Talk of how automation and artificial intelligence would change the workforce once conjured images of robots taking our jobs. But so far, the robots have not replaced humans on the factory floor -- or the cubicle row. Automation has taken over some tasks, particularly repetitive ones, but it hasn't exactly reduced the number of employees businesses need. In many different industries and job functions, automation and AI are making work easier and more efficient -- in some cases driving growth that's spurring companies to hire more people. But are metro Detroit and Michigan businesses ready for the massive transformation these technological advances will bring to every sector?
Researchers at the University of Michigan have been exploring the need to set ethics standards and policies when it comes to the use of artificial intelligence, and they now have their own place to do so. The university has created a new Center of Ethics, Society and Computing (ESC) that will focus on AI, data usage, augmented and virtual reality, privacy, open data and identity. According to the center's website, the name and abbreviation alludes to the "ESC" key on a computer keyboard, which was added to interrupt a program when it produced unwanted results. "In the same way, the Center for Ethics, Society and Computing (ESC -- pronounced'escape') is dedicated to intervening when digital media and computing technologies reproduce inequality, exclusion, corruption, deception, racism or sexism," the center's mission statement reads. The center will bring together scholars who are committed to "feminist, justice-focused, inclusive and interdisciplinary approaches to computing," the university said in a news release.
The day is approaching when commuters stuck in soul-crushing traffic will be freed from the drudgery of driving. Companies are investing billions to devise sensors and algorithms so motorists can turn our attention to where we like it these days: our phones. But before the great promise of multitasking on the road can be realized, we need to overcome an age-old problem: motion sickness. "The autonomous-vehicle community understands this is a real problem it has to deal with," said Monica Jones, a transportation researcher at the University of Michigan. "That motivates me to be very systematic."
The chart of the nuclides is limited by particle drip lines beyond which nuclear stability to proton or neutron emission is lost. Predicting the range of particle-bound isotopes poses an appreciable challenge for nuclear theory as it involves extreme extrapolations of nuclear masses beyond the regions where experimental information is available. Still, quantified extrapolations are crucial for a variety of applications, including the modeling of stellar nucleosynthesis. We use microscopic nuclear mass models and Bayesian methodology to provide quantified predictions of proton and neutron separation energies as well as Bayesian probabilities of existence throughout the nuclear landscape all the way to the particle drip lines. We apply nuclear density functional theory with several energy density functionals. To account for uncertainties, Bayesian Gaussian processes are trained on the separation-energy residuals for each individual model, and the resulting predictions are combined via Bayesian model averaging. This framework allows to account for systematic and statistical uncertainties and propagate them to extrapolative predictions. We characterize the drip-line regions where the probability that the nucleus is particle-bound decreases from $1$ to $0$. In these regions, we provide quantified predictions for one- and two-nucleon separation energies. According to our Bayesian model averaging analysis, 7759 nuclei with $Z\leq 119$ have a probability of existence $\geq 0.5$. The extrapolations obtained in this study will be put through stringent tests when new experimental information on exotic nuclei becomes available. In this respect, the quantified landscape of nuclear existence obtained in this study should be viewed as a dynamical prediction that will be fine-tuned when new experimental information and improved global mass models become available.
In a major development in how tumors are excised, researchers at the University of Michigan have shown that it's possible to accurately analyze brain tumor tissue within the operating room and assess its nature using artificial intelligence. Tumor tissues typically look just like the healthy stuff around them. When a tumor is removed, parts that are near the edges (margins) are sent to the pathology lab for review. After staining and observations using a microscope, the pathologist can let the surgical team know whether it removed all of the tumor or left some behind. This takes a long time, so much so that typically a follow-up surgery is required if the margins are not completely excised.
The majority of successful players in the automotive industry have transformed their business models over the past few years in response to the challenges of digitization. But while many are getting on track to digitize their core product offerings, the underlying business processes still show a high rate of manual work. Warranty handling is a prime example. Automotive manufacturers spend an estimated $50 billion on managing warranty claims each year1. In response to this and to accelerate digital transformation within key business processes of automotive manufacturers, MSX International (MSX) has joined forces with Boston Consulting Group Digital Ventures (BCGDV).
The AI analyses high-resolution images of tumours produced using a method called stimulated Raman histology (SRH). Todd Hollon at the University of Michigan and his colleagues generated more than 2 million SRH images of brain tumours from 415 people with known diagnoses. Each image showed a small region of an excised tumour and was labelled with which type of brain tumour it was out of the 10 most common types. The team fed them all to the AI so it could learn from the images to identify tissue features linked to these specific types of cancer. The images had either come from biopsies that remove a small sample of a suspected tumour for analysis or from surgeries to remove tumours.
The result was a draw: humans, 93.9 percent correct; A.I., 94.6 percent. The study was paid for by the National Cancer Institute, the University of Michigan and private foundations. Dr. Orringer owns stock in the company that made the imaging system, as do several co-authors, who are company employees. He conducted the research at the University of Michigan, before moving to New York. "Having an accurate intra-operative diagnosis is going to be very useful," said Dr. Joshua Bederson, the chairman of neurosurgery for the Mount Sinai Health System, who was not involved in the study.
Facebook has been regularly removing fake accounts in its fight against disinformation, but the company's latest announcement presented a twist -- artificially generated profile pictures. Facebook recently announced that it had taken down a network of accounts for "inauthentic behavior" (i.e. "Some of these accounts used profile photos generated by artificial intelligence and masqueraded as Americans," the statement said. The social media company said it found the accounts through an internal investigation, but the takedown came just a few days after researchers from independent fact-checking organizations unearthed many of the accounts. Facebook's investigation found accounts linked to U.S.-based media company The BL using AI-generated profile pictures from the site ThisPersonDoesNotExist.com -- a site created by a researcher at the University of Michigan to help educate the public about fake images.
As vehicles become smarter and more connected to Wi-Fi networks, hackers will have more opportunities to breach vehicle systems, warns a study. Connecting your smartphone through an USB port can give a hacker backdoor access to data from both your phone and your car. Additionally, Google Android users who can download apps from unverified sites are even more at risk. The research, published in the Journal of Crime and Justice, applied a criminal justice theory to current forms of vehicle security and provided recommendations for manufacturers and owners to improve safety. "The risk with vehicles isn't just personal data – though that is still a real concern," said Thomas Holt, Professor at Michigan State University in the US.