Automation has had a Ripple Effect on Gender Dynamics in the Workplace

In the 1980s, women were more likely to work in jobs at risk of automation, facing greater displacement from routine-intensive roles.

Over the last four decades, women have pivoted towards higher-skill, higher-wage occupations more than their male counterparts.

As a result, labor markets more reliant on routine tasks witnessed a higher level of gender occupational integration. Labor markets have evolved towards a more balanced representation of men and women across various professions.

The net effect of automation has been decreased occupational segregation by gender.

Local labor markets with higher exposure to automation saw a significant rise in college attainment among younger workers, especially women.

The impact of automation varied widely across regions, influenced by historical industry structure differences, affecting gender-specific job shifts.

Both men and women experienced decreased labor force participation rates due to automation.

These insights underscore the complex interplay between technology, gender, and the evolving job market.

Here’s a link to the entire paper, “Automation and Gender: Implications for Occupational Segregation and the Gender Skill Gap.

Walmart is buying Vizio for $2.3B.

The move highlights several key trends impacting the future of retail, media, and advertising:

1. Retail and Entertainment Convergence: Walmart recognizes the lines between retail and entertainment are increasingly blurring. By owning a platform that combines retail capabilities with entertainment offerings, Walmart can offer a seamless experience from entertainment to the retail self. Ads that include QR codes or “buy now” buttons can lead straight to a Walmart shelf.

2. Expanding Omnichannel Advertising Solutions: Walmart Connect’s growth and its integration with VIZIO’s advertising business reflect a focus on growing omnichannel solutions. Advertisers will have diverse opportunities to reach customers across multiple touchpoints, enhancing the effectiveness of advertising campaigns.

3. Enhanced Customer Engagement through Smart Home Devices: The acquisition indicates a push towards leveraging smart home devices to enhance customer engagement. The move recognizes the growing role connected devices in the home are having in driving and directing content.

The move points to a future where advertising is increasingly integrated with entertainment and smart home technology, offering more personalized and immersive experiences to consumers. It also underscores the growing importance of connected TV platforms and omnichannel strategies in reaching and engaging customers.

Research from Natalia Emanuel, Emma Harrington, and Amanda Pallais highlights the tradeoff of being physically near your coworkers. Being close to coworkers enhances the development of skills and knowledge over the long term but may reduce output in the short run. The findings suggest working from home (WFH) offers mixed outcomes over varying time frames, with immediate benefits potentially undermining the long-term professional growth of employees. Some additional insights from their work:

• Increased Feedback: Engineers sitting near their teammates received 22% more online feedback (e.g. Slack), particularly benefiting younger and less tenured engineers. The pandemic and the shift to remote work significantly reduced the proximity advantage, making this feedback advantage largely disappear.

• Impact on Mentorship: Proximity enhanced both the provision and receipt of mentorship, with significant implications for female engineers who engaged more in both roles.

• Decreased Programming Output: Engineers, especially senior ones, produced less work when seated near junior colleagues, indicating a tradeoff between mentorship and immediate productivity.

• Career Trajectory Impacts: Proximity led to fewer early pay raises due to reduced output but resulted in higher long-term pay raises as engineers developed more human capital.

• Greater Mobility: Engineers trained in close proximity to their teammates were more likely to leave for higher-paying jobs elsewhere, suggesting that the benefits of mentorship extend beyond the current firm.

• Negative Externalities of Mixed Proximity: Having team members in different locations reduced the amount of feedback among co-located workers, highlighting challenges in hybrid team structures. The presence of just one remote team member before the pandemic reduced feedback among co-located workers.

• Gender-Specific Effects: Female engineers experienced more pronounced tradeoffs, receiving and providing more feedback when co-located but also facing greater impacts on their programming output.

