we are going to take a small peak into how our optimizer does all the hard work for us computing gradients for arbitrary functions. And then we are going to look together at the important topic of regularization, which will enable us to train much, much larger models.
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This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI.
“I’m extremely confident that level 5 [self-driving cars] or essentially complete autonomy will happen, and I think it will happen very quickly,” Tesla CEO Elon Musk said in a video message to the World Artificial Intelligence Conference in Shanghai earlier this month. “I remain confident that we will have the basic functionality for level 5 autonomy complete this year.”
Musk’s remarks triggered much discussion in the media about whether we are close to having full self-driving cars on our roads. Like many other software engineers, I don’t think we’ll be seeing driverless cars (I mean cars that don’t have human drivers) any time soon, let alone the end of this year.
I wrote a column about this on PCMag, and received a lot of feedback (both positive and negative). So I decided to write a more technical and detailed version of my views about the state of self-driving cars. I will explain why, in its current state, deep learning, the technology used in Tesla’s Autopilot, won’t be able to solve the challenges of level 5 autonomous driving. I will also discuss the pathways that I think will lead to the deployment of driverless cars on roads.
This is how the U.S. National Highway Traffic Safety Administration defines level 5 self-driving cars: “The vehicle can do all the driving in all circumstances, [and] the human occupants are just passengers and need never be involved in driving.”
Basically, a fully autonomous car doesn’t even need a steering wheel and a driver’s seat. The passengers should be able to spend their time in the car doing more productive work.
Level 5 autonomy: Full self-driving cars don’t need a driver’s seat. Everyone is a passenger. (Image credit: Depositphotos)
Current self-driving technology stands at level 2, or partial automation. Tesla’s Autopilot can perform some functions such as acceleration, steering, and braking under specific conditions. And drivers must always maintain control of the car and keep their hands on the steering wheel when Autopilot is on.
Other companies that are testing self-driving technology still have drivers behind the wheel to jump in when the AI makes mistakes (as well as for legal reasons).
Another important point Musk raised in his remarks is that he believes Tesla cars will achieve level 5 autonomy “simply by making software improvements.”
Other self-driving car companies, including Waymo and Uber, use lidars, hardware that projects laser to create three-dimensional maps of the car’s surroundings. Tesla, on the other hand, relies mainly on cameras powered by computer vision software to navigate roads and streets. Tesla use deep neural networks to detect roads, cars, objects, and people in video feeds from eight cameras installed around the vehicle. (Tesla also has a front-facing radar and ultrasonic object detectors, but those have mostly minor roles.)
There’s a logic to Tesla’s computer vision–only approach: We humans, too, mostly rely on our vision system to drive. We don’t have 3D mapping hardware wired to our brains to detect objects and avoid collisions.
But here’s where things fall apart. Current neural networks can at best replicate a rough imitation of the human vision system. Deep learning has distinct limits that prevent it from making sense of the world in the way humans do. Neural networks require huge amounts of training data to work reliably, and they don’t have the flexibility of humans when facing a novel situation not included in their training data.
This is something Musk tacitly acknowledged at in his remarks. “[Tesla Autopilot] does not work quite as well in China as it does in the U.S. because most of our engineering is in the U.S.” This is where most of the training data for Tesla’s computer vision algorithms come from.
Human drivers also need to adapt themselves to new settings and environments, such as a new city or town, or a weather condition they haven’t experienced before (snow- or ice-covered roads, dirt tracks, heavy mist). However, we use intuitive physics, commonsense, and our knowledge of how the world works to make rational decisions when we deal with new situations.
We understand causality and can determine which events cause others. We also understand the goals and intents of other rational actors in our environments and reliably predict what their next move might be. For instance, if it’s the first time that you see an unattended toddler on the sidewalk, you automatically know that you have pay extra attention and be careful. And what if you meet a stray elephant in the street for the first time? Do you need previous training examples to know that you should probably make a detour?
But for the time being, deep learning algorithms don’t have such capabilities, therefore they need to be pre-trained for every possible situation they encounter.
