Yahoo just announced the latest of its open-source releases, it's computer version code that detects pornography in images. The system detects whether a specific image contains pornography or not by using a special type of artificial intelligence called deep learning. It comprises educating artificial neural networks on lots of data (like porn images) and getting them to make a judgment about new data. Yahoo expects its neural network will authorize others to guard others against pornography, at the same time admitting its neural network isn't perfect because of the technology's very nature. "This model is a general purpose reference model, which can be used for the preliminary filtering of pornographic images," says a post on the Yahoo Engineering Tumblr.

I'm really a noob but.. Has anyone tried to model this using a vanilla convolutional network? And the third network you do the same, and it's 5 second time window. At the top of the third network one would sample the top most layer and convert to the next input's audio sample. I'm also thinking about a deconvolutional network, to deconvolute the top layer and then get many samples without needing to wait 90 minutes for sound generation. I've been trying to use the libraries, but I'm really new at the field and a windows user, so had no luck so far.

Given its potential, it's nagged researchers that getting one's eyes tracked wasn't easier. "It was quite shocking to me that we all don't have eye-trackers," says Aditya Khosla, a graduate student in the computer science and artificial intelligence laboratory of MIT's electrical engineering and computer science department. Khosla and a team of six other researchers from the University of Georgia and the Max Planck Institute of Informatics in Saarbruecken, Germany, set out to achieve a straightforward goal: create eye-tracking software that could run on any mobile phone with a camera.

I'm starting to tinker with Torch and I've come across some difficulties in figuring out how to combine layers with the'rnn' and'nn' libraries. I've spent a couple of days on this and I wasn't sure where else I could post to get some answers, but I apologize in advance is this isn't the appropriate place for Torch Questions (I've also posted on Stack Overflow). I have both text and non-text data, so I'd like to combine an LSTM layer (for the text data) and a hidden layer from an MLP (for the non-text data) into a larger MLP to predict my outcome. Here's where things go wrong, the last line fails, so my hack was to combine the predictions after piping it through the first two models, which is what I do below. I'm not sure where to go from here so any help from all you would be sincerely appreciated.

Major technology firms have joined forces in a partnership on artificial intelligence, aiming to cooperate on "best practices" on using the technology "to benefit people and society." Microsoft, Amazon, Google, Facebook, IBM, and Google-owned British AI firm DeepMind on Wednesday announced a non-profit organization called "Partnership on AI" focused on helping the public understand the technology and practices in the field. The move comes amid concerns that new artificial intelligence efforts could spin out of control and end up being detrimental to society. The companies "will conduct research, recommend best practices, and publish research under an open license in areas such as ethics, fairness, and inclusivity; transparency, privacy, and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability, and robustness of the technology," according to a statement. Academics, non-profit groups, and specialists in policy and ethics will be invited to join the board of the Partnership on Artificial Intelligence to Benefit People and Society (Partnership on AI).

Amsterdam, September 29, 2016 – One of the world's first automated inspections by an intelligent drone with deep learning capabilities was demonstrated at the GPU Technology Conference Europe today by Aerialtronics, a Dutch manufacturer of technologically advanced drones, Neurala, a pioneer in deep learning software, and NVIDIA, the world leader in GPU-accelerated computing. This new "intelligent drone" identifies objects and their condition in flight, which dramatically increases the efficiency and accuracy of documenting assets, lowering costs, and making it easier for frequent inspections. It adds to the use of commercial drones to help businesses access difficult and dangerous areas, such as cell towers and turbines. The resulting system can visually inspect a cell tower and recognize the equipment mounted on the mast. This is the first step required to start automating the documentation of assets, and assessing the mechanical functionality and condition of the cell tower to identify rust and other defects.

In this post, I propose that IoT analytics should be a part of'Smart objects' and discuss the implications of doing so The term'Smart objects' has been around from the times of Ubiquitous Computing. However, as we have started building Smart objects, I believe that the meaning and definition has evolved. Some of these analytics could be performed on the device itself ex computing at the edge concept from Intel, Cisco and others. To manage multiple sensor feeds, we need to understand concepts like sensor fusion (pdf) (source freescale). In addition, the rise of CPU capacity leads to greater intelligence on the device – for example Qualcomm Zeroth platform which enables Deep learning algorithms on ... So, in a nutshell, its a evolving concept especially if we include IoT analytics in the definition of Smart objects (and that some of these analytics could be performed at the Edge) ..

A competition pitting artificial intelligence (AI) against human players in the classic video game Doom has demonstrated just how advanced AI learning techniques have become – but it's also caused considerable controversy. While several teams submitted AI agents for the deathmatch, two students in the US have caught most of the flak, after they published a paper online detailing how their AI bot learned to kill human players in deathmatch scenarios. The computer science students, Devendra Chaplot and Guillaume Lample, from Carnegie Mellon University, used deep learning techniques to train their AI bot – nicknamed Arnold – to navigate the 3D environment of the first-person shooter Doom. By effectively playing the game over and over again, Arnold became an expert in fragging its Doom opponents – whether they were other artificial combatants, or avatars representing human players. While researchers have previously used deep learning to train AIs to master 2D video games and board games, the research shows that the techniques now also extend to 3D virtual environments.

This post aims to cram in a synopsis of Jeremy Howard's talk at the inaugural Data Science Melbourne MeetUp at Inspire9 in Richmond on 12th May so may be a little disjointed in it's flow. Jeremy freestyled his delivery once he had established from the members pretty quickly with a show of hands what it was he should be talking about. There is a lack of intelligence from computers and data and what is at stake is not the proof of things we already know but knowing about the things we did not think of from data or what we should be questioning. This is where machine learning asks the computer to come up with some of the intelligence for you. Using machine learning to find the interesting insights and adding value is the huge appeal Jeremy finds in machine learning and to explain this, he kicked off with talking about Arthur Samuel who essentially came up with machine learning and invented what appeared to be the world's first self-learning program.

Inc. (AMZN) have come together to form a non-profit partnership for artificial intelligence. Called the "Partnership on Artificial Intelligence to Benefit People and Society," the venture is intended to promote understanding of artificial intelligence in the public domain and, also, is a means for the companies to collaborate and publish research regarding the technology under an open license. The list of topics that it intends to cover is broad and range from ethics and fairness in AI to interoperability and collaboration between people and AI systems. Tesla Motors Corp. (TSLA) CEO Elon Musk's organization OpenAI and Apple Inc. (AAPL) are notable absentees from the list of organizations in this partnership. With its Siri chatbot that assists smartphone users, Apple is an AI pioneer and is said to be ramping up its investments in this space.