It was an exciting week to be an electric vehicle fan--if a real up-and-down one. On Monday, Elon Musk welcomed investors to Tesla's Palo Alto headquarters for the company's first Autonomy Day, where he made some serious news: He promised an all-electric, 1-million-car fleet of self-driving Tesla taxis would roam the Earth by next year. The electric carmaker, which loves to do things differently, used the event to tout its new self-driving chip, and double down on its aggressive and heterodox approach to autonomous vehicles. Who cares if the self-driving experts are skeptical? By Thursday, electric vehicle fandom got messier.

Many people in the tech world now have a solid understanding of AI. Others are just getting started and asking questions like: What are the differences between deep learning and machine learning? How are they different, and how can they benefit organizations? Enterprises and their leaders who are looking to get started should first get familiar with the fundamentals of deep learning and the corresponding terminology, as well as understand the current challenges to AI adoption and how to address them. In this article, I'll aim to provide a definitive overview of the topic, along with links to several resources that you may find useful.

This week, I interviewed Yuval Noah Harari, the author of three best-selling books about the history and future of our species, and Fei-Fei Li, one of the pioneers in the field of artificial intelligence. The event was hosted by the Stanford Center for Ethics and Society, the Stanford Institute for Human-Centered Artificial Intelligence, and the Stanford Humanities Center. A transcript of the event follows, and a video is posted below. Nicholas Thompson: Thank you, Stanford, for inviting us all here. I want this conversation to have three parts: First, lay out where we are; then talk about some of the choices we have to make now; and last, talk about some advice for all the wonderful people in the hall. Yuval, the last time we talked, you said many, many brilliant things, but one that stuck out was a line where you said, "We are not just in a technological crisis. We are in a philosophical crisis." So explain what you meant and explain how it ties to AI. Let's get going with a note of ...

Artificial intelligence can work wonders, but often it works in mysterious ways. Machine learning is based on the principle that a software program can analyze a huge set of data and fine-tune its algorithms to detect patterns and come up with solutions that humans may miss. That's how Google DeepMind's Alpha Go AI agent learned to play the ancient game of Go (and other games) well enough to beat expert players. But if programmers and users can't figure out how AI algorithms came up with their results, that black-box behavior can be a cause for concern. It may become impossible to judge whether AI agents have picked up unjustified biases or racial profiling from their data sets.

Traditional recruiting methods have typically had a poor track record at matching candidates with employers. San Francisco-based startup Scoutible is betting its AI-based gaming solution can do better. Most seasoned hiring managers know the sinking feeling that comes with realizing within months of onboarding that a new professional is ill-suited to the role. The pressing work that prompted the hire in the first place may stall, eliciting outcry from stakeholders and frustrating colleagues charged with picking up the slack. Meanwhile, the prospect of letting the employee go and starting the search anew creates even more headaches--not to mention added expense.

Convergence is accelerating disruption… everywhere! Exponential technologies are colliding into each other, reinventing products, services, and industries. As AI algorithms such as Siri and Alexa can process your voice and output helpful responses, other AIs like Face can recognize faces. And yet others create art from scribbles, or even diagnose medical conditions. Let's dive into AI and convergence.

MIT engineers have designed tiny robots that can help drug-delivery nanoparticles push their way out of the bloodstream and into a tumor or another disease site. Like crafts in "Fantastic Voyage" -- a 1960s science fiction film in which a submarine crew shrinks in size and roams a body to repair damaged cells -- the robots swim through the bloodstream, creating a current that drags nanoparticles along with them. The magnetic microrobots, inspired by bacterial propulsion, could help to overcome one of the biggest obstacles to delivering drugs with nanoparticles: getting the particles to exit blood vessels and accumulate in the right place. "When you put nanomaterials in the bloodstream and target them to diseased tissue, the biggest barrier to that kind of payload getting into the tissue is the lining of the blood vessel," says Sangeeta Bhatia, the John and Dorothy Wilson Professor of Health Sciences and Technology and Electrical Engineering and Computer Science, a member of MIT's Koch Institute for Integrative Cancer Research and its Institute for Medical Engineering and Science, and the senior author of the study. "Our idea was to see if you can use magnetism to create fluid forces that push nanoparticles into the tissue," adds Simone Schuerle, a former MIT postdoc and lead author of the paper, which appears in the April 26 issue of Science Advances.

If you live in southwest Virginia, don't be surprised at the sight of a drone winging its way on another airborne delivery run over your neighborhood soon. Wing, the drone delivery service spun off from Alphabet's Google, hopes to start flights to homes and businesses in the Blacksburg and Christiansburg areas by the end of the year now that it has the blessings of the Federal Aviation Administration. The FAA announced earlier this week that it had approved Wing as the first air carrier certified for drone delivery. In receiving the certification, Wing beat Amazon to the punch despite all the attention that the online merchandise giant has drawn over its interest in deliveries by air. Both companies, along with others, have been racing to develop drones as a more cost-effective way of delivering small, high-value orders, like medicine.

Neural architectures inspired by our own human cognitive system, such as the recently introduced world models, have been shown to outperform traditional deep reinforcement learning (RL) methods in a variety of different domains. Instead of the relatively simple architectures employed in most RL experiments, world models rely on multiple different neural components that are responsible for visual information processing, memory, and decision-making. However, so far the components of these models have to be trained separately and through a variety of specialized training methods. This paper demonstrates the surprising finding that models with the same precise parts can be instead efficiently trained end-to-end through a genetic algorithm (GA), reaching a comparable performance to the original world model by solving a challenging car racing task. An analysis of the evolved visual and memory system indicates that they include a similar effective representation to the system trained through gradient descent. Additionally, in contrast to gradient descent methods that struggle with discrete variables, GAs also work directly with such representations, opening up opportunities for classical planning in latent space. This paper adds additional evidence on the effectiveness of deep neuroevolution for tasks that require the intricate orchestration of multiple components in complex heterogeneous architectures.

This work formulates a novel loss term which can be appended to an RGB only image localization network's loss function to improve its performance. A common technique used when regressing a camera's pose from an image is to formulate the loss as a linear combination of positional and rotational error (using tuned hyperparameters as coefficients). In this work we observe that changes to rotation and position mutually affect the captured image, and in order to improve performance, a network's loss function should include a term which combines error in both position and rotation. To that end we design a geometric loss term which considers the similarity between the predicted and ground truth poses using both position and rotation, and use it to augment the existing image localization network PoseNet. The loss term is simply appended to the loss function of the already existing image localization network. We achieve improvements in the localization accuracy of the network for indoor scenes: with decreases of up to 9.64% and 2.99% in the median positional and rotational error when compared to similar pipelines.