Jim Sterne, Author, Artificial Intelligence for Marketing AI and Machine Learning need not be mysterious. We begin with a quick overview of AI and ML (hold the math!), practical applications, and what the future may hold. This review of what it is, how it works, and where it can be useful affords the ability to speak cogently with your colleagues and determine where to apply this innovative technology for marketing.

AS THE NAVY plane swooped low over the jungle, it dropped a bundle of devices into the canopy below. Some were microphones, listening for guerrilla footsteps or truck ignitions. Others were seismic detectors, attuned to minute vibrations in the ground. Strangest of all were the olfactory sensors, sniffing out ammonia in human urine. Tens of thousands of these electronic organs beamed their data to drones and on to computers.

Technologies are increasingly being used to shape public policy, business, and people's lives. AI court judges are helping to decide criminal's sentences and AI is being used to catch murder suspects and even shape your insurance policy. That's why the fact that computers aren't great at detecting lies should be a worry. Researchers from the USC Institute for Creative Technologies recently put AI's capability for lie detection to the test, and the test results left a lot to be desired. The USC Institute for Creative Technologies research team recently tested algorithms using basic tests for truth detectors and found that the AIs failed these tests.

Data sets and benchmarks have been some of the most effective tools to speed progress in AI. Our current renaissance in deep learning has been fueled in part by the ImageNet benchmark. Recent advances in natural language processing have been hastened by the GLUE and SuperGLUE benchmarks. "Deepfake" techniques, which present realistic AI-generated videos of real people doing and saying fictional things, have significant implications for determining the legitimacy of information presented online. Yet the industry doesn't have a great data set or benchmark for detecting them.

Thieves used voice-mimicking software to imitate a company executive's speech and dupe his subordinate into sending hundreds of thousands of dollars to a secret account, the company's insurer said, in a remarkable case that some researchers are calling one of the world's first publicly reported artificial-intelligence heists. The managing director of a British energy company, believing his boss was on the phone, followed orders one Friday afternoon in March to wire more than $240,000 to an account in Hungary, said representatives from the French insurance giant Euler Hermes, which declined to name the company. The request was "rather strange," the director noted later in an email, but the voice was so lifelike that he felt he had no choice but to comply. The insurer, whose case was first reported by the Wall Street Journal, provided new details on the theft to The Washington Post on Wednesday, including an email from the employee tricked by what the insurer is referring to internally as "the false Johannes." Now being developed by a wide range of Silicon Valley titans and AI start-ups, such voice-synthesis software can copy the rhythms and intonations of a person's voice and be used to produce convincing speech.

When it comes to AI in drug discovery we don't yet know the limits of its abilities, though we are making progress. What can it do, what can't it do, and what factors will determine the answers to those questions? With the help of Bryn Roberts, Global Head of Operations for Roche Pharmaceutical Research & Early Development in Basel, Switzerland, here we discuss some of the factors that will help us set the upper and lower limits AI's capabilities. In order for a machine to learn, it must be fed categorized data. For example, if you wanted to train a machine to recognize human faces in pictures, you would need to upload pictures and point out the faces in each one in a way the computer can understand.

BROOKLYN, New York, Tuesday, September 3, 2019 - As artificial intelligence and data science enable computer tools to make predictions previously made by skilled humans, a different knowledge gap looms: These black-box tools often offer highly trained medical personnel little understanding of their inner workings. Equally little understood: how deploying these tools affects experts' work practices, perceptions of the value of work, and the expert-patient relationship. Researchers from New York University and Georgia Tech are conducting foundational research to understand and improve expert work in an age of data-intensive enhanced cognition, especially in healthcare, where new technologies are rapidly being deployed. The National Science Foundation recently awarded the team $2 million for the four-year project, which is expected to transform the future of expert work through a combined redesign of technology, workflow, and interactions. "Better understanding of how new technologies impact healthcare expert work will lead to more effective use of healthcare technologies, a healthier and better-informed population, and the more efficient use of human capabilities in restructured healthcare occupations," said NYU Tandon School of Engineering Professor of Technology Management and Innovation Oded Nov, the principal investigator.

It seems like every few days there's another example of a convincing deepfake going viral or another free, easy-to-use piece of software (some even made for mobile) that can generate convincing video or audio that's designed to trick someone into believing a piece of virtual artifice is real. But according to The Wall Street Journal, there may soon be serious financial and legal ramifications to the proliferation of deepfake technology. The publication reported last week that a UK energy company's chief executive was tricked into wiring €200,000 (or about $220,000 USD) to a Hungarian supplier because he believed his boss was instructing him to do so. But the energy company's insurance firm, Euler Hermes Group SA, told the WSJ that a clever AI-equipped fraudster was using deepfake software to mimic the voice of the executive and demand his underling pay him within the hour. "The software was able to imitate the voice, and not only the voice: the tonality, the punctuation, the German accent," a Euler Hermes spokesperson later told The Washington Post.

Artificial intelligence (AI) will have a huge impact on health care. It is currently moving out of the laboratory and into real-world applications for health care and medicine. Many startups are using modern data and AI technologies to tackle problems related to workflow optimization and automation, demand forecasting, treatment and care, diagnostics, drug discovery, personalized medicine, and many other areas. Some of these companies are beginning to speak publicly about their AI initiatives; our upcoming Artificial Intelligence conferences in San Jose and London have a strong roster of speakers who will describe applications of AI in health and medicine. AI's transition to the real world can be challenging.

Researchers at the Nanoscience Center and Faculty of Information Technology in the University of Jyväskylä, Finland, have achieved a significant step forward in predicting atomic structures of hybrid nanoparticles. A research article published in Nature Communications on 3 September 2019, demonstrates a new algorithm that learns to predict binding sites of molecules at the metal-molecule interface of hybrid nanoparticles by using already published experimental structural information on nanoparticle reference systems. The algorithm can in principle be applied to any nanometer-size structure consisting of metals and molecules provided that some structural information already exists on the corresponding systems. The research was funded by the AIPSE research program of the Academy of Finland (Novel Applications of Artificial Intelligence in Physical Sciences and Engineering Research). Nanometre-sized hybrid metal nanoparticles have many applications in different processes, including catalysis, nanoelectronics, nanomedicine and biological imaging. Often, it is important to know the detailed atomic structure of the particle in order to understand its functionality.