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) …
Editor's Note: This piece was written by Gary Newgaard, Vice President, Public Sector at Pure Storage. The opinions represented in this piece are independent of Smart Cities Dive's views. Ask average citizens about their biggest frustrations in dealing with government organizations and you're likely to conjure up at least a few stories of never-ending lines at the Department of Motor Vehicles (DMV). Bureaucracy and manual processes have, fairly or not, become synonymous with the business of government. They upset constituents, and chances are they don't help government workers get their jobs done, either.
Photoshop could become a thing of the past thanks to new technology that can touch-up badly damaged photos. The Nvidia software uses AI and deep-learning algorithms to predict what a missing portion of a picture should look like and recreate it with incredible accuracy. As well as restoring old physical photos that have been damaged, the technique could also be used to fix corrupted pixels or bad edits made to digital files. Graphics specialist Nvidia, based in Santa Clara, California trained its neural network using a variety of irregular shaped holes in images. The system then determined what was missing from each and filled in the gaps.
Drug interactions, including drug-drug interactions (DDIs) and drug-food constituent interactions (DFIs), can trigger unexpected pharmacological effects, including adverse drug events (ADEs), with causal mechanisms often unknown. However, current prediction methods do not provide sufficient details beyond the chance of DDI occurrence, or require detailed drug information often unavailable for DDI prediction. To tackle this problem, Dr. Jae Yong Ryu, Assistant Professor Hyun Uk Kim and Distinguished Professor Sang Yup Lee, all from the Department of Chemical and Biomolecular Engineering at Korea Advanced Institute of Science and Technology (KAIST), developed a computational framework, named DeepDDI, that accurately predicts 86 DDI types for a given drug pair. The research results were published online in Proceedings of the National Academy of Sciences of the United States of America (PNAS) on April 16, 2018, which is entitled "Deep learning improves prediction of drug-drug and drug-food interactions." DeepDDI takes structural information and names of two drugs in pair as inputs, and predicts relevant DDI types for the input drug pair.
Code for this project can be found on: Github. This article can also be found on my website here. As part of completing the second project of Udacity's Self-Driving Car Engineer online course, I had to implement and train a deep neural network to identify German traffic signs. In total, the dataset used consisted of 51,839 RGB images with dimensions 32x32, and is publicly accessible on this website. A validation set was used to assess how well the model is performing.
In popular culture, Artificial Intelligence (AI) often refers to machines that can perform any intellectual task that humans can. Such machines are heavily romanticized and are still very far from becoming a reality. However, weak (or narrow) AIs, algorithms that are designed to perform a specific task, have shown a formidable intellectual prowess that surpasses human capabilities in certain tasks. These machines must have integrative decision-making capability based on what they receive and what they predict would happen. Take, for example, AlphaGo, the AI that famously defeated world champions at the ancient game "Go."
In this video, we explain the difference between three key concepts artificial intelligence vs machine learning vs deep learning – to understand how they relate to the field of data science. First up, artificial intelligence or AI! What is it? Artificial intelligence is simply any code, technique or algorithm that enables machines to mimic, develop and demonstrate human cognition or behavior. We are in, what many refer to as, the era of "weak AI". The technology is still in its infancy and is expected to make machines capable of doing anything and everything humans do, in the era of "strong AI".
This is a very important consideration that is often overlooked by many in the field of Artificial Intelligence (AI). I suspect there are very few academic researchers who understand this aspect. The work performed in academe is distinctly different from the work required to make a product that is sustainable and economically viable. It is the difference between computer code that is written to demonstrate a new discovery and code that is written to support the operations of a company. The former kind turns to be exploratory and throwaway while the the latter kind tends to be exploitive and requires sustainability.
The artificial intelligence (AI) industry will be worth $1.2 trillion in 2018, with customer experience solutions creating the most business value. On Wednesday, Gartner released estimates on the projected value of AI over the course of this year. According to the research firm, the global enterprise value derived from AI will total $1.2 trillion this year, a 70 percent increase from 2017. AI-derived business value is projected to reach up to $3.9 trillion by 2022. "AI promises to be the most disruptive class of technologies during the next 10 years due to advances in computational power, volume, velocity and variety of data, as well as advances in deep neural networks (DNNs)," said John-David Lovelock, research vice president at Gartner.
APIs, or application processing interfaces, are packages of code critical to AI functionality in products and software. They can add more value to AI capabilities with descriptions, and call outs. The future of AI is marked with a race against time, as man strives to make machines more intelligent than humans! What was a fascinating aspect of science fiction has now become the most powerful technology disruptive everyday processes in industries and businesses, and human touchpoints? With continuous breakthroughs in AI research, across domains and use cases, AI is being implemented by one company after another, at a breakneck speed. Thus, AI is based on several disciplines that contribute to intelligent systems – mathematics, biology, logic/philosophy, psychology, linguistic, computer science, and engineering. You need to have a certain level of expertise in math, probability, statistics, algebra, calculus, logic, and algorithms.
Most IT leaders have heard of deep learning, but few really understand how this new technology works. Deep learning burst onto the public consciousness in 2016 when Google's AlphaGo software, which was based on deep learning, beat the human world champion at the board game Go. Since then, deep learning has begun appearing in news reports and product literature with more frequency, but few organizations are actually using it today. The 2018 O'Reilly survey report How Companies Are Putting AI to Work Through Deep Learning found that only 28% of the more than 3,300 respondents were currently using deep learning. However, 92% believed that deep learning would play a role in their future projects, with 54% saying it would play a large or essential role in those initiatives.