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Killexams : Decision Tree Learning

As discussed in the last lecture, the representation scheme we choose to represent our learned solutions and the way in which we learn those solutions are the most important aspects of a learning method. We look in this lecture at decision trees - a simple but powerful representation scheme, and we look at the ID3 method for decision tree learning.

6.1 Decision Trees

Imagine you only ever do four things at the weekend: go shopping, watch a movie, play tennis or just stay in. What you do depends on three things: the weather (windy, rainy or sunny); how much money you have (rich or poor) and whether your parents are visiting. You say to your yourself: if my parents are visiting, we'll go to the cinema. If they're not visiting and it's sunny, then I'll play tennis, but if it's windy, and I'm rich, then I'll go shopping. If they're not visiting, it's windy and I'm poor, then I will go to the cinema. If they're not visiting and it's rainy, then I'll stay in.

To remember all this, you draw a flowchart which will enable you to read off your decision. We call such diagrams decision trees. A suitable decision tree for the weekend decision choices would be as follows:

We can see why such diagrams are called trees, because, while they are admittedly upside down, they start from a root and have branches leading to leaves (the tips of the graph at the bottom). Note that the leaves are always decisions, and a particular decision might be at the end of multiple branches (for example, we could choose to go to the cinema for two different reasons).

Armed with our decision tree, on Saturday morning, when we wake up, all we need to do is check (a) the weather (b) how much money we have and (c) whether our parent's car is parked in the drive. The decision tree will then enable us to make our decision. Suppose, for example, that the parents haven't turned up and the sun is shining. Then this path through our decision tree will tell us what to do:

and hence we run off to play tennis because our decision tree told us to. Note that the decision tree covers all eventualities. That is, there are no values that the weather, the parents turning up or the money situation could take which aren't catered for in the decision tree. Note that, in this lecture, we will be looking at how to automatically generate decision trees from examples, not at how to turn thought processes into decision trees.

There is a link between decision tree representations and logical representations, which can be exploited to make it easier to understand (read) learned decision trees. If we think about it, every decision tree is actually a disjunction of implications (if ... then statements), and the implications are Horn clauses: a conjunction of literals implying a single literal. In the above tree, we can see this by reading from the root node to each leaf node:

If the parents are visiting, then go to the cinema
If the parents are not visiting and it is sunny, then play tennis
If the parents are not visiting and it is windy and you're rich, then go shopping
If the parents are not visiting and it is windy and you're poor, then go to cinema
If the parents are not visiting and it is rainy, then stay in.

Of course, this is just a re-statement of the original mental decision making process we described. Remember, however, that we will be programming an agent to learn decision trees from example, so this kind of situation will not occur as we will start with only example situations. It will therefore be important for us to be able to read the decision tree the agent suggests.

Decision trees don't have to be representations of decision making processes, and they can equally apply to categorisation problems. If we phrase the above question slightly differently, we can see this: instead of saying that we wish to represent a decision process for what to do on a weekend, we could ask what kind of weekend this is: is it a weekend where we play tennis, or one where we go shopping, or one where we see a film, or one where we stay in? For another example, we can refer back to the animals example from the last lecture: in that case, we wanted to categorise what class an animal was (mammal, fish, reptile, bird) using physical attributes (whether it lays eggs, number of legs, etc.). This could easily be phrased as a question of learning a decision tree to decide which category a given animal is in, e.g., if it lays eggs and is homeothermic, then it's a bird, and so on...

6.2 Learning Decision Trees Using ID3

We now need to look at how you mentally constructed your decision tree when deciding what to do at the weekend. One way would be to use some background information as axioms and deduce what to do. For example, you might know that your parents really like going to the cinema, and that your parents are in town, so therefore (using something like Modus Ponens) you would decide to go to the cinema.

Another way in which you might have made up your mind was by generalising from previous experiences. Imagine that you remembered all the times when you had a really good weekend. A few weeks back, it was sunny and your parents were not visiting, you played tennis and it was good. A month ago, it was raining and you were penniless, but a trip to the cinema cheered you up. And so on. This information could have guided your decision making, and if this was the case, you would have used an inductive, rather than deductive, method to construct your decision tree. In reality, it's likely that humans reason to solve decisions using both inductive and deductive processes.

We can state the problem of learning decision trees as follows:

We have a set of examples correctly categorised into categories (decisions). We also have a set of attributes describing the examples, and each attribute has a finite set of values which it can possibly take. We want to use the examples to learn the structure of a decision tree which can be used to decide the category of an unseen example.

Assuming that there are no inconsistencies in the data (when two examples have exactly the same values for the attributes, but are categorised differently), it is obvious that we can always construct a decision tree to correctly decide for the training cases with 100% accuracy. All we have to do is make sure every situation is catered for down some branch of the decision tree. Of course, 100% accuracy may indicate overfitting.

In the decision tree above, it is significant that the "parents visiting" node came at the top of the tree. We don't know exactly the reason for this, as we didn't see the example weekends from which the tree was produced. However, it is likely that the number of weekends the parents visited was relatively high, and every weekend they did visit, there was a trip to the cinema. Suppose, for example, the parents have visited every fortnight for a year, and on each occasion the family visited the cinema. This means that there is no evidence in favour of doing anything other than watching a film when the parents visit. Given that we are learning rules from examples, this means that if the parents visit, the decision is already made. Hence we can put this at the top of the decision tree, and disregard all the examples where the parents visited when constructing the rest of the tree. Not having to worry about a set of examples will make the construction job easier.

This kind of thinking underlies the ID3 algorithm for learning decisions trees, which we will describe more formally below. However, the reasoning is a little more subtle, as (in our example) it would also take into account the examples when the parents did not visit.

Putting together a decision tree is all a matter of choosing which attribute to test at each node in the tree. We shall define a measure called information gain which will be used to decide which attribute to test at each node. Information gain is itself calculated using a measure called entropy, which we first define for the case of a binary decision problem and then define for the general case.

Given a binary categorisation, C, and a set of examples, S, for which the proportion of examples categorised as positive by C is p+ and the proportion of examples categorised as negative by C is p-, then the entropy of S is:

The reason we defined entropy first for a binary decision problem is because it is easier to get an impression of what it is trying to calculate. Tom Mitchell puts this quite well:

"In order to define information gain precisely, we begin by defining a measure commonly used in information theory, called entropy that characterizes the (im)purity of an arbitrary collection of examples."

Imagine having a set of boxes with some balls in. If all the balls were in a single box, then this would be nicely ordered, and it would be extremely easy to find a particular ball. If, however, the balls were distributed amongst the boxes, this would not be so nicely ordered, and it might take quite a while to find a particular ball. If we were going to define a measure based on this notion of purity, we would want to be able to calculate a value for each box based on the number of balls in it, then take the sum of these as the overall measure. We would want to reward two situations: nearly empty boxes (very neat), and boxes with nearly all the balls in (also very neat). This is the basis for the general entropy measure, which is defined as follows:

Given an arbitrary categorisation, C into categories c1, ..., cn, and a set of examples, S, for which the proportion of examples in ci is pi, then the entropy of S is:

This measure satisfies our criteria, because of the -p*log2(p) construction: when p gets close to zero (i.e., the category has only a few examples in it), then the log(p) becomes a big negative number, but the p part dominates the calculation, so the entropy works out to be nearly zero. Remembering that entropy calculates the disorder in the data, this low score is good, as it reflects our desire to reward categories with few examples in. Similarly, if p gets close to 1 (i.e., the category has most of the examples in), then the log(p) part gets very close to zero, and it is this which dominates the calculation, so the overall value gets close to zero. Hence we see that both when the category is nearly - or completely - empty, or when the category nearly contains - or completely contains - all the examples, the score for the category gets close to zero, which models what we wanted it to. Note that 0*ln(0) is taken to be zero by convention.

We now return to the problem of trying to determine the best attribute to choose for a particular node in a tree. The following measure calculates a numerical value for a given attribute, A, with respect to a set of examples, S. Note that the values of attribute A will range over a set of possibilities which we call Values(A), and that, for a particular value from that set, v, we write Sv for the set of examples which have value v for attribute A.

The information gain of attribute A, relative to a collection of examples, S, is calculated as:

The information gain of an attribute can be seen as the expected reduction in entropy caused by knowing the value of attribute A.

As an example, suppose we are working with a set of examples, S = {s1,s2,s3,s4} categorised into a binary categorisation of positives and negatives, such that s1 is positive and the rest are negative. Suppose further that we want to calculate the information gain of an attribute, A, and that A can take the values {v1,v2,v3}. Finally, suppose that:

s1 takes value v2 for A
s2 takes value v2 for A
s3 takes value v3 for A
s4 takes value v1 for A

To work out the information gain for A relative to S, we first need to calculate the entropy of S. To use our formula for binary categorisations, we need to know the proportion of positives in S and the proportion of negatives. These are given as: p+ = 1/4 and p- = 3/4. So, we can calculate:

Entropy(S) = -(1/4)log2(1/4) -(3/4)log2(3/4) = -(1/4)(-2) -(3/4)(-0.415) = 0.5 + 0.311 = 0.811

Note that, to do this calculation with your calculator, you may need to remember that: log2(x) = ln(x)/ln(2), where ln(2) is the natural log of 2. Next, we need to calculate the weighted Entropy(Sv) for each value v = v1, v2, v3, v4, noting that the weighting involves multiplying by (|Svi|/|S|). Remember also that Sv is the set of examples from S which have value v for attribute A. This means that:

Sv1 = {s4}, sv2={s1, s2}, sv3 = {s3}.

