Dont look for the right answer. Try this instead

Dont look for the right answer. Try this insteadDont look for the right answer. Try this insteadIn solving harte nusss, our first instinct is to find the right answer.In boardrooms across America, executives fall over each other to be the first to deliver the correct answer to a perceived herausforderung. Doctors assume theyve got the right diagnosis based on symptoms theyve seen in the past.Heres the problem.When we immediately launch into answer mode, we end up chasing the wrong problem. When we rush to identify solutions - when we fall in love with our diagnosis - our initial answer hides better ones lurking in plain sight. The difficulty lies, as John Maynard Keynes put it, not in the new ideas, but in escaping from the old ones.This is known as the Einstellung effect. In German,einstellungmeans set and in this context, it refers to a mental set. When were familiar with a problem, and when we think we have the right answer, we stop seeing alternatives. The initial framing of th e question, and the initial answer, both stick.Our education system reinforces the Einstellung effect. In schools, were taught to master problems, not to reframe them. The problems are handed to - no, forced upon - students in the fasson of problem sets. The phrase problem set makes this clear The problems have been set (einstellung) and cant be changed or questioned. A typical problem sets out allof its constraints, all of its given information, comprehensively and in advance, as high school teacher Dan Meyerexplains. The students then take the prepackaged and preapproved problem and plug it into a formula they memorized, which, in turn, spits out the right answer.We then bring this conditioning into our adult lives. We continue our search for formulas, life hacks, and right answers. We ask the same cliche questions over and over again - How do we think outside the box?- but expect different results. Over time, we become a hammer, and every problem looks like a nail.But finding the right question is often the key to finding the solution. Every answer, as Harvard business school professor Clayton Christensen puts it, has a question that retrieves it. The answer is often embedded within the question itself, so the framing of the question becomes crucial to the solution.If you define your problem as a missing hammer, theres only one possible solution finding a hammer. But if you reframe the problem as a nail thats sticking out, other solutions might work just as well.Albert Einstein reportedly said that if he had an hour to solve a problem hed spend 55 minutes thinking about the problem and 5 minutes thinking about solutions. Theres no evidence heactually saidthis, but he did believe that the formulation of the problem is often mora essential than its solution.Research supports this approach. Expert physicists spend more time than novices in understanding the problem before they begin crafting solutions. The most creative art students spend more time in the p reparation stage than their less creative counterparts. Even after spending time viewing the problem from different angles, the more creative individuals keep an open mind and stand ready to make changes to their initial definition of the problem.The next time youre tempted to engage in problem-solving, try problem finding instead. Ask yourself,Am I asking the right question?Is there a different way of framing the problem? If I changed my perspective, how would the problem change?Breakthroughs, contrary to popular wisdom, dont begin with a smart answer.They begin with a smart question.Ozan Varol is a rocket scientist turned law professor and bestselling author.Click hereto download a free copy of his e-book, The Contrarian Handbook 8 Principles for Innovating Your Thinking. Along with your free e-book, youll get the Weekly Contrarian - a newsletter that challenges conventional wisdom and changes the way we look at the world (plus access to exclusive content for subscribers only).Th isarticlefirst appeared onOzanVarol.com.

