If you thought cybersecurity was a challenging and often weird part of the tech industry, be prepared for it to get even odder. The next couple of years will bring a new range of threats that will take tech security far beyond its traditional boundaries and will require a whole new set of skills and alliances. One example: tech analyst Forrester predicts that deepfakes could end up costing businesses a lot of money next year: as much as $250m. That might happen in a couple of ways. There's the risk to your share price if someone creates a deepfake of your CEO apparently resigning from the company.

If Google's Pixel 4 is your daily driver, good news: It's now able to screen robocalls -- and more. Google announced this morning an update to the Pixel 4's Call Screen feature in the U.S. that automatically declines calls from unknown parties and filters out suspected robocallers, alongside an improved video calling experience on Duo, the rollout of the new Google Assistant to more users, and a zippier software experience made possible by memory usage optimizations. It's a part of what Google's calling feature drops, which will deliver "bigger updates" to Pixel devices with "more helpful and fun features" going forward. The first arrives starting today, with others to follow on a monthly cadence. "Pixel phones have always received monthly updates to improve performance and make your device safe," wrote Google group product manager Shenaz Zack in a blog post.

The average Bengalurean spends about two hours a day stuck in traffic. The sheer volume of vehicles is impossible to manage manually, through traffic policemen on ground. While Bengaluru has led the way in terms of a tech-savvy traffic police force, there is room for improvement. Artificial Intelligence and technology have revolutionised traffic management across the world but have not yet been adopted in Karnataka, which prefers to use traffic policemen in some areas and a Westernised Webster model that doesn't suit our traffic conditions in others. Civic agencies have also fallen short in terms well-designed roads, construction regulations and meeting infrastructure needs of a fast growing population, leading to congested roads, reports M.K. Ashoka.

Beyond trendy names like Tesla and Alphabet chasing self-driving cars, a host of auto brands and other tech heavyweights are also investing in autonomous R&D. Private companies working in auto tech are attracting record levels of deals and funding, with autonomous driving startups leading the charge. Along with early-stage startups, VCs, and other investors, large corporations are also angling to get a slice of the self-driving pie. From autonomy to telematics to ride sharing, the auto industry has never been at more risk. Get the free 67-page report PDF. Using CB Insights' investment, acquisition, and partnership data, we identified over 40 companies developing road-going self-driving vehicles. They are a diverse group of players, ranging from automotive industry stalwarts to leading technology brands and telecommunications companies. This list is organized alphabetically and focuses on larger corporate players in the space (as opposed to earlier-stage startups). Companies working on industrial autonomous vehicles were not included in this analysis. A few of the companies or brands listed below belong to the same parent organization but are detailed separately if they are operating distinct autonomous development programs. Some companies are grouped together by key partnerships or alliances. Given the complex web of relationships between these players, other collaborations are also noted in each profile. This is not intended to be an exhaustive list of corporations working on autonomous vehicle technology. This brief was originally published on 9/25/2015 and featured 25 select corporations. It was updated and expanded on 5/17/2017, 9/4/2018, and 8/28/2019. Over the last decade, Amazon has spent billions of dollars working on finding ever-better solutions to the last-mile problem in delivery. It's built its own fleet of cargo jets, explored delivery by drone in the form of "Prime Air," and more. More recently, an increasing percentage of that investment has been directed toward autonomous vehicle technology. In February 2019, Amazon invested in Aurora Innovation, an autonomous tech startup run by former executives from two other firms with strong ties to self-driving technology: Google and Tesla. "Autonomous technology has the potential to help make the jobs of our employees and partners safer and more productive, whether it's in a fulfillment center or on the road, and we're excited about the possibilities." The Aurora investment isn't the only autonomous technology play that Amazon is pursuing. In January 2019, the company introduced the Amazon Scout, a six-wheeled electric-powered delivery robot.

Magnetic Resonance Imaging (MRI) of the brain can come in the form of different modalities such as T1-weighted and Fluid Attenuated Inversion Recovery (FLAIR) which has been used to investigate a wide range of neurological disorders. Current state-of-the-art models for brain tissue segmentation and disease classification require multiple modalities for training and inference. However, the acquisition of all of these modalities are expensive, time-consuming, inconvenient and the required modalities are often not available. As a result, these datasets contain large amounts of \emph{unpaired} data, where examples in the dataset do not contain all modalities. On the other hand, there is smaller fraction of examples that contain all modalities (\emph{paired} data) and furthermore each modality is high dimensional when compared to number of datapoints. In this work, we develop a method to address these issues with semi-supervised learning in translating between two neuroimaging modalities. Our proposed model, Semi-Supervised Adversarial CycleGAN (SSA-CGAN), uses an adversarial loss to learn from \emph{unpaired} data points, cycle loss to enforce consistent reconstructions of the mappings and another adversarial loss to take advantage of \emph{paired} data points. Our experiments demonstrate that our proposed framework produces an improvement in reconstruction error and reduced variance for the pairwise translation of multiple modalities and is more robust to thermal noise when compared to existing methods.