We now have need to carry out these calculations:

(|Sv1|/|S|) * Entropy(Sv1) = (1/4) * (-(0/1)log2(0/1) - (1/1)log2(1/1)) = (1/4)(-0 -(1)log2(1)) = (1/4)(-0 -0) = 0

(|Sv2|/|S|) * Entropy(Sv2) = (2/4) * (-(1/2)log2(1/2) - (1/2)log2(1/2))
                                      = (1/2) * (-(1/2)*(-1) - (1/2)*(-1)) = (1/2) * (1) = 1/2

(|Sv3|/|S|) * Entropy(Sv3) = (1/4) * (-(0/1)log2(0/1) - (1/1)log2(1/1)) = (1/4)(-0 -(1)log2(1)) = (1/4)(-0 -0) = 0

Note that we have taken 0 log2(0) to be zero, which is standard. In our calculation, we only required log2(1) = 0 and log2(1/2) = -1. We now have to add these three values together and take the result from our calculation for Entropy(S) to provide us the final result:

Gain(S,A) = 0.811 - (0 + 1/2 + 0) = 0.311

We now look at how information gain can be used in practice in an algorithm to construct decision trees.

The calculation for information gain is the most difficult part of this algorithm. ID3 performs a search whereby the search states are decision trees and the operator involves adding a node to an existing tree. It uses information gain to measure the attribute to put in each node, and performs a greedy search using this measure of worth. The algorithm goes as follows:

Given a set of examples, S, categorised in categories ci, then:

1. Choose the root node to be the attribute, A, which scores the highest for information gain relative to S.

2. For each value v that A can possibly take, draw a branch from the node.

3. For each branch from A corresponding to value v, calculate Sv. Then:

  • If Sv is empty, choose the category cdefault which contains the most examples from S, and put this as the leaf node category which ends that branch.
  • If Sv contains only examples from a category c, then put c as the leaf node category which ends that branch.
  • Otherwise, remove A from the set of attributes which can be put into nodes. Then put a new node in the decision tree, where the new attribute being tested in the node is the one which scores highest for information gain relative to Sv (note: not relative to S). This new node starts the cycle again (from 2), with S replaced by Sv in the calculations and the tree gets built iteratively like this.

The algorithm terminates either when all the attributes have been exhausted, or the decision tree perfectly classifies the examples.

The following diagram should explain the ID3 algorithm further:

6.3 A worked example

We will stick with our weekend example. Suppose we want to train a decision tree using the following instances:

Weekend (Example) Weather Parents Money Decision (Category)
W1 Sunny Yes Rich Cinema
W2 Sunny No Rich Tennis
W3 Windy Yes Rich Cinema
W4 Rainy Yes Poor Cinema
W5 Rainy No Rich Stay in
W6 Rainy Yes Poor Cinema
W7 Windy No Poor Cinema
W8 Windy No Rich Shopping
W9 Windy Yes Rich Cinema
W10 Sunny No Rich Tennis

The first thing we need to do is work out which attribute will be put into the node at the top of our tree: either weather, parents or money. To do this, we need to calculate:

Entropy(S) = -pcinema log2(pcinema) -ptennis log2(ptennis) -pshopping log2(pshopping) -pstay_in log2(pstay_in)
                   = -(6/10) * log2(6/10) -(2/10) * log2(2/10) -(1/10) * log2(1/10) -(1/10) * log2(1/10)
                   = -(6/10) * -0.737 -(2/10) * -2.322 -(1/10) * -3.322 -(1/10) * -3.322
                   = 0.4422 + 0.4644 + 0.3322 + 0.3322 = 1.571

and we need to determine the best of:

Gain(S, weather) = 1.571 - (|Ssun|/10)*Entropy(Ssun) - (|Swind|/10)*Entropy(Swind) - (|Srain|/10)*Entropy(Srain)
                          = 1.571 - (0.3)*Entropy(Ssun) - (0.4)*Entropy(Swind) - (0.3)*Entropy(Srain)
                          = 1.571 - (0.3)*(0.918) - (0.4)*(0.81125) - (0.3)*(0.918) = 0.70

Gain(S, parents) = 1.571 - (|Syes|/10)*Entropy(Syes) - (|Sno|/10)*Entropy(Sno)
                          = 1.571 - (0.5) * 0 - (0.5) * 1.922 = 1.571 - 0.961 = 0.61

Gain(S, money) = 1.571 - (|Srich|/10)*Entropy(Srich) - (|Spoor|/10)*Entropy(Spoor)
                          = 1.571 - (0.7) * (1.842) - (0.3) * 0 = 1.571 - 1.2894 = 0.2816

This means that the first node in the decision tree will be the weather attribute. As an exercise, convince yourself why this scored (slightly) higher than the parents attribute - remember what entropy means and look at the way information gain is calculated.

From the weather node, we draw a branch for the values that weather can take: sunny, windy and rainy:

Now we look at the first branch. Ssunny = {W1, W2, W10}. This is not empty, so we do not put a default categorisation leaf node here. The categorisations of W1, W2 and W10 are Cinema, Tennis and Tennis respectively. As these are not all the same, we cannot put a categorisation leaf node here. Hence we put an attribute node here, which we will leave blank for the time being.

Looking at the second branch, Swindy = {W3, W7, W8, W9}. Again, this is not empty, and they do not all belong to the same class, so we put an attribute node here, left blank for now. The same situation happens with the third branch, hence our amended tree looks like this:

Now we have to fill in the choice of attribute A, which we know cannot be weather, because we've already removed that from the list of attributes to use. So, we need to calculate the values for Gain(Ssunny, parents) and Gain(Ssunny, money). Firstly, Entropy(Ssunny) = 0.918. Next, we set S to be Ssunny = {W1,W2,W10} (and, for this part of the branch, we will ignore all the other examples). In effect, we are interested only in this part of the table:

Weekend (Example) Weather Parents Money Decision (Category)
W1 Sunny Yes Rich Cinema
W2 Sunny No Rich Tennis
W10 Sunny No Rich Tennis

Hence we can calculate:

Gain(Ssunny, parents) = 0.918 - (|Syes|/|S|)*Entropy(Syes) - (|Sno|/|S|)*Entropy(Sno)
                          = 0.918 - (1/3)*0 - (2/3)*0 = 0.918

Gain(Ssunny, money) = 0.918 - (|Srich|/|S|)*Entropy(Srich) - (|Spoor|/|S|)*Entropy(Spoor)
                          = 0.918 - (3/3)*0.918 - (0/3)*0 = 0.918 - 0.918 = 0

Notice that Entropy(Syes) and Entropy(Sno) were both zero, because Syes contains examples which are all in the same category (cinema), and Sno similarly contains examples which are all in the same category (tennis). This should make it more obvious why we use information gain to choose attributes to put in nodes.

Given our calculations, attribute A should be taken as parents. The two values from parents are yes and no, and we will draw a branch from the node for each of these. Remembering that we replaced the set S by the set SSunny, looking at Syes, we see that the only example of this is W1. Hence, the branch for yes stops at a categorisation leaf, with the category being Cinema. Also, Sno contains W2 and W10, but these are in the same category (Tennis). Hence the branch for no ends here at a categorisation leaf. Hence our upgraded tree looks like this:

Finishing this tree off is left as a tutorial exercise.

6.4 Avoiding Overfitting

As we discussed in the previous lecture, overfitting is a common problem in machine learning. Decision trees suffer from this, because they are trained to stop when they have perfectly classified all the training data, i.e., each branch is extended just far enough to correctly categorise the examples relevant to that branch. Many approaches to overcoming overfitting in decision trees have been attempted. As summarised by Tom Mitchell, these attempts fit into two types:

  • Stop growing the tree before it reaches perfection.
  • Allow the tree to fully grow, and then post-prune some of the branches from it.

The second approach has been found to be more successful in practice. Both approaches boil down to the question of determining the correct tree size. See Chapter 3 of Tom Mitchell's book for a more detailed description of overfitting avoidance in decision tree learning.

6.5 Appropriate Problems for Decision Tree Learning

It is a skilled job in AI to choose exactly the right learning representation/method for a particular learning task. As elaborated by Tom Mitchell, decision tree learning is best suited to problems with these characteristics:

  • The background concepts describe the examples in terms of attribute-value pairs, and the values for each attribute range over finitely many fixed possibilities.
  • The concept to be learned (Mitchell calls it the target function) has discrete values.
  • Disjunctive descriptions might be required in the answer.

In addition to this, decision tree learning is robust to errors in the data. In particular, it will function well in the light of (i) errors in the classification instances provided (ii) errors in the attribute-value pairs provided and (iii) missing values for certain attributes for certain examples.

Adapted from S. Colton. Parts: © Simon Colton 2006.

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Killexams : Be the first to know

LINCOLN — Casey Thompson outlined his future in pen and crayon.

This was eighth grade — March 2014 — long before he was a high-profile Power Five football player and even before quarterback became his position of choice. When Thompson put his dreams on paper.

“Story of My Life,” reads the title written neatly on a sketch of an orange book. A winding timeline illustrates childhood highlights and predicts the next 13 years of his life. Of 11 entries, eight have something to do with football.

The sport has been Thompson’s consuming focus for as long as he can remember. It has brought him fame and accolades. He’s earning name, image and likeness profits and, probably, a shot at pro ball.

But there are places he can go where his own story is a footnote against the backdrop of time.

This is both improbable and refreshing for the 23-year-old who has lived much of his life in the public eye. Son of an Oklahoma quarterback. Brother of an NFL player. Former starting QB at Texas. Odds-on favorite for the same job at Nebraska.

