“Delivering real-time AI in the palm of your hand” (Style transfer was invented hardly a year ago or so, but it’s gone from requiring hours to now being doable in realtime on an ordinary smartphone at higher quality. Progress in deep learning is fast, and this also illustrates the optimization potential of neural networks: they start off grossly overparameterized, but can be specialized down to much more powerful faster systems…)
“Effects of dietary restriction on adipose mass and biomarkers of healthy aging in human”, Lettieri-Barbato et al 2016 (Not keen on how they lump together caloric restriction with intermittent fasting etc, or casually dump a few correlational trials in with the randomized ones… I’d rather see it split by intervention type, the correlation trials excluded entirely, and maybe redone as a network meta-analysis or better yet, a network SEM, since there are multiple measures of body fat and inflammation under consideration.)
“Liquid Haskell: Haskell as a Theorem Prover”, Vazou 2016 (One encouraging aspect of programming language theory is how SMT solvers and dependent typing are gradually making progress and enabling languages of the future to express and prove many invariants and properties about code. It’s true that maybe you don’t want to do full-blown theorem proving on a random script or IO processing program, but it’s good for everyone that libraries and infrastructure can be easily locked down and rendered extremely reliable. The larger our systems get, the more important it is that the foundations not be buggy.)
“A Ghost, a Real Ghost”, Randall Jarrell (pg262–263, The Complete Poems; originally, The Woman at the Washington Zoo):
I think of that old woman in the song
Who could not know herself without the skirt
They cut off while she slept beside a stile.
Her dog jumped at the unaccustomed legs
And barked till she turned slowly from her gate
And went—I never asked them where she went.
The child is hopeful and unhappy in a world
Whose future is his recourse: she kept walking
Until the skirt grew, cleared her head and dog—
Surely I thought so when I laughed. If skirts don’t grow,
If things can happen so, and you not know
What you could do, why, what is there you could do?
I know now she went nowhere; went to wait
In the bare night of the fields, to whisper:
“I’ll sit and wish that it was never so.”
I see her sitting on the ground and wishing,
The wind jumps like a dog against her legs,
And she keeps thinking: “This is all a dream.”
“Who would cut off a poor old woman’s skirt?
So good too. No, it’s not so:
No one could feel so, really.” And yet one might.
A ghost must; and she was, perhaps, a ghost.
The first night I looked into the mirror
And saw the room empty, I could not believe
That it was possible to keep existing
In such pain: I have existed.
Was the old woman dead? What does it matter?
—Am I dead? A ghost, a real ghost
Has no need to die: what is he except
A being without access to the universe
That he has not yet managed to forget?
Florence Foster Jenkins (An improbable-sounding plot, but it works and is moving, offering rewards beyond cringe-humor and the usual watching Hollywood recreate the 1940s. I checked WP on the original person, and was amazed how close the film hews to history and how few & reasonable liberties it took with events.)
Going Clear (Scientology expose; one can learn much more about Scientology online reading articles like “The Apostate: Paul Haggis vs. the Church of Scientology”, and this documentary is no substitute for them or for books like Bare-Faced Messiah: A Biography of L. Ron Hubbard, but benefits from the use of archival footage—it definitely adds something to see footage of Hubbard himself and especially David Miscavige, to put faces & voices to the quotes & facts.)
Newsletter tag: archive of all issues back to 2013 for the gwern.net newsletter (monthly updates, which will include summaries of projects I’ve worked on that month (the same as the changelog), collations of links or discussions from my subreddit, and book/movie reviews.)
This page is a changelog for Gwern.net: a monthly reverse chronological list of recent major writings/changes/additions.
Following my writing can be a little difficult because it is often so incremental. So every month, in addition to my regular /r/Gwern subreddit submissions, I write up reasonably-interesting changes and send it out to the mailing list in addition to a compilation of links & reviews (archives).
A subreddit for posting links of interest and also for announcing updates to gwern.net (which can be used as a RSS feed). Submissions are categorized similar to the monthly newsletter and typically will be collated there.
Autonomous AI systems (Agent AIs) trained using reinforcement learning can do harm when they take wrong actions, especially superintelligent Agent AIs. One solution would be to eliminate their agency by not giving AIs the ability to take actions, confining them to purely informational or inferential tasks such as classification or prediction (Tool AIs), and have all actions be approved & executed by humans, giving equivalently superintelligent results without the risk.
