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Discover Tech Research Studies: Opportunities to Participate, Earn, and Get Paid

Are you interested in the latest advancements in technology? Do you want to have a say in shaping the future of digital innovation? Tech research studies offer you the perfect opportunity to participate, earn rewards, and get paid while making a significant impact.

By joining tech research studies, you become a valuable contributor to the development of cutting-edge products and services. Your insights and opinions are crucial in helping companies understand user preferences and improve their offerings. Whether you’re a tech enthusiast or simply curious about the latest trends, participating in these studies can be both rewarding and fulfilling.

Not only do tech research studies provide a platform for sharing your expertise and feedback, but they also offer attractive incentives. Earn rewards for your time and input, and even get paid for your valuable contributions. It’s a win-win situation that allows you to stay up-to-date with the latest tech and earn while doing so.

Key Takeaways:

• Participating in tech research studies allows you to shape the future of technology.
• By joining these studies, you can earn rewards and get paid for your valuable contributions.
• Tech research studies offer a platform to share your expertise and insights.
• Companies rely on user feedback to develop and improve their products and services.
• Stay up-to-date with the latest tech trends while earning rewards and getting paid.

The Importance of Tech Research Studies and How to Get Involved

Tech research studies play a crucial role in shaping technology and driving innovation in today’s rapidly evolving digital landscape. These studies provide valuable insights and data that help companies develop and improve their products and services. The active involvement of users is essential in these research projects to ensure that technology meets their needs and preferences. By participating in tech research studies, users have the opportunity to contribute directly to the development of future technologies.

Getting involved in tech research studies is easier than one might think. Companies often seek participants who can provide valuable feedback on their products or test new prototypes. There are various ways for users to participate, including surveys, interviews, focus groups, and beta testing programs. These opportunities allow individuals to share their opinions, preferences, and experiences, ultimately influencing the direction of technology.

Benefits of User Participation in Tech Research Studies

User participation in tech research studies is beneficial for both individuals and companies. For users, it offers the chance to have a voice and make a meaningful impact on technology. By actively participating, users can shape the future of products and services they rely on daily. Additionally, many research projects offer incentives such as monetary rewards, gift cards, or product samples as a token of appreciation for the user’s time and input.

On the other hand, companies greatly benefit from the insights gained through user participation. Research studies help companies understand their target audience better, uncover user expectations, identify pain points, and gather actionable feedback to enhance their products or services. This user-centric approach enables companies to create technology that is more user-friendly, relevant, and aligned with market demands.

Participating in tech research studies empowers individuals to shape the technology they use and depend on. By sharing their perspectives and experiences, users contribute to the development of innovative solutions that enhance everyday life. Their valuable input serves as a driving force behind new features, improved functionality, and user-centered design principles.

Moreover, by participating in tech research studies, users also gain the opportunity to stay up-to-date with the latest advancements in technology. They get early access to cutting-edge products, provide feedback before widespread release, and play a vital role in shaping the future of various industries.

Types of Tech Research Studies and Available Opportunities

In the field of technology, research studies play a vital role in shaping the future of innovation. These studies offer diverse opportunities for individuals to contribute their valuable insights and expertise. Let’s explore the different types of tech research studies and the roles available to participants.

Research Assistant

A research assistant is an integral part of a tech research study. They work closely with the coordinator and investigator to gather and analyze data. Research assistants play a crucial role in ensuring the smooth execution of the study, from participant recruitment to data collection and interpretation.

Participant

Participants are the lifeblood of tech research studies. They provide essential feedback and data that help researchers gain valuable insights for product development and improvement. As a participant, you have the opportunity to influence the technology of tomorrow while potentially earning rewards for your contribution.

Coordinator

A coordinator acts as the bridge between the research team and the participants. They play a crucial role in organizing and managing study logistics, recruiting participants, and ensuring ethical standards are upheld throughout the research process.

Technicians are responsible for setting up and maintaining the technical aspects of a research study. They ensure that devices, software, and equipment are functioning properly, providing a seamless experience for participants and accurate data for researchers to analyze.

Investigator

An investigator leads the research study, overseeing the entire process from design to analysis. They are responsible for developing research protocols, ensuring ethical guidelines are followed, and drawing meaningful conclusions from the collected data.

Sibling Study

One of the unique opportunities in tech research studies is participating in sibling studies. Sibling studies focus on understanding how technology affects individuals within the same family. By involving siblings, researchers can gain insights into how technology impacts different individuals and familial dynamics.

Available Opportunities in Tech Research Studies

By understanding the various opportunities available in tech research studies, you can actively contribute to the development and improvement of technology while gaining valuable experience. Whether you choose to become a research assistant, participant, coordinator, technician, or investigator, your involvement can make a meaningful impact on the future of technology.

How to Participate in Tech Research Studies and Earn Rewards

If you’re interested in shaping the future of technology while earning rewards, participating in tech research studies is the way to go. These studies offer you the opportunity to contribute your insights, opinions, and feedback, while also compensating you for your time and effort. Here’s a step-by-step guide on how to get involved and start earning:

• Submit your application: Look out for ongoing research studies and submit your application to express your interest. You may need to provide some basic information, such as your contact details and demographic data, to determine your eligibility.
• Check your eligibility: Once you’ve submitted your application, the research team will review your information to see if you meet the criteria for participation. If you’re eligible, you’ll receive a confirmation and further instructions on how to proceed.
• Choose a study: Explore the available research projects and select the one that aligns with your interests. Whether it’s testing a new app, providing feedback on a website, or participating in a focus group, there’s a variety of studies to choose from.
• Participate in-person sibling study and earn: If you have a sibling who meets the study requirements, you may be eligible to take part in an in-person sibling study. These studies often offer additional compensation for both you and your sibling, making it a great opportunity to earn extra rewards together.
• Take part in our 90 minutes: Many tech research studies can be completed in your own time and at your own pace. However, some studies may require a specific time commitment, such as a 90-minute session. Be sure to plan accordingly and set aside dedicated time for participation.
• Submit your feedback: Once you’ve completed the study requirements, submit your feedback, whether it’s through surveys, interviews, or user testing. Your input is valuable in helping companies develop and improve their products and services.

