The stalwart is here on stage filmmaker lyricist poet writer and journalist He adjoined reliance entertainment when he served as a chairman for 15 years under his guidance Reliance Entertainment became a major player in Hollywood He will be talking about the future of digital marketing ladies and gentlemen mr. Amit Khanna. Give it up for him, please You can use those mics. Thank you so much for my DJ at the console. He's got into the mood finally folks, I know it's the tail end and people have been at the receiving end of a lot of verbias and The problem when you talk about future is as they say the future is now so it's a bit of an oxymoron and Yet some of us who have Actually got a real gray hair as through time and experience it sometimes It makes as imperative for us to look into the future gaze into the future and I will try and Look not too distant into a future But say maybe in the next five years seven years horizon and that's perhaps the most one can Look into This space especially in the digital world. I Spend the last hour hour and a half here listening to some of the the previous panel and some of talking to some of the people outside and One thing which struck me straight away was that this room was still predominantly made up of digital migrants. I Could hardly spot the digital native here Now digital native of people Who actually belong to Gen Z? the post-millennials People born in the 90 late mid 90s and and later These people this entire universe is going to be that of digital natives people who do not understand or Relate to the analog world in the way we did or Or even in the way you did in some form or the other This by the in the next five years. We will perhaps be a completely digital universe That said there are issues which we need to Look I was looking at some recent studies done globally and The one estimate says by 2025 the total amount of data consumed Will be one X a bite Per week now if I put it in perspective is that the total amount of all written Audio and visual data generated in the first 2000 years of human history was one or one or one and a half X a bite Which would include you know everything from time immemorial The other thing which struck me is that in India for example when we talk of of digital India we talk of You know I heard a mention of Geo and how it is revolutionized the space yes, it has but unfortunately In the younger Demographic of let's say 15 to 25 in outside metros only 30% of people are online regularly So we have still a long way to go India may have scaled up its data consumption, but we are nowhere in the picture Even in the top 20 or 30 in terms of penetration we confuse Devices and access to Consumption Which is not really The right way of looking at things I'm not a pessimist. I'm an optimist by temperament, but We have to be realized and Look at certain things which we need to look at in today's day and age first of all you know the hierarchical structure of Industry of goods and services is going to change. We are moving into a Society which will operate in swirls and swamps the first beginnings of this are What were seen in the 90s when The internet was being perceived as a collection of communities. So whether it was a community of friends a community of Nationalities of passions of interests of You can share the economic pursuit Not that if you exponentially extrapolate and look at The future we are going to be talking about these large swirls or swamps of people who will then be the It's like a beehive You know the the societal structure will be more like a beehive. So you'll have a honeycomb structure Now to be able to monetize That structure Through any sale or transfer of use of goods and services requires a completely New approach Not only a paradigm shift, but perhaps reworking the whole theorem there are certain Obvious take a ways which one can see from the last Decade which of digitization globally one is that the emphasis on ROI on Traditional metrics of measurement and consumption of data content Information is actually becoming irrelevant What we are trying to do is Adapt existing models from the analog world and hope that that will service our needs in the future This will be impossible already you know unlike Most corporations in India all the larger companies even companies The new age companies like Google and Facebook or the traditional companies like PNG or Unilever are now employing Large number of cultural anthropologists because that they are the people who will actually be able to read trends rather than conventional measurement metric of how Consumer behavior how societal behavior will work There has always been a peculiar anomaly that technology grows in geometric progression But society moves or changes only in arithmetic progression. So there is always a big gap the whole Key here is to bridge this gap. That is the role of a market here and an advertiser That is what translates into a transaction We were we have seen very rapidly evolve from an attention economy from a knowledge economy We we morphed into the attention economy from attention economy. We moved to the To engagement economy and now we are actually Already stepping into a transactional economy Anything which does not translate translate into a transaction has no value as foreign as an engagement So whether it is listening to a song watching a movie Reading an online News or or even watching the FIFA World Cup Unless there is a transaction at the end of that engagement. It loses value Let's look look at YouTube There was a time when YouTube started and I remember visiting Mountain View in those early days and talking to a group of early Googlers and I was One of the things which I learned was that YouTube was it was supposed to be an interim step because even Google did not see it as something which is going to survive two decades or or become a revenue earner and Fortunately for them The advertising and marketing community was so far behind that they continue to use the conventional method of measurement and hence, you know reach and Connecting with the consumer so Whether it was views or whether it was other touch points were still in in in Pretty much in currency The problem now is going to be quite different one We are already, you know all the leading technical Technology Giants have incorporated AI into their Services We are talking of algorithms, which which are completely You know quasi intelligent and all we are now in the midst of What we call the intuitive web and IoT is the next big thing everyone is talking about but actually Already the next big thing is knocking on our doors, which is IIoT, which is the Internet of industrial things so Where machine to machine M2M learning is going to translate into This intermediation between traditional vendors manufacturers and sellers This is very peculiar because while the first and the second wave of digital disruption Was all about this intermediation Where you know you had a Google you did a search you disintermediated the whole set of people and you connected And that's why with the rise of apps now unfortunately, I Don't see a very long life for apps perhaps a Decade that's it because we are now again moving into an area Which I'd like to call the area of reintermediation Where the word which is quite often used nowadays is curated People want curated personalized, you know Bert Manning's the one of the early Chairman of of J. Walter Thompson and The person who set up the MIT lab media lab once said way back in 1980 That the future of media is personalized segmentation That looked almost like science fiction in those days, but today when we are in 2018 this is the future. It's we are moving from many to many from one to many and many to one to a situation where Everything will be individualized this is This is the key for a for in digital marketing You you also have to keep in mind that today there are enough Technology technological tools available to you which are related with Sensory stimuli Now, you know when we talk of a lot of video content We have to realize that There is a certain amount which will be used earlier called stickiness Now that stickiness is changed because two things one attention spans have Got shorter to there is an information overload Or or even in a broader sense our data overload So the amount of what and how you retain that Information that message is becoming more complex Earlier we worked in a hierarchy of You know inputs now it's it's multiple in's multiple outs and To be able to do that many years ago in a Exchange for media conference, I think 15 20 years if I'm not is around I use the word that by In 2000 in at that time 20 years from then which is 2010-15 we'd come to what was called round casting now. What what did I mean by round casting round casting is exactly what Discussed in the previous panel that you use multiple media and send the same message across But we are going to go beyond that that is going to be dead in another five years because the fact is when you are looking at numbers which are you know In excess of five six billion people who are networked all the time and Are on you know, we will move into this an area where? which instantly Actually Geo is pioneering in India where you know, it's no longer about the cost of data Because if you look in from the telecom angle from the other side of the from the service providers angle In 10 years, we'll move to what is called the SDR the software-driven radio Where spectrum will become fungible? So the scarcity of spectrum will no longer be there, you know that they say it won't matter whether I'm You know using a 700 megahertz band or 23,000 23,000 megahertz band it really won't make a difference because everything will be fungible in that scenario when everyone has access to the equal amount of The pipe and the equal speed all the time What will become important is? how you not how you deliver the message but What you put in the message and? How much of that message is going to be retained the post millenial or Gen Z? is You know the consumer is very very Selective it's going to be far less brand conscious than what we are That's the first important thing They will experiment a lot more They will not be swayed by Things like social media Social media will outlive its present form in the next five or ten years, you know No wonder Facebook and Google and all these guys and an apple Are already thinking in terms of what they are going to do with existing services because It's important that as I said it every every such use translates into a transaction for them and you know merely had sense or or You know existing paradigm will not give you enough results because You know if for example today India 80% of the revenue from Advertising or is a focus is going to two companies that there are 2,000 people fighting for that 20% pie That's all historical We have to look at What the new world will be it will be immersive It'll be hyper connected. It will be individualized. It will be segmented and The value would be in trying to you know currently we are in a phase of in in the post Digital world or what is what I'd like to call mass customization So people and companies and services are being mass customized for large communities of users and buyers That will give way to to the personalized segmentation and