Jaguar Land Rover, taking a page from the European luxury car playbook, is offering increasingly attractive performance versions of its entry-level sports cars. Quicker, faster and better-handling than the base F-Type, the SVR model is a high-octane sports car disguised as a luxury car. The SVR versions of Jaguar Land Rover vehicles represent a still smaller slice of the pie. The F-Type SVR's size, limited storage and seating configuration will disqualify it for a lot of buyers.
So, ShopClues plans to use advanced technologies to make it easier for shoppers to find the right size when buying clothes online, according to Utkarsh Biradar, vice-president of product at the company. It's also applying these technologies to help advertisers expand their reach effectively, using machine learning to identify "lookalike" targets that are similar to existing users as well as figuring out what kinds of ads users don't want to see. Ola, one of India's leading ride-hailing apps, is using data science and machine learning to track traffic, improve customer experience, understand driver habits and extend the life of a vehicle. Machine learning models log each customer's gender, brand affinity, store affinity, price preference, frequency, volume of purchases, and more, which become more accurate as the company collects more data.
They were mechanical marvels of technology that could perform many impressive functions within and unto themselves, but artificial intelligence (AI), machine learning, true driver personalization, and external data exchange capabilities were still conceptual. Its value will be judged by how elegantly it understands and communicates with its users using speech and natural language, while accessing and delivering a world of information from a wide range of "expert" sources to instantly and/or proactively deliver the right answer, content, or action. Similarly, the automotive assistant, while highly capable itself, delivers the best experience for users by intelligently coordinating all pieces of the connected world ecosystem. Taken together, rapid advances in AI interoperability, personalization, and contextualization will allow automotive assistants to significantly enhance car mobility for drivers and passengers.
This year artificial intelligence (AI) with the associated technologies such as smart IoT sensors and increasingly powerful and seamless human machine interfaces (HMIs) proved to be the cynosure of all eyes that passed by. Most global automobile companies are working on driverless cars that are based on continuous advances in computer vision and deep learning technologies. This is another example where smart sensors and human machine interactions, when combined with artificial intelligence technologies could create tangible advances in the way we drive, work and play. For instance, Cisco is working with Hyundai to create a strong network backbone for their vehicles that would help Hyundai to simplify its network and seamless connect to other vehicles, through the cloud.
When he turned two, I unwisely thought he would enjoy a monster truck rally and purchased tickets, imagining father and son duo making great memories together. Prototype approaches can be created relatively quickly, requiring publishable mathematics to prove convergence, pounded home with surprisingly good results. This is why I am excited about this Hessian-free approach, because although it is currently not mainstream, and it lacks the rock star status of stochastic gradient descent (SGD) approaches, it has the potential to save the user significant user processing time. I personally love algorithm development and enjoy spending my waking hours seeking to make algorithms faster and more robust.
In fact, unit shipments of artificial intelligence (AI) systems used in automobiles are expected to rise from 7 million in 2015 to 122 million by 2025, according to IHS. Specifically in ADAS, deep learning, which mimics human neural networks, presents several advantages over traditional algorithms. For example, deep learning allows detection and recognition of multiple objects, improves perception, reduces power consumption, supports object classification, enables prediction of actions, and will reduce development time of ADAS systems. In the infotainment human machine interfaces currently installed, most of the speech recognition technologies already rely on algorithms based on neural networks running in the cloud.