Welcome to the fourth edition of the AI Index Report. This year we significantly expanded the amount of data available in the report, worked with a broader set of external organizations to calibrate our data, and deepened our connections with the Stanford Institute for Human-Centered Artificial Intelligence (HAI). The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Its mission is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI. The report aims to be the most credible and authoritative source for data and insights about AI in the world.

Explainable Artificial Intelligence (XAI) is a rising field in AI. It aims to produce a demonstrative factor of trust, which for human subjects is achieved through communicative means, which Machine Learning (ML) algorithms cannot solely produce, illustrating the necessity of an extra layer producing support to the model output. When approaching the medical field, we can see challenges arise when dealing with the involvement of human-subjects, the ideology behind trusting a machine to tend towards the livelihood of a human poses an ethical conundrum - leaving trust as the basis of the human-expert in acceptance to the machines decision. The aim of this paper is to apply XAI methods to demonstrate the usability of explainable architectures as a tertiary layer for the medical domain supporting ML predictions and human-expert opinion, XAI methods produce visualization of the feature contribution towards a given models output on both a local and global level. The work in this paper uses XAI to determine feature importance towards high-dimensional data-driven questions to inform domain-experts of identifiable trends with a comparison of model-agnostic methods in application to ML algorithms. The performance metrics for a glass-box method is also provided as a comparison against black-box capability for tabular data. Future work will aim to produce a user-study using metrics to evaluate human-expert usability and opinion of the given models.

Now that the reviewing period is over, we would like to share with you some statistics and insights about the reviewing process we used this year. We received 12115 abstract submissions, which resulted in 9467 full paper submissions. Compared to 2019, the number of submissions increased by 40%, which is very similar to the growth from 2018 to 2019. After more than three months of hard work from our reviewers, area chairs and senior area chairs (thank you, all!!), we have accepted exactly 1900 papers, including 105 oral presentations and 280 spotlight presentations. Note that this year we introduced "Social Aspects of Machine Learning", with topics like fairness and privacy.

People battle over which language is better for Statistical modeling, R or python or Julia. But if you know what you are doing, then any language or software is just a tool. Fluency in any language is less important than Concepts themselves. Here is Naive Bayes Learning explained clearly and implemented on Tableau from scratch with Data used-Predicting Churn for Bank Customers. Naive Bayes is a probabilistic model that assigns the probability of an event by calculating the individual probability of the variables.

Building a sketch of an n-by-n empirical kernel matrix is a common approach to accelerate the computation of many kernel methods. In this paper, we propose a unified framework of constructing sketching methods in kernel ridge regression (KRR), which views the sketching matrix S as an accumulation of m rescaled sub-sampling matrices with independent columns. Our framework incorporates two commonly used sketching methods, sub-sampling sketches (known as the Nystr\"om method) and sub-Gaussian sketches, as special cases with m=1 and m=infinity respectively. Under the new framework, we provide a unified error analysis of sketching approximation and show that our accumulation scheme improves the low accuracy of sub-sampling sketches when certain incoherence characteristic is high, and accelerates the more accurate but computationally heavier sub-Gaussian sketches. By optimally choosing the number m of accumulations, we show that a best trade-off between computational efficiency and statistical accuracy can be achieved. In practice, the sketching method can be as efficiently implemented as the sub-sampling sketches, as only minor extra matrix additions are needed. Our empirical evaluations also demonstrate that the proposed method may attain the accuracy close to sub-Gaussian sketches, while is as efficient as sub-sampling-based sketches.

WHEN IT comes to using artificial intelligence (AI), intelligence agencies have been at it longer than most. In the cold war America's National Security Agency (NSA) and Britain's Government Communications Headquarters (GCHQ) explored early AI to help transcribe and translate the enormous volumes of Soviet phone-intercepts they began hoovering up in the 1960s and 1970s. Your browser does not support the audio element. Yet the technology was immature. One former European intelligence officer says his service did not use automatic transcription or translation in Afghanistan in the 2000s, relying on native speakers instead.

With increased interest in adopting AI methods for clinical diagnosis, a vital step towards safe deployment of such tools is to ensure that the models not only produce accurate predictions but also do not generalize to data regimes where the training data provide no meaningful evidence. Existing approaches for ensuring the distribution of model predictions to be similar to that of the true distribution rely on explicit uncertainty estimators that are inherently hard to calibrate. In this paper, we propose to train a loss estimator alongside the predictive model, using a contrastive training objective, to directly estimate the prediction uncertainties. Interestingly, we find that, in addition to producing well-calibrated uncertainties, this approach improves the generalization behavior of the predictor. Using a dermatology use-case, we show the impact of loss estimators on model generalization, in terms of both its fidelity on in-distribution data and its ability to detect out of distribution samples or new classes unseen during training.

Phytoparasitic nematodes (or phytonematodes) are causing severe damage to crops and generating large-scale economic losses worldwide. In soybean crops, annual losses are estimated at 10.6% of world production. Besides, identifying these species through microscopic analysis by an expert with taxonomy knowledge is often laborious, time-consuming, and susceptible to failure. In this perspective, robust and automatic approaches are necessary for identifying phytonematodes capable of providing correct diagnoses for the classification of species and subsidizing the taking of all control and prevention measures. This work presents a new public data set called NemaDataset containing 3,063 microscopic images from five nematode species with the most significant damage relevance for the soybean crop. Additionally, we propose a new Convolutional Neural Network (CNN) model defined as NemaNet and a comparative assessment with thirteen popular models of CNNs, all of them representing the state of the art classification and recognition. The general average calculated for each model, on a from-scratch training, the NemaNet model reached 96.99% accuracy, while the best evaluation fold reached 98.03%. In training with transfer learning, the average accuracy reached 98.88\%. The best evaluation fold reached 99.34% and achieve an overall accuracy improvement over 6.83% and 4.1%, for from-scratch and transfer learning training, respectively, when compared to other popular models.

Breast cancer is one of the most common cause of deaths among women. Mammography is a widely used imaging modality that can be used for cancer detection in its early stages. Deep learning is widely used for the detection of cancerous masses in the images obtained via mammography. The need to improve accuracy remains constant due to the sensitive nature of the datasets so we introduce segmentation and wavelet transform to enhance the important features in the image scans. Our proposed system aids the radiologist in the screening phase of cancer detection by using a combination of segmentation and wavelet transforms as pre-processing augmentation that leads to transfer learning in neural networks. The proposed system with these pre-processing techniques significantly increases the accuracy of detection on Mini-MIAS.

WHEN IT COMES to artificial intelligence (AI), spy agencies have been at it longer than most. In the cold war, America's National Security Agency (NSA) and Britain's Government Communications Headquarters (GCHQ) explored early AI to help transcribe and translate the enormous volumes of Soviet phone-intercepts they began hoovering up in the 1960s. Yet the technology was immature. One former European intelligence officer says his service did not use automatic transcription or translation in Afghanistan in the 2000s, relying on native speakers instead. Now the spooks are hoping to do better. The trends that have made AI attractive for business--more data, better algorithms, and more processing power to make it all hum--are giving spy agencies big ideas, too.