the one-on-one individualized 3d printing for example is One such area where you will be able to customize products for Literally for individuals and and all are in Concurrently Let's look at the content side what how it will impact and why am I talking in in a such a Sort of wider perspective is because ultimately this all in In a nutshell in a paragraph will relate to digital the future of digital marketing What we are seeing in content is The humanization of content Emotion is what will not be machine replicated no algorithm at the moment or in the foreseeable future can replicate the Felicity of The human brain or mind to shift from one emotional gear to the other So to be able to to Replicate that or to be able to keep that synapse Going it is important for the for the creative fraternity to also understand that We are now fast approaching the end cycle of the you know the large spectacle Films which are which have dominated or television series which have dominated the mindscape for the last 20 years So we will now start discovering the human aspect of it I Meaningful connect is is the key Now, how do you measure or how do you communicate? Meaningful connect that is something which at the moment nobody has a clue and I don't see the present Matrix Have any clue of of that Yet that's where The effectiveness of the of advertising and marketing Really counts Two or three things are pointing towards certain trends The rise of live events That's going to be huge in the next five or ten years. You know, this is what I call the post experiential phase of media where people would like to Have human contact with events with with goods with places. So that's going to be big We will also have a lot more customization of content Curation is the first part You know, I was remembered talking to Steven Spielberg ten years ago and he's a very very prescient guy and he said, you know Two decades from now When I make a film and if I make a film I will have multiple story lines Because ultimately if I have to engage Every individual in the world He will have the choice at and convenience It is not going to be that you have to press buttons and you have to You know go to your remote again and again, which will determine through intelligence Which in artificial intelligence of which way do you want a particular plot to progress? Similarly, we will see that getting translated and or actually happened Before it starts happening in movies already there are a couple of things being made in Hollywood, which have Multiple story line. In fact, not of many of you will be aware that when Dunkirk was filmed a part of that film was Also shot in something called Cinema VR, which is a new technology and started by Intel now What happens in in such a case is that you will have the ability to have different perspectives so FIFA World Cup or the next cricket World Cup will not only have multiple cameras, but it will have the ability depending upon your choice to tailor make viewing for you Future is an exciting thing one can go on, you know, I mean there are Navigates sitting here and jotting down some things of what is likely to happen It is course so vast and scary about the bell has sounded and you have more to come I'm around in case anybody wants to ask this specific question. Thank you Thank you so much. Mr. Khanna. I request you to remain on stage.  Thank you so much. Mr. Khanna Always look up to you followed your journey very very closely. Give it up for Mr. Khanna. Ladies and gentlemen. Thank you Ladies German, I am Siddharth Kanan hashtag Take munch on social media make a strength


I volunteer because I want to do things that I enjoy and in doing that I bring joy to other people. Emotionally it's just a really good feeling that you get when you're helping.  It makes me feel proud because I'm helping. I feel excited. You get to kind of see what your city's trying to do for you. You get more of like an inside look and you can feel you're part of a bigger thing. You're needed.  What we want in Fort Worth is we want a city of healthy people who are loving and compassionate.


Welcome to my channel, I am Arpita Karwa and in this video I am going to address and answer the most frequently asked question which is how much time should we devote for paper one and paper two preparation if we want to clear net in next four to five months. Now in this video I am going to address this question separately for paper one and then for paper two and at the end I am going to give you three bonus tips. So watch this video till the end. But before I begin with paper one I would like to tell you that the observations and the study technique that I am going to talk about is based on my personal opinion. I have talked to more than thousand students in past two years and I am telling that on the basis of my experience and my interaction with these students. This might vary for you depending upon your lifestyle, your speed of learning and also depending upon how much prior preparation you have already done. If a person who has studied for net from past two years he might have to devote very less time in comparison to a person who is studying for the first time and who is going to give his first attempt. Similarly if you are a person who takes a lot of time to understand things for example you might not be so good with reasoning or DI and you take a lot of time to understand those concepts to brush up upon the basics in that case you will have to give more time in that area. If a person is very good with theory he might have to give less time in that area. So it depends upon a lot of factors but I am going to give you a generalized plan which you have to change and tailor made according to your own requirement. So let's begin. Now when it comes to paper one preparation we simply know that there are 10 units. Now the simple strategy that every student can follow is that you take one unit at a time. Suppose I took reasoning okay now in reasoning you have to go through the previous year papers and finally pick up all the subtopics from which questions are asked. For example if you go through the five years papers you will figure out that there are questioned hours on direction. So the first subtopic is direction. Then you see that there was a question asked on blood relation. So the second subtopic becomes blood relation. Then you see there was a question asked on alphabetical ordering okay. You have to fill in the alphabet which might come in the question mark section okay. So that becomes the third subtopic. After doing this research you will get a list of all the subtopics. According to my understanding in any unit in paper one there are somewhere around 10 to 15 subtopics okay. So the first day's job in any unit would be to get a syllabus of all the subtopics. Now after that you take two subtopics every day. So from the next day you have to start preparing notes for two subtopics okay. Suppose you took directions and blood relation. So it is going to take you 15 to 20 minutes to understand the basics of directions. How to attempt questions on directions then the next 30 minutes you should involve in solving some 10 to 15 questions on directions and seeing where you are making mistakes. Once you have noticed the mistakes make sure you make a note of all these mistakes so that from the next time you don't make the same mistake. So if you do this job in one hour or so you will complete one subtopic. So very simply if you devote one hour to one subtopic you can clearly make notes and also practice questions on that subtopic the same applies to theory. For example in research variables becomes one subtopic okay variables and types of variable. You take any book on paper one or you go to internet or you have if subscribed to any online course you go through the video or audio of that particular topic. You make your own notes then you attempt questions on variables and see if you are getting them right. If you are making mistakes you try to rectify those mistakes. So very simply on the first day you have made a list of all the subtopics. Next day onwards you take two or three subtopics and in five days or six days very simply you will be able to complete all the subtopics. Once you complete all the subtopics you have completed that particular unit. And finally on the seventh day you have to revise all the subtopics so that it gets into your long-term memory and finally do some practice questions on all these topics total in total. For example you can take two questions from direction to from blood relations and accordingly solve a 50 or 100 questions paper so that you know how many questions you are getting it right. So if you look at this plan first day somewhere around one to two hours you have to invest in order to go through all the previous year papers and make a list of all the subtopics. Then next five days you have to allocate two to three hours every day depending upon how many subtopics you are touching and on a final day you revise everything all the notes that you have made that is going to take one to two hours and then one hour you practice. So if you give two hours on an average very easily you can complete one unit in one week and if you look at all the ten units then very simply you can complete it in two and a half months. So two hours time investment for next two and a half months can give you a very profound and a very simplistic preparation strategy for paper one. Now let's talk about paper two. The first thing that you must understand before you begin your preparation for paper two is that the syllabus of any paper two is very vast when compared to paper one syllabus. The vastness of syllabus makes it very difficult to complete it. So the first thing that you must remember here is to know what you will be studying in the next three to four months. You cannot complete the entire syllabus if you are preparing for the first time in just three to four months. So you have to pick and choose the most important topics. Make sure that you make a list of all the topics that must be covered in paper two that you can do simply either by going through the previous year papers or if you have the NTA syllabus you can even look at it. For example in English literature the syllabus given by NTA is very brief. So I have prepared a detailed syllabus for my students which is available free of cost on the website arpitaakarava.com. You can get that syllabus. It is divided in 13 modules. Now if you look at the syllabus it's very huge. The next thing that you have to do is to tick mark the sections that are the most important ones. For example in case of English literature, British literature, theory and criticism. Module one, six and seven are the most important modules from which you can expect 80% of the questions. So first thing you have to do is to put all your energy in completing this chunk. Make sure that you don't start in the ascending order. Most of the students do this mistake. Suppose your syllabus has 10 units. You will start from unit number one. Now you have to first see how many questions are asked from first unit. Maybe only four questions are asked from first unit and 25 questions are asked from seventh unit. So first do seventh unit and later you can do first unit. This is common sense that you have to apply. So make a syllabus. Then we can choose the topics as per the importance level. And finally make a timetable for yourself. Make sure if you have five months in your hand, in first three months you complete the most important topics first. And while you are completing the topics don't just read them. Make your own proper concise notes which you can revise in the last one month. I was preparing notes when I was preparing for UGC net and I prepared 12, I filled 12 notebooks. Now when I started revising them I used to take out the most important elements which I am forgetting and put them in a separate copy. So at the end there were 56 pages which I had to revise just one day before the exam.  