Source: https://www.bbvaopenmind.com/en/perplexity-throughout-history/
[Excerpt] We can’t build societies where people have less ability to choose their future.
[...]
The printing press enabled mass and global dissemination of ideas which took humanity to peaks to knowledge from where it is being dragged down by the creations from Silicon Valley. However, its invention also led to criticisms, which are not too dissimilar to the arguments of current perplexed critics. In praise of scribes, the 15th-century monk Johannes Trithemius defended that copying books by hand meant that their content was assimilated into the minds of copyists.
[...]
“The Egyptians built the pyramids, buildings that caused great perplexity because only the engineers knew how it was possible to create them. For the average person, the pyramids were the work of the gods, something incomprehensive, like it happens now with artificial intelligence,” Miguel Ángel Quintanilla added. A similar reaction was caused by the gates at the temple in Alexandria; they opened automatically with the assistance of what is believed to have been the first steam-powered machine. “The believers thought it was a miracle,” said Quintanilla. “In unequal societies, technologies are controlled partly because of the mystery involving their internal operation,” he added. [/Excerpt]
2) [Harvard Business Review] Liz Kislik: How to Retain and Engage Your B Players
Source: https://hbr.org/2018/09/how-to-retain-and-engage-your-b-players[Excerpt] We’ve heard for decades that we should only hire A players, and should even try to cut non-A players from our teams. But not only do the criteria for being an A player vary significantly by company, it’s unrealistic to think you can work only with A players. Further, as demonstrated by Google’s Aristotle project, a study of what makes teams effective, this preference for A players ignores the deep value that the people you may think of as B players actually provide.
As I’ve seen in companies of all sizes and industries, stars often struggle to adapt to the culture, and may not collaborate well with colleagues. B players, on the other hand, are often less concerned about their personal trajectories, and are more likely to go above and beyond in order to support customers, colleagues, and the reputation of the business. For example, when one of my clients went through a disastrous changeover from one enterprise resource planning system to another, it was someone perceived as a B player who kept all areas of the business informed as she took personal responsibility for ensuring that every transaction and customer communication was corrected.
How can you support your B players to be their best and contribute the most possible, rather than wishing they were A players? Consider these five approaches to stop underestimating your B players and help them to reach their potential.
i) Get to know and appreciate them as the unique individuals they are.
ii) Reassess job fit.
iii) Consider the possibility of bias in your assignments.
iv) Intentionally support them to be their best.
v) Give permission to take the lead.
[/Excerpt]
3) [Fortune] Beth Kowitt: Barack Obama Shares His Lessons Learned on Leadership and Power
Source: http://fortune.com/2017/05/10/barack-obama-leadership-power/
[Excerpt] It’s not enough to be the squeaky wheel. Obama said that politicians and governments respond to people making noise and demands. But the biggest mistake made by activists “is once you’ve gotten the attention of people in power then you have to engage them and have sensible ideas.” Obama added that you have to do you homework, have your facts straight, and be willing to compromise. He is addressing this issue head on with the Obama Presidential Center, which is designed to help the next generation of activist leadership. [/Excerpt]
4) [Linkedin Pulse] Adam Bryant: Your Job As A Leader Is Not To Be An Expert. It's To Be An Expert Learner
Source: https://www.linkedin.com/pulse/your-job-leader-expert-its-learner-adam-bryant
[Excerpt]
Question: A big pressure people face, in addition to the workload, is a sense of uncertainty. With so much disruption, there is no playbook to follow any more.
Answer: People do struggle with not knowing. With the digitalization of business, there’s a lot going on, and if executives are really honest with themselves, they don’t know. Some thrive in the not knowing. Others really have an issue with that because they define leadership as someone who knows everything better than anybody else.
Question: So what do you say to people?
Answer: The first thing I do is comfort them that they’re not the only ones who feel that way. And then we talk about how they define leadership. If you define leadership as having all the answers, you’re going to struggle. That leads to a conversation around being an expert or an expert learner, and the difference between those two. It’s about having the capacity to navigate your way through different fields of knowledge, and to hold different viewpoints at the same time.
