The Role of Machine Learning and Artificial Intelligence in Robotics #Sciencefather #researchers #robotics

 

“Robotics are beginning to cross that line from absolutely primitive motion to motion that resembles animal or human behavior,” said J. J. Abrams. Fundamentally created in the early 1950s, robotics, today, is well-known as a self-operating machine with precisely trained and learned inputs, and its prevalence is omnipresent. The evolution of robotic intelligence shows a wide range of hierarchies since the time it was first created. Introduced to be deployed in factories for industrial use, it isn’t easy to find a sector where robotics is not used today. In the initial days of its advent, robots were merely designed to perform a trained set of repetitive tasks. However, with the role of artificial intelligence in robots, the scope of robotics has expanded. 

Now, robotics operates exclusively on artificial intelligence and machine learning. The 2000s trace the utilization of artificial intelligence in digitally programmed industrial robots. The global scenario has widely changed since then. Skillful integration of machine learning and robotics has been developed to advance the alleged ambit of robotic intelligence, enabling it to attain a sound human vision to detect potent stimuli. Huge algorithms and datasets go into this process of evolving the robotics potential into a human-like vision.

The current generation of blending machine learning and robotics allegedly seems to be the most powerful combination in the history of technological innovations. A completely new era of automation is set to disrupt every possible institution of human civilization. AI-driven robots are considered more efficient than the ones without this technology. For instance, the industrial sector stands as the biggest consumer of functions like robotics and further automation, saving time and human effort and ensuring validity, accuracy, and minor errors. In such an occupied workspace, one cannot risk the security of the place with basic traditional robots.

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Information Technology for Open Science: Innovation for Research | #Sciencefather #researcher #professors

What does innovation mean for higher education? I've asked many college/university and association presidents this question. The typical response points to successful startups, economic development programs, and entrepreneurship training. Yes, these are important ways that we serve society through innovation. But how do higher education institutions themselves innovate?

Colleges and universities are much more facile at outside innovation than inside innovation. By that, I mean that they have created cultures and rewards for moving ideas and inventions outside of the institution through technology transfer or student education but are not as adept at achieving change within.

For fifteen years, while serving as vice president of research at the University of Southern California (USC), I witnessed—and encouraged—a transformation in research practices. Under the umbrella of the "Creativity and Collaboration in the Academy" initiative, USC explored and experimented with how higher education institutions might evolve to a research culture that, aligned with information technology, could achieve innovation inside the institution so as to be more impactful on the outside.

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Artificial Intelligence in Science | #Sciencefather #reserachawards #AI

The rapid advances of artificial intelligence (AI) in recent years have led to numerous creative applications in science. Accelerating the productivity of science could be the most economically and socially valuable of all the uses of AI. Utilising AI to accelerate scientific productivity will support the ability of OECD countries to grow, innovate and meet global challenges, from climate change to new contagions.

                                         


Accelerating the productivity of research could be the most economically and socially valuable of all the uses of artificial intelligence (AI). While AI is penetrating all domains and stages of science, its full potential is far from realised. Policy makers and actors across research systems can do much to accelerate and deepen the uptake of AI in science, magnifying its positive contributions to research. This will support the ability of OECD countries to grow, innovate and address global challenges, from climate change to new contagions.


A distinctive contribution is the book’s examination of policies for AI in science. Policy makers and actors across research systems can do much to deepen AI’s use in science, magnifying its positive effects, while adapting to the fast-changing implications of AI for research governance.


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Research Scientist

Research scientists delve deep into the study of machine learning and computational intelligence systems. Candidates typically hold a PhD or master’s degree in mathematics or computer science. Companies seek individuals with a solid background in AI for these positions, which offer high salaries. Demand for research scientists is expected to remain strong over the next decade.

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How to become Research Scientist | #ScienceFather #researchers

 A Research Scientist is a professional who conducts scientific research and contributes to the advancement of knowledge in their respective fields. They work in various domains such as natural sciences, social sciences, engineering, medicine, technology, and more. Research Scientists are typically employed by academic institutions, research organizations, government agencies, and private companies.

