This is another installment of Mighty AI's "Conversations in Machine Learning" blog series. Since meeting them at June's Conference on Computer Vision and Pattern Recognition (CVPR), we've been chatting with a Corporate Research Engineer and Automated Driving Research Engineer from a behemoth of a company that's working on self-driving car technology. So autonomous vehicles require advanced computer vision, and advanced computer vision requires excellent training data--that's why Mighty AI's in the picture here. Before they came to know about Mighty AI's Training Data as a Service (TDaaS) solution and our talented tasking community, they'd never found a resource other than their own employees that could annotate images to their specifications at a meaningful velocity.
Engineers at the University of California, Riverside (UCR) have developed a new online energy management system (EMS) that they say can improve PHEV fuel efficiency by more than 30%. In "Development and Evaluation of an Evolutionary Algorithm-Based Online Energy Management System for Plug-In Hybrid Electric Vehicles," published in IEEE Transactions on Intelligent Transportation Systems, Xuewei Qi and colleagues explain that improving the efficiency of current PHEVs is limited by shortfalls in their energy management systems (EMS), which control the power split between engine and battery. The EMS developed by Qi and his team combines vehicle connectivity information (such as cell networks and crowdsourcing platforms) and evolutionary algorithms – a mathematical way to describe natural phenomena such as evolution, insect swarming and bird flocking. "We combined this approach with connected vehicle technology to achieve energy savings of more than 30 percent.
Audi and Nvidia are putting their partnership into high gear with the aim of putting cars with artificial intelligence on the road by 2020. The Q7 Deep Learning Concept car can learn to understand and plot a course through its environment, even as it changes, but it also learns from a driver. "Audi's adoption of our DRIVE computing platform for AI cars will accelerate the introduction of next-generation autonomous vehicles, moving us closer to a future of higher driving safety and new mobility services," said Nvidia founder and CEO Jen-Hsun Huang. "In our mutual pursuit for safer roads, the partnership between Audi and NVIDIA will expand to deep learning and artificial intelligence to bring higher automation into production more quickly," said Scott Keogh, President of Audi of America.
Exploring the Artificially Intelligent Future of Finance With technological enhancements increasing computing power and decreasing its cost, easing access to big data and innovating algorithms, there has been a huge surge in interest of artificial intelligence, machine learning and its subset, deep learning, in recent years. What have been the leading factors enabling recent advancements and uptake of deep learning? Yuanyuan: Customer experience could be significantly improved using AI by analyzing individual level attributes to make traditional service much more tailor-made. Alesis: One of the main challenges for start-ups when applying Machine Learning specifically to financial services is educating the customers on the importance of data and access to it.
Technology company Nauto has entered into agreements with BMW i Ventures and Toyota Research Institute, as well as with Allianz Ventures, part of the leading global financial service provider and insurance company Allianz Group. Under the agreements, Nauto and its auto and insurance industry partners will license data and technologies, including Nauto's artificial intelligence-powered vehicle network. Insurers get a more precise view of each and every driver that can help personalize coverage, deliver precision risk assessments, reduce fraudulent claims and provide enhanced urban mobility services for commercial fleets. Nauto's artificial intelligence platform drives deep learning that goes beyond the basics of recorded events by capturing driver behavior, inside-the-vehicle activity and correlations from road, weather and traffic conditions.
In the long run AI, will completely change our investment industry, but (certainly on the institutional investment side) we are only at the beginning of a long and slow transition of 50 years. Financial advisory is another under developing area, where in future, individuals could expect a machine to suggest best investment portfolios based on their own family balance and consumption behaviors. Alesis: One of the main challenges for start-ups when applying Machine Learning specifically to financial services is educating the customers on the importance of data and access to it. To continue on the above examples, advances in NLP assure that bots will be able to handle simple tasks in customer service and AI systems will on the other hand provide automated information from news, press releases and other textual documents to prices.
Telematics company Geotab is using IoT to help fleet management companies significantly reduce accidents. Telematics and Onboard Diagnostics (OBD) are helping fleet management companies and insurance firms collect a wealth of information about vehicles and drivers, including measurable events such as speeding, seatbelt usage, sharp cornering or over-acceleration. IoT sensors and smart cement (cement equipped with sensors) can monitor the structural status of roads and bridges under dynamic conditions and alert us about deficiencies before they turn into catastrophes. The gleaned insights can help in a number of scenarios, including optimizing the use of limited maintenance resources and equipment, as well as predicting and alerting about possible hazards and accidents that may take place because of poor road and weather conditions.
We consider ourselves a machine learning or data science company first, and everything else second. To put it another way, we're a data science company working on heavy-duty vehicles right now. That could be because what we're doing right now as a data science company is iterating a lot, building our algorithms, improving their accuracy, and making them better. I don't do too much of the in-depth data science or ML work at the company, but I know that the team is using Cassandra, which is an Apache tool, and what it does is essentially provide a repository of different functions other people have created to make data science and machine learning with Apache much easier.