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内容：cherish huang 编辑：Gavin huang
Wechat ID: NativeStudy / Weibo: http://weibo.com/u/3476904471
Part I: Speaker
What are the most important moral problems of our time?
Of all the problems facing humanity, which should we focus on solving first? In a compelling talk about how to make the world better, moral philosopher Will MacAskill provides a framework for answering this question based on the philosophy of "effective altruism" -- and shares ideas for taking on three pressing global issues.
Source: TED https://www.ted.com/talks/will_macaskill_how_can_we_do_the_most_good_for_the_world/transcript
[Rephrase 1 | 11:54]
Part II: Speed
How Investing in AI is About Investing in People, Not Just Technology By Jessica Groopman | 21 September 2018 True AI readiness must go far beyond the data, and empower (and reassure) the people responsible for its success.
[Time 2] How is your organization preparing for artificial intelligence (AI)? Ask this question of businesses investing in this field today, and the answer almost always comes down to "data"-- with leaders talking about "data preparations" or "data science talent acquisition."
While there would be no AI without data, enterprises that fail to ready the other side of the equation-- people-- don’t just stunt their capacity for good AI, they risk sunk investment and jeopardize employee trust, brand backlash or worse.
After all, people are the ones building, measuring, consuming and determining the success of AI in enterprise and consumer settings. They're the ones whose jobs will change; whose tedium will be eased by automation; whose consumption or rejection of AI's outcomes will be the focus.
People, in short, are those who'll feel AI's myriad impacts. That's why investing in AI is as much about investing in people as it is data.
I wanted to dig deeper into this issue. So, my co-founders and other industry analysts at Kaleido Insights and I surveyed more than 25 businesses that have deployed AI at scale to learn about the ways they've invested in people. Here is what we found:
1. Investment in factors beyond technical talent Hiring a team of data scientists will not cause business processes to magically become automated overnight. Some liken this mistaken assumption to hiring electrical engineers to run a bakery: While the mechanics of ovens are important, it is the experienced baker who best knows how to innovate recipes and inspire customer delight!
Across industries, we found that the successful AI deployments we saw involved at least eight distinct personae:
Product leaders Front-line associates (e.g., customer support agents, field technicians) Subject matter experts (e.g., doctors, security admins, legal, etc.) Designers Sales Leadership End users Data scientists & technical builders
In addition to identifying these stakeholders, businesses have to make AI accessible and build trust by educating people and quelling fears. The top recommendation here is to prepare stakeholders by using tactics that put AI into context for each role.
Leadership requires a demonstration of ROI and visualization. AI leaders at FedEx, for example, built simulated dashboards and reports to illustrate the difference between traditional analytics and machine-learning-driven recommendations.
Meanwhile, readying the sales team requires both equipping agents with the knowledge, tools and confidence to sell the benefits of AI, and re-evaluating their metrics and incentive models to preserve quality and integrity. For effective roll-out, the unique needs and pain points for each of the above staff members have to be addressed. [424 words]
[Time 3] 2. Investment in addressing AI’s cultural stigma AI is distinct from other technologies in that it can challenge people’s sense of importance and relevance. Some 58 percent of organizations in international settings have not discussed AI’s impact on the workforce with employees, according to a recent survey by the Workforce Institute. Yet AI’s success is driven by people’s willingness to adopt it.
Thus, enterprises deploying AI are well advised to assess how people’s sentiments, fears, questions and insecurities impact their proclivity to adopt. Instead of ignoring concerns, companies interviewed suggested discussing and developing positions and initiatives to address:
Job displacement Algorithmic bias Privacy, surveillance Security threats Autonomous machines Societal manipulation Environmental impacts The notion of "killer robots"
These “elephants in the room” don’t just threaten employee morale, they highlight opportunities for companies to improve engagement and reinforce a healthy and trustworthy company culture. Address concerns of job displacement at your own company by evangelizing the limitations of AI. Articulate where AI will augment or accelerate human workflows. Provide clarity on governance models. And support employee upskilling and continued education programs.
Microsoft’s Professional Program for AI is an example: This is a massive open online course (MOOC) designed to guide aspiring AI builders through a range of topics, from statistics to ethics to research design. Other companies, like Starbucks and Kaiser Permanente, have partnered with elearning platforms like Coursera or Linda.com to facilitate professional development. [252 words]
[Time 4] 3. Investment in building an AI mindset While investing in a mindset might sound squishy or disconnected from the bottom line, preparing employees with the education, ownership, tools and processes they need to engage with AI has tangible business benefits. According to a recent survey of 1,075 companies in 12 industries, the more companies embraced active employee involvement in AI design and deployment, the better their AI initiatives performed in terms of speed, cost savings, revenues and other operational measures.
The following “3 D’s” of what I call the AI mindset reflect three universal truths about AI and serve as starting points for building people’s engagement in an organization’s AI journey:
Think "diversified": AI must be designed and managed by multiple skill sets. Those responsible for the day-to-day administration of the workflow are the ones who best understand where the breakdowns occur, where products fall short, where they, the staffers, spend most of their time and where customer sensitivities lie.
