Top 100 Insightful Machine Learning Quotes By Andrew Ng

Top Insightful Machine Learning Quotes By Andrew Ng learn to code youtube thumbnail Factober Inspiration for Artificial Intelligence, Data Science & ML

Written by Vishal for Factober


October 24, 2020

Andrew Ng Quotes on Machine Learning

Machine Learning is one of the hottest trends in 2020. All major IT corporations have switched their brainstorming to machine learning, big data, data science & artificial intelligence systems, when are you going to become serious about it? Read these 25 most insightful quotes about machine learning by expert Andrew Ng and tell us your opinion in the comments.

  1. A lot of the game of AI today is finding the appropriate business context to fit it in. I love technology. It opens up lots of opportunities. But in the end, technology needs to be contextualized and fit into a business use case.
  2. A lot of the progress in machine learning – and this is an unpopular opinion in academia – is driven by an increase in both computing power and data. An analogy is to building a space rocket: You need a huge rocket engine, and you need a lot of fuel.
  3. A single neuron in the brain is an incredibly complex machine that even today we don’t understand. A single ‘neuron’ in a neural network is an incredibly simple mathematical function that captures a minuscule fraction of the complexity of a biological neuron.
  4. AI has been making tremendous progress in machine translation, self-driving cars, etc. Basically, all the progress I see is in specialised intelligence. It might be hundreds or thousands of years or, if there is an unexpected breakthrough, decades.
  5. AI is creating tremendous economic value today.
  6. AI is witnessing an early innings in India. It has a thoughtful government, and India can race ahead if it chooses to.
  7. Animals see a video of the world. If an animal were only to see still images, how would its vision develop? Neuroscientists have run experiments in cats in a dark environment with a strobe so it can only see still images – and those cats’ visual systems actually underdevelop. So motion is important, but what is the algorithm?
  8. As leaders, it is incumbent on all of us to make sure we are building a world in which every individual has an opportunity to thrive. Understanding what AI can do and how it fits into your strategy is the beginning, not the end, of that process.
  9. As the founding lead of the Google Brain team, former director of the Stanford Artificial Intelligence Laboratory, and now overall lead of Baidu’s AI team of some 1,200 people, I’ve been privileged to nurture many of the world’s leading AI groups and have built many AI products that are used by hundreds of millions of people.
  10. Baidu and Google are great companies, but there are a lot of things you can do outside them. Just as electricity and the Internet transformed the world, I think the rise of modern A.I. technology will create a lot of opportunities both for new startups and for incumbent companies to transform.
  11. Baidu Research has three labs – two in Beijing that are already largely built up, and the Silicon Valley one is being built from scratch. We’re hiring pretty rapidly, about one person a week, but we are about a month in, so honestly, we haven’t done that much work yet.
  12. Baidu’s AI is incredibly strong, and the team is stacked up and down with talent; I am confident AI at Baidu will continue to flourish. After Baidu, I am excited to continue working toward the AI transformation of our society and the use of AI to make life better for everyone.
  13. Beyond helping other people build AI systems with, I also hope to build some AI systems myself!
  14. Deep learning is a very capital-intensive area, and it’s rare to find a company with both the necessary resources and a company structure where things can get done without having to pass through too many channels and committee meetings.
  15. Deep-learning will transform every single industry. Healthcare and transportation will be transformed by deep-learning. I want to live in an AI-powered society. When anyone goes to see a doctor, I want AI to help that doctor provide higher quality and lower cost medical service. I want every five-year-old to have a personalised tutor.
  16. Despite all the hype and excitement about AI, it’s still extremely limited today relative to what human intelligence is.
  17. Education is not about thinning the herd. Education is about helping every student succeed.
  18. Education is one of the industry categories with a big potential for AI. And Coursera is already doing some of this work.
  19. Elon Musk is worried about AI apocalypse, but I am worried about people losing their jobs. The society will have to adapt to a situation where people learn throughout their lives depending on the skills needed in the marketplace.
  20. Even companies like Baidu and Google, which have amazing AI teams, cannot do all the work needed to get us to an AI-powered society. I thought the best way to get us there would be creating courses to welcome more people to deep learning.
  21. Every company has messy data, and even the best of AI companies are not fully satisfied with their data. If you have data, it is probably a good idea to get an AI team to have a look at it and give feedback. This can develop into a positive feedback loop for both the IT and AI teams in any company.