I argue that this is not an effective solution for two major reasons. First, because Agent AIs will by definition be better at actions than Tool AIs, giving an economic advantage. Secondly, because Agent AIs will be better at inference & learning than Tool AIs, and this is inherently due to their greater agency: the same algorithms which learn how to perform actions can be used to select important datapoints to learn inference over, how long to learn, how to more efficiently execute inference, how to design themselves, how to optimize hyperparameters, how to make use of external resources such as long-term memories or external software or large databases or the Internet, and how best to acquire new data. All of these actions will result in Agent AIs more intelligent than Tool AIs, in addition to their greater economic competitiveness. Thus, Tool AIs will be inferior to Agent AIs in both actions and intelligence, implying use of Tool AIs is a even more highly unstable equilibrium than previously argued, as users of Agent AIs will be able to outcompete them on two dimensions (and not just one).
My laptop in my apartment receives Internet via a WiFi repeater to another house, yielding slow speeds and frequent glitches. I replaced the obsolete WiFi router and increased connection speeds somewhat but still inadequate. For a better solution, I used a directional antenna to connect directly to the new WiFi router, which, contrary to my expectations, yielded a ~6× increase in speed. Extensive benchmarking of all possible arrangements of laptops/dongles/repeaters/antennas/routers/positions shows that the antenna+router is inexpensive and near optimal speed, and that the only possible improvement would be a hardwired Ethernet line, which I installed a few weeks later after learning it was not as difficult as I thought it would be.
Designed formal notations & distinct vocabularies are often employed in STEM fields, and these specialized languages are credited with greatly enhancing research & communication. Many philosophers and other thinkers have attempted to create more generally-applicable designed languages for use outside of specific technical fields to enhance human thinking, but the empirical track record is poor and no such designed language has demonstrated substantial improvements to human cognition such as resisting cognitive biases or logical fallacies. I suggest that the success of specialized languages in fields is inherently due to encoding large amounts of previously-discovered information specific to those fields, and this explains their inability to boost human cognition across a wide variety of domains.
Background: Genetic variants which determine amount of coffee consumed have been identified in genome-wide association studies (GWAS) of coffee consumption; these may help to further understanding of the effects of coffee on health outcomes. However, there is limited information about how these variants relate to caffeinated beverage consumption more generally.
To improve phenotype definition for coffee consumption related genetic risk scores by testing their association with coffee, tea and other beverages.
Methods: We tested the associations of genetic risk scores for coffee consumption with beverage consumption in 114,316 individuals of European ancestry from the UK Biobank. Drinks were self-reported in a baseline questionnaire and in detailed 24 dietary recall questionnaires in a subset.
Results: Genetic risk scores including two and eight single nucleotide polymorphisms (SNPs) explained up to 0.39%, 0.19% and 0.77% of the variance in coffee, tea and combined coffee and tea consumption respectively. A one standard deviation increase in the 8 SNP genetic risk score was associated with a 0.13 cup per day (95% CI: 0.12, 0.14), 0.12 cup per day (95%CI: 0.11, 0.14) and 0.25 cup per day (95% CI: 0.24, 0.27) increase in coffee, tea and combined tea and coffee consumption, respectively. Genetic risk scores also demonstrated positive associations with both caffeinated and decaffeinated coffee and tea consumption. In 48,692 individuals with dietary recall data, the genetic risk scores were positively associated with coffee and tea, (apart from herbal teas) consumption, but did not show clear evidence for positive associations with other beverages. However, there was evidence that the genetic risk scores were associated with lower daily water consumption and lower overall drink consumption.
Conclusion: Genetic risk scores created from variants identified in coffee consumption GWAS associate more broadly with caffeinated beverage consumption and also with decaffeinated coffee and tea consumption.
“Overcoming catastrophic forgetting in neural networks”, James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, Raia Hadsell (2016-12-02):
The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Neural networks are not, in general, capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks which they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on the MNIST hand written digit dataset and by learning several Atari 2600 games sequentially.
We introduce the value iteration network (VIN): a fully differentiable neural network with a ‘planning module’ embedded within. VINs can learn to plan, and are suitable for predicting outcomes that involve planning-based reasoning, such as policies for reinforcement learning. Key to our approach is a novel differentiable approximation of the value-iteration algorithm, which can be represented as a convolutional neural network, and trained end-to-end using standard backpropagation. We evaluate VIN based policies on discrete and continuous path-planning domains, and on a natural-language based search task. We show that by learning an explicit planning computation, VIN policies generalize better to new, unseen domains.
One of the key challenges of artificial intelligence is to learn models that are effective in the context of planning. In this document we introduce the predictron architecture. The predictron consists of a fully abstract model, represented by a Markov reward process, that can be rolled forward multiple "imagined" planning steps. Each forward pass of the predictron accumulates internal rewards and values over multiple planning depths. The predictron is trained end-to-end so as to make these accumulated values accurately approximate the true value function. We applied the predictron to procedurally generated random mazes and a simulator for the game of pool. The predictron yielded significantly more accurate predictions than conventional deep neural network architectures.
