This session, in the ordinary course, normally we have around one hour, we have made it a short interest and we have taken few questions which normally comes in the mind of a person as to how the best could be done and what are the investment options and how to invest, what is the ideal age, etc. Taking into consideration all these aspects, we requested Mr. Bharat Chandra who is otherwise having the qualification from the, as a doctor, but his passion to learn the investments as well as thereafter to share with a common man so that they could also be enriched. Speaks volume that his own YouTube channel is doing tremendously well and the person who got us connected, we see him may also logged in Mr. Prashant Chauhan who is from our same profession, he has created his niche on the mediation. Therefore, both the brothers have a diverse thought process, same way the portfolios of money investment is also different and diverse from what ordinarily a professional thinks. Without taking much time, I would request Mr. Bharat to introduce and thereafter we will take some questions and he has agreed to share with us. Certain slides thereafter, we will also share it on the WhatsApp group of Beyond Law CLC which we normally ask people to connect with us on the WhatsApp so that any notes etc. which are shared by the speaker, they can have the knowledge of the same from that coupled with, they can also share like and subscribe the Beyond Law CLC, YouTube, LinkedIn and Facebook page updates. Or do you Dr. Bharat Chandra? Thank you Vikas Ji, speaking from a long distance in Chandigarh. I hope you are able to listen to what I am speaking and ladies and gentlemen, to introduce myself, I am Dr. Bharat Chandra, medical doctor, behavioral therapist and by hobby I am a trainer and specially training people into better investments. As Mr. Vikas Ji just now told, today's topic will be creating wealth for professionals. So when I say wealth, people should understand it is not 5,000, 10,000, 1 lakh you make in a month and talking about crores and crores of money that you are going to keep it aside as probably a retirement purpose or could be for the children education and children marriage or post retirement a house. Now I am going to request my friend Vikas Ji to ask his questions so that I can appropriately answer those questions to the satisfaction of the professionals who are present today. Like we said that we have taken 10 broad questions which normally comes in the mind of a professional and so we have framed these questions, we have also shared them on our social media. The questions would be Mr. Bharat, what are the investment options available for the professionals? I am going to explain everything by slide so that everybody will be very, very happy to note down if they want to. The investment options are the first one I am going to go along with the returns that are available. First one is everybody knows bank deposits, fixed deposits and public, private and fund post office deposits then comes the most favorite gold then real estate everybody knows how to invest and how to make money then comes the mutual funds and finally the stock markets. To explain further the bank FDs give approximately 5 to 6% of returns per annum. PPF is a government instrument gives around 7.1% per annum. Gold average, everybody thinks gold is the thing that we have to use for our upper Bhandau. It can be used at the last resort as a very good investment and I am going to give you bad news. The gold investment on a long run gives only 4 to 5% per annum. It looks as though it is appreciating but at the same time the other day it went up to 55,000 for 10 grams and it has come down to 45,000 degrees. And then real estate conventionally gives about 9 to 10% per annum for a long run but the sad news is very poor liquidity. Mutual funds depending on which mutual fund you choose there are many varieties 10 to 14% or long time and stock markets surprisingly can give you anywhere from 15 to 25% per annum over a long term. So this is what happens with the investment options the professionals have in front of them. And let me explain one more important thing. So now look at this long term inflation in India is around 5 to 7% per annum currently. That means if a professional is going to keep his money in the post office or in bank deposits whatever the interest he is getting 5 to 6% gets nullified by the inflation. It's very important that people start investing in some other source by which he is going to get more money more than the inflation, more than the tax rate of India. Now the next question flowing is the consequence would be why should a person invest a portion of his savings in stock markets because everybody feels it's quite jittery to invest in stock markets. Perfectly said. In fact, most of the people have a misconception that if you put it into stock market the money will go down so much volatility. In fact, I am telling you as a joke. If you invest in stock market before investing you should give a copy of your ECG to stock market to broker because in case the market comes down you may have a heart attack. So you must have a very strong heart before applying in before buying in the stock market. Hence if you invest all our savings in bank FDs, PPF etc. it will lose its value due to inflation will not be sufficient for our future investment. So as Vikas ji very clearly said very beautifully asked the question we have to save a bit of our money into what is called as stock market because compounding works best when given lot of time. When I am saying investing a portion of our money in stock market if you symbolically take that 100 rupees is your total investment 50% can be in real estate, 10% could be in the bank, 5% invested in gold 5% goes for insurance and another 30% can go to stock markets out of which 15% can be mutual funds in case you don't have knowledge about stock market and 15% more in stock markets. Now a lot of people believe that what is the ideal age for investing in stock markets? Very beautifully asked question in fact a large number of people we have done a survey large number of people think about the retirement corpus only post 50 years and after 50 years you don't have time for compounding benefits. Somebody asked Albert Einstein there are 7 wonders in the world and he said there is an 8th wonder and the 8th wonder he said is the effect of compounding and Einstein went one step further and very beautifully said if you do not take the advantage of compounding in your lifetime you are the most unfortunate person. Now since you asked me when is the right time to start investing let me tell you the story of Amar Akbar Anthony everybody knows there are 3 of them are twins and then my dear friends Amar Akbar Anthony let me give an example Amar Akbar Anthony 25 years old all of them Amar at the age of 25 went on investing rupees 1000 in stock market and that gave 15% returns per annum and from 25 years to retirement 60 years at the age of 60 Amar got exactly 1 crore and then Akbar suddenly remembered the age of 35 even I should get 1 crore so if he has to get 1 crore at the age of 60 starting at the age of 35 Akbar had to invest rupees 3400 not 1000 rupees Anthony realized at the age of 45 that he too should get 1 crore retirement corpus starting at the age of 45 he had to invest somewhere around 16400 Amar Akbar Anthony had a very old friend very dull and very unintelligent person and that old person you know Dr. Bharachandra he started investing at the age of 55 then we have to invest 1 lakh 20,000 rupees can you see the postponement has costed you 1000 rupees became 3400 and 3400 became 16,400 and it became 1 lakh 20,000 rupees if all of them had started investing from 25,000 rupees would have been more than enough to get 1 crore and hence the most important rule in investing is invest early in fact the specialist say start investing from today and one of my friend told me today is too late should have started yesterday that is the importance of investing and invest regularly when I am talking about regularity suppose Amar misses one instalment of SIP 1000 rupees at the age of 25 to 60 one instalment of 1000 rupees misses the penalty is at the age of 60 instead of getting 1 crore he will get only 99 lakhs that is the power of investing regularly and invest for long term not for short term now Vikas I want to ask you more questions regarding the I have talked about in fact when we were discussing one of our good friend has already written why my law college did not teach me how to earn money they just taught me how to get job I will just share with him that is why we had asked Mr Bharath so that the earlier they will learn we have to only decide whether we are Amar, whether we are Akbar or Anthony and next is the same question rising from there off we just can hyperlink with that the power of long term investing yeah if I tell you the power of long term investing many of you will be totally repented why did I not start in fact my name is Dr Bharachandra let me tell you I am the most foolish man in the whole world my graduation is medical but I have no idea about the whole finance field the amount of mistakes I have done in the field of investing nobody should do that is why I have been conducting workshops on stock market and wise investment and whatever mistakes I have committed in my life I have been correcting in the form of guiding my children to amass a good income for the retirement plus large number of people who are probably my children's age so that they get the benefit now let me tell you the story of long term in stock market rupees 9500 invested in forces in 1993 today is valued at 13.85 crores many of you may be wondering 1993 I must have spent that 9500 paying a rent or going on a picnic and now if you had kept that stocks it would have gone and given you 13.85 crores now look at this rupees 10,000 invested in repro shares in 1980 is today valued at rupees 1180 crores you can't believe 1980 you paid 10,000 it is 1180 crores now let me go further and because everybody will be totally flabbergasted looking at this particular statistics rupees 10,000 invested as one time investment in January 2000 repeat January 2000 you paid only 10,000 rupees purchase the following shares look at the story in Ayshar Motar's one time investment when I am saying one time investment many people think monthly monthly I must have paid for 10,000 no only once January 3000 invested 10,000 Ayshar Motar's would fetch you 48 lakhs at 34% Bajaj finance would fetch you 93 lakhs at 38% Kotak Mahindra Bank investment you paid 10,000 you are going to get 15 lakhs at 27% now going have those look at this 10,000 has given you 84 lakhs at 37% Madasam Suni would give you 19 lakhs at 38% and LNT rupees 10,000 would give you 2 lakhs and at the rate