Such titles describe Thompson’s football background but a card he’s carried since early in college reminds him there’s more to life than athletics and himself. That his bloodlines include not only gridiron stars but historical figures whose influence carries on more than a century later.

Thompson is an enrolled member of the Kiowa Tribe of Oklahoma. He receives prayers and support from the tribe headquartered 90 miles southwest of Oklahoma City in the town of Carnegie. He could go to a Sun Dance or powwow there or in neighboring states and be welcomed as much because of who his ancestors are as who he is.

“The best way to stay connected is through the elders of the family and the tribe,” Thompson says. “They’re like a walking dictionary. They’re always there to answer questions and to remind me where I’m from.”

A descendant of chiefs and warriors, Thompson is believed to be the first prominent Husker football player with Native roots. It’s easier to have perspective on thumb injuries and quarterback competitions after growing up hearing tales of men who fought life-and-death battles, including two great-great-great-great-grandfathers who were once held hostage by a duplicitous U.S. military commander named George Custer in the late 1860s.

That fighting spirit resides in Thompson, too. There’s something about football that has always brought it out, from his days around Oklahoma City as a standout running back to his emergence as a four-star quarterback recruit and, last year, a starring role with the Longhorns.

Tough, unplanned moments inevitably happen. Thompson has never felt alone in them.

“I’m going to control what I can control and the results will take care of themselves,” Thompson says. “Usually what happens is I come out on top or victorious.”

Ancestry of warriors

Kendal Thompson can’t wait to be there when his younger brother receives a Kiowa name.

The eldest of Charles and Kori Thompson’s three boys was in a situation similar to Casey when he experienced the honor in the summer of 2015. The quarterback had transferred from Oklahoma to Utah late in his college career and found himself more appreciative and curious than ever about his indigenous origins.

The Kiowa are given English names at birth, with Native names usually coming later in life when the family decides an individual is ready. The Kaubin family — Kori’s father, Norman, who was Native — has traditionally handed down names from earlier generations.

Kendal received his at a ceremony that included an elder blessing him by waving over him a smoking bundle of cedar, sage and sweet grass in front of gathered family and friends. The smoke and prayer symbolize armor against the dangers ahead.

Kendal, wearing beaded moccasins and other items provided by the tribe, concluded the event by performing a dance by himself. Then family, including Casey, joined in. Tribal members are welcome to provide donations, with standard practice being for the honoree to provide the gifts back to the community and its members in need.

The oldest Thompson’s tribal name is pronounced “Kooey-shun” — Kiowa language isn’t written — and translated as “Little Wolf.” He now shares the honor with his great-grandfather.

“It’s something we take pride in, especially being athletes,” Kendal Thompson says. “Being Native American athletes, I feel like we can inspire some of the Native American youth and show that those kids can aspire to be great in sports or off the field, as well.”

Football and family ancestry have overlapped since the game began capturing the nation’s imagination in the early 1890s.

Casey Thompson had relatives who played under coach Pop Warner at the Carlisle (Pa.) Indian Industrial School at the turn of the 20th century.

His maternal grandfather, Norman Kaubin, was a standout offensive guard who reached the state college level and later became a longtime coach at Capitol Hill High School in Oklahoma City.

Kaubin, who died in 2003 when Casey was 5, revered Nebraska coach Tom Osborne. Family members buzzed when Thompson chose the Huskers out of the transfer portal in January. It felt like more than a coincidence.

Casey Thompson was often around his mother’s family growing up, especially early on as his father, Charles, pursued a career in the Canadian Football League. Everyone had nicknames — grandpa Norman was “Deda,” great-grandma Ida was “GR” and great-aunt Franda Flyingman is “FA.”

Ida, who died in her late 80s in 2007, was the “OG of the family,” Casey says. She was healthy and strong until the end, with a notoriously stern personality that served the family well. Flyingman — Norman’s sister who in Kiowa terms is considered a grandmother to Casey — is an expert on her family history and active in tribal affairs. Both were central figures in Casey’s Native upbringing.

Flyingman doesn’t yet know what Casey’s Kiowa name will be, but it will be decided by those who know and love him in the months to come. He doesn’t provide up — that could fit in a Kiowa name — as he’s shown through winning a starting job at Texas and overcoming a thumb injury to likely repeat the feat in Lincoln. He obsessively pursues perfection, manifested in ways both obvious (like thousands of hours and counting of film study) and subtle (his closet wardrobe is color-coded).

“I know we need to do Casey’s name soon,” Flyingman says. “It’s his time. He’s a strong character. He comes from a line of men that were tough and mean. They were shrewd and smart.”

One was Lon Ahpeatone (pronounced ahp-ee-ton), the last chief of the Kiowa and a great-great-great-grandfather to Casey. He was most known for dispelling to his people the “Messiah Craze” of the 1890s — the belief of an imminent apocalypse with life as they knew it ending after the Dawes Act of 1887 established breaking up reservations for individual Natives to farm.

Another great-great-great-grandfather and chief is named in a U.S. Supreme Court case — Lone Wolf v. Hitchcock — that ultimately decided in 1903 Congress could unilaterally alter a treaty with an American Indian tribe.

Perhaps the most famous is Satanta (pronounced say-TAN-day), a Kiowa war chief in the 1860s and 1870s and great-great-great-great-grandfather to the Nebraska quarterback. He signed multiple treaties. He once stole a bugle from a skirmish with U.S. soldiers and played it in future encounters to cause confusion.

Satanta was taken into custody multiple times including once by Custer, who went back on a truce and held him until the Kiowa people moved to a reservation in southwestern Oklahoma — the tribe had once lived as far north as Montana and the Rocky Mountains. Satanta later died in a prison in Huntsville, Texas, jumping out of a second-story window in 1878 when told he would never be released.

Casey Thompson lists other major nations he grew up around in Oklahoma City. Cherokee. Chickasaw. Darrell Flyingman, Franda’s husband, for a time was governor of the Cheyenne and Arapaho Tribes of Oklahoma.

Thompson’s direct ancestors have as compelling a history as any of them.

“Kiowa is right there,” Thompson says. “I’m happy to be part of the tribe.”

Planner and perfectionist

That piece of paper on which Thompson mapped out his life? So far, so good.

He accurately predicted a year in advance when he would earn his first football scholarship offer — it came from SMU in May 2015.

He correctly called that he would be an early high school graduate so he could enroll in college sooner.

He’d take a mulligan on his school choice (he drew an Oklahoma Sooners logo but nothing from Texas or Nebraska). Same with his NFL draft year (he wasn’t selected in 2021).

Thompson is still theoretically on track to be married with children and a reigning world champion by 2027.

“He had it a little bit off. He wanted to get in the draft earlier,” Kori Thompson says. “He could still win the Super Bowl in 2026. He’s dead serious about it.”

Casey Thompson is a capital-P Planner. Always has been. He put together a coaching gig by age 7, borrowing cones and other training equipment from his father to set up at a football field a few houses down. Neighborhood kids paid him for lessons, with young Casey running drills with a whistle around his neck and a clipboard in hand.

He also started a shoe-cleaning business, earning $5-10 for each service using materials he would purchase every year from a popup tent outside the Cotton Bowl before the Texas-Oklahoma game. He bought retro Jordans and other footwear, cleaned them and sold them at a profit.

Says Flyingman: “When we would see him, he would say, ‘Your shoes need a little bit of work.’”

Thompson can still expound on the finer details of shoe maintenance. Knowing the fabric is priority No. 1, he says. Next is understanding how to clean the laces — some can go in the washer, some can be bleached, some should be scrubbed. He uses a toothbrush and soap in a pinch.

Thompson traded toys for tidying up and vacuuming the house on his own by the time he was 3. These days he regularly cleans the inside of his football helmet and washes his shoulder pads every couple weeks. He washes his workout shoes to keep them from getting smelly.

“Nobody else does this,” he says with a smile. “I’ve always been a cleaner and perfectionist at heart.”

An eye for details and unusual memory recall shocked his mother growing up. She would bump a lined-up shoe a quarter inch and hear about it. He once asked her about the brand of a shoe worn by a stranger he saw in a convenience store a year earlier, intricately describing the design.

That focus is now on football and football only.

Thompson removed emotion from his offseason transfer decision, writing out pros and cons about each school’s coaching staff and offensive history. He knows enough about Kenny Pickett — the Heisman Trophy finalist that NU offensive coordinator Mark Whipple worked with last year at Pittsburgh — to fill a few scouting reports.

Thompson took Nebraska offensive linemen out to dinner and bowling in the winter and brought his iPad along to study plays. Outside pressure at Texas never bothered him — missed in-game opportunities, often during some of his best performances, eat at him more. He’s practical, Kori Thompson says, to the point that he’ll easily choose getting a teammate right on the field even if it risks the friendship.

“He’s tough; he’s brutal,” Kori Thompson says. “In his mind, he’s like, ‘Why do you want to go out every night on the weekend when you don’t even know all your plays? You can have fun after you go to the league.’”

Kendal Thompson likened his brother’s work ethic and drive to Los Angeles Rams coach Sean McVay, whom Kendal spent time with in Washington and L.A. What can come off as “weird” behavior at first is actually all-in commitment.

“I’ve been in the pros and been around a lot of quarterbacks who get paid a lot of money to do what he does,” Kendal Thompson says. “I think he prepares and goes about his days just like those guys. I think if he continues to do that, good things will happen for him and good things will happen for the University of Nebraska.”

Extensive preparation and memory allow him to reference specific moments in just about any game or practice, exact or otherwise. He does it with media members, coaches and teammates alike. Fellow Texas transfer and receiver Marcus Washington has quickly adjusted to Nebraska’s offense in part because of how detailed Thompson has been in their offseason film sessions translating terminology.

