Gartner, a world leading research company, predicts that by 2020 AI … through advances in artificial intelligence, automation and interconnectedness.

Billions of text posts, photos, and videos are uploaded to social media every day, a firehose of information that's impossible for human moderators to sift through comprehensively. And so companies like Facebook and YouTube have long relied on artificial intelligence to help surface things like spam and pornography. Something like a white supremacist meme, though, can be more challenging for machines to flag, since the task requires processing several different visual elements at once. Automated systems need to detect and "read" the words that are overlaid on top of the photo, as well as analyze the image itself. Memes are also complicated cultural artifacts, which can be difficult to understand out of context. Despite the challenges they bring, some social platforms are already using AI to analyze memes, including Facebook, which this week shared details about how it uses a tool called Rosetta to analyze photos and videos that contain text.

Boasting a 6.5inch OLED screen, the iPhone XS Max is the biggest phone Apple has ever made. While on paper the screen may seem almost too big, the curved design means that in the flesh, it's incredibly easy to pick up and handle - and absolutely stunning to look at. There is something of a jaw dropping moment the first time you load up a picture of press play on the movie. Apple's new iPhone XS Max is shown (right) beside the smaller XS That screen is sharp and incredibly bright, and the phone (and even the still present notch) are almost invisible, making this the closest thing yet to Apple's'all screen' vision. The sound has also had a huge boost, and even in the crowded scrum like experience of the hands on area at Apple's event, audio was loud enough to hear clearly - if you want to annoy the neighbours of fellow commuters, this is definitely the speaker system to do it with.

Many papers on generative models report the log-marginal likelihood in order to quantitatively compare different generative models. Since the log-marginal likelihood is intractable, the Importance Weighted Autoencoder (IWAE)'s bound is commonly reported instead. I don't understand how the bound is computed. I assume that the IWAE is first trained on the dataset and then some synthetic samples from the model in question are used to compute the marginal LL bound. However, I am not entirely sure about the procedure.

Facebook is expanding its fake news spotting systems to include photos and videos as part of its ongoing battle to halt the spread of misinformation on its service. Following successful trials in France, India, and Mexico, the company said it will now roll-out the system in 17 countries worldwide in a bid to staunch what it has branded'misinformation in these new visual formats.' The Artificial Intelligence (AI) system feeds potentially fake content to human fact-checkers, who use visual verification techniques such as reverse image searching and analysing image metadata to check the veracity of photos and videos. Previously, the company's efforts to tackle misinformation had been focused on rooting out false articles and webpage links. Russian agents and other malicious groups seeking to influence democratic elections in the US and elsewhere have repeatedly used images and video.

What will happen as we enter the era of human augmentation, artificial intelligence and government-by-algorithm? James Barrat, author of Our Final Invention, said: "Coexisting safely and ethically with intelligent machines is the central challenge of the twenty-first century." A lot of folks are earnestly exploring the topic. "Will scientists soon be able to create supercomputers that can read a newspaper with understanding, or write a news story, or create novels, or even formulate laws?" asks J. Storrs Hall in Beyond AI: Creating the Conscience of the Machine (2007). "And if machine intelligence advances beyond human intelligence, will we need to start talking about a computer's intentions?" Sharing this concern, SpaceX/Tesla entrepreneur Elon Musk has joined with Y Combinator founder Sam Altman to establish OpenAI, an endeavor that aims to keep artificial intelligence research -- and its products -- accountable by maximizing transparency and openness. Among the most-worried is Swiss author Gerd Leonhard, whose new book Technology Vs. Humanity: The Coming Clash Between Man and Machine, coins an interesting term, "androrithm," to contrast with the algorithms that are implemented in every digital calculating engine or computer. Some foresee algorithms ruling the world with the inexorable automaticity of reflex, and Leonhard asks: "Will we live in a world where data and algorithms triumph over androrithms…i.e., all that stuff that makes us human?"

Three US Representatives have sent a letter to the Director of National Intelligence asking for a report on deepfake technology, how it could be used to harm the US and any countermeasures that can be taken to detect and deter nefarious use of the technology. While deepfakes gained notoriety when Reddit users began swapping celebrity faces onto porn stars, the potential for the technology's use in misinformation campaigns has generated a fair amount of concern. "Forged videos, images or audio could be used to target individuals for blackmail or for other nefarious purposes," the lawmakers said in their letter. The added, "Of greater concern for national security, they could also be used by foreign or domestic actors to spread misinformation. As deep fake technology becomes more advanced and more accessible, it could pose a threat to United States public discourse and national security, with broad and concerning implications for offensive active measures campaigns targeting the United States."

In sentiment analysis (SA) of product reviews, both user and product information are proven to be useful. Current tasks handle user profile and product information in a unified model which may not be able to learn salient features of users and products effectively. In this work, we propose a dual user and product memory network (DUPMN) model to learn user profiles and product reviews using separate memory networks. Then, the two representations are used jointly for sentiment prediction. The use of separate models aims to capture user profiles and product information more effectively. Compared to state-of-the-art unified prediction models, the evaluations on three benchmark datasets, IMDB, Yelp13, and Yelp14, show that our dual learning model gives performance gain of 0.6%, 1.2%, and 0.9%, respectively. The improvements are also deemed very significant measured by p-values.