Understanding #CNNs are tough as it uses various filters/kernels to extracts various features from the #images, uses pooling for down scalling, uses flatten layer to convert into 1D array and uses Fully connect layer for feed forward and back-propogation.... Here's how CNN extracts the features from the images and classify the object. Check out how #AI can meet you with your forefathers and make you cry after watching them live in front of you.

Would you like to be part of a team focused on building adoption of Amazon Web Services with partners and prospective customers? Do you have the business savvy and the technical acumen necessary to help establish Amazon Web Services as the premier cloud provider? Amazon Web Services is expanding into the US Public Sector market, and this new group within Amazon offers a creative, fast paced, entrepreneurial work environment where you'll be at the center of Amazon innovation. In this role, you will be focused on Enterprise accounts within the National Security arena. The ideal candidate has some previous sales experience with a terrific perspective on connecting with customers and building relationships.

Four of the newfound quadruply imaged quasars are shown here: From top left and moving clockwise, the objects are: GraL J1537-3010 or "Wolf's Paw;" GraL J0659 1629 or "Gemini's Crossbow;" GraL J1651-0417 or "Dragon's Kite;" GraL J2038-4008 or "Microscope Lens." The fuzzy dot in the middle of the images is the lensing galaxy, the gravity of which is splitting the light from the quasar behind it in such a way to produce four quasar images. By modeling these systems and monitoring how the different images vary in brightness over time, astronomers can determine the expansion rate of the universe and help solve cosmological problems. With the help of machine-learning techniques, a team of astronomers has discovered a dozen quasars that have been warped by a naturally occurring cosmic "lens" and split into four similar images. Quasars are extremely luminous cores of distant galaxies that are powered by supermassive black holes.

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. For decades, we've been trying to develop artificial intelligence in our own image. And at every step of the way, we've managed to create machines that can perform marvelous feats and at the same time make surprisingly dumb mistakes. After six decades of research and development, aligning AI systems with our goals, intents, and values continues to remain an elusive objective. Every major field of AI seems to solve part of the problem of replicating human intelligence while leaving out holes in critical areas.

It may have been created with the help of AI. Mackmyra, an award-winning Swedish distillery, has launched Intelligens, the world's first whisky created using an artificial intelligence program. Mackmyra partnered with Finnish technology company Fourkind to develop an AI system that augments and automates some of the tasks of the distillery's master blender, who is responsible for whisky flavor and product development. Master blenders spend their time meticulously tasting and experimenting to create the best flavors possible, and that process can be time-consuming. Mackmyra wanted machine learning to work its magic in sifting through massive amounts of data to find new combinations.

Electrical batteries are increasingly crucial in a variety of applications, from integration of intermittent energy sources with demand, to unlocking carbon-free power for the transportation sector through electric vehicles (EVs), trains and ships, to a host of advanced electronics and robotic applications. A key challenge however is that batteries degrade quickly with operating conditions. It is currently difficult to estimate battery health without interrupting the operation of the battery or without going through a lengthy procedure of charge-discharge that requires specialized equipment. In work recently published by Nature Machine Intelligence, researchers from the Smart Systems Group at Heriot-Watt University in Edinburgh, UK working together with researchers from the CALCE group at the University of Maryland in the US developed a new method to estimate battery health irrespective of operating conditions and battery design or chemistry, by feeding artificial intelligence (AI) algorithms with the raw battery voltage and current operational data. Darius Roman, the Ph.D. student that designed the AI framework said: "To date, the progress of data-driven models for battery degradation relies on the development of algorithms that carry out inference faster. Whilst researchers often spend a considerable amount of time on model or algorithm development, very few people take the time to understand the engineering context in which the algorithms are applied. By contrast, our work is built from the ground up. We first understand battery degradation through collaborations with the CALCE group at the University of Maryland, where in-house degradation testing of batteries was carried out. We then concentrate on the data, where we engineer features that capture battery degradation, we select the most important features and only then we deploy the AI techniques to estimate battery health."

This type of machine learning (ML) grants AI applications the ability to learn and find hidden patterns in large datasets without human supervision. Unsupervised learning is also crucial for achieving artificial general intelligence. Labeling data is labor-intensive and time-consuming, and in many cases, impractical. That's where unsupervised learning brings a big difference by granting AI applications the ability to learn without labels and supervision. Unsupervised learning (UL) is a machine learning technique used to identify patterns in datasets containing unclassified and unlabeled data points. In this learning method, an AI system is given only the input data and no corresponding output data.

In this project, we'll build a model for Anomaly Detection in Time Series data using Deep Learning in Keras with Python code.

Microsoft's recent shopping spree reached a new climax this week with the announcement of its $19.7 billion acquisition of Nuance, a company that provides speech recognition and conversational AI services. Nuance is best known for its deep learning voice transcription service, which is very popular in the health care sector. The two companies had already been working together closely before the acquisition. Nuance had built several of its products on top of Microsoft's Azure cloud. And Microsoft had been using Nuance's Dragon service in its Cloud for Healthcare solution, which launched last year in the midst of the pandemic.

US-based phishing detection and response solution provider Cofense announced last week the acquisition of Israel's Cyberfish, a provider of next-generation phishing protection powered by computer vision and advanced machine learning technology. Cofense said that by integrating innovative machine learning capabilities from Cyberfish with Cofense's detection and response technology, the American company will bring to market a holistic, advanced automation solution for email protection, detection, and response. According to Cofense, organizations are rethinking their email security architecture and technology stack with the acceleration of digital transformation and migration to cloud email services from Microsoft 365 and Google Workspace. Cofense's phishing intelligence, already deployed in thousands of enterprises around the world, will be used to train and evolve Cyberfish's machine-learning algorithms to block malicious emails in real-time, the company said. "Cyberfish is excited to join forces with Cofense to solve the phishing problem and take email security to another level," said Dima Kagan, Cyberfish CEO and Co-founder.