"If you define it as having all the answers, you’re going to struggle."
I see executives sometimes being unable to make decisions because they’re a victim of linear thinking. They have a question, but a lot of people have different views, which lead to more questions. What a lot of executives have learned to do is, for a moment, hold those different views in their head and decide from there, instead of the decision being seen as coming at the end of a linear process.
It also is important in that context to talk about the role of small experiments. Sometimes the answers aren’t there, so instead of relying solely on analyzing something to death, you can do a pilot or little experiment.
[...]
Question: What are some of the tougher conversations you have with clients?
Answer: One is holding up a mirror to them about their leadership style and why it may not be working. One example is that the requests they are sometimes handing down to their teams are very much flawed because there’s not enough context. I will tell my clients that they have to spend more time being clear because that will make everything more efficient.
But then they sometimes say, “Well, I assume they understand me. I pay them all that money to know what I’m talking about.” That’s where I see a lot of efficiency being lost these days. The requests are flawed, so people start second-guessing themselves, and then what comes back isn’t right.
"The requests are flawed, so people start second-guessing themselves."
A defining moment in my career was when I was working for British American Tobacco in the UK, and the CEO at the time was Paul Adams. We were working on organizing ourselves to become more of a globally integrated company, and we were really struggling as an executive team. Then Paul interrupted an executive meeting and said, “Am I the only one who is out of his comfort zone here?”
That was fantastic because it made everybody feel like they were in the same boat together, and I use that as an important example. When the request can be clear, make it clear. But when you don’t know and you’re exploring, share with your people that you’re exploring and that you don’t know either. I don’t see enough leaders doing either of those.
[/Excerpt]
5) [Inc.com] J.T. O'Donnell: 1 Leadership Mistake Millennials Say Makes You Look Like a Liar (and a Bit Desperate, Too)
Source: https://www.inc.com/jt-odonnell/millennials-say-this-old-school-leadership-technique-makes-you-look-like-a-liar-heres-why.html
[Excerpt] In this age of emotional intelligence, the qualities employees look for in good leaders have shifted. In particular, how leaders react to challenges or criticism is observed intensely. We live in an age of transparency. Employees believe they have the right to assess the skills of their leadership teams.
"Defend and Deflect" = The Sign of a Desperate Liar
For decades, managers were trained to fight back against negativity and criticism with the "defend and deflect" method. In an effort to appear right and superior in the face of being wrong, the solution is to get intensely vocal, display confidence in your position, and try to pivot the blame.
The defend and deflect communication model comes from an outdated assumption that to be a good leader, you must always have the answers. Today's more emotionally intelligent leadership approaches (i.e., servant leadership) don't have that expectation.
Replace It With "Examine and Engage"
In the face of conflict, top leaders now realize it's important to examine the situation and engage those complaining in a discussion to explore and understand where they are coming from. This provides savvy leaders with valuable information to help them respond in a more positive, effective fashion in particular two situations:
i) When employees don't have enough, or the right, information.
ii) Unexpected challenges or aspects of the situation leaders are unaware of.
[/Excerpt]
6) [Thrive Global] Nora Battelle: How to Be a Great Leader, According to 7 Political and Business Greats
Source: https://thriveglobal.com/stories/best-leadership-advice-business-political-leaders-barack-obama-jeff-bezos/
[Excerpt]
Be a little deaf
Ruth Bader Ginsburg got some great advice from her mother-in-law, which she says she has applied to everything from her marriage to her role on the Supreme Court: It helps to be a little deaf sometimes. “When a thoughtless or unkind word is spoken, best tune out. Reacting in anger or annoyance will not advance one’s ability to persuade,” she wrote in a New York Times opinion piece. [/Excerpt]
7) [Standard Graduate School of Business] Louise Lee: The Decline of the IPO
Source: https://www.gsb.stanford.edu/insights/decline-ipo
[Excerpt] From a company’s standpoint, staying private and obtaining capital through the private markets may be simpler and more desirable than going public.
