In 2013, IBM and University of Texas Anderson Cancer Center developed an AI based Oncology Expert Advisor. According to IBM Watson, it analyzes patients medical records, summarizes and extracts information from vast medical literature, research to provide an assistive solution to Oncologists, thereby helping them make better decisions. According to an article on The Verge, the product demonstrated a series of poor recommendations. Like recommending a drug to a lady suffering from bleeding that would increase the bleeding. "A parrot with an internet connection" - were the words used to describe a modern AI based chat bot built by engineers at Microsoft in March 2016. 'Tay', a conversational twitter bot was designed to have'playful' conversations with users. It was supposed to learn from the conversations. It took literally 24 hours for twitter users to corrupt it.
The authors of the Harrisburg University study make explicit their desire to provide "a significant advantage for law enforcement agencies and other intelligence agencies to prevent crime" as a co-author and former NYPD police officer outlined in the original press release. At a time when the legitimacy of the carceral state, and policing in particular, is being challenged on fundamental grounds in the United States, there is high demand in law enforcement for research of this nature, research which erases historical violence and manufactures fear through the so-called prediction of criminality. Publishers and funding agencies serve a crucial role in feeding this ravenous maw by providing platforms and incentives for such research. The circulation of this work by a major publisher like Springer would represent a significant step towards the legitimation and application of repeatedly debunked, socially harmful research in the real world. To reiterate our demands, the review committee must publicly rescind the offer for publication of this specific study, along with an explanation of the criteria used to evaluate it. Springer must issue a statement condemning the use of criminal justice statistics to predict criminality and acknowledging their role in incentivizing such harmful scholarship in the past. Finally, all publishers must refrain from publishing similar studies in the future.
As AI adoption brings out changes in the workplace, workers are challenged to obtain needed AI skills and business leaders are working to adapt. And as the COVID-19 pandemic has led to a shift to online learning, companies such as Udacity--who have been in that business for years--are in a good position to help. Business leaders may be caught between competing objectives of continuing to deliver strong financial performance while making investments in hiring, workforce training and new technologies that support growth, suggested the author of a recent piece in Harvard Business Review. A team at the MIT-IBM Watson AI Lab has been studying how work is being changed by AI. "By examining these findings, we can create a roadmap for leaders intent on adapting their workforce and reallocating capital, while also delivering profitability," stated author Martin Fleming, a VP and Chief Economist at IBM. He made three suggestions for reskilling the workforce to better prepare for AI.
This story was published in partnership with The Moonshot Catalog. In the late 1960s, Nobel Prize-winning economist Herbert Simon posed the following thought exercise: Imagine you are an alien from Mars visiting a college on Earth, and you spend a day observing how professors teach their students. Simon argued that you would describe the process as "outrageous." "If we visited an organization responsible for designing, building and maintaining large bridges, we would expect to find employed there a number of trained and experienced professional engineers, thoroughly educated in mechanics and the other laws of nature that determine whether a bridge will stand or fall," he wrote in a 1967 issue of Education Record. "We find no one with a professional knowledge in the laws of learning, or the techniques for applying them," he wrote. Teaching at colleges is often done without any formal training. Mimicry of others who are equally untrained, instinct, and what feels right tend to provide the guidance. Reading back over a textbook or taking lecture notes with a highlighter at the ready is often done by students, for instance, but these practices have proven of limited merit, and in some cases even counterproductive in aiding recall. And while many educators believe that word problems in math class are tougher for students to grasp than ones with mathematical notation, research shows that the opposite is true.
The first time I met Adam Rauh was earlier this year at the 2020 National Retail Federation (NRF) in New York. Adam is a lead solutions engineer at Tableau, a Salesforce company, and was at NRF with the purpose of demonstrating some of the most advanced analytics use cases in the retail industry. Rauh and I did a quick video demonstration of retailers can use analytics and machine learning to improve revenue, profitability, and customer experience. Our video had nearly 10K views at the conference. When data is visualized, everyone can take action.@tableau
'Passive' visual experiences play a key part in our early learning experiences and should be replicated in AI vision systems, according to neuroscientists. Italian researchers argue there are two types of learning – passive and active – and both are crucial in the development of our vision and understanding of the world. Who we become as adults depends on the first years of life from these two types of stimulus – 'passive' observations of the world around us and'active' learning of what we are taught explicitly. In experiments, the scientists demonstrated the importance of the passive experience for the proper functioning of key nerve cells involved in our ability to see. This could lead to direct improvements in new visual rehabilitation therapies or machine learning algorithms employed by artificial vision systems, they claim.
With a shortage of medical staff, hospitals around the world turn to robots to assist with the ongoing increase of work due to the Coronavirus outbreak. Here are a few examples of how Robots are helping human doctors and nurses throughout this outbreak. Links to more details can be found on Welcome.AI https://www.welcome.ai/news_info/the-... Streamline Machine Learning Projects https://spell.run/
The machine learning approach works well when these new cases are similar to the examples in the training data. The ability of machine learning algorithms to identify subtle patterns in the training data can allow it to make a faster and possibly better predictions than a human. However, if the new cases are radically different from the training data, and especially if we are playing by a whole new rulebook, then the patterns in the training data will no longer be a useful basis for prediction. Some algorithms are designed to continuously add new training data and therefore update the algorithm, but with large changes this gradual updating will not be sufficient. To learn completely new rules, machine learning algorithms need large amounts of new data.
Tissue biopsy slides stained using hematoxylin and eosin (H&E) dyes are a cornerstone of histopathology, especially for pathologists needing to diagnose and determine the stage of cancers. A research team led by MIT scientists at the Media Lab, in collaboration with clinicians at Stanford University School of Medicine and Harvard Medical School, now shows that digital scans of these biopsy slides can be stained computationally, using deep learning algorithms trained on data from physically dyed slides. Pathologists who examined the computationally stained H&E slide images in a blind study could not tell them apart from traditionally stained slides while using them to accurately identify and grade prostate cancers. What's more, the slides could also be computationally "de-stained" in a way that resets them to an original state for use in future studies, the researchers conclude in their May 20 study published in JAMA Network. This process of computational digital staining and de-staining preserves small amounts of tissue biopsied from cancer patients and allows researchers and clinicians to analyze slides for multiple kinds of diagnostic and prognostic tests, without needing to extract additional tissue sections.