Soft Skills for Work

Source: Fast Company, Nov 2019

According to a 2015 LinkedIn report, people with high EQ make on average $29,000 more than their non-emotionally intelligent counterparts. The bottom line is that you’ll thrive in the job market if you have strong interpersonal skills.


It’s easy to get absorbed in our work (or ourselves) and forget about common courtesy, but demonstrating respect for others is key to developing personal relationships.

When you’re in a meeting—or anywhere else, really—wait for people to finish what they’re saying before you chime in. Thank others when they’ve shared an idea, acknowledge their contribution, and build upon it. If you’re leading the meeting, acknowledge everyone’s presence by inviting comments from each person and thanking them for participating.

Another way to convey respect is by showing up on time for appointments and meetings. (And if you come into a meeting late, don’t try to justify it by saying, “I had a meeting with our chairman,” or “I got stuck in traffic.” Just show up on time.)


A just-released study reveals that 48% of employees have felt embarrassed because they didn’t know a coworker’s name. This should go without saying, but make it a point to learn the names of your colleagues (even if they work in other departments or offices) and use them.

Once you get to know someone, remember what they’ve told you. If someone has given a big presentation or has a family event, don’t let that slip from your mind. Ask about it, and make sure you talk more about them than about yourself.


One of the best ways to make sure you sustain your focus on the person you’re talking with is to put your phone away, and use body language to keep yourself centered on the other person.

Look others directly in the eye and align your body with theirs. Facial expressions, too, can help show you’re focused. These sorts of body language cues will show that you are paying attention, which will also help you stay connected.


Listening is a delicate art, but there are three simple ways to listen: physically, mentally, and emotionally.

Physical listening means watching the body language of others, and responding accordingly. If someone has a frown or closed arms, realize you’re not getting through, and revamp the conversation.

Mental listening involves connecting with what others are thinking, and probing to get to the heart of what they are saying. So ask, “Do you think we should launch this program? Tell me more.”

Emotional listening means listening for what others are feeling, and showing that you understand and care. You might say to a team member, “Do you feel comfortable with this assignment?” Or, “Did you enjoy the conference?” Avoid the more generic, “How’s it going?” (That cliché is bound to prompt others to respond with a cliché of their own: “Not bad.”)


While it’s rare for us to think of love in the workplace, there are absolutely grounds for doing so. Sigal Barsade, professor of management at the Wharton School, writes about the importance of “companionate love” in the office. By this she means “feelings of affection, compassion, caring, and tenderness for others.”

Ford versus Ferrari

Terry Tao Interview

Source: Princeton, Nov 2019

erence Tao *96’s book, Solving Mathematical Problems: A Personal Perspective, is an engagingly slender volume, full of insights on how to approach problems in number theory, algebra, Euclidean geometry, and analytic geometry.

Tao began by setting out some sensible strategies for problem-solving, including these: Understand the problem, understand the data, understand the objective, select good notation, and write down everything you know. He also hoped for something less rote. “A solution,” Tao proposed, “should be relatively short, understandable, and hopefully have a touch of elegance. It should be fun to discover.”

Tao wrote Solving Mathematical Problems in 1990, when he was 15 years old.

The process of problem-solving, he emphasizes, is “non-linear.” In the end, though, is mathematics — is the universe — orderly or random? Tao warms to the question.

“It depends on where you look,” he says. “At the extremely microscopic level, the laws of nature are ordered. Particles and quantum waves obey very rigid waves of mechanics. But as you go to more complicated objects, molecules and living creatures, then it becomes more chaotic and unpredictable.

“There’s this weird mathematical phenomenon called universality. You get very complicated systems, of atoms or people, but if you look at it at a large-enough scale, order starts emerging. Einstein once said that the most incomprehensible thing about the universe is that it is comprehensible. It is very complicated, but at certain levels, patterns appear again.

“So there is order — sometimes — but there is also chaos.” 

Neuro-Evolution with Novelty Search

Source: Quanta, Nov 2019

the steppingstone principle — and, with it, a way of designing algorithms that more fully embraces the endlessly creative potential of biological evolution.

The steppingstone principle goes beyond traditional evolutionary approaches. Instead of optimizing for a specific goal, it embraces creative exploration of all possible solutions. By doing so, it has paid off with groundbreaking results.

Earlier this year, one system based on the steppingstone principle mastered two video games that had stumped popular machine learning methods. And in a paper published last week in Nature, DeepMind — the artificial intelligence company that pioneered the use of deep learning for problems such as the game of Go — reported success in combining deep learning with the evolution of a diverse population of solutions.

Biological evolution is also the only system to produce human intelligence, which is the ultimate dream of many AI researchers.