The landscape of love has always been complex, but the digital age has introduced a new layer of complexity. Now AI is making things even more complex. The Modern Love research, an annual study from McAfee that surveyed 7,000 people across seven countries, reveals insights into how AI and the internet are reshaping love and relationships. Nearly one in four Americans admit to enhancing their online dating profiles with AI-generated photos and content. Yet, paradoxically, 64% would mistrust a love interest who does the same. This dichotomy is just the tip of the iceberg in the evolving narrative of digital romance.

AI: A Double-Edged Sword in Dating
The allure of AI in dating is undeniable. Approximately 70% of respondents reported garnering more interest and better responses with AI-crafted content than their own. However, the reception of AI-generated sentiments is not as warm. Over half of the participants expressed they would feel hurt or offended upon discovering their Valentine’s message was penned by AI. While AI can enhance attraction, it simultaneously undermines trust.

The challenge deepens with the difficulty in discerning AI-generated content. Only a quarter of those surveyed were confident in their ability to identify messages or love letters crafted by AI. Meanwhile, 42% encountered fake profiles or photos in the past year, underscoring the prevalent issue of deception in online dating.

Navigating the Minefield of Online Dating
In response to these challenges, online daters are becoming amateur detectives. Many utilize reverse image searches and scrutinize social media profiles to authenticate the identities of potential partners. This diligence has led to a mix of outcomes, from enhancing attraction to uncovering scams or discovering that the person is already in a relationship.

Embracing AI with Caution and Confidence
In the intricate dance of modern love, where AI plays an increasingly central role, we stand at a crossroads between embracing technological assistance and guarding the sanctity of genuine human connection. The revelations from McAfee’s Modern Love research illuminate the nuanced ways in which AI reshapes the quest for love, highlighting a landscape rife with both potential and pitfalls. As we navigate this evolving domain, the balance we seek is not just in using AI to our advantage but in maintaining a critical eye towards the authenticity and trust that form the bedrock of meaningful relationships. In this era of digital romance, our challenge is to harness the power of AI as a tool for connection, not as a substitute for the deep, irreplaceable nuances of human interaction. Embracing AI with caution and confidence, we are tasked with redefining the future of love, ensuring it remains rooted in the genuine, the authentic, and the truly human.

Amid a shifting economic climate with diminishing fears of recession, businesses continue to grapple with economic uncertainty and emerging risks. The concept of a “soft landing” underscores the acute challenge of dwindling demand, which hampers companies’ efforts to boost revenue and, more critically, to increase profits faster than revenue. The difficulty of expanding profit margins amidst declining demand is the result of reduced pricing power that often accompanies weaker demand.

To mitigate these economic uncertainties, business leaders are increasingly prioritizing streamlining operations, a strategy that entails cutting costs and enhancing operational efficiency. Some of the layoffs announced this year are precisely focused on cost-cutting measures that can help organizations maintain profit margins in the face of slower demand growth.

This strategic adjustment coincides with companies’ advancing use of artificial intelligence (AI). For example, research from Bain indicates that nearly 60% of pharmaceutical executives have moved from AI ideation to implementation, with 55% anticipating the demonstration of multiple proofs of concept or minimum viable product builds by the end of last year. Notably, 40% of these executives are integrating anticipated savings from generative AI into their 2024 budgets. In other words, executives are recognizing generative AI’s ability to drive productivity, warranting less operational spending to achieve revenue targets.

It’s important to remember that productivity measures the output from a set of inputs, with higher productivity translating to increased revenue without a proportional rise in costs.

AI’s Strategic Importance in Reducing Costs

The scope for AI to improve operational efficiency is broad. It includes augmenting and automating customer service, streamlining HR processes, improving financial operations, and personalizing marketing efforts. By leveraging AI, organizations can pinpoint inefficiencies, predict maintenance requirements to avoid expensive downtime, and tailor customer experiences to increase satisfaction and loyalty. Actions like these can lower costs and drive higher profit margins.

