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) …
Artificial intelligence (AI) is positively impacting our world in previously unimaginable ways across many different industries. The use of AI is particularly interesting in the cybersecurity industry because of its unique ability to scale and prevent previously unseen, aka zero-day, attacks. But remember, similar to how drug cartels built their own submarines and cellphone towers to evade law enforcement, and the Joker arose to fight Batman, so too will cyber-criminals build their own AI systems to carry out malicious counter-attacks. An August 2017 survey commissioned by Cylance discovered that 62% of cybersecurity experts believe weaponized AI attacks will start occurring in 2018. AI has been heavily discussed in the industry over the past few years, but most people do not realize that AI is not just one thing, but that it is made up of many different subfields.
Grab your beverage of choice and enjoy the read! Do hit reply if you're up for a brainstorming session on use cases, new research or ways to future proof your SaaS or enterprise product by implementing ML where it makes sense. On the current "AI revolution": In a lovely piece, Prof. Michael Jordan of Berkeley explores many of the central tenets driving the excitement around AI today. He makes the case for a new engineering discipline, defines the differences between human-imitative AI (i.e. "The current focus on doing AI research via the gathering of data, the deployment of "deep learning" infrastructure, and the demonstration of systems that mimic certain narrowly-defined human skills -- with little in the way of emerging explanatory principles -- tends to deflect attention from major open problems in classical AI. These problems include the need to bring meaning and reasoning into systems that perform natural language processing, the need to infer and represent causality, the need to develop computationally-tractable representations of uncertainty and the need to develop systems that formulate and pursue long-term goals. These are classical goals in human-imitative AI, but in the current hubbub over the "AI revolution," it is easy to forget that they are not yet solved."
We have spoken about machine learning and the internet of things as tools to optimize location analytics in logistics and supply chain management. It's an accepted fact that technology, especially cloud-based, can benefit companies by optimizing routes and predicting the accurate estimated time of arrivals (ETAs). The direct business value of this optimization lies in the streamlining of various fixed and variable costs associated with logistics. The Internet of Things (IoT) world may be exciting, but there are serious technical challenges that need to be addressed, especially by developers. In this handbook, learn how to meet the security, analytics, and testing requirements for IoT applications.
Artificial intelligence is quickly growing in importance in the'smart building' sector. Paul Skelton looks at the road ahead for a complex technology. When Mark Chung received an unexpectedly high $500 monthly electricity bill, he turned to his utility for help and answers. However, despite'smart' meters being installed in his home, they were no help. So Mark – an electrical engineer trained at Stanford University – took matters into his own hands.
We have spoken about machine learning and the internet of things as tools to optimize location analytics in logistics and supply chain management. It's an accepted fact that technology, especially cloud-based, can benefit companies by optimizing routes and predicting the accurate estimated time of arrivals (ETAs). The direct business value of this optimization lies in the streamlining of various fixed and variable costs associated with logistics.
Today at the Frankfurt motor show, one of the biggest and most prestigious motor shows in the world, Sheryl Sandberg, COO of Facebook, spoke before German Chancellor Angela Merkel. Now what is Facebook and most importantly, Sheryl Sandberg doing at an automotive industry event? The obvious answer that comes to mind when one relates Facebook and the car industry is the billions of advertising dollars the industry spends on marketing and advertising. However, that does not seem to be Facebook's game plan, as highlighted by Sheryl and shown at their pavilion. Facebook seems to have a strategy of leveraging its capabilities in social marketing, AR & VR and interestingly, who would have thought of it, leveraging its advanced AI and deep learning capabilities to support the development of autonomous vehicles.
This work introduces my initial experiment to study Artificial Perception in Self-Driving technology. Vehicle Artificial Perception is known as a capability that helps Self-driving cars to understand the surrounding environment through a computer based-system. The system can consist of several different sensors such as Cameras, Lidar, Radar, GPS, IMU...to gather information around the car. An intelligent software then processes the data collected from the sensors to recognize and classify surrounding objects such as cars, humans, road marks, traffic signs.... Based on the understanding of the detected objects, the intelligent software can predict behavior and plan appropriate reactions according to the situations. Creating such an intelligent software has been a challenge for Artificial Intelligence researchers for decades. However, Deep Learning has recently offered a promising solution in the field of Artificial Intelligence, in which Deep Learning software has the ability to learn to create its own Artificial Neural Networks.
Machine learning is becoming an increasingly important artificial intelligence approach to building autonomous and robotic systems. One of the key challenges with machine learning is the need for many samples, the amount of data needed to learn useful behaviors is high. In addition, the robotic system is often non-operational during the training phase. This requires debugging to occur in real-world experiments with an unpredictable robot. Microsoft's Aerial Informatics and Robotics platform has a solution for these two problems: It will provide realistic simulation tools for designers and developers to generate the training data needed and will also leverage recent innovations in physics to create accurate, real-world simulations.
It's relatively easy to develop a drone that can fly on its own, but it's another matter developing one that can navigate the many obstacles of real life. That's where Microsoft thinks it can help. It just published an open source simulator, the Aerial Informatics and Robotics Platform, that helps designers test and train autonomous machines in realistic conditions without wrecking expensive prototypes. The tool has vehicles move through randomized environments filled with the minutiae you see on a typical street, such as power lines and trees -- if your drone can't dodge a tree branch, you'll find out quickly. You can see what the vehicle would see (including simulated sensor data), and the software ties into both existing robotic hardware platforms and machine learning systems to speed up development.
Connected cars and the Internet of Things go together like peanut butter and jelly. But realizing the future of autonomous vehicles will demand close attention to be paid to cybersecurity, functional-safety standards, and other critical factors. IoT will advance the era of self-driving cars, which currently is dominated by Tesla Motors. At the same time, it will change some of the dynamics in this market. On one hand, it will turn automotive manufacturers into technology companies, which could provide new revenue streams for carmakers. On the other hand, it will open the door for new players that have never had a viable entry point in the automotive market.