The Internet of Things (IoT) is a popular technology trend, which in recent years has been joined with another popular trend, Artificial Intelligence (AI) to form a brand new keyword: "AIoT". But what exactly is "AIoT"? The traditional Internet of Things refers to the deployment of a large number of physical sensors in a specific space, which can collect and return environmental data at regular intervals, such as temperature, pressure and sound. After integrating Artificial Intelligence, this IoT system can now not only sense environmental data, but as long as it also includes video or surveillance cameras, it can use "computer vision" enabled by AI deep learning technology to "see" and "recognize" surrounding objects, such as people, vehicles, numbers and animals, evolving this Internet of Things into an intelligent Internet of Things. The value of adopting AIoT technology not only lies in assisting to reduce labor costs but also in its ability to enable data analytics.

This is also evident in the number of technologies referring to artificial intelligence (AI). Autonomous cars and personal assistants like Apple's Siri are often spoken about, while machine learning, deep learning and neural networks are frequently featured in written text. What do these terms mean, and what is the difference between them? How far has technology based on elements of AI progressed? We discussed these topics in a series of interviews with Juraj Jánošík, an expert on artificial intelligence of the ESET company. If we can simulate human intelligence, consciousness and thinking with some technology, we achieve artificial intelligence. There is a term for it - artificial general intelligence - but there is also a concept called super intelligence. While artificial general intelligence (AGI) is meant to imitate human thinking, including its faults, super intelligence (SI) should go even further and exceed the limits of human consciousness and thinking, and considerably surpass them. However, there are more philosophical discourses involved, and we have to admit that currently, we are still far behind, even in the development of AGI.

I work in a research department where one of my seniors are authoring a position paper on trustworthy AI, and we came into a discussion regarding the phrase "...understanding the theoretical basis for (human) intelligence has gone hand in hand with improvements in the capabilities of real systems." Even though it was referenced to Russel and Norvigs book, I think the statement is misleading and a bit sensationalist, as more or less none of the recent advances in deep learning I can think of are inspired by neuroscience. In fact, the two seems more detached now than ever. I've tried searching for good papers to back my claim on this, but I have not been able to find any. Are there any good material on the subject of the connection between applied AI and the theory of human intelligence?

Today, the recycling market is changing rapidly due to global changes where the quality requirements of the incoming and outgoing material are increased. As a result, systems that are separating waste material from the target material need to be improved continuously to cope with this change. Stora Enso's Langerbrugge Mill in northwest Belgium, which is one of the largest paper mills in Europe, required a more effective paper-cardboard sorting solution and technology that can easily be retrained for anomaly detection. This technology was developed by Robovision, a company specializing in deep learning-based machine vision and robot programming, and Imec, which is the world-leading R&D and innovation hub in nanoelectronics and digital technologies within the framework of the AUTOWARE project. A big challenge in paper recycling is the separation of cardboard and waste materials from paper.

Humans are naturally prone to having bias. External factors, opinions and feelings all help influence the decisions we make. Machine learning forms the core of modern AI systems, with deep learning algorithms being particularly popular. These algorithms are very data hungry. Specifically, what makes these systems effective is a large quantity of good training data that is relevant to the area in which you're trying to achieve some machine learning objective.

To be a real "full-stack" data scientist, or what many bloggers and employers call a "unicorn," you have to master every step of the data science process -- all the way from storing your data, to putting your finished product (typically a predictive model) in production. But the bulk of data science training focuses on machine/deep learning techniques; data management knowledge is often treated as an afterthought. Data science students usually learn modeling skills with processed and cleaned data in text files stored on their laptop, ignoring how the data sausage is made. Students often don't realize that in industry settings, getting the raw data from various sources to be ready for modeling is usually 80% of the work. And because enterprise projects usually involve a massive amount of data that their local machine is not equipped to handle, the entire modeling process often takes place in the cloud, with most of the applications and databases hosted on servers in data centers elsewhere. Even after the student landed a job as a data scientist, data management often becomes something that a separate data engineering team takes care of. As a result, too many data scientists know too little about data storage and infrastructure, often to the detriment of their ability to make the right decisions at their jobs. The goal of this article is to provide a roadmap of what a data scientist in 2019 should know about data management -- from types of databases, where and how data is stored and processed, to the current commercial options -- so the aspiring "unicorns" could dive deeper on their own, or at least learn enough to sound like one at interviews and cocktail parties.

As a general rule of thumb for learning, you will only understand something in detail once you are able to build it on your own. This is particularly true for technical subjects like machine learning and neural networks. During your first encounter with deep learning, you can take a few large steps by learning and mastering a high level framework such as TensorFlow or PyTorch. However, if you want to go really deep, you need to go back to square one: you need to build your own neural network and optimizer from scratch! This is a common rite of passage for deep learning engineers and researchers.

Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2. You can access this material here. For other free tutorials (including from Berkeley, Harvard, Columbia, Google, Microsoft and so on), follow this link.

With the digital medicine revolution in full swing, just about every specialty will experience some form of health care technology impact. AI algorithms, deep learning systems, and neural networks are already being used to detect lung cancer, screen skin lesions, and predict acute kidney injury. In the surgical realm, technological advancements previously involved the use of computer-assisted surgery (CAS) to improve precision and facilitate minimally invasive approaches. The da Vinci Surgical System obtained FDA approval in 2000 and, according to the company website, has been used in more than 6 million procedures world-wide. In orthopedics, CAS was introduced in the 1990s with perhaps joint replacement surgery as its most popular and widespread application.

Associative memory is a strategy developed by living organisms over many years of evolution. This is true of humans, as we use associative memory to tell a story about two unrelated things to grasp more information in less time. Memory optimisation is important in case of resource-hungry deep learning tasks as well. Though the analogy between neuroscience and artificial neural networks has been beaten to a pulp, mimicking nature is still opening up exciting avenues in the world of AI. Memory association in machine learning is of great significance.