of 15% I deliberately added LNT because there are stocks which are giving more benefit there are stocks which are giving moderate benefits and less benefits but 10,000 becoming 2 lakhs is not too bad in any case this is the power of long-term investing like we are also receiving messages regarding SIP etc could you also explain the concept of system equity plan that is SAP and what is the difference between system equity plan and systematic investment plan is there any subtle difference between the two I am just giving you some more data before I go further SBI gives you 1.44 lakhs, HDFC gives you about 7.82 lakhs HDFC Bank gives you 7.97 lakhs and remember now I am coming to the concept of systematic equity plan or also an alternate could be SIP systematic investment plan systematic equity plan or SIP SIP when you invest in mutual funds on a regular basis is called SIP when you invest the same money in stock market is called SEP now look at this rupees 3,000 invested per month as SEP from January 2000 to April 2021 let me repeat again rupees 3,000 invested per month as SEP from January 2000 to April 2021 that is 21 years and 4 months 21 years and 4 months you would have invested at the rate of 3,000 7.68 lakhs and today's value of patient paid if you sell those shares is 3.07 crores at the rate of 25.52% to give a comparison I have put one more small table at the bottom rupees 3,000 invested per month in Bank RD from January 2000 to April 2021 and Bank RD earlier was giving 8.5 now only around 5.5 to 6.5 and that would have given you 20.81 lakhs and in case of patient paid you are getting 3.07 crores a sizeable portion of money and then if you deduct out of that 3.07 7.68 approximately 2.99 crores is a net profit you are going to make from SEP in patient paid this is a story of one patient paid in fact if had plenty of time I would have made a fight for each one of the more profitable stocks like SDFC, SDFC Bank, Bajaj Finance financial services and Titan and this is the way professionals can do it you know somebody very beautifully said in fact Warren Buffett said if you are not going to create a system of making money when you are sleeping then throughout your lifetime you need to earn money to keep yourself floating I don't know how many of you understood what I said if your money is not going to earn more money the money has to earn more money not you earn money every month and the professionals can do this and we have done a very small survey and later I am going to share it we have asked more than 1 lakh people until today how much money you can save in a month without getting hurt without feeling bad even if that money suddenly goes or you lose that money you should not feel bad and remember more than 60-70% of the people said even 5000 rupees I can save in a month we are talking to mostly a lot of advocates judges and medical professionals and large other people 5000 rupees is a pittance it is a money you spend approximately on a family dinner and then that is what I am telling you please make arrangements to make sure that you get enough wealth for retirement and one beauty about creating wealth for retirement is once you have somewhere around 2 or 3 crores that money in stock market will give you approximately 30 lakhs a month throughout your lifetime I am giving an example let us imagine my name is Vikas at the age of 55 years I created a corpus of somewhere around 3 crores and after that every month, every year you can withdraw 45 lakhs without hurting your principal next year again 3 crores is ready so this is the power of systematic equity plan people feel that you can invest by a blinder or what we say is normal without actually going to the fundamentals do you feel that role of fundamentals and technical analysis is a very great way to understand and invest this thing or without understanding the rules you start investing on the asking of someone you have asked a question and let me tell you a question which I have got weight in its gold see anybody can invest but a person who understands and invest who is the one who will be the winner and one more important thing if you invest without knowing anything and that is called gambling fundamental analysis is analysis of companies to assess which company to invest in it must be done once in 3 months to check the financial performance of the companies decide whether to continue investing in that company or not large number of people feel the fundamental analysis and technical analysis is difficult to understand I am a medical graduate I learnt it by continuously looking at it it is a matter of few hours of training maybe 5 or 6 hours of training you understand what is profit and loss account what is balance sheet what is cash flow etc everybody should be knowing for example before investing in any plot of land you are first doing a research you go to advocate and ask him the veracity of the title similarly before buying any company you must know the fundamental analysis and now I am going to technical analysis technical analysis is done to decide when to buy and when to sell a particular stock it involves observing the price graph of a stock for example fundamental analysis says which stocks to buy and technical analysis says when to buy when to sell and how to buy just to show you I am showing you the graph of a particular company in this case it is a graph of Titan company and it is called 30 days moving average graph you can see a red line we can see a green line and red line going up green line going up a blue line going up the red line is called as buy indication and the blue line coming and touching from the top is called as sell indication if a professional understand you this is a matter of another 5 or 6 hours to understand very easy to learn I have learned if a dull person an unintelligent person like me can understand you all should be able to understand because you are all advocates of the supreme court, high court and then you are the advocates of the greatest intelligence and professionals with a tremendous capacity to understand very easy you can see the graph and decide when to buy this is the time to buy this is the time to sell if you do this you will be able to you will be able to amass somewhere around 20 to 30 percent profit for the money that you have invested in stock market maybe you will invest 10 lakhs you can easily get about 3 lakhs and like we understand the fundamentals and technical before we take things forward I have just received a message on the group itself he is regularly following our webinars he says that he met you 20 years ago and he has relived the memories again and it is a pleasure connecting with you and how much time is required to monitor the stocks because sometimes one feels that it is a whole time job you never know that you may miss the bus when you have to buy when you have to sell or is it that simplicity you have to devote full time is it a 24-7 job few minutes job or how things have to go about very very good question one misconception people have is that it is a full time job if you are investing for long term minimum 8 years to maybe 35 years in retirement then 30 minutes a month is sufficient to manager investment just 30 minutes and then if you are doing short term trading which I don't recommend for all professionals few days to be with then 20 minutes a day is required and that is not during the working hours at that middle of the night or whatever time you are comfortable that 20 minutes can be spent in the name of Dr. Bharat Chandra very very interesting question you asked yeah then what is the minimum amount required to invest in stocks one feels that to buy money to have money you have to have more money or I was just hearing one of the YouTube this blogger and he was also saying he said that even 500 rupees is good enough because the effect of compounding is very good it can always increase better 500 rupees a month even 100 rupees also is more than enough but not as sizable income or corpus you get for retirement to get a reasonably reasonably good corpus retirement I advised minimum 5000 rupees a month for a person of 25 years person of 35 years it could be in the range of around 10,000 for a person of 45 years it could be 20,000 rupees a month so that he can comfortably retire at the end of his tenure at the age of 60 years with a corpus of somewhere around 225 crores once you have 225 crores retirement your retirement is done you are happy, no problem at all this is exactly what I am trying to achieve in all my workshops talking to people now there are certain misconceptions about short term trading though I know that it's a long it's a good few hours sessions to understand but could you give a bird eye view regarding what are the misconceptions and how to dispel about the same very good question again I am no intelligent person like you will definitely ask this question misunderstanding is the percentage returns in short term trading looks good but the quantum of money is usually very small I don't know how many people understand this you get 5000, 10,000, 15,000 per month or even 1 lakh per month but that is not as good as 5 crores, 10 crores retirement it is not suitable for busy professionals since it requires a lot of time and stress so very important and today I have to tell you one more interesting concept probably that is in the mind of my friend Vikasji also what is the difference between trade or an investor my aim as being Dr. Bharachandra is to make people investors not traders trader is a person who earns his money livelihood from stock market trading he does frequent transactions to earn regular income the objective is to earn income rather than create wealth now coming to the investor is a person whose primary activity is their profession and business you can advocate, you are a doctor you are a chartered accountant you are architect continue I never say you come into stock market full time they invest their savings in stocks to create wealth not long term and not for regular income this is a far time activity I again urge everybody who are listening to this particular webinar today and in the future become an investor not a trader and if you do not create a wealth of not less than 2 to 3 or 4 crores for your retirement planning from today you are going to repent definitely later the broader question we have taken few wrap up questions which have been posted on the group we will take how to manage if invested fund is not performing after 7 to 10 years already we have spent 7 years with that fund we are left with less years to give time for compounding what is the way forward the first question is why did you invest in a company which is not fundamentally strong for example there are 1900 stocks in national stock exchange but profitable stocks are only 200 suppose