Conditional GANs [29] (cGANs) are a type of GAN that use conditional information such as class labels to guide the training of the discriminator and the generator. Most frameworks of cGANs either augment a GAN by injecting (embedded) class information into the architecture of the real/fake discriminator [31], or add an auxiliary loss that is class based [36]. We place the class conditional structure at the forefront of the generative model by proposing a loss that ensures generator updates are always class specific. Rather than training a function that measures the information theoretic distance between the generative distribution and one target distribution, we generalize the successful hinge-loss [28] that has become an essential ingredient of many GANs [38, 7] to the multi-class setting and use it to train a single generator classifier pair [38]. While the canonical hinge loss made generator updates according to a class agnostic margin a real/fake discriminator learned [28], our multi-class hinge-loss GAN updates the generator according to many classification margins. With this modification, we are able to accelerate training and achieve state of the art Inception Scores on CIFAR10, CIFAR100, and STL10.

Sparse representations have been shown to be useful in deep reinforcement learning for mitigating catastrophic interference and improving the performance of agents in terms of cumulative reward. Previous results were based on a two step process were the representation was learned offline and the action-value function was learned online afterwards. In this paper, we investigate if it is possible to learn a sparse representation and the action-value function simultaneously and incrementally. We investigate this question by employing several regularization techniques and observing how they affect sparsity of the representation learned by a DQN agent in two different benchmark domains. Our results show that with appropriate regularization it is possible to increase the sparsity of the representations learned by DQN agents. Moreover, we found that learning sparse representations also resulted in improved performance in terms of cumulative reward. Finally, we found that the performance of the agents that learned a sparse representation was more robust to the size of the experience replay buffer. This last finding supports the long standing hypothesis that the overlap in representations learned by deep neural networks is the leading cause of catastrophic interference.

In multi-agent domains (MADs), an agent's action may not just change the world and the agent's knowledge and beliefs about the world, but also may change other agents' knowledge and beliefs about the world and their knowledge and beliefs about other agents' knowledge and beliefs about the world. The goals of an agent in a multi-agent world may involve manipulating the knowledge and beliefs of other agents' and again, not just their knowledge/belief about the world, but also their knowledge about other agents' knowledge about the world. Our goal is to present an action language (mA+) that has the necessary features to address the above aspects in representing and RAC in MADs. mA+ allows the representation of and reasoning about different types of actions that an agent can perform in a domain where many other agents might be present -- such as world-altering actions, sensing actions, and announcement/communication actions. It also allows the specification of agents' dynamic awareness of action occurrences which has future implications on what agents' know about the world and other agents' knowledge about the world. mA+ considers three different types of awareness: full-, partial- awareness, and complete oblivion of an action occurrence and its effects. This keeps the language simple, yet powerful enough to address a large variety of knowledge manipulation scenarios in MADs. The semantics of mA+ relies on the notion of state, which is described by a pointed Kripke model and is used to encode the agent's knowledge and the real state of the world. It is defined by a transition function that maps pairs of actions and states into sets of states. We illustrate properties of the action theories, including properties that guarantee finiteness of the set of initial states and their practical implementability. Finally, we relate mA+ to other related formalisms that contribute to RAC in MADs.

Gene therapy, stem cells, artificial intelligence, the bionic eye – incredible advances in research and technology are opening up new frontiers for eye health. With each step forward, eye researchers are unlocking exciting possibilities for earlier detection of eye disease, better treatments and potentially even restoring sight. At the Centre for Eye Research Australia's Looking to the Future Community Forum, held in October 2019, a selection of CERA's leading experts discussed the current eye research landscape and what the future holds. Over two panels – 'Gene therapy demystified' and'Everything you need to know about artificial intelligence and new technology' – they explored everything from the bionic eye, to corneal cells grown from skin, to computer programs that can detect eye disease with better-than-human accuracy. Here is a snapshot of some of the key ideas discussed, and the areas of research shaping the future of eye health.

Technology has upended one business after another across the United States. To cite only the most recent developments: Lyft and others have utterly changed personal transportation, and Airbnb has done the same for hospitality. And in January 2018, the first Amazon Go store opened, sans checkout clerks, promising similar upheaval for grocers. What is happening is fairly well understood, if initially underestimated. Digitization and other technological advances are exposing the vulnerabilities in every industry, particularly retail. And now, logistics companies are starting to feel the heat. Our new research has turned up five trends that offer startling indicators of impending change for the trucking, rail, warehousing, and logistics companies that move America's merchandise. Start with autonomous trucks (ATs), which will change the cost structure and utilization of trucking--and with that, the cost of consumer goods. Sixty-five percent of the nation's consumable goods are trucked to market.