So make sure in the first three months you make proper notes and learn them. In the fourth month you touch upon topics which are not so important but are easier which can get you those extra marks. So in the fourth month you take some tits and bits of topics. For example research methodology was included in UGC net paper to English literature and it's a very short topic. So you might take it in the fourth month. With that you might also take some other topic for example literary movements in group. It's a comparatively small section which you can complete easily in one week. So all these small small chunks which are easy and relatively small can be picked up in the fourth month.  Don't neglect revision even if you have three months in hand. Make sure you study for two months and revise in the last one month. If you don't revise in the last one month whatever you've studied in the past few months you will not be able to answer those questions on the day of the exam. What I studied today if I don't revise before the exam I will not remember it at all. So revision is the key. So if you follow the strategy and you use your common sense in picking and selecting the topic it becomes very easy or else you can even join some online course which can give you full-fledged materials so that you don't have to go here and there in order to gather material. So that is what we do in our online course. Everything is given to you so that you just have to plug in your earphone, listen to the lecture, make quick notes and revise it. You're done. You don't need to do a lot of research in gathering the material and then understanding and seeing what makes sense and what doesn't make sense. We answer all those questions in just one place. So make sure that you streamline your preparation and make a very simple basic plan. This will I think going to work for the next net exam. I wish you all the best for the upcoming net exam. Make sure that you listen to the three golden tips that I'm going to just give you so that you can see where that fits in your preparation and you can use the best out of it. Three golden tips that I would like to give you before I end this video are number one. Make sure you are consistent in your preparation. I have seen a lot of students who would start as if they are going to complete the syllabus in one day but then that motivation doesn't last. So why we need discipline? Because motivation doesn't last long. You can be motivated one day but then you have to continue doing your preparation for the next five months so you need to have self-discipline. There was a very beautiful saying by Warren Buffett where he says that we don't need to be smarter than everyone else. We just need to be more disciplined than everyone else. So discipline is that magic power which can make you virtually unstoppable. So make sure you are disciplined and consistent throughout your preparation journey. Second important thing, whenever you want to remember theoretical information, data, facts make sure you study these things right before going to the bed. So suppose you go to bed at 11 in the night, make sure at 9 o'clock till 10, 30 or 11 you study and learn the facts and figures that you want to memorize because when you sleep whatever you've studied in the last few hours is going to move into your subconscious mind. So it is going to be retained far more than what you are going to retain in the morning. So even if you are a morning person, make your notes in the morning. But the retaining thing should happen in the night just before you sleep. The third and the last tip that I would like to put across your table is that whenever you are making notes make sure they are concise. Don't write everything that is there in the textbook in your notebook. Then in that case your notebook and textbook will be similar. You don't have to be a photocopy machine. Make concise notes. Only jot down points which are important and use bullet points rather than writing in paragraphs because our brain remembers the bullet points more than it remembers paragraphs. So make sure that you write bullet points and concise notes. Suppose you are reading a page. Now you should not write more than 10% of what is written on the page. So suppose you are reading a textbook. There is a page which has 2000 words. Now out of these 2000 words you cannot write more than 200 words in your notebook. That is what notes are. So you can only reproduce 10% of what you have read if you want to make proper notes. So make sure you use that tip to save a lot of your time. With that note, I want to take your leave. I hope these tips will help you to understand and strategize your preparation for upcoming net exam. I wish you all the best from the entire team of arpitakarva.com. That's it for this video lecture. We will meet soon in the next video lecture. Till the time we meet next, happy learning, keep loving literature and stay tuned to arpitakarva.com.


You may be thinking right now that it's all your fault, that the way you feel, the isolation, the loneliness, the void, you know, the complete absence of love and attention and validation, like you're in this on your own. There's nothing to lean on, there's nothing to rest on. It's a hard feeling. It's a really, really difficult feeling. It's a difficult sense because get rid of the word feeling, right? Because you can feel sad, you can feel angry, and a lot of people say feel lonely, but to me loneliness is more of like a sense. It's kind of around you. You can really get into it. It surrounds you too. It's not just in you as a feeling.  So you feel like you're lost in the ocean almost because where is my bearing? I don't know where I am. And what you most likely like to do is question, contemplate, dig deep into the reasons why you're experiencing this loneliness. And rather than pointing outward, you're pointing directly at yourself. Loneliness is completely my fault. It's completely my fault. The external factors don't matter. This is my fault. It's because I didn't make friends at work. It's because I chose the remote job and I moved away from a city. It's because the city I'm in, I don't really feel like I belong. It's not at home. I feel like I should have kept in touch with my family more. I feel like I should have kept in touch with these friends and we had a falling out. And if I was their friend, I'd feel less lonely contemplating different scenarios, different situations where, oh man, would life be different if I just had a bunch of people around me? And if I can be honest with you, in my experience, it doesn't change. It's complicated as this may sound. The number of people around you doesn't get rid of the loneliness. You know, these cheesy sayings, it's not about being, or if you're feeling alone, it's not the same as being lonely. If you've heard that saying before, raise your hand, right? If you heard that loneliness isn't just the absence of people, it's the feeling that what you do and share doesn't matter to anybody. It's about being seen. It's about being understood. It's about being validated. It's like, it's being noticed like, hey, I see you exist. I see the hard work you're putting in. I see the struggle. I see the joys. I notice the things that you really like. Hey, I notice the things that you're really, really good at. That's awesome. That's what we're all searching for. So if you think that a bunch of people around you and people even seeing you is going to make a difference, a lot of the time it doesn't. They need to see you as in really see you. Not for the bunch of molecules in flesh and not for your tan and your blue eyes, brown eyes, long hair, short hair.  And it's not the size of the cups, baby. It goes way beyond that and we know it. Because if that's what brings you attention, the loneliness is still there.  So I want to talk about some strategies and some ways to think about this. Okay. So number one, really, it's not fully your fault. Communities and cities aren't built the way they used to. More people are living alone now or they have specific roommates. We're living in smaller spaces. There's less, they call this like, oh my gosh, what's it called? Like a third living or community space, something about community spaces that are meant for gathering. You go to Europe, you got all the piazzas and everything like that, right? It's amazing for gathering.  The culture in North America, go do your job. You have to work to live. You have to go home. Everything's so incredibly expensive that a lot of people work two jobs. When do you have actually time to socialize too? There's so many factors. There's a lot. And that's not to pull away personal responsibility from you. That's not what this is about. It's just about giving yourself a little bit of grace, a little bit of understanding that, okay, there are other factors at play here. Fair enough? Fair enough? Like dealing with depression is very, the feeling is associated with that loneliness because under that umbrella of depression is, yeah, loneliness, it's anger, it's misunderstanding, it's grief, it's a whole bunch of stuff. So even in depression, people point the finger at them again, regardless of the biological factors, regardless that you just got broken up with, regardless that you just move cities or move towns and the change is just absolutely unbearable, regardless of physical injury that you're recovering from and you can't exercise or move so you're becoming depressed. It's all my fault. I should be able to control everything.   Study some biology. Study some chemistry. Study the digestive system. Study neurobiology and tell me how much control, conscious control you have over these things. Did you really control how your breakfast was digested today?  You had control of your pancreas, didn't you? All the liver enzymes, all your bile ducts to break down all the fat, I'm sure you did. You didn't. When it comes to a sense of control my friends and like the feeling of loneliness, let's look at the whole picture. So here's the whole picture. It's got some to do with you. It's got some to do with the environment. Cool? Cool. What can we do about it? If I were to ask you why you don't hang around assholes, you'd probably give me a pretty straight answer. Oh, they put me down. They make me feel terrible. They're always negative. They can't see the sunshine even when the sun is shining. Why would I hang out with an asshole? They put me down. They make fun of me when I do something. They judge me. They don't let me be myself. You know, if I'm good at something, they never say it's good enough or sorry, they never say it's really good. It just barely good enough if enough at all. When I learn something new, they don't say great job. They say, oh, I've already done that. Or that's not even that good. That's not even cool. Who plays the ukulele anymore? What? Why are you learning the harmonica? That's pretty idiotic.  