Thompson’s mind also takes him into other nuanced directions.

How many laces are on a football. The difference between leathers on the ball. How to optimize daily and weekly schedules. He’s working with Nebraska sports psychology to be in the moment but still finds himself mulling over — if not always planning for — the future.

“I wonder if the broadcasters or ESPN or these TV companies realize (that) to watch the game you have to see the defense and the coverages,” Thompson said. “But they just like always zoom in (on the quarterback). Little things like that are what I think about when I’m watching the game of football.”

Blending of worlds

The Thompsons would usually attend major tribal gatherings during summers and holidays. Red Earth festivals. The Gourd Clan celebration.

They weren’t regulars at every weekend powwow and Sun Dance, though, while growing up with busy sports schedules.

In a way, Casey says, the split time is reflective of his diverse heritage. Extended family that sat in the bleachers for his games often ran the color gamut, from African American relatives on his father’s side to Whites and Natives on his mother’s.

His full name is Casey Deion Thompson, with the middle name an homage to former NFL star Deion Sanders and a reminder from Dad of Casey’s potential. Charles embraced Kori’s Native background as well — he used to wear a head band at Oklahoma that read “Hanta Yo,” a Lakota-Sioux term understood to mean “clear the way.”

Thompson — the middle of three boys — knows the diversity serves him well, especially in the melting pot that is a college football locker room. He doesn’t identify more as one ethnicity than another.

“Warriors never quit” is a message a Native chief once put in Kendal’s locker during his time at Utah, and Casey feels it, too. He’s playing for himself and his team, yes, but also Native generations before him and ones ahead of him that can still be inspired by what is possible.

Recent pro quarterbacks like Sam Bradford (Cherokee), Bryce Petty (Chickasaw) and Tyler Bray (Citizen Band Potawatomi) have done it. Now Thompson figures it’s his time, on a Nebraska stage new to Native influence.

Channel that warrior spirit, says one side of his family. Have a dog mentality, says another.

Somewhere in the overlap is Thompson. A planner. A fighter. Armed with a conviction that has endured the test of time.

“He’s smart and he’s not afraid,” Flyingman says. “He doesn’t provide up and he doesn’t get mad. He looks at options and figures it out — that’s the best thing about him.”

Sat, 18 Dec 2021 12:38:00 -0600 en text/html
Killexams : Using AI To Speed Up Edge Computing

AI is being designed into a growing number of chips and systems at the edge, where it is being used to speed up the processing of massive amounts of data, and to reduce power by partitioning and prioritization. That, in turn, allows systems to act upon that data more rapidly.

Processing data at the edge rather than in the cloud provides a number of well-documented benefits. Because the physical distance is shorter between where data is generated and where it gets processed, latency is significantly reduced. That also reduces the amount of infrastructure needed to move data, because there is less of it to route after the initial processing. And it reduces the amount of power needed to move that data, as well as the cost of storing it. Yet all of those benefits can be extended by leveraging some form of artificial intelligence.

“The cloud certainly will play a role,” said Thomas Rosteck, Connected Secure Systems Division president at Infineon. “But there has to be some intelligence to reduce amount of data that goes to the cloud, concentrating it, and then getting an answer back. That’s an architectural issue.”

AI is a relatively new twist on edge design, where it is being used to identify and prioritize resources at both the chip and system levels. So while edge computing already is broadly deployed in many different sectors — including multiple layers of processing, which can span everything from within a sensor to layers of on-premise and off-premise servers — there is a recognition that much more value can be extracted from data and the systems that process it.

“If the wall box for my car can communicate with my solar system on the roof, they can agree when it’s a good time to load up the battery in the car,” Rosteck said. “If it’s cloudy, maybe that’s not the best time, so I only load it to 50% and wait until I have better weather conditions for the rest. Or if I know I’m driving to Los Angeles tomorrow, I will need a full battery so I can override my system and say I need more than 80%. In a building, we save energy by controlling the blinds to keep the sun out.”

According to the State of the Edge Report 2021, cumulative investments up to $800 billion will be spent between 2019 and 2028 on edge computing. About half of those investments will be on edge devices, with the other half on edge infrastructure.

Fig. 1: Market segments utilizing edge computing.

Fig. 1: Market segments that use edge computing.

Some of those expenditures will include AI/ML, which helps to optimize the compute systems at the edge, particularly on the inferencing side. While most experts believe training will continue to be done in large data centers using huge datasets, inferencing can be done locally using a variety of processing elements, including GPUs, FPGAs, eFPGAs, NPUs, and accelerators/co-processors. This local variety is important for a wide range of applications where bandwidth is limited or inconsistent, and where processing is constrained by the size or type of battery.

“Edge computing has many applications in the consumer space, such as virtual conferencing, either using a laptop or conferencing platform,” said Ashraf Takla, founder and CEO of Mixel. “During virtual conferencing, edge processing can be used for face detection, background blur, gesture control, intelligent muting, and object detection. Consumer wearables also could benefit from edge computing to detect important objects and sounds around the user, including local voice commands, advanced wake words to simplify device UI, and for facial and object recognition. However, because the resources — including power — are limited at the edge, AI is key to identifying and prioritizing the most relevant information to be further processed or transmitted to the cloud.”

Fig. 2: Comparison of edge and cloud computing. Source: Mixel

Fig. 2: Comparison of edge and cloud computing. Source: Mixel

The initial idea for IoT devices has evolved over time, merging into the broader edge computing concept. But rather than just a collection of dumb sensors that send data off to the cloud for processing through some gateway, many edge devices are now much more advanced.

“We have seen our customers use MIPI for SoCs powering smart devices like security cameras or other IoT devices relying on video and audio inputs. The SoC needs to interface with external sources (video and audio) and send the data to a pre-processing unit to make the image and audio more usable for the neural network,” Takla said. “The neural network clusters and SRAM are where the main processing occurs, including segmentation, identification, inferences, and where other functions take place.”

Many of these devices can locally partition and prioritize data, and they can do it using very little power. But they are more difficult and costly to design and manufacture.

“There are multi-level simulations needed,” said Roland Jancke, design methodology head in Fraunhofer IIS’ Engineering of Adaptive Systems Division. “You have a complex model that reflects all the functionality, and for different parts of the model you go deeper into the details. You don’t need that kind of detail if you’re just bringing in the data or seeing how it connects to other parts, but you do need to decide which parts to model in detail.”

Designing AI into chips
AI architectures frequently are designed around high data throughput with numerous processing elements working in parallel, often with small, localized memories. And for complex edge devices, which could include anything from a car to a smart phone, design tools utilize AI to create better AI chips, which are often combined with other chips in a package.

“It’s not just the silicon or the device that matters,” said John Lee, vice president and general manager of the Ansys Semiconductor business unit. “It’s also the software that goes along with that. The challenge that we see in this area is dynamic thermal management, where you’re designing silicon and you want maximum performance at some point. You need to throttle back that performance because temperature limits are being exceeded. And if you don’t do dynamic thermal management properly, then the performance of your system may be substandard. The only way to do that well is to understand what the real workloads are, which is the software itself, and then use emulation to boot up and run a complete workload. So it’s extremely important. AI/ML techniques are being used to address those challenges.”

Still, working with AI/ML adds other issues. “Our customers utilize AI/ML in various capacities to add value to their ADAS systems,” said Paul Graykowski, senior technical marketing manager at Arteris IP. “These systems use the capabilities of cameras, lidar, and other sensors to build a view of the world around the vehicle. For instance, when reading a speed limit sign, machine learning may detect the presence of a new speed limit. A new limit may then be fed to the cloud for additional learning, and then that data can be pushed back down to other vehicles on the road.”

But what is good enough accuracy for these AI systems, which typically is measured in probabilities based on data distributions, may vary by application and by user. “In the case of striving toward automation with ADAS, we must ensure compliance with standards such as ISO 26262,” Graykowski said. “Safety islands in SoCs, redundancy, and failure mode analysis are just a few of the techniques in play. All of these must be tested accordingly.”

The higher the accuracy, in general, the more compute resources and energy required to achieve it. This is especially evident in automotive, where design teams often are facing conflicting goals. “The real problem is meeting the environmental requirements in Europe, the United States, and Japan, because they all have different emissions targets,” said David Fritz, vice president of hybrid-physical and virtual systems for automotive and mil/aero at Siemens Digital Industries Software. “At the same time, chipmakers need to reduce the power consumption and add more compute capability as these cars get more intelligent. So you have to balance those two things. But how do you make it smarter without consuming more power?”

Many of these systems have a fixed power budget, and that can determine how a device is used or even what kind of batteries are used.

“If you put something like a camera up on the front of your house, you don’t want to have to go and change the batteries constantly,” said Rob Aitken, R&D fellow at Arm (at the time of this interview). “You just want it to work. That’s a fairly isolated application, but it’s representative of what’s going to happen in these other situations. If you have three different battery types in a car that operate in different modes at different times, you’ll have to design the system that includes the sensors, and whatever local processing you do or don’t do on those sensors that feeds that back into some kind of centralized compute system. All of that stuff is going to have some battery profile, but it’s operating on there. And the way that the computer is run — basically the partitioning of tasks on that stuff, the orchestration of them — is going to depend on what that battery is. From an Arm standpoint, we were mostly in the position of, ‘Tell us what your battery does and we can figure out maybe something that will optimize its performance. And we can produce a combination of hardware/firmware/software that will allow you to tailor your operation to whatever it is that you’re trying to optimize in your particular battery world.'”