you invest some dum dum engineering dum dum careers farm farm automobiles god will save you because you are unnecessary invested in non-performing assets 5 to 7 years later your portfolio is not working it is time to sell it and probably I am just killing you please attend a workshop on stock market understand how to do analysis then once in 3 months please watch your stocks if you invest blindly and sleep nothing will happen it is as good as buying a plot of land and not going there for 7 years somebody else would have constructed house there similarly you must look at your stocks once in 3 months don't do it you are not an investor next question would be if we decided to have a set of rupees 10,000 in EALS for 35 years do we need to verify the fund every 3 months if fund is not performing what is the alternative I am just 26 years old this is by Shri Vastwa very important very good question you have to watch all your mutual funds minimum 6 months or once in a 1 year in the case of a mutual fund the mutual fund gives you return you have to check the returns with the benchmark index benchmark landmark and check whether your mutual fund is performing or not not performing you have to simply get rid of that come to stocks come to another mutual fund but having attended this program today I am urging all my youngsters here that instead of spending too much time on mutual fund if you can spend more time with the stocks same amount of time with the stocks you are going to get 3x benefits not 1x Akshay what is your thought on investing more for short term that is 1 year along with long term in this pandemic time so somebody is asking this question 1 year how about long term especially keeping in view the pandemic times see there is no pandemic nothing question is if the market is coming down if I am a long term investor it is a discount sale for me now the market is coming down can you see the smile on my face I have somewhere around 2-3 crores of investment bought for you in our house but we are very happy because I can buy some stocks at a better rate now market goes up short term investments I am selling see market goes up and up or down it doesn't make any difference for a seasoned investor 1 year term is too less anybody is thinking of 1 year in stock market is as good as looking at buying a plot selling after 1 year buying a plot and selling after 1 year probably the registration cost would not have been what then Sachin Pai any good suggestions for investment and mutual funds or what is to be checked on it Sachin Pai is asking a question he is asking a question as good as can you tell me about Rama end in the next 1 year I can only tell Rama was born in Ayodhya and then he went to forest he got married to Sita Sita was taken by Ram and Ram was killed and he came back to him this question is the concept which we explain in 30 to 60 hours workshop yeah so next is by Mr. K. Sengla he says what are the fundamentals to select prospective stocks for long term investment again the same question there are 17 points to see in fundamental analysis first one is the past return second one is very important profit and loss account balance sheet there are 17 such things I think the way they have taken up we will have to have a separate session on these 17 fundamentals that will make better sense and last question we will take upon he says how to open the DMAT account and what is the benefit of the same again if I ask about benefit I am gone because of the big story let me tell you how to open wherever you are whichever place you are go for a walking there will be a board which says angel broking zeroed up broking then share Khan etc etc go there and give your details for 8 days your DMAT account will be ready there is no question of asking what is the benefit of DMAT account because without the DMAT account you cannot buy any stocks that is for sure and then it is as good as asking what is the benefit of taking a PAN card without PAN card how can you open the bank account how can you pay the income tax how can you identify yourself to the government of India and DMAT account is similarly the services by which you keep the shares in safe custody with a government company yeah so I can understand that we had a bird eye view but as I say if you have the bird eye view then you will always like that if the bird could be in the hand and you are made as I say we have seen the cake we have seen the cherry but that can only be eaten if we have a better session we have a broader perspective today we can say that it was a trader as I say this is the trailer so we can have separate sessions for fundamentals etc so that people can understand so thank you to Mr Bharat Dr Bharat Chandra who has given us a good peep into as to what could be the way forward I think a lot of minds would have got a good blocking and the engine for investments would be much much better than what they thought and before we thank Dr Chandra finally I have to say one sentence we keep asking people are you are you interested in starting to invest from the beginning or somebody will be asked were you thinking of investing everybody says sir I have been thinking of investing in stock market for the last 10 years but that time has not come so you have to create a time start investing later you will repent why I did not start like the way I am feeling at this age and I am collecting it with the help of talking to youngsters so that they will start the earliest possible time and no excuses start your investment the earliest possible time thank you and thank you to Mr Rohan also who has been able to connect us and somebody has asked do you have some workshops on this also and how to join that the best technique is you must get into our youtube everyday we are making two videos in english two videos in Canada on our website on our youtube channel this is the youtube channel we are going to give you the details about the youtube channel english channel is Dr Bharat Chandra channel channel is Dr Bharat Chandra institute facebook is there website is there if you go through that and then if you ask us the same question more details will be coming through youtube channel only we will ask after you we do away with the slide I will ask Rohan also to login so that they also should know that just like a compounding effect why Dr Bharat Chandra and Rohan are doing well one by one and one by one and one by one so I would like to thank Mr Rohan also to login and we would like to thank him and we will connect soon Rohan is coming very shortly for a second


What's going on if in a family? Welcome back to another video. It's your girl Janice. If y'all are new to the channel, make sure you hit that subscribe button, enjoying the family. Guys, today I am going to be having the time of my life because I'm getting married. You heard it right, guys. I'm getting married today, but Isaiah doesn't know it. So guys, yes, I am going to be pranking Isaiah by wearing a wedding dress. I have flowers, I have a veil, I have the whole nine. We're supposed to go out to eat, but what he doesn't know is I'm gonna show up downstairs, all dressed and ready for our wedding. I'm going to convince him that it's our wedding day and we are going to get his reaction. He is going to think I'm going absolutely crazy. So if you guys are ready for today's video, don't forget to smash that like button and comment down below, Team Janice. Let's go put that wedding dress on. Oh my gosh, guys, this is crazy. I have all the props on. I've got the dress on, but this is actually insane. To think that I really look like I'm about to get married, imagine when the day actually comes. This is just making me look forward to that even more, but right now is pranking time. So let's go ahead and prank Isaiah and convince him that today is our wedding. Let's get it, guys. How is she trying to jump and she got a crotch?  Babe, why are you not ready? She has a whole crotch and she's trying to jump. Yo, you know what bothers me? When someone is six foot and they can't jump. I don't care about someone who's six foot. If I was six foot, I'd be in the NBA. Why are you not ready? Ready for what? To mean what? The girl in crutches deserves an Oscar for trying.  That's what the comment said. The girl in crutches deserves an Oscar. Hello!  More important.  Babe, come to this right now. Since when are we, when? What? We're not married. Since when did we get married? Yeah, we're not married. Yes, we're gonna get married later. Like, we're gonna be late. One thing I propose. One thing I make this official, like you're wearing a wedding dress. Um, don't we not? And we weren't planning this for years? Negative.   We're not like, you look really good in a wedding dress. I actually like, I might make this a reality soon. Isn't this bad luck like you've seen me before? We are not married. Are we not married yet? No, they're not real. Obviously, you know we have to- What type of wedding are we going to if you have a fake bouquet?  Sunflowers? I said sunflowers. I heard sunflowers. I was gonna say, you don't take sunflowers. My gosh, does that matter? Like seriously. I have all my friends and family waiting for me. Waiting where? I have no idea what's going on.  I'm dead serious.   We're supposed to eat with Tiffany. Actually, they're flying in like, come on. Even your mom is flying in, bye bye. Like, are you serious? I won't lie. Four hours? One thing I propose. One thing I propose. You propose? Bro, I have this ring on my finger because I match your energy. Okay. When I got you your ring years ago, it was a promise ring, right?  That didn't work. Babe, that was a proposal. However, I'm matching your energy. I'm matching your energy. That's why it's right here. That was a proposal. That was not a proposal. I'm not wearing anything right now because I'm getting ready for the real thing. I gave you that ring when you were in my car. You think I would propose to you in my car. You did propose to me. How do you not? In my car. In my car. I'm gonna propose to you in my car.  That was literally what you said. That's why you gave me that ring. My car is irrelevant. I don't propose it. That makes no sense. Babe, are you serious?  Did you hit your head? No.  Go put on some tuxedo. I don't even have a tuxedo. I don't have one. Babe, we bought one two days ago for you.   I'm not. Yeah, you are because I don't have a tuxedo. Oh my God. Are you serious right now?   I don't know who's... He's your best name. My girl is working. No, he's not. I was just texting him. He showed me the picture of the car he's working on. Bro, him and Debra, yes they are a test message so we can leave. I don't know who's worse. You or the girl on crushes here trying to jump. I don't know who's worse. Babe, for real, like he's seriously waiting. All of our family and friends are waiting. Can you go get dressed? We are not married. I never propose to you. Where's the ring? You're not even... You need to wait X amount of time more because I never propose. Let me see what time is it? Oh my God. We are... Bro, stop playing. I'm not playing for the caterers to get there.  