DataHammer/scimrc huggingface dataset:
Question: Summarize disruptive innovation in two paragraphs
Evidence: disruptive innovation is innovation that creates a new market and value network or enters at the bottom of an existing market and eventually displaces established market-leading firms, products, and alliances.The concept was developed by the American academic Clayton Christensen and his collaborators and has been called the most influential business idea of the early 21st century. 

Disruptive innovations tend to be produced by outsiders and entrepreneurs in startups, rather than existing market-leading companies. The business environment of market leaders does not allow them to pursue disruptive innovations when they first arise, because they are not profitable enough at first and because their development can take scarce resources away from sustaining innovations
Answer: In business theory, disruptive innovation is innovation that creates a new market and value network or enters at the bottom of an existing market and eventually displaces established market-leading firms, products, and alliances. The concept was developed by the American academic Clayton Christensen and his collaborators beginning in 1995,[full citation needed] and has been called the most influential business idea of the early 21st century. Lingfei Wu, Dashun Wang, and James A. Evans generalized this term to identify disruptive science and technological advances from more than 65 million papers, patents and software products that span the period 1954–2014. Their work was featured as the cover of the February 2019 issue of Nature  and was included among the Altmetric 100 most-discussed work in 2019.

Not all innovations are disruptive, even if they are revolutionary. For example, the first automobiles in the late 19th century were not a disruptive innovation, because early automobiles were expensive luxury items that did not disrupt the market for horse-drawn vehicles. The market for transportation essentially remained intact until the debut of the lower-priced Ford Model T in 1908. The mass-produced automobile was a disruptive innovation, because it changed the transportation market, whereas the first thirty years of automobiles did not.

Disruptive innovations tend to be produced by outsiders and entrepreneurs in startups, rather than existing market-leading companies. The business environment of market leaders does not allow them to pursue disruptive innovations when they first arise, because they are not profitable enough at first and because their development can take scarce resources away from sustaining innovations (which are needed to compete against current competition). Small teams are more likely to create disruptive innovations than large teams. A disruptive process can take longer to develop than by the conventional approach and the risk associated to it is higher than the other more incremental, architectural or evolutionary forms of innovations, but once it is deployed in the market, it achieves a much faster penetration and higher degree of impact on the established markets.
Question: what was the evaluation metrics studied in this work?
Evidence: Performance figures are computed using tail prediction on the test sets: For each test triple $(h,r,t)$ with open-world head $h \notin E$ , we rank all known entities $t^{\prime } \in E$ by their score $\phi (h,r,t^{\prime })$ . We then evaluate the ranks of the target entities $t$ with the commonly used mean rank (MR), mean reciprocal rank (MRR), as well as Hits@1, Hits@3, and Hits@10.
We then evaluate the ranks of the target entities $t$ with the commonly used mean rank (MR), mean reciprocal rank (MRR), as well as Hits@1, Hits@3, and Hits@10.
Answer: The answers are shown as follows:
* mean rank (MR), mean reciprocal rank (MRR), as well as Hits@1, Hits@3, and Hits@10
Question: What's the moroccan biodiversity ?
Evidence: Morocco has a wide range of biodiversity. It is part of the Mediterranean basin, an area with exceptional concentrations of endemic species undergoing rapid rates of habitat loss, and is therefore considered to be a hotspot for conservation priority. Avifauna are notably variant. The avifauna of Morocco includes a total of 454 species, five of which have been introduced by humans, and 156 are rarely or accidentally seen.

The Barbary lion, hunted to extinction in the wild, was a subspecies native to Morocco and is a national emblem. The last Barbary lion in the wild was shot in the Atlas Mountains in 1922. The other two primary predators of northern Africa, the Atlas bear and Barbary leopard, are now extinct and critically endangered, respectively. Relict populations of the West African crocodile persisted in the Draa river until the 20th century.

The Barbary macaque, a primate endemic to Morocco and Algeria, is also facing extinction due to offtake for trade human interruption, urbanisation, wood and real estate expansion that diminish forested area – the macaque's habitat.

