Topology optimization has emerged as a popular approach to refine a component's design and increasing its performance. However, current state-of-the-art topology optimization frameworks are compute-intensive, mainly due to multiple finite element analysis iterations required to evaluate the component's performance during the optimization process. Recently, machine learning-based topology optimization methods have been explored by researchers to alleviate this issue. However, previous approaches have mainly been demonstrated on simple two-dimensional applications with low-resolution geometry. Further, current approaches are based on a single machine learning model for end-to-end prediction, which requires a large dataset for training.

Picture this: You're using an AI model when it recommends a course of action that doesn't seem to make sense. However, because the model can't explain itself, you've got no insight into the reasoning behind the recommendation. Your only options are to trust it or not -- but without any context. It's a frustrating yet familiar experience for many who work with artificial intelligence (AI) systems, which in many cases function as so-called "black boxes" that sometimes can't even be explained by their own creators. For some applications, black box-style AI systems are completely suitable (or even preferred by those who would rather not explain their proprietary AI).

Through a collaboration with MakarenaLabs srl, a leader in scientific research in multimedia recognition and feature extractions, iWave Systems is excited to collaborate with MakarenaLabs enabling customers with rugged AI Solutions and edge computing systems. Combining the strong edge computing and FPGA Machine Learning expertise of iWave Systems & MakarenaLabs, through this partnership we aim to deliver an optimized AI platform deploying AI to the edge and helping customers to speed up implementation across AI Applications in Face Recognition and Social Distancing. With strong expertise in deep learning frameworks and FPGA Acceleration, MakarenaLabs helps customers implement scalable AI Solutions on the field with reduced development costs and a faster time to market. "iWave Systems is excited to partner with MakarenaLabs to offer customers an EdgeAI embedded platform with intelligence. Complementing iWave Systems expertise in the design and development of edge computing platforms, MakarenaLabs's expertise in frameworks and AI software helps deliver complete AI Solutions to customers. With the anticipated growth of the edge computing market, this collaboration is an important aspect of our growth strategy," said Abdullah Khan, President at iWave Systems Technologies Pvt. Ltd. "MakarenaLabs always looked for an international partner for the deployment of its ML & AI algorithms. FPGA offers an incredible computational power with very little Watt per operation and maintenance costs related. In this period of international crisis it is fundamental to build a strong network based on innovation and technological progress, so we are very humbled by this partnership with iWave Systems." said Enrico Giordano, CEO and CTO of MakarenaLabs srl.

We live in a world which is more opinionated than ever. Any service that we consume leaves us either satisfied or unsatisfied. And with the advent of social media, we make our views public in no time. Vast sources of data are available in the form of reviews, customer satisfaction surveys, customer complaints, etc. Businesses can use this data to understand what customers are talking about, and make data driven decisions to improve their services. Let's talk in terms of Machine Learning now! Sentiment Analysis is the process of understanding how satisfied customers are w.r.t. a service.

First, Google's chatbot Meena and Facebook's chatbot Blender demonstrated that dialog agents can achieve close to human-level performance in certain tasks. Then, OpenAI's GPT-3 model made lots of people wonder whether Artificial General Intelligence (AGI) is already here. While we are still a long way off true AGI, conversations with GPT-3 based chatbots can be very entertaining. Are you interested to learn more about the latest research breakthroughs in Conversational AI? Check out our premium research summaries covering open-domain chatbots, task-oriented chatbots, dialog datasets, and evaluation metrics. Subscribe to our AI Research mailing list at the bottom of this article to be alerted when we release new summaries.

Of all the innovations in artificial intelligence and machine learning space this year, the most significant ones have turned out to be in the healthcare and medicine field, the credit of which could be given to the unprecedented pandemic situation around the world. In this article, we discover some of the major breakthroughs in 2020. Termed as a groundbreaking innovation, DeepMind's AI system AlphaFold was developed to present a solution to the 50-year-old grand challenge of determining the protein structure, also referred to as'protein folding problem'. This system demonstrated high levels of accuracy in predicting the 3D structure of a protein. By bringing about significant progress over the core challenges in biology, this system paves the way for disease understanding and drug discovery, even in case of COVID-19, among other fields.

Our panel of leading experts reviews 2020 main developments and examines the key trends in AI, Data Science, Machine Learning, and Deep Learning Technology.

AI is hungry, hyperscale AI ravenous. As AI models rapidly get larger and more complex (an estimated 10x a year), a recent MIT study warns that computational challenges, especially in deep learning, will continue to grow. Service providers, large enterprises and others also face unrelenting pressures to speed up innovation, performance, and rollouts of neural networks and other low-latency, data-intensive applications, often involving exascale cloud and High-Performance Computing (HPC). These dueling demands are driving technology advances and adoption of a growing universe of Field Programmable Gate Arrays (FPGAs). In the early days of exascale computing and AI, these customer-configurable integrated circuits played a key role.

As artificial technology gains traction in the enterprise, many on the business side remain fuzzy on AI techniques and how they can be applied to drive business value. Machine Learning and deep learning, for example, are two AI techniques that are often conflated. But machine learning can involve a wide variety of techniques for building analytics models or decision engines that don't involve neural networks, the mechanism for deep learning. And there is a whole range of AI techniques outside of machine learning as well that can be applied to solve business problems. Business managers who recognize these distinctions will have a greater understanding of the business value of AI and be better prepared to have productive conversations with data scientists, data engineers, end users and executives about what's feasible and what's required.

"Let Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model": Researchers from New Hyde Park, N.Y.-based Northwell Health's research arm developed an artificial intelligence tool to predict which patients will remain stable overnight and don't need to be awoken for vital monitoring. The tool cut in half the number of patients who were awoken during the night for vital sign checks, misclassifying less than two of 10,000 cases. "Evaluation of the use of combined artificial intelligence and pathologist assessment to review and grade prostate biopsies": Researchers developed an artificial intelligence tool to improve pathologists' grading of prostate needle biopsies, finding significant increases in grading agreement. "Development and validation of a real-time artificial intelligence-assisted system for detecting early gastric cancer: A multicentre retrospective diagnostic study": The research team developed and validated a real-time deep convolutional neural networks system for the detection of early gastric cancer. "Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography": Researchers developed a deep learning-based artificial intelligence algorithm to help detect myocardial infarction using electrocardiography to speed up the diagnosis process.