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) …
If you've ever seen a self-driving car in the wild, you might wonder about that spinning cylinder on top of it. It's a "lidar sensor," and it's what allows the car to navigate the world. By sending out pulses of infrared light and measuring the time it takes for them to bounce off objects, the sensor creates a "point cloud" that builds a 3D snapshot of the car's surroundings. Making sense of raw point-cloud data is difficult, and before the age of machine learning it traditionally required highly trained engineers to tediously specify which qualities they wanted to capture by hand. But in a new series of papers out of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), researchers show that they can use deep learning to automatically process point clouds for a wide range of 3D-imaging applications.
The University of Toronto and the affiliated Vector Institute for Artificial Intelligence have announced the recruitment of two rising stars in machine learning research as part of a continued drive to assemble the best AI talent in the world. Chris Maddison and Jakob Foerster will both come to U of T having completed their doctoral research at the University of Oxford. He earned his undergraduate and master's degrees in computer science at U of T – the latter under the supervision of University Professor Emeritus Geoffrey Hinton. A senior research scientist at Google-owned AI firm DeepMind, Maddison will join U of T's departments of computer science and statistical sciences in the Faculty of Arts & Science as an assistant professor next summer. Foerster, a research scientist at Facebook AI Research, will start as an assistant professor in the department of computer and mathematical sciences at U of T Scarborough in fall of 2020.
The development of driverless car technology is on the rise, and automakers are investing millions and billions to be the first to market with their lineup of autonomous vehicles. But which company has made the largest investment in self-driving cars? Here's a look at what some of the top companies have invested in their driverless vehicle programs so far. The investment into the autonomous vehicle industry has reached over $100 billion, with the leader in spending investing more than half of this number, according to a report by Leasing Options. The report indicated that Volkswagen is driving the charge when it comes to driverless technology with an investment of $54.2 billion and 57 percent share in total industry investment of self-driving cars.
AI is poised to benefit a multitude of industries in a variety of different ways. What does artificial intelligence in the near-term look like? How is it impacting industries and what should companies know about AI to remain competitive over the next few years? What are the early adopters of AI doing right now? Early adopters of AI include everything from automotive to marketing.
The autonomous vehicle industry is in the process of rerouting. Early AV leaders said fully autonomous cars would hit the mass market by 2020 or 2021--Elon Musk even promised a self-driving Tesla by 2017. But with the end of the decade in sight, two things are certain: The autonomous future remains a long way off, and AV-makers are going to have to change their plan for how to get there. In this presentation, we show you what this new path looks like and lay out the step-by-step changes we'll see on the way to full autonomy. We make the case that AV developers' early shortcomings have ushered in a new era of collaboration and realism.
In the next 10 minutes, you'll possibly be amazed, amused, blown away, frightened, or lost in thought. As this is the beginning of a new year and we'd all rather feel joyful – let's focus on the amusing part. Here are 8 mind-boggling technology acceleration outcomes awaiting for us in the (near) future. Artificial Intelligence (AI) by definition is an artificially created ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. AI could carry out a complete simulation of the human brain and even exceed it.
In the world of AI (artificial intelligence) there are a lot of exciting companies making their mark in a pretty impressive way in the automotive industry. Some of these disruptive startups are truly giving us all a glimpse of where the AI market is headed. Ford and its Ford City Insights program, which uses its AI and data from traffic cameras, parking garages, and other sources to share mobility-related information is a perfect example. This AI-powered database is able to predict where collisions are more likely to happen or where microtransit solutions will be most valuable within city limits. Three of the six new cities are Austin, Indianapolis, and Detroit, and reports suggest the other three will be Miami, Pittsburgh, and Grand Rapids.
There's no doubt that we've come a long way from the days when crank windows and stick shifts were the norm and cruise control was a new phenomenon. Modern cars now come equipped with a plethora of autonomous bells and whistles that help people drive safer on the roads. However, the ever-approaching advent of the era of self-driving cars continues to remain several years in the future. The question is, where are we in the process of developing self-driving cars, and how does big data play a role currently as well as in the future of the autonomous transportation industry? While there is a great deal of technology that has been poured into the development of self-driving cars, there's no doubt that big data has held a leading role.
Real-World Examples of IOT Edge: IOT Edge computing is a framework that brings computing in the internet of things closer to the devices, which are the endpoint of the IOT network. By bringing the data processing closer to the Edge, it accelerates the processing, reduces the time taken for making decisions, and thus drives real-time action. IOT edge computing has gained enormous frenzy in recent years. Different versions of edge computing like fog computing have also gained momentum. Various applications, solutions, hardware, software have come into the market to enhance and propagate the adoption of edge computing among the IOT implementers.
A few months ago, Fr Philip Larrey published his book called "Artificial Humanity". In this article, we will explain what would happen if we have an inhumane AI. First of all, what does inhumane mean? Primarily, when we say Artificial Inhumanity, we are referring to an AI which is not concerned with humans. It does not exhibit any human feeling, and humans are just animate objects roaming our world. Even though AI was initially conceived to serve humans, we do not exclude the possibility of eventually having an AI, which ultimately only serves its interests. If that happens, then we are definitely in big trouble. The question of whether machines can think is about as relevant as the question of whether submarines can swim. Using the same line of thought, if machines exhibit humanity, does that mean that they are human?