Is that what you're trying to do? What are you talking about? Are you trying to like get down with something? Like what are you trying to do here? I don't understand the motive. I don't get your motive. I'm literally telling you all the stuff that's going on today. Like what do you not get? Let me see this. It looks like cilantro. No it doesn't. It's eucalyptus. It smells like plastic too. All right, it is made out of plastic Let's do the thing or I'll go like this and I'll catch you. Babe! Bro! Yeah, you're not going to play. You're literally... Let's try this again. No! Oh look, you're getting married next. You caught it. Yeah, literally, tonight. Can we stop playing games? Can you stop playing games? Can you stop playing games? I'm not playing any games. Can you stop playing games? You're not even supposed to see me without my veil now. I feel like the whole wedding is about to... You came downstairs. Are you going to cry? Why are you going to cry? We're not getting married.  I don't understand you right now. Are we...  I think you're crazy now. You're starting to scare me. How? You're starting to scare me. This is the worst... We are not married. We are not engaged. We are just girlfriend and boyfriend. That is all we are. Okay? Now, the proposal will happen eventually. Okay? I can't tell you when because then there's... That's not a proposal. You know what I mean? But... Yeah, I think you just need to go like sleep or something because you're starting to scare me. If you're trying to back out, just tell me. No one's trying to back out. I'm still here. If I wasn't in a back out, I would have been back out a long time ago. No, if you're trying to back out of the wedding today, tell me now. Where is... There's no wedding! Where's the wedding happening? Give me a location right now for your series. I can't. Exactly.  Look at you. You're smiling. I'm not buying it. You're crazy. You got me to cry, though. I'm not alive. Crying was... That was... That was good. The wedding is to you and Janice. Where's the book here? Yes. Now, take it off before I propose like tonight. So... No, I want that to happen. I know you do, but how unfortunate... You want to go get married? I got things to do before I go ahead and propose. Let's go get hitched, baby.  That means like we could literally go and get married right now without the propose or anything. No, man. That's not how I want to do things. Why? No, because I have a whole idea and stuff and I'm not talking about this. I'm done. This screw is loose.  All right, I'm over this. There's gonna be a big problem if I fall through this chair. I promise you the whole neighborhood is gonna feel it. I'm such a good prankster. Bro, shut it.  All right, Infinite Fam. So that is it for today's video. I feel like I got him even though he's acting like... Crazy. I did get you. You're crazy. He literally was like convinced that I was convinced that it was our wedding and that was my goal. So team Jenice, we got a banger and that's it. You... I think you're crazy. Like I was starting to think you're crazy. That's the whole point. You didn't get me. I thought you were crazy because you were really believing that it was our wedding day because what sold me was you started crying. Period. All right, so that's cool. So that I got him and that's it. All right, so team Jenice, we're going with the four, five, six.  Six pranks in a row down there, but he knows damn well that team Jenice is the best team to be on and that's our period. So with all that being said, we will catch you in the next banger. Love you, Infinite Fam.


All right, well, welcome back everyone again. It's my distinct pleasure to introduce the moderator of our panel, following the fascinating lecture by Professor Freeman. The panel will be moderated by Professor Stanley Chan, who is an Elmer Accessory Professor in the Elmer Family School of Electrical and Computer Engineering, and also the Department of Statistics at Purdue University. So Dr. Chan does research in computational imaging. He is particularly interested in the photorevigor imaging, imaging through atmospheric turbulence and robust machine learning. Dr. Chan is the recipient of the Best Paper Award in the IEEE International Conference of Images Processing in 2016, and he has also received the number of distinguished teaching awards at Purdue University. He is currently serving as an Associate Editor of the IEEE Transactions and Computational Imaging, and was a former Associate Editor of the OSA Optics Express. So with that, thank you again, Stanley. Okay, so I know Bill has a very interesting, interactive activities, which I'm really, really looking forward to. So maybe I'll just pass the time. Bill, let's do some exercise. I'm gonna raise my hand if you ask a question, okay? Fedders, Ways, and Future of Computer Vision Research. And then after that, we have a couple of faculty here. We can recheck about these decisions. And just to, how much time should I, I can really tell you whatever length of time I should spend on the slides. What would you like? Sure, how about 15 minutes? Great, perfect. Okay, thanks. Hi again. So the prompt was, you know, what's ahead for the future and I have, I wanna share with you this, so there's slides that kind of talks about the future of computer vision and my viewpoint on it. So the first thing to say is a disclaimer. That this is a cartoon you're gonna see. This is just a cartoon story. It's, you know, there's a lot of subtlety that I'm just not gonna go into. So please don't get upset if I sound like I'm being kind of black or white. It's just, that's gonna be the style of this presentation. A cartoon is something that maybe looks like the original, but it's just very simplified. And that's what this is. Okay, so, you know, as we all know, there's been a revolution in, well, in science really, but in particular in computer vision and image processing and a sort of cartoon description of how we generated papers between 2013 and 2021, is you could say that you take this standard textbook on computer vision by four sides and ponds, open it to any random page and look at the topic on that page and put the word deep in front of it or add the word GAN behind it or something. And then, you know, collect a training set, build a model that implements that and publish. Again, a cartoon, you know, forgive me, but that's a lot of what happened. And you can actually almost verify this. You can go look for, you know, say the topic is texture and go Google deep texture of CEPR and you find a paper on a deep epipolar geometry, CEPR, you find a paper on it. Just anything in the textbook on computer vision, you put deep in front of it, CEPR behind it, you find a paper. And what's, let's see, and what is it that worked so well? Well, you know, there was this really very simple model of processing developed by Hugo and Wiesel and of course others of, you know, convolutions with a non-linearity following it and then repeat that in many layers. Again, the cartoon version, Jan LeCun implemented this in a tastefully done network of convolutions, non-linearities, multiple layers and, you know, to emulate the biology. And what was the result of this kind of very first step in emulating the biology, unprecedented impact and academic success? Just unprecedented, you know, just amazing and, you know, touring awards to the leaders in the field but so much revolution from this, just taking the first step toward doing things the way people do. So if taking one step gives you these incredible results making, maybe taking two steps in that direction is gonna be even better. So that's the kind of point of this talk. And so let's use something else from human vision and visual neurophysiology and incorporate that into our vision systems and maybe that'll take us to an even better place. So there's a lot of things you could use, you know, from psychology, from perception literature. So then here now is therefore a model for how to make papers and computer vision for the next five years. Again, it's just a cartoon, you know, forgive the flippantness of all this but you could take this, there's a really wonderful, it's getting old now, but a wonderful vision science, human vision textbook, vision science and open it to some random page and look at that concept and develop an architecture which implements that concept into your vision system and then publish it. So that's the model for the next five years. And if you don't wanna use that older textbook you can use the most recent VSS, Visual Sciences Society Conference abstracts. Okay, but there's one tweak and that is there's a lot of kind of human and neurophysiology which may be idiosyncratic to biology. And my image for that is kind of related to flight. Like of course, you know, all birds fly and birds have wings but they also have feathers and it seems to me that, you know, feathers are kind of idiosyncratic to the biology. I'm sure they're aerodynamically very important but we've got a whole aerospace industry built on winged featherless flying machines. And so somehow we've got the sense that the wings are important and the feathers are kind of side effects of the biology. And so the game is when you look at that human neurophysiology textbook to decide what of these concepts are feathers and which ones are wings. You know, which ones are idiosyncratic aspects of the biology and which ones are really fundamental things. And just as a mnemonic to help us remember that, we note that if you take a human and add wings to them, you get a flying human. And if you take a human and add feathers to them, well, you know, not so much. So anyway, this part is interactive. I don't know if it's gonna work so well on Zoom. Let's try it. So here's a list of human psychology or neurophysiology features. And I'd like you to tell me whether you think they correspond to feathers or to wings. You know, are they something we should put into our computer vision algorithms or can we kind of ignore them? Okay, here's the first one. And I don't know how to do this, but it's fine with me if you're on mute, if you're allowed to. I don't wanna see the chat in my stream so I'm not sure how I'll do that. But okay, first one is dorsal and ventral path, visual pathways. So the visual processing kind of breaks into two streams, a wear system and with handles motion and things and what system which handles kind of static imagery. Any ideas whether this is feathers or wings? And how do I get feedback from the audience? Hmm, so I can't hear anyone. Maybe