By following these steps, you can actively contribute to tech research studies, share your perspectives, and earn rewards for your valuable input. So why wait? Start participating today and make your voice count!

Ensuring Privacy and Trust in Tech Research Studies

Tech research studies rely on the involvement of individuals to gather valuable insights and shape the future of technology. To ensure a trustworthy and secure environment for participants, privacy and trust are of utmost importance. The following factors contribute to maintaining privacy and building trust:

1. Privacy Policies

Tech research studies implement comprehensive privacy policies that outline how participant information is collected, stored, and used. These policies aim to protect the privacy of individuals and ensure their data remains confidential.

2. Obtaining Consent

Prior to participating in a tech research study, individuals are required to provide explicit consent. This consent allows researchers to collect and analyze their data while respecting their privacy and confidentiality.

3. Transparent Use of Cookies

Cookies play a crucial role in optimizing the user experience and ensuring efficient data collection in tech research studies. Transparent communication about the use of cookies enables individuals to make informed decisions about their participation.

4. Demographic Considerations

Tech research studies often collect demographic information to gain insights into how individuals from different backgrounds interact with technology. Ensuring anonymity and confidentiality of this information is essential to maintain privacy and build trust.

5. Interactions with Participants

Researchers and study coordinators prioritize clear and open communication with participants. This includes providing regular updates, addressing concerns, and being responsive to any queries or feedback, thus fostering a trusting relationship.

By implementing these measures, tech research studies aim to create a secure and trustworthy environment, respecting the privacy of participants and reinforcing their trust in the research process.

How Tech Research Studies Contribute to Technology Development

Tech research studies play a crucial role in the development and improvement of products and services offered by companies. Through market research conducted in these studies, companies gain valuable insights into user preferences and make informed decisions that shape the future of technology.

The Importance of Market Research

Market research is a vital component of tech research studies as it enables companies to understand the needs and preferences of their target audience. By conducting in-depth surveys, interviews, and observations, companies gather data that helps them identify gaps in the market and uncover opportunities for innovation and improvement.

With the help of market research, companies can gain a competitive edge by developing products and services that cater to the specific demands of their customers. By understanding what consumers want and need, companies can create solutions that address their pain points and exceed their expectations.

Driving Product and Service Innovation

Tech research studies provide companies with valuable feedback that drives innovation in their products and services. By actively involving users in the research process, companies obtain insights that shape the design, features, and functionalities of their offerings.

Through user feedback, companies can identify areas for improvement and prioritize the implementation of new features or enhancements. This iterative process allows them to create products and services that better meet the needs of their target market.

Enhancing User Experience

One of the primary goals of tech research studies is to enhance user experience. By involving users in the development process, companies can identify usability issues, understand user behavior, and optimize their products and services accordingly.

Through user testing and feedback, companies can improve the user interface, streamline workflows, and ensure that their products and services are intuitive and user-friendly. This focus on user experience not only boosts customer satisfaction but also creates a competitive advantage in the market.

Impact of Tech Research Studies on Technology Development

By actively engaging users in tech research studies, companies can harness the power of market research to develop and improve their products and services. The insights gained enable them to make data-driven decisions, enhance user experience, and stay ahead in an increasingly competitive tech landscape.

Participating in tech research studies is not only a great way to earn rewards but also an opportunity to shape the future of technology. The data collected from these studies plays a crucial role in analyzing user preferences, understanding market trends, and boosting technological advancements.

By actively participating in tech research studies, individuals contribute to the development and improvement of products and services offered by companies. Their valuable feedback and insights help companies make informed decisions to create better technological solutions that meet the evolving needs of users.

As technology continues to evolve, it becomes increasingly important to engage in research studies to stay at the forefront of innovation. The analysis of data gathered through these studies fuels technological growth and ensures that future developments align with user expectations. So, seize the opportunity to have your voice heard and shape the future by participating in tech research studies today!

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The societal impacts of technological change can be seen in many domains, from messenger RNA vaccines and automation to drones and climate change. The pace of that technological change can affect its impact, and how quickly a technology improves in performance can be an indicator of its future importance. For decision-makers like investors, entrepreneurs, and policymakers, predicting which technologies are fast improving (and which are overhyped) can mean the difference between success and failure.

New research from MIT aims to assist in the prediction of technology performance improvement using U.S. patents as a dataset. The study describes 97 percent of the U.S. patent system as a set of 1,757 discrete technology domains, and quantitatively assesses each domain for its improvement potential.

“The rate of improvement can only be empirically estimated when substantial performance measurements are made over long time periods,” says Anuraag Singh SM ’20, lead author of the paper. “In some large technological fields, including software and clinical medicine, such measures have rarely, if ever, been made.”

