Replatforming a business of our size and scale takes time. However, our pace of innovation is accelerating. We're increasingly using structured data and artificial intelligence to transform shopping on eBay, delivering more personalization capabilities, continuing to iterate our mobile experience, and bringing more unique inventory and categories to our customers. We've got more work to do, but I'm confident we're on the right path. Now, let me turn it over to Scott, and he'll provide more details on our Q3 results.

I had a background in financial sales and portfolio strategy, but Fintech was on the rise, the RoboAdvisor was gaining momentum, and Bogleheads were taking over. They were consolidating to the point of monopoly and had determined that fee income and cross-sell ratio were more important than service and fiduciary responsibility to client wealth. "Too big to fail" became the justification for mass abuse in the financial system. We only have to look to Wells Fargo's recent crisis to see the results. The problem is systemic, encouraged, and will get worse before it gets better.

It does not pay to underestimate a monkey with a rock. Scientists studying the stone-smashing habits of bearded capuchin monkeys in Brazil have found that the primates inadvertently produce stone flakes that look very similar to the flakes used as cutting tools by early humans. The findings, published in the journal Nature, could snarl the links that paleoanthropologists make between early Stone Age artifacts and the emergence of primitive human technology. "It does raise interesting questions about the level of cognitive complexity -- how intelligent a hominin has to be in order to produce what we thought was a sophisticated technology," said lead author Tomos Proffitt, a paleoanthropologist at Oxford University. When anthropologists explore early human settlements, they typically search for signs of tool use, whether by looking at the cuts on butchered animal bones or finding the tools themselves.

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.