The Power of Diversity in Education: Building High-Quality Learning Experiences for All

Diversity is a driving force behind building high-quality education. By embracing diversity, we can create learning environments that cater to different needs, interests, and backgrounds. This approach ensures that every individual has access to quality education, regardless of their geographical location or socio-economic status. The benefits of diverse education are numerous, and it's essential to recognize the value it brings to individuals, communities, and society as a whole.

One of the key aspects of diversity in education is the use of various learning tools and techniques. A combination of different approaches can lead to more effective learning outcomes. For instance, using deep learning techniques alongside traditional methods can enhance student understanding and retention. Moreover, incorporating diverse perspectives and experiences into the curriculum can foster empathy, critical thinking, and problem-solving skills.

The role of technology in education is another significant aspect of diversity. The use of advanced technologies like machine learning, artificial intelligence, and natural language processing has revolutionized the way we learn. These tools have made it possible to create personalized learning experiences, automate grading and assessment, and provide real-time feedback. Moreover, online platforms and educational resources have expanded access to quality education, enabling people from all over the world to participate in learning.

Despite the many benefits of diversity in education, there are still challenges that need to be addressed. One of the significant concerns is the issue of intellectual property rights. As AI systems become more advanced, there's a risk that they may infringe on existing patents and trademarks. This raises questions about ownership and control over intellectual property, particularly in the context of educational resources.

Another challenge is the issue of bias in AI systems. Like all complex systems, AI algorithms can perpetuate biases and prejudices if not designed carefully. This can have serious consequences, including discriminatory outcomes and unequal access to education. Therefore, it's essential to develop AI systems that are fair, transparent, and accountable.

The impact of government policies on education is another critical aspect of diversity. Governments play a significant role in shaping the educational landscape, and their policies can either promote or hinder diversity. By investing in education infrastructure, governments can create environments that support diverse learning experiences. Moreover, policymakers can implement policies that encourage innovation and creativity, such as research funding programs and entrepreneurship initiatives.

The role of community and collaboration is also essential in promoting diversity in education. Building partnerships between educational institutions, industry partners, and governments can help bridge the gaps in access to quality education. Moreover, encouraging collaboration among educators, researchers, and policymakers can lead to the development of more effective learning strategies and technologies.

In conclusion, diversity is a powerful driver of high-quality education. By embracing diverse approaches, technologies, and policies, we can create learning environments that cater to different needs and interests. While challenges exist, it's essential to recognize the value of diversity in education and work together to overcome them. With collaboration, innovation, and a commitment to excellence, we can build a future where every individual has access to quality education.

The Economic Benefits of Deep Learning

Deep learning is a type of machine learning that has revolutionized various industries, including education. In recent years, deep learning techniques have been shown to be highly effective in improving student outcomes, particularly in areas such as natural language processing and computer vision. One of the key benefits of deep learning is its ability to analyze large amounts of data quickly and accurately.

The economic benefits of deep learning are significant. Companies that invest in deep learning technologies can reap substantial rewards in terms of cost savings, increased efficiency, and improved productivity. For instance, Google's investment in deep learning has led to significant improvements in its search engine technology, making it more accurate and effective.

Moreover, deep learning has the potential to create new business models and revenue streams. By leveraging deep learning algorithms, companies can develop personalized learning experiences that cater to individual students' needs. This approach can lead to increased engagement and motivation among students, resulting in improved academic outcomes.

However, some critics argue that the economic benefits of deep learning come at a cost. The development and deployment of deep learning technologies require significant investments in terms of time, money, and resources. Moreover, there are concerns about job displacement and the potential for automation to replace human workers.

Despite these concerns, I believe that the economic benefits of deep learning far outweigh the costs. By investing in deep learning technologies, we can create more efficient, effective, and personalized learning experiences that cater to individual students' needs. This approach has the potential to transform the education sector, making it more accessible, affordable, and equitable.

The Importance of Community and Collaboration

As I look around, I see a community of educators, researchers, and policymakers working together to promote diversity in education. This collective effort is essential in creating learning environments that cater to different needs and interests. By building partnerships between educational institutions, industry partners, and governments, we can create more effective learning strategies and technologies.

Moreover, collaboration among educators, researchers, and policymakers can lead to the development of more innovative approaches to education. For instance, combining expertise from multiple fields can lead to the creation of new teaching methods, curricula, and assessment tools.

