Mark Moore at AI Frontiers : Uber Elevate

•

2 likes•1,482 views

AI Frontiers

Uber Elevate

Technology

$60
$39
$90
Cost to
Operate
Personal
Vehicle
uberX
uberPOOL
uberAIR
Mode
of Transit
Cost Per
Passenger
Total
Minutes
60 min
69 min
72 min
10 min
11 min
5 min = 33 min
Price : Time Tradeoffs
(Typical 25 mile trip)
Skyport
access time
Passenger
pooling time
Skyport
egress
time
2 min
3 min 2 min

A
B
C
D
E
F
G
H
I
J
K
60 mi Sizing Mission
25 mi Nominal Mission
150 mph Cruise
1 Pilot + 4 Pax
Reserves: Balked Landing + 6 mi
0%
25%
50%
75%
100%
BatterySoC 3 hrs Continuous Ops

Transformational Costs
Enabled by paradigm shifts in technology and operations
$0
$2
$4
$6
$8
$10
Helicopter uberAIR - Launch uberAIR - Near Term uberAIR - Long Term
$/passengermile
$0.44
$1.84
$5.73
$8.93
eVTOL
Technology
Energy Cost - $0.12 / kWh
$400 per kW Battery
750 Cycle Battery Life
$1.7M Vehicle Cost
Ridesharing
Economics
700 to 2100 hr/year Utilization
2 -> 2.68 Avg Passengers
Scaled Manufacturing
& Autonomy
2000 Cycle Battery
$200 per kW Battery
$750k Vehicle
4 Passengers / No Pilot

Battery
Characteristics
+
Charge
Hover climb
Hover climb
Cruise
Balked
landing

Dallas
LosAngeles
3rd International City Selection

Similar to Mark Moore at AI Frontiers : Uber Elevate

Dep final presentation

John Macnamara

Throttle Energy Business PPT

Kolawole E. Akinmuyiwa

Prelim final presentation

Zack White

ALTERNATE FUEL SOURCE TRAINER; AVIRIDIS H2

jandamd

Smart Flyer Challenge

Aviationshared

Flying robot corp pitch deck jan 2019 compressed

William Piedra

HTML5 in automotive - web2day 2014

Alexandre Morgaut

Srushti Rath - Mode choice modeling for air taxis

Joseph Chow

Design and Operation of UAV

Sai Shubhankar

Design and Operation of UAV

Sai Shubhankar

Drone Delivery System up.pptx

RakibulHassan77

CK2017: Benefits of Electric Buses

WRI Ross Center for Sustainable Cities

Amazon Prime Air is a drone-based delivery service that aims to deliver packages to customers in under 30 minutes using autonomous flying vehicles. Here is a case study on the project: Overview: Amazon Prime Air was launched in 2013 with the goal of providing faster delivery times to customers using unmanned aerial vehicles (UAVs). The project has faced numerous regulatory and technical challenges, but Amazon remains committed to the project and continues to invest in its development. Challenges: One of the primary challenges facing Amazon Prime Air is regulatory approval. The Federal Aviation Administration (FAA) has strict rules governing the use of drones for commercial purposes, including restrictions on flying beyond the line of sight of the operator and flying over people. Amazon has been working with the FAA to gain approval for the service and has conducted numerous tests to demonstrate the safety and reliability of its drones. Another challenge facing the project is the technical complexity of developing and operating a fleet of drones. The drones must be able to navigate complex urban environments, avoid obstacles, and deliver packages safely and accurately. Additionally, the drones must be able to communicate with each other and with ground-based systems to ensure that they are flying safely and efficiently. Solution: To address the challenges facing Amazon Prime Air, the company has invested heavily in research and development to create a fleet of drones that are safe, reliable, and efficient. The drones are equipped with a range of sensors, including GPS, cameras, and sonar, that enable them to navigate and avoid obstacles. Amazon has also developed a sophisticated logistics system to manage the delivery process. Packages are loaded onto the drones at fulfillment centers and then flown to delivery locations using pre-determined flight paths. The drones are equipped with software that enables them to adjust their flight paths in real-time based on weather conditions and other factors. Results: Although Amazon Prime Air has not yet been fully launched, the company has conducted numerous successful tests of its drone technology. In December 2016, Amazon completed its first drone delivery to a customer in Cambridge, England. Since then, the company has continued to refine its technology and conduct additional tests. Amazon Prime Air has the potential to revolutionize the delivery industry by providing faster, more efficient, and more environmentally-friendly delivery services. However, the project still faces numerous challenges, including regulatory approval and technical complexity. Amazon continues to invest in the project, and it is likely that we will see further developments in the coming years.

