2017-sniekers.pdf: “Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence”, (2017-05-22; ):
Intelligence is associated with important economic and health-related life outcomes1. Despite intelligence having substantial heritability2 (0.54) and a confirmed polygenic nature, initial genetic studies were mostly underpowered3,4,5. Here we report a meta-analysis for intelligence of 78,308 individuals. We identify 336 associated SNPs (METAL p < 5 × 10−8) in 18 genomic loci, of which 15 are new. Around half of the SNPs are located inside a gene, implicating 22 genes, of which 11 are new findings. Gene-based analyses identified an additional 30 genes (MAGMA p < 2.73 × 10−6), of which all but one had not been implicated previously. We show that the identified genes are predominantly expressed in brain tissue, and pathway analysis indicates the involvement of genes regulating cell development (MAGMA competitive p = 3.5 × 10−6). Despite the well-known difference in twin-based heritability2 for intelligence in childhood (0.45) and adulthood (0.80), we show substantial genetic correlation (rg = 0.89, LD score regression p = 5.4 × 10−29). These findings provide new insight into the genetic architecture of intelligence.
2017-wehby.pdf: “Genetic Predisposition to Obesity and Medicare Expenditures”, (2017-05-10; ):
Background: The relationship between obesity and health expenditures is not well understood. We examined the relationship between genetic predisposition to obesity measured by a polygenic risk score for body mass index ( ) and Medicare expenditures. Methods: Biennial interview data from the Health and Retirement Survey for a nationally representative sample of older adults enrolled in fee-for-service Medicare were obtained from 1991 through 2010 and linked to Medicare claims for the same period and to Genome-Wide Association Study ( ) data. The study included 6,628 Medicare beneficiaries who provided 68,627 complete person-year observations during the study period. Outcomes were total and service-specific Medicare expenditures and indicators for expenditures exceeding the 75th and 90th percentiles. The BMI polygenic risk score was derived from data. Regression models were used to examine how the BMI was related to health expenditures adjusting for demographic factors and -derived ancestry. Results: Greater genetic predisposition to obesity was associated with higher Medicare expenditures. Specifically, a 1 SD increase in the BMI was associated with a $805 (p < 0.001) increase in annual Medicare expenditures per person in 2010 dollars (~15% increase), a $370 (p < 0.001) increase in inpatient expenses, and a $246 (p < 0.001) increase in outpatient services. A 1 SD increase in the was also related to increased likelihood of expenditures exceeding the 75th percentile by 18% (95% CI: 10%–28%) and the 90th percentile by 27% (95% : 15%–40%). Conclusion: Greater genetic predisposition to obesity is associated with higher Medicare expenditures.
“Inferring and Executing Programs for Visual Reasoning”, (2017-05-10):
Existing methods for visual reasoning attempt to directly map inputs to outputs using black-box architectures without explicitly modeling the underlying reasoning processes. As a result, these black-box models often learn to exploit biases in the data rather than learning to perform visual reasoning. Inspired by module networks, this paper proposes a model for visual reasoning that consists of a program generator that constructs an explicit representation of the reasoning process to be performed, and an execution engine that executes the resulting program to produce an answer. Both the program generator and the execution engine are implemented by neural networks, and are trained using a combination of backpropagation and REINFORCE. Using the CLEVR benchmark for visual reasoning, we show that our model substantially outperforms strong baselines and generalizes better in a variety of settings.
“Bayesian Reinforcement Learning: A Survey”, (2016-09-14):
Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. In this survey, we provide an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are: 1) it provides an elegant approach to action-selection (exploration/exploitation) as a function of the uncertainty in learning; and 2) it provides a machinery to incorporate prior knowledge into the algorithms. We first discuss models and methods for in the simple single-step Bandit model. We then review the extensive recent literature on for model-based RL, where prior information can be expressed on the parameters of the Markov model. We also present for model-free RL, where priors are expressed over the value function or policy class. The objective of the paper is to provide a comprehensive survey on Bayesian RL algorithms and their theoretical and empirical properties.