The role of community and collaboration is also critical in addressing challenges such as intellectual property rights and bias in AI systems. By working together, we can develop more effective solutions that balance individual needs with societal requirements.

In conclusion, the importance of community and collaboration cannot be overstated. By building partnerships and working together, we can create more inclusive, equitable, and effective learning environments that cater to diverse needs and interests.

The Future of Education: A Vision for Diversity and Innovation

As I look to the future, I envision a world where education is accessible, affordable, and inclusive for all. This vision is built on the principles of diversity, innovation, and collaboration.

In this future, educational institutions will be hubs of creativity, entrepreneurship, and community engagement. They will provide students with access to cutting-edge technologies, research opportunities, and internships that foster personal and professional growth.

Moreover, governments will play a critical role in promoting education as a human right, ensuring that all individuals have access to quality education regardless of their geographical location or socio-economic status. Policymakers will invest in education infrastructure, develop innovative policies, and provide incentives for entrepreneurs and innovators who are developing new learning technologies.

As educators, researchers, and policymakers, we must work together to create a future where every individual has the opportunity to succeed. This vision requires us to be adaptable, resilient, and committed to excellence. By embracing diversity, innovation, and collaboration, we can build a future that is bright, inclusive, and equitable for all.

In conclusion, the future of education is built on the principles of diversity, innovation, and collaboration. As educators, researchers, and policymakers, we must work together to create a world where every individual has access to quality education, regardless of their background or circumstances. With this vision, we can build a brighter future for all.