Pre-trained models reduce ML training time
AI can help in all of these cases. The problem is that training AI to do what you want is time-consuming. A proliferation of pre-trained models has simplified this process, even if they are not fully optimized for a particular device. Integrating a pre-trained model is more cost-effective than ML training from scratch. For a retail application, for example, it would take extensive vision training for the edge device to learn what a human being looks like and how to count humans as they move around.

It is not unusual for ML to go through millions of images to do accurate predictions. Additionally, programming GPUs may also need to go through a learning curve. By using a model pre-trained on YOLOv4 object detection, developers potentially can bypass most of the training process. YOLOv4 is a flexible state-of-the-art framework written in a low-level language to detect real-time objects.

“AI-based edge device developers often encounter the challenge of coming up with a cost-effective approach to training the devices,” said Sam Fuller, senior director of inference marketing, Flex Logix. “With pre-trained models, developers can reduce the design cycle and go to test much quicker. Using the pre-trained EasyVision platform with an X1M chip (50 FPS of YOLOv4 detection), ML can yield great results. As a rough comparison, this combination will produce a performance 80 times greater than the same algorithm running on an Intel i5 processor without acceleration.”

Understanding PPA requirements is key
One of the big challenges for design teams is figuring out all the various possibilities and tradeoffs for a particular design, and understanding how a device will be used. Unlike in the past, when chips were primarily designed for a specification, even fabless design teams often have a good understanding of how and where a chip will be used. In some cases, they are working directly with the manufacturer to fine-tune a solution.

“Edge deployment scenarios can result in various potential solutions,” said Suhas Mitra, product marketing director for Tensilica AI products at Cadence. “To decide which hardware solution is more appropriate for an application depends a lot on understanding the key power, performance, and area (PPA) requirements during the design phase. This can result in many different possibilities or variants. For instance, a battery-operated tiny edge device (hearable or wearable) may require very low power and energy, but may not require high throughput. A well-tuned AI accelerator, DSP or MCU class of hardware could all suffice, but the final choice could depend on the area and power budget for the SoC.”

AI adds a whole new level of optimization possibilities, as well as some challenges.

“For software, end users will have to train their network with data to fine-tune AI workloads,” Mitra said. “Many deployment scenarios use open-source AI models that can alleviate the need to create and iterate on new AI models. At a network compile stage, there are two fundamental flows for processing AI workloads. One involves ahead-of-time (AOT) compilation, and the other one is more similar to a run-time based flow. Both of those flows exist today in various deployment scenarios based on the application needs. With programmable IP like DSPs, the end product could also receive over-the-air (OTA) updates in the same way our phones and various electronics do. This way, as the AI algorithm gets more refined/tuned/accurate, it could be sent over to the end product, leading to an overall better experience and performance improvement.”

AI design also involves numerous parameters. Developers have to consider what Arm or RISC-V control codes to use to run the operating system. Then the embedded CPUs, including DSP vectors and NPU accelerators, must be taken into account. The big question is how to optimize the partitioning to achieve maximum performance.

One approach is to integrate all of these functions into a single IP with code optimization. “Integrating the NPU, DSP, and the real-time CPU into a single IP saves developers a great deal of programming time and headaches,” said Steve Roddy, CMO of Quadric. “Digging into a function to determine the best partitioning across three different processor IP blocks takes a lot of effort. It is more efficient to run the performance-critical control and the DSP and ML graph codes on a single core.”

Security at the edge
Edge devices connected to sensors and networks are often vulnerable and need to be secured. The best way is to incorporate solutions with built-in security, and that can be both passive and active. It also can affect overall performance and power, and ultimately cost.

“It is expected that most edge devices will be self-contained,” said Gijs Willemse, senior director of product management at Rambus. “Nevertheless, they are vulnerable due to their interfaces and ability to receive software updates. Secure boot, device authentication, and secure communication, along with protection of provisioned key material are critical for devices that operate in the public domain and/or could be confiscated. This requires a hardware root of trust, and depending on the performance and latency requirements of the application, hardware acceleration to encrypt/decrypt the data transferred over its interfaces. These hardware security cores should include anti-tamper protections to guard against side-channel and fault injection attacks.”

Edge security is a growing concern. More connected devices widen the attack surface for other connected devices, as well as the device where the initial breach occurs.

“We don’t want someone to interfere with devices sitting at the edge,” said Arteris’ Graykowski. “One of the techniques we employ, which is built into our network-on-chip, is the ability to have firewalls embedded with the network to ensure only intended traffic reaches critical systems.”

Latest AI-based edge design test methods
The AI-based edge design process is complex because it has many moving parts. It involves machine learning, training, inferencing, and selecting the best AI chips/solutions and sensors. In addition, an AI system by definition is supposed to adapt and optimize. Choosing the correct test methods and models to reduce errors early in the design cycle is crucial.

This becomes more challenging the longer chips are in use in the field, and more important as they are used for longer periods of time in safety- or mission-critical applications. So a chip that adapts over a couple decades may look very different than when it was first manufactured. And if it is interfacing with other systems, its behavior may be difficult to predict at the outset.

“Starting even at the design stage, how do we add these in-silicon monitors into the chip itself — whether it’s a single die or a multi-die system — and then collect data as we go from production to in-field operation?” asks Bari Biswas, senior vice president for the Silicon Realization Group at Synopsys. “That falls into that space of software-defined hardware, where not only do we monitor, but we actually optimize there. We do a similar type of optimization that we do with our EDA design software. There are autonomous design systems that will optimize the design creation process. Now imagine those kinds of systems operating in the field, and then optimizing the variables that allow configuration of GPUs and CPUs.”

Still, there are a lot of moving pieces in this equation, and figuring out how a device will behave over time is difficult. “In general, testing for AI edge design involves various aspects, starting from model training to inference and the deployment phase,” said Cadence’s Mitra. “The goal is to design better, more robust AI networks. Monitoring various KPIs during pilot run phases and detecting anomalies are important prior to the deployment phase. Adopting a more continuous cycle of testing and monitoring helps in understanding how to make better networks by collecting and monitoring both normal and adversarial use cases.”

As chips perform more and more of edge computing and AI functions, it is important to reduce errors in the design process. Today register transfer level (RTL) is still the most popular language used in designing SoC, FPGA, and ASIC. Whether it is an edge only or an AI-based edge design, the ultimate goal is to achieve performance, power, and area (PPA) optimization. If errors can be caught early in the design cycle, it means major cost savings.

Digital hardware design has moved from the gate level to the register transfer level. Today, High-Level Synthesis (HLS) is used to synthesize the algorithm design in C++ or SystemC code to RTL. In a system design, when the functional verification is performed at the RTL successfully, the error rate will be reduced to the absolute minimum.

“In AI-based edge computing, decisions at the edge, including smart IoT, are made based on sensor input and analytics. This way, the cloud servers do not need to be involved unless extensive computations are required, cutting down on the cloud traffic,” commented Anoop Saha, senior manager, Strategy and Business Development, Siemens EDA. “While there are benefits to AI-based edge computing, designing such systems can be challenging. Because of the complexity of AI and AI chips, errors can be introduced in the design process. To reduce the cost of redesign, it is important to use the right verification tools such as HLS to carefully perform pre-HLS and post-HLS verifications. By taking this approach, the designers will be able to eliminate errors in the AI-based algorithm and architecture.”

Fig. 3: A good HLS test method includes both pre-HLS and post-HLS verifications. Source: Siemens EDA

Fig. 3: A good HLS test method includes both pre-HLS and post-HLS verifications. Source: Siemens EDA

Edge computing has many benefits, including low latency, reduced cloud traffic, local decision making, and overall cost reduction. While embedded AI will enhance edge computing performance, its deployment presents some challenges. The necessity to implement ML training and inferencing is now being aided by the use of pre-trained models, standard-based models, and integrated IP. Prioritizing security and using the latest AI-based edge design test methods will also help.

The bottom line: AI is expected to be an increasingly integral part of edge computing.

—Ed Sperling contributed to this report.


MIPI in Next Generation of AI IoT Devices at the Edge | Mixel, Inc.

State of the Edge Report 2021 – State of the Edge

Download SNUG presentation: Using machine learning for characterization of NoC components (

Mon, 18 Jul 2022 19:03:00 -0500 en-US text/html
Killexams : 15 OUTSTANDING THINGS TO DO IN SAN LUIS OBISPO No result found, try new keyword!Meet at the middle ground of LA’s fast life and the futuristic San Francisco and take things a bit ‘SLO-er’ in San Luis Obispo. Whether you’re seeking new outdoor adventures or want a laid back place ... Tue, 02 Aug 2022 03:33:41 -0500 en-us text/html Killexams : Hollywood’s Chief Diversity Officers Tell All

After George Floyd’s killing by police in May 2020, demands on D&I leaders "exploded," with burnout and a critical balancing act between CEOs and employees constantly at hand — but also signs of progress.

This is the first in an ongoing series on the status of progress with inclusion in Hollywood.


Latasha Gillespie remembers having a doctor’s appointment on May 30, 2020. It was five days after George Floyd was suffocated by a police officer, his excruciating nine-minute, 29-second death — captured on video and witnessed by millions around the world — igniting an unprecedented social response of outrage and horror. Gillespie, like many other heads of inclusion in Hollywood and at companies across the country, had been working nonstop since that moment, meeting with leadership to provide guidance and messaging and with employees to listen and hold space.