A previous MIT study provided empirical measures for 30 technological domains, but the patent sets identified for those technologies cover less than 15 percent of the patents in the U.S. patent system. The major purpose of this new study is to provide predictions of the performance improvement rates for the thousands of domains not accessed by empirical measurement. To accomplish this, the researchers developed a method using a new probability-based algorithm, machine learning, natural language processing, and patent network analytics.

Overlap and centrality

A technology domain, as the researchers define it, consists of sets of artifacts fulfilling a specific function using a specific branch of scientific knowledge. To find the patents that best represent a domain, the team built on previous research conducted by co-author Chris Magee, a professor of the practice of engineering systems within the Institute for Data, Systems, and Society (IDSS). Magee and his colleagues found that by looking for patent overlap between the U.S. and international patent-classification systems, they could quickly identify patents that best represent a technology. The researchers ultimately created a correspondence of all patents within the U.S. patent system to a set of 1,757 technology domains.

To estimate performance improvement, Singh employed a method refined by co-authors Magee and Giorgio Triulzi, a researcher with the Sociotechnical Systems Research Center (SSRC) within IDSS and an assistant professor at Universidad de los Andes in Colombia. Their method is based on the average “centrality” of patents in the patent citation network. Centrality refers to multiple criteria for determining the ranking or importance of nodes within a network.

“Our method provides predictions of performance improvement rates for nearly all definable technologies for the first time,” says Singh.

Those rates vary — from a low of 2 percent per year for the “Mechanical skin treatment — Hair removal and wrinkles” domain to a high of 216 percent per year for the “Dynamic information exchange and support systems integrating multiple channels” domain. The researchers found that most technologies improve slowly; more than 80 percent of technologies improve at less than 25 percent per year. Notably, the number of patents in a technological area was not a strong indicator of a higher improvement rate.

“Fast-improving domains are concentrated in a few technological areas,” says Magee. “The domains that show improvement rates greater than the predicted rate for integrated chips — 42 percent, from Moore’s law — are predominantly based upon software and algorithms.”

TechNext Inc.

The researchers built an online interactive system where domains corresponding to technology-related keywords can be found along with their improvement rates. Users can input a keyword describing a technology and the system returns a prediction of improvement for the technological domain, an automated measure of the quality of the match between the keyword and the domain, and patent sets so that the reader can judge the semantic quality of the match.

Moving forward, the researchers have founded a new MIT spinoff called TechNext Inc. to further refine this technology and use it to help leaders make better decisions, from budgets to investment priorities to technology policy. Like any inventors, Magee and his colleagues want to protect their intellectual property rights. To that end, they have applied for a patent for their novel system and its unique methodology.

“Technologies that improve faster win the market,” says Singh. “Our search system enables technology managers, investors, policymakers, and entrepreneurs to quickly look up predictions of improvement rates for specific technologies.”

Adds Magee: “Our goal is to bring greater accuracy, precision, and repeatability to the as-yet fuzzy art of technology forecasting.”

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McKinsey Technology Trends Outlook 2023

After a tumultuous 2022 for technology investment and talent, the first half of 2023 has seen a resurgence of enthusiasm about technology’s potential to catalyze progress in business and society. Generative AI deserves much of the credit for ushering in this revival, but it stands as just one of many advances on the horizon that could drive sustainable, inclusive growth and solve complex global challenges.

To help executives track the latest developments, the McKinsey Technology Council  has once again identified and interpreted the most significant technology trends unfolding today. While many trends are in the early stages of adoption and scale, executives can use this research to plan ahead by developing an understanding of potential use cases and pinpointing the critical skills needed as they hire or upskill talent to bring these opportunities to fruition.

Our analysis examines quantitative measures of interest, innovation, and investment to gauge the momentum of each trend. Recognizing the long-term nature and interdependence of these trends, we also delve into underlying technologies, uncertainties, and questions surrounding each trend. This year, we added an important new dimension for analysis—talent. We provide data on talent supply-and-demand dynamics for the roles of most relevance to each trend. (For more, please see the sidebar, “Research methodology.”)

New and notable

All of last year’s 14 trends remain on our list, though some experienced accelerating momentum and investment, while others saw a downshift. One new trend, generative AI, made a loud entrance and has already shown potential for transformative business impact.

Research methodology

To assess the development of each technology trend, our team collected data on five tangible measures of activity: search engine queries, news publications, patents, research publications, and investment. For each measure, we used a defined set of data sources to find occurrences of keywords associated with each of the 15 trends, screened those occurrences for valid mentions of activity, and indexed the resulting numbers of mentions on a 0–1 scoring scale that is relative to the trends studied. The innovation score combines the patents and research scores; the interest score combines the news and search scores. (While we recognize that an interest score can be inflated by deliberate efforts to stimulate news and search activity, we believe that each score fairly reflects the extent of discussion and debate about a given trend.) Investment measures the flows of funding from the capital markets into companies linked with the trend. Data sources for the scores include the following:

• Patents. Data on patent filings are sourced from Google Patents.
• Research. Data on research publications are sourced from the Lens (www.lens.org).
• News. Data on news publications are sourced from Factiva.
• Searches. Data on search engine queries are sourced from Google Trends.
• Investment. Data on private-market and public-market capital raises are sourced from PitchBook.
• Talent demand. Number of job postings is sourced from McKinsey’s proprietary Organizational Data Platform, which stores licensed, de-identified data on professional profiles and job postings. Data is drawn primarily from English-speaking countries.