Evidence of AI’s Impact on Profitability

Across various industries, the positive effects of AI on profit margins are becoming increasingly evident. For example, in the retail sector, AI-enabled inventory management systems can more accurately forecast stock requirements, reducing both excess inventory and stockouts, thereby reducing costs and averting lost sales. Maybe AI can even solve the stockout of Dr. Pepper everywhere I shop. In manufacturing, AI-driven predictive maintenance can foresee equipment failures before they happen, significantly lowering repair expenses and reducing downtime. These instances highlight the direct link between AI-induced productivity improvements and profit growth, especially critical in an economic setting that demands high operational efficiency.

The Productivity-Profit Connection

By automating processes and facilitating quicker, more informed decision-making, AI effectively raises the output from a constant set of inputs, be it labor, capital, or materials. This surge in productivity can lead to cost reductions and, crucially, enables companies to reduce costs while maintaining a set output, which in turn increases profits more rapidly than revenue.

This connection is especially pertinent today, as businesses face external pressures such as variable demand, increasing material costs, and the push for sustainability. AI presents an avenue to overcome these hurdles, not by significantly boosting sales in a tough market, but by redefining the cost structure to improve profitability.

Adapting to AI-Driven Change

Adopting AI to enhance profit growth goes beyond technological investments. It necessitates a comprehensive revision of existing processes and a shift in corporate culture towards innovation and continuous improvement. Organizations must:

  1. Pinpoint key areas where AI can yield substantial cost savings or efficiency improvements.
  2. Invest in the talent and technology needed to develop or acquire AI capabilities.
  3. Cultivate a culture that values data-driven decision-making, experimentation, and learning from setbacks.
  4. Commit to the ethical and responsible application of AI, ensuring trust among consumers and employees

Looking Forward

By thoughtfully incorporating AI into their business models, companies can not only navigate the current economic challenges but also lay the groundwork for sustained, long-term success. The path to AI-enabled profitability is fraught with challenges, but for those prepared to embrace change, the potential rewards are significant.

The playing field for hospitals is changing significantly due to various factors, including technological advancements, policy changes, patient expectations, and economic pressures. Let’s delve into some of the key areas of change.

Technological Advancements: The landscape of healthcare is being reshaped by technological advancements, with telemedicine, AI, electronic health records (EHRs), and the Internet of Medical Things (IoMT) at the forefront of this transformation. Telemedicine extends healthcare access to remote areas, enhancing accessibility, and convenience. AI is revolutionizing diagnostics and patient care, with algorithms that interpret medical images with exceptional speed and accuracy, and robotic surgery systems that offer precise, minimally invasive procedures. EHRs streamline patient management, improving care coordination and data analysis, while blockchain technology promises enhanced data security and supply chain efficiency.

Moreover, the IoMT integrates wearable devices, smart beds, and connected medical instruments, such as inhalers, into healthcare delivery, enabling real-time monitoring of patient vitals and a more personalized care experience. Technology is also facilitating asynchronous communication between patients and providers. These technological innovations not only elevate the quality of patient care but also drive operational efficiencies within healthcare settings. Together, they represent a paradigm shift in how healthcare services are delivered, emphasizing the role of technology in advancing medical practices and patient outcomes.

Policy and Regulatory Changes: With the Affordable Care Act in the U.S., there has been an increased focus on expanding access to care and improving healthcare quality. These changes necessitated adjustments within hospitals and other healthcare institutions to meet increased demand, adhere to new quality and reporting standards, and manage the financial implications of the law, such as adjustments to reimbursement models and the introduction of penalties for readmissions. In Europe, the cross-border healthcare directive aims to ensure patient rights to receive care in any EU country. Such policies compel hospitals to adjust their operational, financial, and care delivery models to comply with new standards and to accommodate the evolving healthcare landscape, including dealing with cross-border health data exchange and patient mobility.

Shift Towards Value-based Care: The implementation of value-based care models often involves establishing Accountable Care Organizations (ACOs) where groups of doctors, hospitals, and other healthcare providers come together voluntarily to give coordinated high-quality care to their Medicare patients. An example of this shift can be seen in bundled payment models, where a single bundled payment covers all services performed by multiple providers for a specific episode of care, encouraging collaboration and efficiency.