Because of biology’s track record, Stanley and others have come to believe that if we want algorithms that can navigate the physical and social world as easily as we can — or better! — we need to imitate nature’s tactics. Instead of hard-coding the rules of reasoning, or having computers learn to score highly on specific performance metrics, they argue, we must let a population of solutions blossom. Make them prioritize novelty or interestingness instead of the ability to walk or talk. They may discover an indirect path, a set of steppingstones, and wind up walking and talking better than if they’d sought those skills directly.

After Picbreeder, Stanley set out to demonstrate that neuroevolution could overcome the most obvious argument against it: “If I run an algorithm that’s creative to such an extent that I’m not sure what it will produce,” he said, “it’s very interesting from a research perspective, but it’s a harder sell commercially.”

He hoped to show that by simply following ideas in interesting directions, algorithms could not only produce a diversity of results, but solve problems. More audaciously, he aimed to show that completely ignoring an objective can get you there faster than pursuing it. He did this through an approach called novelty search.

To test the steppingstone principle, Stanley and his student Joel Lehman tweaked the selection process. Instead of selecting the networks that performed best on a task, novelty search selected them for how different they were from the ones with behaviors most similar to theirs. (In Picbreeder, people rewarded interestingness. Here, as a proxy for interestingness, novelty search rewarded novelty.)

“Novelty search is important because it turned everything on its head,” said Julian Togelius, a computer scientist at New York University, “and basically asked what happens when we don’t have an objective.”

A key element of these algorithms is that they foster steppingstones. Instead of constantly prioritizing one overall best solution, they maintain a diverse set of vibrant niches, any one of which could contribute a winner. And the best solution might descend from a lineage that has hopped between niches.

Now even DeepMind, that powerhouse of reinforcement learning, has revealed its growing interest in neuroevolution. In January, the team showed off AlphaStar, software that can beat top professionals at the complex video game StarCraft II, in which two opponents control armies and build colonies to dominate a digital landscape. AlphaStar evolved a population of players that competed against and learned from each other.

In last week’s Nature paper, DeepMind researchers announced that an updated version of AlphaStar has been ranked among the top 0.2% of active StarCraft II players on a popular gaming platform, becoming the first AI to reach the top tier of a popular esport without any restrictions.

As in the children’s game rock-paper-scissors, there is no single best game strategy in StarCraft II. So DeepMind encouraged its population of agents to evolve a diversity of strategies — not as steppingstones but as an end in itself. When AlphaStar beat two pros each five games to none, it combined the strategies from five different agents in its population. The five agents had been chosen so that not all of them would be vulnerable to any one opponent strategy. Their strength was in their diversity.

AlphaStar demonstrates one of the main uses of evolutionary algorithms: maintaining a population of different solutions.

To mirror this open-ended conversation between problems and solutions, earlier this year Stanley, Clune, Lehman and another Uber colleague, Rui Wang, released an algorithm called POET, for Paired Open-Ended Trailblazer.

For example, one bot learned to cross flat terrain while dragging its knee. It was then randomly switched to a landscape with short stumps, where it had to learn to walk upright. When it was switched back to its first obstacle course, it completed it much faster. An indirect path allowed it to improve by taking skills learned from one puzzle and applying them to another.

POET could potentially design new forms of art or make scientific discoveries by inventing new challenges for itself and then solving them. It could even go much further, depending on its world-building ability. Stanley said he hopes to build algorithms that could still be doing something interesting after a billion years.

In a recent paper, Clune argues that open-ended discovery is likely the fastest path toward artificial general intelligence — machines with nearly all the capabilities of humans.

Clune thinks more attention should be paid to AI that designs AI. Algorithms will design or evolve both the neural networks and the environments in which they learn, using an approach like POET’s.

Such open-ended exploration might lead to human-level intelligence via paths we never would have anticipated — or a variety of alien intelligences that could teach us a lot about intelligence in general. “Decades of research have taught us that these algorithms constantly surprise us and outwit us,” he said. “So it’s completely hubristic to think that we will know the outcome of these processes, especially as they become more powerful and open-ended.”

Measuring Originality in Science

Source: Springer, Nov 2019

We conceptualise originality as the degree to which a scientific discovery provides subsequent studies with unique knowledge that is not available from previous studies.

Specifically, we measure the originality of a paper based on the directed citation network between its references and the subsequent papers citing it. We demonstrate the validity of this measure using survey information. In particular, we find that the proposed measure is positively correlated with the self-assessed theoretical originality but not with the methodological originality.

We consider originality to be rooted in a set of information included in a focal scientific paper. However, we argue that the value of the paper is realised through its reuse by other scientists, and that its originality is established through its interaction with other scientists and follow-on research (Latour and Woolgar 1979; Merton 1973; Whitley 1984).