Amazon case study: Amazon prime air - A delivery by aerial drone project

Harender Singh

Airspector - industrial inspections with drones and AI.pdf

chzhungahora

Similar to Mark Moore at AI Frontiers : Uber Elevate (14)

Dep final presentation

Throttle Energy Business PPT

Prelim final presentation

ALTERNATE FUEL SOURCE TRAINER; AVIRIDIS H2

Smart Flyer Challenge

Flying robot corp pitch deck jan 2019 compressed

HTML5 in automotive - web2day 2014

Srushti Rath - Mode choice modeling for air taxis

Design and Operation of UAV

Drone Delivery System up.pptx

CK2017: Benefits of Electric Buses

Amazon case study: Amazon prime air - A delivery by aerial drone project

Airspector - industrial inspections with drones and AI.pdf

More from AI Frontiers

As video content is becoming mainstream, video summarization is becoming a hot research topic in academia and industry. Video thumbnail generation and summarization has been worked on for years, but deep learning and reinforcement learning is changing the landscape and emerging as the winner for optimal frame selection. Recent advances in GANs are improving the quality, aesthetics and relevancy of the frames to represent the original videos. Come join this session to get an understanding of various challenges and emerging solutions around video summarization.

Divya Jain at AI Frontiers : Video Summarization

AI Frontiers

Topic: How to use big data to enhance AI Outline: 1. Spark ETL Spark SQL Spark Streaming 2. Spark ML Spark ML pipeline Distributed model tuning Spark ML model and data lineage management 3. Spark XGboost XGboost introduction XGboost with Spark XGboost with GPU 4. Spark Deep Learning pipeline Transfer learning Build Spark ML pipeline with TensorFlow Model selection on distributed TF model

Training at AI Frontiers 2018 - LaiOffer Data Session: How Spark Speedup AI

AI Frontiers

Topic：Heuristic Search and how does it apply to self-driving cars? (for Beginners) Outline: 1. Technologies behind self-driving vehicles - Motion Planning - Decision Making 2. Graph Search Algorithms - Depth-First Search - Breadth-First Search - A* Search 3. Incremental Heuristic Search Algorithms - Repeated A* Search - Adaptive A* - Generalized Fringe-Retrieving A*

Training at AI Frontiers 2018 - LaiOffer Self-Driving-Car-Lecture 1: Heuristi...

AI Frontiers

In this tutorial I will introduce recent work in applying weak supervision and reinforcement learning to Questions Answering (QA) systems. Specifically we discuss the semantic parsing task for which natural language queries are converted to computation steps on knowledge graphs or data tables and produce the expected answers. State-of-the-art results can be achieved by novel memory structure for sequence models and improvements in reinforcement learning algorithms. Related code and experiment setup can be found at https://github.com/crazydonkey200/neural-symbolic-machines. Related paper: https://openreview.net/pdf?id=SyK00v5xx.

Training at AI Frontiers 2018 - Ni Lao: Weakly Supervised Natural Language Un...

AI Frontiers

Training at AI Frontiers 2018 - LaiOffer Self-Driving-Car-lecture 2: Incremen...

AI Frontiers

Instructor: Mat Leonard Outline 1. Text Processing Using Python + NLTK Cleaning Normalization Tokenization Part-of-speech Tagging Stemming and Lemmatization 2. Feature Extraction Bag of Words TF-IDF Word Embeddings Word2Vec GloVe 3. Topic Modeling Latent Variables Beta and Dirichlet Distributions Laten Dirichlet Allocation 4. NLP with Deep Learning Neural Networks Recurrent Neural Networks (RNNs) Word Embeddings Sentiment Analysis with RNNs

Training at AI Frontiers 2018 - Udacity: Enhancing NLP with Deep Neural Networks

AI Frontiers

Training at AI Frontiers 2018 - LaiOffer Self-Driving-Car-Lecture 3: Any-Angl...

AI Frontiers

Training at AI Frontiers 2018 - Lukasz Kaiser: Sequence to Sequence Learning ...

AI Frontiers

In recent years, machine learning has undoubtedly been hugely successful in driving progress in AI applications. However, as we will explore in this talk, even state-of-the-art systems have "blind spots" which make them generalize poorly out of domain and render them vulnerable to adversarial examples. We then suggest that more unsupervised learning settings can encourage the development of more robust systems. We show positive results on two tasks: (i) text style and attribute transfer, the task of converting a sentence with one attribute (e.g., sentiment) to one with another; and (ii) solving SAT instances (classical problems requiring logical reasoning) using end-to-end neural networks.