In addition, we updated the selection and definition of trends from last year’s study to reflect the evolution of technology trends:

• The generative-AI trend was added since last year’s study.
• We adjusted the definitions of electrification and renewables (previously called future of clean energy) and climate technologies beyond electrification and renewables (previously called future of sustainable consumption).
• Data sources were updated. This year, we included only closed deals in PitchBook data, which revised downward the investment numbers for 2018–22. For future of space technologies investments, we used research from McKinsey’s Aerospace & Defense Practice.

This new entrant represents the next frontier of AI. Building upon existing technologies such as applied AI and industrializing machine learning, generative AI has high potential and applicability across most industries. Interest in the topic (as gauged by news and internet searches) increased threefold from 2021 to 2022. As we recently wrote, generative AI and other foundational models  change the AI game by taking assistive technology to a new level, reducing application development time, and bringing powerful capabilities to nontechnical users. Generative AI is poised to add as much as $4.4 trillion in economic value from a combination of specific use cases and more diffuse uses—such as assisting with email drafts—that increase productivity. Still, while generative AI can unlock significant value, firms should not underestimate the economic significance and the growth potential that underlying AI technologies and industrializing machine learning can bring to various industries.

Investment in most tech trends tightened year over year, but the potential for future growth remains high, as further indicated by the recent rebound in tech valuations. Indeed, absolute investments remained strong in 2022, at more than $1 trillion combined, indicating great faith in the value potential of these trends. Trust architectures and digital identity grew the most out of last year’s 14 trends, increasing by nearly 50 percent as security, privacy, and resilience become increasingly critical across industries. Investment in other trends—such as applied AI, advanced connectivity, and cloud and edge computing—declined, but that is likely due, at least in part, to their maturity. More mature technologies can be more sensitive to short-term budget dynamics than more nascent technologies with longer investment time horizons, such as climate and mobility technologies. Also, as some technologies become more profitable, they can often scale further with lower marginal investment. Given that these technologies have applications in most industries, we have little doubt that mainstream adoption will continue to grow.

Organizations shouldn’t focus too heavily on the trends that are garnering the most attention. By focusing on only the most hyped trends, they may miss out on the significant value potential of other technologies and hinder the chance for purposeful capability building. Instead, companies seeking longer-term growth should focus on a portfolio-oriented investment across the tech trends most important to their business. Technologies such as cloud and edge computing and the future of bioengineering have shown steady increases in innovation and continue to have expanded use cases across industries. In fact, more than 400 edge use cases across various industries have been identified, and edge computing is projected to win double-digit growth globally over the next five years. Additionally, nascent technologies, such as quantum, continue to evolve and show significant potential for value creation. Our updated analysis for 2023 shows that the four industries likely to see the earliest economic impact from quantum computing—automotive, chemicals, financial services, and life sciences—stand to potentially gain up to $1.3 trillion in value by 2035. By carefully assessing the evolving landscape and considering a balanced approach, businesses can capitalize on both established and emerging technologies to propel innovation and achieve sustainable growth.

Tech talent dynamics

We can’t overstate the importance of talent as a key source in developing a competitive edge. A lack of talent is a top issue constraining growth. There’s a wide gap between the demand for people with the skills needed to capture value from the tech trends and available talent: our survey of 3.5 million job postings in these tech trends found that many of the skills in greatest demand have less than half as many qualified practitioners per posting as the global average. Companies should be on top of the talent market, ready to respond to notable shifts and to deliver a strong value proposition to the technologists they hope to hire and retain. For instance, recent layoffs in the tech sector may present a silver lining for other industries that have struggled to win the attention of attractive candidates and retain senior tech talent. In addition, some of these technologies will accelerate the pace of workforce transformation. In the coming decade, 20 to 30 percent of the time that workers spend on the job could be transformed by automation technologies, leading to significant shifts in the skills required to be successful. And companies should continue to look at how they can adjust roles or upskill individuals to meet their tailored job requirements. Job postings in fields related to tech trends grew at a very healthy 15 percent between 2021 and 2022, even though global job postings overall decreased by 13 percent. Applied AI and next-generation software development together posted nearly one million jobs between 2018 and 2022. Next-generation software development saw the most significant growth in number of jobs (exhibit).

Image description:

Small multiples of 15 slope charts show the number of job postings in different fields related to tech trends from 2021 to 2022. Overall growth of all fields combined was about 400,000 jobs, with applied AI having the most job postings in 2022 and experiencing a 6% increase from 2021. Next-generation software development had the second-highest number of job postings in 2022 and had 29% growth from 2021. Other categories shown, from most job postings to least in 2022, are as follows: cloud and edge computing, trust architecture and digital identity, future of mobility, electrification and renewables, climate tech beyond electrification and renewables, advanced connectivity, immersive-reality technologies, industrializing machine learning, Web3, future of bioengineering, future of space technologies, generative AI, and quantum technologies.

End of image description.

This bright outlook for practitioners in most fields highlights the challenge facing employers who are struggling to find enough talent to keep up with their demands. The shortage of qualified talent has been a persistent limiting factor in the growth of many high-tech fields, including AI, quantum technologies, space technologies, and electrification and renewables. The talent crunch is particularly pronounced for trends such as cloud computing and industrializing machine learning, which are required across most industries. It’s also a major challenge in areas that employ highly specialized professionals, such as the future of mobility and quantum computing (see interactive).

Michael Chui is a McKinsey Global Institute partner in McKinsey’s Bay Area office, where Mena Issler is an associate partner, Roger Roberts  is a partner, and Lareina Yee  is a senior partner.