"WEBVTTKind: captionsLanguage: enokay so the first question you know I'm thinking of the past why did you guys open here oh please Imogene I understand that you were considering other cities right so why don't you let me give them a I may be the most important in destructive technology of our time and transform multiple industries transform society they can see estimated 30 trillion u.s. dollars of value creation to be grown globally by the year 2030 today about the work done by the is led by a small handful of gaps by Silicon Valley in Beijing and I think the rugby's morning IHOP's so you're not to go over you're not God we were looking around to try to decide where to sell about for us international office where she spent about six months of looking at cities in Latin America in Asia and in Europe and on TV including me you know but John spreadsheets treaty criteria visit multiplicity is collar-lok redeems the center at universities in different countries and also the business here in what I would like to see in the future is if very young I think that they are so young that every major cities things that I want to count of energy plan he because I think your plays various divisions I didn't have positive lunch today you know around the time that you went to work for Google and for by 2 that is to say 2011 2013 there were certain breakthroughs all of a sudden certain a high technologies that is to be kind of decent became really really good is one of them translating from voice to text or also face recognition right all of a sudden Facebook was telling you all of this is your friend Edwin do you want to talk about exactly that is there any other technology as we speak that you can comment on that face from being good to being really excellent mm even though that the fall 2011 not the practice they are restricted by scale we we already have a mobile data that computers of CP refusing and especially people using working past and present because I'm your networks to take advantage of the data where we have all some I saw interviewed communities using more traditional methods in using teeth learning writing speech recognition was actually one of the first but one of the presses that affects the work of people like Don the end of gelatin computer vision in a little bit later and I thought that beautifully after that and in robotics you know I think care workers dr. Burton as well even though the technologies improved dramatically one of the things that our community so has a gap though instant integrate research results and translate them into track more more practical declare themselves the gap Regina piece a paper around the department is this one what about our communities getting better at that um in terms of future technologies it turns out that of all the resume I was driven by the illogic consumer Internet companies like Google Facebook myself I do then have ababa users and therefore would have a Model T one of the things I'll be excited about is the rise of small data you read about the rise of big data and if you have any images with two million user records and between China and Network they're entrusting but what's responsible outside because you made that for example if you have a manufacturing plant that is inspecting Sparkletts to steal their scratches at them or not fortunately no factory has been attached to them again cell phones the scratches of that that it feeds they're on the way and some lackeys just did not have that it's a picture if you're able to get you know that was the world even small take you know ten pictures or hundred pages with your pictures it dramatically opens up there seven applications so but the advances in things like fuchsia I mean what about the degeneration and necessarily see exciting progress and small things are actually the work that our team is doing of all of these innovations are allowing us to enter industries that just you've not never pursuant to that you can go a little bit in that direction you know of course when everything can be disclosed but could you tell us about some areas that the company making is working on at broad areas so let's see one thing something sensible is evolve a small David is there cutting edge deeper it's not the only ones well the cutting edge here is the deep learning and a lot of the work that our team is doing about the work that maybe is doing a small theater is done by the team here in so I think we're team is doing cutting edge you know learning work that I don't think is being done in the marathon yet for example and I mean other than that the team in energy has ignored dozen projects ranging from educational initiative interested here and this taking a 19 years poverty as providing machine learning training and then helping them get placements companies the way I were to even major projects from visual inspections that you think you've heard mention and she just acted it as relieving the team the networks are building PCB printed circuit board manufacturing the game new networks is not different than anything any publish papers like that was the cutting-edge work process and healthcare projects fuel-saving sub projects that improve job recommendations so the multi pyrimidine includes the Mashti mapping they are which is helping countries just like adoption switches adjusted if I million u.s. dollars office to do that how I mentioned which is building many different economies including the for portfolio companies where the president's imagining and then also the learning value which is our educational cheap you know position which as well as the I for everyone and as part of a commitment to this region depend on has also tremendous releasing established translations of all the videos so that so spanish-speaking leaders give us access the Ministry of Technology Columbia has a plan to form for over 4,000 people in artificial intelligence engineers that will be capable of doing machine learning and artificial intelligence do you think that plan makes sense so what do you think will be the impact of such a plan in Colombia if we accomplish to form over 4,000 of washing 30 19 years I think training 4,000 people is a great start I would love to see a drone over a few years the 40,000 of them maybe 400,000 a lot but I would love to see it eventually end up secret you know today is the devaglia Beijing were ours of a guy it weren't always this way by the way in 2014 when I went to China I drove back to debilitate on China but my American friends Aussie is a Andrew y-you done Billy but at October 2014 I swore while the other is a director picture husband instead the eye colour varies greatly in China and maybe hopefully I play the rabbit down one up something one of the early encouraging signs I see today is that our office in addition to attracting people and also some mornings are not receive managing become is a look at least on Paul but maybe even about talent hub where if someone in Poland says I work a I went like blue well they could move to Silicon Valley maybe they could go to Beijing or why not think of artists even fairies answer that now because my dignity