Percy Liang at AI Frontiers : Pushing the Limits of Machine Learning

AI Frontiers

I will present several advances in deep learning from OpenAI. First, I will present OpenAI Five, a neural network that learned to play on par with some of the strongest professional Dota 2 teams in the world in an 18-hero version of the game. Next, I will present Dactyl, a human-like robot hand trained entirely in simulation with reinforcement learning that has achieved unprecedented dexterity on a physical robot. I will also present our results on unsupervised learning in language, that show that pre-training and finetuning can achieve a significant improvement over state of the art. Finally, I will present an overview of the historical progress in the field.

Ilya Sutskever at AI Frontiers : Progress towards the OpenAI mission

AI Frontiers

The availability of affordable electronics components, powerful embedded microprocessors, and ubiquitous internet access and WiFi in the household has enabled a new generation of connected consumer robots. In 2015, iRobot launched the Roomba 980, introducing intelligent visual navigation to its successful line of vacuum cleaning robots. In 2018, iRobot launched the Roomba i7, equipped with the latest mapping and navigation technology that provides spatial information to the broader ecosystem of connected devices in the home. In this talk, I will describe the challenges and the potential of introducing consumer robots capable of developing spatial context by exploring the physical space of the home, and I will elaborate on the impact of AI in the future of robotics applications. Moreover, I will describe our vision of the Smart Home, an AI-powered home that maintains itself and magically just does the right thing in anticipation of occupant needs. This home will be built on an ecosystem of connected and coordinated robots, sensors, and devices that provides the occupants with a high quality of life by seamlessly responding to the needs of daily living – from comfort to convenience to security to efficiency.

Mario Munich at AI Frontiers : Consumer robotics: embedding affordable AI in ...

AI Frontiers

Arnaud Thiercelin at AI Frontiers : AI in the Sky

AI Frontiers

As the data and models scale, it becomes necessary to have multiple processing units for both training and inference. SignSGD is a gradient compression algorithm that only transmits the sign of the stochastic gradients during distributed training. This algorithm uses 32 times less communication per iteration than distributed SGD. We show that signSGD obtains free lunch both in theory and practice: no loss in accuracy while yielding speedups. Pushing the current boundaries of deep learning also requires using multiple dimensions and modalities. These can be encoded into tensors, which are natural extensions of matrices. These functionalities are available in the Tensorly package with multiple backend interfaces for large-scale deep learning.

Anima Anandkumar at AI Frontiers : Modern ML : Deep, distributed, Multi-dimen...

AI Frontiers

Wei Xu at AI Frontiers : Language Learning in an Interactive and Embodied Set...

AI Frontiers

The use of AI for voice search and image recognition is talked about often. Enterprises, however, have different challenges and requirements. In this talk, we will focus on talking about use cases in the enterprise and challenges in building out AI solutions. We will talk about how an Auto-machine learning software for videos and images called PowerAI Vision enables quick AI model training & deployment for various enterprise use cases.

Sumit Gupta at AI Frontiers : AI for Enterprise

AI Frontiers

Deep Reinforcement Learning (DRL) has made strong progress in many tasks, such as board games, robotics, navigation, neural architecture search, etc. I will present our recent open-sourced DRL frameworks to facilitate game research and development. Our framework is scalable so we can can reproduce AlphaGoZero and AlphaZero using 2000 GPUs, achieving super-human performance of Go AI that beats 4 top-30 professional players. We also show usability of our platform by training agents in real-time strategy games, and show interesting behaviors with a small amount of resource.

Yuandong Tian at AI Frontiers : Planning in Reinforcement Learning

AI Frontiers

The latest AI advances have the potential to massively improve our health and well being. However, most of the work is yet to be done. In this talk, we will explore the most important opportunities for AI in healthcare. For example, we will explore how AI can diagnose major life-threatening conditions even before those conditions emerge. We will talk about AI ability to recommend dramatically more effective and less harmful treatment plans based on AI understanding of patient's medical history and current conditions. Finally, we will talk about AI role in making our healthcare system effective and affordable for everyone.

Alex Ermolaev at AI Frontiers : Major Applications of AI in Healthcare

AI Frontiers

Games have been leveraging AI since the 1950s, when people built a rules-based AI engine that played tic-tac-toe. With technological advances over the years, AI has become increasingly popular and widely used in the gaming industry. The typical characteristics of games and game development makes them an ideal playground for practicing and implementing AI techniques, especially deep learning and reinforcement learning. Most games are well scoped; it is relatively easy to generate and use the data; and states/actions/rewards are relatively clear. In this talk, I will show a couple of use cases where ML/AI helps in-game development and enhances player experience. Examples include AI agents playing game and services that provide personalized experience to players.

Long Lin at AI Frontiers : AI in Gaming

AI Frontiers

Melissa Goldman at AI Frontiers : AI & Finance

AI Frontiers

Li Deng at AI Frontiers : From Modeling Speech/Language to Modeling Financial...

AI Frontiers

More from AI Frontiers (20)