The authors wish to thank the following McKinsey colleagues for their contributions to this research: Bharat Bahl, Soumya Banerjee, Arjita Bhan, Tanmay Bhatnagar, Jim Boehm, Andreas Breiter, Tom Brennan, Ryan Brukardt, Kevin Buehler, Zina Cole, Santiago Comella-Dorda, Brian Constantine, Daniela Cuneo, Wendy Cyffka, Chris Daehnick, Ian De Bode, Andrea Del Miglio, Jonathan DePrizio, Ivan Dyakonov, Torgyn Erland, Robin Giesbrecht, Carlo Giovine, Liz Grennan, Ferry Grijpink, Harsh Gupta, Martin Harrysson, David Harvey, Kersten Heineke, Matt Higginson, Alharith Hussin, Tore Johnston, Philipp Kampshoff, Hamza Khan, Nayur Khan, Naomi Kim, Jesse Klempner, Kelly Kochanski, Matej Macak, Stephanie Madner, Aishwarya Mohapatra, Timo Möller, Matt Mrozek, Evan Nazareth, Peter Noteboom, Anna Orthofer, Katherine Ottenbreit, Eric Parsonnet, Mark Patel, Bruce Philp, Fabian Queder, Robin Riedel, Tanya Rodchenko, Lucy Shenton, Henning Soller, Naveen Srikakulam, Shivam Srivastava, Bhargs Srivathsan, Erika Stanzl, Brooke Stokes, Malin Strandell-Jansson, Daniel Wallance, Allen Weinberg, Olivia White, Martin Wrulich, Perez Yeptho, Matija Zesko, Felix Ziegler, and Delphine Zurkiya.

They also wish to thank the external members of the McKinsey Technology Council.

This interactive was designed, developed, and edited by McKinsey Global Publishing’s Nayomi Chibana, Victor Cuevas, Richard Johnson, Stephanie Jones, Stephen Landau, LaShon Malone, Kanika Punwani, Katie Shearer, Rick Tetzeli, Sneha Vats, and Jessica Wang.

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Few technological advances have generated as much excitement as AI. In particular, generative AI seems to have taken business discourse to a fever pitch. Many manufacturing leaders express optimism: Research conducted by MIT Technology Review Insights found ambitions for AI development to be stronger in manufacturing than in most other sectors.

Manufacturers rightly view AI as integral to the creation of the hyper-automated intelligent factory. They see AI’s utility in enhancing product and process innovation, reducing cycle time, wringing ever more efficiency from operations and assets, improving maintenance, and strengthening security, while reducing carbon emissions. Some manufacturers that have invested to develop AI capabilities are still striving to achieve their objectives.

This study from MIT Technology Review Insights seeks to understand how manufacturers are generating benefits from AI use cases—particularly in engineering and design and in factory operations. The survey included 300 manufacturers that have begun working with AI. Most of these (64%) are currently researching or experimenting with AI. Some 35% have begun to put AI use cases into production. Many executives that responded to the survey indicate they intend to boost AI spending significantly during the next two years. Those who haven’t started AI in production are moving gradually. To facilitate use-case development and scaling, these manufacturers must address challenges with talents, skills, and data. Following are the study’s key findings:

• Talent, skills, and data are the main constraints on AI scaling. In both engineering and design and factory operations, manufacturers cite a deficit of talent and skills as their toughest challenge in scaling AI use cases. The closer use cases get to production, the harder this deficit bites. Many respondents say inadequate data quality and governance also hamper use-case development. Insufficient access to cloud-based compute power is another oft-cited constraint in engineering and design.
• The biggest players do the most spending, and have the highest expectations. In engineering and design, 58% of executives expect their organizations to increase AI spending by more than 10% during the next two years. And 43% say the same when it comes to factory operations. The largest manufacturers are far more likely to make big increases in investment than those in smaller—but still large—size categories.
• Desired AI gains are specific to manufacturing functions. The most common use cases deployed by manufacturers involve product design, conversational AI, and content creation. Knowledge management and quality control are those most frequently cited at pilot stage. In engineering and design, manufacturers chiefly seek AI gains in speed, efficiency, reduced failures, and security. In the factory, desired above all is better innovation, along with improved safety and a reduced carbon footprint.
• Scaling can stall without the right data foundations. Respondents are clear that AI use-case development is hampered by inadequate data quality (57%), weak data integration (54%), and weak governance (47%). Only about one in five manufacturers surveyed have production assets with data ready for use in existing AI models. That figure dwindles as manufacturers put use cases into production. The bigger the manufacturer, the greater the problem of unsuitable data is.
• Fragmentation must be addressed for AI to scale. Most manufacturers find some modernization of data architecture, infrastructure, and processes is needed to support AI, along with other technology and business priorities. A modernization strategy that improves interoperability of data systems between engineering and design and the factory, and between operational technology (OT) and information technology (IT), is a sound priority.

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Women in AI: Anna Korhonen studies the intersection between linguistics and AI

To give AI-focused women academics and others their well-deserved — and overdue — time in the spotlight, TechCrunch is launching a  series of interviews focusing on remarkable women who’ve contributed to the AI revolution. We’ll publish several pieces throughout the year as the AI boom continues, highlighting key work that often goes unrecognized. Read more profiles here .

Anna Korhonen is a professor of natural language processing (NLP) at the University of Cambridge. She’s also a senior research fellow at Churchill College , a fellow at the Association for Computational Linguistics, and a fellow at the European Laboratory for Learning and Intelligent Systems.

