One of the keys to my success throughout the years has been my ability to understand technology. Innovation in digital marketing has been rapid but consistent… until now. As I'm watching artificial intelligence (AI) advancements, I fear that I'm falling behind… and it could cost me a great career where I've spent every spare minute studying, applying, and implementing with my customers. And, because it's AI, I know that once I fall behind, the machines will far outpace any chance I have of catching up. And I should disclose that I have one incredible edge over most business professionals, my son Bill is the Chief Data Scientist at OpenINSIGHTS with a PhD in Mathematics from the University of Illinois.

Artificial intelligence (AI) has made tremendous strides over the recent years and in a wide range of applications and the benefits of AI stem from rapid growth of data and upsurge of mobile devices. Artificial intelligence is a computational model that enables computer to learn from data and create solutions for complex functions. AI has been extensively applied in large scale fields from robotics to airplane flight control. Artificial intelligence (AI) generally refers to the stimulation of human intelligence in machines which are programmed to have a thinking process similar to humans and mimic their actions. AI-based applications are developing rapidly in terms of deployment, adaptation, computing speed, and capabilities.

Toshiba Corporation, the industry leader in solutions for large-scale optimization problems, today announced a scale-out technology that minimizes hardware limitations, an evolution of its optimization computer, the Simulation Bifurcation Machine (SBM), that supports continued increases in computing speed and scale. Toshiba expects the new SBM to be a game changer for real-world problems that require large-scale, high-speed and low-latency, such as simultaneous financial transactions involving large numbers of stock, and complex control of multiple robots. The research results were published in Nature Electronics on March 1. Speed and scale are keys to success in industrial sectors as different as finance, logistics, and communications, all of which have to deal with large numbers and make complex decisions in the shortest time possible. Aiming to bring higher efficiencies to these and other businesses, Toshiba has addressed combinatorial optimization problems by developing high-speed, high-accuracy algorithms and corresponding practical computer solutions.

Kobe – Japan's Fugaku supercomputer, the world's fastest in terms of computing speed, went into full operation Tuesday, earlier than initially scheduled, in the hope that it can be used for research related to the novel coronavirus. The supercomputer, named after an alternative word for Mount Fuji, became partially operational in April last year to visualize how droplets that could carry the virus spread from the mouth and to help explore possible treatments for COVID-19. "I hope Fugaku will be cherished by the people as it can do what its predecessor K couldn't, including artificial intelligence (applications) and big data analytics," said Hiroshi Matsumoto, president of the Riken research institute that developed the machine, in a ceremony held at the Riken Center for Computational Science in Kobe, where it is installed. Fugaku, which can perform over 442 quadrillion computations per second, was originally scheduled to start operating fully in the fiscal year from April. It will eventually be used in fields such as climate and artificial intelligence applications, and will be used in more than 100 projects, according to state-sponsored Riken. The supercomputer, which was developed jointly with Fujitsu Ltd., was ranked the world's fastest for computing speed in the twice-yearly U.S.-European TOP500 project for the first time in June, and retained the top spot in November.

An updated version of Marc Andreessen's famous quote, "Software is eating the world" probably is "AI is eating software." We tend to think of AI in incremental ways, and we need to urgently change that thought process because our approach to AI will demarcate the difference between linear thinking and transformational thinking. Most organizations want to use AI to cut costs and do the thing they are already doing faster and quicker; this is an incremental approach to AI, whereas we need to focus on the next-level use of AI, that exponentially transforms the way we have been doing things thus far by creating new systems. For example, Amazon Go (Amazon's retail store) isn't using AI to simply remove the role of the cashier, but it is designing a new retail experience that is data and information-driven. Thus, instead of simply putting a layer of AI on top of existing processes, Amazon Go is changing the average grocery-shopping exercise into an experience-driven activity that is all about data, understanding people, behavior and design layout. Similarly, the objective of driverless autonomous vehicles is not merely to eliminate the cost of the driver, but to change the way we travel and redesign the entire transportation industry as well as create ripple effects in the e-commerce and delivery industries.

I believe artificial intelligence (AI) will be a key driver of change in PPC in 2018 as it leads to more and better PPC intelligence. So far, I've discussed the roles humans will play when PPC management becomes nearly fully automated and six strategies agencies can take to future-proof their business. In this final post on the state of AI in PPC, I'll cover the technology of AI. AI has been around since 1956, and PPC has existed since the late 1990s. So why did it take until now for AI's role in paid search to become such a hot topic in our industry? It's because we've recently hit an inflection point where, due to the exponential nature of technological advances, we're now seeing improvements that used to take years happen in weeks.

Joachim Buhmann, Professor for Computer Science and Head of the Institute for Machine Learning at ETH Zurich, offers a more sober assessment of the situation: "The Go player's algorithm has, of course, set a milestone in machine learning, but it's a milestone in a very limited, artificial field," he says. Since the early days of computer science as a scientific discipline, one of the challenges against which it has been relatively easy to measure progress has been strategy games. It started with simple games such as Nine Men's Morris and Draughts. In 1997, IBM's computer Deep Blue beat the reigning chess world champion Garry Kasparov. Soon thereafter, programmers set their sights on the considerably more complex game Go as the next potential milestone.