Corporate leaders have long understood that demonstrating value to shareholders must include navigating and managing change. From the early days of Kurt Lewin’s change management model, it has been well understood that companies need to adequately prepare for both sudden unexpected shifts and gradual changes.
The current economic and health crises have propelled organizations toward long-overdue examinations of the role of employee training and development in shaping corporate readiness. It’s often said that 70% of change initiatives fail. While the Harvard Business Review has estimated that number is actually around 10%, it should still be no surprise that failure to adapt to changes due to the coronavirus can have far-reaching ramifications for employees and stockholders.
Although industries have seen several sudden disruptions due to advancements in technology, sudden changes due to Covid-19 have revealed unexpected challenges. Some organizations quickly overcame or adapted to these
Tandem mass spectrometry is a powerful analytical tool used to characterize complex mixtures in drug discovery and other fields.
Now, Purdue University innovators have created a new method of applying machine learning concepts to the tandem mass spectrometry process to improve the flow of information in the development of new drugs. Their work is published in Chemical Science.
“Mass spectrometry plays an integral role in drug discovery and development,” said Gaurav Chopra, an assistant professor of analytical and physical chemistry in Purdue’s College of Science. “The specific implementation of bootstrapped machine learning with a small amount of positive and negative training data presented here will pave the way for becoming mainstream in day-to-day activities of automating characterization of compounds by chemists.”
Chopra said there are two major problems in the field of machine learning used for chemical sciences. Methods used do not provide chemical understanding
BioTalent Canada today released new findings from its most recent bio-economy Labour Market Information (LMI) study. The data in this new brief—The Talent Differential: The case for work-integrated learning in the bio-economy —was collected from a series of three facilitated roundtable discussions, a survey of 573 bio-economy employers in 2020, and an analysis of data from BioTalent Canada’s current wage subsidy programs.
The results indicate work-integrated learning (WIL) such as co-op, work placements, internships, and clinical placements that combine practical work experience with formal classroom learning are a key component of many Canadian post-secondary education models. The programs also offer a key source of talent recruitment for bio-economy employers.
“Students who take advantage of work-integrated learning opportunities have an easier time transitioning to the workforce,” says Rob Henderson, President and CEO of BioTalent Canada. “But this brief uncovers some challenges. While women account for the majority of WIL participants, they
Question: When was ASTRO founded and what is its mission?
My cousin Michael and I founded ASTRO in 2014. Our mission is to champion equity among diverse populations by innovating health-promoting programs that improve the way people live, learn, work, and play.
Q: On Oct. 5, the Social Enterprise Greenhouse announced that it was providing ASTRO with a $25,000 loan. What will you use the money for?
Funds acquired through this loan will provide working capital for ASTRO to expand our operations and offer day care services to adult populations. We originally started out in adult services, but the community need that took precedence was providing a safe place for kids to be during the out-of-school time, and this is where we have focused the majority of our efforts over the years.
Now that many adults have been displaced from their day programs due to COVID-19 restrictions on numbers, there
These would affect all aspects of HR functions such as the way HR professionals on-board and hire people, and the way they train them
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Artificial intelligence (AI) is changing all aspects of our lives and that too at a rapid pace. This includes our professional lives, too. Experts expect that in the days ahead, AI would become a greater part of our careers as all companies are moving ahead with adopting such technology. They are using more machines that use AI technology that would affect our daily professional activities. Soon enough, we would see machine learning and deep learning in HR too. It would affect all aspects of HR (human resources) such
With the rise in remote work, the cloud industry has experienced extraordinary growth, largely due to enterprise businesses transitioning their physical IT infrastructure to the cloud. Along with this rapid expansion into cloud technology comes the need for a workforce with cloud expertise.
At the moment, the IT needs are changing faster than the employees in charge of these programs can handle. In fact, only 56% of cloud leaders report having an actionable plan to upskill their workforce in cloud environments.
The lack of planning surrounding employee training is only one of the pain points that comes with navigating the complexity of the cloud. Other barriers to success include a lack of internal skills and knowledge, balancing competing priorities with day-to-day work, and providing enough time for employees to study the ins and outs of the major cloud providers, while also doing their existing full-time jobs.
DNA and RNA have been compared to “instruction manuals” containing the information needed for living “machines” to operate. But while electronic machines like computers and robots are designed from the ground up to serve a specific purpose, biological organisms are governed by a much messier, more complex set of functions that lack the predictability of binary code. Inventing new solutions to biological problems requires teasing apart seemingly intractable variables—a task that is daunting to even the most intrepid human brains.
Two teams of scientists from the Wyss Institute at Harvard University and the Massachusetts Institute of Technology have devised pathways around this roadblock by going beyond human brains; they developed a set of machine learning algorithms that can analyze reams of RNA-based “toehold” sequences and predict which ones will be most effective at sensing and responding to a desired target sequence. As reported in
TEA Announces Additional Innovative Learning Solutions for K-12 English and Spanish, and K-5 Science to Support Schools Across Texas
AUSTIN, Texas, Oct. 5, 2020
AUSTIN, Texas, Oct. 5, 2020 /PRNewswire/ — ICYMI The Texas Education Agency announced Great Minds as the creator of PhD Science TEKS Edition for Texas home learning for Grades K–5. This follows the agency’s selection of Great Minds to create Eureka Math in Sync TEKS Edition for Grades K–5. The TEA news release is below. Great Minds contact: Chad Colby, Chad.Colby@greatminds.org, 202-297-9437.
The Texas Education Agency today announced the next set of instructional materials – covering K-12 English Language Arts and Reading (ELAR), K-5 Spanish Language Arts and Reading (SLAR), and K-5 Science – that will be made available to school systems through the Texas Home Learning 3.0 (THL 3.0) initiative. Like other THL 3.0 offerings, these instructional materials are
A machine learning technique rapidly rediscovered rules governing catalysts that took humans years of difficult calculations to reveal — and even explained a deviation. The University of Michigan team that developed the technique believes other researchers will be able to use it to make faster progress in designing materials for a variety of purposes.
“This opens a new door, not just in understanding catalysis, but also potentially for extracting knowledge about superconductors, enzymes, thermoelectrics, and photovoltaics,” said Bryan Goldsmith, an assistant professor of chemical engineering, who co-led the work with Suljo Linic, a professor of chemical engineering.
The key to all of these materials is how their electrons behave. Researchers would like to use machine learning techniques to develop recipes for the material properties that they want. For superconductors, the electrons must move without resistance through the material. Enzymes and catalysts need to broker exchanges of electrons, enabling new medicines
Daniel Ratner, head of SLAC’s machine learning initiative, explains the lab’s unique opportunities to advance scientific discovery through machine learning.
DOE/SLAC National Accelerator Laboratory
Machine learning is ubiquitous in science and technology these days. It outperforms traditional computational methods in many areas, for instance by vastly speeding up tedious processes and handling huge batches of data. At the Department of Energy’s SLAC National Accelerator Laboratory, machine learning is already opening new avenues to advance the lab’s unique scientific facilities and research.
For example, SLAC scientists have already used machine learning techniques to operate accelerators more efficiently, to speed up the discovery of new materials, and to uncover distortions in space-time caused by astronomical objects up to 10 million times faster than traditional methods.
The term “machine learning” broadly refers to techniques that let computers “learn by example” by inferring their own conclusions from large sets of