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As tech companies shell out millions for top AI talenteven reportedly billionsregular rank-and-file employees are left wondering how to get in on the action and land a job in artificial intelligence. One report found that job postings that mention needing at least one AI skill had salaries 28% higher than other jobs, which translates to $18,000 more. Jobs that required two AI skills had a 43% salary jump. To begin with, its worth considering where the AI jobs are and how this intersects with your interests and existing skills. Many jobs in AI can roughly be divided into five different categories: researchers engineers business strategists domain experts policymakers Researchers bring a deep understanding of neural networks and algorithm design to the table and can push the technology forward, but this is a very small pool and typically requires a PhD. Engineers typically have programming skills that they can use to build AI applications. Business strategists can fold AI into their companys workflows and processes, or spearhead product development. Domain experts understand how to apply AI to their field, while policymakers can craft AI ethics and use guidelines. But what do you do once youve identified where you want to go? Getting experience in AI, and developing skills in it, is a tricky proposition because the field is still so nascent. Plus, things are evolving at breakneck speed; what worked a couple years ago may not be a silver bullet today. But some strategiesbeing scrappy, curious, and adaptablecould prove timeless. We interviewed both HR and recruiting pros, as well as people who have managed to build up their AI skills to land a job in the industry, to learn: What AI industry insiders at LinkedIn and Amazon recommend are the surefire ways to get a hiring manager’s attention How workers are turning their regular jobs into “AI jobs” to get experience Where one talent recruiter looks to see if someone is working on developing AI skills 1. Figure out ways to learn on the job While companies such as Boston Consulting Group (BCG) and Thomson Reuters are rolling out company-wide initiatives to ensure their entire staff gets trained in AI, that isnt true of most companies. Only 2 in 5 employees report receiving AI training on the job. If your company doesnt have AI training, get on projects that do involve AI. Get some experience at your existing company before you try to jump into a truly AI-focused role, says Cheryl Yuran, chief human resources officer at Absorb Software, an AI-powered learning platform provider. Have something on your résumé to talk about from an AI standpoint. Yuran points out that Absorb isnt able to find enough people with AI experience for all of their teams. Thats how few people are out there in the workforce with an actual background in it. Instead, the company makes sure there are one or two members with AI experience on their teams. The remaining jobs go to candidates or insiders who demonstrate they can add value, whether its deep product knowledge or excellent communication skills. If there arent AI projects or initiatives at your job, create them. Or experiment with ways to use AI to help you do your job. Gabriel Harp, a former product manager for multiple companies in academic publishing, oversaw the launch of an AI-powered writing assistant in 2023 at Research Square, an Inc. 5000 company. Although my degree is in English and German, I’ve spent more than a decade building software products, Harp says. For the AI writing assistant, Harp set the initial vision and scope of the project, working on the branding and go-to-market strategy, conducting quality analysis, and much more. Harp wasnt an engineer, yet he still leveraged his background to get great AI experience just before it was popular (or needed) to have any. Since then, hes served as head of product strategy at a startup that uses AI to build privacy tools. When Harp went on the job market, he had plenty to discuss during interviews, although he has a degree in the humanities. Since Id been using AI in the workplace, I was more familiar than the average person with these tools, he says. He recently landed a senior staff product manager job at Mozilla. Were seeing a lot of emerging talent or people who want to shift their career path, says Prashanthi Padmanabhan, VP of engineering at LinkedIn, who regularly hires for AI talent. Nothing beats showing youve actually [used AI] on the job.” 2. Take a course If getting close to an AI project at work isnt an option, you can always take courses. Right before the pandemic, Amanda Caswell was working as a copy lead at Amazon when she became interested in AI. She started listening to podcasts about AI and signed up for courses, including an online prompt engineering class at Arizona State University, an AI boot camp by OpenAI, and a generative AI and prompt engineering master class by LinkedIn. Start at the 101 level, even if you have some experience, she says. That