"Please think forward to the year 2030. Analysts expect that people will become even more dependent on networked artificial intelligence (AI) in complex digital systems. Some say we will continue on the historic arc of augmenting our lives with mostly positive results as we widely implement these networked tools. Some say our increasing dependence on these AI and related systems is likely to lead to widespread difficulties. Our question: By 2030, do you think it is most likely that advancing AI and related technology systems will enhance human capacities and empower them? That is, most of the time, will most people be better off than they are today? Or is it most likely that advancing AI and related technology systems will lessen human autonomy and agency to such an extent that most people will not be better off than the way things are today? Please explain why you chose the answer you did and sketch out a vision of how the human-machine/AI collaboration will function in 2030.
The unreasonable effectiveness of data is possibly the greatest surprise coming out of the last twenty years of Artificial Intelligence (AI): pretty simple algorithms and tons of data seem to almost invariably beat complex solutions with small-to - none training set. In the seminal words of (Halevy, Norvig, and Pereira, 2009): "now go out and gather some data, and see what it can do". The perfect storm has been set in motion by the convergence of the big data hype (Hagstroem et al 2017), the general availability of specialized hardware and scalable infrastructure, and some "computational tricks" (e.g. Hochreiter S., Schmidhuber S., 1997, Hinton et al, 2013): all together, they unlocked the Deep Learning (DL) Revolution and created a tremendous amount of business value (Chui et al 2018). The A.I. wave is so disruptive that a great deal of commentators, practitioners (Radford et al 2019) and entrepreneurs (Musk 2017) inevitably started to wonder what is the place of humans in this new world: is A.I. going to replace humanity (in the world of Silicon Valley, Joy in 2001 was already stating that "the future doesn't need us")? In this position paper, we shall argue for two surprising perspectives: 1) the future of A.I. is about less data, not more; 2) human-machine collaboration is, at least for the foreseeable future, the only way to outpace humans and outsmart machines effectively. The paper is organized as follows: Section 2 contains a review of the current state of the A.I. landscape, with particular attention to the origins of the DL Revolution; the section casts some doubts on the general applicability of DL to language problems, drawing from theoretical considerations from academia and industry use cases in the space of Tooso. Section 3 details a real use-case from the industry that is challenging for the DL paradigm, and outlines a different framework to tackle the problem; finally, Section 4 concludes with remarks and roadmap for a new type of A.I., what we call "A.I. with humans and for humans." 2
Radhika previously worked in content marketing at three technology firms, and graduated from Sri Krishna College Of Engineering And Technology with a degree in Information Technology. According to the National Institute of Mental Health, the United States is currently battling a mental health epidemic. One in every five Americans struggles with mental illness in one form or another. According to the Center for Workplace Mental Health founded by the American Psychiatric Association, up to 7% of full-time workers in the U.S. suffer from major depressive disorder, the economic cost of which is estimated to be $210.5 billion per year. When compared to other developed nations, traditional healthcare in the U.S. is notoriously costly; mental healthcare, even more so.
Many complex systems operate with loss. Mathematically, these systems can be described as non-Hermitian. A property of such a system is that there can exist certain conditions--exceptional points--where gain and loss can be perfectly balanced and exotic behavior is predicted to occur. Optical systems generally possess gain and loss and so are ideal systems for exploring exceptional point physics. Miri and Alù review the topic of exceptional points in photonics and explore some of the possible exotic behavior that might be expected from engineering such systems. Singularities are critical points for which the behavior of a mathematical model governing a physical system is of a fundamentally different nature compared to the neighboring points. Exceptional points are spectral singularities in the parameter space of a system in which two or more eigenvalues, and their corresponding eigenvectors, simultaneously coalesce. Such degeneracies are peculiar features of nonconservative systems that exchange energy with their surrounding environment. In the past two decades, there has been a growing interest in investigating such nonconservative systems, particularly in connection with the quantum mechanics notions of parity-time symmetry, after the realization that some non-Hermitian Hamiltonians exhibit entirely real spectra. Lately, non-Hermitian systems have raised considerable attention in photonics, given that optical gain and loss can be integrated as nonconservative ingredients to create artificial materials and structures with altogether new optical properties. As we introduce gain and loss in a nanophotonic system, the emergence of exceptional point singularities dramatically alters the overall response, leading to a range of exotic functionalities associated with abrupt phase transitions in the eigenvalue spectrum. Even though such a peculiar effect has been known theoretically for several years, its controllable realization has not been made possible until recently and with advances in exploiting gain and loss in guided-wave photonic systems. As shown in a range of recent theoretical and experimental works, this property creates opportunities for ultrasensitive measurements and for manipulating the modal content of multimode lasers. In addition, adiabatic parametric evolution around exceptional points provides interesting schemes for topological energy transfer and designing mode and polarization converters in photonics.