Their view of the world and their view of you, they don't support you at all. It can make you feel pretty lonely, can't it? Hang around someone that doesn't appreciate you, doesn't see you, doesn't honor the struggle, doesn't honor the work you're putting into your life?  You see how I'm going with this?   Oh my God, it's huge sometimes. Just massive.  Does that make you feel more lonely then if your internal dialogue is always putting you down? Are you always putting yourself down?  The old saying is, if you feel lonely when you're alone, that means you're in bad company. If you feel lonely when you are alone, or feel lonely with a group of people, that means you're not in good company. So where do you start? How do you start loving yourself? First of all, acknowledge awesome things that you do. And notice your inner critic coming in. I'm starting to learn guitar from scratch. All I learned was heavy metal learning, heavy metal when I was starting in 2009, believe it or not. So I can play all these riffs, okay? But as far as my pinky skills, as far as my work around the fretboard, I'm new. So I'm actually using a musician. This isn't an ad or anything, but I'm using musicians starting from scratch. And the internal dialogue is, oh my God, Scott, you can barely get around that D chord and then go into fret five and you've been playing for over 10 years. Oh my God. Right? I noticed that. I'm like, are you trying to put me down? I was having fun. I was having a lot of fun right now doing something alone and in comes the person that doesn't want me to have a good time. Doesn't want me to enjoy myself. Enjoy the spirit. They come in quick, don't they?  I see you. I see you. Oh, I was having a good time and that came in. Okay. Even if you're alone sometimes, it'll come in. You're lonely. You're alone again. Even if you enjoy being alone at times, you can feel guilty for the enjoyment. You're sitting here watching a bit of Netflix. You finish work a long day. You get home and you're like, man, it's going to feel good being on the couch and in comes a voice. Why aren't you working out?  Right? Why don't you have more friends? Why didn't you go to the bar with people after work? All your work colleagues wanted you to go, but I wanted to be alone. Why do you want to be alone? Because I'm enjoying myself. Why are you enjoying yourself? You should be with people. Notice the inner critic, my friends. This is the meditative work I've been doing for, I don't know, that specific work like four or five years now. And it gets easier. It gets easier to notice and to almost laugh at these things and be like, oh, there you are. You're coming in. I just turned on the computer and I was about to learn a different chord and here you come. Hey, what's up, buddy? Okay, I'm going to keep playing. Notice. Okay. Really notice. All right. The next thing which is really important is to label the loneliness. Don't just use the umbrella term like I'm depressed. Don't use the umbrella term like I'm just sad. You know, sometimes that is just flat out. That's it. A lot of the time, if we're feeling a sort of stress and maybe if we're on edge, maybe if we're really low, the loneliness is in that. And that's the void we're feeling. That's maybe the disconnection we're feeling. That's the loss we're feeling. That's the inability to touch edges of the pool and know where we are, the ability to touch walls, right? So label loneliness saying, okay, what am I feeling right now? I'm pretty lonely right now. I'm feeling real lonely right now. And by admitting it, you remove the mystery and then we can go into number three is label it, remove the mystery, and then go into it. This is really, really important, everyone. And this is what a lot of us can't do because it's very difficult. It's very uncomfortable. And don't think that I'm going to pressure you to be like, you have to go into it right now. When you end this video, go into your loneliness. It's the only way. The best way to live is to be uncomfortable. If you're too comfortable, you're not making process. Why not making progress? I can't even say it. These things that I see on YouTube are so absolutely damaging and so ridiculously one sided that there's no balance on social media. And what I'm telling you is that there is balance, man. So number three is why don't you try to go into it? And what I mean by going into it and give this a thumbs up, give this a like, write me a comment if you'd like me to create a beautiful meditation for you around how to go into this sense, this sensible oneness, and I'll guide you right through a baby. Okay.  I think I talked about this, to talk I did about a month ago is you ask yourself, okay, Scott, okay, Jimmy, okay, Laura, okay, Sandra, okay, Jennifer, okay, Isaac, if you're if that's your name, please comment. You say, what is this all about? Beautiful words, beautiful words. What is this sense all about? Is this sense telling me something? What's it telling me? Sometimes emotions tell us a lot. Sometimes they don't have to tell us anything. It's like I'm sad. I'm just sad. I don't want to, I don't want to zen into it. I don't need, I'm just sad. I'm going to have a day. Cool. I'm lonely. I'm going to have the day. Cool. Once in a while, ask yourself, what is this all about? Write it down. This is free education, baby. Isn't YouTube amazing? Isn't it amazing? It's so great that I can share this with you because it's really helped me a lot in so many focusing exercises of what is this all about? You know, I yell at Vanessa or I get angry at this or I'm short with this person on the phone and I just want to rush through the day and I'm like, what's the rushing all about, man? What's going on? Why are you angry, huh?  Just be kind to yourself and see what it's all about, okay? So that's number three. If you can go into it, that's awesome. And here's number four. You don't have to go into it if you don't want to. How about that one? Distract yourself. If you're lonely, you don't want to go into it. You don't want to think about it. I don't know. Play some video games, watch some TV, read a book, get involved in somebody else's story to relieve yourself of the tedium of existence, of your own living. Because the more we focus on ourselves, man, the more miserable we can be if it's not in balance, right? It's always me, me, me, that gets exhausting, that gets boring, gets self-centered, the world becomes small. Open up your world a bit. Watch some Arthur, learn some beautiful life lessons. Watch some Ninja Turtles from 1987. Play some old Mario, right? Watch Lizzie McGuire. I'm trying to hit the audience right here. What else would you buy? I don't know. Watch old episodes at Dexter. I'm watching House right now. I've never seen House and it's awesome, but House, like, is he ever going to change? The demeanor, he's such a dink. House is almost like the internal critic, right? It's that internal dialogue of, oh my gosh, you're never good enough. It's unbelievable. Do something to distract yourself, okay? Now, this is a big one too. I might play you a song. I might play you a song. We'll see. The title of this video is by a band called Third World. I think they're out of somewhere in the Caribbean.  Check it out. And it goes, if you're out there somewhere feeling lonely, you're not the only one. You're not the only one. If you're out there somewhere feeling lonely, you're not the only one. I'm fringing myself out. You're not the only one. Repeat it. Ooh, that can be your mantra, man. You're talking to other people.  You're saying, I'm not the only one. You're not the only one. It's beautiful. Make it a mantra. Sing a song about it. So the song is amazing. Check it out. Makes me feel better when I'm in those times of loneliness and put your loneliness to work. That's what this is for. That's what this notebook's for too. Put your loneliness to work. The best songs, man. The best song come out of, unfortunately, like desperation, loneliness, heartbreak, challenge, suffering. Listen to the best songs of all time. Except for like Earth, Wind, and Fire. Those people, those guys were just happy, nonstop. So it can come from happiness for sure. But all you Taylor Swift fans, man, I can't wait till she gets another broken heart, right? Adele, I can't wait till she goes through another breakup before she gets a new album out because it's a source of so much. Put your loneliness to work. You never try poetry?  You never play an instrument? Try the recorder. Try a little keyboard. Get GarageBand for free on the app store and write a little synthesizer thing with your keyboard. Hum. Something. Anything. Draw. But Scott, I don't draw. I don't care. Draw out. Excuse me. Draw out what loneliness feels like to you. Feels like a squiggle. Close your eyes with it.  Whatever it feels like to you, write it out. Express it. Move through it. There's times where we can avoid, distract. I'm not going to say that's a bad thing, man. It's a pattern interruption. It's a change. It's changing your state. Getting out of loneliness is a good thing. And then there's other moments where the sense calls for it to be heard. Give it a try. Go in. Ask it. Okay? So you know this is available all the time. It's Patreon.com slash Scott St. Marie. If you're looking to express your loneliness, talk to me and other people and just have a chat. We meet every month. It's just a way for you to support these videos because they rarely have sponsorships, if at all. It's a way for you to support it. A couple bucks, you get a community feel every month. What I'm also releasing, Vanessa and I, it's going to be eight weeks with me about going into the senses, about how to deal with anxiety, how to deal with an overwhelming sense of loneliness, sadness, depression. It's not going to be eight weeks to your perfect self. I'm not a snake oil salesman, but it is going to help so much because we're going to meet every single week for eight weeks. We're going to meet eight times and I'm giving you specific things to do every single day between each class. This is what mindfulness based stress reduction did for me for the eight week program. It was amazing and it changed me so much and I actually might sign up to it again to get a refresh because it keeps you disciplined every single day for two whole months and you take that with you. If you're interested in joining this, I'm going to take like 15 people max, small group maybe 20, and we're going to work together for eight weeks. The link is going to be in the description or in tagged comment thing. Click it if you want to join the wait list and if you don't make it this time, I'll be sure to include you next time. That's about it. Have yourself an awesome week. If you're lonely, if you're out there, you're not the only one. I feel you. I see you and it'll be okay. It'll be okay. Take care. Oh, like and subscribe and stuff. If you like the video, comment. Make yourself seen in the comments. Tell me something that you like, something that you're good at and I'll read it and a lot of people will make yourself seen. Take care.


