Due to physiological variation, patients diagnosed with the same condition may exhibit divergent, but related, responses to the same treatments. Hidden Parameter Markov Decision Processes (HiP-MDPs) tackle this transfer-learning problem by embedding these tasks into a low-dimensional space. However, the original formulation of HiP-MDP had a critical flaw: the embedding uncertainty was modelled independently of the agent's state uncertainty, requiring an unnatural training procedure in which all tasks visited every part of the state space--possible for robots that can be moved to a particular location, impossible for human patients. We update the HiP-MDP framework and extend it to more robustly develop personalized medicine strategies for HIV treatment.

Student-$t$ processes have recently been proposed as an appealing alternative non-parameteric function prior. They feature enhanced flexibility and predictive variance. In this work the use of Student-$t$ processes are explored for multi-objective Bayesian optimization. In particular, an analytical expression for the hypervolume-based probability of improvement is developed for independent Student-$t$ process priors of the objectives. Its effectiveness is shown on a multi-objective optimization problem which is known to be difficult with traditional Gaussian processes.

We consider the problem of non-parametric regression with a potentially large number of covariates. We propose a convex, penalized estimation framework that is particularly well-suited for high-dimensional sparse additive models. The proposed approach combines appealing features of finite basis representation and smoothing penalties for non-parametric estimation. In particular, in the case of additive models, a finite basis representation provides a parsimonious representation for fitted functions but is not adaptive when component functions posses different levels of complexity. On the other hand, a smoothing spline type penalty on the component functions is adaptive but does not offer a parsimonious representation of the estimated function. The proposed approach simultaneously achieves parsimony and adaptivity in a computationally efficient framework. We demonstrate these properties through empirical studies on both real and simulated datasets. We show that our estimator converges at the minimax rate for functions within a hierarchical class. We further establish minimax rates for a large class of sparse additive models. The proposed method is implemented using an efficient algorithm that scales similarly to the Lasso with the number of covariates and samples size.

Datasets containing large samples of time-to-event data arising from several small heterogeneous groups are commonly encountered in statistics. This presents problems as they cannot be pooled directly due to their heterogeneity or analyzed individually because of their small sample size. Bayesian nonparametric modelling approaches can be used to model such datasets given their ability to flexibly share information across groups. In this paper, we will compare three popular Bayesian nonparametric methods for modelling the survival functions of heterogeneous groups. Specifically, we will first compare the modelling accuracy of the Dirichlet process, the hierarchical Dirichlet process, and the nested Dirichlet process on simulated datasets of different sizes, where group survival curves differ in shape or in expectation. We, then, will compare the models on a real-world injury dataset.

Artificial intelligence (AI) is destined to be more efficient than ever reachable for humans in most instances. The growing emergence of AI across many industries means that people's jobs are under threat. Across the working spectrum, it will impact those in the blue-collar occupations such as taxi drivers factory employees as well as white-collar professions such as doctors and managers. Therefore leaving the majority of the working population being affected by either complete automation or partial automation of their careers. One of the first industries to be disrupted will be trucking.

The'smart' speakers that won't stop talking to each other: Watch Amazon's Echo Dot get stuck in an'infinite loop' chatting to Google's Home Google's $130 Home speaker went on sale earlier this month Amazon's Alexa has been a huge hit with 5.1m sold Both can do everything from control lights to answer questions Google's $130 Home speaker went on sale earlier this month Amazon's Alexa has been a huge hit with 5.1m sold Has YOUR Google account been hacked? Researchers say... Apple goes Red for World AIDS day as firm is revealed to... Britain traded with the Middle East 1,300 years ago: Bitumen... The original human ancestor'Lucy' was a tree climbing... Has YOUR Google account been hacked? Researchers say... Apple goes Red for World AIDS day as firm is revealed to... Britain traded with the Middle East 1,300 years ago: Bitumen... The original human ancestor'Lucy' was a tree climbing... Google Home AI speaker (left) shows the incredible potential of a smart home assistant - but still has a little bit of learning to do before it become indispensable.

Should the United States create robotic war weapons that decide on their own whether to kill? That's the type of question involving the intersection of ethics and artificial intelligence that could be considered by a new research center funded with a $10 million gift from K&L Gates, the New York Times reports. The center at Carnegie Mellon University will be called the K&L Gates Endowment for Ethics and Computational Technologies. The center will use the money to create two endowed chairs for faculty members and three presidential scholarships for doctoral students, according to a press release. It will also host a biennial international conference to share research.

From the early days of Amazon, Machine learning (ML) has played a critical role in the value we bring to our customers. Around 20 years ago, we used machine learning in our recommendation engine to generate personalized recommendations for our customers. Today, there are thousands of machine learning scientists and developers applying machine learning in various places, from recommendations to fraud detection, from inventory levels to book classification to abusive review detection. There are many more application areas where we use ML extensively: search, autonomous drones, robotics in fulfillment centers, text processing and speech recognition (such as in Alexa) etc. Among machine learning algorithms, a class of algorithms called deep learning has come to represent those algorithms that can absorb huge volumes of data and learn elegant and useful patterns within that data: faces inside photos, the meaning of a text, or the intent of a spoken word.After over 20 years of developing these machine learning and deep learning algorithms and end user services listed above, we understand the needs of both the machine learning scientist community that builds these machine learning algorithms as well as app developers who use them.

Rumors are flying about the latest Samsung flagship smartphone. Presumably to be called the Galaxy S8, it'll attempt to revive Samsung's burned edges from the Galaxy Note7 debacle. Here's everything we have heard so far about what Samsung is putting together. The details are pretty much in the rumor category at this point, but we'll continually update this article as we get more information. The latest Samsung phone usually wins the "best display" crown from Displaymate.

The US Department of Veterans Affairs (VA) has signed a five-year deal with Flow Health, an AI company, to develop personalized healthcare plans for veterans using deep learning. Deep learning is a tool in machine learning that is useful for sifting through huge heaps of data to find useful information. Flow Health is focusing on building a knowledge graph, a database containing information about people's genomes and phenotypes, to identify disease risks and recommend treatments. "Our mission is to advance healthcare by applying the latest artificial intelligence techniques to improve the detection, diagnosis, treatment and management of diseases," said Alex Meshkin, CEO of Flow Health. "The VA supports millions of Americans who have served our nation and deserve our honor, respect and the best care our country has to offer. Through our partnership with the VA, Flow Health is working to unleash the power of AI to benefit our nation's veterans."