March Networks, a global video security and video-based business intelligence leader, is pleased to introduce its ME6 Series IP Cameras, a new 6MP camera line powered by Artificial Intelligence (AI). Incorporating advanced system-on-chip (SoC) technology from California-based company Ambarella, the ME6 Series use Deep Neural Network processing power to accurately distinguish between people and vehicles. This built-in intelligence is combined with next-generation security analytics for the most accurate, real-time analysis and detection of events. Organizations can enhance security and rapidly respond to incidents with the cameras' highly accurate notifications on perimeter and zone breaches, loitering, and atypical activity involving vehicles. The ME6 Series' analytics are embedded on the cameras for convenient out-of-the-box use – no training required.

For our February AI Ethics Twitter Chat, we invited expert guest, Dr. Brandie Nonnecke, Founding Director, Citris Policy Lab at UC Berkeley to discuss "Deconstructing Deepfakes". Mia Dand: Dr. Nonnecke, Let's start off with the basics, what are deepfakes? Dr. Brandie Nonnecke: "Deepfakes" are deceptive audio or visual media created with AI to depict real people saying or doing things they did not. The term "deepfake" is a portmanteau of "deep learning" (a type of machine learning) & "fake". Don't confuse deepfakes w/ "cheap fakes" or "shallow fakes", which are created w/out AI.

In a paper scheduled to be presented at the upcoming International Conference on Learning Representations, Amazon researchers propose an AI approach that greatly improves performance on certain meta-learning tasks (i.e., tasks that involve both accomplishing related goals and learning how to learn to perform them). They say it can be adapted to new tasks with only a handful of labeled training examples, meaning a large corporation could use it to, for example, extract charts and captions from scanned paperwork. In conventional machine learning, a model trains on a set of labeled data (a support set) and learns to correlate features with the labels. It's then fed a separate set of test data (a query set) and evaluated based on how well it predicts that set's labels. By contrast, during meta learning, an AI model learns to perform tasks with their own sets of training data and test data and the model sees both. In this way, the AI learns how particular ways of responding to the training data affect performance on the test data.

Google today announced the release of a new data set -- the Free Universal Sound Separation data set, or FUSS for short -- intended to support the development of AI models that can separate distinct sounds from recording mixes. The use cases are potentially endless, but if it were to be commercialized, FUSS could be used in corporate settings to extract speech from conference calls. It follows on the heels of a study by Google and the Idiap Research Institute in Switzerland describing two machine learning models -- a speaker recognition network and a spectrogram masking network -- that together "significantly" reduced the speech recognition word error rate (WER) on multispeaker signals. Elsewhere, tech giants including Alibaba and Microsoft have invested significant time and resources in solving the sound separation problem. As Google Research scientists John Hershey, Scott Wisdom, and Hakan Erdogan explain in a blog post, the bulk of sound separation models assume the number of sounds in a mixture to be static, and they either separate mixtures of a small number of sound types (such as speech versus nonspeech) or different instances of the same sound type (like a first speaker versus a second speaker).

With the advent of powerful neural language models over the last few years, research attention has increasingly focused on what aspects of language they represent that make them so successful. Several testing methodologies have been developed to probe models' syntactic representations. One popular method for determining a model's ability to induce syntactic structure trains a model on strings generated according to a template then tests the model's ability to distinguish such strings from superficially similar ones with different syntax. We illustrate a fundamental problem with this approach by reproducing positive results from a recent paper with two non-syntactic baseline language models: an n-gram model and an LSTM model trained on scrambled inputs.

Learning to navigate in a realistic setting where an agent must rely solely on visual inputs is a challenging task, in part because the lack of position information makes it difficult to provide supervision during training. In this paper, we introduce a novel approach for learning to navigate from image inputs without external supervision or reward. Our approach consists of three stages: learning a good representation of first-person views, then learning to explore using memory, and finally learning to navigate by setting its own goals. The model is trained with intrinsic rewards only so that it can be applied to any environment with image observations. We show the benefits of our approach by training an agent to navigate challenging photo-realistic environments from the Gibson dataset with RGB inputs only.

Understanding cross-cultural differences is an important application of natural language understanding. This problem is difficult due to the relativism between cultures. We present a computational approach to learn cultural models that encode the general opinions and values of cultures from multi-lingual Wikipedia. Specifically, we assume a language is a symbol of a culture and different languages represent different cultures. Our model can automatically identify statements that potentially reflect cultural differences. Experiments on English and Chinese languages show that on a held out set of diverse topics, including marriage, gun control, democracy, etc., our model achieves high correlation with human judgements regarding within-culture values and cultural differences.

Invariant risk minimization (IRM; Arjovsky et al., 2019) is a recently proposed framework designed for learning predictors that are invariant to spurious correlations across different training environments. Because IRM does not assume that the test data is identically distributed as the training data, it can allow models to learn invariances that generalize well on unseen and out-of-distribution (OOD) samples. Yet, despite this theoretical justification, IRM has not been extensively tested across various settings. In an attempt to gain a better understanding of IRM, we empirically investigate several research questions using IRMv1, which is the first practical algorithm proposed in (Arjovsky et al., 2019) to approximately solve IRM. By extending the ColoredMNIST experiment from (Arjovsky et al., 2019) in multiple ways, we find that IRMv1 (i) performs better as the spurious correlation varies more widely between training environments, (ii) learns an approximately invariant predictor when the underlying relationship is approximately invariant, and (iii) can be extended to multiple environments, multiple outcomes, and different modalities (i.e., text). We hope that this work will shed light on the characteristics of IRM and help with applying IRM to real-world OOD generalization tasks.

The growing interest for adversarial examples, i.e. maliciously modified examples which fool a classifier, has resulted in many defenses intended to detect them, render them inoffensive or make the model more robust against them. In this paper, we pave the way towards a new approach to defend a distant system against adversarial examples, which we name the luring of adversarial perturbations. A component is included in the target model to form an augmented and equally accurate version of it. This additional component is designed to be removable and to give false indications on the way to fool the target model alone: the adversary is tricked into fooling the augmented version of the target model, and not the target model. We explain the intuition of our defense with the principle of the luring effect, inspired by the notion of robust and non-robust features, and experimentally justify its validity. Eventually, we propose a simple prediction strategy which takes advantage of this effect, and show that our defense scheme on MNIST, SVHN and CIFAR10 can efficiently thwart an adversary using state-of-the-art attacks and allowed to perform large perturbations.

Neural Machine Translation (NMT) methodologies have burgeoned from using simple feed-forward architectures to the state of the art; viz. BERT model. The use cases of NMT models have been broadened from just language translations to conversational agents (chatbots), abstractive text summarization, image captioning, etc. which have proved to be a gem in their respective applications. This paper aims to study the major trends in Neural Machine Translation, the state of the art models in the domain and a high level comparison between them.