If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Can AI Make A Channel Trailer? everydAI Channel Trailer 989 views 2 months ago everydAI is a YouTube channel focused on highlighting the ways we interact with artificial intelligence every day. Follow me on Twitter! http://twitter.com/jordanbh... Become a Patron! Why Are Dating Apps So Bad? - Duration: 6 minutes, 10 seconds. Your Identity Is NOT Private - Duration: 6 minutes, 12 seconds. THIS IS A DEEPFAKE #AI101 - Duration: 9 minutes, 23 seconds.
Where have we been, where are we now, and where are we headed? I've written this in first person format so the reader can see it through my eyes from the trenches, including observations, conclusions and questions. In looking back over time, it helps me to revisit milestone articles I've written and read. Although my first published paper on our AI R&D was in 2002, I'll start by looking back at a futuristic scenario on the American healthcare system published in 2010. A decade is a nice round number and was also the same year my old friend and former business partner Russell Borland passed away unexpectedly. Russell was a close friend from the early 1980s on. He was involved with our journey at KYield since inception in the mid 1990s, so losing him was a shock. I emailed the healthcare scenario to Vint Cerf who asked me if he could share it -- the paper was on the web so of course I said yes. The next thing I know enormous numbers of downloads were occurring (don't underestimate Vint's network, or Google's). We stopped counting at several million views from healthcare institutions all over the world, and that's just on our site (others have published the paper on the web without permission).
Sign in to report inappropriate content. Robot being trained for 500 iterations to learn to control inclination of torso. This is done by selecting goal inclinations the robot attempts to reach, while creating a network of postures to move between. In the final evaluation (0:49), goals are selected manually, to force the robot to roll around 360 degrees to reach them.
In this blog, I'll discuss how I worked collaboratively with various domain experts, using reinforcement learning to develop innovative solutions in rocket engine development. In doing so, I'll demonstrate the application of ML techniques to the manufacturing industry and the role of the Machine Learning Product Manager. Machine learning (ML) has had an incredible impact across industries with numerous applications such as personalized TV recommendations and dynamic price models in your rideshare app. Because it is such a core component to the success of companies in the tech industry, advances in ML research and applications are developing at an astonishing rate. For industries outside of tech, ML can be utilized to personalize a user's experience, automate laborious tasks and optimize subjective decision making.
In previous posts (here and here) I introduced Double Q learning and the Dueling Q architecture. These followed on from posts about deep Q learning, and showed how double Q and dueling Q learning is superior to vanilla deep Q learning. However, these posts only included examples of simplistic environments like the OpenAI Cartpole environment. These types of environments are good to learn on, but more complicated environments are both more interesting and fun. They also demonstrate better the complexities of implementing deep reinforcement learning in realistic cases.
Planning methods can solve temporally extended sequential decision making problems by composing simple behaviors. However, planning requires suitable abstractions for the states and transitions, which typically need to be designed by hand. In contrast, reinforcement learning (RL) can acquire behaviors from low-level inputs directly, but struggles with temporally extended tasks. Can we utilize reinforcement learning to automatically form the abstractions needed for planning, thus obtaining the best of both approaches? We show that goal-conditioned policies learned with RL can be incorporated into planning, such that a planner can focus on which states to reach, rather than how those states are reached.
The study of object representations in computer vision has primarily focused on developing representations that are useful for image classification, object detection, or semantic segmentation as downstream tasks. In this work we aim to learn object representations that are useful for control and reinforcement learning (RL). To this end, we introduce Transporter, a neural network architecture for discovering concise geometric object representations in terms of keypoints or image-space coordinates. Our method learns from raw video frames in a fully unsupervised manner, by transporting learnt image features between video frames using a keypoint bottleneck. The discovered keypoints track objects and object parts across long time-horizons more accurately than recent similar methods.