Incode, a San Francisco startup developing what it describes as an omnichannel biometric identity platform, today announced that it's raised $10 million in seed funding from undisclosed investors. Founder and CEO Ricardo Amper said that the newfound capital will enable Incode to accelerate the development and rollout of its tools globally, some of which are already being used by major banks, financial institutions, governments, and retailers. "The modern consumer is all about experiences and convenience," said Amper. "What they want is a seamless, consistent and secure way to perform daily tasks like access their ATM, make payments, and access online accounts. Yet, what they get today is quite the opposite. The ecosystem is fragmented by multiple vendors and devices, making processes clunky and inefficient. That's precisely why we've built Incode Omni: to help companies provide a frictionless, secure and convenient experience for the next generation of consumers."

Machine learning in finance might work magic, although there's no secret powering it (well, perhaps just a bit of bit). Nevertheless, the good results of machine learning task depends much more on creating effective infrastructure, collecting ideal datasets, and putting on the proper algorithms. Machine learning is actually making considerable inroads within the financial services sector. Let us see why financial companies must care, what answers they could put into action with AI as well as machine learning, and just how exactly they are able to use this technology. We are able to define machine learning (ML) being a subset of information science that makes use of statistical models to bring insights as well as whip predictions.

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.

We propose a method for causal inference using satellite image time series, in order to determine the treatment effects of interventions which impact climate change, such as deforestation. Simply put, the aim is to quantify the 'before versus after' effect of climate related human driven interventions, such as urbanization; as well as natural disasters, such as hurricanes and forest fires. As a concrete example, we focus on quantifying forest tree cover change/ deforestation due to human led causes. The proposed method involves the following steps. First, we uae computer vision and machine learning/deep learning techniques to detect and quantify forest tree coverage levels over time, at every time epoch. We then look at this time series to identify changepoints. Next, we estimate the expected (forest tree cover) values using a Bayesian structural causal model and projecting/forecasting the counterfactual. This is compared to the values actually observed post intervention, and the difference in the two values gives us the effect of the intervention (as compared to the non intervention scenario, i.e. what would have possibly happened without the intervention). As a specific use case, we analyze deforestation levels before and after the hyperinflation event (intervention) in Brazil (which ended in 1993-94), for the Amazon rainforest region, around Rondonia, Brazil. For this deforestation use case, using our causal inference framework can help causally attribute change/reduction in forest tree cover and increasing deforestation rates due to human activities at various points in time.

W e consider the problem of inverse kinematics (IK), where one wants to find the parameters of a given kinematic skeleton that best explain a set of observed 3D joint locations. The kinematic skeleton has a tree structure, where each node is a joint that has an associated geometric transformation that is propagated to all its child nodes. The IK problem has various applications in vision and graphics, for example for tracking or reconstructing articulated objects, such as human hands or bodies. Most commonly, the IK problem is tackled using local optimisation methods. A major downside of these approaches is that, due to the non-convex nature of the problem, such methods are prone to converge to unwanted local optima and therefore require a good initialisation. In this paper we propose a convex optimisation approach for the IK problem based on semidef-inite programming, which admits a polynomial-time algorithm that globally solves (a relaxation of) the IK problem. Experimentally, we demonstrate that the proposed method significantly outperforms local optimisation methods using different real-world skeletons.

There is no bigger industry on our planet than food and agriculture, with a consistent, loyal customer base of 7 billion. In fact, the World Bank estimates that food and agriculture comprise about 10% of the global GDP, meaning that, food and agriculture would be valued at about $8 trillion globally based on the projected global GDP of $88 trillion for 2019. On the food front, a record $1.71 trillion was spent on food and beverages in 2018 at grocery stores and other retailers and away-from-home meals and snacks in the United States alone. During the same year, 9.7% of Americans' disposable personal income was spent on food -- 5% at home and 4.7% away from home -- a percentage that has remained steady amidst economic changes over the past 20 years. However, despite a stalwart customer base, the food industry is facing unprecedented challenges in production, demand and regulations stemming from consumer trends.

With the industry 4.0 revolution around, Research N Reports presents a detailed analysis of Cognitive Computing market that offers latest insights for business professionals. Using BI tools such as Factiva and Hoover, the report offers a comprehensive analysis and is a mix of market intelligence studies and industry insights. Prepared by a panel of highly experienced market analysts and consultants, the report is spread across 137 pages offering chapter wise detailed market analysis that enables the clients with multiple data points and encourages them to have a 360 degree overview of the market performance. Clients can ask for sample of this report that gives a detailed overview of the market conditions, driving and restraining factors, segments, trends and opportunities. Covering the latest information about the market, the samples can give a basic understanding upon the report contents and its format.

Drone point of view of a Tractor spraying on a cultivated field. What does the future hold for machine learning/AI within the agricultural sector? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world. From the food we eat to the clothing we wear and the gasoline in millions of cars, agriculture touches our daily existence like few other industries. This year alone, we've experienced several major supply shocks--including massive flooding in the American Midwest, a trade war, and the outbreak of serious crop and animal diseases in China--all of which highlight just how unpredictable the system is. Fortunately, we've also reached the point where there are nearly infinite amounts of data available to understand and forecast the complex interplay between global agricultural markets.

Around 2017, demand for microtasking crowdwork changed quickly and significantly, both in quantity and quality. Florian Alexander Schmidt tried to figure out, among other things, whether this was "a short-lived phenomenon or [something offering] long-term economic prospects for crowdworkers". This post is my own summary of the resulting report, titled "Crowdsourced Production of AI Training Data" and published in February 2019. What caused the sudden change in the demand for microtasking crowdwork was the equally sudden need of lots of high quality training data for autonomous vehicles. Those data are fed to the so-called self-learning algorithms that "drive" self-driving cars.

ABSTRACT Transformer with self-attention has achieved great success in the area of nature language processing. Recently, there have been a few studies on transformer for end-to-end speech recognition, while its application for hybrid acoustic model is still very limited. In this paper, we revisit the transformer-based hybrid acoustic model, and propose a model structure with interleaved self-attention and 1D convolution, which is proven to have faster convergence and higher recognition accuracy. We also study several aspects of the transformer model, including the impact of the positional encoding feature, dropout regularization, as well as training with and without time restriction. We show competitive recognition results on the public Librispeech dataset when compared to the Kaldi baseline at both cross entropy training and sequence training stages. For reproducible research, we release our source code and recipe within the PyKaldi2 toolbox.