In computer science, Monte Carlo tree search (MCTS) is a heuristic search algorithm for some kinds of decision processes, most notably those employed in software that plays board games. In that context MCTS is used to solve the game tree.
Despite the success of CNNs, selecting the optimal architecture for a given task remains an open problem. Instead of aiming to select a single optimal architecture, we propose a "fabric" that embeds an exponentially large number of architectures. The fabric consists of a 3D trellis that connects response maps at different layers, scales, and channels with a sparse homogeneous local connectivity pattern. The only hyper-parameters of a fabric are the number of channels and layers. While individual architectures can be recovered as paths, the fabric can in addition ensemble all embedded architectures together, sharing their weights where their paths overlap. Parameters can be learned using standard methods based on back-propagation, at a cost that scales linearly in the fabric size. We present benchmark results competitive with the state of the art for image classification on MNIST and CIFAR10, and for semantic segmentation on the Part Labels dataset.
Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing text-to-image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) to generate 256×256 photo-realistic images conditioned on text descriptions. We decompose the hard problem into more manageable sub-problems through a sketch-refinement process. The Stage-I GAN sketches the primitive shape and colors of the object based on the given text description, yielding Stage-I low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. It is able to rectify defects in Stage-I results and add compelling details with the refinement process. To improve the diversity of the synthesized images and stabilize the training of the conditional-GAN, we introduce a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold. Extensive experiments and comparisons with state-of-the-arts on benchmark datasets demonstrate that the proposed method achieves significant improvements on generating photo-realistic images conditioned on text descriptions.
In this paper, we propose a novel generative model named Stacked Generative Adversarial Networks (SGAN), which is trained to invert the hierarchical representations of a bottom-up discriminative network. Our model consists of a top-down stack of GANs, each learned to generate lower-level representations conditioned on higher-level representations. A representation discriminator is introduced at each feature hierarchy to encourage the representation manifold of the generator to align with that of the bottom-up discriminative network, leveraging the powerful discriminative representations to guide the generative model. In addition, we introduce a conditional loss that encourages the use of conditional information from the layer above, and a novel entropy loss that maximizes a variational lower bound on the conditional entropy of generator outputs. We first train each stack independently, and then train the whole model end-to-end. Unlike the original GAN that uses a single noise vector to represent all the variations, our SGAN decomposes variations into multiple levels and gradually resolves uncertainties in the top-down generative process. Based on visual inspection, Inception scores and visual Turing test, we demonstrate that SGAN is able to generate images of much higher quality than GANs without stacking.
The Schiehallion experiment was an 18th-century experiment to determine the mean density of the Earth. Funded by a grant from the Royal Society, it was conducted in the summer of 1774 around the Scottish mountain of Schiehallion, Perthshire. The experiment involved measuring the tiny deflection of the vertical due to the gravitational attraction of a nearby mountain. Schiehallion was considered the ideal location after a search for candidate mountains, thanks to its isolation and almost symmetrical shape.
Whether China and the United States are destined to compete for domination in international politics is one of the major questions facing DoD. In a competition with the People’s Republic of China, the United States must explore all of its advantages and all of the weaknesses of China that may provide an asymmetry for the United States. This study examines one such asymmetry, the strategic consequences of Chinese racism. After having examined the literature on China extensively, this author is not aware of a single study that addresses this important topic. This study explores the causes of Chinese racism, the strategic consequences of Chinese racism, and how the United States may use this situation to advance its interests in international politics.
the study finds that xenophobia, racism, and ethnocentrism are caused by human evolution. These behaviors are not unique to the Chinese. However, they are made worse by Chinese history and culture.
considers the Chinese conception of race in Chinese history and culture. It finds that Chinese religious-cultural and historical conceptions of race reinforce Chinese racism. In Chinese history and contemporary culture, the Chinese are seen to be unique and superior to the rest of the world. Other peoples and groups are seen to be inferior, with a sliding scale of inferiority. The major Chinese distinction is between degrees of barbarians, the “black devils,” or savage inferiors, beyond any hope of interaction and the “white devils” or tame barbarians with whom the Chinese can interact. These beliefs are widespread in Chinese society, and have been for its history…
evaluates the 9 strategic consequences of Chinese racism.
virulent racism and eugenics heavily inform Chinese perceptions of the world…
racism informs their view of the United States…
racism informs their view of international politics in three ways.
states are stable, and thus good for the Chinese, to the degree that they are unicultural.