The business benefits: Diversifying AI design and development helps companies identify important features, UX/UI needs and use cases that might otherwise go unseen, or take more resources to surface. Companies like Wells Fargo have cross-functional centers of excellence to accelerate this process, emphasizing the value of using trusted internal influencers to facilitate onboarding.
Think "directional": AI implementation is not a linear, "completed" destination, but rather one that calls for continual learning and iterations based on feedback loops.
The business benefits: Instilling a “directional” mindset reduces time to at-scale deployment. Even though people want to see results quickly, the extent of experimentation determines how strong any AI model is, and how many problems it can solve. Often, deployment time is based on user adoption, and the more people who can help train and optimize the system, (again) the more problems adoption can solve. This is also why companies like SEB, a Swiss bank, deployed its virtual agent, Aida, to 600 employees; then to 15,000 employees, before rolling the agent out across its million-plus customers.
Think "democratized": AI is more sustainable when organizations enable accessible tools, training and multi-functional contribution and collaboration.
The business benefits: Democratizing access via easy-to-use tools means employees don't have to have a data science degree to contribute value to AI systems. The more simple, reliable and “self-service” enterprise data portals become, the more employees of all stripes can activate enterprise data -- an invaluable metric to any business.
In sum, the culture of an organization is inextricably linked to the willingness of its people to adapt, adopt, engage and innovate. Technology is only half the battle. Hierarchies, silos, complexity, distrust and complacency can choke innovation. Given that the most powerful AI involves both humans and machines, true AI readiness must go far beyond the data, and empower the people responsible for its success. [478 words]
Source： Entrepreneur https://www.entrepreneur.com/article/320422
5 Major Artificial Intelligence Hurdles We're on Track to Overcome by 2020 By Jayson DeMers | 23 January 2017 Unless you're in AI development yourself, you need to start thinking about tech partnerships that could bring AI to your business.
[Time 5] Artificial intelligence (AI) gets more advanced every year, but there are still some major limitations keeping us from seeing a futuristic reality that includes robot butlers and near-complete societal automation.
Fortunately, some of these limitations are on the verge of being overcome, and if you watch and plan carefully, you’ll be able to take advantage of those improvements for your business.
Here are some of the biggest hurdles we may overcome as early as 2020: 1. Unsupervised learning Right now, most AI systems “learn” new information through a kind of structured force-feeding, relying on information given to those systems by humans. However, this form of “supervised learning” isn’t scalable, and doesn’t mimic the way that human beings naturally learn.
In fact, we humans are immersed in our environments, perceiving pretty much everything that crosses our path and naturally filtering out what’s unimportant. We also experiment to learn how objects interact and how the world works.
Currently, we’re still a few years away from machines that can learn this way, but when we get there, we’ll have the ability to use them to generate or augment ideas, and produce concepts we couldn’t come up with on our own.
2. Creativity and abstract thinking Humans tend to think of ideas -- and solutions to problems -- in terms of abstractions. For example, think about a horse. Chances are, you aren’t thinking about a very specific example of a horse, and you don’t need to list out all the required “ingredients” that constitute a horse. Instead, you conceptualize the general idea of what a “horse” is.
A modern computer, on the other hand, would need thousands of examples of horses to understand what “horse” means, and even then, it would have to define this conceptual equine concretely and completely to use that idea in any application. If we want machines that can take raw data and turn it into intuitive concepts that can be grasped, we'll need to create machines that can think abstractly.
What's intriguing here is that we’re already on our way, having created deep learning programs that understand games like Go, and chess, as more than brute-force possibilities. By 2020, we could be taking the next step.
3. Public trust Self-driving cars run on sophisticated AI software to avoid collisions and drive better than slow-thinking, distracted, mistake-prone human drivers. To date, self-driving cars' record has been relatively clean, compared to that of human drivers.
However, only 39 percent of U.S. consumers claim they would feel safe in an autonomous vehicle. AI is still a foreign concept to most of us, and thanks to decades of science fiction, many of us are inherently distrustful of any fully mechanized solution. However, thanks to the gradual introduction of AI systems, public trust is steadily increasing, and may reach a point by 2020 that allows for widespread adoption. [504 words]
[Time 6] 4. Integration AI doesn’t exist by itself. It needs to be combined with something to be practical, such as those aforementioned self-driving cars. Integration into existing products, such as standard appliances and software programs, will be a major hurdle to overcome -- and one we’re already overcoming. We’re already seeing a plethora of “smart” devices, but few of these feature true machine learning or AI tech.
Being able to incorporate deep learning elements into existing systems could instantly multiply our capabilities -- and this may start happening soon.
5. General use We have AI systems that can beat human Go masters, write poetry and pass the Turing test. But these were all created for specific applications. Could we develop an AI program that serves a more general, all-around use?
Personal digital assistants like Siri, Google Home, Amazon Alexa and Cortana are a good start, but they only scratch the surface of what modern AI is capable of when put to a single application. By 2020, I suspect we’ll either see the beginnings of “general” AI development, or further fracturing into more specific niche functions.
