Welcome to the Hackett Group First Quarter Earnings Conference Call. Your lines have been placed on listed only mode. Hosting tonight's call are Mr. Ted Fernandez, Chairman and CEO; and Mr. Rob Ramirez, Chief Financial Officer. Mr. Ramirez, you may begin. Good afternoon, everyone, and thank you for joining us to discuss The Hackett Group's First Quarter Results. Speaking on the call today and here to answer your questions are Ted Fernandez, Chairman and CEO of The Hackett Group; and myself, Rob Ramirez, Chief Financial Officer. Our press announcement was released over the wires at 4:14 p.m. Eastern Time. For a copy of the release, please visit our website at www.thehackettgroup.com. We will also place any additional financial or statistical data discussed on this call that is not contained in the release on the Investor Relations page of our website. Before we begin, I would like to remind you that in the following comments and in the Q&A session, we will be making statements about expected future results, which may be forward-looking statements for the purposes of the federal securities laws. These statements relate to our current expectations, estimates and projections and are not a guarantee of future performance.

Thanks to great experimental work by several research groups studying the behavior of Stochastic Gradient Descent (SGD), we are collectively gaining a much clearer understanding as to what happens in the neighborhood of training convergence. The story begins with the best paper award winner for ICLR 2017, "Rethinking Generalization". This paper I first discussed several months ago in a blog post "Rethinking Generalization in Deep Learning". One interesting observation in that paper is the role of SGD. Indeed, in neural networks, we almost always choose our model as the output of running stochastic gradient descent.

On May 11, 1997, a computer showed that it could outclass a human in that most human of pursuits: playing a game. The human was World Chess Champion Garry Kasparov, and the computer was IBM's Deep Blue, which had begun life at Carnegie Mellon University as a system called ChipTest. One of Deep Blue's creators, Murray Campbell, talked to the IDG News Service about the other things computers have learned to do as well as, or better than, humans, and what that means for our future. What follows is an edited version of that conversation. IDGNS: Is it true that you and Deep Blue joined IBM at the same time?

My dad got me my first computer for Christmas when I was 11 years old. It was a Texas Instruments 99-4a. He must have gotten it second hand because it had no box or any form of storage like a tape drive. I had been obsessed with the movie War Games, so my first program on the TI was designed to replicate the computer in the movie which uttered the famous line "shall we play a game." My program gave static responses to a huge variety of programmed questions, and mostly followed the script from the movie.

On May 11, 1997, a computer showed that it could outclass a human in that most human of pursuits: playing a game. The human was World Chess Champion Garry Kasparov, and the computer was IBM's Deep Blue, which had begun life at Carnegie Mellon University as a system called ChipTest. One of Deep Blue's creators, Murray Campbell, talked to the IDG News Service about the other things computers have learned to do as well as, or better than, humans, and what that means for our future. What follows is an edited version of that conversation. IDGNS: Is it true that you and Deep Blue joined IBM at the same time?

What are the most interesting recent videos on YouTube about artificial intelligence (AI)? We save your time filtering mega-hours of videos uploaded each day to select the most relevant and popular ones, by view-count as of 1 May 2017. The description is as appeared at YouTube. This video shows that GeForce GTX G-Assist takes advantage of cutting-edge NVIDIA artificial intelligence to bring you the next revolution in gaming. This is a video for the first-ever entire songs composed by Artificial Intelligence: "Daddy's Car" and "Mister Shadow", created by scientists at SONY CSL Research Lab.

In 1999 he represented the Italian universities at the Platform Aerospace in Paris, in 2012 he won the Lamarck Prize at SMAU thanks to Leonardo Human Language Code, the embryonic version of XSENSE and last April he was speaker at TEDx in Rome. Let's get to know him better and meet him at BLAST 2017: book your pass now! Let's start from the beginning: who is Marco Menichelli? How did your passion for artificial intelligence start? Sincerely, I still do not know enough myself to say who I am.

Ladies and gentlemen, thank you for standing by, and welcome to Nuance's Second Quarter Fiscal 2017 Conference Call. At this time, all lines are in a listen-only mode. Later, there'll be an opportunity for your questions, and instructions will be given at that time. As a reminder, this conference is being recorded. With us today from Nuance are Chairman and CEO, Paul Ricci; CFO, Dan Tempesta; EVP of Corporate Strategy and Development, Bruce Bowden; and Director of Investor Relations, Christine Marchuska. Now, I would like to turn the call over to Ms. Marchuska. Before we begin, I remind, everyone, our discussion this afternoon includes predictions, estimates, expectations, and other forward-looking statements. These statements are subject to risks and uncertainties that could cause material differences in our actual results. Please refer to our recent SEC filings for a discussion of these risks.

Windows may dominate the worldwide desktop market, but Microsoft is working to adapt the operating system for all kinds of devices, from giant displays like the Surface Studio to mixed reality headsets like the HoloLens. The event comes after Microsoft recently debuted new products including Windows 10 S and the Surface Laptop . Harman Kardon also just revealed its new Invoke smart speaker, which is powered by Microsoft's Cortana virtual assistant. Terry Myerson, Microsoft's executive vice president of the Windows and devices group, sat down with TIME to discuss the future of Windows, Microsoft's hardware efforts, virtual reality, and more. Below is a transcript of our conversation edited for length and clarity.

In our recent post, we described our encounter with the Google Cloud Natural Language API. Let's see where we can find the desired information: With this analysis at our disposal, we make a few experiments to qualitatively compare the Google Natural Language API and the Stanford engine. Stanford CoreNLP also provides similar feature, allowing to perform entity linking of detected entities to their Wikipedia page. The Natural Language API returned "negative", "positive" and "positive" for these inputs.