way youll know industry best practices, which can help you teach others. Because who knows? You might have to do a job in AI training. In 2020, Caswell started getting gigs as a prompt engineer at Upwork and has made close to $200,000 on the platform, only working about 20 hours a week. In addition, her knowledge of prompt engineering helped her land a job as an AI journalist at Toms Guide. Similarly, Cesar Sanchez, a full-stack engineer (who is now an AI engineer) became interested in AI in 2023. He immediately signed up for a Coursera course on generative AI with large language models to get an understanding of the fundamentals. It was a great decision. It offered me a strong foundation and helped me understand the theory, Sanchez says. He also signed up for another course that offered im access to a network of AI engineers. While I didnt necessarily learn new things, I was able to connect with other engineers and compare my skills to what else was out there in the market, he adds. Plus, I got lots of free credits for using tools and platforms. 3. Take on a side project However, even if you arent able to fold AI into the job or take a course, recruiters say theres always the trusty side project. Having a side gig is often a privilege thats unavailable to some, but having one can sometimes grow into something that’s more full-time, sustainable, and meaningful, regardless of the field. AI, experts say, may be no different. A lot of candidates will say, I just focus full-time on my current role, says Taylor King, CEO of Foundation Talent, which recruits for top tech startups. But the ones really thriving are the people who dive headfirst into new AI or LLM tools, constantly experimenting and building on the side,” he adds. “An active GitHub tells you theyre genuinely curioussomeone whos growing beyond the boundaries of their job, not defined by it. (A McKinsey report found that people who are adaptable are 24% more likely to be employed.) Nico Jochnick had no background in AI, but managed to land a job as lead engineer at Anara, an AI startup that helps research teams organize and write scientific papers. He says he got a job in AI because of his experience using AI for side projects. I was fascinated with AI and using Cursor to code side projects, and was doing hackathons, he says. [Anaras founder] and I knew these tools were giving us tons of leverage, and we connected over that. While Harp, now at Mozilla, was job searching, he also worked on AI side projects, such as using AI coding tools to create a bingo game for his favorite podcast, as well as a recruiting tool in ChatGPT that allowed recruiters to ask questions about his work experience. I was worried about getting rusty, he says. I needed to continue experimenting with the tools out there. 4. Create your own job Ben Christopher, a screenwriter, taught himself to code in order to keep the lights on. He started experimenting with AI in 2022 and built Speed Read AI, a tool that summarizes scripts and provides business insights, such as budget estimates, for Hollywood executives. I started showing it to some people in the industry, and got enough feedback where people said, Well pay for that, Christopher said. Today, his team is five people strong with a growing customer base. (Christopher is careful to stress the point of Speed Read AI is to help Hollywood executives dig through massive slush piles and find more unique scripts.) Meanwhile, Victoria Lee originally trained as a lawyer but then took a coding boot camp when she felt like she was getting pigeonholed in her career development. She graduated from the boot camp and got her first coding job in 2022, a few months before ChatGPT launched publicly. In her spare time, she had started putting publicly available legal contracts into ChatGPT for analysis and comparing them with her own. She built an understanding of what ChatGPT did well, and where it had gaps. Lee realized the legal industry was embracing AI, and that she was perfectly positioned to fill a gap; she knew what lawyers wanted and also knew how to speak to engineers. She landed a job in product strategy at eBrevia, which uses AI in mergers and acquisitions (M&A) due diligence. However, Lee realized she could add more value by creating her own company. Today, she provides legal services for, as well as works with, mid-market law firms to help them implement AI and craft AI policies. Lee recommends that people who want to go into AI should identify their specialty and build knowledge to understand how it can work better with AI, or where AI currently falls short. Jochnick has since left Anara to found his own AI-powered company, which is still in stealth mode. The people Id hire are already building projects and putting them out in the world, he says. In fact, Jochnick notes the biggest mistake you can make today when experimenting with AI is not trying. Its insane to see how much more powerful you can become in a few months. This is a really fun journey to be on. Everyone should be upskilling themselves.”