But from the standpoint of Securities and Exchange Commissioner Kara Stein, a market in which a huge fraction of companies remain private may not serve the interests of investors and society at large. For example, private companies have to disclose only limited information about their businesses, while publicly traded companies must regularly disclose a far larger amount, providing investor protections and benefiting the market as a whole, says Stein, a recent visiting speaker in the Corporations and Society Program at Stanford Graduate School of Business.
“There’s a lot of transparency in the public space,” says Stein, who stressed in an interview that she was expressing her own views and not those of the other commissioners or staff of the SEC. By global standards, “our public markets here are the deepest, they’re the most liquid, and they’re enabling companies to access very large amounts of capital and grow and create jobs,” she says.
Staying private, though, is a route that companies are following with rising frequency, Stein notes.
The number of publicly traded companies in the U.S. was about 4,300 as of 2015, down from a 1996 peak of about 8,000, according to a 2017 study from accounting firm EY. And in 2016, 112 initial public offerings occurred, down from 291 in 2014, the year IPOs reached their highest level since 2000.
Companies can tap a deep well of private capital from such sources as hedge funds and private equity funds. Staying private also lets a business avoid hiring lawyers and accountants to make the required, ongoing disclosures and regulatory filings to the SEC. Currently, just over half of capital raised is coming from the private market.
But, Stein asks, what might happen to the overall market when too many companies stay private? For starters, more investors could be hurt, since investor protections in the private market are less robust than in the public market. And as the proportion of private companies grows, there’s less information flowing into the financial ecosystem, reducing the market’s overall transparency, Stein says. [/Excerpt]
8) [MIT Sloan Management Review] Megan Beck and Barry Libert: The Machine Learning Race Is Really a Data Race
Source: https://sloanreview.mit.edu/article/the-machine-learning-race-is-really-a-data-race/
[Excerpt] Machine learning — or artificial intelligence, if you prefer — is already becoming a commodity. Companies racing to simultaneously define and implement machine learning are finding, to their surprise, that implementing the algorithms used to make machines intelligent about a data set or problem is the easy part. There is a robust cohort of plug-and-play solutions to painlessly accomplish the heavy programmatic lifting, from the open-source machine learning framework of Google’s TensorFlow to Microsoft’s Azure Machine Learning and Amazon’s SageMaker.
What’s not becoming commoditized, though, is data. Instead, data is emerging as the key differentiator in the machine learning race. This is because good data is uncommon.
[...]
While machine learning can occasionally surprise us with a flash of rare brilliance that no one has yet to discover, the technology isn’t capable of providing these insights with consistency. This doesn’t mean the tool is broken. It means we have to apply it wisely. This is easier said than done: For instance, in our research of the alternative data market, we found that more than half of new data providers are still focused on measuring physical and financial assets.
The step that many organizations omit is creating a hypothesis about what matters. Where machine learning really excels is taking an insight that humans have — one based on rules of thumb, broad perceptions, or poorly understood relationships — and developing a faster, better understood, more scalable (and less error-prone) method for applying the insight.
In order to use machine learning in this way, you don’t feed the system every known data point in any related field. You feed it a carefully curated set of knowledge, hoping it can learn, and perhaps extend, at the margins, knowledge that people already have.
All this has three specific implications for companies wanting to create impactful and valuable machine learning applications:
- [Differentiated data is key to a successful AI play.] You won’t uncover anything new working with the same data your competitors have. Look internally and identify what your organization uniquely knows and understands, and create a distinctive data set using those insights.
- [Meaningful data is better than comprehensive data.] You may possess rich, detailed data on a topic that simply isn’t very useful. If your company wouldn’t use that information to help inform decision-making on an ad hoc basis, then that data likely won’t be valuable from a machine learning perspective.
- [What you know should be the starting point.] Companies that best use machine learning begin with a unique insight about what matters most to them for making important decisions. This guides them about what data to amass, as well as what technologies to use.
9) Partner at Silicon Valley venture capital firm Andreessen Horowitz, Benedict Evans: Does AI make strong tech companies stronger?