you're not able to unmute yourselves. And I don't really see the chat in my, when I'm presenting. So. So I can click the reaction button. Okay. Can you see the reaction here? I can, yes, I can see that. So how about we use green check to say this is wing and then a red cross to say this is feather. Okay, okay, good. So I see green wing. So there's a feathers and I see two votes. One, okay, a couple of greens and then green wings. Okay. And then in the interest of time, I there just kind of go through some of this. So I think it's feathers. I'm not sure really that it's essential for vision. Of course, some people disagree with this, but I just think you can do it other ways. Okay, next one. Sorry. Explicit representation of border ownership. So here's a picture from a segmented image database. And if you look at all the horse pixels, it looks like this. This one, sorry, I'll just, I'll run through this one because I really enjoyed the time. I think if you don't really explicitly represent which ones are the edge borders and who owns the border, you'll just get, it'll be so hard to learn what the shape of a horse is and so forth because this shape on the right of horse pixels includes the occluder in front of it and because you haven't represented it properly by properly saying that the border between her leg and the horse is owned by her, it's not owned by the horse, whereas the border from the horse to the background is owned by the horse. Anyway, so I think therefore that this is wings. I think it's essential. And this is part of my little program to try to put the vision back in vision learning. I think we need to recruit some of these concepts from human psychophysics and put them back into our machine learning algorithms. Actually, really interesting time. I think I won't do the interactive version of this one because it's kind of hard for me to pull the people but I'll go through these and you should think please on your own, think about the answer to this one. Feathers are wings, counter completion. I think it's essential. So there's a little video on the top left is a thing of a chair and we could only get the correct contour as if we really analyze the grouping properly. If you just look at motion alone it just totally fails on this problem. So I think that counter completion is an essential piece which we're not really putting into our algorithms currently. That's why I can see. Foviation, we see the world in this really strange way where there are foveas, high resolution, everything else is low resolution. Is that critical? Well, I think for consumer applications it's critical. You know, it's got to be efficient. And for, but for academic research I just think it's not essential. I think it's feathers for that. And Jeffrey, I'm trying to infer what you're thinking by your expression but I'll hear from you in the panel. Perceptual grouping, I just, I'm so in favor of that it's just got to be wings. Otherwise, how do you put things together? Trichromacy, do we need RGB to perceive the world? So sometimes I think wings, sometimes I think feathers, I think people are persuading me it's probably feathers.  The brain is amazingly power efficient, low heat dissipation, portable. Is that a critical part of vision? Well, again, I think for consumer applications yes but I think for academic research perhaps no. And then finally, having both feed forward and feedback connections. That's something that's very prevalent in the human visual system and it's present in some artificial systems but not many, but I think that's also a critical piece in terms of kind of validating your internal representations of what's out in the world. I think you have to have feedback connections. So that's my little spiel and I don't want to take up more of the panel time and I really look forward to what other people have to say and so I'll stop sharing now and thank you very much. Sorry that the interactive part is harder over Zoom. Great, thank you, thank you Bill. So let me just introduce our panel today. So we have Professor Kathi Kramani from Mechanical Engineering and we have Professor Jeff Siskine from Electrical Engineering. We also have Maggie Zhu from Electrical Engineering. So I really like this exercise. I think this is really, really interesting in a sense that I guess we're looking at a problem, right? We're looking at a problem of what would be the interesting things to think about in the next five or 10 years or 20 years ahead of time. So I want to hear the perspective of the panels, right? We hear some perspective from Dr. Freeman. Maybe I'll just open up the floor and ask what do the panels think about in the future of computer vision? What are the interesting problems that in your opinion, we should spend some effort on? Anyone? Jeff, do you want to start first? Okay, so I share Bill's general view that we're lacking a lot of incorporation of what we know about human vision in our computer vision research, both at the psychophysical level and at the neurophysiology level. I think there's a fundamental difference between engineering problems and scientific problems. And a lot of the approaches that people take might be good from an engineering perspective, but they don't actually exhibit human level behavior and they don't actually work the way human vision or biological vision would generally work. And one critical difference is that machine learning systems are trained on absolutely huge data sets and humans are not. Humans, when they're growing up, don't see a million or 14 million images from ImageNet flickering in front of their eyes at five hertz for the first few years of their lives and they don't actually see images, the same image more than once and the images don't come labeled. So how human vision works, I don't believe that object recognition is a process that is learned. I believe that that is almost completely innate and we only learn that to it in the linguistic labels that we assign to categories that the innate human visual system can differentiate. And I think that whole approach to vision is something that largely the community is not addressing right now and it might not be relevant from an engineering perspective, but it's definitely relevant from a scientific perspective. Maybe I should ask Kati from an engineering or science perspective. Yeah, it's a really good question. I tend to sort of inform the basics from perspectives that you gather in the real world in some sense and to Bill's point of view, I was just looking at some of his recent papers, what can you learn when staring at a blank wall? It sort of was analogous to some of the examples he was giving, but I also was looking at some of his patents. One of your most cited patents built from 1997 was this hand gesture machine control system with Wasteman. I still work in that area, but of course, and being publishing in computer vision and ECCV, CVPR and ICCV, all of them. But of course at times change, the sensing modalities change, the context changes and we want to solve problems with fewer assumptions about the environment and context. So I think to that point of view, your analogies of feed forward, feedback and perceptual grouping, I completely agree with this control completion, it's not used, but I don't think that you have to be a wing or a feather, you can be both. So I sort of buy into this perspective of A plus B equals C that combining applied and basic together can actually feed back into the basics as well as feed forward into the applied, partly because I'm biased, I kind of do that all the time. So my perspective there is, you can be both a wing as well as a feather, depending on where you want to go with the publications. But yeah, I kind of buy into the notions that both Bill as well as Jeff point out to that a lot of computer vision problems, of course, learning consumes so much of energy, we do so many things in our brain with extremely less energy and we are able to make out a lot of things just by looking at shadows and so on and so forth, right? So certainly inspiring computer vision problems with the kinds of ideas that Bill actually very nicely presented. I think it's really good for science perspectives. Wonderful. How about Maggie, do you have any comment on that? I think it's very interesting exercise and thanks Dr. Freeman for doing that. Zoom is difficult, but I think we still see some consensus, I guess, based on these set of exercises. So I think I sort of agree with the concept of depending on what perspective we're looking in terms of whether it's a wing or a feather problem. But sometimes that can be ambiguous, right? So for example, I think one of the question we little quiz we had was looking at efficiencies, right? Of these models, but I think in terms of efficiency, there are also different ways of interpreting this, right? It could be, do we want to build a very efficient model architecture from the point of power consumption and so on as Dr. Romani pointed out. But also what about understanding the design itself, right? So are we trying to build just a black box design type of efficient network or so on? Or are we really thinking about the theoretical side, the physics foundations of doing this design and then making this a white box and maybe that is more efficient. So I think there's a lot of interesting perspective and thoughts behind how we look at whether or not the problem itself is a feather wing, right? So another example I think is important is, we're trying to at least attempt to mimic or have a better understanding of modeling of the human vision system. But a lot of times the problems that we encounter do need to include human as a input, right? And then how do we do that by engaging the human but also provide that level of autonomy to a lot of these practical problems? So these are all things, you know, I'm very interested and I think these are kind of, I hope the future direction of where we're going. That's a good point. So Maggie, you're actually a very good point. So human eye, right? So I want to ask another question. It would be human eye, right? So when we think of a computer vision research, one of the really, really ultimate goal is that I'm gonna make an artificial eye that can do as much as a human eye or maybe more. Should this be the goal of computer vision or actually that should be something else, right? Should we ask the computer vision system to do something complementary to human or do we want it to just replace human eyes? Well, I know there's a sort of domain specific but I want to just hear comments from maybe Maggie, the folks here. Well, I mean, maybe we have a lot of answers coming. The comments. Hey, well, I'm not allowed to turn on my video so I guess I'll just turn on the video. Can we make Ed turn on the video, Maria? Let me see. We're working