Trade of animals and plants for food, pets, medicinal purposes, souvenirs and photo props is common across Morocco, despite laws making much of it illegal. This trade is unregulated and causing unknown reductions of wild populations of native Moroccan wildlife. Because of the proximity of northern Morocco to Europe, species such as cacti, tortoises, mammal skins, and high-value birds (falcons and bustards) are harvested in various parts of the country and exported in appreciable quantities, with especially large volumes of eel harvested – 60 tons exported to the Far East in the period 2009‒2011.
Answer: Morocco has a large variety of Biodiversity. It has an exceptional concentrations of endemic species. The avifauna includes a total of hundred species. The Barbary lion was a subspecies native to Morocco. The other two primary predators of northern Africa, the Atlas bear and Barbary leopard, are now extinct and critically endangered, respectively
The Barbary macaque a primate endemic to Morocco.
Question: What type of persimmon can be eaten raw?
Evidence: Numerous cultivars have been selected. Some varieties are edible in the crisp, firm state but it has its best flavor when allowed to rest and soften slightly after harvest. The Japanese cultivar 'Hachiya' is widely grown. The fruit has a high tannin content, which makes the unripe fruit astringent and bitter. The tannin levels are reduced as the fruit matures. Persimmons like 'Hachiya' must be completely ripened before consumption. When ripe, this fruit comprises thick, pulpy jelly encased in a waxy thin-skinned shell.[citation needed]

Commercially and in general, there are two types of persimmon fruit: astringent and non-astringent.

The heart-shaped Hachiya is the most common variety of astringent persimmon. Astringent persimmons contain very high levels of soluble tannins and are unpalatable if eaten before completely softened. The astringency of tannins is removed in various ways. Examples include ripening by exposure to light for several days and wrapping the fruit in paper (probably because this increases the ethylene concentration of the surrounding air).