Patient Expectations and Consumerism: Healthcare consumerism is marked by patients increasingly seeking convenience, transparency, and personalized care, similar to what they experience in other consumer-oriented sectors. The rise of healthcare apps and platforms that offer appointment booking, teleconsultations, and access to health records online exemplify this trend. This has also driven new competitors into the marketplace like Walgreens, BestBuy, and Dollar General. Insurance companies like Cigna, UnitedHealth, and Aetna have expanded their roles beyond traditional insurance offerings. Hospitals are also adopting CRM systems to better understand and engage with patients. Cleveland Clinic and Mayo Clinic, for example, have developed comprehensive online portals and mobile apps that provide patients with access to their health records, appointment scheduling, and telehealth services, enhancing convenience and patient satisfaction.

Financial Pressures: Hospitals are exploring alternative revenue streams such as offering specialized services, engaging in partnerships with private sector companies, and expanding into outpatient services to reduce reliance on inpatient care, which is often more costly. Additionally, implementing lean management techniques and automation in administrative and operational processes can significantly reduce waste and improve efficiency, helping to alleviate financial pressures.

Home Hospitals: The concept of home hospitals is gaining traction as a way to provide acute care services in a patient’s home, offering a comfortable and familiar environment that can improve outcomes and patient satisfaction. Programs like the Hospital at Home initiative, developed by Johns Hopkins Medicine, demonstrate significant reductions in healthcare costs and improvements in clinical outcomes by treating patients with conditions such as pneumonia, heart failure, and COPD at home. This model relies on technologies such as remote monitoring, telehealth consultations, and mobile diagnostic tools, and is supported by visiting nurses and physicians. The expansion of home hospital programs represents a paradigm shift in thinking about where and how hospital-level care can be delivered. Brigham and Women’s Hospital in Boston, and Mount Sinai Health System in New York are both expanding their hospital-at-home programs.

Population Health Management: The strategic focus on prevention and management has proven to significantly improve patient outcomes by reducing the progression of chronic diseases, enhancing the quality of life, and increasing lifespan. Additionally, by preventing disease exacerbation and avoiding unnecessary hospital admissions, healthcare systems can effectively reduce healthcare costs. Kaiser Permanente is an example of a healthcare system that has implemented population health management strategies, using data analytics to identify at-risk populations and implementing targeted preventive care and chronic disease management programs.

Cybersecurity Threats: Ransomware attacks, which affected many healthcare organizations, highlight the critical importance of cybersecurity in healthcare. Hospitals are investing in advanced cybersecurity measures, such as next-generation firewalls, intrusion detection systems, and cybersecurity training for staff to mitigate these risks.

Sustainability and Social Responsibility: Healthcare systems like Gundersen Health System in Wisconsin have invested in renewable energy projects and achieved energy independence by producing more energy than they consume. Other hospitals are reducing their environmental footprint by minimizing medical waste, implementing recycling programs, and using eco-friendly materials and technologies.

These changes are often interlinked, with each influencing how hospitals operate, how care is delivered, and how success is measured. Hospitals that can effectively navigate these changes are better positioned to thrive in the evolving healthcare landscape.

Institutions are increasingly leveraging AI to rethink how they interact with prospective and current students, streamline admissions processes, and enhance student support and retention. From personalized outreach efforts that resonate with individual student preferences to sophisticated algorithms automating admissions tasks and virtual assistants providing round-the-clock support, AI is at the forefront of educational innovation. Here are a few examples of how AI is reshaping education.

Personalized Outreach and Engagement:

AI can analyze vast amounts of data to identify prospective students’ interests, preferences, and educational backgrounds. This information can be used to tailor outreach efforts, making them more relevant and appealing to each prospective student. By personalizing communication, institutions can better engage with students, addressing their specific questions, concerns, and aspirations.