“I was in heavy mode doing this,” says the Amazon Studios head of global diversity, equity and inclusion of her state of mind upon showing up at the doctor’s office. The last section of her new patient forms, on mental health, gave her pause: Are you feeling anxiety? Do you feel depressed? Are you having trouble sleeping? “Every question, my answer was yes,” she says. “I’d been in go-go-go mode; I hadn’t stopped to acknowledge I wasn’t OK. Not from a human perspective — as a daughter of a Black father, wife of a Black man, mother of two Black boys. But I also understood: When things like this happen, you have a window to make real change, not just performative stuff.”

Many of her counterparts around town were feeling the same way. “We evolved into counselors and therapists, trying to help people make sense of what is happening out in the world and how it impacts them in the workplace. Individually, we were forced to compartmentalize our own feelings,” says Sony Pictures chief diversity officer Paul Martin. “I had feelings about watching [Floyd’s murder] and how to explain this to my son, but my feelings had to become less of a priority as I began to deal with this from a more organizational business lens.”

CDOs found their responsibilities suddenly amplified in the summer of 2020. “My schedule exploded,” says NBCUniversal CDO Craig Robinson. “Within a week’s time, I had 25 invitations to speak to virtual town halls across the company. I spoke to eight or nine thousand of our employees over about 10 days.”

In the early months of a pandemic that physically blurred the boundaries of work and home life, many people turned to their companies not just to help navigate their thoughts and emotions, but also to demand action. “Folks wanted to make us social justice experts. That’s not my lane, but our partners are,” says Motion Picture Association vp external and multicultural affairs John Gibson, who works with 56 media equity groups as part of his job.

And organizations without a CDO scrambled to get one. “I’ve never had more job offers in my life,” says Jeanell English, an operations and human resources veteran at Discovery who left in November 2020 to expand the office of representation, inclusion and equity at the Academy of Motion Picture Arts and Sciences. “Everyone was like, ‘Oh my God, we need someone who knows what they’re doing and can help us think about diversity.’ “

Whether motivated by mounting pressure from employees and consumers to reflect certain ideals — a 2020 Glassdoor study found “culture and values” as the top driver of employee satisfaction, well above “business outlook,” “work-life balance” and “compensation and benefits” — or by the evidence that diverse businesses yield greater returns, organizations are seeking out and leaning on chief diversity officers more than ever. But whether the CDO becomes a vital member of executive leadership or is simply a shield or scapegoat when criticism arises depends on how the role is deployed. “You cannot hire one person and expect them to change an organization,” Gillespie says. “If you’re not ready to staff them up with a team and provide them resources and a budget, it’s performative, disheartening and you’re setting that person up for failure. Usually that person is a woman, from an LGBTQ community or a person of color, and when it doesn’t go well, then that person is the problem, not the organization. It’s dangerous.”

The C-Suite’s Hottest Seat

The number of diversity heads across organizations worldwide rose 107 percent between 2015 and 2020, according to LinkedIn, which also reported a spike in D&I-related job posts in June 2020 following Floyd’s killing, 4.3 times the number of openings listed 60 months prior.

In Hollywood, the major studios and most of the big guilds and streamers already had CDOs for at least a few years before that, thanks in part to earlier industry-specific inflection points, like #OscarsSoWhite in 2016. “While [inclusion] is everyone’s job, we need a professional just like we would with finance, legal or content,” says Netflix vp inclusion strategy and former outside consultant Vernā Myers of the streamer’s decision to bring her in-house in 2018. The past two years have seen agencies and awards bodies adopt the role as well. CAA and WME hired heads of inclusion in November 2020 (WME parent Endeavor created a chief inclusion officer role in 2019, during its first attempt to go public), while UTA added its first chief diversity officer in January.

But not all CDO jobs are created equal, and how the role is structured varies widely. The corporate D&I practice originally served to monitor compliance with workplace anti-discrimination and equal opportunity laws signed in the 1960s, but now encompasses wider functions. “We’re starting to move outside of just counting heads to figuring out how to make every head count,” says Martin. “Where we started was trying to help our creative execs stay out of trouble; we were program administrators, whereas now we’ve evolved into strategic partners.”

DEI objectives at entertainment and media companies have developed a robust focus on content. “We have the ability to inform social and political narratives with a single piece of content, and that is a lot of power,” says English, who began her career at Lockheed Martin. The purviews of Hollywood CDOs cover some version of what the industry sees as the front lines of the issue: staff diversity metrics, inclusive work culture, supplier diversity and representative content. DEI teams (not including volunteer employee diversity groups) at the biggest companies are often organized like a wheel, with a central hub and staffers specializing within geographic regions and/or business units, as is the case with Netflix’s team of 25.

Endeavor head of impact and inclusion Romola Ratnam oversees a staff of 16, split into three teams to tackle people-centered hiring and retention; systemic research on pipeline and compensation structures; and workplace cultural programming. “It’s three different groups that are synergistic but have different responsibilities,” she says. “Inclusion means so many things, and there are so many jobs to do.”

Team sizes vary: Gillespie leads 20 DEI heads across Amazon Studios, Prime Video and IMDb, while Gennean Scott, who became the Broadway League’s first director of equity, diversity and inclusion in July 2021, describes herself as an executive who freely moves among departments. “One of the things we talked about is that EDI should not be separate,” says Scott of the trade association, which has only about 20 total employees. “I utilize every staff — communications, marketing, education, union, IT and social media. It lets me know that the organization understands EDI is part of everything we do.”

While today’s DEI certified universally caution against companies confining their work within a silo, D&I practitioners have traditionally sat inside HR departments, a positioning that still exists in some companies. “A lot of people feel that can be a great model, and others feel that can be difficult,” says CAA head of global inclusion strategy Sharoni Little, who reports to the agency’s chief HR officer. “For me, because we’re people-centered, I think it’s a natural place to be partnered.” As senior vp HR at Starz, Jamila Daniel reports to the CEO, and after Floyd’s death she took on a second role as CDO of parent company Lionsgate, through which she reports to the studio’s chief HR officer.

There is an emerging belief among business management strategists that the head of diversity position is best served directly reporting to the CEO. At a salon dinner hosted by WarnerMedia equity and inclusion execs in December at Mastro’s in Beverly Hills, the then-company’s then-chief enterprise inclusion officer Christy Haubegger touted the significance of reporting directly to the CEO, calling it evidence of the studio’s prioritization of the 50-plus E&I team. But after Discovery closed on its merger with Warners in April, Haubegger and her CEO were out, leaving the new Warner Bros. Discovery as the only major in town with a CDO vacancy. WBD CEO David Zaslav is seeking to fill a position that will report to him and chief people and culture officer Adria Alpert Romm, a dual structure that some remaining E&I staffers fear will be a demotion and potentially diminish their work.

“In general, I think CDOs should report to the CEO,” says Gillespie, who reports to Amazon Studios head Jennifer Salke. “A lot of times the hard work is actually in HR, so it’s harder when you’re [there] versus being agnostic. When you solely relegate diversity to HR, you send a clear message how you think about it. There’s this rut when we think about DEI from an HR perspective: How many times are you going to unconscious-bias train people to death?”

Adds Robinson, who reports to NBCUniversal CEO Jeff Shell, “There’s the advantage of having a direct line to the person that runs the company, which facilitates and makes conversations easier.” Like other members of Shell’s executive team, Robinson is expected to provide a report at every leadership meeting: “Making [diversity] a recurring item just as you would a financial update keeps it top of mind, not something that one only talks about at times of crisis.”

A Balancing Act Like No Other

One side effect of 2020’s corporate embrace of black squares and justice pledges has been increased expectation among the public and staffers that companies should make business decisions in line with cultural values they’ve heartily espoused. Emboldened employees and a more vigilant audience are quicker to hold organizations accountable to their publicity-friendly commitments, leaving CDOs in the position of pushing for progress internally while serving as, literally, the organization’s face of diversity externally.

This year, after Florida’s legislature passed the “Don’t Say Gay” bill banning teachers from discussing gender and sexual orientation in classrooms, Disney (given its theme parks and significant investment in the state) came under pressure from its employees for its financial contributions to anti-gay Florida officials as well as its initial silence over the legislation. On March 2, CDO Latondra Newton sent out an internal statement, titled “Showing Support for Our LGBTQ+ Community,” promising that the company would discuss the “issues of concern” at gatherings scheduled for March 22 and April 13. “Our hope in having a CDO would be to hold the company to account, rather than release statements that toe the company line,” read a response to Disney leadership signed by “the queer/LGBTQIA+ employees of The Walt Disney Co. and their allies” that was posted on social media March 8. “We need an advocate, not a figurehead.”

Disney declined to make Newton available for comment, but her counterparts acknowledge the skepticism that surrounds the job. “We’re trying to make a change within a capitalist corporate situation, so you have to be a lot of different people for a lot of different people,” says Netflix’s Myers. “I try to stay close to the people experiencing the most direct impact, and you have to come up and down [the organizational ranks]. As a leader, to the extent I can, [I] communicate the stakes and details. [Sometimes] you have to call people out, but you’ve got to do it in a compassionate way that they still want to be part of the change.”

Netflix experienced its own public employee protest in October over transphobic content in its Dave Chappelle special The Closer. “How do you hold space when people are coming with real hurts? It was a lot about translating, being empathetic and making sure the very difficult conversations could happen,” says Myers, whose role was to provide a two-way conduit of communication between aggrieved employees and the business leaders. “There are differences in power, representation and lived experiences, and trying to create a bridge for people to actually see each other as human beings is the hardest work I’ve ever done. We weren’t able to make everybody happy, but we were there for all the many stakeholders.”

