Click here to learn more about Gilad David Maayan. There are a significant number of investments in the automotive industry nowadays. The majority of these investments focus on artificial intelligence (AI) and the optimization of self-driving technology. Meanwhile, new mobility systems and players are making their way into the automotive market. Tesla is trying to improve its autopilot system, Uber is testing robo-taxis, and Google is developing self-driving cars.
Amazon recently bought up a self-driving autonomous ride-hailing startup Zoox, which is being claimed as the most ambitious step that the tech giant has taken in the recent past. Reportedly a $1.2 billion deal, the acquisition of the Robo-taxi company is not just to build upon its capabilities to deliver packages but actively set foot in the autonomous driving industry. While Amazon has invested heavily in developing drones or autonomous delivery robots in the past, its investment in self-driving vehicles has recently gained traction. Some of the other ventures of the company have been in self-driving truck Embark when CNBC reported that it had been hauling Amazon cargo on some of its test runs. For instance, in drones, Amazon has designed a future delivery system to safely deliver packages to customers in a short period of time.
This part of the series looks at the future of AI with much of the focus in the period after 2025. The leading AI researcher, Geoff Hinton, stated that it is very hard to predict what advances AI will bring beyond five years, noting that exponential progress makes the uncertainty too great. This article will therefore consider both the opportunities as well as the challenges that we will face along the way across different sectors of the economy. It is not intended to be exhaustive. Machine Learning is defined as the field of AI that applies statistical methods to enable computer systems to learn from the data towards an end goal. The term was introduced by Arthur Samuel in 1959. Deep Learning refers to the field of Neural Networks with several hidden layers. Such a neural network is often referred to as a deep neural network. Neural Networks are biologically inspired networks that extract abstract features from the data in a hierarchical fashion. Deep Reinforcement Learning will be considered in greater detail in part 3 of this series. For the purpose of this article I will consider AI to cover Machine Learning and Deep Learning. Narrow AI: the field of AI where the machine is designed to perform a single task and the machine gets very good at performing that particular task.
Disclaimer: The following is based on my observations of machine learning teams -- not an academic survey of the industry. For years, the biggest bottleneck to production deep learning was simple: we needed models that worked. And over the last decade--thanks to companies with access to unprecedented amounts of data and computer power, as well as new model architectures--we've largely cleared that hurdle. We may not have fully autonomous vehicles or Bladerunner-esque AI, but when you call an Uber, you get an accurate ETA prediction. When you open an email in Gmail, you get a contextually appropriate suggestion from Smart Compose.
Right now, a minivan with no one behind the steering wheel is driving through a suburb of Phoenix, Arizona. And while that may seem alarming, the company that built the "brain" powering the car's autonomy wants to assure you that it's totally safe. Waymo, the self-driving unit of Alphabet, is the only company in the world to have fully driverless vehicles on public roads today. That was made possible by a sophisticated set of neural networks powered by machine learning about which very is little is known -- until now. For the first time, Waymo is lifting the curtain on what is arguably the most important (and most difficult-to-understand) piece of its technology stack. The company, which is ahead in the self-driving car race by most metrics, confidently asserts that its cars have the most advanced brains on the road today. Anyone can buy a bunch of cameras and LIDAR sensors, slap them on a car, and call it autonomous. But training a self-driving car to behave like a human driver, or, more importantly, to drive better than a human, is on the bleeding edge of artificial intelligence research.
Alphabet is using its dominance in the search and advertising spaces -- and its massive size -- to find its next billion-dollar business. From healthcare to smart cities to banking, here are 10 industries the tech giant is targeting. With growing threats from its big tech peers Microsoft, Apple, and Amazon, Alphabet's drive to disrupt has become more urgent than ever before. The conglomerate is leveraging the power of its first moats -- search and advertising -- and its massive scale to find its next billion-dollar businesses. To protect its current profits and grow more broadly, Alphabet is edging its way into industries adjacent to the ones where it has already found success and entering new spaces entirely to find opportunities for disruption. Evidence of Alphabet's efforts is showing up in several major industries. For example, the company is using artificial intelligence to understand the causes of diseases like diabetes and cancer and how to treat them. Those learnings feed into community health projects that serve the public, and also help Alphabet's effort to build smart cities. Elsewhere, Alphabet is using its scale to build a better virtual assistant and own the consumer electronics software layer. It's also leveraging that scale to build a new kind of Google Pay-operated checking account. In this report, we examine how Alphabet and its subsidiaries are currently working to disrupt 10 major industries -- from electronics to healthcare to transportation to banking -- and what else might be on the horizon. Within the world of consumer electronics, Alphabet has already found dominance with one product: Android. Mobile operating system market share globally is controlled by the Linux-based OS that Google acquired in 2005 to fend off Microsoft and Windows Mobile. Today, however, Alphabet's consumer electronics strategy is being driven by its work in artificial intelligence. Google is building some of its own hardware under the Made by Google line -- including the Pixel smartphone, the Chromebook, and the Google Home -- but the company is doing more important work on hardware-agnostic software products like Google Assistant (which is even available on iOS).
Autonomous driving is of great interest to industry and academia alike. The use of machine learning approaches for autonomous driving has long been studied, but mostly in the context of perception. In this paper we take a deeper look on the so called end-to-end approaches for autonomous driving, where the entire driving pipeline is replaced with a single neural network. We review the learning methods, input and output modalities, network architectures and evaluation schemes in end-to-end driving literature. Interpretability and safety are discussed separately, as they remain challenging for this approach. Beyond providing a comprehensive overview of existing methods, we conclude the review with an architecture that combines the most promising elements of the end-to-end autonomous driving systems.
Planning is an essential topic in the realm of automated driving. Besides planning algorithms that are widely covered in the literature, planning requires different software tools for its development, validation, and execution. This paper presents a survey of such tools including map representations, communication, traffic rules, open-source planning stacks and middleware, simulation, and visualization tools as well as benchmarks. We start by defining the planning task and different supporting tools. Next, we provide a comprehensive review of state-of-the-art developments and analysis of relations among them. Finally, we discuss the current gaps and suggest future research directions.
Matt Velloso, a technical advisor to Microsoft's CEO, got 24,000 likes on this tweet posted in November 2018: "Difference between machine learning and AI: If it is written in Python, it's probably machine learning. If it is written in PowerPoint, it's probably AI." This sums up the AI frenzy that has seized marketing departments and media pundits for the last three years. With the coming of age of machine learning and deep learning, many have hastily jumped to the conclusion that, at long last, humans are on the verge of creating a machine in their own image, capable of autonomous thinking--general artificial intelligence somehow emerging from more and more complex algorithms. Yes, neural networks have revolutionized the computer vision space and transformed natural language processing.
The Evaluation Committee has completed the evaluations for the AI Time Journal TOP 25 Artificial Intelligence Companies 2019. The objective of the AI Time Journal TOP 25 Artificial Intelligence Companies 2019 Initiative is to give recognition and showcase AI companies for their contribution in 2019 to applying Artificial Intelligence, Machine Learning and Deep Learning to solve significant and complex problems and improve people's lives in a multitude of domains including Healthcare, Education, Finance, Autonomous Vehicles and more. Note: companies that employ evaluation committee members have not been included in the evaluations. Alvin Foo: "The adoption of Artificial Intelligence, Deep Learning and Machine Learning to facilitate human decision-making will continue to accelerate. While it creates opportunities to automate, it will also open up new challenges for IT team to address the potential increase in cyberattack. The advancement of AI provides a scalable cybersecurity solution for companies to automate and protect their IT assets. The future of cybersecurity will be AI-powered!"