In this note, I develop my personal view on the scope and relevance of symbolic computation in software science. For this, I discuss the interaction and differences between symbolic computation, software science, automatic programming, mathematical knowledge management, artificial intelligence, algorithmic intelligence, numerical computation, and machine learning. In the discussion of these notions, I allow myself to refer also to papers (1982, 1985, 2001, 2003, 2013) of mine in which I expressed my views on these areas at early stages of some of these fields. It is a great joy to see that the SCSS (Symbolic Computation in Software Science) conference series, this year, experiences its 9th edition. A big Thank You to the organizers, referees, and contributors who kept the series going over the years! The series emerged from a couple of meetings of research groups in Austria, Japan, and Tunisia, including my Theorema Group at RISC, see the home pages of the SCSS series since 2006. In 2012, we decided to define "Symbolic Computation in Software Science" as the scope for our meetings and to establish them as an open conference series with this title. As always, when one puts two terms like "symbolic computation" and "software science" together, one is tempted to read the preposition in between - in our case "in" - as just a set-theoretic union. Pragmatically, this is reasonable if one does not want to embark on scrutinizing discussions. However, since I was one of the initiators of the SCSS series, let me take the opportunity to explain the intention behind SC in SS in this note. Also, this note, for me, is a kind of revision and summary of thoughts I had over the years on the subject of SCSS and related subjects.

Algorithms were around for a very long time before the public paid them any notice. The word itself is derived from the name of a 9th-century Persian mathematician, and the notion is simple enough: an algorithm is just any step-by-step procedure for accomplishing some task, from making the morning coffee to performing cardiac surgery. Computers use algorithms for pretty much everything they do -- adding up a column of figures, resizing a window, saving a file to disk. But all those things usually just happen the way they're supposed to. We don't have to think about what's going on under the hood. But algorithms got harder to ignore when they started taking over tasks that used to require human judgment -- deciding which criminal defendants get bail, winnowing job applications, prioritizing stories in a news feed.

Computers use algorithms to do everything from adding up a column of figures to resizing a window. Computers use algorithms to do everything from adding up a column of figures to resizing a window. Algorithms were around for a very long time before the public paid them any notice. The word itself is derived from the name of a 9th-century Persian mathematician, and the notion is simple enough: an algorithm is just any step-by-step procedure for accomplishing some task, from making the morning coffee to performing cardiac surgery. Computers use algorithms for pretty much everything they do -- adding up a column of figures, resizing a window, saving a file to disk.

Every company is now a data company, capable of using machine learning in the cloud to deploy intelligent apps at scale, thanks to three machine learning trends: data flywheels, the algorithm economy, and cloud-hosted intelligence. That was the takeaway from the inaugural Machine Learning / Artificial Intelligence Summit, hosted by Madrona Venture Group* last month in Seattle, where more than 100 experts, researchers, and journalists converged to discuss the future of artificial intelligence, trends in machine learning, and how to build smarter applications. With hosted machine learning models, companies can now quickly analyze large, complex data, and deliver faster, more accurate insights without the high cost of deploying and maintaining machine learning systems. "Every successful new application built today will be an intelligent application," Soma Somasegar said, venture partner at Madrona Venture Group. "Intelligent building blocks and learning services will be the brains behind apps."

"Drawing on your phone or computer can be slow and difficult -- so we created AutoDraw, a new web-based tool that pairs machine learning with drawings created by talented artists to help you draw," wrote Google Creative Lab's "creative technologist," Dan Motzenbecker, earlier this week. AutoDraw is one of Google's artificial intelligence (AI) experiments, working across platforms to let anyone, irrespective of their artistic flair, create something super quick with little more than a scribble. It guesses what you're trying to draw, then lets you pick from a list of previously created pictures. No worries!" is the general idea here. First up, AutoDraw is a super fun tool that gets increasingly addictive -- that much is clear. But what's also clear is that the tool is more a display of AI smarts than it is a tool to improve your artwork, because it would be just as easy to embody the exact same functionality within a text-based search engine. I mean, why bother drawing a crap dolphin ...

Every company is now a data company, capable of using machine learning in the cloud to deploy intelligent apps at scale, thanks to three machine learning trends: data flywheels, the algorithm economy, and cloud-hosted intelligence. That was the takeaway from the inaugural Machine Learning / Artificial Intelligence Summit, hosted by Madrona Venture Group* last month in Seattle, where more than 100 experts, researchers, and journalists converged to discuss the future of artificial intelligence, trends in machine learning, and how to build smarter applications. With hosted machine learning models, companies can now quickly analyze large, complex data, and deliver faster, more accurate insights without the high cost of deploying and maintaining machine learning systems. "Every successful new application built today will be an intelligent application," Soma Somasegar said, venture partner at Madrona Venture Group. "Intelligent building blocks and learning services will be the brains behind apps."

Every company is now a data company, capable of using machine learning in the cloud to deploy intelligent apps at scale, thanks to three machine learning trends: data flywheels, the algorithm economy, and cloud-hosted intelligence. That was the takeaway from the inaugural Machine Learning / Artificial Intelligence Summit, hosted by Madrona Venture Group* last month in Seattle, where more than 100 experts, researchers, and journalists converged to discuss the future of artificial intelligence, trends in machine learning, and how to build smarter applications. With hosted machine learning models, companies can now quickly analyze large, complex data, and deliver faster, more accurate insights without the high cost of deploying and maintaining machine learning systems. "Every successful new application built today will be an intelligent application," Soma Somasegar said, venture partner at Madrona Venture Group. "Intelligent building blocks and learning services will be the brains behind apps."

Thanks to an abundance of digital data, and powerful GPUs, we are now capable of teaching computers to read, see, and hear. Just this year, a handful of high-profile experiments came into the spotlight, including Microsoft Tay, Google DeepMind AlphaGo, and Facebook M. These experiments all relied on a technique known as deep learning, which attempts to mimic the layers of neurons in the brain's neocortex. This idea – to create an artificial neural network by simulating how the neocortex works – has been around since the 1980s. During the training process, the algorithm learns to discover useful patterns in the digital representation of data, like sounds and images.

At Algorithmia, we believe in democratizing access to state-of-the-art algorithmic intelligence. That's why we're introducing a solution for hosting and distributing trained deep learning models on Algorithmia using GPUs in the cloud. Today, researchers and developers can train their neural nets locally, and deploy them to Algorithmia's scalable, cloud infrastructure, where they become smart API endpoints for other developers to use. We're excited to announce initial native support for the Caffe, Theano, and TensorFlow frameworks, and have added 16 open source deep learning models that run as microservices to start. Support for Torch and MxNet are coming soon.

Every company is now a data company, capable of using machine learning in the cloud to deploy intelligent apps at scale, thanks to three machine learning trends: data flywheels, the algorithm economy, and cloud-hosted intelligence. That was the takeaway from the inaugural Machine Learning / Artificial Intelligence Summit, hosted by Madrona Venture Group* last month in Seattle, where more than 100 experts, researchers, and journalists converged to discuss the future of artificial intelligence, trends in machine learning, and how to build smarter applications. With hosted machine learning models, companies can now quickly analyze large, complex data, and deliver faster, more accurate insights without the high cost of deploying and maintaining machine learning systems. "Every successful new application built today will be an intelligent application," Soma Somasegar said, venture partner at Madrona Venture Group. "Intelligent building blocks and learning services will be the brains behind apps."