People have been dreaming about Artificial Intelligence for hundreds, if not thousands of years. Well, it's starting to feel like the future is actually here, and AI can be seen almost everyone nowadays. So how should you feel about it? Here are 42 facts about the past, present and future of artificial intelligence to help you decide for yourself. In Ancient Greek mythology, the blacksmith god Hephaestus was believed to have built what were essentially robots. His "automatons," as they were called, were crafted from metal and designed to perform different tasks for him or other gods.
A necessary condition of an intelligence explosion is that for some large number of possible beliefs, the updated probability of each of those beliefs being true has greatly increased (perhaps close to 1) over a relatively short time. In the 18th century, the French mathematician, Nicolas de Condorcet, proposed a model for how collective intelligence could be used to determine facts with near certainty. That model is today known as Condorcet’s jury theorem. Going back to Condorcet’s 18th century model, we will update it to provide a proof of concept of how to model intelligence explosions for the social good. The model will pinpoint the kind of social and political institutions that must be in place for the kind of intelligence explosion, described by the model, to occur. The hope is that by providing this kind of proof of concept model, future research will be able tweak, add, or remove different model assumptions to better fit the circumstances with which researchers are concerned, and figure out what kinds of institutions would need to be necessary to facilitate an altruistic intelligence explosion.
"The great scientific breakthroughs in artificial intelligence are still ahead of us," Professor Patrick Winston predicted in his opening remarks to Rethinking Artificial Intelligence, a corporate briefing held at MIT on September 24-25. "Assuming that the science of AI is a 100-year enterprise that began in 1950, 2000 will be the halfway point," said Dr. Winston, the Ford Professor of Engineering in the Department of Electrical Engineering and Computer Science. "Molecular biology reached its halfway point when Watson and Crick discovered DNA. That discovery shifted everything -- it changed the world. About 300 senior technical management and corporate strategists from industries as diverse as aerospace and advertising attended the three-part seminar, which focused on how AI-based systems have evolved, where their impact is felt and what AI means for corporate strategy and revenue.
Blog posts can be strange and unpredictable things. There are times when I pour a ton of energy and creativity into a post only to have it largely ignored. Other times I quickly and haphazardly put something together and it ends up attracting thousands of hits. Such was the case with my recent post, Must-know terms for today's intelligentsia. Owing to all the interest, feedback and requests, I've decided to revise the list and provide greater detail and links. I apologize for not providing this in the first place. Before I get into the list, however, I'd like to clarify the purpose of this exercise. First, I am trying to come up with a list of the most fundamental and crucial terms that are coming to define and will soon re-define the human condition, and that subsequently should be known by anyone who thinks of themselves as an intellectual. I admit that there's an elitist and even pompous aspect to this exercise, but the fact of the matter is that the zeitgeist is quickly changing. It's not enough anymore to be able to quote Dostoevsky, Freud and Darwin. This said, while my list of terms is'required' knowledge, I am not suggesting that it is sufficient. My definition of an'intellectual' also requires explanation. To me an intellectual in this context is an expert generalist -- a polymath or jack-of-all-trades who sees and understands the Big Picture both past, present and future.
Is research and thinking on artificial intelligence stuck in a local minimum? Those in the field have attested to major advances in the last decade, but are these advances merely a renaming of approaches that were taken decades ago? This book does not address these questions as its major goal, but instead attempts to give a broad overview of how A.I. got started and where it is now, and where it might be going. The reader is lead to ask the questions above though after reading the book, for the author seems to ask them implicitly. Its validity as a science are questioned, and the future of A.I. is addressed in detail.