VB036/06 Štěpánek, Libor. Oral Presentations Seminar. http://online.cjv.muni.cz/opc/click-to-start.html Williams, Erica J. Presentations in English. Honkong: MacMillan, 2008. Greenberg, Rebecca. “Machines Are Better Than Humans at Hiring the Best Employees.” Bloomberg. http://www.bloomberg.com/news/articles/2015-11-17/machines-are-better-than-humans-at-hiring-top-employees Jones, Leo. New Cambridge Advance English. Cambridge: Cambridge UP, 2004. Week 6 Presentations 4. Conclusion – Make your final message clear: Stay in control until the very last second and follow these steps at the ‘finish’ of your presentation. Firstly, pause briefly and signal clearly that you are now ready to finish the presentation. The audience will start to listen again closely at this point. Then, make your summary, giving a brief overview of what has already been said. The summary is a reflection of your ‘what’ and looks back. It should not be too long as you will lose your audience’s attention again, but detailed enough to cover your points. This can be a difficult balance to achieve! A good summary gives your listeners time to reflect on the content and builds up to your conclusion, making your conclusion stronger, more powerful and more effective. A conclusion without a summary can sound incomplete as your audience may not have listened to every point during the main part of the presentation and the purpose can be lost. Avoid giving any conclusions while you are making your summary. After this, give your conclusion. This is a reflection of your ‘why’ and looks forward to what you want people to do or think after your presentation. it should follow logically from your summary. There are different kinds of conclusions: you can make a call for action, make a recommendation or assure your audience that they’re better informed. This is the destination of your journey and the most important part of your presentation. Finally, make your closing remarks by thanking your audience, asking for questions or passing round your presentation hand-outs. Phrases related to Conclusion: 1. So, that brings me to the end of my presentation 2. Let me summarize what we’ve looked at. 3. Thank you for your attention. 4. I’ll briefly summarize the main issues. 5. I’ll now hand out ... 6. I suggest Johannes ... and Michel ... 7. I’d like to summarize. 8. I’d like to conclude by strongly recommending ... 9. So, that completes my presentation. 10. Let me just go over the key points again. 11. To sum up ... 12. I trust you gained an insight into ... 13. To conclude, I’d like to leave you with the following thought ... 14. Well, that covers everything I want to say. 15. If you have any questions, I’d be happy to answer them. 16. At this stage, I’d like to go over ... 17. In my opinion, the only way forward is to ... 18. Thank you for listening. 19. To summarize, I’ll run through my three topics. VB036/06 Štěpánek, Libor. Oral Presentations Seminar. http://online.cjv.muni.cz/opc/click-to-start.html Williams, Erica J. Presentations in English. Honkong: MacMillan, 2008. Greenberg, Rebecca. “Machines Are Better Than Humans at Hiring the Best Employees.” Bloomberg. http://www.bloomberg.com/news/articles/2015-11- 17/machines-are-better-than-humans-at-hiring-top-employees Jones, Leo. New Cambridge Advance English. Cambridge: Cambridge UP, 2004. Machines Are Better Than Humans at Hiring the Best Employee. By Rebecca Greenfield 1) Complete the text with the words below. deviated, worse, run through, based, tendency, picked, finds, duration, confirming, productive, bias, completed, themselves, gain, across, instincts People want to believe they have good 1) ___________, but when it comes to hiring, they can’t best a computer. Hiring managers select worse job candidates than the ones recommended by an algorithm, new research from the National Bureau of Economic Research 2) ___________. Looking 3) ___________ 15 companies and more than 300,000 hires in low-skill service-sector jobs, such as data entry and call center work, NBER researchers compared the tenure of employees who had been hired 4) ___________ on the algorithmic recommendations of a job test with that of people who’d been 5) ___________ by a human. The test asked a variety of questions about technical skills, personality, cognitive skills, and fit for the job. The applicant’s answers were 6) ___________ an algorithm, which then spat out a recommendation: Green for high-potential candidates, yellow for moderate potential, and red for the lowest-rated. First, the researchers proved that the algorithm works, 7) ___________ what previous studies have found. On average, greens stayed at