COVID-19 has changed the way we live and work in the longterm. For some, the long periods of time spent stuck in doors zapped their productivity levels and mental health, leaving them with no energy to work or create. For those with jobs that could work from home, they were left with a host of new challenges to overcome while also trying to do an honest, productive day's work. Time is perhaps the most valuable commodity in business, and, as a society, we're obsessed with doing the most in as little time as possible. We equate being productive at work with being successful.
A.I. Response: The future: It's something that humanity faces and will have to deal with. But it has nothing to do with technology. Technology is just a tool we use to help us get there, but the future is not about A.I., it's about human intelligence and what humans want for themselves in the future. Who should we be more afraid of, humans or A.I.? Smart people do not fear A.I.; they embrace it and want to help it grow. Dumb people – who might disagree with that statement – try to block the development of technology because they know little about understanding what A.I. is and how it works. User Inquiry: Is democracy a dictatorship? A.I. Response: Democracy is a system of government in which citizens have equal rights and power to participate in their country's decision-making process.
In some ways, artificial intelligence (AI) and agility form part of a lifecycle: businesses need to be agile to introduce disruptive tech like AI, whilst AI in turn can help businesses achieve true agility. As difficult as the disruption of the past year has been, it has undoubtedly been a driver for businesses to get their priorities straight. The effects of the global pandemic have clearly distinguished between those organizations that are agile and proactive, and those that are not. The latter group has generally found it much harder to cope with market disruptions and will continue to struggle to seize new opportunities. Chris Pope is VP Innovation at ServiceNow.
Artificial intelligence and machine learning are the most disruptive technologies, according to IT professionals in the 2020 CIO Tech Priorities Poll. Respondents say these solutions -- more so than cloud, IoT, and analytics -- have the potential to significantly alter the way businesses and entire industries operate. But where is machine learning having the most impact? That's the question we posed to the IDG Influencer Network, a community of industry analysts, IT professionals, and journalists who contribute their knowledge and expertise to the broader IDG community. Here are some key takeaways from their responses.
Our goal is to provide quality news content regarding machine learning in medicine for you in this and coming versions. Dataset shift is one of the main challenges for AI model generalization. For example, in a clinical setting, training data may differ from the data used by the model to provide diagnostic, prognostic, or treatment advice. Finlayson et al. have published letters in the New England Journal of Medicine outlining how to identify and potentially mitigate familiar sources of dataset shift in machine learning systems. Casto et al. have considered causal reasoning to tackle different types of shifts in medical imaging.
Fifteen tech start-ups have raised £6.3 million in investment and grants after taking part in a new AI accelerator programme set up by the University of Edinburgh. The accelerator ran for five months from February and sought to help AI startups with high-growth potential. The final 15 companies were chosen from 89 applicants and include Sharktower, a project management software firm that recently raised £400,000 in seed funding, and Reath, a software firm that helps companies become more environmentally friendly. Since enrolling on the accelerator, Reath has secured £313,000 in funding and signed up Marks & Spencer to a pilot scheme, helping the retailer to improve and track re-use of its packaging. Another participant, MyWay, is a diabetes management app that uses AI to predict the efficacy of treatment, employs 30 people and recently raised £1.2 million in grant funding.
Why is today's narrow artificial intelligence (AI) not real? Most of the machine learning, deep learning algorithms and models are heavily relying on the statistical learning theory instead of causal learning, thus predicting spurious correlations instead of meaningful causation. This makes a critical difference for the whole enterprise, its applications, prospects, and impacts on every part of human life. We have to be intelligently critical and fully objective as modern science demands it, and as far as it concerns all of us and our human future. The AI world has been flooded with a series of gigantic language model projects promoted as the last word in AI.
Posted on July 27th, 2021 by Dr. Francis Collins Proteins are the workhorses of the cell. Mapping the precise shapes of the most important of these workhorses helps to unlock their life-supporting functions or, in the case of disease, potential for dysfunction. While the amino acid sequence of a protein provides the basis for its 3D structure, deducing the atom-by-atom map from principles of quantum mechanics has been beyond the ability of computer programs--until now. In a recent study in the journal Science, researchers reported they have developed artificial intelligence approaches for predicting the three-dimensional structure of proteins in record time, based solely on their one-dimensional amino acid sequences . This groundbreaking approach will not only aid researchers in the lab, but guide drug developers in coming up with safer and more effective ways to treat and prevent disease.
National Olympic teams are using machine learning to gain an edge in competition over their opponents at the Tokyo Olympic Games 2020. Machine learning technologies are being used at the international sports event from athlete data tracking, coaches' real-time feedback that can tell athletes when to train and when to stop, to predicting sports injuries with algorithms. Machine learning algorithms analyze athlete data collected from multiple systems like Alibaba Group and Intel which partnered to run a 3D athlete-tracking system that allows coaches to probe into every minute movement of their Olympic athletes. The system relies on algorithms to understand the biomechanics of the movement of athletes captured by cameras and estimate the position of key body joints. As a field of artificial intelligence, computer vision enables machines to perform image processing tasks with the aim of imitating human vision.
Illustration: Chaitanya Dinesh Surpur Artificial intelligence (AI) is fast becoming a topic that is relevant to everyone today and, therefore, a subject that everyone ought to learn at least the rudiments of, say experts. From the humble milkman delivering packets of milk to households in the morning to the highest lawmakers and biggest industrialists, AI will increasingly touch everyone. "A lot of people look at AI as a vertical that calls for experts to develop," says Amit Anand, founding partner at Jungle Ventures, a VC firm in Singapore that has invested in several tech startups in India. However, both in his own mind and as an advisor to the Singapore government on the ethical use of AI, "We have taken a view that AI is going to affect everybody, and hence everyone should be knowledgeable and have a certain level of understanding of AI." Click here to see Forbes India's comprehensive coverage on the Covid-19 situation and its impact on life, business and the economy You can buy our tablet version from Magzter.com. To visit our Archives, click here.)