For too long, robots have had it too easy, and it's starting to show. To get these freeloading bots back to work, neuroscientists from University of Southern California suggest programming them to fear death. By forcing robots to operate in terms of self-preservation -- in addition to whatever other tasks they were assigned -- the scientists suggest that they'll make better choices and become more productive, Popular Mechanics reports. The researchers even suspect that fear of death could be an important stepping stone along the way to true artificial intelligence. The ultimate goal is to build robots and artificial intelligence systems that can evaluate their own behavior, according to research published by the team last month in Nature Machine Intelligence.

Lift and shift has not been the greatest friend to those migrating to cloud. Many hustled legacy applications to cloud to little, if any, positive effect; some wound up "repatriating" apps once the bill arrived. Likewise, valuable data from on-premises systems can't be dumped cold into the cloud; after all, next-gen machine-learning applications in cloud stand or fall on well-governed, quality data. Older systems hold operational-processing data that helps machine-learning algorithms -- in cloud or on-prem -- make predictions. However, there is a generational gap to bridge between old data and advanced analytics technologies.

Intel Experience Day 2019, organized by Intel, one of the major innovative hardware and technology corporations worldwide, took place in Moscow at the end of October. Intel and partner companies presented the latest Intel hardware and software product implementations advancing IoT, AI, computer vision, machine learning, object recognition, and more. Many speakers shared their ideas and insights on trending industrial innovations like cloud computing, Big Data, and analytics, including Al Diaz, Intel's Vice President, Natalya Galyan, Intel's Regional Director for Russia, and Marina Alekseeva, CEO of R&D of Intel in Russia. Intel Experience Day 2019 attracted many IT market players who use Intel solutions in their work daily, and Auriga experts were among them. Several years ago, Auriga became a pioneer user of the Intel Multi-OS Engine tool to develop an innovative iPad application for patient monitoring.

As tensions rise between the US and China, there's been some chatter of an AI arms race that would see each country scrambling to get and retain some kind of advantage in the field. But Microsoft cofounder Bill Gates, who spoke at the Bloomberg New Economy Forum in Beijing on Thursday, says he has some difficulty understanding how separating or limiting the sharing of scientific research would even work. "You can't, you know, carry around little notes to each other saying don't give this to someone because their grandmother is Chinese," he said. Gates said that the US has long benefitted from openly sharing scientific research, and that it remains a huge advantage, especially within the field of artificial intelligence. "AI is very hard to put back in the bottle and whoever has the open system will so vastly get ahead," Gates said.

In the U.S. alone, revenues from the trucking industry rose to $796.7 billion in 2018, up from $700.1 billion the previous year, according to the American Trucking Associations. Trucks moved more than 70% of the country's freight. A major factor for businesses in choosing self-driving trucks is greater fuel efficiency, which cuts fuel costs by at least 15%, according to Plus.ai. "There's no question that autonomous trucks will be ready before autonomous cars," Plus.ai COO and co-founder Shawn Kerrigan said in a statement to CNBC.

Within the cloud space, AI is being considered for collaboration more and more as the likes of IBM, Amazon and Microsoft delve into this kind of technology. Automated management of hard drive-free data is bound to speed up the process of storage management. Also, AI, with its need for a large amount of processing power, can thrive within the cloud, which is known for its ability to manage large projects with ease. Cloud adoption is directing enterprises to AI, as vendors offer a growing number of tools and services, without big upfront investments. But according to Domo's VP of Data and Curiosity, Ben Schein, it's vital that the agility and speed that AI can provide is balanced with integration and control. To achieve this, Schein said it "comes down to a sense of empathy for the people that have to use intelligence".

Chatbots are growing in influence, and developers are now applying artificial intelligence to improve chatbot performance by deepening their personality and making them more lifelike. But this raises some problems. The acceptance of chatbots is still a challenge. Even worse, some attempts at building a chatbot with personality have been taken very negatively, such as with the ethical questions arising from Google Duplex imitating humans. Along with the business and ethical challenges, there's also the technical issue of controlling AI chat learning, as shown in the now classic example of Microsoft Tay quickly learning racism, misogyny and antisemitism.

Thanks to approvals from the Food and Drug Administration (FDA) for applications such as primary disease diagnosis, digital pathology is rapidly becoming the new standard of care. However, this advancement creates challenges that artificial intelligence could help solve. Digital pathology enables capturing pathology information, such as whole slide images (WSI), and working with it digitally using a specialized scanner. Acquiring, studying and managing data in this way allows sharing between parties on a computer or mobile device. According to experts, the global digital pathology market was worth $689.2 million in 2018.

With the advent of wearable technologies such as the Fitbit and the Apple Watch, devices are collecting billions of data points on consumers -- everything from their sleep cycles to their step counts to their heart rates. While that information is useful, what is even better is the insight machine learning can glean from analyzing this data. In the future, patient screenings will include software that analyzes health over time and allows doctors to predict health complications before they happen. Because behavioral changes are the most effective way to keep us healthy, proactive directions from doctors can replace diagnoses after the fact. Significant strides have already been made in this direction at the Georgia Institute of Technology, where deep learning is already able to predict heart failure.

As I read Melanie Mitchell's "Artificial Intelligence: A Guide for Thinking Humans," I found myself recalling John Updike's 1986 novel "Roger's Version.'' One of its characters, Dale, is determined to use a computer to prove the existence of God. Dale's search leads him into a mind-bending labyrinth where religious-metaphysical questions overwhelm his beloved technology and leave the poor fellow discombobulated. I sometimes had a similar experience reading "Artificial Intelligence." In Mitchell's telling, artificial intelligence (AI) raises extraordinary issues that have disquieting implications for humanity. AI isn't for the faint of heart, and neither is this book for nonscientists. To begin with, artificial intelligence -- "machine thinking," as the author puts it -- raises a pair of fundamental questions: What is thinking and what is intelligence? Since the end of World War II, scientists, philosophers, and scientist-philosophers (the two have often seemed to merge during the past 75-odd years) have been grappling with those very questions, offering up ideas that seem to engender further questions and profound moral issues. Mitchell, a computer science professor at Portland State University and the author of "Complexity: A Guided Tour," doesn't resolve these questions and issues -- she as much acknowledges that they are irresolvable at present -- but provides readers with insightful, common-sense scrutiny of how these and related topics pervade the discipline of artificial intelligence. Mitchell traces the origin of modern AI research to a 1956 Dartmouth College summer study group: its members included John McCarthy (who was the group's catalyst and coined the term artificial intelligence); Marvin Minsky, who would become a noted artificial intelligence theorist; cognitive scientists Herbert Simon and Allen Newell; and Claude Shannon ("the inventor of information theory"). Mitchell describes McCarthy, Minsky, Simon, and Newell as the "big four'' pioneers of AI.