The ability to argue, to express our reasoning to others, is one of the defining features of what it is to be human. Processes of argumentation run our governments, structure scientific endeavour and frame religious belief. So should we worry that new advances in artificial intelligence are taking steps towards equipping computers with these skills? As technology reshapes our lives, we are all getting used to new ways of working and new ways of interacting. Millennials have known nothing else.

Marco Ribeiro, a graduate student at the University of Washington in Seattle, probes DNNs using what are called "counterfactual probes" -- a trick related to that used by the MIT researchers to spoof DNNs. The idea is to give the DNN inputs carefully varied over a wide range, and see which way the outputs of the DNN jump. For example, consider a neural network that reads in the text of movie reviews, and then flags those that give a movie a thumb's-up. To do this, the DNN would first be trained by being given reviews flagged as being positive, plus reviews flagged as being negative, with the DNN's ability then using the training from these examples to flag reviews appropriately itself.

I am currently of the opinion that unbounded receptive field of RNNs is often a curse, have tried many models where hard truncation (resetting the memory) at a fixed or even random interval was important to get it to actually work in generation. I think a lot of what people care about in generative models are more like "medium term" dependencies (more exactly, do true "long term dependencies" exist? At least one case in particular here is burned in my brain forever. Hierarchies are often useful, whether you get it from multiple RNNs and skip connections, HM-RNN, SampleRNN, fixed interval hidden passing from a fast RNN to "slow" one, WaveNet style dilated convolutions, or in more roundabout ways using trees, memory, stacks, etc. One really interesting part of these convolutional generative models was pointed out to me by Laurent Dinh, I mention it in this review of PixelRNN/CNN - growing the dependency chain over depth makes tons of sense for a lot of problems, and is a general idea that is useful in a ton of domains.

That's my summary anyway, and while I understand if you feel like I'm glossing over one or two important events, I would ask you to remember what those important events actually were. Best to concentrate on those bots, a least for a little while. Shitty robots will forever have a special place in my heart because they remind me of myself, a shitty human. YouTuber Simone Giertz has had this genre sewed up since 2016, but my personal favorite shit-bot this year was a grubby specimen that lives in a pizza box and uses facial recognition to shoot lasers into the eyes of anyone it sees. God bless the Knightscope K5, for it is truly a trailblazer in human-robot relations.

The practice of data science and machine learning capabilities are increasingly being adopted across a wide range of industries and applications. The challenges in data analytics are now being addressed by machine learning.

Christmas Eve is a busy time for some, but for others, it's a day to relax and watch TV. Luckily, there are plenty of Christmas movies and TV specials to watch on Dec. 24. Marathons are always big on the holidays. The first three "Home Alone" movies will air back-to-back on Encore all day long on Christmas Eve. Meanwhile, CMT will kick off their "Love Actually" movie loop at noon.

The NORAD Tracks Santa website, NoradSanta.org, also features a mobile version. This year, you can ask Alexa, Amazon's artificial intelligence software, for fun facts about Santa as well as where he is and when he'll be over your house.

Recently it seems that every time you open your browser or scroll through a news feed, some people are writing articles about machine learning and its influence on our lives. Machine learning has been highlighted in articles covering everything from Virtual Assistant to androids and cars on autopilot that can accomplish the same tasks as humans. A number of huge companies are determining machine learning as'the future' -- but what we should really expect from that? Machine learning is a core subfield of artificial intelligence, that enables computers to get into a form of self-learning without being explicitly programmed. These programs are enabled to learn, develop, change, and grow by themselves when exposed to a new data.

The most terrifying film of the year didn't come from Hollywood. It came from a think tank looking to save us all from killer machines. In the movie's near future dystopia, palm sized drones loaded up with explosives use facial recognition to hunt down and slaughter people with pin point precision. Swarms of the micro murderers tear through congress, massacring Senators based on ideology. Terrorists unleash a horde of the flying monsters into schools to take out the kids of parents who dare to speak out against the threat. What makes it all so horrifying is how close we are to making it a reality.

It is impossible to escape artificial intelligence in the fields with which human mind and talents cannot keep up. In Turkey, we have important scientists specializing in artificial intelligence, enriching the academic work of their universities and creating economic value through their enterprises.