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Many news outlets have reported an increaseor surgein attention-deficit/hyperactivity disorder, or ADHD, diagnoses in both children and adults. At the same time, health care providers, teachers, and school systems have reported an uptick in requests for ADHD assessments. These reports have led some experts and parents to wonder whether ADHD is being overdiagnosed and overtreated. As researchers who have spent our careers studying neurodevelopmental disorders like ADHD, we are concerned that fears about widespread overdiagnosis are misplaced, perhaps based on a fundamental misunderstanding of the condition. Understanding ADHD as one end of a spectrum Discussions about the overdiagnosis of ADHD imply that you either have it or you dont. However, when epidemiologists ask people in the general population about their symptoms of ADHD, some have a few symptoms, some have a moderate level, and a few have lots of symptoms. But there is no clear dividing line between those who are diagnosed with ADHD and those who are not since ADHDmuch like blood pressureoccurs on a spectrum. Treating mild ADHD is similar to treating mild high blood pressureit depends on the situation. Care can be helpful when a doctor considers the details of a persons daily life and how much the symptoms are affecting them. Not only can ADHD symptoms be very different from person to person, but research shows that ADHD symptoms can change within an individual. For example, symptoms become more severe when the challenges of life increase. ADHD symptoms fluctuate depending on many factors, including whether the person is at school or home, whether they have had enough sleep, if they are under a great deal of stress, or if they are taking medications or other substances. Someone who has mild ADHD may not experience many symptoms while they are on vacation and well rested, for example, but they may have impairing symptoms if they have a demanding job or school schedule and have not gotten enough sleep. These people may need treatment for ADHD in certain situations, but may do just fine without treatment in other situations. This is similar to what is seen in conditions like high blood pressure, which can change from day to day or from month to month, depending on a persons diet, stress level, and many other factors. Can ADHD symptoms change over time? ADHD symptoms start in early childhood and typically are at their worst in mid-to late childhood. Thus, the average age of diagnosis is between 9 and 12 years old. This age is also the time when children are transitioning from elementary school to middle school and may also be experiencing changes in their environment that make their symptoms worse. Classes can be more challenging beginning around fifth grade than in earlier grades. In addition, the transition to middle school typically means that children move from having all their subjects taught by one teacher in a single classroom to having to change classrooms with a different teacher for each class. These changes can exacerbate symptoms that were previously well-controlled. Symptoms can also wax and wane throughout life. For most people, symptoms improvebut may not completely disappearafter age 25, which is also the time when the brain has typically finished developing. Psychiatric problems that often co-occur with ADHD, such as anxiety or depression, can worsen ADHD symptoms that are already present. These conditions can also mimic ADHD symptoms, making it difficult to know which to treat. High levels of stress leading to poorer sleep, and increased demands at work or school, can also exacerbate or cause ADHD-like symptoms. Finally, the use of some substances, such as marijuana or sedatives, can worsen, or even cause, ADHD symptoms. In addition to making symptoms worse in someone who already has an ADHD diagnosis, these factors can also push someone who has mild symptoms into full-blown ADHD, at least for a short time. The reverse is also true: Symptoms of ADHD can be minimized or reversed in people who do not meet full diagnostic criteria once the external cause is removed. Kids with ADHD often have overlapping symptoms with anxiety, depression, dyslexia, and more. How prevalence is determined Clinicians diagnose ADHD based on symptoms of inattention, hyperactivity, and impulsivity. To make an ADHD diagnosis in children, six or more symptoms in at least one of these three categories must be present. For adults, five or more symptoms are required, but they must begin in childhood. For all ages, the symptoms must cause serious problems in at least two areas of life, such as home, school, or work. Current estimates show that the strict prevalence of ADHD is about 5% in children. In young adults, the figure drops to 3%, and it is less than 1% after age 60. Researchers use the term strict prevalence to mean the percentage of people who meet all of the criteria for ADHD based on epidemiological studies. It