Cognitive Space is building the intelligent infrastructure of the New Space domain. Join our highly dedicated team of engineers, data scientists, and domain experts in reshaping the future of space. At Cognitive Space, we value passion, curiosity and a drive to succeed. We take ownership of our work, and work as a team to solve difficult problems while not letting our egos get in the way. Our three guiding principles are: seek excellence, foster engagement, and build trust.

Cognitive Space announced the highlights of a very successful year in its mission to dramatically improve the way we monitor the Earth for economic, environmental, and national security understanding. The company helps organizations fly their satellites with new tools for New Space – providing satellite operators and space infrastructure companies with sophisticated SaaS services for optimizing revenue and performance yield, forecasting future capacity, and orchestrating collection management as satellite constellations grow and scale. "The New Space economy is attracting massive investment and is growing exponentially. Space will be filled with thousands of new commercial satellites," said Scott Herman, CEO of Cognitive Space. "But building out the required ground architecture is a major hurdle for New Space companies and usually represents a significant monetary investment, a multi-year time commitment, and major execution risk as they build their business. Cognitive Space provides a blueprint and an operational capability that de-risks and accelerates their buildout schedule, controls costs, and then optimizes their ongoing operations to power their business vision."

There was a time not so long ago that space was known as the final frontier--the exclusive domain of governments and a small handful of aerospace companies who could muster the technology and resources to depart the Earth's atmosphere. Today, however--similar to what we've observed with technologies like artificial intelligence and quantum computing that were once accessible only to universities and research labs--space technologies are being democratized thanks in part to the cloud. I closed out last year with a prediction that space will be the area where we see some of the greatest advancements when it comes to novel application of cloud capabilities. Now, only six months later, a new crop of space pioneers are preparing to supercharge their efforts with cloud technology. Within the emerging commercial space industry, where it's feasible for even small startups to make a big impact by introducing innovative new space technologies, the cloud will be critical to accelerating experimentation, expanding automation, and delivering deeper insights. Getting to this point of expanded commercial activity was no accident.

In 2014, the Swedish philosopher and cognitive scientist Peter Gärdenfors went to Krakow, Poland, for a conference on the mind. He was to lecture at Jagiellonian University, courtesy of the Copernicus Center for Interdisciplinary Studies, on his theory of conceptual, or "cognitive," spaces. Gärdenfors had been working on his idea of cognitive spaces, which explain how our brains represent concepts and objects, for decades. In his book Conceptual Spaces, from 2000, he wrote, "It has long been a common prejudice in cognitive science that the brain is either a Turing machine working with symbols or a connectionist system using neural networks." In Krakow, Gärdenfors pushed against that prejudice. In his talk, "The Geometry of Thinking," he suggested that humans are able to do things that today's powerful computers can't do--like learn language quickly and generalize from particulars with ease (to see, in other words, without much training, that lions and tigers are four-legged felines)--because we, unlike our computers, represent information in geometrical space.

I am more than happy to see that after the full hype period where everybody was talking about AI and machine learning as the solution for all the problems of the world (with the sky as the only limit), intelligent and honest persons/experts are publishing more and more articles where the things are described as they are and the expectations are set in a correct way. It took more than expected for this to happen but it was inevitable and only a matter of time: as Lincoln said "You can fool all the people some of the time, and some of the people all the time, but you cannot fool all the people all the time". Out of the many articles that have been published recently, let me link this one that is clear, short and that can be understood by nearly everyone. There are some statements that are very important to highlight because they explain very clearly that ML techniques can be useful (very useful in a few specific cases) but are much more limited and simple than many like to think and that they still require a huge amount of work and perspiration (sorry but also here there are no free lunches:-). Despite evocative names like "artificial intelligence," "machine learning" and "neural networks," such technologies have little to do with human thought or intelligence.

In this era of digitization, knowing the user's sociolect aspects have become essential features to build the user specific recommendation systems. These sociolect aspects could be found by mining the user's language sharing in the form of text in social media and reviews. This paper describes about the experiment that was performed in PAN Author Profiling 2017 shared task. The objective of the task is to find the sociolect aspects of the users from their tweets. The sociolect aspects considered in this experiment are user's gender and native language information. Here user's tweets written in a different language from their native language are represented as Document - Term Matrix with document frequency as the constraint. Further classification is done using the Support Vector Machine by taking gender and native language as target classes.