Base measure

We propose to measure the originality of an individual scientific papers based on its cited papers (i.e., references) and citing papers (i.e. follow-on research). We draw on subsequent papers that cite the focal paper to evaluate whether the authors of these subsequent citing papers perceive the focal paper as an original source of knowledge (Fig. 1A).

Suppose that the focal paper X cites a set of prior papers (reference set R) and is cited by a set of subsequent papers (citing set C). If X serves as a more original source of knowledge, then the citing papers (i.e., papers in citing set C) are less likely to rely on papers that are cited by X (i.e., papers in reference set R). In contrast, if X is not original but an extension of R, then C will probably also cite R together with X. In other words, we exploit the evaluation by the authors of follow-on research to measure the originality of the focal paper.

In conclusion, this study proposes a new bibliometric measure of originality. Although originality is a core value in science (Dasgupta and David ; Merton ; Stephan ; Storer ), measuring originality in a large scale has been a formidable challenge. Our proposed measure builds on the network betweenness centrality concept (Borgatti and Everett ; Freeman ) and demonstrates several favourable features as discussed above. 

Moral Grandstanding

Source: ZeroHedge, Nov 2019

moral grandstanding can be defined as “the use and abuse of moral talk to seek status, to promote oneself, or to boost your own brand.”

A moral grandstander is therefore a person who frequently uses public discussion of morality and politics to impress others with their moral qualities. Crucially, these individuals are primarily motivated by the desire to enhance their own status or ranking among their peers.

Across 6 studies (involving 2 pre-registrations involving nationally representative samples), 2 longitudinal designs, and over 6,000 participants, these are their core findings:

Moral grandstanders (those scoring high on the moral grandstanding survey) tend to also score high in narcissistic characteristics and also tend to report status-seeking as their fundamental social motive.

There is no relationship between moral grandstanding and political affiliation. However there is a link between moral grandstanding and political polarization: people on the far left and far right are both more likely to score higher in moral grandstanding characteristics than those who are more moderate democrats and republicans.

Moral grandstanders are more likely to report greater moral and political conflict in their daily lives (e.g., “I lost friends because of my political/moral beliefs”) and they report getting into more fights with others on social media because of their political or moral beliefs. This correlation was found even after controlling for other personality traits, and continued over the course of a one-month longitudinal study.

Grandstanders were more likely to report antagonistic behavior over time, such as attacking others online, or trying to publicly shame someone online because they held a different moral or political belief.

moral grandstanding can be fueled by either:

  1. The need to seek social status by dominating others (“When I share my moral/political beliefs, I do so to show people who disagree with me that I am better than them”)
  2. The need to seek status through being a knowledgeable and virtuous example (“I want to be on the right side of history about moral/political issues”, “If I don’t share my views, others will be less likely to learn the truth about moral/political matters”, “I often share my moral/political beliefs in the hope of inspiring people to be more passionate about their beliefs.”)

The researchers found that the dominance path to social status was much more strongly linked to antagonistic behaviors and conflict in everyday life

Does Perception Depend upon Action?

Source: Quanta, Nov 2019

Kenneth Harris and Matteo Carandini, neuroscientists at University College London, started with a different goal: to characterize the structure of the spontaneous activity in the visual cortex that occurs even when the rodent gets no visual stimulation. They and other members of their joint team at the university’s Cortexlab recorded from 10,000 neurons at once in mice that were free to act as they wanted — to run, sniff, groom themselves, glance around, move their whiskers, flatten their ears and so on — in the dark.

The researchers found that even though the animals couldn’t see anything, the activity in their visual cortex was both extensive and shockingly multidimensional, meaning that it was encoding a great deal of information.

this discovery reflects the fact that fundamentally, the brain evolved for action — that animals have brains to let them move around, and that “perception isn’t just the external input

Sensory information represents only a small part of what’s needed to truly perceive the environment. “You need to take into account movement, your body relative to the world, in order to figure out what’s actually out there,” Niell said.

“We used to think that the brain analyzed all these things separately and then somehow bound them together,” McCormick said. “Well, we’re starting to learn that the brain does that mixing of multisensory and movement binding [earlier] than we previously imagined.”

It’s necessary to know how the body is moving to contextualize and interpret incoming sensory information.

“Our brains aren’t just thinking in our heads. Our brains are interacting with our bodies and the way that we move through the world,” Niell said. “You think, ‘Oh, I’m just thinking,’ or ‘I’m just seeing.’ You don’t think about the fact that your body is playing a role in that.”

“People tend to think of movements as being separate from cognition — as interfering with cognition, even,” Churchland said. “We think that, given this work, it might be time to consider an alternative point of view, that at least for some subjects, movement is really a part of the cognition.”

researchers agree that the work heralds a shift in how they conduct their experiments on perception — namely, it demonstrates that they need to start paying more attention to behavior, too.