Yesterday, AIM published an article on how difficult it is for the small labs and individual researchers to persevere in the high compute, high-cost industry of deep learning. Today, the policymakers of the US have introduced a new bill that will ensure deep learning is affordable for all. The National AI Research Resource Task Force Act was introduced in the House by Representatives Anna G. Eshoo (D-CA) and her colleagues. This bill was met with unanimous support from the top universities and companies, which are engaged in artificial intelligence (AI) research. Some of the well-known supporters include Stanford University, Princeton University, UCLA, Carnegie Mellon University, Johns Hopkins University, OpenAI, Mozilla, Google, Amazon Web Services, Microsoft, IBM and NVIDIA amongst others.

We ask whether recent progress on the ImageNet classification benchmark continues to represent meaningful generalization, or whether the community has started to overfit to the idiosyncrasies of its labeling procedure. We therefore develop a significantly more robust procedure for collecting human annotations of the ImageNet validation set. Using these new labels, we reassess the accuracy of recently proposed ImageNet classifiers, and find their gains to be substantially smaller than those reported on the original labels. Furthermore, we find the original ImageNet labels to no longer be the best predictors of this independently-collected set, indicating that their usefulness in evaluating vision models may be nearing an end. Nevertheless, we find our annotation procedure to have largely remedied the errors in the original labels, reinforcing ImageNet as a powerful benchmark for future research in visual recognition.

Whether or not your organisation suffers a cyber attack has long been considered a case of'when, not if', with cyber attacks having a huge impact on organisations. In 2018, 2.8 billion consumer data records were exposed in 342 breaches, ranging from credential stuffing to ransomware, at an estimated cost of more than $654bn. In 2019, this had increased to an exposure of 4.1 billion records. While the use of artificial intelligence (AI) and machine learning as a primary offensive tool in cyber attacks is not yet mainstream, its use and capabilities are growing and becoming more sophisticated. In time, cyber criminals will, inevitably, take advantage of AI, and such a move will increase threats to digital security and increase the volume and sophistication of cyber attacks.

In After Effects, we can get rid of unwanted objects in our video footage using Adobe Sensei AI. The Content-Aware Fill tool in After Effects simply asks us for the region and the duration for the software to "fill" the video frames to mask things we don't want to see. The tool then samples surrounding contextual pixels to generate pixel patterns in the video frames that "blend in" with the scene -- as if the object never existed. This AI is probably built using Generative Adversarial Networks (GANs) -- the same deep learning algorithms that can create incredibly convincing deepfakes. As a (very) concise overview -- a GAN is composed of two competing neural networks: a generator and a discriminator.

Critical success factors behind a modern analytics landscape lies from the fact that it is not restricted to technical excellence but comes from answering the trickier "why" questions. This includes understanding deep learning models behind business problems; trusting data model predictions and explaining outcomes in a simple yet comprehensive language. Of late, many of the data scientists are more interested to sharpen their skills and unearth interesting nuggets buried in data than engaging themselves to this softer cause. Though this may sound natural with a narrow focus on data and the tools required to explore it, understanding the critical'why' is more mainstream to reach out to more users across the value chain. To understand the nuances of a Data Strategy, let us understand it from a consulting team's point of view who is assisting a large MNC to develop its data strategy.

With the rise of autonomous vehicles, smart video surveillance, facial detection and various people counting applications, fast and accurate object detection systems are rising in demand. These systems involve not only recognizing and classifying every object in an image, but localizing each one by drawing the appropriate bounding box around it. This makes object detection a significantly harder task than its traditional computer vision predecessor, image classification.

Machine learning-based personalization has gained traction over the years due to volume in the amount of data across sources and the velocity at which consumers and organizations generate new data. Traditional ways of personalization focused on deriving business rules using techniques like segmentation, which often did not address a customer uniquely. Recent progress in specialized hardware (read GPUs and cloud computing) and a burgeoning ML and DL toolkits enable us to develop 1:1 customer personalization which scales. Recommender systems are beneficial to both service providers and users. They reduce transaction costs of finding and selecting items in an online shopping environment and improves customer experience.

Welcome (or welcome back!) to the AI for social good series! In the second part, of this two-part series of articles, we will look at how Artificial intelligence (AI) coupled with the power of open-source tools and techniques like deep learning can help us further the quest for finding extra-terrestrial intelligence! In the first part of this two-part series, we formulated our key objective and motivation behind doing this project. Briefly, we were looking at different radio-telescope signals simulated from SETI (Search for Extra-terrestrial Intelligence) Institute data. We leveraged techniques to process, analyze and visualize radio signals as spectrograms, which are basically visual representations of the raw signal.

Recurrent Neural Networks (RNN) are a class of Artificial Neural Networks that can process a sequence of inputs in deep learning and retain its state while processing the next sequence of inputs. Traditional neural networks will process an input and move onto the next one disregarding its sequence. Data such as time series have a sequential order that needs to be followed in order to understand. Traditional feed-forward networks cannot comprehend this as each input is assumed to be independent of each other whereas in a time series setting each input is dependent on the previous input. In Illustration 1 we see that the neural network (hidden state) A takes an xt and outputs a value ht.

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.