Subreddit devoted to discussion of reinforcement learning research and projects, particularly deep (more specialized than
/r/MachineLearning). Major themes include deep learning, model-based vs model-free RL, robotics, multi-agent RL, exploration, meta- , imitation learning, the psychology of RL in biological organisms such as humans, and safety/AI risk. Moderate activity level (as of 2019-09-11): ~10k subscribers, 2k pageviews/daily
“Deep Voice 2: Multi-Speaker Neural Text-to-Speech”, (2017-05-24):
We introduce a technique for augmenting neural text-to-speech (TTS) with lowdimensional trainable speaker embeddings to generate different voices from a single model. As a starting point, we show improvements over the two state-ofthe-art approaches for single-speaker neural TTS: Deep Voice 1 and Tacotron. We introduce Deep Voice 2, which is based on a similar pipeline with Deep Voice 1, but constructed with higher performance building blocks and demonstrates a significant audio quality improvement over Deep Voice 1. We improve Tacotron by introducing a post-processing neural vocoder, and demonstrate a significant audio quality improvement. We then demonstrate our technique for multi-speaker speech synthesis for both Deep Voice 2 and Tacotron on two multi-speaker TTS datasets. We show that a single neural TTS system can learn hundreds of unique voices from less than half an hour of data per speaker, while achieving high audio quality synthesis and preserving the speaker identities almost perfectly.
Physicists at the Large Hadron Collider (LHC) rely on detailed simulations of particle collisions to build expectations of what experimental data may look like under different theory modeling assumptions. Petabytes of simulated data are needed to develop analysis techniques, though they are expensive to generate using existing algorithms and computing resources. The modeling of detectors and the precise description of particle cascades as they interact with the material in the calorimeter are the most computationally demanding steps in the simulation pipeline. We therefore introduce a deep neural network-based generative model to enable high-fidelity, fast, electromagnetic calorimeter simulation. There are still challenges for achieving precision across the entire phase space, but our current solution can reproduce a variety of particle shower properties while achieving speed-up factors of up to 100,000×. This opens the door to a new era of fast simulation that could save significant computing time and disk space, while extending the reach of physics searches and precision measurements at the LHC and beyond.
Deep learning (DL) systems are increasingly deployed in safety and security-critical domains including self-driving cars and malware detection, where the correctness and predictability of a system’s behavior for corner case inputs are of great importance. Existing DL testing depends heavily on manually labeled data and therefore often fails to expose erroneous behaviors for rare inputs.
We design, implement, and evaluate DeepXplore, the first whitebox framework for systematically testing real-world DL systems. First, we introduce neuron coverage for systematically measuring the parts of a DL system exercised by test inputs. Next, we leverage multiple DL systems with similar functionality as cross-referencing oracles to avoid manual checking. Finally, we demonstrate how finding inputs for DL systems that both trigger many differential behaviors and achieve high neuron coverage can be represented as a joint optimization problem and solved efficiently using gradient-based search techniques.
DeepXplore efficiently finds thousands of incorrect corner case behaviors (eg., self-driving cars crashing into guard rails and malware masquerading as benign software) in state-of-the-art DL models with thousands of neurons trained on 5 popular datasets including ImageNet and Udacity self-driving challenge data. For all tested DL models, on average, DeepXplore generated one test input demonstrating incorrect behavior within one second while running only on a commodity laptop. We further show that the test inputs generated by DeepXplore can also be used to retrain the corresponding DL model to improve the model’s accuracy by up to 3%.
“Visual Semantic Planning using Deep Successor Representations”, (2017-05-23):
A crucial capability of real-world intelligent agents is their ability to plan a sequence of actions to achieve their goals in the visual world. In this work, we address the problem of visual semantic planning: the task of predicting a sequence of actions from visual observations that transform a dynamic environment from an initial state to a goal state. Doing so entails knowledge about objects and their affordances, as well as actions and their preconditions and effects. We propose learning these through interacting with a visual and dynamic environment. Our proposed solution involves bootstrapping results across a wide range of tasks in the challenging THOR environment.with imitation learning. To ensure cross task generalization, we develop a deep predictive model based on successor representations. Our experimental results show near optimal
“AIXIjs: A Software Demo for General Reinforcement Learning”, (2017-05-22):
“Categorizing Wireheading in Partially Embedded Agents”, (2019-06-21):
Embedded agents are not explicitly separated from their environment, lacking clear I/O channels. Such agents can reason about and modify their internal parts, which they are incentivized to shortcut or wirehead in order to achieve the maximal reward.