the development of the LNN software and its application to several domains within IBM research. I've only seen two questions so far. Please keep the questions coming and the discussions on the crowdcast. Naweed is a research scientist in AI science at the IBM Research Africa, here in the Johannesburg lab. He's leading the open source development of the LNN in the global neural symbolic AI team at IBM Research. Naweed, please take over. Thanks, Ndiwul. So yeah, my name is Naweed Khan, research scientist at the Johannesburg lab and I'm leading the LNN development in this case. It's really a tough ask to come after Ryan. We did such an excellent job in trying to explain what the LNN is in terms of its breadth and depth. So maybe I'm just gonna take a step back and give you a high level overview of where the LNN is playing at right now. So touching on some of the domains as well as focusing on some research that is released within the space and maybe have a short discussion at the end around what comes next for LNN. So looking beyond research. So LNN really does try and answer a very difficult question of how do you combine neural networks with symbolic reasoning? And the way that we've attempted to do that is to take an individual neuron and to constrain it logically in a way that allows you to take a symbol and apply it to that neuron on a per neuron basis. And as described, we get this very nice class of neural networks that wasn't there before called neural equals symbolic in a way that allows us to truly lift the lid off of neural networks to be more than just a big box models. But I'll describe a bit of that later. So just to give you an overall look at what the landscape looks like. So as part of our first iteration of releasing the LNN, what we've opted for was looking at a specific subset of logic called first order logic and the propositionalization of groundings within those logical statements. So making sure that each fact in our network is in fact just the proposition. And it's a common approach in neural symbolic systems. As Ryan mentioned, we do have bounds on individual propositions. This allows us to have uncertainties about what truth values can be. And wherever you are maximally uncertain, that facilitates unknown inputs. So we can cater towards the open world assumption. The LNN has also been rigorously tested against classical logic and extended towards different classes of real value logic like Lukasziewicz and Godel as Ryan described. And we've really taken it forward in terms of the parameterization on putting weights on a per input basis. So for example, we can actually identify what is the contribution of individual operands in the computation of an operator. So where does the LNN really apply? That will probably be the fundamental question in your mind. And right now the starting point will just be in any system that has first order logic as some kind of a representation of knowledge. If you do have first order logic statements then the LNN is automatically for you. But the one thing that we do have to do differently when training the LNNs compared to normal neural networks is this additional step of constraint optimization. So it's not as simple as just saying, okay, that propagation finds the gradients and updates the weights and the biases. We actually have to say that specific neurons within the system have been defined according to predetermined symbols set by a human. And those symbols may have their own constraints associated with it. For example, if I specified a conjunction or an AND gate as a particular neuron, that means there are constraints placed on that neuron that says if all of the inputs in the conjunction are true then the output should be true. But if a single input is false then the conjunction be false. That's the classical interpretation of an AND gate. And this applies to all kinds of neurons that can be defined. Typically it's a conjunction and disjunction and implication under the hood. And what this really allows us to do at a micro level is to lift the lid off of any neural network defined as an LNN and inspect each neuron within the system to ask that neuron, what are you? What is your symbol? What is the truth of that symbol and how much certainty do you have in that individual truth? Which is something that you couldn't do with normal neural networks because there is no interpretability on a per-neuron basis. But because we have this symbolic system implemented as a neural network what you can do is take an existing knowledge base, the database of some kind and use the symbolic reasoning engine of the AND and in order to identify how logical facts and reasoning might propagate throughout the system. Maybe just to touch on this idea of where rules may come into play. You may have human-generated rules but as Ryan alluded to you don't only have to have human-generated rules. Those rules can also be learned from the facts themselves. And he did demonstrate the one piece of work that looks at templated rule learning. So it is possible to have this combination of both human-generated and machine-generated rules that work together with one another and along with the neural components of the system. But you can imagine that having machine-generated rules introduces a lot of noise within the system. It may not always apply and you always have this interplay between having noise in the system that might introduce some kind of logical inconsistency. And Ryan did flash some of the lost functions there but what it really facilitates is this ability to identify where there's inconsistencies within the system. So you can imagine one part of the network saying a neuron should be true and another part of the network saying that neuron should be false. That inconsistency in a normal symbolic system would actually cause it to raise its hands in a very brittle way and say I can't compute over inconsistencies. It doesn't make any logical sense. But what we do in the LNN is to actually change that inconsistency into a loss. And then as part of the back propagation process through the gradients, we can actually identify what were the sources of those inconsistencies? And then using this weighted formulation allow you to slowly start to change the parameters to reduce the amount of inconsistencies within the system. So that allows the LNN to operate under two modes. Typically a supervised setting if you give any particular label to any neuron within the system as well as a self-supervised system where the whole system will try and converge towards being logically consistent as it tries to learn. There is this combination of the open world assumption along with the knowledge-based reasoning engine that plays nice with one another. So you can imagine a scenario where you may not have all of the information within a neural network. Either some signal that came from a hardware system might have gotten corrupted or there's just missing data or you didn't have any information. The LNN's open world assumption allows you to stipulate that that fact could be unknown in the system. But through the reasoning process and the rules that exist on top of the facts you can actually start to fill in information that was missing if there is a reasoning path that makes sense for the LNN. And through multiple hops of reasoning it does facilitate knowledge-based completion. And finally to just touch on this idea of how LNN integrates with its back propagation engine you can imagine that any individual proposition or atom in the system can be given a truth by a human but it may not be the case. You can also have those propositions being defined by whole deep learning systems. So we get this very nice interplay between a subsymbolic deep neural network with rules that might exist on top of it and the back propagation process of LNNs allows you to actually use one deep learning system through the rules to actually train another deep learning system with those rules to make sense and there's logical coherence. So that's a very high level overview of where the LNN is right now. So just to quickly flash some slides around the research the link is at the bottom I do encourage everybody who is a research oriented to actually go to this link. You'll see that IBM has invested heavily within the space of Neurosombotic AI. Lots of publications, many projects and collaborations. So for those of you who are actively working within these research areas to reach out to the authors of those particular papers. I'm gonna flash three quick pieces of research you would have seen but some pieces of it in Ryan's presentation. The first is the main LNN archive paper that's available right now. It does identify and explain to you how to set up an individual neuron, how to construct a graph, what the reasoning steps and the learning steps might be in order to create an LNN. A second paper is around the soundest guarantees of the bounded computations of LNNs. So how do we go from computing over complete uncertainties and then as new facts come in we start to be more certain about particular positions in the system. Finally is that I guess paper that Ryan mentioned on how to train LNNs within the constraint optimization process. And it is a fairly difficult problem but there's a lot of different approaches that we could use. In this case, there was just one approach that uses an advanced Lagrangian method. Some four papers on where the LNN has been applied building on top of Ryan's papers that he discussed. The first is the knowledge-based question and answering. That ACL paper will be exposed a bit more in depth by my colleague, my colleague Francois will speak after me. The second is using LNN within logical optimal action. You can find that on archive as well along with the other RL applications that Ryan mentioned. And also what comes to mind is Benjio's presentation yesterday where he showed that an RL agent really needs to execute on some logical function. And those logical functions can directly be replaced by LNNs under the book. LNN has also been used within time series classification so weighted signal temporal as a subset of that. And then also in knowledge-based completion. So in this archive paper it looks at how to use the LNNs bounds as an embedding space in order to perform multiple hops of reasoning. So that's a very high level overview of some of the researchers. Please do reach out and build on top of the literature that is there. But just to wrap up the conversation around what's next, what's beyond LNN research. And two things really come to mind. The first is development and the second is integration. So what we're gonna do as part of the development push is to really cement this idea that we wanna move forward in an open framework. So we are trying right now to implement the LNN and will be released on Git. As an open platform that teaches all of those different tasks sets that you saw. It's a really difficult task, but we are in the process of working on that. And as part of this first implementation, the code base will be released with a backbone of Python and PyTorch. So do familiarize yourself with those tools if you haven't already and just go over the research on some level to understand the different mindset that's required in order to do computations with LNN. It is a little bit different from standard neural networks. But when we release the LNN as part of the open framework, what we will do is to continue to innovate and push the state of the art within this new domain that we've really created. And as part of the first push will be requiring the user and the human to put in logical rules. But as Ryan mentioned, you're not restricted to that. We can do role learning. So over time, we will integrate different ways of doing the role learning from data. And