The non-astringent persimmon is squat like a tomato and is most commonly sold as fuyu. Non-astringent persimmons are not actually free of tannins as the term suggests but rather are far less astringent before ripening and lose more of their tannic quality sooner. Non-astringent persimmons may be consumed when still very firm and remain edible when very soft.
Answer: Non-astrigent persimmon like fuyu can be eaten raw while astringent persimmon like Hachiya needs to be ripe to be eaten.
Question: Who won the March Madness Mens Basketball Tournament in 2022?
Answer: The University Kansas Jayhawks
Question: What are the main problems with the description?
Evidence: The Flickr30K dataset BIBREF0 is a collection of over 30,000 images with 5 crowdsourced descriptions each. It is commonly used to train and evaluate neural network models that generate image descriptions (e.g. BIBREF2 ). An untested assumption behind the dataset is that the descriptions are based on the images, and nothing else. Here are the authors (about the Flickr8K dataset, a subset of Flickr30K):
“By asking people to describe the people, objects, scenes and activities that are shown in a picture without giving them any further information about the context in which the picture was taken, we were able to obtain conceptual descriptions that focus only on the information that can be obtained from the image alone.” BIBREF1 
What this assumption overlooks is the amount of interpretation or recontextualization carried out by the annotators. Let us take a concrete example. Figure FIGREF1 shows an image from the Flickr30K dataset.
This image comes with the five descriptions below. All but the first one contain information that cannot come from the image alone. Relevant parts are highlighted in bold:
We need to understand that the descriptions in the Flickr30K dataset are subjective descriptions of events. This can be a good thing: the descriptions tell us what are the salient parts of each image to the average human annotator. So the two humans in Figure FIGREF1 are relevant, but the two soap dispensers are not. But subjectivity can also result in stereotypical descriptions, in this case suggesting that the male is more likely to be the manager, and the female is more likely to be the subordinate. rashtchian2010collecting do note that some descriptions are speculative in nature, which they say hurts the accuracy and the consistency of the descriptions. But the problem is not with the lack of consistency here. Quite the contrary: the problem is that stereotypes may be pervasive enough for the data to be consistently biased. And so language models trained on this data may propagate harmful stereotypes, such as the idea that women are less suited for leadership positions.
This paper aims to give an overview of linguistic bias and unwarranted inferences resulting from stereotypes and prejudices. I will build on earlier work on linguistic bias in general BIBREF3 , providing examples from the Flickr30K data, and present a taxonomy of unwarranted inferences. Finally, I will discuss several methods to analyze the data in order to detect biases.
Rashtchian et al. (2010) do note that some descriptions are speculative in nature, which they say hurts the accuracy and the consistency of the descriptions. But the problem is not with the lack of consistency here. Quite the contrary: the problem is that stereotypes may be pervasive enough for the data to be consistently biased.
Answer: The problem is that stereotypes may be pervasive enough for the data to be consistently biased.
Question: Divide these sneakers into Air Jordan and Air Max: Tokyo 96, Big Bubble Red, Collector's Dream, Monarch, Chicago, Lucky Green
Answer: Air Jordan: Tokyo 96, Chicago, Lucky Green
Air Max: Big Bubble Red, Collector's Dream, Monarch
Question: Classify the following cartridge types by whether they are typically used in a pistol or a rifle: 9mm, .40 S&W, 5.56mm NATO, .45 ACP, 7.62mm NATO, 6.5mm Creedmoor, 6.5mm Grendel,  .357 Magnum
Answer: 9mm - Pistol
.40 S&W - Pistol
5.56mm NATO - Rifle
.45 ACP - Pistol
7.62mm NATO - Rifle
6.5mm Creedmoor - Rifle
6.5mm Grendel - Rifle
.357 Magnum - Pistol
Question: How do they identify abbreviations?
Evidence: The overview of our approach is shown in Figure FIGREF6 . Within ICU notes (e.g., text example in top-left box in Figure 2), we first identify all abbreviations using regular expressions and then try to find all possible expansions of these abbreviations from domain-specific knowledge base as candidates. We train word embeddings using the clinical notes data with task-oriented resources such as Wikipedia articles of candidates and medical scientific papers and compute the semantic similarity between an abbreviation and its candidate expansions based on their embeddings (vector representations of words).
Within ICU notes (e.g., text example in top-left box in Figure 2), we first identify all abbreviations using regular expressions and then try to find all possible expansions of these abbreviations from domain-specific knowledge base as candidates.
Answer: The answers are shown as follows:
* identify all abbreviations using regular expressions
Question: Summarize what GDP means as well as what it is and is not used for.
Evidence: Gross domestic product (GDP) is a monetary measure of the market value of all the final goods and services produced and sold in a specific time period by a country or countries, generally "without double counting the intermediate goods and services used up to produce them".[citation needed] GDP is most often used by the government of a single country to measure its economic health. Due to its complex and subjective nature, this measure is often revised before being considered a reliable indicator.[citation needed] GDP (nominal) per capita does not, however, reflect differences in the cost of living and the inflation rates of the countries; therefore, using a basis of GDP per capita at purchasing power parity (PPP) may be more useful when comparing living standards between nations, while nominal GDP is more useful comparing national economies on the international market. Total GDP can also be broken down into the contribution of each industry or sector of the economy. The ratio of GDP to the total population of the region is the per capita GDP (also called the Mean Standard of Living).

GDP definitions are maintained by a number of national and international economic organizations. The Organisation for Economic Co-operation and Development (OECD) defines GDP as "an aggregate measure of production equal to the sum of the gross values added of all resident and institutional units engaged in production and services (plus any taxes, and minus any subsidies, on products not included in the value of their outputs)". An IMF publication states that, "GDP measures the monetary value of final goods and services—that are bought by the final user—produced in a country in a given period of time (say a quarter or a year)."

