Cadence's Paul McLellan provides an overview of the new IEEE 1838 standard for manufacturing test of 3D stacked ICs and how it aims to enable testing of multi-die chiplet-based designs. In a video, Mentor's Colin Walls investigates the scope and lifetime of pointers in embedded applications. A Synopsys writer checks out the latest mobile memory standard, JESD209-5A, and the enhancements it contains to the existing LPDDR5 standard, including support for Partial Array Refresh Control, Refresh Management, Enhanced Write Clock Always On Mode, and Optimized Refresh. Rambus' Paul Karazuba takes a look at what makes machine learning models vulnerable to side-channel attacks and why differential power analysis detection and prevention techniques are needed for edge devices. In a blog for Arm, OctoML's Logan Weber and Andrew Reusch explain how optimizing and deploying machine learning workloads to bare-metal devices is becoming easier with Apache TVM and broad framework support, compiler middleware, and flexible autotuning and compilation capabilities.

In this Keras tutorial, you'll see how to Extend a Keras Model Generally, you only need your Keras model to return prediction values, but there are situations where you want your predictions to retain a portion of the input. A common example is forwarding unique'instance keys' while performing batch predictions. In this blog and corresponding notebook code, I'll demonstrate how to modify the signature of a trained Keras model to forward features to the output or pass through instance keys. Sometimes you'll have a unique instance key that is associated with each row and you want that key to be output along with the prediction so you know which row the prediction belongs to. You'll need to add keys when executing distributed batch predictions with a service like Cloud AI Platform batch prediction.

In the first article of this three-part series, we saw the basics of the Kaplan-Meier Estimator. Now, it's time to implement the theory we discussed in the first part. It gives us information about the data types and the number of rows in each column that has null values. It's very important for us to remove the rows with a null value for some of the methods in survival analysis. This gives us a general idea about how our data is distributed.

AI techniques are being applied by researchers aiming to extend the life and monitor the health of batteries, with the aim of powering the next generation of electric vehicles and consumer electronics. Researchers at Cambridge and Newcastle Universities have designed a machine learning method that can predict battery health with ten times the accuracy of the current industry standard, according to an account in ScienceDaily. The promise is to develop safer and more reliable batteries. In a new way to monitor batteries, the researchers sent electrical pulses into them and monitored the response. The measurements were then processed by a machine learning algorithm to enable a prediction of the battery's health and useful life.

There's a saying of garbage in, garbage out when it comes to artificial intelligence and machine learning. It's common knowledge that every machine learning solution needs a good algorithm powering it, but what gets far less press is what actually goes into these algorithms: the training data itself. Your model is only as good as the data it's trained on. That's why we built this training data guide. In the Essential Guide to Training Data we'll cover everything you need to know about creating the training data necessary to drive successful machine learning projects.

I was trying my hand on Optical Character Recognition on newspaper images when I realised that most documents have sections and text is not necessarily across the entire horizontal space of the page. Even though Tesseract was able to recognise the text it was jumbled up. To fix this the model should be able to identify sections on the document and draw a bounding box around it an perform OCR. It was this moment when applying Yolo Object detection on such images came into mind. YOLOv3 is extremely fast and accurate.

Artificial Intelligence has multiple applications in the healthcare domain. We have also seen many AI solutions being developed to identify symptoms of COVID-19 among patients. Here's one such case of AI-based screening of visitors at Mumbai railway stations to identify COVID-19 symptoms. Also, the application of Artificial Intelligence in adjusting insulin dose to control glucose levels among Type I Diabetes patients. Body-screening facility "FebriEye thermal cameras" have been set up at Chhatrapati Shivaji Maharaj Terminus and Lokmanya Tilak Terminus in Mumbai to scan passengers for COVID-19 symptoms.

According to Gartner, by 2020, a quarter billion connected vehicles will form a major element of the Internet of Things. Connected vehicles are projected to generate 25GB of data per hour, which can be analyzed to provide real-time monitoring and apps, and will lead to new concepts of mobility and vehicle usage. Uber Technologies Inc is a peer-to-peer ride sharing platform. Uber platform connects the cab drivers who can drive to the customer location. Uber uses machine learning, from calculating pricing to finding the optimal positioning of cars to maximize profits.

RealityEngines.AI, the machine learning startup co-founded by former AWS and Google exec Bindu Reddy, today announced that it is rebranding as Abacus.AI and launching its autonomous AI service into general availability. In addition, the company also today disclosed that it has raised a $13 million Series A round led by Index Ventures' Mike Volpi, who will also join the company's board. Seed investors Eric Schmidt, Jerry Yang and Ram Shriram also participated in this oversubscribed round, with Shriram also joining the company's board. This new round brings the company's total funding to $18.25 million. At its core, RealityEngines.AI's Abacus.AI's mission is to help businesses implement modern deep learning systems into their customer experience and business processes without having to do the heavy lifting of learning how to train models themselves.

Artificial Intelligence (AI) -- and its attendant term, 'Machine Learning' (ML) -- is described as the capability of a computer system to perform tasks that normally require human intelligence, such as visual perception, speech recognition and decision-making. Almost all AI/ML examples in commercial as well as military use today rely on data stores that drive deep learning and natural language processing.[1] The defining feature of an AI/ML system is its ability to learn and solve problems. There has been a gradual change in our understanding of what exactly constitutes AI. While advancements in computer hardware and more efficient software have led to the development of AI systems, hitherto computer-resource-intensive tasks, such as optical character recognition (OCR) are now considered a routine technology and, hence, no longer included in any contemporary discussion of AI/ML.