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Talent Acquisition Tech with Artificial Intelligence

#artificialintelligence

Earlier I wrote about different ways in which HR and recruiting can use artificial intelligence to help bare some of the administrative burden that seems to take up so much of our time. In this article, I mentioned that artificial intelligence is defined as "an ideal'intelligent' machine [that] is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal." It's a branch of computer science that uses machine learning algorithms that mimic cognitive functions; making machines more human-like. I am a firm believer that A.I. technologies can allow HR as well as talent acquisition to be more strategic in our jobs. A.I. is great for pattern matching and prediction, however, I still have many questions especially surrounding whether A.I. is ethical at using these growing number of tools to use as part of your diversity recruiting efforts.


Talent Acquisition Tech with Artificial Intelligence

#artificialintelligence

Earlier I wrote about different ways in which HR and recruiting can use artificial intelligence to help bare some of the administrative burden that seems to take up so much of our time. In this article, I mentioned that artificial intelligence is defined as "an ideal'intelligent' machine [that] is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal." It's a branch of computer science that uses machine learning algorithms that mimic cognitive functions; making machines more human-like. I am a firm believer that A.I. technologies can allow HR as well as talent acquisition to be more strategic in our jobs. A.I. is great for pattern matching and prediction, however, I still have many questions especially surrounding whether A.I. is ethical at using these growing number of tools to use as part of your diversity recruiting efforts.


Apple bolsters continuing machine learning efforts with Tuplejump acquisition

#artificialintelligence

Apple is continuing to add to its team of machine learning experts in Cupertino. TechCrunch reports that Apple has acquired Tuplejump, which describes itself as a service that "presents all your data in a familiar format" on their now-removed website. Apple buys smaller technology companies from time to time, and we generally do not discuss our purpose or plans. The report notes that Tuplejump is based in part in India as well as the United States and doesn't disclose the terms of the acquisition. We're hearing that Apple was particularly interested in "FiloDB", an opensource project that Tuplejump was building to efficiently apply machine learning concepts and analytics to massive amounts of complex data right as it streamed in.


Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding

Neural Information Processing Systems

We consider the problem of active feature acquisition where the goal is to sequentially select the subset of features in order to achieve the maximum prediction performance in the most cost-effective way at test time. In this work, we formulate this active feature acquisition as a jointly learning problem of training both the classifier (environment) and the RL agent that decides either to stop and predict' or collect a new feature' at test time, in a cost-sensitive manner. We also introduce a novel encoding scheme to represent acquired subsets of features by proposing an order-invariant set encoding at the feature level, which also significantly reduces the search space for our agent. We evaluate our model on a carefully designed synthetic dataset for the active feature acquisition as well as several medical datasets. Our framework shows meaningful feature acquisition process for diagnosis that complies with human knowledge, and outperforms all baselines in terms of prediction performance as well as feature acquisition cost.


BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning

Neural Information Processing Systems

We develop BatchBALD, a tractable approximation to the mutual information between a batch of points and model parameters, which we use as an acquisition function to select multiple informative points jointly for the task of deep Bayesian active learning. BatchBALD is a greedy linear-time $1 - icefrac{1}{e}$-approximate algorithm amenable to dynamic programming and efficient caching. We compare BatchBALD to the commonly used approach for batch data acquisition and find that the current approach acquires similar and redundant points, sometimes performing worse than randomly acquiring data. We finish by showing that, using BatchBALD to consider dependencies within an acquisition batch, we achieve new state of the art performance on standard benchmarks, providing substantial data efficiency improvements in batch acquisition. Papers published at the Neural Information Processing Systems Conference.