on it. Yes. There we go. Oh, here we go. That was about the question I was gonna ask and I was gonna make it much more provocative. So I'll address this to our guests. So Bill, why does it have to work, a computer vision system work like a human vision system work? In other words, you can say you get insights maybe from human vision but is that the only way to go? I mean, is there another approach that you can take? And this sort of dovetails off a Stanley's question too. Yeah, and it's a great question. I guess I'm, let's see, I mean, so one answer you might say, which is not my answer, but it's the first really reasonable one. An example of that is like the AlphaGo or the chess programs that totally beat humans and do it in a very different way than humans do it. So why should perception be any different? And certainly that's a great point. I guess I feel like right now human vision is just so far ahead of computational vision that you just can't go wrong by trying to make it more like a human vision system. But I do certainly acknowledge that eventually when we get our act together and understand things better, surely it's just gotta be the case that machines have all these different set of constraints than humans have. And machines don't have to grow up from an instance into an organism. And that's surely gonna be a big constraint that changes things. So in principle, no, it doesn't have to be like people, but we're just so far away from what people can do that I think a very reasonable kind of step right now is to just try to copy what people do somehow and work on that. But I understand that that's not in the long run, maybe what we need to do. Well, I mean, and I agree with Jeff's point. I think object recognition is probably inherent and we just put labels on it. And I think there's some evidence of that, maybe even to a certain extent in animals and certainly also, you know, 3D vision. So, but yeah, I always worry that maybe we want the neurologists to solve our problem for us. Yeah, now I see what you're saying. And I also wanna jump in that, you know, if you look at other biology inspired, you know, camera sensors and so on, they don't necessarily look like human eyes, right? But they have very, you know, specific unique characteristics and then, you know, we have a lot of, I think, faculties, researchers in the field that are exploring those possibilities and they may be designed for specific type of applications, right? But I think, you know, definitely taking inspiration from biology is, you know, one way that, you know, to go as the future direction. Yeah. Oh, you're muted. We can cut it, you need to unmute yourself. I have a comment, you know, tied onto Maggie's comment and also a question to Bill. You know, it's how we think about AI, right? Is it going to, you know, how is it going to be used eventually, right? One is doing the science, but also how does the science get used? You know, is it going to extend humans or is it going to relieve us of doing some work or is it like a backup or is it being designed to replace some of our things, right? So I think there's complementarity of what we want to do that has a lot to do in terms of how you might think about it. I don't know what your perspectives there are, everybody in the panel, but also Bill. Well, just to mention a point that's in the opposite of what I was kind of saying before, you could imagine if you were really successful at building a vision system like a human that then your airport security vision system would also get bored and get distracted and not be able to just stare at something for 10 hours straight as you would like our machines to be able to do and which is so hard for humans to do. So indeed you want to diverge from, as you're saying, you want to have a complementary set of strengths. So I do computational imaging, design sensors, algorithms and so on. I see Charlie Bowman is also in this room. Maybe this question is more towards specific things. As we design algorithms, what we are doing today is you give me a sensor, be it spare camera or event camera. So you provide me that kind of device. And then I think really, really hard for a couple of months, okay, how am I going to use this sensor? So I want to fit this question around and I want to ask maybe Bill or Charlie or other folks in this room who have some experience on this topic. If you can talk to a sensor guy, talk to a sensor guy, I want a new functionality. Can you make it for me? And you can only ask for one functionality. What functionality would you want? And assuming that person can make it for you. So what would you want? Well, I've been looking for time, so let's let him to go first. Hi, Charlie. Oh, okay. Okay. Am I supposed to be answering this? Yeah, you can, Charlie. Okay, yeah. You know, one thing that comes to mind, I don't know if it's an answer to your question, but I think a lot of people know Sergio Goma from Qualcomm. Oh, hi, Bill. I think maybe you know him. And he's, I think they were, he's talked about, and I'm not an expert in this area, but integrating sensors with computation, local computation. They have this technology where they basically just fabricate two devices that are so flat that when you put them together, they effectively bond and they can communicate. You know, maybe so, I mean, maybe the broader question you were asking Stanley is that if we had control of the design of the sensors rather than just the algorithms and we wanted code integrated code design, where should we be going with that? And you know, I think there's a lot of potential along those lines. And maybe that's feathers rather than wings, but maybe it's important feathers. It's sort of like foveation. But what kind of functionality would you want, Charlie? Right? You have a cell phone. You have cameras. You have spray. What do you want, Charlie? I'm not sure. But maybe what I want is probably in real applications, you're always or in future real applications, you can be limited by data rates. So maybe you want a lot of sort of like these normorphic cameras where you have a lot of summary information that you can provide. And then you use that to extract the kinds of information that Bill was talking about doing reconstruction in ways that you might not expect to be able to.  I don't consider myself really a vision researcher per se. So, you know, maybe I don't have the depth of experience, but one thing that I, when I think about vision, it seems like there's a lot of problems where we've made a lot more progress than I expected to make in the last decade or in the last 15, 20 years. That you can really solve some really amazing vision problems in terms of having a static image or you have a video and you can tell me a lot about what's going on there with high reliability using automatic methods, right? But somehow that doesn't seem really intelligent in some sense. Like it's doing something. It's giving you the answer to your problem. It's not insightful. I don't really feel that the computer has a lot of understanding. Okay. And so I don't really have the expertise to be able, I can ask the question. I don't have the expertise to answer it. What does it take for a vision system to have really understanding of what's going on? And I kind of feel like it involves a certain amount of dynamic process where, because understanding relates to action and intent. Okay. So I guess my question to Bill is this. Is dynamic vision like where there's interaction, not just a video in which you process it, but interaction between the vision algorithms and system and the ascending device? Is that wings or feathers? That's a great question. Yeah. So, well, it's something I think a lot about. So I love the simplicity of a passive system. You just set up your algorithm in front of a TV set and let it watch for a year. And then you come up with a good vision system. That would be really cool. But I talk about those with my friend Rich Sutton, who's big on reinforcement learning. And he thinks, no way, no way. You can't do that. You've got to interact. You've got to poke and prod. And that's the only way you can learn how to see or understand the world. And that might be true. I sort of, I'm sure it helps. I'm still somehow feeling like you could just do it by looking at, you know, looking at all the YouTube, spending a year watching YouTube, and you can really understand a lot about the world. So anyway, I'm sure it helps to be interactive. So is it feathers or wings? I don't know. You know, maybe it's wings. You know, it could be that you just got to. Prop hope the world in order to understand the causal structure of the world. And so therefore maybe it's wings. I guess it's as an as a researcher, it's so much easier to compartmentalize everything. If it's, if you can, if you don't have to be able to robot in order to understand how to see. By the way, I just wanted to go back and answer Stanley's question. I would love to have a differentiator as in my sensor that comes comes back with a high dynamic range derivative of what's falling on the sensor.  And I should say I have to credit that. Jack Tumblin wrote a paper on why he wants to differentiate in camera. And I just, I thought that was such a great idea. I want to credit Jack for that. And you can build a really nice analog differentiator. Okay. Yeah. Doesn't have to be digital. Yeah. Okay. Yes. So talking about this dynamics, I wonder if Ed or Jeff, you have any comment on that. I think Charlie asked a very interesting question. Right. So, so you want to understand not just from the static scene. You also have this dynamics that's involved in the scenario. And I know you guys, Ed and Jeff, you have been working a lot on this space. And any thought on that? Is it feather or wings? From your perspective? Maybe Jeff. So, I don't know so much about feathers. But what people lose track of is that biological flights can do things that human aircraft or human-designed aircraft can't do and vice versa. So human-designed aircraft can fly supersonically and birds cannot. But a bird, which has a flexible wing, a non-articulate or complex articulated wing, can be at a high altitude. It could pull its wings in. And it could enter ballistic motion phase, fall ballistically. And just at the right time, spread out its wings and attenuate its motion and catch a prey. And that's something that human-engineered aircraft are not able to do. And each have optimized for a different kind of functionality. The same thing holds true getting back to your initial sensors. There are sensing modalities that we can engineer that can do amazing things that the human eye can't even begin to do. Like MRI and CAT scan and the like. No matter how much you try, a human staring at another human body can't see inside the body. Whereas ordinary everyday