Source: https://www.ben-evans.com/benedictevans/2018/12/19/does-ai-make-strong-tech-companies-stronger
[Excerpt] First, though you need a lot of data for machine learning, the data you use is very specific to the problem that you’re trying to solve. GE has lots of telemetry data from gas turbines, Google has lots of search data, and Amex has lots of credit card fraud data. You can’t use the turbine data as examples to spot fraudulent transactions, and you can’t use web searches to spot gas turbines that are about to fail.
This means that the implementation of machine learning will be very widely distributed. [...] One could argue that this just means the larger companies in each industry get stronger - Vodafone, GE and Amex each have ‘all the data’ for whatever it is that they do and so that forms a moat against their competition. But here again, it’s more complex: there are all sorts of interesting questions about who exactly owns the data, how unique it is and at what levels it’s unique, and where the right point of aggregation and analysis might be.
So: as an industrial company, do you keep your own data and build the ML systems to analyse it (or pay a contractor to do this for you)? Do you buy a finished product from a vendor that’s already trained on other people’s data? Do you co-mingle your data into that, or into the training derived from it? Does the vendor even need your data or do they already have enough? The answer will be different in different parts of your business, in different industries and for different use cases.
To come at this from the other end, if you’re creating a company to deploy ML to solve a real-world problem, there are two basic data questions: how do you get your first data to train your models to get your first customer, and how much data do you actually need?
Of course, the second question breaks down into lots of questions: is the problem solved with a relatively small amount of data that you can get fairly easily (but many competitors can get), or do you need far more, hard-to-get data, and if so is there a network effect to benefit from, and so a winner takes all dynamic? Does the product get better with more data indefinitely, or is there an S curve?
It depends.
- Some data is unique to the business or product or gives a strong proprietary advantage. GE engine telemetry might not be much use for analyzing Rolls Royce engines, but if it is they won’t share it. This might be an opportunity for company creation, but is also a place where lots of internal big company IT and contractor projects happen.
- Some data will apply to a use case that is found in many companies or even many industries. ‘There is something odd about this call’ might be a common analysis across all credit card companies - ‘the customer sounds angry’ might apply to anyone with a call centre. This is the ‘co-mingling’ question. Lots of companies are being created here to solve problems across many companies or indeed across different industries, and there are network effects in data here.
- But there will also be cases at which after a certain point the vendor doesn’t really even need each incremental customer’s data - the product is already working.
10) Other reading/learning during week 51:
https://blog.hackerrank.com/linkedin-scaling-talent-shortages-ai/
https://www.ben-evans.com/benedictevans/2018/12/19/does-ai-make-strong-tech-companies-stronger
https://hbr.org/2018/12/5-questions-we-should-be-asking-about-automation-and-jobs
https://hbrascend.org/topics/dos-donts-reading-the-room-before-a-meeting-or-presentation/
https://hbrascend.org/topics/using-stories-to-persuade/
https://hbr.org/2018/12/the-5-things-all-great-salespeople-do
https://www.forbes.com/sites/dailymuse/2017/03/28/the-simple-change-thatll-convince-people-to-respond-to-your-cold-emails/
https://www.inc.com/justin-bariso/21-ways-to-improve-your-emotional-intelligence-using-just-a-few-minutes-a-day.html
https://www.linkedin.com/pulse/top-trends-can-impact-your-personal-growth-going-2019-john-rampton
https://www.linkedin.com/pulse/agile-implementation-flexibility-comes-ashish-yadav
https://hbr.org/2018/12/the-secret-to-leading-organizational-change-is-empathy
https://hbr.org/2018/09/how-to-help-your-team-manage-grunt-work
https://sloanreview.mit.edu/article/why-people-believe-in-their-leaders-or-not/
https://hbrascend.org/topics/three-leadership-skills-that-make-a-winning-leader/
https://venturebeat.com/2018/12/09/6-things-a-first-time-ceo-needs-to-know/
https://hbrascend.org/topics/4-analytics-concepts-every-manager-should-understand/
https://www.linkedin.com/learning/body-language-for-leaders/
https://hbr.org/video/5561200070001/the-setuptofail-syndrome
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