Korhonen previously served as a fellow at the Alan Turing Institute and she has a PhD in computer science and master’s degrees in both computer science and linguistics. She researches NLP and how to develop, adapt and apply computational techniques to meet the needs of AI. She has a particular interest in responsible and “human-centric” NLP that — in her own words — “draws on the understanding of human cognitive, social and creative intelligence.”

Briefly, how did you get your start in AI? What attracted you to the field?

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Much of my current research is driven by the mission to develop AI that can improve human lives for the better. AI has a huge positive potential for social and global good. A big part of my job as an educator is to encourage the next generation of AI scientists and leaders to focus on realizing that potential.

How do you navigate the challenges of the male-dominated tech industry and, by extension, the male-dominated AI industry?

I’m fortunate to be working in an area of AI where we do have a sizable female population and established support networks. I’ve found these immensely helpful in navigating career and personal challenges.

For me, the biggest problem is how the male-dominated industry sets the agenda for AI. The current arms race to develop ever-larger AI models at any cost is a great example. This has a huge impact on the priorities of both academia and industry, and wide-ranging socioeconomic and environmental implications. Do we need larger models, and what are their global costs and benefits? I feel we would’ve asked these questions a lot earlier in the game if we had better gender balance in the field.

What advice would you give to women seeking to enter the AI field?

AI desperately needs more women at all levels, but especially at the level of leadership. The current leadership culture isn’t necessarily attractive for women, but active involvement can change that culture — and ultimately the culture of AI. Women are infamously not always great at supporting each other. I would really like to see an attitude change in this respect: We need to actively network and help each other if we want to achieve better gender balance in this field.

What are some of the most pressing issues facing AI as it evolves?

AI has developed incredibly fast: It has evolved from an academic field to a global phenomenon in less than a single decade. During this time, most effort has gone toward scaling through massive data and computation. Little effort has been devoted to thinking how this technology should be developed so that it can best serve humanity. People have a good reason to worry about the safety and trustworthiness of AI and its impact on jobs, democracy, environment and other areas. We need to urgently put human needs and safety at the center of AI development.

What are some issues AI users should be aware of?

Current AI, even when seeming highly fluent, ultimately lacks the world knowledge of humans and the ability to understand the complex social contexts and norms we operate with. Even the best of today’s technology makes mistakes, and our ability to prevent or predict those mistakes is limited. AI can be a very useful tool for many tasks, but I would not trust it to educate my children or make important decisions for me. We humans should remain in charge.

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Developers of AI tend to think about ethics as an afterthought — after the technology has already been built. The best way to think about it is before any development begins. Questions such as, “Do I have a diverse enough team to develop a fair system?” or “Is my data really free to use and representative of all the users’ populations?” or “Are my techniques robust?” should really be asked at the outset.

Although we can address some of this problem via education, we can only enforce it via regulation. The recent development of national and global AI regulations is important and needs to continue to guarantee that future technologies will be safer and more trustworthy.

How can investors better push for responsible AI?

AI regulations are emerging and companies will ultimately need to comply. We can think of responsible AI as sustainable AI truly worth investing in.

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Nsf awards fellowships to 60 georgia tech graduate students.

The National Science Foundation (NSF) has awarded 60 Georgia Tech students with Graduate Research Fellowships (GRF). The fellowships, valued at $159,000 each, include funding for three years of graduate study and tuition.

This year’s winners represent areas of study ranging from aerospace engineering to ocean sciences. The purpose of the GRF initiative , the oldest of NSF’s programs, is to develop experts who will contribute significantly to research, teaching, and innovations in science and engineering. Their awards total more than $9.5 million in funding, the most Georgia Tech has ever had in the program.

This year’s recipients are: 

Mihir Natansh Bafna  

Rebecca Kate Banner 

Bareesh Bhaduri 

Jessica A. Bonsu 

Noah S.S. Campbell 

Adrian Alfonso Candocia  

Cailey M Carpenter  

Katherine Elizabeth Cauffiel  

Michael John Cho  

Claudia Chu 

Eric Anthony Comstock 

Sarah Deiters 

Adit Desai 

Ramy Ghanim 

Hannah E. Gilbonio 

Callie L. Goins  

Ashley Alexus Goodnight 

Margaret Gordon 

Jared Nathaniel Grinberg  

Sean Healy 

Alec F. Helbling 

Geoffrey M. Hopping 

Madeline Hoyle 

Joy Michelle Jackson 

Maeve Alexandra Janecka  

Aulden Jones 

Donguk Daniel Kim 

Tara Hashemian Kimiavi 

Michael Klamkin 

Velin H. Kojouharov 

Luke Allen Kurfman 

Aidan S. Labrozzi 

Hee Jun Lee 

Zikang Leng 

Huston Locht 

Emma J. Menardi 

Yash V. Mhaskar 

Madeline Rose Morrell 

Siddharth R. Nathella 

Jennifer Nolan 

Sydney A. Oliver 

Isabelle A. Osuna 

Jorja Y. Overbey 

Robert Patrick Pesch 

Michelle T. Seeler 

Riya Sen 

Matthew So 

Jorik Stoop 

Maggie Emma Straight  

Amanda L. Tang 

Albert Ting 

Darin Tsui 

Julia E. Vallier 

Jacqueline F. van Zyl 

Angel E. Vasquez 

Abhijeet Krishnan Venkataraman 

Alix Wagner 

Matthew Y. Wang 

Samuel E. Wilcox 

Elias G. Winterscheidt 

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The entire "first week" is background information about the university and instructors, along with a short animation related to technical writing with no commentary that seems like it was made to be inserted into a video but was uploaded alone instead.

