Artificial Intelligence has seen radical advances of many kinds over the last years, roundly beating human champions in games like Go and poker that once seemed out of reach. Advances in other domains like speech recognition, machine translation, and photo tagging has become routine. Yet something foundational is still missing: ordinary common sense. Common sense is knowledge that is commonly held, the sort of basic knowledge that we expect ordinary people to possess, like "People don't like losing their money," "You can keep money in your wallet," "You can keep your wallet in your pocket," "Knives cut things," and "Objects don't disappear when you cover them with a blanket." Without it, the everyday world is hard to understand; lacking it, machines can't understand novels, news articles, or movies.

If you're just starting out in Intelligent Automation (IA) or Robotic Process Automation (RPA), it won't be long before you start hearing a certain acronym banded around again and again and again. Indeed, the RPA Centre of Excellence (CoE) retains a special importance in the IA/RPA universe. But what exactly is a CoE? Why should you have one? And, more to the point, how do you go about setting one up? Edge Tech's US Consultant, Greg Hunt, sat down for a chat with Andy Fanning, an RPA leader/executive based out of Lake Ozark, Missouri to dig deep into this topic. Before we dive in, it'd be great for you to set the scene. If you think back to the second industrial revolution when electricity was replacing steam power in the factories, at what point could it have been said that the revolution started? When was the tipping point?

Artificial intelligence (AI) is quickly making inroads into medical practice, especially in forms that rely on machine learning, with a mix of hope and hype.1 Multiple AI-based products have now been approved or cleared by the US Food and Drug Administration (FDA), and health systems and hospitals are increasingly deploying AI-based systems.2 For example, medical AI can support clinical decisions, such as recommending drugs or dosages or interpreting radiological images.2 One key difference from most traditional clinical decision support software is that some medical AI may communicate results or recommendations to the care team without being able to communicate the underlying reasons for those results.3 Medical AI may be trained in inappropriate environments, using imperfect techniques, or on incomplete data. Even when algorithms are trained as well as possible, they may, for example, miss a tumor in a radiological image or suggest the incorrect dose for a drug or an inappropriate drug.

Tesla unleashed the latest twist in driverless car technology last week, raising more questions about whether autonomous vehicles are outracing public officials and safety regulators. The Palo Alto electric car company on Sept. 26 beamed a software feature called Smart Summon to Tesla owners who prepaid for it. Using a smartphone, a person can now command a Tesla to turn itself on, back out of its parking space and drive to the smartphone holder's location -- say, at the curb in front of a Costco store. The car relies on onboard sensors and computers to help it move forward, back up, steer, accelerate and decelerate on its own, braking if it detects people, other vehicles or stationary objects in its path. The "driver" must keep a finger or thumb on the smartphone screen or the car will stop.

A NASA Frontier Development Lab (FDL) team has shown that by using deep learning, it is possible to virtually monitor the Sun's extreme ultraviolet (EUV) irradiance, which is a key driver of space weather. The Sun is vital for survival, but solar flares, which typically occur a few times a year, have the potential to cause severe disruptions in space and on Earth. These disruptions can impact spacecraft, satellites and even systems here on Earth, including GPS navigation, radio communications and the power grid. Deep learning can help get more value out of our current ability to monitor the Sun by providing virtual instruments to supplement physical devices. This research will be published in Science Advances on October 2, 2019 ("A deep learning virtual instrument for monitoring solar extreme ultraviolet spectral irradiance").

Researchers at CHILI Lab (Ecole Polytechnique Fédérale de Lausanne) in Switzerland and GAIPS Lab (University of Lisbon) in Portugal have recently developed an autonomous system designed to assist children in improving their handwriting skills. The system they created, presented in a paper published in Springer's International Journal of Social Robotics, entails the use of a social robot in one-to-one learning sessions with children. For some children, handwriting can be a difficult skill to acquire, yet it is a fundamental stepping stone in their academic path. In fact, poor handwriting can negatively affect a child's academic performance, self-esteem and learning motivation. To master handwriting, a child needs to learn to coordinate cognitive, motor and perceptual abilities, thus he/she might also require a considerable amount of practice.

The machines haven't taken over. However, they are seeping their way into our lives, affecting how we live, work and entertain ourselves. From voice-powered personal assistants like Siri and Alexa, to more underlying and fundamental technologies such as behavioral algorithms, suggestive searches and autonomously-powered self-driving vehicles boasting powerful predictive capabilities, there are several examples and applications of artificial intellgience in use today. However, the technology is still in its infancy. What many companies are calling A.I. today, aren't necessarily so.

Governments around the world are pouring money into AI research and developing detailed AI strategies, but the US has been slow to follow suit. That's leading some to question whether policy makers are doing enough to maintain the country's lead in the technology. Earlier this month the US government announced that the 2020 budget request includes nearly $1 billion worth of funding for non-military research and development in AI. They were eager to note that represents a significant increase in spending on civilian applications--in 2016 the total was $1 billion including defense projects. But the response from industry insiders was markedly lackluster, according to the Wall Street Journal.

Although the future is exciting, it's also unknown. Dell Technologies' Realizing 2030 report found that 85 percent of jobs that will exist in 2030 haven't yet been thought up. Despite fears around the unknown, research from McKinsey reports that, on the whole, job growth will outpace job loss. Researchers found that while 15 percent of the global workforce -- 400 million workers -- could be displaced by automation by 2030, this will be offset by the jobs gained. In the same period, labor demand is predicted to grow by up to 33 percent of the global workforce -- the equivalent of 890 million new jobs.

Have you ever noticed that, after 6 p.m. on weekdays, you tend to listen to harmony-laden, lo-fi, guitar-based songs with medium-to-fast-paced rhythms and a strong backbeat -- but you'll skip ones that are too distorted? As opposed to weekend mornings, when you follow up a local news podcast with slower piano tracks sung by a solo female vocalist, with strings and horns, angular melodies, multiple sections (but no solos) and a touch of melancholy throughout? Chances are, you've never thought about your listening choices in such a detailed way. But Pandora's musicologists and scientists have, and that's how -- with the help of artificial intelligence, machine learning and the analysis of the listening habits of its more than 65 million monthly users -- it knows which song you'll want to hear next. "We treat every individual very specially, and focus on contextual recommendations to understand what you like, what you listen to," says Oscar Celma, Pandora's vice president of data science, of how the company maps the DNA of every piece of audio in Pandora's millions-wide song library and compares that with explicit and implicit user preference feedback to yield bespoke programming.