Last year, Netflix fo-founder Mitch Lowe told a Utah audience that the key to a successful business was finding and solving “transactional stresses.”
Of course, most people are familiar with how efficiently Netflix eliminated the trip to the video store and further enhanced our collective viewing by innovating technology, driven by artificial intelligence, to guess what movies and shows we might like to watch next.
Identifying those transactional stresses and helping companies abolish them is at the heart of so-called customer experience companies, and Utah has a rising reputation for creating some of the best companies out there. Qualtrics, Podium, InMoment and other efforts born and built in the Beehive State have carved out impressive successes in this new tech-created realm.
Now, a new business from veteran entrepreneur and founder of the long-running Utah-based advocacy group Women Tech Council, Cydni Tetro, is aiming to make it easy for technology companies to simulate and communicate how their emerging innovations will make things, well, easier.
The technology industry is not the only sector clamoring to work with artificial intelligence. Moffitt Cancer Center in Tampa is looking to get further involved, and has hired an artificial intelligence officer.
On Monday, the cancer center announced J. Ross Mitchell was appointed AI officer. He will help develop digital tools and computer science technologies to help the quality and efficiency of cancer care.
Data from wearable devices offer a way for people and physicians to track aging and the risk of future disease, according to a study published in Aging.
While other biomarkers of aging exist, like DNA methylation, researchers from Gero and Roswell Park Comprehensive Cancer Center conducted a systematic evaluation and found that wearable devices can be a feasible and cheaper option than getting blood drawn when it comes to tracking aging and potential diseases, Peter Fedichev, founder and chief science officer of Gero, told Healthcare Analytics News™.
An artificial intelligence (AI) algorithm was trained to find patterns in everyday changes of physical activity to estimate a person’s biological age.
At the end of last year, Google announced it was officially pulling the plug on Allo, its latest in a long list of failed messaging apps. That list includes Google Wave, Google Plus, Google Buzz and, most recently, Google Hangouts (which has evolved into the enterprise-focused Hangouts Chat and Hangouts Meet).
But while its consumer messaging strategy remains confusing, Google is making an aggressive two-pronged attack into the world of business messaging. As a result, it appears to be on a collision course with one of its big tech rivals.
I’ve recently written about the risks of machine learning (ML), but with this post I wanted to take a step back and talk about ML and general. I want to talk about the ‘why’ of machine learning and whether you and/or your company should be investigating machine learning. Do you need machine learning? Maybe. Maybe not.
The first question you have to ask yourself (and then answer) is this: Why do you want to be involved with machine learning? What problem(s) are you really trying to solve? Are you trying to forecast revenue for next quarter? You can probably do just fine with standard time series modeling techniques. Are you trying to predict house prices in cities/neighborhoods around the world? Machine learning is probably a good idea.
In 2015, Uber opened a research facility around the corner from Carnegie Mellon University’s National Robotics Engineering Center in a move positioned as a partnership between the two organizations. Within months, dozens of faculty members had left their positions for full-time roles at Uber, draining the center of much of its talent. Other major tech companies have followed a similar path – in 2018, Facebook launched AI labs in Seattle and Pittsburgh headed by former professors.
These stories provide a window into a tug-of-war that’s been playing out between the tech industry and academia. Keen to build products and services that use AI and machine learning, tech firms and other businesses have been hiring away researchers and professors from universities, creating a shortage of academics who can teach the next generation of data scientists. The proportion of computer science PhDs who stay in academia has reached a “historic low,” the Computing Research Association has said.
Uber is in no rush to bring fully autonomous vehicles to public roads, according to Raquel Urtasun, Uber ATG’s Toronto-based chief scientist, who said the company plans to stick to its own timelines.
Urtasun opened up to BetaKit about how the advanced technologies arm of the global ridesharing company is focusing on getting the technology right, making safety a top priority, and how Uber ATG will grow in the coming year.
Imagine a cell phone you can fold up and carry in your wallet. When you drop it, nothing cracks or breaks, or if it does, it repairs itself. And when it’s time for an upgrade, the old phone will biodegrade instead of taking up space in a landfill.
Maybe you’d rather wear your laptop or tablet in the fibers of your clothes, or wear a monitor that provides constant data about your health but feels no different than your own skin.
This is the future of electronics, and it’s happening in UC Merced Professor Yue ‘Jessica’ Wang’s lab.
Wang and her students are synthesizing organic polymeric compounds that conduct electricity and behave like no electronics on the market today.
These are conjugated polymers — plastics — that conduct electricity like copper or silicon. However, conducting plastics have a distinct advantage over metals that are mined.
Computer science researchers at West Virginia University plan to tackle the state’s opioid epidemic through the use of technology and artificial intelligence.
Dr. Yanfang “Fanny” Ye, assistant professor in the Lane Department of Computer Science and Electrical Engineering, recently received a three-year, $1 million grant from the National Institute of Justice to support her work in developing artificial intelligence techniques to combat opioid trafficking, which has evolved along with technology.
Ye’s research will target online opioid trafficking.
Cambridge, MA February 15, starting at 6 p.m. ” Join swissnex Boston and representatives from Switzerland’s top universities, research institutions and companies for the 9th annual Swiss Sciences Night – an evening of conversation, science and opportunities.” [free, registration required]
London, England March 5, starting at 6:30 p.m., IET London: Savoy Place (2 Savoy Place). “The Oxford Internet Institute is excited to present the Director of the OII, Professor Philip Howard.” [free, registration required]
Qatar February 17, starting at 8 a.m., HBKU Research Complex. “This workshop on AI for Social Good, jointly organized by the United Nations Development Programme (UNDP) and the Qatar Center for Artificial Intelligence (QCAI) at QCRI, brings together experts and practitioners from a range of stakeholders, including UN agencies, NGOs, academic institutions and industry partners to present their experience with applying AI to solve real world problems in this domain.”
Brooklyn, NY May 27-31. “The summer school is dedicated to the uses of artificial intelligence (AI) techniques in and for games. After introductory lectures that explain the background and key techniques in AI and games, the school will introduce participants the uses of AI for playing games, for generating content for games, and for modeling players.” Registration opens soon.
“In this article, I will go over how to build a deep learning model using TensorFlow and Keras that accomplishes the task of generally detecting street art by using publicly available social media data on Instagram.”
Data from the world’s largest digital sky survey is being publicly released today by the Space Telescope Science Institute (STScI) in Baltimore, Maryland, in conjunction with the University of Hawai’i Institute for Astronomy in Honolulu, Hawaii. Data from the Pan-STARRS1 Surveys will allow anyone to access millions of images and use the database and catalogs containing precision measurements of billions of stars and galaxies. This data release contains over 1.6 petabytes of data (a petabyte is one million gigabytes), making it the largest volume of astronomical information ever released. The survey data resides in the Mikulski Archive for Space Telescopes (MAST), which serves as NASA’s repository for all of its optical and ultraviolet-light observations.