You can skip straight to Week 2 "Characteristics of Technical Writing" and not be any worse off for it.

", could've easily been combined with the course information in a text document or with "Characteristics of Technical Writing" for a more robust understanding of what technical writers do and how it relates to their writing.)

For how much the course tries to impress that technical writing should be clear, concise, and well-presented, it fails in nearly all of those aspects.It's not unwatchable and there's solid information to be found, but those are only part of what makes a quality educational video.

Great course and got great insights about Technical Writing.

Adding some Technical Writing Tools would have been even nicer.

This course was a great opportunity to learn the pros and cons of technical writing.

This course is very helpful for developing technical writing skill.

Is a product description an example of marketing or technical writing?

I think the information you learn about the reasons for technical writing as well as the types, and intricate parts of a body of work are valuable information.

This course is a great gateway to the world of technical writing.

The course was very well structured... a perfect balance between theoretical and practical aspects of Technical Writing.

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Technical File Reviewer $49k

Logistics Specialist / Technical Support $76k

Technical Field Representative 3 $81k

Technical Service Representative 1 2 $91k

Americas Technical Analyst $104k

Team IT Technical Analyst Lead 3 $108k

Technical Research and Development Engineer $109k

Technical Implementation Specialist Lead 2 $113k

Technical Marketing Mgr $114k

Technical Onsite Project Manager $117k

Distiguished Member of Technical Staff 3 $146k

Senior Program Manager/Technical Project Manager $156k

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UC researchers develop new CPAP device

The vortexpap could result in more patient compliance.

An estimated 40 million adults in the United States have obstructive sleep apnea (OSA), a chronic sleep-related breathing disorder that involves a decrease or complete halt in airflow. Continuous positive airway pressure (or CPAP) is broadly considered first-line therapy for OSA.

CPAP technology is effective, but only if used regularly. Largely due to discomfort related to the mask and associated headgear required to maintain prescribed airway pressures by current devices, today’s CPAP is not well tolerated by patients. Noncompliance is estimated to impact  at least a third of patients .

Researchers at the University of Cincinnati are developing a VortexPAP machine that takes advantage of vortex airflow technology. A preliminary clinical study with current CPAP users demonstrated that the VortexPAP can deliver the pressure levels that are used in the subjects’ CPAP therapy, but the mask is more comfortable to wear. It has a minimalistic design that is less intrusive and barely touches the patient’s face.

“Despite the clinical efficacy for CPAP in controlling OSA, patient compliance with the therapy remains a major cause of treatment failure,” says Liran Oren, PhD, research associate professor in the Department of Otolaryngology, Head and Neck Surgery at the UC College of Medicine. 

Dr. Liran Oren is shown with Dr. Ephraim Gutmark and Dr. Ann Romaker and the VortexPAP device. Photo by Andrew Higley/UC Marketing + Communications.

“The vast majority of complaints from patients in CPAP therapy revolve around improving the comfort of the mask,” explains Oren. “However, regardless of the design, they all require a tight seal over the face, so that the airway can be pressurized. This design requirement for a tight seal is the main limitation for making CPAP therapy more comfortable.”

Oren says the VortexPAP eliminates the requirement for a tight seal since it’s based on reapplication of vortical airflow, a flow control mechanism commonly used in aerospace engineering application.

“The unique aerodynamic properties of vortical airflow enables the device to pressurize the upper airways without needing a tight-fitting mask,” says Oren. “As a result, this game-changing innovation will make CPAP therapy more comfortable, better tolerated and may increase patient compliance, a win for patients, doctors and third-party payers. Over 40 million people in the U.S. are diagnosed with OSA but the actual number is likely even higher because many remain undiagnosed.”

The project is a collaboration between Oren; Roy Kulick, MD, UC Entrepreneur-in-Residence; Ephraim Gutmark, PhD, Distinguished Professor, Ohio Eminent Scholar in the UC Department of Aerospace Engineering; and Ann Romaker, MD, director of the UC Sleep Medicine Center and professor in the UC Department of Internal Medicine.

Dr. Roy Kulick/photo provided.

Gutmark is director of the UC Laryngeal Biomechanics Lab. “The main strength of our lab is marrying advanced knowledge in aerospace with medicine,” Oren says. “For example, we know that vortices emanating from a jet engine would generate sound that people perceive as a type of ‘noise.’ As such, there is much active research on eliminating these vortices to reduce the sound (noise) produced by the jet.

“Our lab takes this knowledge and tries to reverse it. We found that vortices form in the airway when our vocal cords vibrate when we speak.”

Oren says it is well-established in aerospace engineering that vortex airflow has unique aerodynamic properties that are different from conventional, continuous airflow. This type of airflow is used in various applications, such as enhancing fuel mixing in combustion processes, improving convective cooling of electronic components or as a flow control mechanism that reduces drag over a body (such as an airplane wing or a car). The co-inventors and Oren realized the unique properties of vortex airflow could also be leveraged for CPAP therapy.

“The development work of the VortexPAP draws on the strengths that UC offers,” Oren says. “This idea was born from distinguished departments (aerospace engineering and otolaryngology) and developed into a functional, suitable medical device using expertise in biomedical engineering.

“We collaborate with Dr. Ann Romaker, a sleep medicine doctor from UC, who helped us identify the main reasons for patient noncompliance with CPAP therapy and ensure our technology offers an alternative, effective solution.”