In this paper, we provide a taxonomy of ways by which wireheading can occur, followed by a definition of wirehead-vulnerable agents. Starting from the fully dualistic universal agent experimentally demonstrating the results with the GRL simulation platform AIXIjs., we introduce a spectrum of partially embedded agents and identify wireheading opportunities that such agents can exploit,
We contextualize wireheading in the broader class of all misalignment problems—where the goals of the agent conflict with the goals of the human designer—and conjecture that the only other possible type of misalignment is specification gaming. Motivated by this taxonomy, we define wirehead-vulnerable agents as embedded agents that choose to behave differently from fully dualistic agents lacking access to their internal parts.
Advances in artificial intelligence (AI) will transform modern life by reshaping transportation, health, science, finance, and the military. To adapt public policy, we need to better anticipate these advances. Here we report the results from a large survey of machine learning researchers on their beliefs about progress in AI. Researchers predict AI will outperform humans in many activities in the next ten years, such as translating languages (by 2024), writing high-school essays (by 2026), driving a truck (by 2027), working in retail (by 2031), writing a bestselling book (by 2049), and working as a surgeon (by 2053). Researchers believe there is a 50% chance of AI outperforming humans in all tasks in 45 years and of automating all human jobs in 120 years, with Asian respondents expecting these dates much sooner than North Americans. These results will inform discussion amongst researchers and policymakers about anticipating and managing trends in AI.
We propose a novel system which can transform a recipe into any selected regional style (e.g., Japanese, Mediterranean, or Italian). This system has two characteristics. First the system can identify the degree of regional cuisine style mixture of any selected recipe and visualize such regional cuisine style mixtures using barycentric Newton diagrams. Second, the system can suggest ingredient substitutions through an extended word2vec model, such that a recipe becomes more authentic for any selected regional cuisine style. Drawing on a large number of recipes from Yummly, an example shows how the proposed system can transform a traditional Japanese recipe, Sukiyaki, into French style.
Training convolutional networks (CNN’s) that fit on a single GPU with minibatch stochastic gradient descent has become effective in practice. However, there is still no effective method for training large CNN’s that do not fit in the memory of a few GPU cards, or for parallelizing CNN training. In this work we show that a simple hard mixture of experts model can be efficiently trained to good effect on large scale hashtag (multilabel) prediction tasks. Mixture of experts models are not new (Jacobs et. al. 1991, Collobert et. al. 2003), but in the past, researchers have had to devise sophisticated methods to deal with data fragmentation. We show empirically that modern weakly supervised data sets are large enough to support naive partitioning schemes where each data point is assigned to a single expert. Because the experts are independent, training them in parallel is easy, and evaluation is cheap for the size of the model. Furthermore, we show that we can use a single decoding layer for all the experts, allowing a unified feature embedding space. We demonstrate that it is feasible (and in fact relatively painless) to train far larger models than could be practically trained with standard CNN architectures, and that the extra capacity can be well used on current datasets.
1994-loury.pdf: “Self-Censorship in Public Discourse: A Theory of “Political Correctness” and Related Phenomena”, (1994-10-01; ):
Uncertainty about what motivates “senders” of public messages leads “receivers” to “read between the lines” to discern the sender’s deepest commitments. Anticipating this, senders “write between the lines,” editing their expressions so as to further their own ends. I examine how this interactive process of inference and deceit affects the quality and extent of public deliberations on sensitive issues. A principle conclusion is that genuine moral discourse on difficult social issues can become impossible when the risks of upsetting some portion of one’s audience are too great. Reliance on euphemism and platitude should be expected in this strategic climate. Groups may embark on a tragic course of action, believed by many at the outset to be ill-conceived, but that has become impossible to criticize.
2011-gensowski.pdf: “The Effects of Education, Personality, and IQ on Earnings of High-Ability Men”, (2011-01-24; ):
[Preprint version of Gensowski 2018]
This paper estimates the internal rate of return (IRR) to education for men and women of the Terman sample, a 70-year long prospective cohort study of high-ability individuals. The Terman data is unique in that it not only provides full working-life earnings histories of the participants, but it also includes detailed profiles of each subject, including IQ and measures of latent personality traits. Having information on latent personality traits is important as it allows us to measure the importance of personality on educational attainment and lifetime earnings.