the data could come from either an image, the text to different kinds of domains. But really where LNN will shine and we'll include this as a whole new section and it's related to all of the conversations that were had yesterday around explainable and trusted AI is to look at the LNN not only at the micro level of inspecting individual neurons and asking what they are, but also at the macro level to really ask a neuron, why do you believe what you believe? How have you come about to reason about some fact of the universe and having this real human computer interaction where the machine can explain itself, which is something that we really couldn't do before. So that's a high level overview of the domain. And I'm looking forward to some of your questions in the chat. Thanks. All right, thanks, Nahuid, for that great presentation on the development of the LNN and its applications. Seems like I came into the stage too early there. I hope Nahuid has helped solidify some of the theoretical ideas that Ryan introduced. We now move on to the next speaker, Francois Luce, who will delve deeper into the application of Neurosymbolic AI to question answering, contrasting it with end-to-end pre-trained deep learning based language models in a natural language context. Dr. Luce is a lead of the Learnable Reason Sub-Theme and team lead of the LNN at IBM Research. He's also based in the Johannesburg Research Lab. He's leading the global effort in the development of the LNN and its application to several domains within IBM Research. Take it over, Francois. Thank you, Nduvo, and thank you, Ryan, for the comprehensive overview of LNN and Nahuid for the high level summary of LNN yet again. So in this closing talk of our session, I wanted to take a step back and make a case for Neurosymbolism and LNN as a way to achieve next generation advances in natural language understanding or NLU. So what I'm gonna do in this presentation is to review some of the problems that we have with deep learning-based transformer-based language models. And I'm gonna discuss how Neurosymbolism can address those deficiencies. And I'm also gonna show an example of how we apply Neurosymbolism to a question and answer task. So why do we want natural language understanding? So firstly, natural language ability is one of the all marks of human intelligence and language is central to the human experience. So the ability to inject automated NLU into our everyday lives, we can add considerable value to so many industries and human pursuits, industries like healthcare, law, education, but also recreation and other creative outlets. And by injecting automated natural language capability in our everyday lives, we can enhance and enrich the human experience. And so one of the prime examples of this is the BERT language model, which is used by Google search and it serves more than 4 billion people on a daily basis. So looking at the philosophy or the metaphysics of understanding, what is understanding? There are basically three broad categories of understanding. It's referentialism, internalism and pragmatism. So starting with referentialism. Referentialism basically maps your text input into external reference or it maps contexts into truths. So referentialism has the ability to evaluate the truths of sentences given a context. And referentialism is the typical mode that neurosymbolism uses. And this typically gets executed as linking things that you see in the text to an external knowledge base. And then we gain knowledge of things that we see in the text and we can make sense of it to our degree. Secondly, internalism. This is somewhat related to referentialism. With internalism, we retrieve internal representational structure given linguistic data. So basically we map text to internal objects or processes. And so this suggests a little bit of a causal understanding. So referentialism, if we go back one, referentialism supports systematicity. So systematicity is where you systematically arrive at an answer or an output. So you go by it step by step. And this is a very important quality for interpretability because you can examine the inner workings of a model if it's based on referentialism or internalism. So internalism would be intrinsically systematic. In contrast, we have the mode that is predominantly used by deep learning models, the mode of pragmatism. So imagine you have an agent and the agent is intercepting communications from an unknown universe. It might be able to imitate or mimic the conversational patterns that it sees, but it's inability to ground into things that are known would basically eventually betray its lack of understanding. So in this talk, I want to play these two against one another, the approach of referentialism for neurosymbolic AI and pragmatism for deep learning. So looking at the best deep learning models for natural understanding that we have today, we see that deep learning through the attention mechanism in transformers has brought us these large scale language models like ALMO, BERT, different variants of GPT and many other language models that's based on these foundational models. And these models display a very impressive variety of linguistic capabilities and adaptability to so many different situations. And these models can actually train through deep learning on large unstructured data, raw text data, basically through a simple self-supervised objective of auto-recreation. So auto-recreation, you only need to predict the next word given a preceding text. So what's the problem with these transformer based deep learning based models for language understanding? So if we take a large scale language models like GPT-3, these models are exceptional at producing fluent and plausible sounding text without necessarily being grounded in truth. So when I say grounded, I mean being able to link things that you see in the text on the surface form to link that to things that you have knowledge of. So pointing to a knowledge base, for instance, having knowledge of something allows you to reason more systematically and more soundly about the things in your context. So even though these deep learning models can produce text with strike fluency, they eventually lapse into a state of incoherence. And this suggests that they are merely parroting according to the statistics of the data. So these deep learning models they rely on something called emergence where behavior is implicitly induced with minimal priors. So they basically built in the case of transformers they built statistically representations given the current patterns of words. Now, these deep learning models can accompany their outputs and decisions with generative explanations. But there's little assurance of the faithfulness and the soundness of these explanations or whether it actually gives the right kind of insight into the moral behavior. So these models lack an intrinsic interpretability and the systematicity. So that means that they have unmapped some unmapped capabilities. And that means that these models can lead to unintended consequences and unknown failure modes. So an example of some of the reasoning flaws experienced by these transformer based language models here we have a study that was done on the birth classifier birth being the basis for a lot of natural language inference transformer language models. It's very, very capable to perform inference but basically it's a statistical learner. So it's prone to adopting shallow heuristics or so-called shortcut features. And in these examples, the shortcut features or the shallow heuristics that birth adopts is to say if it sees a port of a sentence in another sentence, then it would readily assume that the one entails the other. Let's look at some of the examples of this. So we have three different types of word overlap starting with the constituent word overlap. We have the sentence starting with if the artist slept and then birth immediately says that implies the artist slept. But of course this is a logical error because this precondition is not necessarily fulfilled. Then if we go to sub sequence overlap, we have another example here, the doctor near the actor dons. Bird implies that oh, it's the actor that dons. Which again, this is incorrect. So bird actually gets confused here by the change from passive to activist just because these two sentences share the same words. And it's throwing the incorrect logical conclusion. And similarly with lexical overlap, here we just have a disconnected sub sequence that appears in the consequence of this implication. And bird again makes the mistake due to this change from active to passive voice. So how can neuro symbolism help us with some of these issues? So if we look at like GPT-3, it has actually trained on three to four orders of magnitude more data than humans will ever read or hear. Yet humans achieve a better language understanding. So advancing grounded language learning, this is essential to approaching human language acquisition efficiency. Furthermore, a neuro symbolic approach uses logical rules and these logical rules are either acquired or they're utilized during inference. And these are inspectable and auditable rules. So you get some insight into the model that you perform inference with. And this means that you can map the behavior, the capabilities of the system to a larger extent. And the system behavior can be anticipated more reliably. So here we have an example which was previously shown by Ryan and Awid. An example where we apply logical neural networks for the task, the natural language inference task of question and answering. And this is an approach, a neuro symbolic approach that is based on this mode of reasoning or understanding called referentialism. So if you recall, referentialism is where we link things that we see in the text on the surface form. We link that to things that exist in a knowledge base. So this is the act of grounding. So we'll take entities and the relations that we see in the text, some abstract representation of the text and we'll map those to corresponding entities and the relations that we have in a knowledge base. And that very act gains a lot of information about what we're dealing with. And this gives us the ability to actually perform proper logical reasoning in order to determine the answer to the given query. So looking a bit more closely, here we have the pipeline on the left hand side and we can start out with the natural language input to the pipeline, which is the question. Here the question is which actors stored in Spanish movies produced by Benicio del Toro. The first step that we take is to convert that into an abstract meaning representation, which can be represented as following the example this I am or we didn't take in the second step and we take the entities and the relations that exist inside of this I am or and we point that to our external knowledge base. In this case, we used DB Pedia, but you can also use Wikidata for most of the popular type of Q and A benchmark datasets. And then after we have grounded these pieces of the I am or we now have a grounded I am or representation. And then the next step is to take this grounded I am or representation and to convert that into a logical form. In this case, we have first order logical form for our question. This becomes the input to an Allen or alternatively we express this logic as an Allen and as a neural network. And then we permit Allen and to perform the reasoning in order to obtain the answer. And this would be proper logical reasoning whilst making reference to all of the knowledge that's pertinent to this scope in the knowledge base. So Allen and gets to traverse the knowledge base through a reasoning approach in order to answer this question.  