If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Whether shipping organizations work together with huge companies such as Microsoft and Google or one of the many new maritime startups (whose numbers have recently exploded with "more than $3.3 billion …invested in digital startups in the shipping and logistics sector") it appears that shipping is not just ripe for change anymore, it's changing. If you need more convincing, a recent Inmarsat survey of 125 global ship owners found that "ship owners are far more open to deploying IoT tools for analytic, management, and operational purposes than some other industries, including mining and agriculture" and "average expenditure per business on IoT based solutions will amount to $2.5 million over the next three years" while IDC tells us that "The DX (digital transformation) programs that will receive the most funding in 2018 are digital supply chain and logistics automation ($93 billion)". An industry that has often been described as "behind the times" is now proving itself to be quite the opposite. With this in mind, I ask several experts in shipping and maritime innovation and technology, representing both large organizations and startups, to share their thoughts on how they see AI impacting the shipping industry right now.
We've most certainly learnt a thing or two about what makes a thorough and informative salary report since conducting our first salary survey in 2017. Our European Salary Report for 2020 has seen a response of more than one thousand participants which has enabled us to provide a truly data rich and comprehensive insight on what the Data Science market currently looks like. The top countries to provide responses to our survey during 2019 came from Germany, France, Switzerland, The Netherlands and The UK. Much like our 2019 survey, many respondents were Data Scientists, but we've also collected results from Data Engineers, Researchers, Machine Learning Engineers and C-Level professionals. This report covers a broad scope of professions in the European data science market at all levels.
The Autonomous flying drone uses the computer vision technology to hover in the air avoiding the objects to keep moving on the right path. Apart from security surveillance and Ariel view monitoring, AI drone is now used by online retail giant Amazon to deliver the products at customer's doorstep revolutionizing the transportation and delivery system by logistics and supply chain companies. Cogito and AWS SageMaker Ground Truth have partnered to accelerate your training data pipeline. We are organising a webinar to help you "Build High-Quality Training Data for Computer Vision and NLP Applications". After registering, you will receive a confirmation email containing information about joining the webinar.
Artificial intelligence - enabling and sustainable digital workers with digital workers is an obvious trend for the 2020s. The industry is increasingly defined by its ability to use advanced computer technology to understand and improve business and customer experiences. I suppose you have heard this before, but the way it is defined today is in the field of computer science, which emphasises intelligent machines that work and react like humans. The point is that things are likely to become even more complex, as the use of artificial intelligence as an artist becomes more widespread, as machines can produce creative work better, and as the boundary between works of art made by humans and computers continues to blur. If machines are given large datasets of content from which to learn styles, they will become better and better at mimicking people.
For starters, this article series is a joint effort by a team of three: One member on our team has a background in STEM (Richard Sarpong), and two of us don't (Ava Dobreva and Rafael Knuth). All three of us are new to TensorFlow, and we wanted to validate the hypothesis that TensorFlow is a big leap towards the democratization of AI. How do we prove this hypothesis? That being said, consider this article a documented learning journey resulting from a series of experiments. However, we did not just jump onto the task and got it "somehow" done.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Physically taxing jobs can hinder one's cognitive health, potentially causing a person's brain to age faster and leave them with a poorer memory as they grow older, a new study suggests. In a study published in the Frontiers in Human Neuroscience in July, researchers surveyed nearly 100 cognitively healthy older adults between ages 60 and 80 years old in order to better understand how stress plays a role in how the human brain ages. Their analysis indicated that adults who reported having higher levels of physical stress in their most recent job were also people who had a smaller hippocampal volume and poorer memory performance. The hippocampus is commonly associated with memory.
The Full Stack AI/ML Engineer toolkit needs to include web scraping, because it can improve predictions with new quality data. I thought, how can we angle "Web Scraping for Machine Learning", and I realized that Web Scraping should be essential to Data Scientists, Data Engineers and Machine Learning Engineers. The Full Stack AI/ML Engineer toolkit needs to include web scraping, because it can improve predictions with new quality data. Machine Learning inherently requires data, and we would be most comfortable, if we have as much high quality data as possible. But what about when the data you need is not available as a dataset?
Oliver Hofmann and his research group at the Institute of Solid State Physics at TU Graz are working on the optimization of modern electronics. A key role in their research is played by interface properties of hybrid materials consisting of organic and inorganic components, which are used, for example, in OLED displays or organic solar cells. The team simulates these interface properties with machine-learning-based methods. The results are used in the development of new materials to improve the efficiency of electronic components. The researchers have now taken up the phenomenon of long-range charge transfer.
The COVID 19 situation, has rendered the industry into an unprecedented situation. Businesses across the globe are now resorting to plan out new strategies to keep the operations going, to meet clients' demands. Work-from-Home is the new normal for both the employees and the employers to function in a mitigated manner. Twitter on their tweet had suggested their employees, to function through "Work-from-Home", forever, if they want to. This new trend can be easily surmised as being effective for a while to manage operations, but cannot be ruled out as the necessary solution, for satisfying the customers and clients in the long run.
To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations. In this retrospective study, the proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions, based on a dataset of 9749 chest CT volumes. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities, based on deep learning and deep reinforcement learning. The first measure of (PO, PHO) is global, while the second of (LSS, LHOS) is lobe-wise. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States collected between 2002-Present (April 2020).