How this affects entrepreneurs So, how might these beaten obstacles affect you and your business? Better tools and analytics. First up, the tools and software you use to improve your business are going to get a major overhaul. They’ll be able to use data more efficiently and more accurately than your human data analysts, and you’ll be able to produce more intuitive, visual representations of those conclusions. New customer needs. Your customers’ lives are going to change, and drastically. Their cars, appliances and even their homes will function more intelligently, which means the door’s open for a host of new solutions to address those new circumstances. Human resources shifts. You may see a number of your internal positions become replaceable. At that point, you’ll need to decide between keeping a salaried body on staff or opting for a more cutting-edge, but less personal solution. Partnership opportunities. Unless you’re in AI development yourself, you need to start thinking about tech partnerships that could bring AI to your business. Is there a way to make your product “smarter”? Is there a way to make machine learning improve the value of your services? Demand is about to skyrocket, so you need to be ready.
It’s hard to say exactly how AI will develop, but its momentum is strong, and there are no signs of its stopping. The better prepared you are for the future of AI, the more your business stands to benefit in multiple areas. Get ahead of your competition now, and start planning for the next few years of AI development. [472 words]
Source： Entrepreneur https://www.entrepreneur.com/article/287438
Part III: Obstacle
People Who Graduate During Recessions Earn Less Money — but They’re Happier By Emily D. Bianchi | 21 September 2018
[Paraphrase 7] When the graduating classes of 2009, 2010, and 2011 hit the job market, their employment prospects were depressingly bleak. Unemployment rates were at historic highs and job openings were scarce. Nine months after graduation, only 56% of the class of 2010 had found a job. Many of those who did find work held jobs that were temporary, lacked benefits, or did not require a college degree.
These early career experiences appear to have lasting negative consequences for later career success. For instance, people who graduate in recessions earn less money than their counterparts who graduated in more-favorable economic times, even decades later. They also tend to work for smaller, less-prestigious, and lower-paying firms. Similar patterns emerge among people who reach the pinnacle of corporate life: CEOs. Recession graduates who become CEOs often run smaller, less-prestigious companies than their counterparts who started in more-prosperous economic times.
While there is little doubt that recessions have lasting negative effects on salaries and occupational prestige, they appear to have some surprisingly positive implications for other aspects of people’s working lives.
For one, people who enter the workforce in a recession tend to be happier with their jobs, compared with people who graduate in better economic times. For instance, in one paper I examined the job attitudes of 1,638 people over a 15-year period. Even though they earned less money than people who started their careers in better economic times, recession graduates were significantly happier with their jobs both early in their careers and years later. These effects could not be explained by different industry or occupational choices. Instead, recession graduates tended to think about their jobs in more-positive and ultimately satisfying ways. Rather than ruminating over unfollowed paths or wondering what might have been, they focused on what was good about their jobs and were more grateful for the jobs they held. People who began their careers in prosperous times, on the other hand, were more likely to be plagued by regret, second-guessing, and what-ifs.
Entering the workforce in a recession appears to affect not only how people think about their jobs but also how they think about themselves. One metric of self-focus is narcissism: the belief that one is special, unique, and entitled to good outcomes. This way of being can be costly at work. Narcissists tend focus on their own interests even if doing so is harmful to others. They are also particularly likely to become angry or aggressive and to steal from their employers or shareholders.
One reason that recession graduates might be less likely to develop a grandiose sense of self is that narcissism seems to be tempered by adversity and setbacks. People who begin their careers during economic downturns often have a more difficult time finding work and establishing their careers. Many are forced to move back home, work in jobs that do not require a college degree, or cobble together part-time gigs. While these challenges can make it difficult to establish independence and build a career, they also appear to hamper the development of an overinflated ego.
I tested whether graduating in a recession did in fact temper narcissism, using data from a representative sample of over 30,000 Americans. I found that people who entered adulthood during worse economic times were less narcissistic than those who came of age in more-prosperous times. Similar effects even emerged among CEOs. Company leaders who began their careers in challenging economic times were less narcissistic than CEOs who began their careers in more-prosperous times.
If entering the workforce during a recession affects people’s sense of grandiosity and entitlement, could it also affect their willingness to engage in unethical business practices? Some evidence suggests it could. Past work has shown that narcissists are more likely to behave unethically, sabotage their coworkers, and be convicted of white-collar crime. Given that recession graduates are less likely to be narcissistic, could they also be less likely to be traverse moral and ethical lines? Consistent with this idea, my colleague Aharon Mohliver and I found that CEOs who first began their careers in worse economic times were less likely to backdate their stock options, an unethical, illegal, and common practice in the late 1990s and early 2000s. So not only were recession graduates less likely to regard themselves as supremely important and deserving of outsize attention and praise, but they were also less likely to engage in behavior that enriched themselves at the expense of their organizations.
Many people who started their careers during the Great Recession still bear the scars of entering the workforce during that tumultuous and uncertain time. They may have gaps in their résumés and fewer zeros in their salaries. But these tough experiences may have helped shape them into happier, less-self-absorbed, and more-ethical employees. [796 words]
Source: Harvard Business Review https://hbr.org/2018/09/people-who-graduate-during-recessions-earn-less-money-but-theyre-happier
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发表于 2018-9-24 11:29:12
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