Contact centers across Latin America are looking to transform their customer service solutions by implementing new channels, such as chat, social media, email, video, or updated voice services, which are still hugely relevant in the region. Within that scope, speech recognition technology is often seen as a means to cut costs, improve customer satisfaction, and increase productivity in the call center. But are these solutions worth it? Speech recognition is performed by a machine or program that can identify words or phrases in spoken language and convert them into a machine-readable format. Beyond interactive voice response (IVR) systems, some of the more popular call center speech recognition applications are call routing, speech-to-text, voice dialing, and voice search.

Gartner, Inc. today highlighted the top technology trends that will be strategic for most organizations in 2017. Analysts presented their findings during the sold-out Gartner Symposium/ITxpo, which is taking place here through Thursday. Gartner defines a strategic technology trend as one with substantial disruptive potential that is just beginning to break out of an emerging state into broader impact and use or which are rapidly growing trends with a high degree of volatility reaching tipping points over the next five years. "Gartner's top 10 strategic technology trends for 2017 set the stage for the Intelligent Digital Mesh," said David Cearley, vice president and Gartner Fellow. "The first three embrace'Intelligence Everywhere,' how data science technologies and approaches are evolving to include advanced machine learning and artificial intelligence allowing the creation of intelligent physical and software-based systems that are programmed to learn and adapt. The next three trends focus on the digital world and how the physical and digital worlds are becoming more intertwined. The last four trends focus on the mesh of platforms and services needed to deliver the intelligent digital mesh."

This is Patricia Murphy, Vice President of Investor Relations for IBM. I'd like to welcome you to our third quarter earnings presentation. The prepared remarks will be available within a couple of hours and a replay of the webcast will be posted by this time tomorrow. I'll remind you that certain comments made in this presentation may be characterized as forward-looking under the Private Securities Litigation Reform Act of 1995. Those statements involve a number of factors that could cause actual results to differ materially. Additional information concerning these factors is contained in the Company's filings with the SEC. Copies are available from the SEC, from the IBM website, or from us in Investor Relations. Our presentation also includes certain non-GAAP financial measures in an effort to provide additional information to investors. All non-GAAP measures have been reconciled to their related GAAP measures in accordance with SEC rules. You'll find reconciliation charts at the end of the presentation and in the form 8-K submitted to the SEC today. So with that, I'll turn the call over to Martin Schroeter. In the third quarter, we generated 19.2 billion in revenues, 3.7 billion in pre-tax income and 3.29 of operating earnings per share. As we think back to the discussion 90 days ago, it was around Brexit and its impact on Europe, global spending and sectors like banking and the attractiveness of investment in the emerging markets, all of these topics have the capacity to drive some volatility and results, but what you see in our third quarter results is stability in our revenue with continued strong growth and strategic imperatives and a top and bottom line consistent with what we expected. Our revenue was essentially flat relative to last year. Looking at the revenue dynamics, I want to point out a few things. Our clients are focussed on becoming digital businesses and have strong growth in cloud, security, mobile, and across our analytics portfolio reflects this. In total, we continue to deliver double-digit revenue growth in our strategic imperatives led by our cloud business. Cloud delivered as-a-service is part of a solid recurring revenue base across software and services, and our annuity revenue continued to grow. Of course, the acquisitions we made in the last 12 months contributed to growth about the same amount as last quarter and for the first time in quite a while currency was a modest tailwind to revenue growth.

I write about gadgets, which means everyone asks me what laptop/ phone/ dishwasher to buy. I struggle with this, because the answer often starts, "It depends." Unless you ask about a phone. In that case, I usually say get an iPhone. But the phones can be … frustrating.

A rover headed for the red planet will perform an unprecedented search for rocky remnants of dead Martians--so where should we send it? Fossil stromatolites, like this one from Bolivia, offer clues to the kinds of preserved life we may find on Mars. Nearly four billion years ago, when Earth was coming alive, Mars was gradually choking to death. The thick atmosphere that had warmed the red planet was leaking into space, and plummeting temperatures caused Martian lakes and rivers to freeze, turning the wet surface into a dry wasteland. But it's possible life took root in those early years.

While transportation, hospitality, and financial services are all industries being disrupted by technology, the next big area poised for massive, tech-driven change may be the human workforce. "We are going to move from people to things," explained Jane Fraser, CEO of Citigroup's Latin America business, speaking Monday at Fortune's Most Powerful Women Summit in Laguna Niguel, Calif. "We are expecting 500 billion objects to become connected to the internet and this automation is going to hollow out middle and working class jobs," explained Fraser. "Technology is replacing these jobs." The technology Fraser is referring to is artificial intelligence--the machine learning that powers driverless cars and other intelligent machines that are slowly taking over human tasks.

