Join us for an online webinar on Tuesday, June 30th at 9:30 a.m. ET with MIT faculty member and expert in machine learning, Professor Devavrat Shah.
This webinar is a way to understand the topics covered in the ‘Machine Learning: From Data to Decisions’ online course, ask questions, and get a preview of the content.
This is a 60-minute webinar with Prof. Devavrat Shah to learn more about the upcoming Machine Learning: From Data to Decisions (Online) Program, followed by a Q&A session.
Time.
The FBI is concerned that AI is being used to create deepfake videos that are so convincing they cannot be distinguished from reality.
The alarm was sounded by an FBI executive at a WSJ Pro Cybersecurity Symposium held recently in San Diego. “What we’re concerned with is that, in the digital world we live in now, people will find ways to weaponize deep-learning systems,” stated Chris Piehota, executive assistant director of the FBI’s science and technology division, in an account in WSJPro.
The technology behind deepfakes and other disinformation tactics are enhanced by AI. The FBI is concerned natural security could be compromised by fraudulent videos created to mimic public figures. “As the AI continues to improve and evolve, we’re going to get to a point where there’s no discernible difference between an AI-generated video and an actual video,” Piehota stated.
New types of antibiotics are being developed using an AI machine-learning approach that scans a pool of more than 100 million molecules, including one that works against strains of bacteria previously considered untreatable, according to a recent account in Nature.
For an indication about bifurcating the levels of self-driving, see my indication here: https://aitrends.com/ai-insider/reframing-ai-levels-for-self…-autonomy/
Conclusion
The consensus among self-driving car aficionados is that a robot driver is a long way away from being practical. A robot driver is considered generally to be more futuristic than trying to develop a self-driving car instead.
The lifecycle starts when data is collected or ingested from any source. With the advent of 5G, this includes ever more data that’s streamed and generated in real-time.
Companies realize that in order to grow, connect products and services, or protect their business, they need to become data-driven. In selecting the tools to realize these goals, organizations effectively have two choices: a self-selected combination of analytics tools and applications or a unified platform that handles all. In this blog we will discuss the challenges of the former choice that will provide justification for the latter. Let’s take a step back and ask: what do organizations need in terms of analytics to realize their data-driven goals? What is needed to combat customer churn, provide a predictive maintenance service or identify fraud as it happens? One thing is clear: it is not one single analytical capability. Implementing innovative and differentiating business use cases is not simply selecting the perfect data warehouse solution and calling it good. Today’s solutions require more than a better individually functional tool. Going from data to insight to action demands a complete range of capabilities that spans the data life cycle from the edge to AI.
The kickoff of Lyft’s second challenge comes months after Waymo expanded its public driving data set and launched the $110,000 Waymo Open Dataset competition. Winners were announced mid-June during a workshop at the 2020 Conference on Computer Vision and Pattern Recognition (CVPR), which was held online this year due to the coronavirus pandemic.
Following the release of the Perception Dataset and the conclusion of its 2019 object detection competition, Lyft today shared a new corpus — the Prediction Dataset — containing the logs of movements of cars, pedestrians, and other obstacles encountered by its fleet of 23 autonomous vehicles in Palo Alto. Coinciding with this, the company plans to launch a challenge that will task entrants with predicting the motion of traffic agents.
A longstanding research problem within the self-driving domain is creating models robust and reliable enough to predict traffic motion. Lyft’s data set focuses on motion prediction by including the movement of traffic types its fleet crossed paths with, like cars, cyclists, and pedestrians. This movement is derived from data collected by the sensor suite mounted to the roof of Lyft’s vehicles, which captures things like lidar and radar readings as the vehicles drive tens of thousands of miles:
Logs of over 1,000 hours of traffic agent movement.