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) …
Australian giant Downer has a 30-year contract with the New South Wales government to manage and maintain its fleet of 78 Waratah trains that operate in the greater Sydney metro area. With 2041 not approaching any time soon, the company recognised a perfect opportunity to maximise technology to make the most of its data and plan for proactive, rather than reactive, maintenance of Sydney's trains. In December 2016, the NSW government ordered 24 Waratah Series 2 trains under its Sydney Growth Trains Project and in February 2019, announced the decision to order 17 more trains. The new trains are touted as providing passengers with improved safety and comfort, fitted with air-con, more CCTV cameras, and improved accessibility. Downer general manager of Digital Technology and Innovation Mike Ayling said his company saw this as the perfect opportunity to leverage additional sensor data from the fleet.
So Uber is finally going public. The ride-hail company perhaps best known for sketchy business practices and a culture of sexual harassment released on April 11 a lengthy prospectus detailing its plan to get that money. The document, required by the Securities and Exchange Commission and written for the benefit of potential investors, serves as a compendium of the company's hopes, dreams, and fears. SEE ALSO: Here's what we learned from Travis Kalanick's hidden 2007 Twitter account However, at 285 pages (not including the index), you have better things to do than attempt to parse this beast of a document. Thankfully, we've done a bit of that for you.
Niccolo Mejia covers AI applications across industries at Emerj. He holds a bachelor's degree in Writing, Literature, and Publishing from Emerson College. In this article, we explore the applications of AI software within the automotive industry from production and manufacturing to insurance and transportation. We will discuss the equipment involved in collecting and analyzing data along with the potential value they offer to manufacturers, shared mobility companies, insurers, and drivers. We begin our overview of AI in the automotive industry with how machine vision technology could improve the robots that car manufacturers use to build vehicles and maintain quality control.
In a little over a decade since researchers uncovered novel techniques to improve its efficiency and effectiveness, deep learning has become a practical technology that now underpins a number of applications that need artificial intelligence (AI). Many of these applications are hosted in the cloud on powerful servers, as tasks sometimes involve the processing of data-rich sources such as images, videos, and audio. Those servers often call on the additional performance of acceleration hardware, which range from graphics processing units to custom devices. These become particularly important for the numerically-intensive process, during which a neural network is trained on new data. Typically, the inferencing process, which uses a trained network to assess new data, is far less compute-intensive than training.
IoT is already affecting most areas of our lives and transportation is no exception. Even before the age of autonomous cars IoT is improving road safety and I'm going to express this through my own personal experience and other uses. I am currently working in Dublin and I travel back home to Waterford every weekend to see my family. For the first few months, I would take the bus and with the frequent stops, distance and Dublin Traffic the journey would take 4 hours! This meant I was travelling on average for 8 hours over the weekend.
Every new technology that comes to prominence has always made the life of humans better. Remember, the time fire was first discovered by your ancestors to cook food. And then came the wheel. Now, it is digital payments and internet of things. Are you a person who keeps a keen eye on the scientific developments happening in the world?
The never-ending loop of customers and business has taught us one thing: there are millions of ways using which we can reach the elite tier in any sector if we are willing to adapt. Business process outsourcing, the industry that once was dependent on call centers is now a contradistinctive sector. With Artificial Intelligence now fusing into business process services, the blueprint of the business industry has been given a systemic makeover/redesigned. Customers hold the whip of a businesses' success and no organization is ready to slip the slightest revenue making cede the slightest profit-making chance to its rivals, owing to technological disruptions that are remodeling everything from top to bottom. Therefore, every BPS company today looks to integrate its operations with the latest technologies like AI and RPA to ensure high performance, flexibility, seamless operations, and an encouraging ROI.
If you just include "machine learning" in your pitch you can add a zero on to the end of your valuation. Machine Learning (ML) and the Internet of Things (IoT) have been huge buzzwords in the past few years, much of which has been hype and much of which reflects their profound potential. The above quote came somewhat jokingly from an investor, but it has some truth to it too. Given the hype around machine learning and IoT and the broad range of application to which they can be brought to bear, it can be difficult to cut through the noise and understand where the actual value lies. In this post, I'll explain how machine learning can be valuable for IoT, when it's appropriate to use, and some machine learning applications and use cases currently out in the world today.
Machine learning (ML) and artificial intelligence (AI) continue to capture media attention and business investments. IDC estimates machine learning and AI spending to increase from $19.1 billion in 2018 to $52.2 billion by 2021. With billions of dollars invested in machine learning and AI, it's no surprise that tech giants Google, Microsoft and Amazon are investing billions in cloud infrastructure and development tools to accelerate the delivery of custom machine learning applications. Case in point, machine learning was the third-highest category for the number of patents granted between 2013 and 2017. So, what exactly is machine learning?
At IBM Think in February, IBM made several announcements around the expansion of Watson's availability and capabilities, framing these announcements as the launch of "Watson Anywhere." This piece is intended to provide guidance to data analysts, data scientists, and analytic professionals seeking to implement machine learning and artificial intelligence capabilities and evaluating the capabilities of IBM Watson's AI and machine learning services for their data. IBM declared that Watson is now available "anywhere" – both on-prem and in any cloud configuration, whether private, public, singular, multi-cloud, or a hybrid cloud environment. Data that needs to remain in place for privacy and security reasons can now have Watson microservices act on it where it resides. The obstacle of cloud vendor lock-in can be avoided by simply bringing the code to the data instead of vice versa.