Dr. Oren shown with Dr. Gutmark and the VortexPAP device. Photo by Andrew Higley/UC Marketing + Communications.

Dr. Oren and the Dr. Romaker discuss the function of the VortexPAP device. Photo by Andrew Higley/UC Marketing + Communicatiions.

Romaker says the biggest design change is the lack of a need for a seal. The interface will not be tight nor will it have to be enclosed, so people with a degree of claustrophobia will be much more comfortable, as well as everyone else.

“The lower pressures produced by this method will also decrease air swallowing, which can be uncomfortable for some CPAP users and, again, will be more comfortable,” Romaker says. 

According to Oren, the development work is also done with the goal of eventually commercializing the VortexPAP in the U.S. As such, they received strong support from the UC Venture Lab. They paired with Entrepreneur-in-Residence Kulick, who has been instrumental in helping build out a business plan for the VortexPAP. 

“I think this project is the epitome of UC,” Oren adds.

Featured image at top of the VortexPAP displayed in the laboratory of Dr. Liran Oren in the UC College of Medicine. Photo by Andrew Higley/UC Marketing + Communications. 

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Study shows people check their phones 144 times a day. Here's how to detach from your device.

*Checks phone*

The common practice can be deemed as an addiction that has captured many Americans. With a 4-to-5-inch screen many smartphone devices hold most of our daily life activities. From apps like Facebook, Instagram and TikTok to help us stay connected, to work-related apps like Slack, Google, Microsoft and Zoom that keep us tethered.

As a society we have ditched alarm clocks to wake us up or a notebook to write things down. When we get a new smartphone, those apps are already embedded within its interface. The dependence we have on a smartphone has grown exponentially over the past decade, too.

In 2023, research showed that Americans checked their phones 144 times a day.

• Nearly 90% of those respondents check their phone within the first 10 minutes of waking up.
• About 75% of the population said that they checked their phone when they're in the restroom.
• At least 60% of the people in the study admitted that they sleep with their phone at night.
• About 57% of the respondents acknowledged they were addicted to the devices, according to results from Reviews.org.

Can you relate?

If so, here are some ways you can break up with your cell phone.

Advice from an expert: Eye strain in a digital age

USA TODAY Tech columnist Kim Komando shares ways to detach from your devices

Kim Komando wrote in a column for USA TODAY that people who are attached to their smartphones need to cut the screen time in half.

Here are some of her suggestions:

Notifications

Instead of running to pick up your phone every time it pings, Komando suggests that smartphone users should put their phone on "Do Not Disturb" on weekends, vacations and holidays in order to spend time with the people you care about.

Limit your screen times for Android and iPhone users

If Do Not Disturb doesn't help, you can have your phone monitor your usage for you.

With the Screen Time function in the iPhone settings and the Digital Well-Being app in Android, smartphone users can set time limits for apps they use often to lower the amount of time spent on it per day. These features will create a lock-out function that will prohibit you from using the app until the following day.

Ahjané Forbes is a reporter on the National Trending Team at USA TODAY. Ahjané covers breaking news, car recalls, crime, health, lottery and public policy stories. Email her at  [email protected] . Follow her on  Instagram ,  Threads  and  X @forbesfineest.

Technology behind ChatGPT better with eye problem advice than non-specialist doctors, study test finds

AI won't replace doctors but tools could soon be transformative for NHS waiting lists, researchers say, after a surprising set of findings which put doctors of different backgrounds through their paces.

Technology correspondent

Thursday 18 April 2024 12:53, UK

The technology behind ChatGPT scored better at assessing eye problems and providing advice than non-specialist doctors, a new study has found.

A study led by the University of Cambridge has found that GPT-4 , the large language model (LLM) developed by OpenAI , performed nearly as well as specialist eye doctors in a written multiple-choice test.

The AI model, which is known for generating text based on the vast amount of data it is trained on, was tested against doctors at different stages of their careers, including junior doctors without a specialism, as well as trainee and expert eye doctors.

Each group was presented with dozens of scenarios where patients have a specific eye problem, and asked to give a diagnosis or advise on treatment by selecting from one of four options.

The test was based on written questions, taken from a textbook used to test trainee eye doctors, about a range of eye problems - including sensitivity to light, decreased vision, lesions, and itchy eyes.

The textbook on which the questions are based is not publicly available, so researchers believe it is unlikely the large language model has been trained on its contents.

GPT-4 scored significantly higher than junior doctors.

The model achieved similar scores to trainee and expert eye doctors, but it was beaten by the top-performing experts.

Keep up with all the latest news from the UK and around the world by following Sky News

The research was conducted last year using the latest available large language models.

The study also tested GPT-3.5, an earlier version of OpenAI's model, Google's PaLM2, and Meta's LLaMA on the same set of questions. GPT-4 gave more accurate responses than any of the other models.

The researchers have said that large language models will not replace doctors, but they could improve the healthcare system and reduce waiting lists by supporting doctors to deliver care to more patients in the same amount of time.

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Dr Arun Thirunavukarasu, the lead author of the paper, said: "If we had models that could deliver care of a similar standard to that delivered by humans, that would help overcome the problems of NHS waiting lists.

"What that requires is trials to make sure it's a safe and effective model. But if it is, it could be revolutionary for how care is delivered."

He added: "While the study doesn't indicate deployment of LLMs in clinical work immediately, it gives a green light to start developing LLM-based clinical tools as the knowledge and reasoning of these models compared well to the expert ophthalmologists."

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