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) …
Once upon a time I was trying to train a speaker recognition model with TIMIT dataset. I used Alexnet since I wanted to try this with a smaller model first. I have used a softmax layer at the end. The inputs were spectrograms of voices of different people and labels were the speaker IDs. MSELoss was used with the PyTorch library.
Today, every organization in almost every industry is keen to leverage the power of artificial intelligence (AI) to better understand its business, clients, products, and processes. The applications of AI continue to grow. Data is everywhere, but to make it speak we need the right project goals, mindset and resources. Being a black belt in martial arts helped me reinforce six core life skills in my personal and professional life – belief, communication, respect, honesty, self-esteem, and discipline. As I continued to train, I realized that AI project principals are no different.
Before we do any further analysis using our data we need to split the entire data set into three different subsets: training set, validation set and test set. The test set is also referred to as hold-out set and once we split it from the remaining data we do not touch it again until we have trained and tweaked our Machine Learning models to a point where we think they will perform well on data that they have never seen before. We split the remaining data into a training and a validation set. This allows us to train our models on the training data and then evaluate their performance on the validation data. In theory, we can then go and tweak our models and evaluate them on the validation data again and thereby find ways to improve model performance.
To demonstrate a practical use of an ML pipeline, this example uses the sample HVAC.csv data file that comes pre-loaded on the default storage for your HDInsight cluster, either Azure Storage or Data Lake Storage. HVAC.csv contains a set of times with both target and actual temperatures for HVAC (heating, ventilation, and air conditioning) systems in various buildings. The goal is to train the model on the data, and produce a forecast temperature for a given building.
In our previous blog, we outlined that Supervised Machine Learning (ML) models need labeled data, but majority of the data collected in the raw format lacks labels. So, the first step before building a ML model would be to get the raw data labeled by domain experts. To do so, we had outlined how Doccano is an easy tool for collaborative text annotation. However, not all data that gets collected is in text format, many a times we end up with a bunch of images but the end goal is again to build a Supervised ML model. Like stated previously, the first step would be to tag these images with specific labels.
Full-stack Developer with a front-end preference is sought after by a data analytics company in Essex. Due to recent growth and funding, the company are looking to grow their development team to work on their bespoke client portal and mobile app for their international clients. You will also have the opportunity to get involved with their AI projects involving Computer Vision and 3D modelling. The following would be nice to have, however, if there is something you do not have experience with, you would have the opportunity to be trained accordingly. The technologies, various projects and predicted growth make this role a challenging and exciting prospect for any successful candidate, especially if you are interested in the AI space and appreciate the opportunity to develop and learn new skills.
It seems like only last year that we were arguing about whether the slow-release rollout of the 1.5 billion parameter Generative Pretrained Transformer-2 (GPT-2) was reasonable. If the debate seems recent, that's because it is (writing from 2020): The notorious GPT-2 model was announced by OpenAI in February 2019, but it wasn't fully released until nearly 9 months later (although it was replicated before that). The release schedule was admittedly somewhat experimental, meant more to foster discussion of responsible open publishing, rather than a last-ditch effort to avert an AI apocalypse. All that is a bit moot by now because not only has OpenAI trained a much larger language model in GPT-3, but you can sign up to access it through their new API. Comparing GPT-3 to GPT-2 is like comparing apples to, well, raisins, because the model is about that much larger.
If you'll permit us to spoil a little bit of movie magic, many of the sound effects you hear in film and TV are actually recreated and edited in later by Foley artists. Now, researchers are attempting to create sound effect-generating artificial intelligence to see if they can do their jobs well enough to fool the general population. In a recent study, a small cohort of participants fell for the trick: Most they believed that the AI-generated noises were real, IEEE Spectrum reports. Sometimes, they even chose the AI version over a video's original audio. In the study, which was published in June in the paper IEEE Transactions on Multimedia, 41 of the 53 participants were fooled by the AI-generated sounds.
In a new short video that has surfaced on TikTok, apes have been spotted flying drones. The drone is an Autel Robotics Evo and the apes are located in a Myrtle Beach Safari in South Carolina. The video was taken by photographer Nick B. and shows two apes flying a drone. One is standing up using the drone's controller while the other sits beside him holding the drone's case. The video is particularly impressive as the ape seems very much in control of the drone.
Last week, researchers of the University of Sherbrooke presented the Sherbrook Multimodal Autonomous Drone (S-MAD) at the Living Machine 2017 conference at Stanford University, where it won the "Best Robotics Paper Award." According to NewAtlas, the Createk Design Lab and Sherbrooke researchers looked to birds and their last-minute perching instincts and abilities and infused those into their unmanned aerial vehicle. Not only that, but the company went with a fixed-wing approach--again, hewing closer to a bird's anatomy as opposed to the typical rotor-based drone design. Makes sense, as the Living Machine conference is all about the symbiotic relationship between nature and machinery, rewarding those companies most creative and effective in their designs. The Sherbrooke researchers apparently tested thousands of aerodynamic model simulations in order to perfect the design's flight and perching capabilities, before actually succeeding in developing a capable fixed-wing drone with the proper thrust and pitch required to pull the aforementioned behavior off correctly.