Consider the artificially intelligent voices you hear on a regular basis. Are any of them men? Whether it's Apple's Siri, Microsoft's Cortana, Amazon's Alexa, or virtually any GPS system, chances are the computerized personalities in your life are women. This gender imbalance is pervasive in fiction as well as reality. Films like "Her" and "Ex Machina" reflect our anxieties about what intelligent machines mean for humanity.
They might apply to one of these futuristic job ads one day. Fifteen years ago, people would have looked at you sideways if you told them you were a data scientist, driverless car engineer, or drone operator. It's hard to believe, but in 2006 those industries didn't really exist. By 2030, automation is expected to hit a midpoint, "something like 16 percent of occupations would have been automated--and there would be impact and dislocation as a result of these technologies." Artificial Intelligence, spatial computing (augmented and virtual reality), brain-computer interfaces, are all set to substitute labor or complement it in some way.
Minsky and McCarthy, in the 1950s, described artificial intelligence as any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task. The AI systems typically demonstrate behaviors associated with human intelligence like planning, learning, reasoning, problem-solving, knowledge representation, perception, motion, manipulation, and to a lesser extent, social intelligence, and creativity. Nowadays, artificial intelligence is all around us in computers, speech and language recognition of the Siri virtual assistant on the Apple iPhone, in the vision-recognition systems on self-driving cars, in the recommendation engines that suggest products you might like based on what you bought in the past, interpreting video feeds from drones carrying out visual inspections of infrastructure such as oil pipelines, organizing personal and business calendars, responding to simple customer-service queries, coordinating with other intelligent systems to carry out tasks like booking a hotel at a suitable time and location, helping radiologists to spot potential tumors in X-rays, flagging inappropriate content online, detecting wear and tear in elevators from data gathered by IoT devices, the list goes on and on. There is a flood of virtual assistants, such as Apple's Siri, Amazon's Alexa, Google Assistant, and Microsoft Cortana, etc. The AI is capable of executing vastly different tasks, anything from giving you a haircut to building complex robots as commonly seen in movies, the likes of HAL in 2001, or Skynet in The Terminator, though doesn't exist today certainly a reality of tomorrow. What is machine learning: Machine learning is where a computer system is fed large amounts of data which it then uses to learn how to carry out a specific task such as understanding speech or captioning a photograph.
What kind of job do you think your children will have in the future? They might apply to one of ... [ ] these futuristic job ads one day. Fifteen years ago, people would have looked at you sideways if you told them you were a data scientist, driverless car engineer, or drone operator. It's hard to believe, but in 2006 those industries didn't really exist. By 2030, automation is expected to hit a midpoint, "something like 16 percent of occupations would have been automated--and there would be impact and dislocation as a result of these technologies."
Amazon has released its first Echo device for use outside of the house, allowing users to take Alexa in their car. The company revealed the device in 2018 but it has finally come to customers in the UK and Ireland. Echo Auto plugs into a car's 12V power outlet or built-in USB port and connects to the in-car stereo via either audio jack cable or Bluetooth to enable the use of voice assistant Alexa inside the vehicle. Users are then able to use Alexa voice commands to control music, check the news, make phone calls or check their schedule without taking hands off the wheel or eyes off the road. The device gets internet connectivity by connecting to a user's smartphone and the Alexa app and using its existing data plan.
Here are a few predictions about how several industries that impact our everyday lives will be impacted by AI not only this year but beyond. The buzz surrounding AI and its impact in 2020 and beyond shows no signs of slowing down. Driven by the emergence of virtual assistants, such as the Alexa, Siri, and Google Assistant ecosystems of devices, AI has now been incorporated into the everyday life of consumers. While it's impossible to predict the future with certainty, technologies that incorporate AI and automation are maturing at an incredibly rapid rate across some industries. Here are a few predictions about how several industries that impact our everyday lives – specifically healthcare, manufacturing, and mobility – will be impacted by AI not only this year but beyond.
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.
Alphabet is using its dominance in the search and advertising spaces -- and its massive size -- to find its next billion-dollar business. From healthcare to smart cities to banking, here are 10 industries the tech giant is targeting. With growing threats from its big tech peers Microsoft, Apple, and Amazon, Alphabet's drive to disrupt has become more urgent than ever before. The conglomerate is leveraging the power of its first moats -- search and advertising -- and its massive scale to find its next billion-dollar businesses. To protect its current profits and grow more broadly, Alphabet is edging its way into industries adjacent to the ones where it has already found success and entering new spaces entirely to find opportunities for disruption. Evidence of Alphabet's efforts is showing up in several major industries. For example, the company is using artificial intelligence to understand the causes of diseases like diabetes and cancer and how to treat them. Those learnings feed into community health projects that serve the public, and also help Alphabet's effort to build smart cities. Elsewhere, Alphabet is using its scale to build a better virtual assistant and own the consumer electronics software layer. It's also leveraging that scale to build a new kind of Google Pay-operated checking account. In this report, we examine how Alphabet and its subsidiaries are currently working to disrupt 10 major industries -- from electronics to healthcare to transportation to banking -- and what else might be on the horizon. Within the world of consumer electronics, Alphabet has already found dominance with one product: Android. Mobile operating system market share globally is controlled by the Linux-based OS that Google acquired in 2005 to fend off Microsoft and Windows Mobile. Today, however, Alphabet's consumer electronics strategy is being driven by its work in artificial intelligence. Google is building some of its own hardware under the Made by Google line -- including the Pixel smartphone, the Chromebook, and the Google Home -- but the company is doing more important work on hardware-agnostic software products like Google Assistant (which is even available on iOS).
From The Terminator to Blade Runner, pop culture has always leaned towards a chilling depiction of artificial intelligence (AI) and our future with AI at the helm. Recent headlines about Facebook panicking because their AI bots developed a language of their own have us hitting the alarm button once again. Should we really feel unsettled with an AI future? News flash: that future is here. If you ask Siri, the helpful assistant who magically lives inside your phone, to read text messages and emails to you, find the nearest pizza place or call your mother for you, then you've made AI a part of your everyday life.
The room was packed at the annual Machine Learning and the Market for Intelligence conference in Toronto last week. Now in its fifth year, the lengthy name of the event matches the depth of the discussions. But one speaker and her talk stood out to me in particular: Marzyeh Ghassemi, who also happens to be a veteran of Alphabet's Verily, presented "Machine Learning From Our Mistakes." Ghassemi, an assistant professor at the University of Toronto, talked about the importance of predicting actionable insights in health care, the regulation of algorithms, and practice data versus knowledge data. But at the very end, saving the best for last, she emphasized the importance of treating health data as a resource.