So how does this system perform? So we call this system NSQA. In the bottom line here, we can see the performance and this is for two very quite difficult Q and A datasets. And if we compare our method against these state of the art Q and A systems on these benchmark datasets, we see for a F1 accuracy score, a higher score being better that we outperform the state of the art. And in some cases with statistical significance. And the hypothesis here is that because we symbolize everything in our pipeline as early as possible, we get the best opportunity, the best chance to perform sound reasoning in order to obtain the answer. And this produces better answer and this could contribute to the better performance of our system. So this is a quick overview for this specific case and the paper is linked here. And then in closing, answering the question, what is next in natural language understanding, we firstly recognize that still the growth in moral resource requirements will still forexceed the generational hardware improvements. And that means that we're gonna have to keep working at these models. We're gonna have to keep introducing further useful priors into these models in order to improve model performance. We're then we're also gonna see a trend of using deep learning and back box models for high stakes decisions being scrutinized more and more. And then we're also gonna see our greater emphasis based on intrinsic interpretability, explainability and causality of these future language models. And we're gonna see to that end a greater administration of experimental protocols that examines the internal dynamics or the inner workings of these models. And we're gonna see the deep learning community turn to neurosymbolic approaches integrating that more and more in order to retain all of the benefits of deep learning, but also gain the intrinsic interpretability and systematicity of a neurosymbolic approach. And we're gonna see neurosymbolic representations that are powerful like LNN being adopted more and more in these future desirable language model approaches. And with that, I'm gonna conclude and hand over to Ndiva. Thank you. All right, thanks Franco for the great talk. I hope that has helped clarify the advantages of neurosymbolic AI and LNN language understanding tasks. So that concludes this series on advances in learning and reasoning. I'd like to thank our speakers for their amazing presentations. I hope we've managed to get you excited about our journey to neurosymbolic AI and the application examples have helped make the material relatable. I'd like to invite all our speakers back to the stage for the final discussions. We have Antwa and Nawid. I don't know if Ryan is joining. All right. I will pick three questions from the audience, one for each speaker. I'm not sure if we have enough time for all the questions. I'll read all three questions and you guys can answer once I've read all the questions. Starting with Ryan, we have a question from Hope. The question is ultimately, what's the general problem or concept of LNN is based on or trying to solve? We move on to Nawid. What problems can a young researcher start working on in LNNs? And then Francois, can you give us a concrete example of a problem that is generally tackled with traditional neural networks and contrast how LNN will solve it better? I do feel like your presentation did kind of touch on that. Over this question was asked earlier on before you have a chance to speak. Ryan, your question. Sure, okay. So what is the general problem solved by LNN? I think that LNN has a lot of different fronts, a lot of different things that it's trying to do. So if we boil that question down to something like what sorts of problems, like business tasks, for instance, might LNN be well suited for? LNN is very good at prediction problems in the same sense that neural nets are very good at prediction problems. They can be trained in the same way that neural nets are in the sense that I have a training data set in different examples of observed facts and particular answers, ground truth answers for what prediction should have been. So given thousands of such examples, I can do the gradient based optimization in order to make those predictions accurately, though of course with the distinction being that LNNs are thinking to also maintain logical consistency throughout. LNNs are more flexible than that. They can be used in a setting that is not very typical of neural networks, where for instance, you have multiple different things that you are trying to predict at different times. These are still, I'm still phrasing that as a prediction problem, but it's just predicting different things at different times kind of problem. You could, as a point of comparison, the various language models like BERT and GPT and so forth are also just prediction problems. They're attempting to predict, what's the next token in a sequence? So you could map that kind of problem into LNN as well. But I would stress, it's meant to be a very general model. It's almost at a paradigm level in the sense of, so what can you use neural nets to solve? And the answer is almost anything. Yeah, with the added benefits of interpretability and generalization. Yes, yes. Yeah, you can incorporate domain knowledge much more easily. Yes, definitely. Yeah, so I guess to get to that aspect of the question of, so why neural nets and not some other method, those are the concrete things that we can say is, it's trivial to incorporate domain knowledge. You just directly represent those formulae expressing domain knowledge as extensions to the neural net. And another thing too is, I didn't stress in my talk the modularizability. I can take a portion of a logical neural network and reuse it elsewhere. I can combine logical neural networks that are trained on different tasks, but in the same setting, because the truths of the different formulae learned should be compatible with each other. Making a larger network, making a larger network, training out of two, and then retraining that might constitute a good method of building up a large network from smaller independently trained components, for instance. So there's, again, lots of desirable reasons to investigate elements. All right, thanks Ryan. Naweed, what problems can a young researcher start working on in NNNs? It's actually a really interesting question because Ryan just touched on so many of those topics right now. The one thing that stands out for me when you look at new or young researchers coming into the space is some researchers don't have a lot of experience within logic or they don't have a lot of experience within deep neural networks, but they may come from different environments. So a physics background, applied math background, many different areas that are starting to only now look into how to apply neural networks within those fields. So you may come from a different environment. The question is when you're looking at neural networks being applied in those spaces, LNN becomes applicable where you say you don't just have data, you don't just have neural networks trying to do pattern matching on their data, but you actually have some kind of domain knowledge as well that you can enforce with that. And it's truly this interaction between the knowledge that you can reason about that provides some gains that we might be able to see as young researchers because new fields may not have as much data as existing fields. So for example, if you're working in computer vision, there's a lot of data that's associated within those fields, but if you're coming from a physics background, it's more likely that you'll have knowledge that you can represent than having a whole lot of clean data that you can actually work with. And this is where the LNN can shine in these kinds of environments because LNN fundamentally can operate as a zero shot learning process because a rule can be something you learn on without even having any facts associated with that system. But as a little bit of data starts to come in, that's where this interaction between the rules and the facts starts to shine. So you can kind of see how coming from a different environment has less data which makes deep learning based systems less likely because for deep learning, you need a lot of data to train. Whereas the LNN, you just have to simply train on those rules itself. So it's this interplay between the knowledge that you have and the data that might shine in those ways. All right, thanks, Nawud. Front work, maybe you can just re-emphasize some of the examples you showed in your presentation. Yeah, I wanted to give the most prominent example that I can think of a concrete example of where LNN does better than normal neural networks is with knowledge-based computation or link prediction. And so we showed a couple of papers in the list of things that we've done with LNN before. And the reason this for me is a very strong example is because you do have the use of neural networks in combination with embeddings, which is a very, very popular approach for link prediction. So they would use neural networks in the way of learning a knowledge base, essentially as a neural network operation so that they can go from one embedding to another embedding given a certain relation. And LNN outperforms some of those methods. And we have a very strong hypothesis of why that is the case. And it's simply because LNN has the right kind of inductive bias for rule induction. So we place the template in exactly the form that you can outfit after you have learned everything that you need to learn. And you also have the added capability of a parameterization of that model where you can learn nuance if that is necessary. So this for me a very prominent example of where LNN does all of the right things in order to outperform neural networks. Additionally, with this right kind of inductive bias that LNN has, you can directly leverage the knowledge base as it is. And you don't have to start approximating it with a neural network. So that means LNN can leverage human authored data directly and we have the right kind of inductive bias for reasoning the way that we wish to interpret it. And that means that we go beyond what neural networks can do in terms of their lack of concise interpretability. So that's the answer. All right, thanks Francois. Yeah, so we've come to the end of the learning and reasoning session. I hope you guys enjoy the talks and the discussions at the end. I hope to see more participation in this journey going forward. I would like to invite John to close. Thank you colleagues for this excellent session. And also for all of you for bearing with us during this last part of what's next in AI session. I'd like to thank you all for being with us during this particular round of the lecture series and looking forward to having you when we run it next. On behalf of my colleagues at IBM Research, we'd like to thank you for all for being here and just to remind you that you can learn a lot more on the topic of AI through the various resources that we as IBM make available to our academic partners. Through the IBM academic initiative program, IBM gives a lot of resources, learning resources and also software and access to IBM Cloud through the academic initiative. It's all available free of charge. Just log on to ibm.com forward slash academic and through that you can access the academic initiative and many other resources from IBM for your learning on artificial intelligence. On behalf of my colleagues at IBM, thank you so much for being with us during this two part series and looking forward to host you for the coming quantum hackathon that my colleague talked to you about. The links will be available in the chat. Please register for those and we look forward to hosting you once again. Thank you very much and goodbye for now.


