I am the co-founder and CEO of Neurala Inc., a Boston-based company building Artificial Intelligence emulating brain function in software. Neurala's deep learning tech makes robots, drones, cars, consumer electronics, toys and smart devices more useful, engaging and autonomous. Neurala stems out of 10 years of research at Boston University Neuromorphics Lab, where as AI Professor I have pioneered the research and fielding of brain-inspired (also called Deep Learning, or Artificial Neural Networks) algorithms that allow robots and drones to perceive, navigate, interact and learn real-time in complex environments. Over my academic and industrial career, I have lectured and spoken at dozens of events and venues, including TEDx, keynote at Mobile World Congress Drone Summit, NASA, the Pentagon, GTC, InterDrone, Los Alamo National Lab, GE, Air Force Research Labs, HP, iRobot, Samsung, LG, Qualcomm, Huawei, Ericsson, BAE Systems, AI World, Mitsubishi, ABB and Accenture, among many others. My work has been featured in TIME, IEEE Spectrum, Fortune, CNBC, The Boston Globe, Xconomy, The Chicago Tribune, TechCrunch, VentureBeat, Nasdaq, Associated Press and many other media.

Keras is a simple-to-use but powerful deep learning library for Python. In this post, we'll build a simple Convolutional Neural Network (CNN) and train it to solve a real problem with Keras. This post is intended for complete beginners to Keras but does assume a basic background knowledge of CNNs. My introduction to Convolutional Neural Networks covers everything you need to know (and more) for this post - read that first if necessary. The full source code is at the end.

Chitchat dialogue is a very difficult NLP task to master. However, creating a dialogue system (chatbot) with a simple LSTM Seq2Seq model with controls can deliver relatively good performance. Below, we've deployed a chatbot with two control features (conditional training [CT] and weighted decoding [WD] in order to control four attributes in chitchat dialogue: repetition, specificity, response-relatedness and question-asking. The chatbot leverages a baseline twitter dataset that was fine-tuned on the PersonaChat ConvAI2 dataset found here. In addition, we've also added a transformer based chatbot called Poly-Encoder.

Meet Foodvisor, a startup that has built a mobile app that helps you log everything you eat in order to lose weight, follow a diet or get healthier. You can add data by capturing a photo of your plate before you eat. "We've spent a little over two years doing research and development before we launched the app in 2018 in France," co-founder and CMO Aurore Tran told me. Foodvisor has raised $1.5 million so far (€1.4 million). The company is using deep learning to enable image recognition and detect what you're about to eat. In addition to identifying the type of food, the app tries to estimate the weight of each item.

Do you want to upgrade your skills with Best Data Analytics Certification Online to stand out in the industry? Here is a list of Best Data Analytics Courses Online, Training, Tutorials, and Classes to assist you to become a top Data Analyst. Now Big data, Data Science, Machine Learning, Deep Learning, Artificial Intelligence (AI), Analytics, Python, R, r-stats are the most trending and highly demanding subjects in every sector for almost every industry. Learn business analytics to get hands-on knowledge of big data analytics, data visualization, data management, and data mining as an analytics professional. Majority of the business professionals are upgrading their skills with Best Data Analytics Training to standout in their industry.

"Nokia's AI driven access analytics solution has given us the ability to proactively address issues, reducing customer calls by solving multiple issues in a single intervention and creating overall efficiencies in our troubleshooting process." High-resolution video, cloud services and the multiplication of connected devices require higher bandwidth as well as increased reliability. Today, for the copper medium to remain competitive relative to fiber, a similar quality of experience is expected from both an end-user and maintenance perspective. This is especially true given the uptake of Fiber in Europe, which stands at less than 50% for home subscribers. As a Tier-1 European service provider, POST Luxembourg was looking to improve the overall performance of its copper troubleshooting process to reduce OPEX and improve satisfaction among its customers.

KT Corporation and Portugal-based WeDo Technologies have signed a Cooperation Agreement for AI-FMS (Artificial Intelligence based Fraud Management System) development and sales. KT's Deep Learning-based Artificial Intelligence (AI) module has been implemented and tested on WeDo's RAID FMS system. This AI module, trained with KT Big Data, has showed strong results for fraud detection and prevention, and has reportedly proved to be effective for a number of fraud use cases, with a high degree of accuracy. KT and WeDo plan to supply the AI-based International Revenue Share Fraud (AI-IRSF) module with the RAID platform to communication service providers (CSPs) by the end of 2019. KT's DL (Deep Learning) based AI module has been implemented and tested on WeDo's RAID FMS system.

At the recently-concluded PyTorch DevCon, the team released updates that can probably cement PyTorch position as one of the top frameworks for machine learning. The new release includes experimental support for features such as seamless model deployment to mobile devices, model quantization for better performance at inference time, and front-end improvements along with a number of additional tools and libraries to support model interpretability and bringing multimodal research to production. To play down the reputation of ML models as black models, the developers at PyTorch introduced a new tool called Captum. This tool enables the users to visualise what their models are detecting. For example, when an image, say of an animal is given, it visualize the attributions for each pixel by overlaying them on the image as can be seen in the picture above.

The rapid adoption of artificial intelligence (AI) for practical business applications has introduced a number of uncertainties and risk factors across virtually every industry, but one fact is certain: in today's AI market, hardware is the key to solving many of the sector's key challenges, and chipsets are at the heart of that hardware solution. Given the widespread applicability of AI, it is almost certain that every chip in the future will have some sort of AI engine embedded. The engine could take a wide variety of forms, ranging from a simple AI library running on a CPU to more sophisticated custom hardware. The potential for AI is best fulfilled when the chipsets are optimized to provide the appropriate amount of compute capacity at the right power budget for specific AI applications, a trend that is leading to increasing specialization and diversification in AI-optimized chipsets. During the past 2 years, the deep learning chipset market has experienced a dramatic period of evolution, led by NVIDIA and Intel.