Mapping forest aboveground biomass (AGB) has become an important task, particularly for the reporting of carbon stocks and changes. AGB can be mapped using synthetic aperture radar data (SAR) or passive optical data. However, these data are insensitive to high AGB levels (\textgreater{}150 Mg/ha, and \textgreater{}300 Mg/ha for P-band), which are commonly found in tropical forests. Studies have mapped the rough variations in AGB by combining optical and environmental data at regional and global scales. Nevertheless, these maps cannot represent local variations in AGB in tropical forests. In this paper, we hypothesize that the problem of misrepresenting local variations in AGB and AGB estimation with good precision occurs because of both methodological limits (signal saturation or dilution bias) and a lack of adequate calibration data in this range of AGB values. We test this hypothesis by developing a calibrated regression model to predict variations in high AGB values (mean \textgreater{}300 Mg/ha) in French Guiana by a methodological approach for spatial extrapolation with data from the optical geoscience laser altimeter system (GLAS), forest inventories, radar, optics, and environmental variables for spatial inter-and extrapolation. Given their higher point count, GLAS data allow a wider coverage of AGB values. We find that the metrics from GLAS footprints are correlated with field AGB estimations (R 2 =0.54, RMSE=48.3 Mg/ha) with no bias for high values. First, predictive models, including remote-sensing, environmental variables and spatial correlation functions, allow us to obtain "wall-to-wall" AGB maps over French Guiana with an RMSE for the in situ AGB estimates of ~51 Mg/ha and R${}^2$=0.48 at a 1-km grid size. We conclude that a calibrated regression model based on GLAS with dependent environmental data can produce good AGB predictions even for high AGB values if the calibration data fit the AGB range. We also demonstrate that small temporal and spatial mismatches between field data and GLAS footprints are not a problem for regional and global calibrated regression models because field data aim to predict large and deep tendencies in AGB variations from environmental gradients and do not aim to represent high but stochastic and temporally limited variations from forest dynamics. Thus, we advocate including a greater variety of data, even if less precise and shifted, to better represent high AGB values in global models and to improve the fitting of these models for high values.

Multilingual Text Classification (MLTC) is a text classification task in which documents are written each in one among a set L of natural languages, and in which all documents must be classified under the same classification scheme, irrespective of language. There are two main variants of MLTC, namely Cross-Lingual Text Classification (CLTC) and Polylingual Text Classification (PLTC). In PLTC, which is the focus of this paper, we assume (differently from CLTC) that for each language in L there is a representative set of training documents; PLTC consists of improving the accuracy of each of the |L| monolingual classifiers by also leveraging the training documents written in the other (|L| − 1) languages. The obvious solution, consisting of generating a single polylingual classifier from the juxtaposed monolingual vector spaces, is usually infeasible, since the dimensionality of the resulting vector space is roughly |L| times that of a monolingual one, and is thus often unmanageable. As a response, the use of machine translation tools or multilingual dictionaries has been proposed. However, these resources are not always available, or are not always free to use. One machine-translation-free and dictionary-free method that, to the best of our knowledge, has never been applied to PLTC before, is Random Indexing (RI). We analyse RI in terms of space and time efficiency, and propose a particular configuration of it (that we dub Lightweight Random Indexing LRI). By running experiments on two well known public benchmarks, Reuters RCV1/RCV2 (a comparable corpus) and JRC-Acquis (a parallel one), we show LRI to outperform (both in terms of effectiveness and efficiency) a number of previously proposed machine-translation-free and dictionary-free PLTC methods that we use as baselines.

As the skills gap widens for software developers, machine-learning specialists are stepping in with collaboration platforms designed to streamline time-consuming tasks such as tracking down technical solutions when working with new technologies. Collokia, a New York-based startup, announced the beta launch of a machine-learning platform last month that uses artificial intelligence to promote greater collaboration in software development. The other goal is reducing the "technical debt" of development teams by using AI to track down relevant information about a technology project, including information that already resides in company systems. The platform edits and updates search results as a way to disseminate information more widely among development teams. Automation tools that leverage AI promise to address the growing shortfall of qualified software engineers.

Recognizing the central role that technology now plays in the global economy, the World Economic Forum is establishing a new center in San Francisco to connect tech companies and policymakers in the heart of the world's technology industry. Building off the Forum's thesis of a "Fourth Industrial Revolution," the new facility will focus on bringing government officials and tech companies together to create frameworks for more productive legislative policies that can be implemented worldwide. "Depending on the collective choices we make -– as consumers, as communities, as business, government, and civil society leaders -– these technological breakthroughs could give us the power to move into a world that is even more prosperous, while being more sustainable and more inclusive," reads an early version of remarks prepared by World Economic Forum founder and chairman, Klaus Schwab. "Alternatively, we could end up in a world where our economic, political and social systems are more rigid, more unequal and more conflicted." Despite their deep roots in government-funded research, the relationship between policymakers and the tech companies that have sprung from the civic-minded seeds they nurtured with financing has always been a thorny or even openly antagonistic one (cf.