In 2016, Georgia State University implemented an AI-powered chatbot named “Pounce” to improve communication with prospective students. Pounce answers questions about the enrollment process, financial aid, and other student services, providing personalized responses 24/7. During its initial summer of use, Pounce responded to over 200,000 inquiries from new college entrants. The initiative helped reduce the University’s summer melt by 22%, translating to 324 additional students attending the first day of fall classes.

Automated Admissions Processes:

AI can streamline the admissions process by automating routine tasks such as sorting applications, analyzing academic records, and evaluating eligibility criteria. For example, AI algorithms can quickly process essays and recommendation letters to identify key themes and strengths of applicants. This not only speeds up the admissions process but also ensures a level of consistency and objectivity in evaluating applications. Furthermore, AI can assist in predicting enrollment likelihood, helping institutions to more accurately forecast and manage their incoming classes.

According to a survey conducted in September 2023 by Intelligent, a digital magazine specializing in higher education insights, half of the admissions offices in higher education institutions have integrated AI technologies into their operations and 80% said they would use AI in 2024.

Enhanced Student Support and Retention:

Once students have enrolled, AI can continue to play a crucial role in supporting them through personalized learning experiences and support services. AI-powered chatbots and virtual assistants can provide 24/7 support to answer students’ questions, from administrative queries to academic guidance. Additionally, AI can identify students who may be at risk of dropping out by analyzing patterns in their academic performance, engagement levels, and other indicators. Early identification allows institutions to intervene with targeted support, improving student retention and success rates.

Georgia Institute of Technology experimented early with an AI teaching assistant named “Jill Watson” to manage the high volume of student inquiries in a master’s-level AI class. Jill, powered by IBM’s Watson, was able to answer students’ questions with a 97% success rate, significantly reducing the workload on human teaching assistants and allowing them to focus on more complex student needs​​. The latest iteration of Jill is capable of autonomously responding to roughly 60% of student introductions in the first week of classes, as well as handling approximately one-third of all administrative inquiries related to class assessments, including assignments, projects, and exams. Jill has worked with over 50 human TAs and interacted with over 1500 students.

As we look to the future, the integration of AI within higher education institutions is not just a fleeting trend but a fundamental shift in how institutions operate and engage with student populations. The success stories of “Pounce” at Georgia State University and “Jill Watson” at the Georgia Institute of Technology highlight the tangible benefits AI can bring to the table, from enhancing communication to supporting teaching staff and improving student retention rates. With a substantial number of institutions planning to adopt AI in their operations, the stage is set for a transformative impact on the higher education landscape. This technological evolution promises to make higher education more accessible, personalized, and efficient, ensuring that institutions can meet the diverse needs of their students while preparing them for the challenges of the future.

This semester, Ferris State University in Michigan has taken a creative step into the future of education by introducing two AI-powered “freshman” students, Ann and Fry, to their academic community. Unlike typical students, Ann and Fry won’t navigate campus life as humanoid robots. Instead, they will interact with their peers and professors through computers, equipped with microphones and speakers.

The innovative experiment is the brainchild of Associate Professor Kasey Thompson, who unveiled the project during the university’s AI Day. The initiative aims to bridge the gap between emergent AI technologies and educational pathways. By integrating AI students into classrooms, Ferris State hopes to learn how to better cater to the needs and preferences of future students.

Ann and Fry are designed to simulate the complete student experience. Their activities range from participating in class discussions and working on assignments to selecting their majors, thereby emulating the educational journey of their human counterparts. This novel approach facilitates a dynamic, real-time learning experience, allowing both the AI entities and the university’s faculty to adjust and evolve the experiment based on ongoing observations and findings.

