By 1433, the Chinese admiral Zheng He had already sailed from China to India, Indonesia, and even Africa on caravels twice as large as those Christopher Columbus used 59 years later for his fateful journey. China could have been the country to discover America. Instead, its government surprisingly decided to put an end to its naval activities and burn its entire fleet of ships, indirectly allowing Spain to conquer America and bring prosperity to Europe. It took more than 5 centuries for China to recover from this political decision. What could make such an advanced country deliberately turn away from its future?

Marissa Mayer vanished from the Silicon Valley landscape two years ago when she resigned from Yahoo Inc. shortly after it was sold to Verizon Communications Inc. for $4.48 billion. Her tumultuous 5-year reign at the eponymous tech media company, on the heels of a historic run at Alphabet Inc.'s GOOGL, -1.03% GOOG, -1.06% Google in the search division, made her one of the industry's most recognizable faces -- to her professional benefit and personal dismay. On Monday, at the Techonomy conference here, she resurfaced with a new startup and some pointed comments on the valley. Mayer was interviewed on stage for about 20 minutes by Techonomy founder and journalist David Kirkpatrick. Like Twitter Inc. TWTR, 0.82% Chief Executive Jack Dorsey, Mayer opposes automated ads from politicians, calling them "very dangerous."

The banana has been the subject of Andy Warhol's cover art for the Velvet Underground's debut album, can arguably be the most devastating item in the Mario Kart video game franchise and is one of the world's most consumed fruits. And humanity's love of bananas may still be on the rise, according to data from the Food and Agriculture Organization of the United Nations. On average, says Chris Barrett, a professor of agriculture at Cornell University, citing that U.N. data, every person on earth chows down on 130 bananas a year, at a rate of nearly three a week. But the banana as we know it may also be on the verge of extinction. The situation led Colombia--where the economy relies heavily on the crop, as it does in several other countries including Ecuador, Costa Rica and Guatemala--to declare a national state of emergency in August.

To view the code, training visualizations, and more information about the python example at the end of this post, visit the Comet project page. While much of the writing and literature on deep learning concerns computer vision and natural language processing (NLP), audio analysis -- a field that includes automatic speech recognition (ASR), digital signal processing, and music classification, tagging, and generation -- is a growing subdomain of deep learning applications. Some of the most popular and widespread machine learning systems, virtual assistants Alexa, Siri and Google Home, are largely products built atop models that can extract information from audio signals. Many of our users at Comet are working on audio related machine learning tasks such as audio classification, speech recognition and speech synthesis, so we built them tools to analyze, explore and understand audio data using Comet's meta machine-learning platform. This post is focused on showing how data scientists and AI practitioners can use Comet to apply machine learning and deep learning methods in the domain of audio analysis.

Humanity's rich history has left behind an enormous number of historical documents and artifacts. However, virtually none of these documents, containing stories and recorded experiences essential to our cultural heritage, can be understood by non-experts due to language and writing changes over time. For instance, archaeologist have unearthed tens of thousands of clay tablets from ancient Babylon [1], yet only a few hundred specially trained scholars can translate them. The vast majority of these documents have never been read, even if they were uncovered in the 1800s. To give a further illustration of the challenge posed by this scale, a tablet from the Tale of Gilgamesh was collected in an expedition in 1851, but its significance was not brought to light until 1872.

The IEEE Symposium on Security and Privacy (IEEE S&P) is one of the top-tier conferences in computer security and electronic privacy. This year, the IEEE S&P was held in May, in San Francisco. It was not a regular edition, as this flagship conference marked its 40th anniversary. This year's symposium was a special celebration that included a plenary session with some exceptional panelists from the S&P community, Test of Time awards for papers that have made a lasting impact on the field, and even an amazing birthday cake! I had the pleasure of presenting two research papers at two different workshops while at the conference: the Deep Learning and Security Workshop (DLS 2019) and the Workshop on Traffic Measurements for Cybersecurity (WTMC 2019). Both papers were based on my master's thesis, that I developed for the most part when I was a research intern at the Austrian Institute of Technology (AIT) in Vienna, Austria.

