EVs are poised to age like smartphones. For most of its short life, my Tesla Model 3 has aged beautifully. Since I bought the car, in 2019, it has received a number of new features simply by updating its software. My navigation system no longer just directs me to EV chargers along my route--it also shows me, in real time, how many plugs are free. With the push of a button, I can activate "Car Wash Mode," and the Tesla will put itself in neutral and disable the windshield wipers.

Each question in PTR is associated with a functional program built from a set of basic functions. A.1 Data Types Our basic functional building blocks operate on values of the following types: Object: A single object in the scene. Part-level functions are listed in Table 4. B have certain spatial relationships. For NS-VQA, we first use Mask-RCNN to propose segmentations for objects and parts. If an object is unstable, possible changes (to_left, to_right, to_front, to_behind) are predicted.

Is there more to AI than automation and data processing? And now that some of the more basic functions of AI have matured, we're going to see a sharp increase in the sophistication of AI, as it becomes more and more human. The following is a brief look at where the enterprise is when it comes to AI, and where we're about to start heading. These are the enterprise AI trends making headlines. As I noted above, there are some basic functions of AI that most companies have already adopted, and that we as users can feel confident about when it comes to functionality.

Is there more to AI than automation and data processing? And now that some of the more basic functions of AI have matured, we're going to see a sharp increase in the sophistication of AI, as it becomes more and more human. The following is a brief look at where the enterprise is when it comes to AI, and where we're about to start heading. As I noted above, there are some basic functions of AI that most companies have already adopted, and that we as users can feel confident about when it comes to functionality. Those include things like personalization (personalizing ads, email messaging, auto-texts, etc.) and recommendations (such as those you see on apps like Spotify, Netflix, or in your Amazon shopping card), and other martech, which is now widely available aaS for just about any size enterprise.

Is there more to AI than automation and data processing? And now that some of the more basic functions of AI have matured, we're going to see a sharp increase in the sophistication of AI, as it becomes more and more human. The following is a brief look at where the enterprise is when it comes to AI, and where we're about to start heading. As I noted above, there are some basic functions of AI that most companies have already adopted, and that we as users can feel confident about when it comes to functionality. Those include things like personalization (personalizing ads, email messaging, auto-texts, etc.) and recommendations (such as those you see on apps like Spotify, Netflix, or in your Amazon shopping card), and other martech, which is now widely available aaS for just about any size enterprise.

Visual Question Answering (VQA) is a challenging task for evaluating the ability of comprehensive understanding of the world. Existing benchmarks usually focus on the reasoning abilities either only on the vision or mainly on the knowledge with relatively simple abilities on vision. However, the ability of answering a question that requires alternatively inferring on the image content and the commonsense knowledge is crucial for an advanced VQA system. In this paper, we introduce a VQA dataset that provides more challenging and general questions about Compositional Reasoning on vIsion and Commonsense, which is named as CRIC. To create this dataset, we develop a powerful method to automatically generate compositional questions and rich annotations from both the scene graph of a given image and some external knowledge graph. Moreover, this paper presents a new compositional model that is capable of implementing various types of reasoning functions on the image content and the knowledge graph. Further, we analyze several baselines, state-of-the-art and our model on CRIC dataset. The experimental results show that the proposed task is challenging, where state-of-the-art obtains 52.26% accuracy and our model obtains 58.38%.

Lux has finally released a follow-up to its Lux Geo smart thermostat. The Kono is largely in line with the Geo's basic sensibilities of delivering smart features and style in a lower-priced high-tech thermostat. Priced at $150, the Kono is an affordable way to get smart connectivity in your heating and cooling controls, but you won't get much in the way of bells and whistles. One potential sticking point is pure aesthetics: The Kono's industrial design could appear stylish and techno-hip, or tacky and plastic looking depending upon your taste. Lux has cleverly made it so you can purchase an array of colored faceplates for the unit and swap them out in a second to suit your room's color scheme or general mood.

Predicting epidemic dynamics is of great value in understanding and controlling diffusion processes, such as infectious disease spread and information propagation. This task is intractable, especially when surveillance resources are very limited. To address the challenge, we study the problem of active surveillance, i.e., how to identify a small portion of system components as sentinels to effect monitoring, such that the epidemic dynamics of an entire system can be readily predicted from the partial data collected by such sentinels. We propose a novel measure, the gamma value, to identify the sentinels by modeling a sentinel network with row sparsity structure. We design a flexible group sparse Bayesian learning algorithm to mine the sentinel network suitable for handling both linear and non-linear dynamical systems by using the expectation maximization method and variational approximation. The efficacy of the proposed algorithm is theoretically analyzed and empirically validated using both synthetic and real-world data.

Microsoft has launched an app that'narrates the world' to people who are blind or visually impaired using AI. The app can, for example, recognize friends' faces and guess their emotions, read text out loud when it comes into view and even has experimental features that can describe scenes - for example, it identified a young girl throwing a frisbee in the park. The app, which is free and available on iOS-only, relies on holding up one's smartphone camera to hear information about the world. The app can also describe strangers around you. In a video demonstrating the app's abilities, the app described a young woman with glasses (left) by saying: '28-year-old female, wearing glasses, looking happy' (right) To use the app, the user must point their phone's camera, select a channel and hear a description of their surroundings.

Amazon's Alexa can now handle commands for the Hyundai G90 luxury car (Photo: Genesis) Hyundai announced Thursday that it has partnered with Amazon to let motorists use their home digital assistant Alexa to boss around their new Genesis luxury car. From inside their homes, owners of Hyundai's Genesis G90 sedan will able to use voice commands through their Amazon Echo speakers to command the Alexa system to have their cars perform certain basic functions, Hyundai says. So, for instance, the customer who wants the car running and air conditioning blasting for a daily commute need only say something like, "Alexa, tell Genesis to start my car at 70 degrees." Or an owner might bark, "Alexa, tell Genesis to lock my car" from the comfort of their living room. In addition, Alexa can flash the car's headlights, honk the horn or turn off a running engine.