The initiative goes beyond merely showcasing technology. It is deeply rooted in a commitment to enhancing educational strategies for future generations. By closely analyzing the interactions and progress of Ann and Fry, researchers at Ferris State hope to gain valuable insights into the potential of AI to make education more accessible and effective.
This experiment represents an opportunity to augment the human learning experience through the use of AI. As educators explore the capabilities of artificial intelligence in this context, we also open the door to redefining AI’s role in education. The potential benefits extend beyond the immediate educational community, suggesting a future where AI and human learners collaborate in ways we are only beginning to imagine.

Through this pioneering effort, Ferris State University is not just teaching its students about the possibilities of AI; it is actively demonstrating those possibilities. As Ann and Fry progress through their academic journey, they will undoubtedly provide invaluable lessons on the integration of AI into educational settings, potentially shaping the future of how we learn and teach.

Springer Nature recently announced a new AI-powered in-house writing assistant to support researchers. The tool has been trained on scholarly literature that covers 447+ disciplines, encompasses over 2,000 specialized subjects, and incorporates feedback from more than 1 million revisions on papers—including those featured in esteemed Nature publications.

Studies indicate scientists who are non-native English speakers spend 51% more time writing papers on average. This disparity creates an imbalance in the research field, hindering the progression of knowledge and affecting the contribution of top-tier research from various parts of the world.

This underscores the trend towards creating LLMs tailored for particular uses with specialized domain knowledge.

 

 

Vision-language models (VLMs) are AI models that combine both vision and language modalities. These models can process both images and natural language. Researchers are expanding VLMs by including an action layer. These models can process visual and textual information and generate sequences of decisions for real-world scenarios. This fusion of vision, language, and action within computational models is emerging as a potentially useful AI paradigm for a wide range of applications. Vision-Language-Action Models (VLAs) are designed to perceive visual data, interpret it using linguistic context, and subsequently generate a corresponding action or response. In essence, VLAs emulate human-like cognition, where sight, comprehension, and action intertwine.

At its core, VLAs marries computer vision with natural language processing. The vision component enables machines to “see” or interpret visual data. This is complemented by the language component which processes this visual information in linguistic terms, enabling the machine to “understand” or describe what it sees. Finally, the action component facilitates a response, whether that be a decision, movement, or another specific output.

Wayve recently introduced LINGO-1, an open-loop driving commentator. Some key quotes from their announcement:

The use of natural language in training robots is still in its infancy, particularly in autonomous driving. Incorporating language along with vision and action may have an enormous impact as a new modality to enhance how we interpret, explain and train our foundation driving models. By foundation driving models, we mean models that can perform several driving tasks, including perception (perceiving the world around them), causal and counterfactual reasoning (making sense of what they see), and planning (determining the appropriate sequence of actions). We can use language to explain the causal factors in the driving scene, which may enable faster training and generalisation to new environments.

We can also use language to probe models with questions about the driving scene to more intuitively understand what it comprehends. This capability can provide insights that could help us improve our driving models’ reasoning and decision-making capabilities. Equally exciting, VLAMs open up the possibility of interacting with driving models through dialogue, where users can ask autonomous vehicles what they are doing and why. This could significantly impact the public’s perception of this technology, building confidence and trust in its capabilities.

In addition to having a foundation driving model with broad capabilities, it is also eminently desirable for it to efficiently learn new tasks and quickly adapt to new domains and scenarios where we have small training samples. Here is where natural language could add value in supporting faster learning. For instance, we can imagine a scenario where a corrective driving action is accompanied by a natural language description of incorrect and correct behaviour in this situation. This extra supervision can enhance few-shot adaptations of the foundation model. With these ideas in mind, our Science team is exploring using natural language to build foundation models for end-to-end autonomous driving.

These models enable us to ask questions so we can better understand what the model “sees” and to better understand its reasoning.  Here’s an example:

https://www.youtube.com/watch?v=6X51pxPJpa4&list=PL5ksjZd5b6SK5X_u1Ix-flUjNS97_fk4r&index=9

Language can help interpret and explain AI model decisions, a potentially useful application when it comes to adding transparency and understanding to AI. It can also help train models, enabling them to more quickly adapt to changes in the real-world.