Amid advances in precision medicine, healthcare is facing the twin challenges of having to curate and tailor the use of patient data to drive genomics-powered breakthroughs. That was the takeaway from the AI & data sciences track of last week's Precision Medicine World Conference in Santa Clara, California. "There aren't a lot of physicians saying, 'Bring me more AI,' " said John Mattison, MD, emeritus CMIO and assistant medical director of Kaiser Permanente. "Every physician is saying bring me a safer and more efficient way to deliver care." Mattison recalled his prolonged conversations with the original developers of IBM's Watson AI technology.

Although the opportunities to apply analytics in the public sector are abundant, cultural and technical challenges must be overcome before government agencies can claim to be fully developed, enterprise-wide, analytically competitive organizations. Building an analytical culture where data is widely used to evaluate hypotheses is crucial for an analytically competitive organization. Despite the successes that the public sector has seen in the past with analytics, data analysis is not integrated into most decision-making processes. This can partly be attributed to the enormous variety of tasks in many different fields that government organizations perform. In such varied environments, one-size-fits-all approach to cultural change is often ineffective, and customized approaches training, policies, and incentives are necessary.

Data volumes in mobile networks are increasing at an unprecedented rate. In our latest mobility report, we forecast that mobile data traffic will grow fourfold by 2025, reaching up to 160 exabytes (EB) per month. This is amazing of course and offers all kinds of opportunities for communications service providers, but there is also a potential downside to this rapid surge in data traffic: its impact on the energy consumption and carbon footprint of mobile networks. That's not the only downside for communications service providers, as it also raises a significant cost concern. As we found in our AI report, the demand to reduce operational costs already ranks among the top priorities for today's operators.

Grand View Research estimates the global AI market will grow at a compound annual rate of 57% between 2017 and 2025, reaching $36 billion. Forrester predicts that 2020 is the year executives will focus on how to drive and measure the value of their investments in AI. A recent survey of healthcare executives conducted by Optum found that not only is use of AI on the rise, but also that most executives expect a faster return on their investments than first anticipated. What's missing from these lofty projections are more substantive discussions about what's required to ensure that AI can deliver on its promise, such as the importance of data governance and management. There are also fewer conversations about the role AI and machine learning can play in the healthcare supply chain, compared with other areas, such as improved disease diagnosis and drug development. But when you stop and think about how AI is being applied elsewhere in healthcare, you begin to see implications and opportunities for the supply chain.

Delivering the right products and prices, for every sales engagement, at lightning speed, requires the power of proven AI technology. But how do you get your sales team to get on board with prescriptive pricing recommendations when they've been doing it their way for so long? Follow these tips to help ensure your sales team follows your customer deal pricing recommendations. Once they see how AI-powered pricing shortens approval cycles, enables them to price quickly and accurately, and helps identify new opportunities, they'll be hooked. Alex Smith has over 10 years at PROS helping companies Outperform in pricing & sales effectiveness across all our industries and regions.

Seyfarth Synopsis: As companies face increasing competition for the best talent within the marketplace, a growing number of businesses are turning to artificial intelligence and data driven strategies to more effectively identify and evaluate potential employees. The first installment of our artificial intelligence series will focus on some of the ways that employers are using these technologies in the area of talent acquisition. Business has always been in a search for "the next big thing." Something to give them an edge over competitors or allow them to anticipate shifts in the marketplace before they happen. Companies who moved from hand production to large-scale manufacturing were able to dominate nascent markets around the turn of the 20th Century.

Every year, companies that operate self-driving cars in California are required to submit data to the state's Department of Motor Vehicles listing the number of miles driven and the frequency at which human safety drivers were forced to take control of their autonomous vehicles (also known as a "disengagement"). And every year, those same companies raise a huge stink about it. Waymo, which drove 1.45 million miles in California in 2019 and logged a disengagement rate of 0.076 per 1,000 self-driven miles, says the metric "does not provide relevant insights" into its technology. Cruise, which drove 831,040 miles last year and reported a disengagement rate of 0.082, says the "idea that disengagements give a meaningful signal about whether an [autonomous vehicle] is ready for commercial deployment is a myth." Aurora, which only drove 13,429 miles and recorded a disengagement rate of 10.6 per 1,000 miles, calls them "misguided."

Pony.ai, a self-driving startup based in Silicon Valley and Guangzhou, China, is deepening its ties to Toyota. The two companies announced a pilot program to test self-driving cars on public roads in two Chinese cities, Beijing and Shanghai. The Japanese auto giant plans to invest $400 million in Pony.ai, valuing the startup at $3 billion. Pony.ai has been working with Toyota since 2019 on public autonomous vehicle testing. With this new investment, their relationship will become even closer, with the automaker and the startup "co-developing" mobility products like "mobility services."

The "The Business Use of Artificial Intelligence" training has been added to ResearchAndMarkets.com's offering. Your competitors are using AI to analyze sales, what do you do? Organizations today are applying artificial intelligence capabilities to a wide variety of uses especially in operations such as for process enablement. Each organization is focusing on performance improvements using AI. This explosion of interest in AI poses a challenge to managers to effectively make sense of and use AI effectively.

Artificial Intelligence applications are expanding into nearly every area of industry including government services, transportation, healthcare, cybersecurity, autonomous systems, finance and more. Forbes includes artificial intelligence as one of the "Hottest Career Paths of 2020 and Beyond." In order to meet the increasing demand for AI professionals, the University of North Texas, a Tier One research university, is offering the only Master of Science degree in artificial intelligence in Texas and one of only a few programs nationwide. The new degree offers students the choice of three concentrations: machine learning, autonomous systems and biomedical engineering. Students will be able to take classes that allow them to explore specific interests in AI and leave the program with marketable skills.