Becoming a Research Scientist typically involves a combination of education, research experience, and a passion for scientific inquiry. While the specific path can vary depending on the field of study, here are general steps to pursue a career as a Research Scientist:

• Choose a Field of Study: Determine the field of research that aligns with your interests and career goals. This could be in natural sciences (biology, chemistry, physics), social sciences (psychology, sociology, economics), engineering, medicine, or any other discipline with a strong research component.
• Obtain a Bachelor’s Degree: Start by earning a bachelor’s degree in a relevant field. Choose a program that provides a strong foundation in scientific principles, research methodology, and analytical skills. It’s advisable to select courses and research projects that align with your area of interest.
• Gain Research Experience: Seek opportunities to gain research experience while pursuing your undergraduate degree. This can include working as a research assistant in a lab, participating in research projects, or engaging in internships or summer research programs. This hands-on experience will expose you to the research process and help develop essential skills.

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WHO guidance for digital health application and future research | nesin.sciencefather.com

The World Health Organisation (WHO) has released guidance for digital health after performing a critical and in-depth review on the literature available. It has generated an overview on the benefits, harms, acceptability, feasibility, resource use and equity considerations of digital health interventions. It also provides insights for researchers engaged in designing, developing and validating the digital interventions. The guidelines are also foreseen to greatly impact the future research for digital applications.

It is certain that digital solutions support patients and physicians in achieving their medical goals. They have been also increasingly adopted in health care. However, data is lacking on the impact of digital applications on health care. As number and types of digital solutions increasing, there is an immediate requirement to develop evidence-based insights for the integration of these solutions in medical practices. This paper provides an overview of the guidelines and insights for researchers engaged in the development and validation of digital interventions. It also recommends that the digital tools should be developed and implemented in accordance with the principles of digital development. It extends guidance for building a supportive environment for better adoption of digital health.

Using digital means to complement the traditional education, in training and education of healthcare professionals is definitely recommended. However studies evaluating the validation of learning content are limited that makes it difficult to draw firm conclusions. Researchers need to be vigilant in using over increasing digital tools. Additionally, sustained efforts are demanded in the development of novel digital tools that could benefit both patient and physicians in health care.

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Pure Water: The Unsung Hero Of The Lab

 Pure water plays a pivotal role in almost every life science experiment, diagnostic test or chemical reaction. It’s so important, there’s even a high impact journal dedicated to reporting on new water-based research and purification methods. Sadly, lab water still rarely gets the attention it deserves. 

Without pure water, very few experiments, tests or reactions would be possible. Thanks to its favorable chemical and physical properties, water is often considered the ‘universal solvent’ as it dissolves more substances than any other liquid. Perhaps even more importantly, many biochemical reactions will only take place within aqueous solutions.

One potential limitation of water is that it is a weak eluent and a poor solvent of most organic compounds, due to it being highly polar at ambient temperatures. However, superheated water at 200°C has similar properties to typical organic solvents such as methanol, so it can be used to dissolve many organic compounds. This adds even more credibility to water’s status as the universal solvent and means you can use it in a wider range of applications, such as separation and extraction science. Even better, water is cheaper, less flammable and more environmentally friendly than most organic solvents – more great reasons why water is an essential reagent in the lab!

Ultra-Sensitive Analytics that Rely on UltraPure Water are Fuelling Cutting-Edge Science

Some of the most interesting science being carried out at the moment pushes the boundaries of analytical sensitivity. Techniques such as inductively-coupled plasma mass spectrometry (ICP-MS), thermal ionization mass spectrometry (TIMS) and ICP atomic emission spectrometry (ICP-AES) allow researchers to explore biological and chemical systems in more detail and at greater resolution than ever before, including being able to detect the presence of chemicals and factors at trace (<100 ppm) and ultra-trace (<1 ppm) concentrations.

As an example, such sensitivity allows us to detect low expression biomarkers so that we can better diagnose and treat patients, helping to turn the promise of personalized medicine into a reality. High sensitivity analysis is also highly useful when working with environmental samples, so that researchers can better track global climate and environmental change.

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