medical imaging does that to very, very good purpose. So there are sensors that if I could have in my wildest dreams, if you could provide me, it would be great. Generally, if you can increase the spatial and temporal resolution by several orders of magnitude of brain imaging, that would be amazing. If you could image directly the neurologic activity, not indirectly to both, that would be amazing engineering capability. And it would have nothing to do with computer vision, but it would be tremendous for neuroscience. I don't really have the answer to what kind of sensor I would want if I were trying to do computer vision. Just a completely different question with a completely different range of possible answers that I haven't thought about very much. Thank you, Jeff. Thank you very much. I see a hand raised. Chi, do you want to comment something here? Yes, yes. Hi Bill, hi everyone. So I just joined here this month and all the way from Boston. So very nice to see you from Boston. So regarding, I first want to echo with what Bill has said about the differentiator camera. So I was thinking maybe like building such kind of sensor that outputs the differentiation of images can be a combination of both the optics and the electronics. So if we are able to make the optical design such that the pointer function obtained for the images follows certain kind of properties, maybe like it will be very easy for a like, maybe it's very easy for electronic operation to obtain a robust image that is a differentiation of the original ones. And so that leads to my imagination of a future camera. So I really hope that the optics side of the camera can perform much more type of computation than what we currently can do. So currently I see a lot of people using diffractive optical elements or some people are doing nano photonics. So they are able to manipulate, for example, the phase and intensity of the light wave that pass through it. I'm wondering if we are able to do non-linearity operations on the light that goes through certain kind of optics. If so, the kind of computation we are able to enable for a piece of camera can be much, much more expanded. So those are my comments. That's very exciting. That's very exciting. Yeah. I will just add a couple of comments from my very selfish perspective. I've been working on sensor for a couple of years and I know analog processing. I know digital processing, 14-bit, 12-bit, 16-bit processing. I want to have a sensor that is 1-bit, 1-bit binary. 1-bit binary, as soon as the signal comes to your sensor, I want it to just dump your binary response, 1 or 0. And then what do you get? Well, you get speed. You get resolution because the pitch can be a lot smaller. You also will be able to sense the signal in a completely different way. You are still digital, but then you are binary digital. And so you can do motion if you can track them. You can do dynamic range because I can fuel stamp. So in my imagination, some kind of sensors along the slide. Well, we've been doing something along the slide. We just quantized the sensor with Eric Foster. But I think that's still a really, really long way to go, right? This binary sensor that's replacing the whole sensor today, not analog, not digital, but it's a binary sensor. That would be really, really, really cool if we can fuel it. So that's why I've been thinking. Sounds great. So, all right. So I want to switch gear a little bit to another question, which is, I know there are lots of students in this room and they are really exciting about computer vision and they want to publish in CPI and so on. Maybe I can ask Bill and also the other panels for their opinion. What is publication? This is really big. What is publication? In your mind, what cost to become a good publication? There are different metrics. I know schools, people, employers, there are different metrics. But in your mind, what is a good piece of paper? What is good? That's a great question. So I'll just jump in and I really want to hear what the other people say. So I'll be brief. So this is something I wrestle with all the time. Also in evaluating faculty candidates and so forth. And I have to say, I've really been thrown for a loop by the neural network revolution. It's much harder for me to evaluate things now than it used to be. You know, it's possible to get in and make contributions with a much earlier in one's career than it used to be possible because the tools are so accessible. Which is great, but it's just finding confusing. So I'd love to have some crazy new ideas, some new way of looking at things. Something that you'll do differently now that you've read the paper than you would have done if you hadn't read the paper. Or something you're excited about. Those are all things I love. And that's of course in conflict with what's a great way to get a CVPR paper in. Which is to have a table with all these rows and your rows have all the bold numbers in them. You know, that's a great way to get a paper in. But it's not necessarily the one with the great new ideas. And so I'm really torn about that. And I really love the great ideas papers, but they're hard to evaluate. And somehow even harder to evaluate now. Anyway, I want to hear other people say too. Yeah, yeah, yeah. Maybe I'll start with Ed. Ed should have a lot of experience with advising students. Well, I'll just label myself part of the walking wounded here. But, you know, my concern is many times people that are jumping in and using all these neural network tools, for example, they lack understanding on how they really work. I don't know how many times that I've been at CVPR in the last couple of years where I walk up to somebody and listen to their poster. And then I asked them, explain to me how it really works. And you get a lot of people that are sort of choking when I do that, when I ask those questions. So I think it's important to have insight. You know, one of the things I teach a, Bill, we have this course here. It's called vertically integrated projects where we form teams of students from freshmen all the way up to seniors. And they work on research problems. Okay. And, you know, 99 out of 100 students, they want to do something with machine learning, whatever that means. Okay.  And they didn't really understand how it works. We want your grandmother on that plane. And you can figure out what the answer is. So I think that's what I would see. I'd also like to see students doing computer vision image processing. I learned a little bit more about, you know, statistics and probability theory. Like your introductory thing where you say, okay, let's just take a Bayesian model, make some Gaussian assumptions and let's see where it goes. I bet there's a lot of people that say they do computer vision or image processing. I couldn't do that or understand it because everything has to be solved by a neural network. You know, why did you take 100,000 samples and put it into ResNet or something like that, Professor Friedman? What's wrong with you? You're swimming in the opposite of a side of the river. You know what I mean? So that's what I would like to see is more understanding about how things work. Yeah. I'm there with you. So let me ask the other folks on the panel. Maybe Katik, do you have any comments?  I like to build up foundations for spatial geometry in computer vision. So I like to see a lot of more geometric content based on the foundations, you know, can you solve certain higher order problems, especially guiding it with these structure that you can garner from outside the vision area. So I kind of believe in more special purpose AI algorithms in computer vision and also one more human input into the AI system. So there's a lot of gap between what the AI systems can do today and what humans are capable of doing. So even if we think about AI as helping or aiding humans, you know, the gap is substantial and leads a lot of issues such as trust, as you point out and so on and so forth. Right. So in many of my colleagues in EC, like Saurabh and others are looking deeper into those types of issues. I think there are a lot of important aspects. But I like to think of AI and vision also being used for, you know, non-damaging AI versus more things that can cost more damage. There are a lot of areas where vision can be used but not be harmful if it fails. So and, you know, this is a mix of these problems. And of course we are entering this autonomy age and major questions of reliability and trust all come in. So there are a lot of interesting cans of worms. This AI has opened as Bill pointed out. We are in that world, unfortunately, unfortunately. So, yeah, I still like to inspire my problems with basic geometry. Yeah. Maggie. Yes. So I think, you know, there's another point sort of to bring to this discussion related to your original question of how do we advise students, right? Because, you know, we're sort of in this era and then there is, I feel like a larger environment in which whether you are applying a job, you're applying a school, you're evaluating a candidate, it's about where you publish, how much you publish and how reputable those publication venues are, right? And sometimes it becomes difficult to, you know, see, you know, the potential of the work. And then I don't know about, you know, those of you in the panel and the audience, but, you know, I've had experience where, you know, we work on something we feel is interesting and then, you know, it's a little bit immature, but then, yes, the reviewers agree it is an interesting problem and then, you know, it's something worthy to explore. However, we're not seeing enough of these big tables with bold numbers. So, I mean, I feel like it's a struggle, right?  