I feel like for us the visa is impossible for us to be successful by ourselves which is fine Obama what we want to do this yeah I'm gonna cue sucessfully I don't think if you are 80 years if you learned about 90 Bernie doesn't either suyana top this is a great time to drinking but also the business leaders it turns out that a nine mashido need to be successful turkey is most efficient so the team of 10:13 engineers and then for the rest of the car didn't know nothing about bi so I think providing education at all levels which people that big dive does doesn't become one of the reasons I did that was because I thought if a CEO of a company or you know VP of activity thinks they are everyone it would be in a much better position to set the machine in a teapot for success so the part of my secret plan was to get more you know cut the executives in the eye every month that this will make them want to hire even more years last time you mentioned some of the hot topics that were there what year ago that were hot topics in machine learning one of them with resume that already was being able to train neural networks with smaller datasets qualify understand small Dana what what other hot topics in machine Hareton or AI would you signal as it was significant one once product ringing the most important changes mention a few things I'll be excited by the investor Kansai I'm excited about the rise of machinery as a systemic injury discipline so to make an analogy I think you know thousands of years ago if you wanted to build a bridge that gives me these wise old man buys a woman and you have to build bridges that stay up you oughta take no further they had a piece of wood there and if you had that piece of with the zombies of the bridge stays up but eventually we had to keep the ancient discipline of civil engineering and today people all around the world on the Bible bridges I think machine learning and deep learning is so in that earlier era where you know a student may trade in your network for some reason it doesn't work so they go along but what did I do it at home and says oh she's activation function one of the most personal things I've ever seen discipline discipline given we are one of the teams that are in the process of making up new processes all the time so for example one of the processes for example in software engineer you may have further you know strong and two-week sprints one of the very appealing things that I do missionary to is about one day Sprint's because executing for two weeks and see me on occasion when I worked at missionary project sometimes we read country today from the mascara overnight the mix only we do error analysis to decide what to do things then we basically decide what code to write the second day we run expense second at night and so this is one example of a very strange process especially what they've spent that's crazy right but but the machine learning world but some projects not all projects for some projects this allows us to drive much faster process so this is one because of the genie it took us a long time to figure out that you know version control was a great night hero and in fact it took a long time before right I guess CVS is that version a long time to figure out that code review is a three-night year I think of machine learning work was still making of those processes I was in terms of just mentioned what this meant that if everything else was there anything they wrote down that people have a pap a Chelsea fan openly I do agree well not that I think what type of articles overly words observation hard to become a robot I'm seeing signs of traction into the prosperity and then I'll mention one of them we have become I said about which is supervised learning as you know I spent the whole day at the self supervised learning workshop and this is idea that you can make up supervised learning tasks into presentations so for example one of the plaza could have is take a picture cut them into B by the previous picture cuts it so yeah nine pieces but the shuttle these are pieces back to me and then also supervised learning algorithm to tell me what was the original mapping a piece not pieces so you can income unlimited amounts of they both data because you just taking people if you just cutting it up and they shackled upon pieces that you drink infinite lots of labeled data but what can we design hacked official toss that usually for people danger see very encouraging signs even traded jogging I had work done fantastic a presentation to transfer to Utah a couple times I hope you we do rent the game I gave one of them some of the next questions have to do with the careers of the people who are sitting here so if you don't mind I would love to know a little bit better about the audience how many here is raise your hands if are still if your students okay those who are undergraduate students but if you graduate students and who are professors teachers like yourselves and the races who are practitioners of you know professionals of twenty years or partition heirs of any other people it is okay so your question you have even intern in Colombia wants to pursue a career in a what should these developer do what should she or he start learning and what would your recommendation be time to join the field is often whether it's still taking off and is still taking off he wanted people they covered on the ice so this is a great time to be jumping into it and confidence perception of building the eye projects this is all machine learning engineers are using tools available to train your network Secession not true a good be 19 in order to build practical system social products often has a mix of this cloud machine our engineers as we're solving for socket advantages front a backer although the you adapter Pisco working together in terms of software engineer as it turns out that let me let me tell you what I saw Silicon Valley about ten years I read ten years ago one of the ways that women for me as a back in engineer was just work on big penises in everyone else right so you know you could imagine in the barbers with a value 2 in G as we get together and I'll say I process appended by the day time that you can kind of I think we're moving into an era where school as a software engineer is not about new processes the most data is about other things and what their prices I mean I era a lot of the most complicated problems that intersection I was front or back tell you one example let's say you we throw these rocks better you work a book she may exist that takes us into the x-ray image and transit doc knows if the patient has cancer what turns out the operative machinery have we have made their chest x-ray and say oh there's a big win here of the south-- we're there but 20% chances comes in there mr. chair the way we build pipelines we are building very complicated pipelines are not seen anywhere else khadijah we're actually it comes back it's a sewer pipe ends with a small data age or computing faculty connections and what they see very complicated problems driven put the need written by the existence sadly many people have