on but I love this powder. I mean the fact that I got it in a different color should tell you that I love it a lot. I did get another setting powder and this one, you guys, I could cry. I am so happy to have this. This is La Mer, the powder. So I have a super old extra expired powder that I keep in my collection and I only use on special occasions because she pricey. So I took the opportunity to refresh, get a non-expired one. This is my favorite setting powder of all time, you guys. I don't talk about it because I don't use it because it's so pricey. But this is my special occasion powder. There's nothing that looks more seamless on the skin. I just find this to be the most perfecting loose setting powder ever. I use it sparingly and I still feel like my skin looks airbrush. Now I do have the older one with the previous packaging. You actually get more product with that. I did hear the formula was changed a little bit when they released this packaging. So that I'm not sure about. I haven't opened or used this yet, but I hope she's just as great as the old one. This is so heavy, you guys. Like I think half the price is within this packaging. It is so luxe. I need to leave it out on my desk because it's just perfect. I'm so excited. The best powder ever, I swear. So the next item that I picked out was out of pure curiosity. I have heard so many people rave about Tower 28. I do think that I am going to pick up more items from this event and more items from Tower 28 because there's a lot more that I'm curious about. They're pretty fairly priced and I ended up picking up the Bronzino West Coast. It is a cream bronzer and I'm a huge cream bronzer fan. I love this clear acrylic packaging and I've always been attracted to that. So I love the packaging. So this looks a little bit odd in the packaging. It has a sheen to it that I thought might be on flattering on the skin, but once it's blended out, I just use a beauty blender. It's extremely natural and glowy. The gold shift that I kind of saw that turned me off, it actually looks much better on the skin and it applied very good as well. So far so good. I definitely need to continue using this. I did get another cheek product and you guys know I have been hounding to get more colors of the Patrick Todd Double Take Cream and Powder Blush. So I got one other color. I'm planning to place and order myself for the coral color, but I did get She's So LA and I was really scared with this one. I felt like it looked like it was a little bit brown and not good for my skin tone, but it looked so good on everybody else that I was like, I need to get this and I'm so happy that I ended up with it. I just feel like it doesn't look like on the skin what it does in the pan. It actually has much more of a rosier turn kind of on the skin. It's what I'm wearing now.  Patrick Todd actually does it the opposite way, but I still find that the glowiness kind of shows through and I just love the cream by itself so much that sometimes I don't even bother with the powder, but the powder is also very nice. Don't get me wrong, but if you're a neutral blush lover, this is incredible, especially if you love playing with different textures. This one I feel like was a little bit more glowy than the pink one that I got, but I love it. This is so good for the average person because it's so neutral and beautiful. I can't wait to get the next color. I just love the formula of this so much. I think he did a fantastic job. I'm so happy I could pick up another color. So let's move on to my favorite category, eyeshadow. So there aren't really any super big palettes out right now that are super interesting to me that I haven't already picked up. You know, I already picked up the Patrick Star and stuff like that, but there were two not necessarily super new eye products that I've been interested in and I had to get another Tom Ford quad as I continued to grow my collection. So I ended up getting Noir Foume? Foume? I'm so sorry, but this is so beautiful. I wanted to pick this up the last Sephora event, but by the time I got around to it, it like sold out really quickly. So this was one of the first things that I put in my basket. You guys know how I feel for every day about my cool tone neutrals. This is it, you guys. And it swatched so buttery smooth. I didn't use it today, but I can't wait to use it. And I know this is a quad that I feel comfortable reaching for on an everyday basis. This is my kind of everyday color story, pop in a bronze shade and fantastic. But for me, this is going to be one of my new essential Tom Ford quads, assuming that I love it, which I'm pretty sure I will. So I had to get this one, not a super new one, but it's one that I've been wanting for a while. I wanted to get disco dust as well, but this one just sold out so quickly last time that I had to get this one. And then the next thing I got, it was something that I didn't know I wanted until I saw it online and it's odd. It came in this plastic bag and then the palette came with this. And I cannot for the life of me figure out what it is now. Granted, I did not do any research, but I showed my mom. She was like, is that like maybe it's a bracelet and I mean, I don't know how I feel about orgasm acts being a bracelet. I think it's supposed to fit around the palette because it, I'll show you. Like it makes it a little bit easier to grip because this is really slippery packaging. Anyways, that aside, this ended up being one of the items that I'm most excited for. And I never saw it coming. I haven't purchased from NARS in a really long time. I just felt like sometimes