So, how do we advise them? I mean, how do we, you know, I don't know influence change, you know, to cultivate a better environment for kind of educating the next generation of students, right? So, you know, we want them to understand how things work, right? We want, I mean, they're going to be engineers, you know, research scientists, so they're going to influence what comes next in the future. And that's more like a question for me, because I struggle with that too. That's actually a good question. It's actually a good question for us as faculty to think about, because we sort of, on a different side of the river compared to students, right? Because because students, they really in a rush to graduate and to get jobs, they need those numbers. But on us, we sort of, okay, now, if I have, I have a, like an e-gap, I can do sabbatical, I can just do work on something else. I think, Maggie, you're raising a really, really good point. And I can see the tension, but I don't have a solution. All I can do is just to encourage students to think big, to think big. But as it goes to the publication, we also want to face the reality to create big tables. I don't have a solution either. I'm not sure if you say it's a building culture already to this community because it's getting big, because it's getting competitive. I don't have a solution, but I can sort of just simplify with all our students. Maybe I can hear more from our other faculties or anything that we as faculty nowadays can do to help our students. On one hand, they want to get a job, I understand. On the other hand, don't lose their great talent of just spending so much time on the table. What can we as faculty do to help our students? I think that's such a good question. I'll just jump in. I don't have an answer. I wanted to hear what other people said too. But as you say, we have so much more patience because of our different career stage than the students do. And so for us, for a paper to get rejected or even to get rejected twice, it's not the end of the world. There's paper deadlines coming all the time. Just use the valuable feedback from the viewers and make it better. But that's a really hard, it's just very hard I think to be at an early stage in one's career and receive one or two rejections on a piece of work. And I don't know the solution to that. I try to tell people, the students, don't worry about it. Sometimes it goes from rejection to a best paper. It really can be, you just keep working at it. But it's a hard message to hear, I think. Do other people have any, so what do other people do? I wonder what other people think. Ed or maybe Charlie, any input on this? Yeah, I would just say my philosophy, I don't know if it's the best philosophy, okay? But I just try to ignore a lot of the hubbub. And I know it sounds kind of naive, but I try to do the right thing. Like, I try to just say, you know, what is really important here? And if you have a paper rejected, you know, take the feedback and try to incorporate it, try to look at it from the reviewer's point of view. And really try to keep focus on making a valuable contribution as opposed to hitting some specific goal. I mean, I feel like particularly as the success of these fields in computer vision, you know, image processing, computational imaging has led to the participation of a lot of people. So I think that the communities tend to naturally create barriers to entry. So you have to know the secret handshake and they pretend that the secret handshake is really important. But I believe many times the secret handshake is silly. It's just something they've created so that they could cull down the number of people they have to consider and reduce their competition. So I try to just really focus on doing valuable work that I think is important. And I try to have a small group of friends that are, that I, whose opinion I trust, and I ask them to give me honest feedback. And if they think of what I'm doing is jump, they go, you know, Charlie, we like you, but this is just isn't very interesting. So I move on. Okay. I have another question I want to ask, not to change the subject, but Bill, you have a really unique perspective because you've been in industry really deeply embedded in industry, and it's related to the previous question, but also deeply embedded academia. You know, as an academic, I tend to think that academics have more silly bean counting, but I'm sure there's a lot of silly bean counting in industry too. Probably, you know, because the grass is always a little greener on the other side. But I'm just curious, and but there's another dynamic that's occurring in the past, maybe for other fields that in areas like machine learning, let's say specifically, there's an argument that being made that industry is ahead of academia. Maybe others will disagree, but at least some people believe that's true because, you know, it's really core to their mission. There's a lot of resources. You know, okay, let me, the salaries are dramatically higher. Okay. And so they attract really enormous talent. So, but you know, academics, we like to think that we're free thinking and the dark side of tenure and so forth. So do you have any perspective on that on the relative balance between those and also how we can better get synergy between those, because maybe they have different things to bring to the table? Thanks. Sure. I'm happy to comment on that. But then I want to put it on the stack that I want to ask other people to, you know, how to protect students from the CVPR steamroller. I mean, we have, you know, we have a lot of experts on industry and academia. Yeah, well, industry is exploring this notion that everything bigger is better and still even more bigger is even more better. And they're exploring that and we'll see where it goes. But my friend, yeah, your wife, you know, reminds me and others that this whole thing, this whole juggernaut, frenzy of neural networks started with one academic and one graduate student working on something for two years in isolation, you know, that's where it came from. And surely the next big thing is also going to come from that kind of environment where, you know, academics do have more freedom than people in the industry. On the other hand, yes, it is my I always wonder whether the right model for what's going on is what happened with the semiconductor industry where, you know, industry has the best semiconductor manufacturing and academia dozens. And is that the way it's going to go with machine learning to I hope not. I still think there's a lot of room for creative, crazy ideas to change to change the industry coming from academia. But I fear that that I don't know. That's that's that's what keeps me hopeful, actually. Okay, good. Can I chime in for a second? Yes, yes. Because I'd like to follow along the lines of what Bill quoted yesterday, which is something that I try to remind my students, which a lot of young, entering PhD students these days, see the deep learning revolution and think the world started without extending 2012. And what they don't have the perspective that some of us older folks have is that the nature of the entire field of computer science, not just computer vision, not just AI, but all computer science, is that there's a progression of what you might call paradigm shifts, what you might call facts. And every paradigm shift, every paradigm and every fact lasts for a while. Some last longer than others, but they all come to an end. They necessarily have to come to an end. Because if you think about it, if you have a topic and an approach that a huge community, a community as large as the combined computer vision, machine learning, AI, national language, robotics, community is at this point with many tens of thousands of people, it can't go on for very long until one of two things happens. Either we solve all the problems and then there's nothing left to work on, or the methods run out of steam, they ask until and the field ceases to make progress. And the only way people make progress is by a paradigm shift that creates a discontinuity. So if you look at the way things have worked, not just with the current paradigm, but with all paradigm shifts over the past 50 years or so since the dawn of computing, in fact, there are these discontinuous increases in performance or ability functionality that happens when there's a paradigm shift. And then there's a slow march up from the curve until it asks until and then there's another discontinuity. So I have no doubt that whatever we're working on now is going to, asymptote is going to dry up at some point. It might be two years it might be five years, it might be 10 years, I don't know exactly how long, but it will. And at some point, there will be a new paradigm shift. I don't know what it is. I wish I did. I'd be a head of the curve at this point. But what I tell my students is when you graduate with a PhD, you have another 30 or 40 years in your career ahead of you. And over the course of that career, there are going to be five or eight paradigm shifts. And you have to prepare yourself to ride all of those, to persist through all of those. And so it's not so important to become an expert just at the current time. You have to get the good sense about how to run your entire research career to persist across all of the paradigm shifts that are going to happen over the next 30 years or 40 years. Great. That's an excellent point. Yeah. So maybe I should ask Maria, how much time do we have left? Is Maria here? I don't see her here. So I'm just looking at that gender. I think we are at a time, 4.45. So I wonder how the audience feel about us? Do you have more questions we want to chat about? Or we should also, or we should honor this time. And should we get a stop here? I'm open. I have a lot of time. Okay, this afternoon. So I just wonder what do you guys want to do? Dr. Chan, I just need to just I want to just let everybody know that we were actually out at time. So okay, okay. Sorry to do that to you. I'm so sorry. Sure. Sure. No problem. No problem. Thank you. No problem. Thank you for monitoring this. But I guess so let me just say a couple words to conclude this panel. I just first of all, I really want to thank Bill for for spending this afternoon with us. I think this is really, really, really stimulating. The the talk is just so stimulating. I just cannot believe at least I'm learning a lot, right? How to become persistent on the problem for a long time and then make some real progress. Although, though we see a lot of failures, never mind, just keep doing this. This is a really good lesson for me. And there are lots of great ideas that I'm learning. Lots of great discussions in this panel. Thank you, everyone for participating, sharing your comments. I guess one challenge I will just bring it to everyone is really how do we help our students. I think this is really a burden for me. Okay, how do I help our students? How do we encourage them to rethink back? While at the same time, we also only do acknowledge the need for those job markets. That's a challenge for us. Maybe in the near future, when we meet in person, maybe we will have some ideas. I hope Bill can visit us. Hopefully later this year, I really want to show you around campus. I'll have a student show you some interesting images through turbulence work that we can talk about. So I really look forward to that. Again, thank you everyone for coming. Thank you, students, faculty for joining us. Thank you, Bill, for virtually coming to this event. Thank you, everyone. Thank you. And thank you so much for inviting me. I will take you up on that invitation. I will come visit you. And I look forward to it. Thank you. I've learned a lot in this panel. And thanks a lot, everyone. Thank you. Thank you very much. Have a wonderful evening. Look forward to see everyone. Hopefully on campus very soon. Thanks, Bill. We really appreciate it.