Did you miss a session at the Data Summit? Companies developing artificial intelligence (AI)-powered marketing tools typically claim that their solutions drive strategic decision-making better than software without an algorithmic component. But -- as is often the case -- the reality is more complicated. AI learns to make predictions from large amounts of high-quality data, and so can be hamstrung (e.g., make mistakes) if that data is not available. The complex nature of marketing stacks, which sprawl across disparate, disconnected systems, can put up logistical roadblocks to implementation.

Accurately forecasting extreme rainfall is notoriously difficult, but is also ever more crucial for society as climate change increases the frequency of such extremes. Global numerical weather prediction models often fail to capture extremes, and are produced at too low a resolution to be actionable, while regional, high-resolution models are hugely expensive both in computation and labour. In this paper we explore the use of deep generative models to simultaneously correct and downscale (super-resolve) global ensemble forecasts over the Continental US. Specifically, using fine-grained radar observations as our ground truth, we train a conditional Generative Adversarial Network -- coined CorrectorGAN -- via a custom training procedure and augmented loss function, to produce ensembles of high-resolution, bias-corrected forecasts based on coarse, global precipitation forecasts in addition to other relevant meteorological fields. Our model outperforms an interpolation baseline, as well as super-resolution-only and CNN-based univariate methods, and approaches the performance of an operational regional high-resolution model across an array of established probabilistic metrics. Crucially, CorrectorGAN, once trained, produces predictions in seconds on a single machine. These results raise exciting questions about the necessity of regional models, and whether data-driven downscaling and correction methods can be transferred to data-poor regions that so far have had no access to high-resolution forecasts.

For years, beer drinkers have had to pretend to enjoy dodgy-tasting beer served up by hipster breweries and enthusiastic home-brewers. Now researchers in Japan say two different facial expressions can truly reveal whether or not we enjoyed a beer immediately after trying it. In experiments, the scientists used facial recognition technology to scan people's facial expressions to reveal their true beer preferences. 'Lip suck', where the lips are drawn inwards as if we're saying'mmmmm', indicate that we enjoy a beverage, the experts claim. Conversely, 'lip press', where the lips are pressed down on top of each other, reveals that we actually thought a beer tasted horrible.

It was February 11, his birthday, and the 58-year-old billionaire CEO and cofounder of Yandex, the Russian tech behemoth, was in the sort of open, engaging mood that could be called privetliviy, after the casual Russian word privet for hello. He was speaking from his car in Tel Aviv, bragging about his father--an oil geologist in his eighties who had "discovered" oil in Israel, Volozh said--as we chatted about my upcoming trip to Tel Aviv to interview him for this story. For more than 20 years, Yandex has been known as "Russia's Google": It began as a search engine in 1997 and still has a 60 percent share of the Russian search market. But for the past decade, this tag has understated the company's inescapable ubiquity in Russians' daily life. Yandex Music is the country's leader in paid music streaming, and Yandex Taxi is the top ride-hailing app.

The COVID-19 pandemic has demonstrated the increasing need of policymakers for timely estimates of macroeconomic variables. A prior UNCTAD research paper examined the suitability of long short-term memory artificial neural networks (LSTM) for performing economic nowcasting of this nature. Here, the LSTM's performance during the COVID-19 pandemic is compared and contrasted with that of the dynamic factor model (DFM), a commonly used methodology in the field. Three separate variables, global merchandise export values and volumes and global services exports, were nowcast with actual data vintages and performance evaluated for the second, third, and fourth quarters of 2020 and the first and second quarters of 2021. In terms of both mean absolute error and root mean square error, the LSTM obtained better performance in two-thirds of variable/quarter combinations, as well as displayed more gradual forecast evolutions with more consistent narratives and smaller revisions. Additionally, a methodology to introduce interpretability to LSTMs is introduced and made available in the accompanying nowcast_lstm Python library, which is now also available in R, MATLAB, and Julia.