There’s already a body of evidence that shows Tesla’s deep learning algorithms are not very good at dealing with unexpected scenery even in the environments that they are adapted to. In 2016, a Tesla crashed into a tractor-trailer truck because its AI algorithm failed to detect the vehicle against the brightly lit sky. In another incident, a Tesla self-drove into a concrete barrier, killing the driver. And there have been several incidents of Tesla vehicles on Autopilot crashing into parked fire trucks and overturned vehicles. In all cases, the neural network was seeing a scene that was not included in its training data or was too different from what it had been trained on.
Tesla is constantly updating its deep learning models to deal with “edge cases,” as these new situations are called. But the problem is, we don’t know how many of these edge cases exist. They’re virtually limitless, which is what it is often referred to as the “long tail” of problems deep learning must solve.
Musk also pointed this out in his remarks to the Shanghai AI conference: “I think there are no fundamental challenges remaining for level 5 autonomy. There are many small problems, and then there’s the challenge of solving all those small problems and then putting the whole system together, and just keep addressing the long tail of problems.”
#blog #artificial intelligence #deep learning #demystifying ai #self-driving cars #deep learning
The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.
Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:
By Dipanjan Sarkar
**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.
By Divye Singh
**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.
By Dongsuk Hong
About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.
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The autonomous tech world has 2 giants: Tesla & Waymo.
In my last article, I broke down Tesla’s computer vision system.
Today, we’ll take a look at what Waymo, Google’s self-driving car division, is doing under the hood.
Waymo has driven more than 20 million miles on public roads in over 25 cities. They also drove tens of billions of miles in simulations (as we’ll see later in the article). Additionally, Waymo is operating a taxi service in the United States, transporting passengers—for real—without a driver.
Given their growing presence in the real world, I want to dive deep into Waymo’s technology so you can understand what’s actually behind this giant.
As with every self-driving vehicle, Waymo implements their tech using our 4 main steps:** perception, localization, planning, and control.**
In this post, the only thing I’ll not talk about is control. For Waymo, prediction (which is part of planning), is another core pillar, and it will be treated independently here.
Let’s start with perception.
The core component of most robotics systems is the perception task. In Waymo’s case, perception includes estimations of obstacles and the localization of the self-driving vehicle.
#machine-learning #self-driving-cars #deep-learning #computer-vision #heartbeat #deep learning
One of the coolest things that happened in last decade is that Google released a framework for deep learning called TensorFlow. TensorFlow makes all that hard work that we’ve done superfluous because now you have a software framework. They can very easily configure and train deep networks and TensorFlow can be run on many machines at the same time. So, in this medium article, we’ll focus on TensorFlow because if one becomes a machine learning expert, these are the tools that people in the trade use everyday.
A convolutional neural network is a specialized type of deep neural network that turns out to be particularly important for self-driving cars.
Deep Learning is an exciting branch of** machine learning (ML) **that uses data, lots of data, to teach computers how to do or learn things only humans can do. Myself, I’m very interested in solving the problem of perception, recognizing what’s in an image what people are saying when they’re talking on their phone, helping robots explore the world and interact with it. Deep Learning emerged as a central tool to solve perception problems in recent time. It’s the state of the art on everything having to do with computer vision and speech recognition. But there is more. Increasingly, people are finding
that Deep Learning is a much better tool to solve complex problems, like discovering new medicines, understanding natural language (NLP), understanding documents (OCR), and, for example, ranking them for search.
Many companies today, have made deep learning a central part of their mission learning toolkit. Facebook, Baidu, Microsoft and Google, are all using deep learning in their products and pushing the research forward. It’s easy to understand why, deep learning shines wherever there is lots of data and complex problems to solve. And all these companies are facing lots of complicated problems. Understanding what’s in an image, to help you find it. Or** translating a document into another language** that you can speak.
Now, I will explore a continuum of complexity from very simple models to very large ones that one will still be able to train in minutes on a personal computer to very elaborate tasks like predicting the meaning of words or classifying images. One of the nice things about deep learning is it’s really a family of techniques that adapts to all sorts of data and all sorts of problems. All of them using a common infrastructure and a common langauge to describe things.
#artificial-intelligence #self-driving-cars #machine-learning #data-science #deep-learning