greater is justina you know software engineer I think is a fine thing to do destructive technology today is an i and I think a software engineer front a packet mobile you know whatever force that figures out how your work means to camera will be doing something very unique and very bad well that very information you citing you don't need to receive machine when useful image one of the new take some processor you can learn enough to get a job as a as a and she but inexperienced back in engineer you don't want to be an energy revolution in each year was much better for your career instead to learn a little bit about AI and then we are on the really hard and really important problems on how back and engineering is changing that era and that's across the problems that they're sorted okay these guys here are already working for for the club from climbing 19 my question raise your hands with people that I know I know that's that's what I put two other ones to work for their company for his company meeting so we hear that the selection process your company's flat different from from the traditional Colombian companies because you know I was joking with under like what's that mean that in the interviews hearing that even when you go to a company it usually has to what's your mother's name what's your father's name what do you like for that whereas in the interview process for instance they they asked them to a whiteboard I'm so lucky that so how what would be your recommendation very practical recommendations for these people to your company to you so I think we are and I think what mistake that he made there are some problems with the not lasting logical disruption divided into them as well but we also I would like to put all the pieces in place maybe from an educational system yes they are also made jobs or passive jobs but it's our responsibility to be trained people have a training education we won't use the and in future as long as already really yeah but one other thing we'd like to do is to be shown this time around that would be showing that wealth is very sheer and for we're only and think it's fair to say that Andrew and his friends at Stanford already changed the world by creating for sale as many change the way education is building high-quality education is delivered to everybody in the world let's hope you're right again with you you know diversity learning something maybe Obama was 10 years ago was driven by the parts of scale or a skin or dangerous killer competition but we look at what the substitute a ideals do today is not that we use deep learning for every I think of me I as a part of all the other many different tools and so you know sometimes question we do Steve learned a lot but sometimes a few months ago versus lean on the Dean debunking implementation or PCA and I think the experience the ideas whether portfolio tools ranging from machine learning and deep learning to maybe a knowledge draft that was graphic also planning algorithms and depend on the problem of music were to offer different elements of this I think so much how about informing because of all of these portfolio tools deep learning is the one of those performances that was so much greater than say five years ago where the contrast I don't know that Bayesian networks work that much better your identity is the business world if they're better now five years ago but it's not that you know hundred eggs and fruit or something every school teacher I think you're a success in life about the I think about each time I think positive general intelligence hopes like years or something did about that irrational exuberance attract investment but if you look at most of the other day I guess you know the media reported of how much money Google has spent but if you look at Google deep learning has been incredibly profitable the country as a whole so I think was he one of the reasons I don't think that we're in a very bad bubble is because I have seen with my own eyes you know the value of other or the money that some logical reason I think community we are big enough for many different approaches and a small number of successes has been done really really well I think we are the community people for some things to help to be dense but our communities are so big that we can afford a 20-person - you doing some piece of work that turns out the wrong direction because chances are they'll get different 50% to you even if a company that does something really amazing so I think I will have to cheer on everyone trying to push for all of the different years of AI even that I think some areas of all policy I would best thought those myself but I'm glad that there others protesting about the area's water hey how come already made many smile and continues I think that's desperate in the education is a big deal and I think government has very strong convening power so what were those aces are contained very effective I think I should actually back in Columbia makes fun speaking about beautiful but certainly events like they have the power of the government to compete against put together the emotional businesses is also very powerful and 99 plants our teams are working on products they're serving or audiences so you know the team is writing software that we use United States are using a using other countries or using that America as well but one thing in addition to it turns out that every country of Colombia what happened allocation suppose that Jenny you need to call a beer so the way that you know beer does coffee on the way down of Columbia expelled time people are in gasps direction mama problems and Carl embarrassment better position to solve than something sent off in slipper family and so I think apartments in proposing VI for the industries of adrenaline kilometer is already to that I don't think repair should build another web search engine that was like the competition for ten years ago a very good search engine sisters use of existing ones but we don't like very good solutions yeah but he had a mining agriculture factory but on the next to the next six years people because our team has an operative here right here and I know how good they were kids and because even but we started you know he spent six months trying to figure out where to go I wanted maybe a little bit strange that I am as an American executive I have a ball of a timidity and what I want to tell you is I need you to also have a pension because if what we have to do is not just think you know other countries and they int my successful but they won't want to but to do is build a community regional or global talent first four thousand and it may be the future 20,000 won even more people trade em in the eye and if you want your city to in the future be one of the cool morning I hope so then the next time some components looking for job is praying that they come join me on TV I I think is also great continent into joining different country so what I want to do all of you ability to notice ecosystem and it had to happen I am offended that you and I mean youth also Tatum energy and to work together with us as a community