Jobs in the Emerging Field of Machine Learning

Jobs in the Emerging Field of Machine LearningJobs in the Emerging Field of Machine LearningAt the top ofLinkedIns 2017 US Emerging Jobs Reportwere two occupations in the Machine Learning field Machine Learning Engineer and Data Scientist. Employment for machine learning engineers grew by 9.8 times between 2012 and 2017 and data scientist jobs increased 6.5 times during the same five year period. If the trend continues, ansicht occupations will have employment outlooks that surpass many others occupations.With a future so bright, could a job in this field be right for you? What Is Machine Learning? Machine learning (ML) is just what it sounds like. This technology involves teaching machines to perform specific tasks. Unlike traditional coding that provides instructions that tell computers what to do, ML provides them with data that lets them figure it out on their own, much like a human being or animal would do.Sounds like magic, but it isnt. It involves the interaction of comput er scientists and others with related expertise. These IT professionals create programs called algorithms- sets of rules that solve a problem- and then feed them large sets of data that teach them to make predictions based on this information. Machine learningis asubset of artificial intelligence that enables computers to perform tasks they havent been explicitly programmed to do(Dickson, Ben. Skills You Need to Land a Machine Learning Job. It Career Finder. January 18, 2017.) It has gotten mora complicated, yet mora commonplace, over the years. Steven Levy,in an article that speaks to Googles prioritization of machine learning and retraining of the companys engineers,writes, For many years, machine learning was considered a specialty, limited to an elite few. That era is over, as recent results indicate that machine learning, powered by neural nets that emulate the way a biological brain operates, is the true path towards imbuing computers with the powers of humans, and in some ca ses, super humans (Levy, Steven. How Google is Remaking Itself As a Machine Learning First Company Wired. June 22, 2016). How is machine learning used in the real world? Most of us come across this technologyon a daily basis without giving it much thought. When you use Google or another search engine, the results that come up at the top of the page are the result of machine learning. The predictive text, as well as the sometimes maligned autocorrect feature, on your smart phones texting app, are also a result of machine learning. Recommended movies and songs on Netflix and Spotify are further examples of how we use this rapidly growing technology while barely noticing it. More recently, Google introduced Smart Reply in Gmail. At the end of a message, it presents a user with three possible replies based on the content. Uber and other companies are currently testing self-driving cars. Industries Using Machine Learning The use of machine learning reaches far beyond the tech world. SAS, an analytical software company, reports that many industries have adopted this technology. The financial services industry uses ML toidentify investment opportunities, let investors know when to trade, recognize whichclients have high-risk profiles, and detect fraud.In health care, algorithms help diagnose illnesses by picking up abnormalities. Have you ever asked the question, why is an ad for that product Im thinking of buying showing up on every web page I visit? ML allows the marketing and sales industry to analyze consumers based on their buying and search histories. The transportation industrys evolutionre anpassung of this technology detects potential problems on routes and helps make them more efficient. Thanks to ML, the oil and gas industry can identify new energy sources(Machine Learning What It Is and Why It Matters.SAS). How Machine Learning Is Changing the Workplace Predictions about machines taking over all our jobs have been around for decades, but will ML finally make that a reality? Experts forecast this technology has and will continue to alter the workplace. But as far as taking away all our jobs? Most experts dont think that will happen. While machine learning cant take the place of human beings in all occupations, it could change many of the job duties associated with them. Tasks that involve making quick decisions based on data are a good fit for ML programs not so if the decision depends on long chains of reasoning, diverse background knowledge or common sense says Byron Spice. Spice is Director of Media Relations at Carnegie Mellon Universitys School of Computer Science (Spice, Byron. Machine Learning Will Change Jobs. Carnegie Mellon University. December 21, 2017). In Science Magazine, Erik Brynjolfsson and Tom Mitchell write, labor demand is more likely to fall for tasks that are close substitutes for capabilities of ML, whereas it is more likely to increase for tasks that are complements for these systems. Each time an ML system crosses the threshold where it becomes more cost-effective than humans on a task, profit-maximizing entrepreneurs and managers will increasingly seek to substitute machines for people. This can have effects throughout the economy, boosting productivity, lowering prices, shifting labor demand, and restructuring industries (Brynjolfsson, Erik and Mitchell, Tom. What Can Machine Learning Do? Workforce Implications. Science. December 22, 2017). Do You Want a Career inMachine Learning? Careers in machine learning require expertise in computer science, statistics, and math. Many people come to this field with a background in those fields. Many colleges that offer a major in machine learning take a multi-disciplinary approach with a curriculum that includes, in addition to computer science, electrical and computer engineering, math, and statistics (Top 16 Schools for Machine Learning. AdmissionTable.com). For those who are already involved in the Information Technology Industr y, the transition to an ML job isnt a far leap. You may already have many of the skills you need. Your employer may even helpyou make this transition. According to Steven Levys article, currently there arent a lot of people who are experts in ML so companies like Google and Facebook are retraining engineers whose expertise lies in traditional coding. While many of the skills you developed as an IT professional will transfer to machine learning, it may be a bit challenging. Hopefully, you stayed awake during your college statistics classes because ML relies on a strong grasp of that subject, as well as math. Levy writes that coders have to be willing to give up the total control they have over programming a system. You are not out of luck if your tech employer isnt providing the ML retraining Google and Facebook are. Colleges and Universities, as well as online learning platforms like Udemyand Coursera, offer classes that teach the basics of machine learning. It is crucial, however, to round out your expertise by taking stats and math classes. Job Titles and Earnings The primary job titles you will come across when looking for a job in this field include machine learning engineer and data scientist. Machine learningengineers run the operations of a machine learning project and are responsible for managing the infrastructure and data pipelines needed to bring code to production. Data scientists are on the data and analysis side of developing algorithms, rather than the coding side. They also collect, clean, and prepare data(Zhou, Adelyn. Artificial Intelligence Job Titles What Is a Machine Learning Engineer? Forbes. November 27, 2017). Based on user submissions from people working in these jobs, Glassdoor.com reports that ML engineers and data scientists earn an average base salary of $120,931. Salaries range from a low of $87,000 to a high of $158,000 (Machine Learning Engineer Salaries. Glassdoor.com. March 1, 2018).Although Glassdoor groups these titles, there are some differences between them. Requirements for the Machine Learning Jobs ML engineers and data scientists do different jobs, but there is a lot of overlap between them. Job announcements for both positions often have similar requirements. Many employers prefer bachelors, masters, or doctoral degrees in computer science or engineering, statistics, or mathematics. To be a machine learning professional, you will need a combination of technical skills- skills learned in school or on the job- and soft skills. Soft skills are ones abilities that they do not learn in the classroom, but instead are born with or acquire through life experience. Again, there is a great deal of overlap between the required skills for ML engineers and data scientists. Job announcements reveal that those working in ML engineering jobs should be familiar with machine learning frameworks like TensorFlow, Mlib, H20 and Theano. They need a strong background in coding including experience with programm ing languages such as Java or C/C and scripting languages such as Perl or Python. Expertise in statistics and experience using statistical software packages to analyze large sets of data are also among the specifications. A variety of soft skills will allow you to succeed in this field. Among them are flexibility, adaptability, and perseverance. Developing an algorithm requires a lot of trial and error, and therefore, patience. One must test an algorithm to see if it works and, if not, develop a new one. Excellent communication skills are essential. Machine learning professionals, who often work on teams, need superior listening, speaking, and interpersonal skills to collaborate with others, and must also present their findings to their colleagues. They should, in addition, be active learners who can incorporate new information into their work. In an industry where innovation is valued, one must be creative to excel.

