Deep fakes are "videos that have been constructed to make a person appear to say or do something that they never said or did. Many commentators worry that voters will be influenced by pure fiction: As the 2020 US election looms, we learn of the fight to stay ahead (CNN, April 26, 2019) of the "growing threat" (ABC Eyewitness News, May 7, 2019) because "The 2020 campaigns aren't ready for deepfakes" (Axios/HBO, June 4, 2019). We Can No Longer Believe What We See, the New York Times warned yesterday. Under the right set of circumstances, deepfakes will be very influential. They don't even have to be particularly good to potentially swing the outcome of an election. As with so much in elections, deepfakes are a numbers game. While the presence of tampering in all but the most sophisticated deepfakes can be quickly identified, not everyone who views them will get that message. More fundamentally, not everyone wants to get that message. As can occur with other forms of online ...

AI is rapidly changing everything. It is transforming society, the very way we live and act as social creatures, how we behave, how we do business, and even the very fabric of our own human identity. Cities are managed by machine driven big data gathered by sensors, constructing together what we known as the Internet of Things. An escalating digital transformation is transforming our vast amounts of paper ledgers into digital records, from traffic to finance to medical records. These, which convey most of the data gathered about us, are now processed in the cloud and augmented with machine learning artificial intelligence's multiple tools, which are stored in blockchain distributed ledgers.

This thesis presents new methods for unsupervised learning of distributed representations of words and entities from text and knowledge bases. The first algorithm presented in the thesis is a multi-view algorithm for learning representations of words called Multiview Latent Semantic Analysis (MVLSA). By incorporating up to 46 different types of co-occurrence statistics for the same vocabulary of english words, I show that MVLSA outperforms other state-of-the-art word embedding models. Next, I focus on learning entity representations for search and recommendation and present the second method of this thesis, Neural Variational Set Expansion (NVSE). NVSE is also an unsupervised learning method, but it is based on the Variational Autoencoder framework. Evaluations with human annotators show that NVSE can facilitate better search and recommendation of information gathered from noisy, automatic annotation of unstructured natural language corpora. Finally, I move from unstructured data and focus on structured knowledge graphs. I present novel approaches for learning embeddings of vertices and edges in a knowledge graph that obey logical constraints.

I don't think this is as significant of a problem as you think it is. If I understood the paper correctly, the problem it actually solves is much smaller than the problem stated. It only applies to non-stationary environments where only one copy of the environment is available. If multiple copies of the environment are available, then I would wager that standard reinforcement learning techniques will far outperform this. Furthermore, reinforcement learning with only one copy of the environment (in other words, non-parallel) has proven to be inadequate for real problems because it is impossible to get enough data.

Much as robots have transformed entire swaths of the manufacturing economy, artificial intelligence and automation are now changing information work, letting humans offload cognitive labor to computers. In journalism, for instance, data mining systems alert reporters to potential news stories, while newsbots offer new ways for audiences to explore information. Automated writing systems generate financial, sports and elections coverage. A common question as these intelligent technologies infiltrate various industries is how work and labor will be affected. In this case, who – or what – will do journalism in this AI-enhanced and automated world, and how will they do it?

Climate change: I am inevitable. Robert Downey Jr.: I am Iron Man. That's basically what went down during the opening keynote at Amazon's new re:MARS tech and innovation conference in Las Vegas. Avengers actor Robert Downey Jr. made the very Tony Stark-like announcement that he'd be launching a new organization focused on solving environmental woes using artificial intelligence and other advanced technologies. "Between robotics and technology, we could probably clean up the planet significantly, if not entirely, within a decade," he said on Tuesday night.

Symbol detection for Massive Multiple-Input Multiple-Output (MIMO) is a challenging problem for which traditional algorithms are either impractical or suffer from performance limitations. Several recently proposed learning-based approaches achieve promising results on simple channel models (e.g., i.i.d. Gaussian). However, their performance degrades significantly on real-world channels with spatial correlation. We propose MMNet, a deep learning MIMO detection scheme that significantly outperforms existing approaches on realistic channels with the same or lower computational complexity. MMNet's design builds on the theory of iterative soft-thresholding algorithms and uses a novel training algorithm that leverages temporal and spectral correlation to accelerate training. Together, these innovations allow MMNet to train online for every realization of the channel. On i.i.d. Gaussian channels, MMNet requires two orders of magnitude fewer operations than existing deep learning schemes but achieves near-optimal performance. On spatially-correlated channels, it achieves the same error rate as the next-best learning scheme (OAMPNet) at 2.5dB lower SNR and with at least 10x less computational complexity. MMNet is also 4--8dB better overall than a classic linear scheme like the minimum mean square error (MMSE) detector.

We focus on the problem of streaming recommender system and explore novel collaborative filtering algorithms to handle the data dynamicity and complexity in a streaming manner. Although deep neural networks have demonstrated the effectiveness of recommendation tasks, it is lack of explorations on integrating probabilistic models and deep architectures under streaming recommendation settings. Conjoining the complementary advantages of probabilistic models and deep neural networks could enhance both model effectiveness and the understanding of inference uncertainties. To bridge the gap, in this paper, we propose a Coupled Variational Recurrent Collaborative Filtering (CVRCF) framework based on the idea of Deep Bayesian Learning to handle the streaming recommendation problem. The framework jointly combines stochastic processes and deep factorization models under a Bayesian paradigm to model the generation and evolution of users' preferences and items' popularities. To ensure efficient optimization and streaming update, we further propose a sequential variational inference algorithm based on a cross variational recurrent neural network structure. Experimental results on three benchmark datasets demonstrate that the proposed framework performs favorably against the state-of-the-art methods in terms of both temporal dependency modeling and predictive accuracy. The learned latent variables also provide visualized interpretations for the evolution of temporal dynamics.

"I have songwriting credits…even though I don't know how to write a song." 1 The speaker of this statement is not a musician and has no musical training. He helped create an app called Endel, which is self-described as "a cross-platform audio ecosystem." 2 Endel is part of a larger part of the current hot debate over works of art being "created" by computers using programs employing "artificially intelligent" modes of computer learning, or AI for short. "Dmitry Evgrafov, Endel's composer and head of sound design, says all 600 tracks were made'with a click of a button.' There was minimal human involvement outside of chopping up the audio and mastering it for streaming. Endel even hired a third-party company to write the track titles." 3 What makes this notable is that Endel has a record deal with Warner Bros. Music. 4 "Five Endel albums have already been released, and 15 more are coming this year -- all of which will be generated by code. In the future, Endel will be able to make infinite ambient tracks." 5 But didn't the Endel engineers create the software in question?

The acquisition brings Tableau's analytics and Salesforce's AI-powered Einstein platform together, which the press release claims "will deliver the most intelligent and intuitive analytics and visualization platform for every department and every user at any company". "We are bringing together the world's #1 CRM with the #1 analytics platform. Tableau helps people see and understand data, and Salesforce helps people engage and understand customers. It's truly the best of both worlds for our customers--bringing together two critical platforms that every customer needs to understand their world." Accenture (NYSE:ACN), along with digital services provider Avanada, has been recognized in a recently published report from IDC (International Data Corporation) for its leadership in worldwide Microsoft implementation services.