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) …
China is shaping up to be the first real test of Big Tech's ambitions in the world of carmaking, with giants from Huawei Technologies Co. to Baidu Inc. plowing almost $19 billion into electric and self-driving vehicle ventures widely seen as the future of transport. While Apple Inc. has long had plans for its own car and Alphabet Inc. has Waymo, its autonomous driving unit, the size -- and speed -- of the move by China's tech titans puts them at the vanguard of that broader push. The lure is an industry that's becoming increasingly high tech as it pivots away from the combustion engine, with sensors and operating systems making cars more like computers, and the prospect of autonomy re-envisioning how people use will them. As the world's biggest market for new-energy cars, China is a key battlefield. Established automakers like Volkswagen AG and General Motors Co. are already slogging it out with local upstarts such as market darling Nio Inc. and Xpeng Inc.
Every day, billions of photos and videos are posted to various social media applications. The problem with standard images taken by a smartphone or digital camera is that they only capture a scene from a specific point of view. But looking at it in reality, we can move around and observe it from different viewpoints. Computer scientists are working to provide an immersive experience for the users that would allow them to observe a scene from different viewpoints, but it requires specialized camera equipment that is not readily accessible to the average person. To make the process easier, Dr. Nima Kalantari, professor in the Department of Computer Science and Engineering at Texas A&M University, and graduate student Qinbo Li have developed a machine-learning-based approach that would allow users to take a single photo and use it to generate novel views of the scene.
MIT researchers developed a picking robot that combines vision with radio frequency (RF) sensing to find and grasps objects, even if they're hidden from view. The technology could aid fulfilment in e-commerce warehouses. System uses penetrative radio frequency to pinpoint items, even when they're hidden from view. In recent years, robots have gained artificial vision, touch, and even smell. "Researchers have been giving robots human-like perception," says MIT Associate Professor Fadel Adib.
Six years ago, Atsushi Nakanishi launched Triple W with nothing but the seed of an idea and an overwhelming passion to realize it. Today, the startup is the creator and seller of DFree -- the world's first wearable device for urinary incontinence. The tiny, noninvasive device uses ultrasound to monitor the volume of urine in the user's bladder in real time. When the bladder reaches its threshold, DFree sends an alert to the user's smartphone to tell them it is time to go to the bathroom. Nakanishi credits the ground-breaking product to a eureka moment in 2013.
One can be forgiven for thinking that everyone in the world is adopting sophisticated, next-gen technologies such as artificial intelligence and autonomous systems, and their company is falling woefully behind. While it's more the case of everyone trying to find their way with still yet-to-be-fully-understood technologies, this fear of falling behind is real, and is driving investment. That's the word from a survey of 200 enterprises from Seeqc, which finds rising investment in deep-tech solutions is largely driven by the threat of industry competition, with substantial R&D budgets and jobs on the line. More than two-thirds, 67%, fear their competitors are further along than their company. That's certainly a way to get the full attention of business leaders controlling the purse strings.
If you have built Deep Neural Networks before, you might know that it can involve a lot of experimentation. In this article, I will share with you some useful tips and guidelines that you can use to better build better deep learning models. These tricks should make it a lot easier for you to develop a good network. You can pick and choose which tips you use, as some will be more helpful for the projects you are working on. Not everything mentioned in this article will straight up improve your models' performance.
As IBM explain, "at its simplest form, artificial intelligence is a field, which combines computer science and robust datasets to enable problem-solving." It includes the sub-fields of machine learning and deep learning. These two fields use algorithms that are designed to make predictions or classifications based on input data. Of course, as technology becomes more sophisticated, literally millions of decisions need to be made every day and AI speeds things up and takes the burden off humans. The World Economic Forum describes AI as a key driver of the Fourth Industrial Revolution.
"Trust is a must," she said. "The EU is spearheading the development of new global norms to make sure AI can be trusted. By setting the standards, we can pave the way to ethical technology worldwide." Any fast-moving technology is likely to create mistrust, but Vestager and her colleagues decreed that those in power should do more to tame AI, partly by using such systems more responsibly and being clearer about how these work. The landmark legislation – designed to "guarantee the safety and fundamental rights of people and businesses, while strengthening AI uptake, investment and innovation" – encourages firms to embrace so-called explainable AI.
Python continues to lead the way when it comes to Machine Learning, AI, Deep Learning and Data Science tasks. Because of this, we've decided to start a series investigating the top Python libraries across several categories: Of course, these lists are entirely subjective as many libraries could easily place in multiple categories. Now, let's get onto the list (GitHub figures correct as of November 16th, 2018): "pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python." "Matplotlib is a Python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell (à la MATLAB or Mathematica), web application servers, and various graphical user interface toolkits."