How to Move Past a Negative Performance Review

How to Move Past a Negative Performance ReviewHow to Move Past a Negative Performance ReviewI recently had lunch with a former colleague who admitted that he had received some negative feedback on his latest performance review. My babo said I dont communicate well, he stated.According to my colleague, the boss didnt cite any specific reasons for the feedback. He didnt provide examples of poor communication in the past, and he didnt offer any guidance on specific actions or activities that would help my colleague communicate effectively in the future.(Lets pause here to appreciate the irony of a boss communicating poorly about his employees bad communication.)Unfortunately, its common for supervisors to be unclearwhen providing feedback. According to a study conducted by World at Work,results show that 53% of employees say that when their boss does praise excellent performance, the feedback does not provide enough useful information to help them repeat it. And 65% of employees say tha t when their boss criticizes poor performance, they dont provide enough useful information to help employees correct the issue.Whether the boss doesnt want to be seen as the bad cop, prefers to avoid conflict, or just hasnt adequately prepared for a feedback discussion, many employees are left wondering what to do after receiving a negative review.Here are four steps to make your negative review work to your advantage.1. Clarify the feedbackEven if you think you know why youre receiving the criticism, make sure you truly are on the same page as your boss. Alison Green of the Ask a Manager blogsays, You cant just let negative feedback go on without addressing it, or you risk having your professional reputation affected or even losing your job. You must address it with your boss.One way to do this is to ask follow up questions, such asWhat gave you the impression that I(dont get along with my co-workers, am not as efficient as I could be, am disorganized)?Are you referring to(the fact that I turned in that last assignment late, the time when Bob and I disagreed over X, the comment that Jane made in the lunchroom)?Can you give me an example of(a time when I didnt communicate well, a time when my manner welches too abrupt, a time when I was tardy)?2. Identify the business impactEmployees can feel singled out for seemingly minor flaws when they dont understand the context for the criticism. Rather than assuming that your boss is picking on you unfairly or doesnt like you, encourage him to explain how this issue is affecting the business.Ask the following types of questions to encourage your boss to elaborate on the big pictureCan you help me understand(who is affected when I play my music without headphones, why it bothers you when my update is 10 minutes late)?Why is it a problem that I(schedule meetings for later the same day, arrive at the office after 10 a.m.)?3. Agree on next stepsIn order to move past negative feedback, you must understand exactly what change s or actions your boss expects to see moving forward. This is the time to take initiative and propose solutions. Think of specific, measurable steps you can take to address your boss concerns and encourage your boss to help you create a mutually agreed upon checklist.Some examples areTo improve my communication, I would like to send out a weekly status update to our team. Do you think this will make people feel more in the loop?I didnt realize that Fred had to answer the phones when I arrive late. Im going to work on showing up 10 minutes early in the future and Ill also forward my work number to my cell phone just in case. Do you think that will help solve this problem?4. Initiate check-insIdeally, your boss should follow up on your progress after providing criticism in order to provide continued guidance. However, many managers do not schedule regular meetings with their staff. If you have received negative feedback, take the initiative to address the situation, as its unlikely to go away on its own. Its critical to regularly check in with your boss to assess your performance.Propose a follow up method to your boss, such asCould we set up a 30-minute weekly check-in to review my progress around this issue?Id like to email you a weekly update detailing my activities to address this issue.Could we set up some milestone meetings over the next 6 months to evaluate my performance regarding this issue?Its one thing to receive this type of criticism once, but its another to be fired later on for failing to deal with it earlier. By proactively taking steps to address critical feedback as soon as it happens, you will demonstrate initiative and maturity that will help you both in the current situation as well as in your future career.