Neural Information Processing Systems http://nips.cc/

Summary and Contributions: The paper presents an algorithm for k-clustering in a sliding window streaming model, where k-clustering means the generalization of k-median and k-means to any fixed l_p-norm. The main theoretical result is an algorithm that achieves O(1)-approximation for points in arbitrary metric space and thus includes the prevalent of Euclidean metric, which is also used in the experimental evaluation. This algorithm is for sliding window streaming, where the algorithm repeatedly solves the clustering problem on the w most recent points in the stream (for parameter w). While the minimal requirement is to estimate the cost of a k-clustering, this algorithm also reports k center points. The usual motivation for this model is to allow old data to expire, and analyze only recent data.

Online algorithms is a field that deals with algorithmic problems in which the input is not entirely known in advance, but rather arrives in a sequential way. The algorithm is required to make irrevocable decisions, only based on the input received at a given point, and to incur the corresponding irrevocable cost for each of them. Traditionally, in the analysis of online algorithms we assume, rather pessimistically, that an adversary always presents the algorithm with the worst case input. More precisely, the performance of online algorithms is evaluated by the competitive ratio [5], which is the worst-case ratio of total algorithm's cost to the cost of a computationally unlimited optimal algorithm that is aware of the entire request sequence in advance. On the other hand, one of the main goals in the field of machine learning is to predict the unknown based on data, and learn what the world looks like, rather than preparing for the worst that can happen. In an effort to exploit this, there has been a trend in recent years that tries to use machine learning predictions in order to deal with the inherent uncertainty in online algorithms, while still providing worst case performance guarantees. Specifically, one might think that directly using machine learning in online problems should enhance their performance, since by knowing, with some error, how the input looks like, we should be able to come up with almost optimal solutions/approximations. In reality, this turns out not to be true, since the error of the learner does not necessarily remain constant, and it could propagate during different phases of the algorithm and cause a much bigger error. In a recent work, Lykouris and Vassilvitski [4] tried to formulate a framework to provide formal guarantees for these learning augmented online algorithms, in terms of consistency and robustness.

Metaverse is one of the hottest buzzwords of the moment. It's basically a virtual world created by combining different technologies, including virtual and augmented reality. While it doesn't technically exist yet, companies like Facebook hope the metaverse will become a place where we go to meet, work, play, study and shop. This'extended reality' is predicted to be the next evolution of the internet and will blur the lines between physical and digital life. Think in-game purchases, where computer gamers can buy virtual goods and services using real money. Jobs in the metaverse might include personalised avatar creator or metaverse research scientist.

On Wednesday, October 13 at 8:30am CT, William Shatner, who starred as Captain Kirk in Star Trek: The Original Series, will be going where no Hollywood star has gone before -- a suborbital sojourn that will take him 66 miles to the edge of space where he will be able to marvel at the curvature of earth and enjoy zero gravity weightlessness with his Blue Origin crew members. The entire experience is expected to last about 10 minutes and will be similar to the ride that Blue Origin and Amazon founder Jeff Bezos took this past summer with his brother and crew. Shatner might be the oldest person to rocket to space at 90 years old, but he's not the first actor to go. Feature filmmakers from Russia landed on the International Space Station last week beating Tom Cruise to bragging rights. The actor has been working on a $200 million Universal Studios film with SpaceX founder Elon Musk which NASA tweeted last year is expected to be shot on the space station.

While our political leaders rail against some of the effects of globalisation our job market could be at a point of no return when it comes to technology in the workplace. Our reliance on technology, and new innovations are just some of the reasons why top economists designated 2016 as a tipping point year for technological advancement and the impact it can have. A recent report by Sodexo estimates that as many as two million jobs will be created by robots in the next eight years. Other reports, however, estimate that twice as many jobs could be lost by advances in automation. But there is so much more to future jobs than just robots – here are some of the careers that the next generation could find themselves in.

From winning Jeopardy in 2011 to helping write a sad song last year, IBM's Watson cognitive computing platform is all over popular culture. Press releases fly out about Watson producing a movie trailer, powering a Macy's shopping app, even controlling lights on an internet-connected dress -- along with more serious applications like working on cancer treatments. It seems, from IBM's hype, that Watson can do everything. But Bernie Meyerson, IBM's chief innovation officer, wants to dial back the hype in some ways, calling Watson "just the first step on a very, very long road." Watson can be helpful in a lot of industries, such as medicine, which are awash in data, but it can't replace people, he says.

From winning Jeopardy in 2011 to helping write a sad song last year, IBM's Watson cognitive computing platform is all over popular culture. Press releases fly out about Watson producing a movie trailer, powering a Macy's shopping app, even controlling lights on an internet-connected dress--along with more serious applications like working on cancer treatments. It seems, from IBM's hype, that Watson can do everything. But Bernie Meyerson, IBM's chief innovation officer, wants to dial back the hype in some ways, calling Watson "just the first step on a very, very long road." Watson can be helpful in a lot of industries, such as medicine, which are awash in data, but it can't replace people, he says.

The World Economic Forum recently published its 2016 list of the Top Ten Emerging Technologies that will likely have the greatest impact on the world in the years to come. The list is compiled by the WEF Meta-Council on Emerging Technologies, a panel of global experts led by Dr. Bernard Meyerson, IBM Fellow and Chief Innovation Officer. "Horizon scanning for emerging technologies is crucial to staying abreast of developments that can radically transform our world, enabling timely expert analysis in preparation for these disruptors," said Dr. Meyerson. "The global community needs to come together and agree on common principles if our society is to reap the benefits and hedge the risks of these technologies." The technologies on the list are not new.

The World Economic Forum has put together a list of the top 10 emerging technologies that will change our lives. The list includes nanosensors that will circulate through the human body, a battery that will be able to power an entire town and socially aware artificial intelligence that will track our finances and health. These are not far-flung visions, according to the forum. They are technologies that are on the cusp of having a meaningful impact. "Horizon scanning for emerging technologies is crucial to staying abreast of developments that can radically transform our world, enabling timely expert analysis in preparation for these disruptors," said Bernard Meyerson, chairman of the World Economic Forum council that compiled the list of the top 10 emerging technologies in 2016.