to tell everyoneokay so the first question you know I'm thinking of the past why did you guys open here oh please Imogene I understand that you were considering other cities right so why don't you let me give them a I may be the most important in destructive technology of our time and transform multiple industries transform society they can see estimated 30 trillion u.s. dollars of value creation to be grown globally by the year 2030 today about the work done by the is led by a small handful of gaps by Silicon Valley in Beijing and I think the rugby's morning IHOP's so you're not to go over you're not God we were looking around to try to decide where to sell about for us international office where she spent about six months of looking at cities in Latin America in Asia and in Europe and on TV including me you know but John spreadsheets treaty criteria visit multiplicity is collar-lok redeems the center at universities in different countries and also the business here in what I would like to see in the future is if very young I think that they are so young that every major cities things that I want to count of energy plan he because I think your plays various divisions I didn't have positive lunch today you know around the time that you went to work for Google and for by 2 that is to say 2011 2013 there were certain breakthroughs all of a sudden certain a high technologies that is to be kind of decent became really really good is one of them translating from voice to text or also face recognition right all of a sudden Facebook was telling you all of this is your friend Edwin do you want to talk about exactly that is there any other technology as we speak that you can comment on that face from being good to being really excellent mm even though that the fall 2011 not the practice they are restricted by scale we we already have a mobile data that computers of CP refusing and especially people using working past and present because I'm your networks to take advantage of the data where we have all some I saw interviewed communities using more traditional methods in using teeth learning writing speech recognition was actually one of the first but one of the presses that affects the work of people like Don the end of gelatin computer vision in a little bit later and I thought that beautifully after that and in robotics you know I think care workers dr. Burton as well even though the technologies improved dramatically one of the things that our community so has a gap though instant integrate research results and translate them into track more more practical declare themselves the gap Regina piece a paper around the department is this one what about our communities getting better at that um in terms of future technologies it turns out that of all the resume I was driven by the illogic consumer Internet companies like Google Facebook myself I do then have ababa users and therefore would have a Model T one of the things I'll be excited about is the rise of small data you read about the rise of big data and if you have any images with two million user records and between China and Network they're entrusting but what's responsible outside because you made that for example if you have a manufacturing plant that is inspecting Sparkletts to steal their scratches at them or not fortunately no factory has been attached to them again cell phones the scratches of that that it feeds they're on the way and some lackeys just did not have that it's a picture if you're able to get you know that was the world even small take you know ten pictures or hundred pages with your pictures it dramatically opens up there seven applications so but the advances in things like fuchsia I mean what about the degeneration and necessarily see exciting progress and small things are actually the work that our team is doing of all of these innovations are allowing us to enter industries that just you've not never pursuant to that you can go a little bit in that direction you know of course when everything can be disclosed but could you tell us about some areas that the company making is working on at broad areas so let's see one thing something sensible is evolve a small David is there cutting edge deeper it's not the only ones well the cutting edge here is the deep learning and a lot of the work that our team is doing about the work that maybe is doing a small theater is done by the team here in so I think we're team is doing cutting edge you know learning work that I don't think is being done in the marathon yet for example and I mean other than that the team in energy has ignored dozen projects ranging from educational initiative interested here and this taking a 19 years poverty as providing machine learning training and then helping them get placements companies the way I were to even major projects from visual inspections that you think you've heard mention and she just acted it as relieving the team the networks are building PCB printed circuit board manufacturing the game new networks is not different than anything any publish papers like that was the cutting-edge work process and healthcare projects fuel-saving sub projects that improve job recommendations so the multi pyrimidine includes the Mashti mapping they are which is helping countries just like adoption switches adjusted if I million u.s. dollars office to do that how I mentioned which is building many different economies including the for portfolio companies where the president's imagining and then also the learning value which is our educational cheap you know position which as well as the I for everyone and as part of a commitment to this region depend on has also tremendous releasing established translations of all the videos so that so spanish-speaking leaders give us access the Ministry of Technology Columbia has a plan to form for over 4,000 people in artificial intelligence engineers that will be capable of doing machine learning and artificial intelligence do you think that plan makes sense so what do you think will be the impact of such a plan in Colombia if we accomplish to form over 4,000 of washing 30 19 years I think training 4,000 people is a great start I would love to see a drone over a few years the 40,000 of them maybe 400,000 a lot but I would love to see it eventually end up secret you know today is the devaglia Beijing were ours of a guy it weren't always this way by the way in 2014 when I went to China I drove back to debilitate on China but my American friends Aussie is a Andrew y-you done Billy but at October 2014 I swore while the other is a director picture husband instead the eye colour varies greatly in China and maybe hopefully I play the rabbit down one up something one of the early encouraging signs I see today is that our office in addition to attracting people and also some mornings are not receive managing become is a look at least on Paul but maybe even about talent hub where if someone in Poland says I work a I went like blue well they could move to Silicon Valley maybe they could go to Beijing or why not think of artists even fairies answer that now because my dignity I feel like for us