Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. In this post, I want to write what I shared when I got invited as a guest lecturer at the University of Indonesia for the Advanced Information Retrieval course. I shared several Information Retrieval implementation ideas that can be used in the real world.

Reinforcement learning is a form of machine learning that allows an algorithm to interact directly with its environment, learning through punishment …

With neural search seeing rapid adoption, more people are looking at using it for indexing and searching through their unstructured data. I know several folks already building PDF search engines powered by AI, so I figured I'd give it a stab too. How hard could it possibly be? This is just a rough and ready roadmap -- so stay tuned to see how things really pan out. If you want to follow along at home (and maybe fix a few of my bugs!), check the repo: I want to build a search engine for a dataset of arbitrary PDFs.

Netskope published a research which found that phishing downloads saw a sharp increase of 450% over the past 12 months, fueled by attackers using search engine optimization (SEO) techniques to improve the ranking of malicious PDF files on popular search engines, including Google and Bing. The top web referrer categories contained some categories traditionally associated with malware, particularly shareware/freeware, but were dominated by more unconventional categories. The ascension of the use of search engines to deliver malware over the past 12 months provides insight into how adept some attackers have become at SEO. Malware downloads referred by search engines were predominantly malicious PDF files, including many malicious fake CAPTCHAs that redirected users to phishing, spam, scam, and malware websites. The report also found that most malware over the past 12 months was downloaded from within the same region as its victim, a growing trend that points to the increasing sophistication of cybercriminals, which more frequently stage malware to avoid geofencing filters and other traditional prevention measures. The findings reveal that attackers tend to target victims located in a specific region with malware hosted within the same region.

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One of the first steps in many text-based social science studies is to retrieve documents that are relevant for the analysis from large corpora of otherwise irrelevant documents. The conventional approach in social science to address this retrieval task is to apply a set of keywords and to consider those documents to be relevant that contain at least one of the keywords. But the application of incomplete keyword lists risks drawing biased inferences. More complex and costly methods such as query expansion techniques, topic model-based classification rules, and active as well as passive supervised learning could have the potential to more accurately separate relevant from irrelevant documents and thereby reduce the potential size of bias. Yet, whether applying these more expensive approaches increases retrieval performance compared to keyword lists at all, and if so, by how much, is unclear as a comparison of these approaches is lacking. This study closes this gap by comparing these methods across three retrieval tasks associated with a data set of German tweets (Linder, 2017), the Social Bias Inference Corpus (SBIC) (Sap et al., 2020), and the Reuters-21578 corpus (Lewis, 1997). Results show that query expansion techniques and topic model-based classification rules in most studied settings tend to decrease rather than increase retrieval performance. Active supervised learning, however, if applied on a not too small set of labeled training instances (e.g.

Forte introduces "DataPack", a standardized data structure for unstructured data, distilling good software engineering practices such as reusability, extensibility, and flexibility into PyTorch-based ML solutions. Machine Learning (ML) technologies are now widely used in many day-to-day applications. For example, the systems behind personal assistants like Siri or Alexa are grounded in complex ML technologies, such as Natural Language Processing, Computer Vision, and many more. While the consumer interface of Machine Learning systems may appear simple, the systems behind the scene can be much more complex than they first appear. For example, building an intelligent medical information retrieval system requires one to stitch together a diverse set of techniques.

Conventional methods for query autocompletion aim to predict which completed query a user will select from a list. A shortcoming of this approach is that users often do not know which query will provide the best retrieval performance on the current information retrieval system, meaning that any query autocompletion methods trained to mimic user behavior can lead to suboptimal query suggestions. To overcome this limitation, we propose a new approach that explicitly optimizes the query suggestions for downstream retrieval performance. We formulate this as a problem of ranking a set of rankings, where each query suggestion is represented by the downstream item ranking it produces. We then present a learning method that ranks query suggestions by the quality of their item rankings. The algorithm is based on a counterfactual learning approach that is able to leverage feedback on the items (e.g., clicks, purchases) to evaluate query suggestions through an unbiased estimator, thus avoiding the assumption that users write or select optimal queries. We establish theoretical support for the proposed approach and provide learning-theoretic guarantees. We also present empirical results on publicly available datasets, and demonstrate real-world applicability using data from an online shopping store.

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In the pooled data problem we are given a set of $n$ agents, each of which holds a hidden state bit, either $0$ or $1$. A querying procedure returns for a query set the sum of the states of the queried agents. The goal is to reconstruct the states using as few queries as possible. In this paper we consider two noise models for the pooled data problem. In the noisy channel model, the result for each agent flips with a certain probability. In the noisy query model, each query result is subject to random Gaussian noise. Our results are twofold. First, we present and analyze for both error models a simple and efficient distributed algorithm that reconstructs the initial states in a greedy fashion. Our novel analysis pins down the range of error probabilities and distributions for which our algorithm reconstructs the exact initial states with high probability. Secondly, we present simulation results of our algorithm and compare its performance with approximate message passing (AMP) algorithms that are conjectured to be optimal in a number of related problems.