  22. Google Brain, which I led, was arguably the single biggest force for turning Google into a great A.I. company. I’m pretty sure I led the team that transformed Baidu as well. So one thing that really excites me is the potential for other companies to become great A.I. companies.
  23. I am always mission driven, and I always ask myself what I want to be working on, what project excites me the most. I figure that out and then find the best place to do that work.
  24. I am looking into quite a few ideas in parallel and exploring new AI businesses that I can build. One thing that excites me is finding ways to support the global AI community so that people everywhere can access the knowledge and tools that they need to make AI transformations.
  25. I am super optimistic about the near-term prospects of AI because every time there is a technological disruption, it gives us the opportunity of making the world a little different.
  26. I believe that the ability to innovate and to be creative are teachable processes. There are ways by which people can systematically innovate or systematically become creative.
  27. I find it a very encouraging sign for a society if employers are bringing online education to their companies, helping employees gain more knowledge.
  28. I had a strong interest in free online education, and I was interested in what videos and formats would work for it. A lot of education workers were very sceptical about what computer scientists were doing. It was only after the first visible success of MOOCs that they started to take it seriously.
  29. I joined Baidu in 2014 to work on AI. Since then, Baidu’s AI group has grown to roughly 1,300 people, which includes the 300-person Baidu Research. Our AI software is used every day by hundreds of millions of people.
  30. I just thought making machines intelligent was the coolest thing you could do. I had a summer internship in AI in high school, writing neural networks at National University of Singapore – early versions of deep learning algorithms. I thought it was amazing you could write software that would learn by itself and make predictions.
  31. I see a minimum living wage as a long-term solution, but I’m not sure that’s my favorite. I think society benefits if all the human race is empowered and aspiring to do great things. Giving people the skill sets to do great things will take work.
  32. I think that AI will lead to a low cost and better quality life for millions of people. Like electricity, it’s a possibility to build a wonderful society. Also, right now, I don’t see a clear path for AI to surpass human-level intelligence.
  33. I think that solving the job impact of AI will require significant private and public efforts. And I think that many people actually underestimate the impact of AI on jobs. Having said that, I think that if we work on it and provide the skill training needed, then there will be many new jobs created.
  34. I think that, hundreds of years from now, if people invent a technology that we haven’t heard of yet, maybe a computer could turn evil. But the future is so uncertain. I don’t know what’s going to happen five years from now. The reason I say that I don’t worry about AI turning evil is the same reason I don’t worry about overpopulation on Mars.
  35. I think the first wave of deep learning progress was mainly big companies with a ton of data training very large neural networks, right? So if you want to build a speech recognition system, train it on 100,000 hours of data.
  36. I think the Indian AI ecosystem is growing rapidly. A lot of Indian entrepreneurs reach out to me seeking feedback about startups and products. And some of them have very interesting business ideas.
  37. I think the next massive wave of value creation will be when you can get a manufacturing company or agriculture devices company or a health care company to develop dozens of AI solutions to help their businesses.
  38. I think the rise of A.I. is bigger than the rise of mobile. Large companies are sometimes as worried about startups as startups are about large companies. Ultimately, it will be about who delivers the best service or product.
  39. I think the world will just be better if AI is helping us. It will reduce the cost of goods, giving us good education, changing the way we run hospitals and the health-care system – there’s just a long list of things.
  40. I thought the best place to advance the AI mission is at Baidu.
  41. I want an AI-powered society because I see so many ways that AI can make human life better. We can make so many decisions more systematically or automate away repetitive tasks and save so much human time.
  42. I will continue my work to shepherd in this important societal change… In addition to working on AI myself, I will also explore new ways to support all of you in the global AI community so that we can all work together to bring this AI-powered society to fruition.
  43. I’m super excited about health care; I’m super excited about education – major industries where AI can play a big role.
  44. I’ve been to so many manufacturing plants. I’ve yet to walk into one where I did not think AI solutions wouldn’t help.
  45. If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.
  46. If we can make computers more intelligent – and I want to be careful of AI hype – and understand the world and the environment better, it can make life so much better for many of us. Just as the Industrial Revolution freed up a lot of humanity from physical drudgery I think AI has the potential to free up humanity from a lot of the mental drudgery.