the visa is impossible for us to be successful by ourselves which is fine Obama what we want to do this yeah I'm gonna cue sucessfully I don't think if you are 80 years if you learned about 90 Bernie doesn't either suyana top this is a great time to drinking but also the business leaders it turns out that a nine mashido need to be successful turkey is most efficient so the team of 10:13 engineers and then for the rest of the car didn't know nothing about bi so I think providing education at all levels which people that big dive does doesn't become one of the reasons I did that was because I thought if a CEO of a company or you know VP of activity thinks they are everyone it would be in a much better position to set the machine in a teapot for success so the part of my secret plan was to get more you know cut the executives in the eye every month that this will make them want to hire even more years last time you mentioned some of the hot topics that were there what year ago that were hot topics in machine learning one of them with resume that already was being able to train neural networks with smaller datasets qualify understand small Dana what what other hot topics in machine Hareton or AI would you signal as it was significant one once product ringing the most important changes mention a few things I'll be excited by the investor Kansai I'm excited about the rise of machinery as a systemic injury discipline so to make an analogy I think you know thousands of years ago if you wanted to build a bridge that gives me these wise old man buys a woman and you have to build bridges that stay up you oughta take no further they had a piece of wood there and if you had that piece of with the zombies of the bridge stays up but eventually we had to keep the ancient discipline of civil engineering and today people all around the world on the Bible bridges I think machine learning and deep learning is so in that earlier era where you know a student may trade in your network for some reason it doesn't work so they go along but what did I do it at home and says oh she's activation function one of the most personal things I've ever seen discipline discipline given we are one of the teams that are in the process of making up new processes all the time so for example one of the processes for example in software engineer you may have further you know strong and two-week sprints one of the very appealing things that I do missionary to is about one day Sprint's because executing for two weeks and see me on occasion when I worked at missionary project sometimes we read country today from the mascara overnight the mix only we do error analysis to decide what to do things then we basically decide what code to write the second day we run expense second at night and so this is one example of a very strange process especially what they've spent that's crazy right but but the machine learning world but some projects not all projects for some projects this allows us to drive much faster process so this is one because of the genie it took us a long time to figure out that you know version control was a great night hero and in fact it took a long time before right I guess CVS is that version a long time to figure out that code review is a three-night year I think of machine learning work was still making of those processes I was in terms of just mentioned what this meant that if everything else was there anything they wrote down that people have a pap a Chelsea fan openly I do agree well not that I think what type of articles overly words observation hard to become a robot I'm seeing signs of traction into the prosperity and then I'll mention one of them we have become I said about which is supervised learning as you know I spent the whole day at the self supervised learning workshop and this is idea that you can make up supervised learning tasks into presentations so for example one of the plaza could have is take a picture cut them into B by the previous picture cuts it so yeah nine pieces but the shuttle these are pieces back to me and then also supervised learning algorithm to tell me what was the original mapping a piece not pieces so you can income unlimited amounts of they both data because you just taking people if you just cutting it up and they shackled upon pieces that you drink infinite lots of labeled data but what can we design hacked official toss that usually for people danger see very encouraging signs even traded jogging I had work done fantastic a presentation to transfer to Utah a couple times I hope you we do rent the game I gave one of them some of the next questions have to do with the careers of the people who are sitting here so if you don't mind I would love to know a little bit better about the audience how many here is raise your hands if are still if your students okay those who are undergraduate students but if you graduate students and who are professors teachers like yourselves and the races who are practitioners of you know professionals of twenty years or partition heirs of any other people it is okay so your question you have even intern in Colombia wants to pursue a career in a what should these developer do what should she or he start learning and what would your recommendation be time to join the field is often whether it's still taking off and is still taking off he wanted people they covered on the ice so this is a great time to be jumping into it and confidence perception of building the eye projects this is all machine learning engineers are using tools available to train your network Secession not true a good be 19 in order to build practical system social products often has a mix of this cloud machine our engineers as we're solving for socket advantages front a backer although the you adapter Pisco working together in terms of software engineer as it turns out that let me let me tell you what I saw Silicon Valley about ten years I read ten years ago one of the ways that women for me as a back in engineer was just work on big penises in everyone else right so you know you could imagine in the barbers with a value 2 in G as we get together and I'll say I process appended by the day time that you can kind of I think we're moving into an era where school as a software engineer is not about new processes the most data is about other things and what their prices I mean I era a lot of the most complicated problems that intersection I was front or back tell you one example let's say you we throw these rocks better you work a book she may exist that takes us into the x-ray image and transit doc knows if the patient has cancer what turns out the operative machinery have we have made their chest x-ray and say oh there's a big win here of the south-- we're there but 20% chances comes in there mr. chair the way we build pipelines we are building very complicated pipelines are not seen anywhere else khadijah we're actually it comes back it's a sewer pipe ends with a small data age or computing faculty connections and what they see very complicated problems driven put the need written by the existence sadly many people have greater is justina