One year after her controversial exit from Alphabet's Google, AI scientist Timnit Gebru launches small lab to continue her research The former Google computer scientist whose controversial exit from the search engine giant this time last year, has resurfaced in a new role. Reuters has reported that Timnit Gebru on Thursday revealed she has launched a small lab to continue her research work freely. Indeed, the Distributed AI Research Institute has reportedly raised $3.7 million so far, and aims to critically study services from big tech companies, as well as propose AI-based solutions to issues such as food insecurity and climate change, Gebru reportedly said. The Distributed AI Research Institute (DAIR) joins a number of other projects such as the Algorithmic Justice League that are advancing ethical use of AI. For many years now critics have worried about the ethical use and safeguards for AI and facial recognition systems, along with credit scoring, which could lead to mass surveillance and racial discrimination.

Natural Language Inference (NLI) is considered a representative task to test natural language understanding (NLU). In this work, we propose an extensible framework to collectively yet categorically test diverse Logical reasoning capabilities required for NLI (and by extension, NLU). Motivated by behavioral testing, we create a semi-synthetic large test-bench (363 templates, 363k examples) and an associated framework that offers following utilities: 1) individually test and analyze reasoning capabilities along 17 reasoning dimensions (including pragmatic reasoning), 2) design experiments to study cross-capability information content (leave one out or bring one in); and 3) the synthetic nature enable us to control for artifacts and biases. The inherited power of automated test case instantiation from free-form natural language templates (using CheckList), and a well-defined taxonomy of capabilities enable us to extend to (cognitively) harder test cases while varying the complexity of natural language. Through our analysis of state-of-the-art NLI systems, we observe that our benchmark is indeed hard (and non-trivial even with training on additional resources). Some capabilities stand out as harder. Further fine-grained analysis and fine-tuning experiments reveal more insights about these capabilities and the models -- supporting and extending previous observations. Towards the end we also perform an user-study, to investigate whether behavioral information can be utilised to generalize much better for some models compared to others.

Social media content routinely incorporates multi-modal design to covey information and shape meanings, and sway interpretations toward desirable implications, but the choices and outcomes of using both texts and visual images have not been sufficiently studied. This work proposes a computational approach to analyze the outcome of persuasive information in multi-modal content, focusing on two aspects, popularity and reliability, in COVID-19-related news articles shared on Twitter. The two aspects are intertwined in the spread of misinformation: for example, an unreliable article that aims to misinform has to attain some popularity. This work has several contributions. First, we propose a multi-modal (image and text) approach to effectively identify popularity and reliability of information sources simultaneously. Second, we identify textual and visual elements that are predictive to information popularity and reliability. Third, by modeling cross-modal relations and similarity, we are able to uncover how unreliable articles construct multi-modal meaning in a distorted, biased fashion. Our work demonstrates how to use multi-modal analysis for understanding influential content and has implications to social media literacy and engagement.

The House minority leader blasted Democratic leadership, saying the current policy is'creating another Syria' in the Middle East. The United States military conducted a drone strike in Syria targeting a senior al-Qaeda leader and planner, a CENTCOM spokesperson says. "U.S. forces conducted a kinetic strike near Idlib, Syria, December 3, targeting a senior al-Qaeda leader and planner," CENTCOM spokesperson Captain Bill Urban told Fox News Digital in a statement. "The strike was conducted using a precision strike method from MQ-9 aircraft." Urban added that an "initial review of this strike indicates the potential for possible civilian casualties."

Legend has it a humanoid creature covered in fur inhabits the forested areas along the west coast of the northern US and although stories of this mythical monster have been told since the 1800s, no one has been able to prove its existence. The closest and most compelling evidence of Bigfoot was captured in 1967, when Bob Gimlin and Roger Patterson shot footage of a furry figure walking through Bluff Creek in Northern California. The grainy, one-minute clip has sparked many investigations into its authenticity and DailyMail.com'The Proof is Out There' episode about Bigfoot will run tonight at 10pm ET. The show has brought on a team of experts to use the latest and greatest technology for this mission, including artificial intelligence and computer vision algorithms.

Promobot, a European artificial intelligence company, has offered someone £150,000 (over €175,000) to do just that. The company want to make their robots super realistic. So, they want to base their looks off real people, with the hope of making them more lifelike. You'd fit the role if you were over 25 and have a "kind and friendly" face. The job includes taking selfies and making a 3D model of a persons face and body to be replicated for the robot's physical features.

Just when you thought it was safe to go back to normal -- are you ready for round two? "There are big changes ahead," says Forrester VP Brandon Purcell. "There are a lot of changes that have been brought about by what happened over the last 2 years. The pace of change is very rapid. There are pretty big things happening." Purcell spoke with InformationWeek about the predictions for AI in 2022 and beyond.

Facial-recognition technology (FRT) is mainly deployed in the cybersecurity and surveillance sectors. It has long been in use at airport borders and on smartphones, and as a tool to help police identify criminals. But it is now creeping further into private and public spaces. From Quito to Nairobi, Moscow to Detroit, hundreds of municipalities have installed cameras equipped with FRT, sometimes promising to feed data to central command centres as part of'safe city' or'smart city' solutions to crime. The COVID-19 pandemic might accelerate their spread.

Tensor network, which originates from quantum physics, is emerging as an efficient tool for classical and quantum machine learning. Nevertheless, there still exists a considerable accuracy gap between tensor network and the sophisticated neural network models for classical machine learning. In this work, we combine the ideas of matrix product state (MPS), the simplest tensor network structure, and residual neural network and propose the residual matrix product state (ResMPS). The ResMPS can be treated as a network where its layers map the "hidden" features to the outputs (e.g., classifications), and the variational parameters of the layers are the functions of the features of the samples (e.g., pixels of images). This is different from neural network, where the layers map feed-forwardly the features to the output. The ResMPS can equip with the non-linear activations and dropout layers, and outperforms the state-of-the-art tensor network models in terms of efficiency, stability, and expression power. Besides, ResMPS is interpretable from the perspective of polynomial expansion, where the factorization and exponential machines naturally emerge. Our work contributes to connecting and hybridizing neural and tensor networks, which is crucial to further enhance our understand of the working mechanisms and improve the performance of both models.

This work examines adaptive distributed learning strategies designed to operate under communication constraints. We consider a network of agents that must solve an online optimization problem from continual observation of streaming data. The agents implement a distributed cooperative strategy where each agent is allowed to perform local exchange of information with its neighbors. In order to cope with communication constraints, the exchanged information must be unavoidably compressed. We propose a diffusion strategy nicknamed as ACTC (Adapt-Compress-Then-Combine), which relies on the following steps: i) an adaptation step where each agent performs an individual stochastic-gradient update with constant step-size; ii) a compression step that leverages a recently introduced class of stochastic compression operators; and iii) a combination step where each agent combines the compressed updates received from its neighbors. The distinguishing elements of this work are as follows. First, we focus on adaptive strategies, where constant (as opposed to diminishing) step-sizes are critical to respond in real time to nonstationary variations. Second, we consider the general class of directed graphs and left-stochastic combination policies, which allow us to enhance the interplay between topology and learning. Third, in contrast with related works that assume strong convexity for all individual agents' cost functions, we require strong convexity only at a network level, a condition satisfied even if a single agent has a strongly-convex cost and the remaining agents have non-convex costs. Fourth, we focus on a diffusion (as opposed to consensus) strategy. Under the demanding setting of compressed information, we establish that the ACTC iterates fluctuate around the desired optimizer, achieving remarkable savings in terms of bits exchanged between neighboring agents.