Shortly after Robinson succeeded Paula Madison as NBCUniversal’s diversity head 11 years ago, some employees told him he wasn’t fighting hard enough for progress. “By some you’re seen as the company watchdog, by others as somebody who’s not doing enough to push this or that agenda. I found sometimes privately speaking truth to power could be more effective because you also need to have the trust of your bosses, and if they feel you are an external advocate, that’s not going to be a winning strategy in the long run. It’s a constant balancing act with a lot of stakeholders, and you must build equity and credibility with each of them every day.”

Martin sees his function as an advisory one. “When we’ve had our own crises, my role has always been to provide the senior-most leader all of the pertinent information to help them make the most informed decision,” says the exec, whose first big test as Sony’s inaugural CDO was dealing with some of the racially insensitive emails revealed in the studio’s 2014 email hack, which occurred mere months after he started the job. “In that room, the head of external policy, legal, CHRO are all there, and we’re different lenses. The leader is being paid to make very tough, very important decisions, and if they make one I wouldn’t have and a crisis happens, I’m still here as a resource on how to move forward.”

Paramount global head of inclusion Marva Smalls sometimes calls herself a “double agent,” but one in which her various interests are laid bare, and she is “not just responding to the company and shareholders, but also helping our employees understand there are tension points in a stance and balancing how you will say it and engage around it.” The veteran exec has offered her resignation to every new CEO since the first time the company was known as Viacom: “I can operate in gray, but [a CEO and I] have got to be clear that we share an equal commitment in diversity, equity and inclusion. I am not here to window-dress, because my personal reputation in this space means more to me.” Adds Gillespie: “There’s a freedom that comes when you understand that you have lived half your career [and you can say], ‘I’m aligned to my values first, and I’m OK speaking truth to power when they don’t align with where we’re going.’ “

That friction between company and CDO may be one reason turnover in the role now averages three years, according to The Wall Street Journal — or a matter of months, as was the case at the Hollywood Foreign Press Association, which, after the February 2021 Los Angeles Times exposé about its racial exclusion issues, cycled through a number of representatives, including a crisis manager and D&I adviser, before hiring its first CDO in November. “The role is very unique in that it requires you to be in two places,” says Neil Phillips, who might have the most scrutinized CDO job in town. “One of them is squarely invested in believing in the organization and its prospects, so I’ve got to be on Team HFPA. That said, I also have to maintain a significant, meaningful level of psychological distance and objectivity to be able to assess where we are as an organization, because I can’t let my confidence in the HFPA and its progress blind me to the work that needs to be done.”

For Phillips, some idealism is necessary to do his job. “We’re in trouble as a society if we get great at calling people out for transgressions but don’t leave room for them to grow,” he says. “The number of organizations that need to do deep dives around diversity, equity and inclusion is big, and the number actually doing the work is small, so I try to afford those a significant amount of grace, because we need them to prove it is possible.”

Keeping Progress Going

“At the end of last year, I realized that I had not put my own oxygen mask on,” says Smalls of hitting a state of exhaustion after 18 intense months of helping her company navigate social and cultural issues amid an ongoing pandemic. “I allowed a little bit of me to be chipped away by [not] taking the time to think about what these moments meant to me, versus always wanting to be on and be there for everybody.” She sent an email to Paramount Global CEO Bob Bakish and four of her critical partners, informing them she was going off the grid for a bit (a whole two days). “I’m recognizing that we’ve all got PTSD in this moment, and that I need to step away to deal with it so I can come back to be the best partner I can be for all the company stakeholders, and for my family and friends as well.”

D&I fatigue is real, say the CDOs, for individuals, organizations and the general public. “People are tired of being told what they’ve done is not enough. Then you add the stress of the pandemic, the possible recession, there’s a lot of different stressful points that the average person is dealing with, so if something has to be moved off your mental plate, D&I would be the first thing,” says Martin. “The challenge is finding a way to keep moving forward but not feel like you’re force-feeding an agenda.”

Multiple CDOs use “ebbs and flows” to describe public enthusiasm for equity, especially in 2022. “In the summer of 2020, with a global pandemic and racial reckoning, we were shaken out of our sleep and ‘woke,’ for lack of a better term,” says Recording Academy vp of DEI Ryan Butler. “For some, it’s been easier to go back to sleep than keep doing the work, so that is presenting new challenges in what CDOs are dealing with.” Adds Gillespie: “We all knew the window was going to close. You can name any movement — Stop Asian Hate, Black Lives Matter — there’s a fervor in the beginning, and then when things look like they’re back to normal, everybody goes back to their business. Those of us who wear marginalized identities know that systemic discrimination and bias is a system that will continue to perpetuate itself. It’s built on centuries of discrimination and doesn’t stop in one or two years. We’ve got to actively work every day to dismantle it.”

Ratnam notes that the slow pace of progress is sometimes inevitable. “If we’re doing the work correctly, it is an evolution,” she says, citing Endeavor’s efforts to add self-identification to its interview process, which may take a year to implement. “We’re tracking [Equal Employment Opportunity] data, and that takes a long time, but having that data will be a game-changer. The work will be slow, but it will be important, structural and permanent.”

In the meantime, what’s keeping CDOs going is seeing gradual change in their organizations. “We’re invited now to more meetings than we can get to; in the past we needed to push our way in,” says Martin. Robinson recently got a note from an NBCUniversal employee, an Asian American woman, expressing her pride over the work of the inclusion team. “You get about 10 on the other side to one like that,” he says with a laugh. “I sent that note to a colleague and said, ‘This will keep me going for a week.’ “

The Expert Panel Courtesy Of Subject (11); Aaron Doggett For Visyoual Media; Richard Harbaugh, ©A.M.P.A.S.

This story first appeared in the July 15 issue of The Hollywood Reporter magazine. Click here to subscribe.

Mon, 18 Jul 2022 01:10:00 -0500 en-US text/html
Killexams : The Tank Announces Programming for Eco-Forward Festivals TRASHFEST & DARKFEST 2022

The Tank Announces Programming for Eco-Forward Festivals TRASHFEST & DARKFEST 2022

The Tank announced programming for their eco-forward festivals, TrashFest and DarkFest, from Saturday July 30 - Sunday August 7, 2022. Both festivals will take place in-person at The Tank NYC (312 West 36th St New York, NY 10018).

For years, The Tank has hosted DarkFest - a week of shows that do not use conventional theatrical lighting in an effort to reexamine how we can make theater more sustainable. With the pandemic forcing their physical theater to close, they challenged that idea further with the first annual TrashFest in 2020. It is often difficult to wrestle with the environmental impact our daily lives and our work can have - especially in theater, where so many resources are needed to develop sets, props, and costumes that are only used for a short amount of time. TrashFest centers work that not only produces no waste, but reuses materials discarded as garbage while DarkFest celebrates innovative performance that utilizes self-sufficient and alternative energy sources.

More information about the programming is below. Tickets begin at $10 with free offerings and are available by visiting


Written and directed by Emma Gometz

July 30 at 3PM

Nenny is an agoraphobic violinist with two weeks left before graduating from her MFA program. Before she can graduate, she has to perform a solo concert in a remote location and accepts an offer from her ex-boyfriend Rob to stay with him and his eccentric parents (who happen to live nearby the venue) and rehearse. Leading up to the big day, she is visited by fluffy, musty, homey entities, threatening to swallow her alive. Who is everyone, and what do they want from her?

Ghosts features sound design by Nicolas Recalde and Akvinder Kaur is the Stage Manager.


Written and directed by Katelynn Kenney

August 3 at 7PM

A climate-conscious Etsy store owner raises her child in her studio apartment over these next 10 years-through them, we experience Earth's rearing of humanity and our rebellion against the planet that birthed us. Through vastness and intimacy, 10 YEARS questions and wrestles with our agency in the waves of the climate crisis already crashing around and against us. As the child's imaginary friends, you, the audience, may or may not affect the course of their life, and our future.

The cast of 10 YEARS features Dani Martineck and Teresa Attridge.


By Philip J. Kaplan

July 31 at 7PM

The President of Ohio is a post-apocalyptic farce about family life in the year 2070. Everything depends on tonight's dinner, where the President of Ohio will be the guest. But the cook just died, war with Kentucky is imminent, the homeowners association wants more barbed wire, and a Girl Scout assassin is waiting. Can Brenda's family survive the night?

The cast of The President of Ohio features Belle Caplis, John Dimino, Jamahl Garrison-Lowe, Meghan Jones, and Amy Stiller.


By Libby Heily

July 31 at 9:30PM

37 Incidents Between Victoria and Her Brain examines the complex relationship between our minds and ourselves. Like most of us, Victoria often feels disconnected from her brain. As she matures, Victoria learns and adapts. She creates both good and bad coping mechanisms that she must confront and shift as she reaches different stages of her life. Through familiar and a few not-so-familiar rites of passage, we see Victoria deal with love, life, death, and trauma and how her brain supports or undermines her, seemingly without her knowledge.

Ali Kresch Levine serves as the Dramaturg.


Written and performed by Rachel Weekley

Directed and designed by Robert Malbrough

August 2 at 7PM

King of Nothing is an experiential solo performance piece inspired by the environmental collapse of modern society in a post-Amazon age and the text of King Lear that asks the question: what is the point of a fool at the end of the world?

Tyler Riley provides voice over and Zeynep Akca is the Stage Manager.


August 2 at 9:30PM

Rudy & Camille

Written by Dean Haspiel and Whitney Matheson

Performed by Sharief Johnson, Morgan Lennon and Adam Files

Rudy & Camille is a psychedelic rom-com that chronicles two office mates who spend most of their time pontificating and pawing at each other -- instead of preventing the impending apocalypse.

