Have you ever noticed your friends getting tagged automatically after you upload a group picture? Though the technology has now gained widespread attention, its history can be traced back to the 1960s. Woodrow Wilson (Woody) Bledsoe, an American mathematician and computer scientist, is one of the founders of pattern and facial recognition technology. Back in the 1960s, he developed ways to classify faces using gridlines. A striking fact was, even during the experimental and inception phase, the application was able to match 40 faces per hour.
What can fly like a bird and hover like an insect? If drones had this combo, they would be able to maneuver better through collapsed buildings and other cluttered spaces to find trapped victims. Purdue University researchers have engineered flying robots that behave like hummingbirds, trained by machine learning algorithms based on various techniques the bird uses naturally every day. This means that after learning from a simulation, the robot "knows" how to move around on its own like a hummingbird would, such as discerning when to perform an escape maneuver. Artificial intelligence, combined with flexible flapping wings, also allows the robot to teach itself new tricks.
In this talk, presented at Accelerate AI East 2019, Ingo Mierswa presents the ideas of understandability, transparency, and governance in machine learning, and how those pieces all work together. Ingo Mierswa is an industry-veteran data scientist since starting to develop RapidMiner at the Artificial Intelligence Division of the TU Dortmund University in Germany. Mierswa, the scientist, has authored numerous award-winning publications about predictive analytics and big data. Mierswa, the entrepreneur, is the founder of RapidMiner. Under his leadership RapidMiner has grown up to 300% per year over the first seven years.
As with most other areas of technology, aviation is experiencing rapid technological shifts which will alter the future landscape of the industry. To better understand disruption in the world of aviation and on-demand mobility, NASA partnered with Quid to analyze over 30,000 research articles and understand the top technologies in AI and Machine Learning, Human-Machine Interaction, Cybersecurity and Energy. Innovations in areas such as visual image recognition, cloud services and augmented reality were surfaced as important tech trends which are beginning to converge with on-demand mobility. Researchers found that current electric propulsion limits are a major barrier to on-demand mobility, indicating future projects should focus on propulsion alternatives that can overcome energy storage and propulsion efficiency barriers. Quid helped NASA create a shortlist of top researchers working on these challenges for invitation to a panel discussion to share their ideas with the organization.
The Fukushima Prefectural Government is leading a collaborative effort involving companies from different industries and a robotics testing field to invent a flying car. In early August, the research center at the test field began accepting applications for four additional companies. The prefecture is focusing on efforts to attract companies to the site, which remains the only facility in the country where development and testing can all be done at the same site. The prefecture hopes to create synergies among various businesses and local parts suppliers and eventually build one of the country's largest industrial centers in Fukushima. Fukushima Gov. Masao Uchibori introduced the concept in Tokyo during a conference on flying car development organized by the industry ministry on Aug. 2. The central government is in the process of putting together a plan to build a working flying car by 2023.
The meeting will be held on the 26th of September at ENAC's Facilities, 7 av. The overall objective of HARVIS project is to identify how cognitive computing algorithms, implemented in a digital assistant, could support the decision making of a single pilot in complex situation. The project will produce a roadmap to help the EU and Clean Sky 2 Joint Undertaking orient the research and development in the next decade. To get input, suggestions and comments from the aviation community, the project decided to involve various stakeholders, like members of the industry, pilots, professional drivers, and professional users of AI and digital assistance in complex systems, through face-to-face meetings, bilateral interviews and surveys. During this First Meeting, several use cases will be described to illustrate how a future digital assistant implemented with artificial intelligence could be helpful.
Imagine that you have built a very precise machine learning model by using clever tricks and non-standard features. You are beyond happy and proud. However, when you present your results to your stakeholders, they are less thrilled. They don't understand what you did and why. They don't comprehend how your clever model makes a prediction.
AI has been facing a PR problem. Too often AI has introduced itself as a misogynist, racist and sinister robot. Remember the Microsoft Twitter chatbot named Tay, who was learning to mimic online conversations, but then started to blur out the most offensive tweets? Think of tech companies creating elaborate AI hiring tools, only to realise the technology was learning in the male-dominated industry to favour resumes of men over women. As much as this seems to be a facepalm situation, this happens a lot and seems not so easy to solve in an imperfect world, where even the most intelligent people have biases.
Vijay Kumar is one of the top roboticists in the world, professor at the University of Pennsylvania, Dean of Penn Engineering, former director of GRASP lab, or the General Robotics, Automation, Sensing and Perception Laboratory at Penn that was established back in 1979, 40 years ago. Vijay is perhaps best known for his work in multi-robot systems (or robot swarms) and micro aerial vehicles, robots that elegantly cooperate in flight under all the uncertainty and challenges that real-world conditions present. This conversation is part of the Artificial Intelligence podcast run by Lex Fridman.
Let's see what the result would be if we were to calculate the Shapley values for a single row: Shapley values for a single data point. This plot shows a base value that is used to indicate the direction of the prediction. Seeing as most of the targets are 0, it isn't strange to see that the base value is negative.