The carbon footprint of algorithms must be measured and transparently reported so computer scientists can take an honest and active role in environmental sustainability. In this paper, we take analyses usually applied at the industrial level and make them accessible for individual computer science researchers with an easy-to-use Python package. Localizing to the energy mixture of the electrical power grid, we make the conversion from energy usage to CO2 emissions, in addition to contextualizing these results with more human-understandable benchmarks such as automobile miles driven. We also include comparisons with energy mixtures employed in electrical grids around the world. We propose including these automatically-generated Energy Usage Reports as part of standard algorithmic accountability practices, and demonstrate the use of these reports as part of model-choice in a machine learning context.

While deep learning has shown tremendous success in a wide range of domains, it remains a grand challenge to incorporate physical principles in a systematic manner to the design, training, and inference of such models. In this paper, we aim to predict turbulent flow by learning its highly nonlinear dynamics from spatiotemporal velocity fields of large-scale fluid flow simulations of relevance to turbulence modeling and climate modeling. We adopt a hybrid approach by marrying two well-established turbulent flow simulation techniques with deep learning. Specifically, we introduce trainable spectral filters in a coupled model of Reynolds-averaged Navier-Stokes (RANS) and Large Eddy Simulation (LES), followed by a specialized U-net for prediction. Our approach, which we call turbulent-Flow Net (TF-Net), is grounded in a principled physics model, yet offers the flexibility of learned representations. We compare our model, TF-Net, with state-of-the-art baselines and observe significant reductions in error for predictions60frames ahead. Most importantly, our method predicts physical fields that obey desirable physical characteristics, such as conservation of mass, whilst faithfully emulating the turbulent kinetic energy field and spectrum, which are critical for accurate prediction of turbulent flows.

Balancing exploration and exploitation is a fundamental part of reinforcement learning, yet most state-of-the-art algorithms use a naive exploration protocol like $\epsilon$-greedy. This contributes to the problem of high sample complexity, as the algorithm wastes effort by repeatedly visiting parts of the state space that have already been explored. We introduce a novel method based on Bayesian linear regression and latent space embedding to generate an intrinsic reward signal that encourages the learning agent to seek out unexplored parts of the state space. This method is computationally efficient, simple to implement, and can extend any state-of-the-art reinforcement learning algorithm. We evaluate the method on a range of algorithms and challenging control tasks, on both simulated and physical robots, demonstrating how the proposed method can significantly improve sample complexity.

Cryo-electron microscopy (cryo-EM) is capable of producing reconstructed 3D images of biomolecules at near-atomic resolution. As such, it represents one of the most promising imaging techniques in structural biology. However, raw cryo-EM images are only highly corrupted - noisy and band-pass filtered - 2D projections of the target 3D biomolecules. Reconstructing the 3D molecular shape starts with the removal of image outliers, the estimation of the orientation of the biomolecule that has produced the given 2D image, and the estimation of camera parameters to correct for intensity defects. Current techniques performing these tasks are often computationally expensive, while the dataset sizes keep growing. There is a need for next-generation algorithms that preserve accuracy while improving speed and scalability. In this paper, we combine variational autoencoders (VAEs) and generative adversarial networks (GANs) to learn a low-dimensional latent representation of cryo-EM images. We perform an exploratory analysis of the obtained latent space, that is shown to have a structure of "orbits", in the sense of Lie group theory, consistent with the acquisition procedure of cryo-EM images. This analysis leads us to design an estimation method for orientation and camera parameters of single-particle cryo-EM images, together with an outliers detection procedure. As such, it opens the door to geometric approaches for unsupervised estimations of orientations and camera parameters, making possible fast cryo-EM biomolecule reconstruction.