Skin Exam

by Ezequiel González Camaño and Cornelia Smith

A young man must have his skin checked every year for signs of melanoma. His body is unwaveringly surveilled and documented by a rotating cast of witnesses: a chiromancer, who discovers a shortened sun line on the young man's hand; a nurse, who examines the body for signs of abnormalities; and a companion, under interrogation, who must recount identifiable markings on the young man's skin.


August 3 at 9:30PM


By Joe Quattro

Two characters are bumbling through a cemetery, literally and figuratively, in search of a plot. Meanwhile, other characters appear from behind tombstones recounting tails of the intentional and unintentional deaths.

Banana Man in a Box

Performed by Michael Galligan

Directed by Bailey Nassetta

Music by Robert Fernandez

Ever wonder where your bananas go when they're bruised and forgotten? Banana Man in a Box follows the tragicomic story of a Dole banana lost in the wasteful world of capitalism. The show combines clown, magic, karaoke, stand-up and more to put you right in your peelings.

The Trial of the Worm Man

Created, written, and performed by Veronika Gribanova

Created and performed by Kevin Mejia

Directed and script and comedy consulted by Carol Lee Sirugo

It's the trial of the century and we're down to the final witnesses. Two children* are tasked with clearing the name of the infamous Worm Man. The judge admitted it was a "strange choice" made by the defense. The press have called it "downright disturbing." Stay tuned for when Lady Guga and her "best friend/nemesis" Buckle take the stand for the first and probably last time.

*They insisted I call them children, though I'm almost certain they are fully grown adults.

In the Dark

by Martin Murray

In The Dark explores "remote viewing," a mysterious method of clairvoyance, where practitioners claim to be able to explore objects and locations at a distance from not only their physical position but their physical body as well. Two petty criminals decide to use this practice to plan a string of heists and robberies, but they soon learn that there are larger forces at work.


By Amanda DeLalla

August 4 at 3PM

Rightful/Sword & Staff is a cautionary tale that becomes a psychological case study. Refined Marian Frey is in jail for a crime we don't yet know about. As she- and later her daughter- tell their stories, we inch closer to the truth and are forced to consider how far we'd go to remedy a perceived injustice.

The original production was directed by Diane Zerega. Tech operation is by Shira Harris.


August 4 at 7PM

Sitting by a body of water in the Dark

Choreographed by Mia Martelli

Performed by Maya Gonzalez, Nina Guevara, and Noa Rui-Piin Weiss

Sitting by a body of water in the Dark is a group work, in-progress, choreographed by Mia Martelli. This work stacks invisible experiences, namely, breath, cerebrospinal fluid, and spiritual ascension. Situated on a backdrop of rave and ambient music, we explore diffraction spikes from light sources, imagined climates/geography, and polyrhythms in the body and among bodies, as a form of spacetime travel.


Choreographed, in collaboration with the below dancers, by Stephen Shynes

Performed by Gabrielle Loren, Kyneijee Wubah, and Jayden Williams.

These theories of dreams and the unconscious believed insanity was the breaking of the chains of logic. Our subconscious mind is variably used to define the state of mind in psychology which is what we use to function through our daily lives.


By Emma Isabel

Directed by Matthew Pezzulich

August 4 at 9:30PM

Fantasma De Noche features two sisters who have learned to both raise each other and tease each other. The oldest of the two, Adelita, has a secret. A secret about someone living under their same roof. Will they both be able to handle the weight of this secret? Will they be able to unfold cyclical patterns in their family? Will this secret finally die? Or will it not?

The cast of Fantasma De Noche will feature Belgys Felix, Yesenia Morales, and Gaby Villeda. Maggie Lally is the Dramaturg and Fernando Mercado is the Fight Choreographer and Board Operator.


Created, performed, and produced by Greg Kamilaw

August 5 at 7PM; August 7 at 3PM

Nature We & I, a solo performance work for voice and guitars, is a love letter to a New York City most don't see. A city as a part of nature. Manhattan, New York City, one of the worlds capitals of business, finance and the arts. Busy people, from all walks of life, and every corner of the world, with ambitions and dreams big and small are drawn to this city. The City confronts these busy people with the reality of their talents and the opportunities of the moment. People must make choices; what are they willing to endure? What will they compromise? How will they adapt? Will they evolve, grow, change so they can reach their goal, fulfill their dreams? Will they persevere? Will they outgrow their dreams, and settle for a way to make a living? And what are the Cost of their life's pursuits? The cost of an individual's busy way of living. What is the cumulative cost of humankind's way, humankind's busy way of living? The cost on others, on our future generations, and on nature. Are we individuals aware of the effects we are having on the rest of nature? The nature that surrounds us, the nature we are all a part of. How much do we care and what will we do when confronted by the consequences of our busy way of living? Nature We & I, asks, explores, and answers these questions.

Guillermo Laporta is the audio-visual lighting artist.


Written by Katelynn Kenney

August 6 at 3PM


50 years in the future, a theatre troupe tells us the tale of the shape-shifting rebels who took down the wall of a wealthy, "green" metropolis and relit the flame of revolution and justice in a climate- and capitalism-ravaged world.


By Karl O'Brian Williams

Directed by Jermaine Rowe

August 6 at 7PM

A young man reluctantly accepts a challenge from a class project that asks students to create a performance art piece about their family history. This prompts him to question his purpose in the world, releasing a flurry of internal conflicts. His story is shaped by music, spoken word, movement and through the guidance of the ancestors who test and prod accordingly. Project Sankofa looks at family, community, love and the endless questioning about oneself.

The cast of Project Sankofa will feature Antonyio Artis, Cailin Chang, Zeinebou Dia, Frida Fritter, Natalya Gammon, Anthony Goss, Christian Pacheco, Luca Rodrigues, and Jack Vine. Andy Evan Cohen is the tech manager and Joel Edwards is the sound designer.


Written and directed by Sara Rahman

Music by Uma Paranjpe

August 6 at 9:30PM

When zombie virus Pep-C-Koli ravages the world, four girls band together (literally) to fight Zombos, form friendships, and make kickass music. When one of the girls is threatened by the virus, the girls must confront their grief, anxiety, and complex female friendships to come out the other side alive. Sleepover Club is a horror coming-of-age, zombie apocalypse, girl band play (you read that right) with a whole lot of heart.


August 7 at 7PM


Created, directed, and produced by Eddie Datz

Performed by Datz and Michael Ortiz

Two characters are suffering through a horribly-written play when they decide to break off and say whatever dialogue they want to say. As they talk, they begin to get philosophical about the who/what/where/when/why of their creation and purpose in their world! Having premiered at the Florida Players New Works Festival in 2019, witness the Off-Off Broadway playwriting premier by NYC's hottest young multi-hyphenated artist, Eddie Datz!


By Anthony Jadus

This is a theatrical character study and an attempt to bridge different people and challenge them on their ways of thinking politically.


Written by Derek Davidson

Directed by Rachel Sabo-Hedges

The play's done-but don't go yet: time for the best part, the Talkback! The dramaturg's moment... btw, what do dramaturgs actually do? Well, sometimes, they can predict the future.


Created by Charlie Wo, Darius Lamont, Jupiter Genesis, Ray Johnson, Stephanie Orta Vázquez, YaYa Harrison, and Tank.

August 7 at 9:30PM

The Mannequin Play is a folk-rap musical devised by NYC drag performers and members of the performance art collective "I Don't Wanna See That?!!?!" The play follows a family of three mannequins, Sale, Glossy, and Liver, working at a Rainbow Shops in Bushwick as their store closes and ultimately separates them. The three are thrust into unfamiliar worlds and are forced to create new families while they try to reunite with each other.

Orchestrations are by Derek Walsh and Projection Design by Lisa Marie Montgomery.


Founded in 2003, The Tank is an Obie Award-winning, multi-disciplinary non-profit arts presenter and producer, which provides a home to emerging artists working across all disciplines, including theater, comedy, dance, film, music, puppetry, and storytelling. Led by Artistic Director Meghan Finn, Managing Producer Molly FitzMaurice and Director of Artistic Development Johnny G. Lloyd, The Tank champions emerging artists engaged in the pursuit of new ideas and forms of expression. In doing so the company removes the economic barriers from the creation of new work for artists launching their careers and experimenting within their art form. From the company's home with two theaters on 36th Street, The Tank serves over 2,500 artists every year, presents over 1,000 performances, and welcomes 36,000 audience members annually. The company fully produces a curated season of 13-18 theatrical World or New York premieres each season. During the ongoing COVID-19 public health crisis, The Tank has launched CyberTank, a virtual gathering space and programming platform for artists to share work. With weekly themed variety shows, ongoing series and evening-length shows made for the virtual frame, CyberTank has already presented the work of over 4,093 artists in over 468 performances to over 20,000 audience members across the country and the world.

Recent Tank-produced work includes The New York Times Critics' Picks Taxilandia by Flako Jimenez (2021); OPEN by Crystal Skillman, directed by Jessi D. Hill (2019); Red Emma & The Mad Monk by Alexis Roblan, directed by Katie Lindsay (2018); and The Offending Gesture by Mac Wellman, directed by Meghan Finn (2016), as well as Drama Desk Award-nominated productions The Hunger Artist (2018), The Paper Hat Game (2017), the ephemera trilogy (2017), Ada/Ava (2016) and youarenowhere (2016).

Wed, 20 Jul 2022 17:07:00 -0500 by Chloe Rabinowitz en text/html
Killexams : Opinions | The consciousness of bees No result found, try new keyword!The French philosopher René Descartes, whose views on animals were highly influential, argued that these creatures acted purely by reflex — they had no intellectual capabilities. But there has been a ... Fri, 29 Jul 2022 02:13:28 -0500 en-us text/html
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