Recruitment has been changing rapidly. At the root of all this change is technology advancement as the ultimate change maker. Some recruiters wonder about the future of recruitment and what role is left for them to fulfil. What if you could articulate yourself without saying a word? The truth is, we already can.

How often do we link terms like data science, artificial intelligence (AI), and machine learning with futuristic advancement, such as highly sophisticated robots and space ships as public transport? Why do we not associate them with a greener area, cleaner air, or flourishing biodiversity? Fourth Industrial Revolution technologies such as AI are enabling humanity to harness information and data to revolutionise education, energy, healthcare, agriculture, transportation, and many other service areas. AI helps us makes the world a better place, from traffic management in urban mobility to enhancing the efficiency of renewable energies to predict crop needs and other innovative solutions in smart agriculture. AI is becoming a key tool for facilitating a circular economy and building smart cities that use their resources efficiently.

On this episode of the eeDesignIt Podcast, we're joined by Dhonam Pemba to explore artificial intelligence (AI) and his new company KidX AI. Dhonam is a neural engineer by PhD, a former rocket scientist and a serial AI entrepreneur. He was CTO of the exited company, Kadho which was acquired by Roybi for its Voice AI technology. At Kadho Sports he was their Chief Scientist which had clients in MLB, USA Volleyball, NFL, NHL, NBA, and NCAA. His latest company, KidX, is in the AI edtech space, where he has built NLP and Voice assessment to serve China's leading robotics company with 4M users.

Artificial intelligence operates on a different plane than human reason. As described by various futurists and technologists, AI is literally an "alien mind." In advanced artificial neural networks, the modes of cognition--the logical steps behind any given conclusion--are completely incomprehensible, even to their creators. In the coming years, this nonhuman intelligence will change everything about our personal lives, our social organization, and how we think. That's the premise of two books published back-to-back this year--one from the West, the other from the East. The Age of AI: And Our Human Future is a primer on technetronic civilization for Western policy makers.

Gas turbine engines are complex machines that typically generate a vast amount of data, and require careful monitoring to allow for cost-effective preventative maintenance. In aerospace applications, returning all measured data to ground is prohibitively expensive, often causing useful, high value, data to be discarded. The ability to detect, prioritise, and return useful data in real-time is therefore vital. This paper proposes that system output measurements, described by a convolutional neural network model of normality, are prioritised in real-time for the attention of preventative maintenance decision makers. Due to the complexity of gas turbine engine time-varying behaviours, deriving accurate physical models is difficult, and often leads to models with low prediction accuracy and incompatibility with real-time execution. Data-driven modelling is a desirable alternative producing high accuracy, asset specific models without the need for derivation from first principles. We present a data-driven system for online detection and prioritisation of anomalous data. Biased data assessment deriving from novel operating conditions is avoided by uncertainty management integrated into the deep neural predictive model. Testing is performed on real and synthetic data, showing sensitivity to both real and synthetic faults. The system is capable of running in real-time on low-power embedded hardware and is currently in deployment on the Rolls-Royce Pearl 15 engine flight trials.

Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics. We review the state of machine learning methods and applications for new physics searches in the context of terrestrial high energy physics experiments, including the Large Hadron Collider, rare event searches, and neutrino experiments. While machine learning has a long history in these fields, the deep learning revolution (early 2010s) has yielded a qualitative shift in terms of the scope and ambition of research. These modern machine learning developments are the focus of the present review.

Tensor decomposition is one of the fundamental technique for model compression of deep convolution neural networks owing to its ability to reveal the latent relations among complex structures. However, most existing methods compress the networks layer by layer, which cannot provide a satisfactory solution to achieve global optimization. In this paper, we proposed a model reduction method to compress the pre-trained networks using low-rank tensor decomposition of the convolution layers. Our method is based on the optimization techniques to select the proper ranks of decomposed network layers. A new regularization method, called funnel function, is proposed to suppress the unimportant factors during the compression, so the proper ranks can be revealed much easier. The experimental results show that our algorithm can reduce more model parameters than other tensor compression methods. For ResNet18 with ImageNet2012, our reduced model can reach more than twi times speed up in terms of GMAC with merely 0.7% Top-1 accuracy drop, which outperforms most existing methods in both metrics.

The increase in people's use of mobile messaging services has led to the spread of social engineering attacks like phishing, considering that spam text is one of the main factors in the dissemination of phishing attacks to steal sensitive data such as credit cards and passwords. In addition, rumors and incorrect medical information regarding the COVID-19 pandemic are widely shared on social media leading to people's fear and confusion. Thus, filtering spam content is vital to reduce risks and threats. Previous studies relied on machine learning and deep learning approaches for spam classification, but these approaches have two limitations. Machine learning models require manual feature engineering, whereas deep neural networks require a high computational cost. This paper introduces a dynamic deep ensemble model for spam detection that adjusts its complexity and extracts features automatically. The proposed model utilizes convolutional and pooling layers for feature extraction along with base classifiers such as random forests and extremely randomized trees for classifying texts into spam or legitimate ones. Moreover, the model employs ensemble learning procedures like boosting and bagging. As a result, the model achieved high precision, recall, f1-score and accuracy of 98.38%.

It is not always the big stuff or the latest packages that help improve the accuracy or performance of our #machine learning models. At times we overlook the basics of Machine Learning and rush to higher order solutions. When the solution is just right there in front of us. Below are 10 simple things you should remember to try first before throwing in the towel and jumping straight to RNNs and CNNs (of course there are datasets which merit you to start straight from LSTMs and BERT).Let us remind ourselves of our checklist before bringing out our Calculus skills. Try to understand as much about the domain as you can.

'Watters' World' host lists the many domestic issues President Biden faces This is a rush transcript from "Watters' World," December 4, 2021. This copy may not be in its final form and may be updated. JESSE WATTERS, FOX NEWS HOST: Welcome to WATTERS' WORLD, I'm Jesse Watters. The Annual White House Christmas Tree lighting is always such a special event, except Joe Biden, the President seemingly forgot he was supposed to light it. Maybe he thought Barack was going to light it. These things just keep happening every single week. I kind of feel bad for LL, they needed to do a second take. Now, President Biden and First Lady, Dr. Jill Biden [CHEERING AND APPLAUSE] (END VIDEO CLIP) WATTERS: So, how are we supposed to feel confident the President can crush the virus when he can't even get it together for a Christmas Tree lighting? I'm not worried about the new variant. I'm worried about how the government is going to overreact to the new variant. Biden has got a new plan. More masks, more testing, but unvaxxed illegals can just pour across the Southern border without testing, without quarantining. And then Joe packs them onto planes and buses and sends them to your neighborhood. Does that make sense to anybody? (BEGIN VIDEO CLIP) PETER DOOCY, FOX NEWS CHANNEL WHITE HOUSE CORRESPONDENT: Dr. Fauci, as you advised the President about the possibility of new testing requirements for people coming into this country? ANTHONY FAUCI, DIRECTOR, NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES: Everybody who is coming into the country needs to get a test within 24 hours of getting on the plane to come here. DOOCY: But what about people who don't take a plane and just these border crossers coming in in huge numbers?