For this poll we've created a list of the greatest AI of all time, featuring AI characeters you know from movies, television, video games and more. Characters like Hal from Space Oddessy 2001 have provided us with some extremely memorable movie lines, such as the famous "I'm sorry Dave, I'm afraid I can't do that." If your favorite fictional artificial intelligence is missing, don't be salty- just add it to the list so that other people can vote for for it too! What is the most popular artificial intelligence that people just can't get enough of? Scroll down and find out who the masses voted for.

Research into word embeddings is one of the most interesting in the deep learning world at the moment, even though they were introduced as early as 2003 by Bengio, et al. Most prominently among these new techniques has been a group of related algorithm commonly referred to as Word2Vec which came out of google research.[2] In this report we are going to investigate the significance of Word2Vec for NLP research going forward and how it relates and compares to prior art in the field. In particular we are going to look at some desired properties of word embeddings, two generally popular approaches centered around the concept of a Bag of Words (which in the following we shall simply refer to as BoW), namely Latent Semantic Analysis and explore its shortcomings. This shall motivate a detailed exposition of how and why Word2Vec works and whether the word embeddings derived from this methodology can remedy some of the shortcomings of BoW based approaches.

Welcome to Fresh Machine Learning! This is my new course dedicated to making bleeding edge machine learning accessible to developers everywhere. I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ I introduce two papers in this video Human Level Concept Learning through Probabilistic Program Induction: http://web.mit.edu/cocosci/Papers/Sci... and it's associated code is in MatLab: https://github.com/brendenlake/BPL I found a great alternative though, these guys are really close to finishing this python library.

You only look once (YOLO) is a state-of-the-art, real-time object detection system. On a Titan X it processes images at 40-90 FPS and has a mAP on VOC 2007 of 78.6% and a mAP of 44.0% on COCO test-dev. Prior detection systems repurpose classifiers or localizers to perform detection. They apply the model to an image at multiple locations and scales. High scoring regions of the image are considered detections.

AI's strongest applications are data-hungry. Pioneers in the field, such as Facebook, Google, and Uber, have each secured a "privileged zone" by gaining access to current and future data, the raw material of AI, from their users and others in ways that go far beyond traditional data harvesting. Their scale gives them the ability to run more training data through their algorithms and thus improve performance. In the race to leverage fully functional self-driving cars, for example, Uber has the advantage of collecting 100 million miles of fleet data daily from its drivers. This data will eventually inform the company's mobility services.

MEPs have called for the adoption of comprehensive rules for how humans will interact with artificial intelligence and robots. The report makes it clear that it believes the world is on the cusp of a "new industrial" robot revolution. It looks at whether to give robots legal status as "electronic persons". Designers should make sure any robots have a kill switch, which would allow functions to be shut down if necessary, the report recommends. Meanwhile users should be able to use robots "without risk or fear of physical or psychological harm", it states.

Hot topics like "big data", "machine learning", "data science" are now dominating in the scientific community. In the past 10 years alone, data availability has increased exponentially (and not even in a squared, or cubed sort of way… we are talking on the order of 1010 if not more). Exabytes (1018 or one QUINTILLION bytes!!?) of information are being passed, stored, saved and analyzed on a monthly (perhaps weekly?) basis. This includes credit card transactions (in November 2015, there were approximately 242 million credit card transactions in the United Kingdom alone;(source: BBA)), web searches (Just think about how many times you use Google in the run of a day, and interpolate that out to the 40% of the world who have access to the internet), and any time a user (you) clicks on a link you found on Facebook. When you combine this with the countless other data coming in, it is nearly overwhelming to think about.

Linear Regression is about fitting a straight line from the scatter plot,key challenge here what constitutes a best fit line in other words what would be best values of and . The general idea is to find a line ( its coefficients) such that total error is at the minimum. There is a standard explanation that we need to minimize the total square error, which means we have to solve a minimization problem to solve optimal values of the coefficients. Obviously this method involves quite a lot of mathematics or calculus etc. which would not provide any institution or illustration, instead we will use a little of vector algebra and associated geometry to build the intuition about the solution.

If manufacturers want to sustain and grow their customer bases in a competitive environment, their products need to fulfill increasingly high quality and reliability standards. Automakers, for example, now have a target defect rate for the integrated systems of less than 1 percent. That's putting pressure on the original equipment makers (OEMs) and their suppliers who have to meet these targets at the same time that products and manufacturing processes are becoming increasingly complex and featuring numerous activities that impact quality, performance, and yield. To prevent failures of components, systems, and ultimately the product, these manufacturers need reliable methods to find defects. But quality control today is, in many cases, still performed by human inspectors, which limits its reliability and efficiency.

Deals to AI startups reached a record high last year, from 160 deals in 2012 to 658 in 2016. Although non-US deal share has been increasing over the time period, well over a majority of deals, around 70%, went to startups in the US in the last 5 years. The top 3 states for deals were California (51%), New York (11%), and Massachusetts (9%). Using the CB Insights database, we mapped the most well-funded AI startups in each of the 35 states where there have been equity deals to an AI company in the last five years. The map only includes companies that have not exited and only includes equity financing.