In 1995, the Internet had had little impact on society. If you went around telling people that the Internet was about to change the world, you would have been with met a lot of skepticism. Most people didn't see how this new thing was relevant to them, and they didn't think that "normal" people would ever find value in the Internet. It's the same today with AI. But things are about to change, on an even larger scale.
Think about it this way: when the Internet became mainstream, a lot of business models were disrupted and a lot of companies had to transform themselves or disappear, such as bookstores, retailers, DVD sellers. And the same is about to happen with AI in the next few decades, except for all business models. For all jobs. AI is not going to be a new industry. AI is going to be in every industry. It's going to be in every application, in every process in our society, in every aspect of our lives. Not just business and jobs, but also culture and art. Everything. AI is going to change what it means to be human.
Mobile/Smart Phone: 2008 => 2013 => 2028
AI: 2015 => 2020 => 2035
So a lot of the occupations that exist in the world today are going to disappear, because we are going to automate nearly all current jobs. That's the nature of AI: automating an ever-growing range of intellectual tasks. And at the same time, a new world of opportunities is going to open up. A more exciting and much broader world of opportunities. We'll free up people's time to do more meaningful things. We'll transition to a different economy altogether --and it will be for the better. We will enter a new era of prosperity.
The Internet has been a huge step forward for humanity as a whole and for each and every one of us. By automating a wide variety of intellectual tasks, AI has the potential to be just as beneficial, if not more. Now is the time to make sure that the transition into this brave new world goes as smoothly as the previous one. Everyone should be able to start using AI to solve their business problems, to answer the questions they have, in much the same way that every business today has a webpage and can leverage the Internet for everything from sales to marketing to inventory sourcing.
Fear #1 - The hollow middle - the number of papers in SIGMOD deal with the core database system stuff is slowly declining in recent years, from 100% in 1977 to 47% in 2017. The conclusion is, we are drifting into "applications" - all the core becomes increasingly well understood. But the applications (NLP, autonomous vehicles, complex analytics) don't have anything to do with each other. We are moving to a world where nothing binds us together except 200 researchers looking favorably on each others' papers, from bifurcation to multi-furation!
Fear #2 - We have been abandoned by our customers. The industry types have largely disappeared, presumably because DBMS research conferences are no longer relevant to their needs. We have become disconnected from the real world.
Fear #3 - Diarrhea of papers. A Ph.D. applicant who wants to get a good academic job should have ~10 papers. A tenure case should have ~40 papers. And there are around 10X the number of researchers, i.e. 100X the number of papers. Nobody can keep up with this deluge. Everybody divides their papers into least publishable units (LPU's). A student has to grind out ~10 papers in 3-4 years. Any serious implementation is impossible in this climate. Postgre would be impossible in this climate. Way too much work for too few publications.
Fear #4 - Reviewing is getting very random. Quality stinks, huge program committees, the overwhelming crush of papers. Leads to revise and resubmit (R&R) mentality. Which is killing both the submitters and the reviewers.
Fear #5 - Research taste has disappeared (customer leaving and whale replacement) - the acceptance and subsequent rejection of MapReduce from Google. We are way too uncritical of the whales. People who don't understand history will be condemned to repeat it.
Fear #6 - We are polishing a round ball - 10% papers are not worth reading, and implementation.
Fear #7 - Irrelevant theory is taking over. It's very difficult to get system papers accepted. Seemingly because they have no theory. Real world doesn't share data. So do the whales. Systems papers are becoming more artificial.
Fear #8 - We are ignoring the most important problems. In favor of ones that are easy to solve. Driven by the need for quickies. A consequence of being detached from the real world. A field is defined to have "lost its way" when it forgets who the customer is.
Fear #9 - Research support is disappearing. NSF success rate down to 7%, and the mouths to feed is increasing. Industry is not picking up the slack. Without a healthy, aggressive, and innovative research community, we are "toast" in the long run.
Fear #10 - Student load. CS departments are overrun with students. 40%+ of MIT undergraduates are majoring in CS! Will likely make academic life less and less attractive of into the future. Best people will depart for greener pastures and a lot more money.
Although the deep learning technology has significant progress in recent years, one of the key differences between the human brain and computer is the energy efficiency. Open AI shows in the past few years the amount of computing resources needed has grown exponentially over 300 thousand times. From the environmental perspective, the carbon footprint of the neural architecture search is actually in an order of magnitude larger than a round-trip flight between New York and San Francisco. Not mentioning the deep learning in the cloud, even for the machine learning in the edge computing, it is reported that a self-driving car prototype uses approximately 2,500 watts of computing power just to process all the sensor data. In the past decades, we're relying on the development of semi-conductor technology to give us smaller faster, and more energy-efficient chips. However, the Moore's Law and Dennard Scaling have been slow down in recent years, and we see the transistors are not becoming more efficient, to drive the deep neural network applications. When we do autonomous navigation, the start-of-the-art approaches to do depth estimation and object segmentation use deep neural networks, which requires up to several hundred millions of operations and weights to compute, that is 100 times more complex than video compression. Therefore, we need to have specialized hardware for significant improvement in the speed and energy efficiency, that is, redesign the computing hardware from the ground up. If we look inside the neural network process, it is found that data movement is the most expensive, consuming the most energy compared to a floating-point multiplication. Techniques like weight pruning or efficient architecture redesign do reduce the number of MACs and weight, but from the system perspective, it doesn't mean energy-saving or reduce latency. The speaker and her team developed an Eyeriss Deep Neural Network Accelerator with an on-chip buffer to achieve over 10 times energy reduction compared to a mobile GPU.
