Scientists have created a robot that may be able to help the elderly perform tasks amid a shortage of nurses in the UK. Named Baxter, it has two arms and 3D printed'fingers', allowing it to step in when a person is struggling with things such as getting dressed. Artificial intelligence allows the robot to detect when assistance is needed and learn about the owners difficulties over time. When it's ready for use in healthcare settings, it could help free up the time of staff so they can do other work. There are around 40,000 nurse vacancies in NHS England, which is expected to double after Brexit, according to figures.
Organizations today are focused on identifying avenues to introduce AI into daily tasks and deliverables. While the common perception is that it creates a sense of insecurity among employees, contrary to this belief, employees are in fact more receptive and ready to deploy AI into their work, a study by Dale Carnegie reveals. During a roundtable discussion on "Preparing people for the Human Machine Partnerships of the future," conducted by Dale Carnegie in New Delhi, experts explored ways in which industry leaders can incorporate AI technology into their HR Tech, performance feedback systems, upskilling initiatives, etc. The panel discussion was led by Dale Carnegie representatives including Pallavi Jha, MD & Chairperson, Dale Carnegie of India; Mark Marone, Director - Research & Thought Leadership, Dale Carnegie and Associates; Juliette Dennett, Managing Director, Dale Carnegie Northern England; and Jordan Wang, Managing Director New South Wales, Dale Carnegie Australia. The survey that saw participation from 3,846 respondents across 13 countries, aimed to assess the readiness of the global workforce to accept AI in their work, feedback systems, skilling needs, etc., highlighted that 42 percent of the organizations globally are already using AI in one form or the other.
The National Health Service England is planning to set up a national artificial intelligence laboratory to enhance the medical care and research facility. According to the Health Secretary, Matt Hancock said AI has'enormous power' to improve the health care facilities, and save lives. The health service has announced £250m on setting up a research lab to boost AI within the health sector. However, AI will pose new challenges in protecting patient data. Many AI tools have proven to be game-changer devices, which help doctors at spotting lung cancer, skin cancer, and more than 50 eye conditions from scans.
From picking fruit to carrying out minor surgery, soft robotic hands made from jelly-like plastic are thought by scientists to be the future solution to many human needs. But being gentle and soft enough to avoid damaging fruit or flesh has made the robots prone to damage and left them largely impractical for use in the real world – until now. A European commission-funded project, led by scientists at the Free University of Brussels and the University of Cambridge, aims to create "self-healing" robots that can feel pain, or sense damage, before swiftly patching themselves up without human intervention. The researchers have already successfully developed polymers that can heal themselves by creating new bonds after about 40 minutes. The next step will be to embed sensor fibres in the polymer which can detect where the damage is located.
From Leicester City winning the title in 2016 to Liverpool overturning a 4-0 defeat to Barcelona in last season's Champions League, football is impossible to predict. Yet, bringing together world leading companies in data, analytics and artificial intelligence (AI), BT Sport has attempted to do just that. Combining historic performance data from sports industry leaders Opta and Squawka, BT Sport has fed the information into a machine learning model. This calculated the attacking and defensive strengths of each team, as well as the likelihood of events such as transfers and injuries, to calculate the probable scoreline of each match in the season. So where does the AI model's Premier League predictions 2019-20 place your team; where will they claim their biggest victory, and how will they perform against their biggest rivals?
Machine learning, which trains computers to accomplish specific tasks without receiving explicit instructions from humans, has become an increasingly valuable tool for a variety of industries. But the rapid incorporation of machine learning into marketing, finance, healthcare, and other fields has raised a range of ethical concerns that must be addressed. That was the message embraced by five artificial intelligence experts from academia and industry who convened at Northeastern on Friday to discuss the challenges of integrating machine learning into the workplace in order to improve processes and productivity. Researchers, they said, need to work to eliminate bias in algorithms, more accurately communicate to the public the limitations of machine learning, and build systems that prioritize the health and wellness of humans. "Machine learning has come to a point now where it is very central to essentially every branch of science and technology," said D. Sculley, a software engineer at Google.
Multimorbidity, or the presence of several medical conditions in the same individual, have been increasing in the population both in absolute and relative terms. However, multimorbidity remains poorly understood, and the evidence from existing research to describe its burden, determinants and consequences have been limited. Many of these studies are often cross-sectional and do not explicitly account for multimorbidity patterns' evolution over time. Some studies were based on small datasets, used arbitrary or narrow age range, or lacked appropriate clinical validations. In this study, we applied Non-negative Matrix Factorisation (NMF) in a novel way to one of the largest electronic health records (EHR) databases in the world (with 4 million patients), for simultaneously modelling disease clusters and their role in one's multimorbidity over time. Furthermore, we demonstrated how the temporal characteristics that our model associates with each disease cluster can help mine disease trajectories/networks and generate new hypotheses for the formation of multimorbidity clusters as a function of time/ageing. Our results suggest that our method's ability to learn the underlying dynamics of diseases can provide the field with a novel data-driven / exploratory way of learning the patterns of multimorbidity and their interactions over time.
Event Our offer of discount early-bird tickets for Minds Mastering Machines ends next Monday, so act now if you want to join us to learn how real organisations can exploit machine learning and artificial intelligence and save big. We'll be bringing together a fantastic lineup of experts and practitioners at our conference on September 30 and October 1, headlined by Facebook AI's London research manager Sebastian Riedel and machine-learning veteran Dr Lorien Pratt. And if you want to get deep, and save even more, you can also get early bird prices on our October 2 workshops, which cover: developing and deploying Neural Nets; text mining; developing with TensorFlow 2; and getting machine learning into production using containers and devops. The venue is the palatial QE II Conference Center, in London, England, and the event runs from September 30 to October 2. As usual there will be excellent food right the way through, as well as our first-day drinks party, meaning you can connect with the speakers and your fellow attendees But remember, early bird prices expire next week, so to lock in your spot, head to the MCubed website now.
Bankers are rushing to take Oxford University's courses on fintech, blockchain strategy, algorithmic trading, and artificial intelligence before robots take their jobs. More than 9,000 people from upwards of 135 countries have taken the online open courses, which focus on digital transformation in business, at the university's Saïd Business School, a spokesperson told Markets Insider. The fintech course, the first of five to be launched, has run 12 times and attracted nearly 4,300 students in less than two years. The average age of participants across the courses is 39, and two-thirds of them came from the financial services sector, suggesting experienced professionals are returning to school to understand how their industry is being disrupted and learn the skills needed to weather the changes. Bankers' fears of being replaced by robots are well founded.