Adoption has more than doubled since 2017, though the proportion of organizations using AI 1 1. In the survey, we defined AI as the ability of a machine to perform cognitive functions that we associate with human minds (for example, natural-language understanding and generation) and to perform physical tasks using cognitive functions (for example, physical robotics, autonomous driving, and manufacturing work). A set of companies seeing the highest financial returns from AI continue to pull ahead of competitors. The results show these leaders making larger investments in AI, engaging in increasingly advanced practices known to enable scale and faster AI development, and showing signs of faring better in the tight market for AI talent. On talent, for the first time, we looked closely at AI hiring and upskilling.

An experiment was initially performed in 2011 where both humans and AI were "asked" to identify what was shown in a blurred image. Human error rated at 5% while AI at 26%. In 2013 the experiment was repeated and AI error dropped to 3%. In 2015 an AI managed to almost beat the top Poker players in the U.S. (and poker is a strategic "thinking" game where not merely the cards "have a role to play". The main point was that it learned how to "bluff", … yes, … really!

For those who may find awkward the reference to "half a decade" and not the "next decade" here is why: Artificial Intelligence (AI) is evolving at such a staggering rate that it is simply not possible to foresee what it will represent in 10 years' time. As Maurice Conti (Chief Innovation Officer at Telefónica Alpha and former director at Autodesk) reminded us in his TEDx talk in February 2017, that in the human history the "Hunter-Gatherer" age lasted for several million years, then the agricultural age lasted several thousand years, the industrial age has been around for a couple of centuries now, the information age has merely a few decades, and the AI age (although the concept was drawn in the 1950s) has in fact effectively started less than half a decade ago. It is very easy to mistake AI for RPA (Robotic Process Automation), so let's start by defining what sets them apart. RPA results from developing detail instructions that are translated into code which a computer interprets while actuating a robot. Therefore, RPA enables the integration with mechatronics (robotic physical machines) to partially or fully automate human activities which are manual, repetitive and rule-based.

As we're nearing the end of 2017, we've come to the 5 year landmark of deep learning really starting to hit the mainstream. For me, I think of AlexNet and the 2012 Imagenet competition as the coming out party (although researchers have definitely been working in this field for quite a bit longer). It's been just 5 years and we've absolutely revolutionized the way we look at the capabilities of machines, the way we build software (Software 2.0), and the ways we think about creating products and companies (Just ask any VC or startup founder). Tasks that seemed impossible just a decade ago have become tractable, granted you have the appropriate labeled dataset and compute power of course. In this post, we'll overview the last couple years in deep learning, focusing on industry applications, and end with a discussion on what the future may hold.

For those who may find awkward the reference to "half a decade" and not the "next decade" here is why: AI is evolving at such a staggering rate that it is simply not possible to foresee what it will represent in 10 years' time. As Maurice Conti (Chief Innovation Officer at Telefónica Alpha and former director at Autodesk) reminded on his intervention at TEDX in February 2017, in human history the "Hunter-Gatherer" age lasted for several million years, then the Agricultural age lasted several thousand years, the Industrial age has been around for a couple of centuries now, the Information age has merely a few decades and the AI age (although the concept was drawn in the 1950s) has in fact effectively started less than half a decade ago. It is very easy to mistake AI for RPA (Robotic Process Automation), so let's start by defining what sets them apart. RPA results from developing detail instructions that are translated into code which a computer interprets while actuating a robot. Therefore, RPA enables the integration with Mechatronics (robotic physical machines), to partially or fully automate human activities which are manual, repetitive and rule-based.

Before we begin talking about the impact of Artificial Intelligence over the next half decade, here is a quick introduction to AI. AI does not aim at accomplishing repetitive tasks based on a given set of rules. It aims at learning new ways of acting from either having performed or watched repetitive tasks; so, having the ability to make subjective decisions with the goals of improving the initially established process. AI means moving away from programming and stepping into "Machine Learning", where an AI is trained to "acknowledge" certain patterns, hence making its own decisions on how to proceed. Nevertheless, once you enable AI to create code which will instruct RPA, then you have reached Cybernetics and created an A2IM (Autonomous Artificial Intelligence Mechatronics), such as some military drones, but we will come back to that ahead in the text. Go is the most difficult game humans have managed to come up with; it has more permutations in terms of possible moves than the sum of all the atoms that have been calculated to exist in the universe!

Enter your email address to subscribe to this blog and receive notifications of new posts by email. Permission to publish provided by Ira Padilla. For those who may find awkward the reference to "half a decade" and not the "next decade" here is why: AI is evolving at such a staggering rate that it is simply not possible to foresee what it will represent in 10 years' time. As Maurice Conti (Chief Innovation Officer at Telefónica Alpha and former director at Autodesk) reminded on his intervention at TEDX in February 2017, in human history the "Hunter-Gatherer" age lasted for several million years, then the Agricultural age lasted several thousand years, the Industrial age has been around for a couple of centuries now, the Information age has merely a few decades and the AI age (although the concept was drawn in the 1950s) has in fact effectively started less than half a decade ago. It is very easy to mistake AI for RPA (Robotic Process Automation), so let's start by defining what sets them apart.

As we're nearing the end of 2017 (and coincidentally the first day of NIPS 2017), we've come to the 5 year landmark of deep learning really starting to hit the mainstream. For me, I think of AlexNet and the 2012 Imagenet competition as the coming out party (although researchers have definitely been working in this field for quite a bit longer). It's been just 5 years and we've absolutely revolutionized the way we look at the capabilities of machines, the way we build software (Software 2.0), and the ways we think about creating products and companies (Just ask any VC or startup founder). Tasks that seemed impossible just a decade ago have become tractable, granted you have the appropriate labeled dataset and compute power of course. In this post, we'll overview the last couple years in deep learning, focusing on industry applications, and end with a discussion on what the future may hold.