Developing robotic intelligent systems that can adapt quickly to unseen wild situations is one of the critical challenges in pursuing autonomous robotics. Although some impressive progress has been made in walking stability and skill learning in the field of legged robots, their ability to fast adaptation is still inferior to that of animals in nature. Animals are born with massive skills needed to survive, and can quickly acquire new ones, by composing fundamental skills with limited experience. Inspired by this, we propose a novel framework, named Robot Skill Graph (RSG) for organizing massive fundamental skills of robots and dexterously reusing them for fast adaptation. Bearing a structure similar to the Knowledge Graph (KG), RSG is composed of massive dynamic behavioral skills instead of static knowledge in KG and enables discovering implicit relations that exist in between of learning context and acquired skills of robots, serving as a starting point for understanding subtle patterns existing in robots' skill learning. Extensive experimental results demonstrate that RSG can provide rational skill inference upon new tasks and environments, and enable quadruped robots to adapt to new scenarios and learn new skills rapidly.

Let's understand NLP and get all fundamental skills from SCRATCH! In this course you are invited to learn all the fundamental skills ... In this course you are invited to learn all the fundamental skills required in any kind of activity related to the Natural Language Processing and you will learn them from a theoretical and practical point of view, in fact you will seat together with me coding and implementing any topic step-by-step, instruction after instruction. Any of these projects will be a real and working use case so you will be able to re-use them in your own apps. In few words, this course is a real journey inside Natural Language Processing starting from the very beginning and finishing with the idea that all modern systems are leveraging: word embeddings. We are exploring NLU, NLG, NLP History, applications and use cases, studing Tokenization, Stopwords, Stemming, Lemmatization, PoS, NER, BoW, TF-IDF and Embeddings.

Computers are quickly gaining on us. Artificial systems are now exceeding the performance of human experts in several domains. However, we do not yet have a deep definition of expertise. This paper examines the nature of expertise and presents an abstract knowledge-level and skill-level description of expertise. A new level lying above the Knowledge Level, called the Expertise Level, is introduced to describe the skills of an expert without having to worry about details of the knowledge required. The Model of Expertise is introduced combining the knowledge-level and expertise-level descriptions. Application of the model to the fields of cognitive architectures and human cognitive augmentation is demonstrated and several famous intelligent systems are analyzed with the model.

We will soon be surrounded by artificial systems capable of cognitive performance rivaling or exceeding a human expert in specific domains of discourse. However, these "cogs" need not be capable of full general artificial intelligence nor able to function in a stand-alone manner. Instead, cogs and humans will work together in collaboration each compensating for the weaknesses of the other and together achieve synthetic expertise as an ensemble. This paper reviews the nature of expertise, the Expertise Level to describe the skills required of an expert, and knowledge stores required by an expert. By collaboration, cogs augment human cognitive ability in a human/cog ensemble. This paper introduces six Levels of Cognitive Augmentation to describe the balance of cognitive processing in the human/cog ensemble. Because these cogs will be available to the mass market via common devices and inexpensive applications, they will lead to the Democratization of Expertise and a new cognitive systems era promising to change how we live, work, and play. The future will belong to those best able to communicate, coordinate, and collaborate with cognitive systems.

Many "How to Data Science" courses and articles, including my own, tend to highlight fundamental skills like Statistics, Math, and Programming. Recently, however, I noticed through my own experiences that these fundamental skills can be hard to translate into practical skills that will make you employable. Therefore, I wanted to create a unique list of practical skills that will make you employable. The first four skills that I talk about are absolutely pivotal for any data scientist, regardless of what you specialize in. The following skills (5–11) are all important skills but will vary in usage depending on what you specialize in.

While the field of data science continues to evolve with exciting new progress in analytical approaches and machine learning, there remain a core set of skills that are foundational for all general practitioners and specialists, especially those who want to be employable with full-stack capabilities. Many "How to Data Science" courses and articles, including my own, tend to highlight fundamental skills like Statistics, Math, and Programming. Recently, however, I noticed through my own experiences that these fundamental skills can be hard to translate into practical skills that will make you employable. Therefore, I wanted to create a unique list of practical skills that will make you employable. The first four skills that I talk about are absolutely pivotal for any data scientist, regardless of what you specialize in.

Deep learning has provided extraordinary advances in problem spaces that are poorly solved by other approaches. This success is due to several key departures from traditional machine learning that allow it to excel when applied to unstructured data. Today, deep learning models can play games, detect cancer, talk to humans, and drive cars. But the differences that make deep learning powerful also make it costly. You may have heard that deep learning success requires massive data, expensive hardware, and even more expensive elite engineering talent.