  47. If you have a lot of data and you want to create value from that data, one of the things you might consider is building up an AI team.
  48. If you want to publish data, you should do it to share knowledge.
  49. Imagine if we can just talk to our computers and have it understand, ‘Please schedule a meeting with Bob for next week.’ Or if each child could have a personalized tutor. Or if self-driving cars could save all of us hours of driving.
  50. In English, there is one word for sister. In Chinese, there are two separate words, for elder and younger sister. This is actually a translation problem because if you see the word sister, you don’t know how to translate it to Chinese because you don’t know if it’s an elder sister or younger.
  51. In healthcare, we are beginning to see that AI can read the radiology images better than most radiologists. In education, we have a lot of data, and companies like Coursera are putting up a lot of content online.
  52. In my own life, I found that whenever I wasn’t sure what to do next, I would go and learn a lot, read a lot, talk to experts. I don’t know how the human brain works, but it’s almost magical: when you read enough or talk to enough experts, when you have enough inputs, new ideas start appearing. This seems to happen for a lot of people that I know.
  53. In Silicon Valley, there are a lot of startups using computer vision for agriculture or shopping – there are a lot for clothes shopping. At Baidu, for example, if you find a picture of a movie star, we actually use facial recognition to identify that movie star and then tell you things like their age and hobbies.
  54. In terms of building consumer products, the U.S. and China are ahead of India. The interesting opportunity for India is whenever there is a disruption in technology, it gives every country a chance to leapfrog and take a lead. To take an example, China is leaping ahead in growing the China electric vehicle ecosystem.
  55. India has a large base of tech talent, and I hope that a lot of AI machine learning education online will allow Indian software professionals to break into AI.
  56. It is difficult to think of a major industry that AI will not transform. This includes healthcare, education, transportation, retail, communications, and agriculture. There are surprisingly clear paths for AI to make a big difference in all of these industries.
  57. It seemed really amazing that you could write a few lines of code and have it learn to do interesting things.
  58. It takes a government to set up public-private partnerships and develop university programmes. I think this is the best path for India, given the rapid progress the country has already made and given the rapid progress we all hope India will continue to make.
  59. Job displacement is so huge, I’m tempted to not talk about anything other than that.
  60. Life is shockingly short; I don’t want to waste that many days.
  61. Machine learning is the most popular course for people from India. There is a window of time when India can embrace and capture a large fraction of the AI opportunity. But it will not remain open for ever.
  62. Many researchers are exploring other forms of AI, some of which have proved useful in limited contexts; there may well be a breakthrough that makes higher levels of intelligence possible, but there is still no clear path yet to this goal.
  63. Most of the value of deep learning today is in narrow domains where you can get a lot of data. Here’s one example of something it cannot do: have a meaningful conversation.
  64. No university is the best place to execute every mission and no one company the best place to execute every mission.
  65. None of us today know how to get computers to learn with the speed and flexibility of a child.
  66. One of my philosophies of building companies is the importance of velocity.
  67. One of my relatives had been asking me on how he could break into AI. For him to learn AI – deep-learning, technically – a lot of facts exist on the Internet, but it is difficult for someone to go and read the right combination of research papers and find blog posts and YouTube videos and figure out themselves on how to learn deep-learning.
  68. One of the things Baidu did well early on was to create an internal platform that made it possible for any engineer to apply deep learning to whatever application they wanted to, including applications that AI researchers like me would never have thought of.
  69. One of the things that Baidu did well early on was to create an internal platform for deep learning. What that did was enable engineers all across the company, including people who were not AI researchers, to leverage deep learning in all sorts of creative ways – applications that an AI researcher like me never would have thought of.
  70. One thing I’ve been doing at Baidu is running a workshop on the strategy of innovation. The idea is that innovation is not these random unpredictable acts of genius but that, instead, one can be very systematic in creating things that have never been created before.
  71. Our education system has succeeded so far in teaching generations to do different routine tasks. So when tractors displaced farming labor, we taught the next generation to work in factories. But what we’ve never really been good at is teaching a huge number of people to do non-routine creative work.
  72. Our educational system globally has not been historically great in reskilling for newer job roles. We need a new social contract to do that. For India, lack of an incumbent structure might be an advantage, where it can use digital education to leapfrog.