their stuff could get a little bit repetitive, but I saw this online. And for some reason in that moment, I really wanted it. I thought it looked gorgeous and I'd seen it before, but this was the first time it sparked interest in me. So this is the orgasm X I quite it's in like a plastic component. And these four shades in here are stunning, you guys. A mauve pinky lover's dream. Now, did I need any more of these colors in my collection? No, but I wanted them. I'm wearing this on my eyes right now and it is so stunning. I love the shimmery shades that they have in here. I love the tone of this quad. Don't sleep on it, you guys. As somebody who has a lot of NARS in their collection, I am very happy to have this. I'm surprised at how much I'm loving this, but I just think it is so beautiful. So this is something that you might not know that you needed. The next item that I have is from milk and this is their new kush liquid liner. I have been trying out a lot of different liquid liners lately. I just love testing them out to see what's the best of the best. And I really like this one. It is a felt tip applicator. I think a lot of you will like this one because I feel like you have a lot of control with it because some liquid liners, they just are so wet. It gets easy to get a little bit messy. This one is not that way. It applies color where you want it and only where you want it. And what's really great about it is it's not super shiny, like a lot of liquid liners. You do get a little bit of a reflection, but it's one of the more matte ones that I own. So I really do like that. And I got a really nice fat precise wing with it today. So I have to obviously continue to test as far as wear time. But so far, I'm really liking this. I'm quite impressed with it. Happy that I was able to pick this one up. Last two final products. They are lips. So this has been on my wish list for a while. This is from Fenty. This is their slip shine sheer shiny lipstick. And I knew I wanted the shade make out breakout. It's kind of a light topy pink shade. And these are exactly what they are called like lip shines. So you're not going to get a lot of pigment from these, which is why I only wanted one color, but it's perfect to throw on on top of a lipstick or a lip liner because it's going to add some gloss and moisture to your lips. But it has some pigmentation to it as well. I have another lipstick underneath that I will talk about in a second. But I just love mixing a formula like this with another lipstick because it just makes your lips so juicy. This is kind of a nude pink color for me. And I really, really like it. I'm happy that I ended up with this color. And I just wanted one of this formula in my collection because I kind of tried this formula before and I really do like it. I think it's nice. The last item that I have for today's haul is actually a Gucci lipstick. I don't talk about their lipsticks enough on my channel, but they really do have one of the best formulas on the market. It's just, it's so luxe, including the price. But look at this gorgeous intricate luxury packaging. Truly one thing that Gucci has been doing amazing is their packaging. You can't deny it. You're paying for the packaging, which is incredible. But their lipstick formula is also incredible. So I've been wanting the shade Linda Beige is basically the lightest color that they have in their collection. And a lot of you guys are going to think that this isn't flattering on you. I think this is actually quite a versatile color and it's going to look good on a lot of different skin tones. Now, this is not a color that you can put all over your lips and when it done, you're good to go. This is a color that needs to stay more so within the center of your lips. So I would start off with either a darker lip liner or even put down a whole lipstick all over your lips.  It's a great mixing shade. Or if you want to use a dark lip liner and mix this in, so good. Once you master that technique down, you're always going to want like the widest, brightest lipstick. This blends in with my skin tone. There's no way this could ever be flattering on me. So I use the buff lip liner from Pat McGrath on the outside of my lips, which is more pinky. It's not too deep. And then I focus this on the center of my lips and then use my fingers to blend it out. I did want the outer bits of my lips to have that pink from the lip liner and it was really beautiful. I think typically I would prefer to have more of the lip liner as a focal point, but then to kind of even everything out. And because I love mixing formulas, I put the Fenty right on top and that's how I got this new pink lip. So these are all of the items that I've currently chosen for this summer event. I do plan on going back for more because I've been out of the loop with these new products lately. It's been like very individual releases coming out. So I haven't picked them up and I feel behind on the times I need to educate myself on these newer products. So do expect probably a part two, I don't know. But anyways, thank you guys so much for your love and continued support. I really truly appreciate it. If there's any items that you might be interested in me experimenting further with new reviews, make sure you comment them down below because that lets me know what you guys want to see. And again, thank you so much to Sephora for partnering with me on this video. If you aren't subscribed to my channel already, I would absolutely love it if you would consider taking the time to do so. And I will see you guys in the next one. Bye guys, have a good one.