the job 12 days longer than yellows, who stayed 17 days longer than reds. The median 8) ___________ of employees in these jobs isn’t very long to begin with, about three months. “That’s still a big deal, on average, when you’re hiring tens of thousands of people,” said researcher Mitchell Hoffman, an assistant professor of strategic management, calling the extra few weeks the algorithm bought a “modest or significant improvement.” Often hiring managers, possibly because of overconfidence or 9) ___________, don’t listen to the algorithm. Those cases, it turns out, lead to worse hires. When, for example, recruiters hired a yellow from an applicant pool instead of available greens, who were then hired at a later date to fill other open positions, those greens stayed at the jobs about 8 percent longer, the researchers found. The more managers 10) ___________ from the testing recommendations, the less likely candidates were to stick around. Recruiters might argue that they make these exceptions to hire more 11) ___________ people, even though they don’t stay as long at the job. The numbers suggest otherwise. For six of the 15 companies, the researchers measured productivity, such as the number of calls 12) ___________ per hour, amount of data entered per hour, or number of standardized tests graded per hour. The exceptions to the algorithm did no better than their peers. “There is no statistical evidence that the exceptions are doing better in this other dimension,” said researcher Danielle Li, an assistant professor of entrepreneurship at Harvard Business School. In some cases, she said, the exceptions did 13) ___________. While hiring algorithms have started to 14) ___________ popularity as a way to reduce hiring and turnover costs, finding employees who fit better within companies, there’s still a 15) ___________ to trust one’s gut over a machine. One study dubbed the phenomenon “algorithm aversion.” People can be blinded by bias, however, especially when it comes to hiring. Some hiring managers gravitate to people like 16) ___________; others are just overconfident in their abilities to predict success. “It’s human nature to think that some of that information you’re learning in an interview is valuable,” added Li. “Is it more valuable than the information in the test? In a lot of cases, the answer is no.” VB036/06 Štěpánek, Libor. Oral Presentations Seminar. http://online.cjv.muni.cz/opc/click-to-start.html Williams, Erica J. Presentations in English. Honkong: MacMillan, 2008. Greenberg, Rebecca. “Machines Are Better Than Humans at Hiring the Best Employees.” Bloomberg. http://www.bloomberg.com/news/articles/2015-11- 17/machines-are-better-than-humans-at-hiring-top-employees Jones, Leo. New Cambridge Advance English. Cambridge: Cambridge UP, 2004. -Ing and to 1) Discuss the difference in meaning (if any) between these sentences. Then decide how each sentence might continue. 1 We stopped to eat our sandwiches when … We stopped eating our sandwiches when … 2 I won’t forget to meet her because … I won’t forget meeting her because … 3 He’d like to study alone because … He likes studying alone because … Studying alone is what he likes because … 4 I used to write a lot of 250-word essays but … I usually write a lot of 250-word essays but … I’m used to writing a lot of 250-word essays but … 5 Sometimes she didn’t remember to hand in her work because … Sometimes she doesn’t remember to hand in her work because … Sometimes she doesn’t remember handing in her work because … 6 The lecturer went on to tell the audience about … The lecturer went on telling the audience about … 7 We tried to get through to her on the phone but … We tried getting through to her on the phone but … 8 I regret to tell you that your application was unsuccessful because … I regret telling you that your application was unsuccessful because … 2) Correct the errors in these sentences: a) Although I was looking forward to meet her, I was afraid to make a bad impression b) To smoke is not allowed in the office but employees are permitted smoking in the canteen. c) Everyone was beginning getting nervous before the exam, but once we began realizing that we were all in the same boat we began to feel better. d) The man denied to have committed the crime but he failed convincing the magistrate. e) They made me to sit down and wouldn’t let me leaving without to apologize for being rude to them. f) To get a good job you have to having the right qualification. g) Don’t forget making notes before you start to write the essay, and remember checking your work through afterwards. h) You can’t expect achieving success without to work hard.