  73. People change jobs much more often, and therefore, companies, on average, invest less in employee development.
  74. Silicon Valley and Beijing are the leading hubs of AI, followed by the U.K. and Canada. I am seeing a lot of excitement in India, going by the number of people who are taking Coursera courses on AI.
  75. Some of the most successful businesses succeed by exploiting their users.
  76. Speech recognition today doesn’t really work in noisy environments.
  77. Text input is certainly useful, but images and speech are a much more natural way for humans to express their queries. Infants learn to see and speak well before they learn to type. The same is true of human evolution – we’ve had spoken language for a long time compared to written language, which is a relatively recent development.
  78. The big AI dreams of making machines that could someday evolve to do intelligent things like humans could – I was turned off by that. I didn’t really think that was feasible when I first joined Stanford.
  79. The biggest ethical challenge AI is facing is jobs. You have to reskill your workforce not just to create a wealthier society but a fairer one. A lot of call centre jobs will go away, and a radiologist’s job will be transformed.
  80. The Chinese market is very different. One of the things that I believe is that the biggest, hottest tech trend in China right now is O2O, or online-to-offline.
  81. The most trusted way to keep moving up that value chain is to keep investing in individuals – to help them grow in knowledge and skills. Education is hard. It takes individuals to do the hard work.
  82. The success, or failure, of a CEO to implement AI throughout the organization will depend on them hiring a leader to build an organization to do this. In some companies, CIOs or chief data officers are playing this role.
  83. The thing that really excites me today is building a new AI-powered society.
  84. The true value proposition of education is employment.
  85. The two things I’m most excited about are self-driving cars and speech. Speech doesn’t sound like that much, but it’s one of those technologies with the potential to change everything. Steve Jobs didn’t invent the touch screen. He just made it work very well, and that’s changed everything.
  86. The way AI complements people’s work, it actually creates a lot of new jobs, a lot of demand. For example, if a automatic visual inspection technology helps spot flaws in manufacturing parts, I think that in some cases, this does create a lot more demand for people to come in to rework or to fix some of the parts that an AI has found to be flawed.
  87. There are so many problems in the world worth working on and so many discoveries to make, you have to make a choice. My preference is to focus my efforts on solving problems that will help people.
  88. There are some outcomes in finance we don’t want, and government should regulate that.
  89. There are two companies that the AI Fund has invested in – Woebot and Landing AI – and the AI Fund has a number of internal teams working on new projects. We usually bring in people as employees, work with them to turn ideas into startups, then have the entrepreneurs go into the startup as founders.
  90. There’s a very long tail of all sorts of creative products – beyond our core web search, image search and advertising businesses – that are powered by deep learning.
  91. Want to train a machine translation system? Train it on a gazillion pairs of sentences of parallel corpora, and that creates a lot of breakthrough results. Increasingly, I’m seeing results on small data where you want to try to take in results even if you have 1,000 images.
  92. We can build a much brighter future where humans are relieved of menial work using AI capabilities.
  93. We think that many companies view Coursera as a quality, convenient, inexpensive way to continue employee development. Is there a contract with a company that might make sense? I don’t have an answer to that yet.
  94. We’re doing a lot of work on self-driving cars. We do not currently have cars in the U.S., but we plan to, for development and testing. I think we are within striking distance of making self-driving cars a reality, and these would be powered by deep learning.
  95. We’re making this analogy that AI is the new electricity. Electricity transformed industries: agriculture, transportation, communication, manufacturing.
  96. When we automated away the elevator operator function, who knew that all the descendants of those operators would become social media marketers, machine learning engineers, and all these other jobs that we didn’t even have a language to describe back then.
  97. When you become sufficiently expert in the state of the art, you stop picking ideas at random. You are thoughtful in how to select ideas and how to combine ideas. You are thoughtful about when you should be generating many ideas versus pruning down ideas.
  98. When you go to Japan, there is such a talent shortage that the debate about AI taking jobs is almost non-existent. The debate is, how can we automate this so we can get all the work done?
  99. With human inspectors, it’s difficult to get even the same person to make consistent judgments.
  100. With the Google Brain project, we made the decision to build deep learning processes on top of Google’s existing infrastructure.
Top Insightful Machine Learning Quotes By Andrew Ng learn to code youtube thumbnail Factober Inspiration for Artificial Intelligence, Data Science & ML
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