is an important number because it provides clinicians and scientists with an estimate on how many people are expected to have ADHD in a given group of people. In contrast, the diagnosed prevalence is the percentage of people who have been diagnosed with ADHD based on real-world assessments by health care professionals. The diagnosed prevalence in the U.S. and Canada ranges from 7.5% to 11.1% in children under age 18. These rates are quite a bit higher than the strict prevalence of 5%. Some researchers claim that the difference between the diagnosed prevalence and the strict prevalence means that ADHD is overdiagnosed. We disagree. In clinical practice, the diagnostic rules allow a patient to be diagnosed with ADHD if they have most of the symptoms that cause distress, impairment, or both, even when they dont meet the full criteria. And much evidence shows that increases in the diagnostic prevalence can be attributed to diagnosing milder cases that may have been missed previously. The validity of these mild diagnoses is well-documented. Consider children who have five inattentive symptoms and five hyperactive-impulsive symptoms. These children would not meet strict diagnostic criteria for ADHD even though they clearly have a lot of ADHD symptoms. But in clinical practice, these children would be diagnosed with ADHD if they had marked distress, disability, or both because of their symptomsin other words, if the symptoms were interfering substantially with their everyday lives. So it makes sense that the diagnosed prevalence of ADHD is substantially higher than the strict prevalence. Implications for patients, parents, and clinicians People who are concerned about overdiagnosis commonly worry that people are taking medications they dont need or that they are diverting resources away from those who need it more. Other concerns are that people may experience side effects from the medications or that they may be stigmatized by a diagnosis. Those concerns are important. However, there is strong evidence that underdiagnosis and undertreatment of ADHD lead to serious negative outcomes in school, work, mental health, and quality of life. In other words, the risks of not treating ADHD are well-established. In contrast, the potential harms of overdiagnosis remain largely unproven. It is important to consider how to manage the growing number of milder cases, however. Research suggests that children and adults with less severe ADHD symptoms may benefit less from medication than those with more severe symptoms. This raises an important question: How much benefit is enough to justify treatment? These are decisions best made in conversations between clinicians, patients and caregivers. Because ADHD symptoms can shift with age, stress, environment, and other life circumstances, treatment needs to be flexible. For some, simple adjustments like classroom seating changes, better sleep, or reduced stress may be enough. For others, medication, behavior therapy, or a combination of these interventions may be necessary. The key is a personalized approach that adapts as patients needs evolve over time. Carol Mathews is a professor of psychiatry at the University of Florida. Stephen V. Faraone is a distinguished professor of psychiatry at SUNY Upstate Medical University. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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When someone opens the door and enters a hospital room, wearing a stethoscope is a telltale sign that theyre a clinician. This medical device has been around for over 200 years and remains a staple in the clinic despite significant advances in medical diagnostics and technologies. The stethoscope is a medical instrument used to listen to and amplify the internal sounds produced by the body. Physicians still use the sounds they hear through stethoscopes as initial indicators of heart or lung diseases. For example, a heart murmur or crackling lungs often signify an issue is present. Although there have been significant advances in imaging and monitoring technologies, the stethoscope remains a quick, accessible, and cost-effective tool for assessing a patients health. Though stethoscopes remain useful today, audible symptoms of disease often appear only at later stages of illness. At that point, treatments are less likely to work and outcomes are often poor. This is especially the case for heart disease, where changes in heart sounds are not always clearly defined and may be difficult to hear. We are scientists and engineers who are exploring ways to use heart sounds to detect disease earlier and more accurately. Our research suggests that combining stethoscopes with artificial intelligence could help doctors be less reliant on the human ear to diagnose heart disease, leading to more timely and effective treatment. History of the stethoscope The invention of the stethoscope is widely credited to the 19th-century French physician René Theophile Hyacinthe Laënnec. Before the stethoscope, physicians often placed