Chinese ethnocentrism and racism drive their outlook to the rest of the world. Their expectation is of a tribute system where barbarians know that the Chinese are superior.
there is a strong, implicit, racialist view of international politics that is alien and anathema to Western policy-makers and analysts. The Chinese are comfortable using race to explain events and appealing to racist stereotypes to advance their interests. Most insidious is the Chinese belief that Africans in particular need Chinese leadership.
the Chinese will make appeals to Third World states based on “racial solidarity,”…
Chinese racism retards their relations with the Third World…
Chinese racism, and the degree to which the Chinese permit their view of the United States to be informed by racism, has the potential to hinder China in its competition with the United States because it contributes to their overconfidence…
as lamentable as it is, Chinese racism helps to make the Chinese a formidable adversary…
the Chinese are never going to go through a civil rights movement like the United States…
China’s treatment of Christians and ethnic minorities is poor…
considers the 5 major implications for United States decision-makers and asymmetries that may result from Chinese racism.
Chinese racism provides empirical evidence of how the Chinese will treat other international actors if China becomes dominant…
it allows the United States to undermine China in the Third World…
it permits a positive image of the United States to be advanced in contrast to China…
calling attention to Chinese racism allows political and ideological alliances of the United States to be strengthened…
United States defense decision-makers must recognize that racism is a cohesive force for the Chinese…
…The study’s fundamental conclusion is that endemic Chinese racism offers the United States a major asymmetry it may exploit with major countries, regions like Africa, as well as with important opinion makers in international politics. The United States is on the right side of the struggle against racism and China is not. The United States should call attention to this to aid its position in international politics.
Policy-makers are interested in early-years interventions to ameliorate childhood risks. They hope for improved adult outcomes in the long run, bringing return on investment. How much return can be expected depends, partly, on how strongly childhood risks forecast adult outcomes. But there is disagreement about whether childhood determines adulthood. We integrated multiple nationwide administrative databases and electronic medical records with the four-decade Dunedin birth-cohort study to test child-to-adult prediction in a different way, by using a population-segmentation approach. A segment comprising one-fifth of the cohort accounted for 36% of the cohort's injury insurance-claims; 40% of excess obese-kilograms; 54% of cigarettes smoked; 57% of hospital nights; 66% of welfare benefits; 77% of fatherless childrearing; 78% of prescription fills; and 81% of criminal convictions. Childhood risks, including poor age-three brain health, predicted this segment with large effect sizes. Early-years interventions effective with this population segment could yield very large returns on investment.
A previous genome-wide association study (GWAS) of more than 100,000 individuals identified molecular-genetic predictors of educational attainment. We undertook in-depth life-course investigation of the polygenic score derived from this GWAS using the four-decade Dunedin Study (N = 918). There were five main findings. First, polygenic scores predicted adult economic outcomes even after accounting for educational attainments. Second, genes and environments were correlated: Children with higher polygenic scores were born into better-off homes. Third, children’s polygenic scores predicted their adult outcomes even when analyses accounted for their social-class origins; social-mobility analysis showed that children with higher polygenic scores were more upwardly mobile than children with lower scores. Fourth, polygenic scores predicted behavior across the life course, from early acquisition of speech and reading skills through geographic mobility and mate choice and on to financial planning for retirement. Fifth, polygenic-score associations were mediated by psychological characteristics, including intelligence, self-control, and interpersonal skill. Effect sizes were small. Factors connecting GWAS sequence with life outcomes may provide targets for interventions to promote population-wide positive development. [Keywords: genetics, behavior genetics, intelligence, personality, adult development]
Health and social scientists have documented the hospital revolving-door problem, the concentration of crime, and long-term welfare dependence. Have these distinct fields identified the same citizens? Using administrative databases linked to 1.7 million New Zealanders, we quantified and monetized inequality in distributions of health and social problems and tested whether they aggregate within individuals. Marked inequality was observed: Gini coefficients equalled 0.96 for criminal convictions, 0.91 for public-hospital nights, 0.86 for welfare benefits, 0.74 for prescription-drug fills and 0.54 for injury-insurance claims. Marked aggregation was uncovered: a small population segment accounted for a disproportionate share of use-events and costs across multiple sectors. These findings were replicated in 2.3 million Danes. We then integrated the New Zealand databases with the four-decade-long Dunedin Study. The high-need/high-cost population segment experienced early-life factors that reduce workforce readiness, including low education and poor mental health. In midlife they reported low life satisfaction. Investing in young people’s education and training potential could reduce health and social inequalities and enhance population wellbeing.