In an out-of-distribution (OOD) detection problem, samples of known classes(also called in-distribution classes) are used to train a special classifier. In testing, the classifier can (1) classify the test samples of known classes to their respective classes and also (2) detect samples that do not belong to any of the known classes (i.e., they belong to some unknown or OOD classes). This paper studies the problem of zero-shot out-of-distribution(OOD) detection, which still performs the same two tasks in testing but has no training except using the given known class names. This paper proposes a novel yet simple method (called ZOC) to solve the problem. ZOC builds on top of the recent advances in zero-shot classification through multi-modal representation learning. It first extends the pre-trained language-vision model CLIP by training a text-based image description generator on top of CLIP. In testing, it uses the extended model to generate candidate unknown class names for each test sample and computes a confidence score based on both the known class names and candidate unknown class names for zero-shot OOD detection. Experimental results on 5 benchmark datasets for OOD detection demonstrate that ZOC outperforms the baselines by a large margin.

Governments and humanitarian groups can use machine learning algorithms and mobile phone data to get aid to those who need it most during a humanitarian crisis, we found in new research. The simple idea behind this approach, as we explained in the journal Nature on March 16, 2022, is that wealthy people use phones differently from poor people. Their phone calls and text messages follow different patterns, and they use different data plans, for example. Machine learning algorithms--which are fancy tools for pattern recognition--can be trained to recognize those differences and infer whether a given mobile subscriber is wealthy or poor. As the COVID-19 pandemic spread in early 2020, our research team helped Togo's Ministry of Digital Economy and GiveDirectly, a nonprofit that sends cash to people living in poverty, turn this insight into a new type of aid program. First, we collected recent, reliable and representative data.

March 17, 2022--Moshe Shenfeld, a computer science Ph.D. candidate at Hebrew University of Jerusalem (HU)'s Rachel and Selim Benin School of Engineering and Computer Science, was selected as an Apple Scholar in AI/Machine Learning for 2022. Shenfeld is one of only 15 awardees worldwide, the other Israeli recipient is from Tel Aviv University. The Ph.D. fellowship in Machine Learning and AI was created by Apple "to celebrate the contributions of students pursuing cutting-edge fundamental and applied machine learning research worldwide." Currently, Shenfeld is researching privacy-preserving machine learning under the supervision of HU Professor Katrina Ligett. His Ph.D. focuses on differential privacy and its relation to adaptive data analysis and machine learning.

The malaria parasite, which is transmitted by several Anopheles mosquito species, requires more time to reach its human-transmissible stage than the average lifespan of mosquito vectors. Monitoring the species-specific age structure of mosquito populations is critical to evaluating the impact of vector control interventions on malaria risk. We present a rapid, cost-effective surveillance method based on deep learning of mid-infrared spectra of mosquito cuticle that simultaneously identifies the species and age class of three main malaria vectors in natural populations. Using spectra from over 40, 000 ecologically and genetically diverse An. gambiae, An. arabiensis, and An. coluzzii females, we develop a deep transfer learning model that learns and predicts the age of new wild populations in Tanzania and Burkina Faso with minimal sampling effort. Additionally, the model is able to detect the impact of simulated control interventions on mosquito populations, measured as a shift in their age structures. In the future, we anticipate our method can be applied to other arthropod vector-borne diseases. Knowing the age of malaria-transmitting mosquitoes is important to understand transmission risk as only old mosquitoes can transmit the disease. Here, the authors develop a method based on mid-infrared spectra of mosquito cuticle that can rapidly identify the species and age class of main malaria vectors.

Much has been said about the potential of artificial intelligence (AI) to transform how we live, work, and interact with each other. But we must also draw attention to a less discussed, but equally important, question -- do we have the skills required to develop AI inclusively and use it responsibly? AI adoption is accelerating, and the overall market is expected to be worth $190 billion by 2025. By 2030, AI technology will add $15.7 trillion to global gross domestic product (GDP). AI is everywhere -- whether we're aware of it or not.