you know software engineer I think is a fine thing to do destructive technology today is an i and I think a software engineer front a packet mobile you know whatever force that figures out how your work means to camera will be doing something very unique and very bad well that very information you citing you don't need to receive machine when useful image one of the new take some processor you can learn enough to get a job as a as a and she but inexperienced back in engineer you don't want to be an energy revolution in each year was much better for your career instead to learn a little bit about AI and then we are on the really hard and really important problems on how back and engineering is changing that era and that's across the problems that they're sorted okay these guys here are already working for for the club from climbing 19 my question raise your hands with people that I know I know that's that's what I put two other ones to work for their company for his company meeting so we hear that the selection process your company's flat different from from the traditional Colombian companies because you know I was joking with under like what's that mean that in the interviews hearing that even when you go to a company it usually has to what's your mother's name what's your father's name what do you like for that whereas in the interview process for instance they they asked them to a whiteboard I'm so lucky that so how what would be your recommendation very practical recommendations for these people to your company to you so I think we are and I think what mistake that he made there are some problems with the not lasting logical disruption divided into them as well but we also I would like to put all the pieces in place maybe from an educational system yes they are also made jobs or passive jobs but it's our responsibility to be trained people have a training education we won't use the and in future as long as already really yeah but one other thing we'd like to do is to be shown this time around that would be showing that wealth is very sheer and for we're only and think it's fair to say that Andrew and his friends at Stanford already changed the world by creating for sale as many change the way education is building high-quality education is delivered to everybody in the world let's hope you're right again with you you know diversity learning something maybe Obama was 10 years ago was driven by the parts of scale or a skin or dangerous killer competition but we look at what the substitute a ideals do today is not that we use deep learning for every I think of me I as a part of all the other many different tools and so you know sometimes question we do Steve learned a lot but sometimes a few months ago versus lean on the Dean debunking implementation or PCA and I think the experience the ideas whether portfolio tools ranging from machine learning and deep learning to maybe a knowledge draft that was graphic also planning algorithms and depend on the problem of music were to offer different elements of this I think so much how about informing because of all of these portfolio tools deep learning is the one of those performances that was so much greater than say five years ago where the contrast I don't know that Bayesian networks work that much better your identity is the business world if they're better now five years ago but it's not that you know hundred eggs and fruit or something every school teacher I think you're a success in life about the I think about each time I think positive general intelligence hopes like years or something did about that irrational exuberance attract investment but if you look at most of the other day I guess you know the media reported of how much money Google has spent but if you look at Google deep learning has been incredibly profitable the country as a whole so I think was he one of the reasons I don't think that we're in a very bad bubble is because I have seen with my own eyes you know the value of other or the money that some logical reason I think community we are big enough for many different approaches and a small number of successes has been done really really well I think we are the community people for some things to help to be dense but our communities are so big that we can afford a 20-person - you doing some piece of work that turns out the wrong direction because chances are they'll get different 50% to you even if a company that does something really amazing so I think I will have to cheer on everyone trying to push for all of the different years of AI even that I think some areas of all policy I would best thought those myself but I'm glad that there others protesting about the area's water hey how come already made many smile and continues I think that's desperate in the education is a big deal and I think government has very strong convening power so what were those aces are contained very effective I think I should actually back in Columbia makes fun speaking about beautiful but certainly events like they have the power of the government to compete against put together the emotional businesses is also very powerful and 99 plants our teams are working on products they're serving or audiences so you know the team is writing software that we use United States are using a using other countries or using that America as well but one thing in addition to it turns out that every country of Colombia what happened allocation suppose that Jenny you need to call a beer so the way that you know beer does coffee on the way down of Columbia expelled time people are in gasps direction mama problems and Carl embarrassment better position to solve than something sent off in slipper family and so I think apartments in proposing VI for the industries of adrenaline kilometer is already to that I don't think repair should build another web search engine that was like the competition for ten years ago a very good search engine sisters use of existing ones but we don't like very good solutions yeah but he had a mining agriculture factory but on the next to the next six years people because our team has an operative here right here and I know how good they were kids and because even but we started you know he spent six months trying to figure out where to go I wanted maybe a little bit strange that I am as an American executive I have a ball of a timidity and what I want to tell you is I need you to also have a pension because if what we have to do is not just think you know other countries and they int my successful but they won't want to but to do is build a community regional or global talent first four thousand and it may be the future 20,000 won even more people trade em in the eye and if you want your city to in the future be one of the cool morning I hope so then the next time some components looking for job is praying that they come join me on TV I I think is also great continent into joining different country so what I want to do all of you ability to notice ecosystem and it had to happen I am offended that you and I mean youth also Tatum energy and to work together with us as a community to tell everyone\n"