Welcome to the Open Group Open Footprint Forum global event. My name is Heidi Carlson, and I am the director of the Open Footprint Forum. Together with Johann Krebers, who will be joining us shortly, we will be covering in our presentation today what is the Open Footprint Forum about, what are the objectives, and what we will be delivering. Johann Krebers' background is, he is the GM of digital emerging technologies and VP of IT innovation at Shell. He is also the co-chair of OFP. As I mentioned, these are the topics that we will be reviewing today. And just to review briefly about HSSE and the Open Group Rules, the commitment to HSSE is very important, as you know, for health, safety, environment, and security. And just as an example, if we could just be, you know, conscious of, as the world slowly opens up after this pandemic, that we're still mindful of all those out there who may be struggling or may be in a different situation. And just be mindful of the others that may need help. And also just be aware for yourself and to stay safe, healthy, and so that we can all move forward in a positive manner. Because this is a Open Group meeting, I just want to stress that please be cognitant of anything that is confidential. So please, please, please during this meeting and sessions for the next two days, that do not share anything that's confidential or proprietary information for any of the organizations. Do not, you know, share prices or products or services about the organization, anything regarding purchasing plans for particular goods or services, and or anything about specific mergers or divestment plans, development plans, inventory's cost of organizations. Thank you. And so everyone just looking at today's agenda, what we're going to be covering in day one is the OFP initiatives, the data platform. We're going to hear from WBCSD. We're going to provide an overview of the GHG and reporting, the reference architecture, the OFP strategy, and developments in the market of GHG and related activities. In day two, we will be covering the recap of day one and developments in the market of GHG and related activities. Again, IBM's plans for OFP, along with AWS plans for OFP, calculations, opportunities for new and existing members, a demo, and a preview of the platform, and what is expected by various industries and how to get it used in the various industries. And then in closing, we will provide some follow up actions. Just in general, as John had mentioned, at the end of each session, there will be five minutes for Q&A, and please write your questions in the Q&A box. All sessions will be recorded and to be made available afterwards. So just a little bit about the open group and today's agenda. As I'd mentioned, we will have an introduction to the open group, just a very high level overview. Some background, talking about what is the open footprint forum, the benefits of the open footprint initiative. We'll review the current members and our active projects, and then provide some useful links. So a little bit about the open group, and we are at the open group, we help our members to achieve their business objective through technology standards and open source software projects. We have over 800 members around the world in various countries and are best known for the standards like UNIX and CHOGAF. And so moving on to the next slide. In the past 10 years or so, we have done a number of industry initiatives that look at transforming various industries, starting with the face initiative, which is the military avionics space. And then we moved into the open process automation forum, OPAF, which also looks at the process automation and process control standards. Then SOSA, the SOSA initiative, which looks at sensors and is developing an open sensor system architecture, and then the open subservice data universe, OSDU, which is developing an energy data platform for the energy sector. And then the initiative that we will be talking about today, the open footprint forum. So with that, I will turn it over to Johan. Thank you very much. Good afternoon, good morning. Maybe good evening, Johan Krabbers, out of The Hague in the Netherlands. It's nice weather out here even today. Very brief about the open footprint, because we're going to have two days about it. And so what's the open footprint all about is really the world of greenhouse gases. And of course, the people in the line most likely will know about this world. But what we really try to address in the world of greenhouse gases is really looking at how to define what are the data definition standards for this information? What are the access standards for this information? So how do you store the data? What are the data definition standards? Because in this whole world of greenhouse gases, we've got our reporting standards from SASB, from WF, from WRI, etc. But are no underpinning standards on what are the data definitions and how do you access their data? And that's very important, especially to talk about supply chain type environment. In supply chain, when you bring data together, your carbon data from several parties into a single environment to really calculate the carbon footprint of a product, it's important that you all speak that same language. Today, we do not do that. So it's very hard in this world and we can spend a lot of time to try to align carbon calculations from different companies to try to align into one is very time consuming. And really, we should not spend our money there. We should spend our money, really our budget on, okay, how can exploit the data and how can exploit the data by using good applications on top of it. We should not raise our money, we're going to just align the data. So we try to do it with open footprint. I'm going to talk about it in the next two days. See the whole story behind that is we align the data definitions of the, of the, of for scope one, for scope two, and for scope three, for a company. So a company will have clear aligned data definitions. And as long as the company is using open footprint, and all the companies also using open footprint, the definitions, the access, the way of access will be exactly the same between these companies. So you can start to like, you can start comparing like for like. That is what I'm trying to do. We try to live that, not just by standard, but also by real implementation, a reference implementation. You see an early version of tomorrow afternoon, when we have a session of an hour, where we give some demos of that, of that reference implementation. But our goal is always not to just use a standard or produce a standard, but also to really the drive a reference implementation with FB one. Relatives are simple, but we grow that with MP two, minimum of output two, minimum of output three, et cetera, into a full blown reference implementation people can use to as their carbon footprint, as their open footprint, sorry, date implementation. That's what we're going to, that's what we're trying to do. We're going to explain in the next two days, the whole story behind it, where we're coming from, how we're going to get it made, where we are, where we are today, how we're seeing for the future, and how does it fit in the overall plan. Because we cannot do it in a world of carbon, in a world of greenhouse gases, it's all about collaboration with lots of other parties. Nobody can do this on their own. It's all about working with the WCSD, with the WVF, in our case, with a whole raft of audiences out there. Now, I'm also very pleased today in the program, we have a number of audiences presenting what their stories now all fit together. So, it's not an open footprint team on its own story, it's a story with rather a much better framework, the company will solution out there. This is the current over the companies, so we're very grateful. Remember, we started this exercise on August the 24th last year, we had an open group kickoff meeting of the open footprint forum on August the 24th last year, and these are companies we haven't seen since. And remember, in the case of open groups, it's all about the companies, the members who do the work. So, there's no miracle out there. The people here, any company on here, whether it's Kev Jenner, whether it's PIDX, whether it's Tata, AWS, you name them all over here, those are people who do the work to create the stands we're talking about, to create the rafts that we're talking about. So, it's all members, members, members.  But I always remember the results, the outcome, whenever we have a formal piece of outcome, will be available to everybody. So, the members have the influence, what we do, what our priorities are, how we go about it. But the outcome will be available to everybody, of course. It's important, we always never forget that. And Heidi will clearly come back on this one. So, let me leave it to Heidi and then we can hand over to Sami. Sure, absolutely. So, if you do, if there are any questions, please reach out to myself and all of this information will be featured throughout the next two days. In addition, we have some wonderful links here that you can also take a look at from our website to looking at the current list of members as well as joining us in LinkedIn.


Hi everyone, Joe for Jaspy's CaseRace.com coming at you with the number block. Four 2022 limited football picker team, two 19 teams in this one and a free spot giveaway. And for those of you who are watching live, we gave away an additional spot. So those are the teams involved. A lot of teams involved right here. Oh yeah, I guess you're right. Yeah, box is only not to be that accurate. There's the extra spot right there. So we only sold eight spots outright. So the first dice roll, top two of those eight are gonna get extra spots after six into two, eight times. Top two after eight, one, two, three, four, five, six, seven and eighth in final time, Ed and Marlon. After eight, extra spots go on your way. So congrats to the both of you. All right, now let's randomize you a number. Let's roll it in randomized names and numbers, two and a five, seven times. One, two, three, four, five, six and seventh in final time. And Steve down to Jason after seven, two and a five, seven times for the numbers. One, two, three, four, five, six and seven. We got five down to eight. Remember, zero gets you any and all redemptions for that big group of teams, including one of one redemptions. One still gets live one of ones, as stated in the item description. C with five, Ed with four and three, Simon with nine, C with seven, Marlon with six, Ed with two, Marlon with zero and one. So it's a moot point, Marlon. You've got your bases covered. And Jason with eight. Let's sort numerically, and we'll see you in the next video for the limited break itself, jaspyscasebreaks.com. Bye bye.