Our analysis addresses two problems of the literature on returns to education: First, we establish causality of the treatment effect of education on earnings by implementing generalized matching on a full set of observable individual characteristics and unobserved personality traits. Second, since we observe lifetime earnings data, our estimates of the IRR are direct and do not depend on the assumptions that are usually made in order to justify the interpretation of regression coefficients as rates of return.
For the males, the returns to education beyond high school are sizeable. For example, the IRR for obtaining a bachelor’s degree over a high school diploma is 11.1%, and for a doctoral degree over a bachelor’s degree it is 6.7%. These results are unique because they highlight the returns to high-ability and high-education individuals, who are not well-represented in regular data sets.
Our results highlight the importance of personality and intelligence on our outcome variables. We find that personality traits similar to the Big Five personality traits are statistically-significant factors that help determine educational attainment and lifetime earnings. Even holding the level of education constant, measures of personality traits have statistically-significant effects on earnings. Similarly, IQ is rewarded in the labor market, independently of education. Most of the effect of personality and IQ on life-time earnings arise late in life, during the prime working years. Therefore, estimates from samples with shorter durations underestimate the treatment effects.
“Algorithmic Entities”, (2018):
In a 2014 article, Professor Shawn Bayern demonstrated that anyone can confer legal personhood on an autonomous computer algorithm by putting it in control of a limited liability company. Bayern’s demonstration coincided with the development of “autonomous” online businesses that operate independently of their human owners—accepting payments in online currencies and contracting with human agents to perform the off-line aspects of their businesses. About the same time, leading technologists Elon Musk, Bill Gates, and Stephen Hawking said that they regard human-level artificial intelligence as an existential threat to the human race.
This Article argues that algorithmic entities—legal entities that have no human controllers—greatly exacerbate the threat of artificial intelligence. Algorithmic entities are likely to prosper first and most in criminal, terrorist, and other anti-social activities because that is where they have their greatest comparative advantage over human-controlled entities. Control of legal entities will contribute to the threat algorithms pose by providing them with identities. Those identities will enable them to conceal their algorithmic natures while they participate in commerce, accumulate wealth, and carry out anti-social activities.
Four aspects of corporate law make the human race vulnerable to the threat of algorithmic entities. First, algorithms can lawfully have exclusive control of not just American LLC’s but also a large majority of the entity forms in most countries. Second, entities can change regulatory regimes quickly and easily through migration. Third, governments—particularly in the United States—lack the ability to determine who controls the entities they charter and so cannot determine which have non-human controllers. Lastly, corporate charter competition, combined with ease of entity migration, makes it virtually impossible for any government to regulate algorithmic control of entities.
2016-bayern.pdf: “The Implications of Modern Business-Entity Law for the Regulation of Autonomous Systems”, (2016-06; ):
Nonhuman autonomous systems are not legal persons under current law. The history of organizational law, however, demonstrates that agreements can, with increasing degrees of autonomy, direct the actions of legal persons. Agreements are isomorphic with algorithms; that is, a legally enforceable agreement can give legal effect to the arbitrary discernible states of an algorithm or other process. As a result, autonomous systems may end up being able, at least, to emulate many of the private-law rights of legal persons. This essay demonstrates a technique by which this is possible by means of limited liability companies (LLCs), a very flexible modern type of business organization. The techniques that this essay describes are not just futuristic possibilities; as this essay argues, they are already possible under current law.
Aaron Smith-Teller works in a kabbalistic sweatshop in Silicon Valley, where he and hundreds of other minimum-wage workers try to brute-force the Holy Names of God. All around him, vast forces have been moving their pieces into place for the final confrontation. An overworked archangel tries to debug the laws of physics. Henry Kissinger transforms the ancient conflict between Heaven and Hell into a US-Soviet proxy war. A Mexican hedge wizard with no actual magic wreaks havoc using the dark art of placebomancy. The Messiah reads a book by Peter Singer and starts wondering exactly what it would mean to do as much good as possible…
Aaron doesn’t care about any of this. He and his not-quite-girlfriend Ana are engaged in something far more important—griping about magical intellectual property law. But when a chance discovery brings them into conflict with mysterious international magic-intellectual-property watchdog UNSONG, they find themselves caught in a web of plots, crusades, and prophecies leading inexorably to the end of the world.