In computer science and mathematical logic, the satisfiability modulo theories (SMT) problem is a decision problem for logical formulas with respect to combinations of background theories expressed in classical first-order logic with equality. Examples of theories typically used in computer science are the theory of real numbers, the theory of integers, and the theories of various data structures such as lists, arrays, bit vectors and so on. SMT can be thought of as a form of the constraint satisfaction problem and thus a certain formalized approach to constraint programming.
In computer science and logic, a dependent type is a type whose definition depends on a value. It is an overlapping feature of type theory and type systems. In intuitionistic type theory, dependent types are used to encode logic's quantifiers like "for all" and "there exists". In functional programming languages like Agda, ATS, Coq, F*, Epigram, and Idris, dependent types may help reduce bugs by enabling the programmer to assign types that further restrain the set of possible implementations.
Randall Jarrelljə-REL was an American poet, literary critic, children's author, essayist, and novelist. He was the 11th Consultant in Poetry to the Library of Congress—a position that now bears the title Poet Laureate of the United States.
A Beautiful Planet is a 2016 American documentary film that explores Earth by showing IMAX footage, recorded over the course of fifteen months by astronauts aboard the International Space Station. It is narrated by actress Jennifer Lawrence.
Florence Foster Jenkins is a 2016 biographical film directed by Stephen Frears and written by Nicholas Martin. It stars Meryl Streep as Florence Foster Jenkins, a New York heiress known for her poor singing. Hugh Grant plays her manager and long-time companion, St. Clair Bayfield. Other cast members include Simon Helberg, Rebecca Ferguson, and Nina Arianda.
Florence Foster Jenkins was an American socialite and amateur soprano who was known, and mocked, for her flamboyant performance costumes and notably poor singing ability. Stephen Pile ranked her "the world's worst opera singer... No one, before or since, has succeeded in liberating themselves quite so completely from the shackles of musical notation."
The Sound of Music is a 1965 American musical drama film produced and directed by Robert Wise, and starring Julie Andrews and Christopher Plummer, with Richard Haydn, Peggy Wood, Charmian Carr, and Eleanor Parker. The film is an adaptation of the 1959 stage musical of the same name, composed by Richard Rodgers with lyrics by Oscar Hammerstein II. The film's screenplay was written by Ernest Lehman, adapted from the stage musical's book by Lindsay and Crouse. Based on the 1949 memoir The Story of the Trapp Family Singers by Maria von Trapp, the film is about a young Austrian postulant in Salzburg, Austria, in 1938 who is sent to the villa of a retired naval officer and widower to be governess to his seven children. After bringing love and music into the lives of the family, she marries the officer and, together with the children, finds a way to survive the loss of their homeland to the Nazis.
Fantastic Beasts and Where to Find Them is a 2016 fantasy film directed by David Yates. A joint British and American production, it is a spin-off of and prequel to the Harry Potter film series. It was produced and written by J. K. Rowling in her screenwriting debut, inspired by her 2001 "guide book" of the same name. The film features an ensemble cast that includes Eddie Redmayne, Katherine Waterston, Dan Fogler, Alison Sudol, Ezra Miller, Samantha Morton, Jon Voight, Carmen Ejogo, and Colin Farrell. It is the first installment in the Fantastic Beasts film series, and ninth overall in the Wizarding World franchise that began with the Harry Potter films.
Going Clear: Scientology and the Prison of Belief is a 2015 documentary film about Scientology. Directed by Alex Gibney and produced by HBO, it is based on Lawrence Wright's book Going Clear: Scientology, Hollywood and the Prison of Belief (2013). The film premiered at the 2015 Sundance Film Festival in Park City, Utah. It received widespread praise from critics and was nominated for seven Emmy Awards, winning three, including Best Documentary. It also received a 2015 Peabody Award and won the award for Best Documentary Screenplay from the Writers Guild of America.
Finding Dory is a 2016 American computer-animated adventure film produced by Pixar Animation Studios and released by Walt Disney Pictures. Directed by Andrew Stanton and written by Stanton and Victoria Strouse, the film is a sequel/spinoff to Finding Nemo (2003) and features the returning voices of Ellen DeGeneres and Albert Brooks, with Hayden Rolence, Ed O'Neill, Kaitlin Olson, Ty Burrell, Diane Keaton, and Eugene Levy joining the cast. The film focuses on the amnesiac fish Dory, who journeys to be reunited with her parents.
Subscription page for the monthly gwern.net newsletter. There are monthly updates, which will include summaries of projects I’ve worked on that month (the same as the changelog), collations of links or discussions from my subreddit, and book/movie reviews. You can also browse the archives since December 2013.
Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.