their ear directly on a patients chest to listen for abnormalities in breathing and heart sounds. In 1816, a young girl showing symptoms of heart disease sought consultation with Laënnec. Placing his ear on her chest, however, was considered socially inappropriate. Inspired by children transmitting sounds through a long wooden stick, he instead rolled a sheet of paper to listen to her heart. He was surprised by the sudden clarity of the heart sounds, and the first stethoscope was born. Over the next couple of decades, researchers modified the shape of this early stethoscope to improve its comfort, portability, and sound transmission. This includes the addition of a thin, flat membrane called a diaphragm that vibrates and amplifies sound. The next major breakthrough occurred in the mid-1850s, when Irish physician Arthur Leared and American physician George Philip Cammann developed stethoscopes that could transmit sounds to both ears. These binaural stethoscopes use two flexible tubes connected to separate earpieces, allowing clearer and more balanced sound by reducing outside noise. These early models are remarkably similar to the stethoscopes medical doctors use today, with only slight modifications mainly designed for user comfort. Listening to the heart Medical schools continue to teach the art of auscultationthe use of sound to assess the function of the heart, lungs, and other organs. Digital models of stethoscopes, which have been commercially available since the early 2000s, offer new tools like sound amplification and recordingyet the basic principle that Laënnec introduced endures. When listening to the heart, doctors pay close attention to the familiar lub-dub rhythm of each heartbeat. The first soundthe lubhappens when the valves between the upper and lower chambers of the heart close as it contracts and pushes blood out to the body. The second soundthe duboccurs when the valves leading out of the heart close as the heart relaxes and refills with blood. Along with these two normal sounds, doctors also listen for unusual noisessuch as murmurs, extra beats, or clicksthat can point to problems with how blood is flowing or whether the heart valves are working properly. Heart sounds can vary greatly depending on the type of heart disease present. Sometimes, different diseases produce the same abnormal sound. For example, a systolic murmuran extra sound between first and second heart soundsmay be heard with narrowing of either the aortic or pulmonary valve. Yet the very same murmur can also appear when the heart is structurally normal and healthy. This overlap makes it challenging to diagnose disease based solely on the presence of murmurs. Teaching AI to hear what people cant AI technology can identify the hidden differences in the sounds of healthy and damaged hearts and use them to diagnose disease before traditional acoustic changes like murmurs even appear. Instead of relying on the presence of extra or abnormal sounds to diagnose disease, AI can detect differences in sound that are too faint or subtle for the human ear to detect. To build these algorithms, researchers record heart sounds using digital stethoscopes. These stethoscopes convert sound into electronic signals that can be amplified, stored, and analyzed using computers. Researchers can then label which sounds are normal or abnormal to train an algorithm to recognize patterns in the sounds it can then use to predict whether new sounds are normal or abnormal. Researchers are developing algorithms that can analyze digitally recorded heart sounds in combination with digital stethoscopes as a low-cost, noninvasive, and accessible tool to screen for heart disease. However, a lot of these algorithms are built on datasets of moderate-to-severe heart disease. Because it is difficult to find patients at early stages of disease, prior to when symptoms begin to show, the algorithms dont have much information on what hearts in the earliest stages of disease sound like. To bridge this gap, our team is using animal models to teach the algorithms to analyze heart sounds to find early signs of disease. After training the algorithms on these sounds, we assess their accuracy by comparing them with image scans of calcium buildup in the heart. Our research suggests that an AI-based algorithm can classify healthy heart sounds correctly over 95% of the time and can even differentiate between types of heart disease with nearly 85% accuracy. Most importantly, our algorithm is able to detect early stages of disease, before cardiac murmurs or structural changes appear. We believe teaching AI to hear what humans cant could transform how doctors diagnose and respon to heart disease. Valentina Dargam is a research assistant professor of biomedical engineering at Florida International University. Joshua Hutcheson is an associate professor of biomedical engineering at Florida International University. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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