Nowadays, we witness the domain of data science is growing at a rapid rate. We see a lot of people entering this space. At the same time, demand for these people is also increasing throughout the globe. The current pandemic followed by lockdown around the world has set the path for people with self-motivation to pursue this amazing career. The Internet is flooded with multiple resources to learn almost anything related to AI as a whole. Most of them are free and some are paid.

This post was originally published at thinkmobile.dev Looking for how to automatically test TensorFlow Lite model on a mobile device? Check the 2nd part of this article. Building TensorFlow Lite models and deploying them on mobile applications is getting simpler over time. There is a set of information that needs to be passed between those steps -- model input/output shape, values format, etc.

DBS is taking steps to future-proof its workforce by announcing a collaboration with Amazon Web Services (AWS) to ensure its employees are equipped with fundamental skills in Artificial Intelligence (AI) and Machine Learning (ML) by the end of the year. The bank has set its sights on accelerating the use of AI and ML across its business. To enable this, DBS and AWS have jointly launched the DBS x AWS DeepRacer League. With this initiative, DBS employees will learn the basics of AI and ML by participating in a series of hands-on online tutorials before putting their new knowledge to the test by programming their own autonomous model race car. These ML models are then uploaded onto a virtual racing environment where employees can experiment and iteratively fine-tune their models as they engage each other in friendly competition. "We have never believed in limiting digital expertise to a small team.

Google has announced a new technology, powered by an acquisition called Socratic, that will let students take photos of their math homework in order to get the solutions. Google says it wants the Lens-powered technology to help parents and caretakers who are homeschooling, likely for the first time, as a result of the global COVID-19 pandemic. Google's search engine is already crammed with autosuggestions that you can tell are seeking homework answers. When you search for a classic novel, the related searches are always things like "Darcy house name" or "Meaning of dance scene." Math is harder to Google because of the array of symbols the average person doesn't know how to type, and people's math anxiety to begin with makes it more difficult to measuredly seek out what they need.

Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypothesis (a set of logical rules) that generalises given training examples. In contrast to most forms of machine learning, ILP can learn human-readable hypotheses from small amounts of data. As ILP approaches 30, we provide a new introduction to the field. We introduce the necessary logical notation and the main ILP learning settings. We describe the main building blocks of an ILP system. We compare several ILP systems on several dimensions. We describe in detail four systems (Aleph, TILDE, ASPAL, and Metagol).

This primer explores the exciting subject of intelligence. Intelligence is a fundamental component of all living things, as well as Artificial Intelligence(AI). Artificial Intelligence has the potential to affect all of our lives and a new era for modern humans. This paper is an attempt to explore the ideas associated with intelligence, and by doing so understand the implications, constraints, and potentially the capabilities of future Artificial Intelligence. As an exploration, we journey into different parts of intelligence that appear essential. We hope that people find this useful in determining where Artificial Intelligence may be headed. Also, during the exploration, we hope to create new thought-provoking questions. Intelligence is not a single weighable quantity but a subject that spans Biology, Physics, Philosophy, Cognitive Science, Neuroscience, Psychology, and Computer Science. Historian Yuval Noah Harari pointed out that engineers and scientists in the future will have to broaden their understandings to include disciplines such as Psychology, Philosophy, and Ethics. Fiction writers have long portrayed engineers and scientists as deficient in these areas. Today, modern society, the emergence of Artificial Intelligence, and legal requirements all act as forcing functions to push these broader subjects into the foreground. We start with an introduction to intelligence and move quickly onto more profound thoughts and ideas. We call this a Life, the Universe and Everything primer, after the famous science fiction book by Douglas Adams. Forty-two may very well be the right answer, but what are the questions?

Most existing metric learning methods focus on learning a similarity or distance measure relying on similar and dissimilar relations between sample pairs. However, pairs of samples cannot be simply identified as similar or dissimilar in many real-world applications, e.g., multi-label learning, label distribution learning. To this end, relation alignment metric learning (RAML) framework is proposed to handle the metric learning problem in those scenarios. But RAML framework uses SVR solvers for optimization. It can't learn positive semidefinite distance metric which is necessary in metric learning. In this paper, we propose two methds to overcame the weakness. Further, We carry out several experiments on the single-label classification, multi-label classification, label distribution learning to demonstrate the new methods achieves favorable performance against RAML framework.

Fatigue is one of the key factors in the loss of work efficiency and health-related quality of life, and most fatigue assessment methods were based on self-reporting, which may suffer from many factors such as recall bias. To address this issue, we developed an automated system using wearable sensing and machine learning techniques for objective fatigue assessment. ECG/Actigraphy data were collected from subjects in free-living environments. Preprocessing and feature engineering methods were applied, before interpretable solution and deep learning solution were introduced. Specifically, for interpretable solution, we proposed a feature selection approach which can select less correlated and high informative features for better understanding system's decision-making process. For deep learning solution, we used state-of-the-art self-attention model, based on which we further proposed a consistency self-attention (CSA) mechanism for fatigue assessment. Extensive experiments were conducted, and very promising results were achieved.

We study the dynamics of optimization and the generalization properties of one-hidden layer neural networks with quadratic activation function in the over-parametrized regime where the layer width $m$ is larger than the input dimension $d$. We consider a teacher-student scenario where the teacher has the same structure as the student with a hidden layer of smaller width $m^*\le m$. We describe how the empirical loss landscape is affected by the number $n$ of data samples and the width $m^*$ of the teacher network. In particular we determine how the probability that there be no spurious minima on the empirical loss depends on $n$, $d$, and $m^*$, thereby establishing conditions under which the neural network can in principle recover the teacher. We also show that under the same conditions gradient descent dynamics on the empirical loss converges and leads to small generalization error, i.e. it enables recovery in practice. Finally we characterize the time-convergence rate of gradient descent in the limit of a large number of samples. These results are confirmed by numerical experiments.

The curiosity in artificial intelligence (AI) is taken to a whole new level these past years. Every day new startups, new tools, new innovations are growing. This term is now always mentioned when we talk about AI. Nowadays, though, people who interested in learning more about this technology won't have time to go back to college or spend a whole year on a training course. For this reason, we decided to created this article.