Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19’s informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naïve Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.
What if you could interact with a therapist, learn new skills to improve your well-being and gain access to information that would normally be learned in therapy for a fraction of the cost, or for free, without leaving your home? The prospect is certainly alluring, and with Artificial Intelligence (AI) technology, there is reason to take this prospect seriously. AI technology for mental health was first developed in the 1960s with ELIZA, a simple computer program. AI has since been developed for use in other areas, including assisting therapists with diagnosing depression and PTSD in the US armed forces. Recently, the integration of AI with smartphone technology had opened up opportunities to provide mental health support that is not possible with traditional in-person therapy.
Elon Musk is renowned for his innovative mind and unceasing desire to improve multiple facets of life such as transportations, space exploration, cities, and now, the human brain. The famed CEO and inventor announced the Neuralink brain microchip that could give humans equal footing with AI technology. Elon Musk is well known for his multiple ventures. Recently the tech genius announced plans to construct a Starbase--a new town in Southern Texas that will function as a miniature Cape Canaveral. Garry Kitchen, a pioneer gamer and engineer, tells The Post.
A new machine learning approach to COVID-19 testing has produced encouraging results in Greece. The technology, named Eva, dynamically used recent testing results collected at the Greek border to detect and limit the importation of asymptomatic COVID-19 cases among arriving international passengers between August and November 2020, which helped contain the number of cases and deaths in the country. The findings of the project are explained in a paper titled "Deploying an Artificial Intelligence System for COVID-19 Testing at the Greek Border," authored by Hamsa Bastani, a Wharton professor of operations, information and decisions and affiliated faculty at Analytics at Wharton; Kimon Drakopoulos and Vishal Gupta from the University of Southern California; Jon Vlachogiannis from investment advisory firm Agent Risk; Christos Hadjicristodoulou from the University of Thessaly; and Pagona Lagiou, Gkikas Magiorkinis, Dimitrios Paraskevis and Sotirios Tsiodras from the University of Athens. The analysis showed that Eva on average identified 1.85 times more asymptomatic, infected travelers than what conventional, random surveillance testing would have achieved. During the peak travel season of August and September, the detection of infection rates was up to two to four times higher than random testing.
To celebrate International Women's Day, we take a look back over the past year of AIhub content and highlight some of our favourite articles, interviews, podcasts and videos, by, or featuring, women in the field. Falaah Arif Khan is an engineer/scientist by training and an artist by nature. She is currently Artist-in-Residence at the Center for Responsible AI at New York University. When we interviewed Falaah in 2020 she had just completed her first comic book, Meet AI. She has since teamed up with other AI researchers on other exciting projects.
Refraction AI, a company developing semi-autonomous delivery robots, today announced that it raised $4.2 million in seed funding led by Pillar VC. Refraction says that the proceeds will be used for customer acquisition, geographic expansion, and product development well into the next year. The worsening COVID-19 health crisis in much of the U.S. seems likely to hasten the adoption of self-guided robots and drones for goods transportation. They require disinfection, which companies like Kiwibot, Starship Technologies, and Postmates are conducting manually with sanitation teams. But in some cases, delivery rovers like Refraction's could minimize the risk of spreading disease.
Researchers have developed a method based on Artificial Intelligence (AI) that rapidly identifies currently available medications that may treat Alzheimer's disease. The method could represent a rapid and inexpensive way to repurpose existing therapies into new treatments for this progressive, debilitating neurodegenerative condition. Importantly, it could also help reveal new, unexplored targets for therapy by pointing to mechanisms of drug action. "Repurposing FDA-approved drugs for Alzheimer's disease is an attractive idea that can help accelerate the arrival of effective treatment -- but unfortunately, even for previously approved drugs, clinical trials require substantial resources, making it impossible to evaluate every drug in patients with Alzheimer's disease," said researcher Artem Sokolov from Harvard Medical School. "We therefore built a framework for prioritising drugs, helping clinical studies to focus on the most promising ones," Sokolov added.
When Amazon envisioned Alexa, an AI-powered, voice-activated customer recommendation system, it was a feat that required machine learning and massive amounts of data to provide answers to conversational queries quickly, even in a noisy environment. Now, the same data analysis capabilities that enable Amazon to become hyper-familiar with consumer purchasing patterns could hold the key to reducing waste in healthcare. Think about the similarities between healthcare and retail. Both industries revolve around the consumer, and they use data to gain context into behavior and draw meaningful conclusions. In healthcare, this includes the ability to predict which consumers could develop type 2 diabetes with 95% accuracy or to pinpoint where and when the Covid-19 virus will spread and how to protect those most vulnerable.
A new robotic puppy developed to help older people, particularly those living with dementia, has been launched in the UK. Ageless Innovation, a US company with ambitions to work with the NHS, makes robotic pets which can be safer and more predictable alternatives to living animals designed to comfort adults who are lonely or who have dementia. The freckled pup robot is capable of responding to human voices, being touched and hugged with realistic dog-like sounds and has a simulated heartbeat to make it appear more life-like. The battery-powered puppy resembles a liver and white cocker spaniel thanks to its soft, tufty fur, and is small and light enough to easily rest on a lap. It will go on sale in the UK for £129 from 15 March, having previously been launched in the US last October.
In Octavia E. Butler's novel "Parable of the Sower" (1993), a climate-change Book of Exodus set in a scorched mid-twenty-twenties California, a preacher's daughter named Lauren Oya Olamina tries to convince a friend that their world has veered off course. Disaster surrounds their fortified suburb of Los Angeles: water shortages, a measles epidemic, fires set by drug-addicted pyromaniacs, and bandits who prey on the unhoused multitudes that roam the lawless highways. Outsiders throw severed limbs over the walls of their neighborhood, "gifts of envy and hate." Lauren knows it's time to get out: I'm talking about the day a big gang of those hungry, desperate, crazy people outside decide to come in. I'm talking about what we've got to do before that happens so that we can survive and rebuild--or at least survive and escape to be something other than beggars. . . .