GDP is often used as a metric for international comparisons as well as a broad measure of economic progress. It is often considered to be the world's most powerful statistical indicator of national development and progress. However, critics of the growth imperative often argue that GDP measures were never intended to measure progress, and leave out key other externalities, such as resource extraction, environmental impact and unpaid domestic work. Critics frequently propose alternative economic models such as doughnut economics which use other measures of success or alternative indicators such as the OECD's Better Life Index as better approaches to measuring the effect of the economy on human development and well being.
Answer: Gross Domestic Product (GDP) is a measurement of all of the goods and services sold in a specific country over a specific time period. It is used as a metric for economic health, economic progress, and for international comparisons. GDP is not a used to measure cost of living differences and inflation rates between countries.
Question: What is detection engineering and what are the benefits?
Answer: Detection engineering is a new approach to threat detection. More than just writing detection rules, detection engineering is a process—applying systems thinking and engineering to more accurately detect threats. The goal is to create an automated system of threat detection which is customizable, flexible, repeatable, and produces high quality alerts for security teams to act upon. Detection engineering is not yet a mature discipline with consistent methods and predictable results, but pioneers in the field are working towards this goal. There is not a lot written about the concept right now, no go-to standards or frameworks. It appears companies are developing their own approaches to incorporate the concept to improve their detection and response capabilities. 

A concept called detection-as-code (DaC), first coined by Anton Chuvakin in 2020, is at the heart of detection engineering, and points to the idea that detections should be treated as code. Essentially, it is about applying software engineering best practices to detections using modern agile CI/CD processes. 

The benefits Of Detection Engineering include 1) reduced mean-time-to-response through automation of detections, 2) detections are more relevant to the environment, 3) the process is structured, repeatable, and informs workflows, 4) detection rules and analytics are version controlled, reused, and modified as needed, 5) Peer review and automated testing are part of the process, to catch mistakes and gaps.
Question: Say that you take 1 step left, then 2 forward, then 4 right, then 3 right, then 3 forward, then 5 back. Take me through the process of determining how far you end up from your starting position.
Answer: First, we find the displacement from the starting position, which we will use to calculate the distance. To do this, collect the steps into groups by direction.

Left: we take 1 step left
Right: we take 4 + 3 = 7 steps right
Forward: we take 2 + 3 = 5 steps forward
Back: we take 5 steps back

We can subtract step counts in opposite directions to get our final displacement

Left/Right: we take 1 step left and 7 right, so we take a total of 1 - 7 = -6 steps left. This is equivalent to 6 steps right.
Forward/Back: we take 5 steps forward and 5 steps back, so we take a total of 5 - 5 = 0 steps forward.

This means we end up 5 steps to the right and 0 steps forward from where we started, which is a distance of 5 steps.
Question: What are the two feature studies we did?
Evidence: We conducted two feature study experiments: 1) a feature ablation study to assess the contribution of feature groups and 2) a feature elimination study to determine the optimal percentile of top ranked features for classifying Twitter tweets in the depression schema hierarchy.
We conducted two feature study experiments: 1) a feature ablation study to assess the contribution of feature groups and 2) a feature elimination study to determine the optimal percentile of top ranked features for classifying Twitter tweets in the depression schema hierarchy.
Answer: 1) a feature ablation study to assess the contribution of feature groups and 2) a feature elimination study to determine the optimal percentile of top ranked features for classifying Twitter tweets in the depression schema hierarchy.
Question: Here's a list of the key characters in the TV Series Picard who also appeared in another Star Trek franchise series': Jean-Luc Picard, Data, Seven of Nine, Deanna Troi, William Riker, Q, Borg Queen, and Wesley Crusher.

List the characters, sorted by the original series in which they appeared.
Answer: All of the characters first appeared on either The Next Generation or Voyager.
The Next Generation: Jean-Luc Picard, Data, Deanna Troi, William Riker, Q, and Wesley Crusher.
Voyager: Seven of Nine and Borg Queen
Question: What are a lot of different options for traveling across New York City?
Answer: You could bike, ride a motorcycle, drive a car, take a bus, use the subway or hail a cab
Question: Where is the best place to see the northern lights?
Answer: The northern lights are experienced most frequently in northern Scandinavia. Tromsø in Norway, Lapland in Sweden and Reykjavik in Iceland are recommended locations. The Aurora Borealis is brightest between the months of September and April.
Question: How were potentially hateful messages identified?
Evidence: To find a large amount of hate speech on the refugee crisis, we used 10 hashtags that can be used in an insulting or offensive way. Using these hashtags we gathered 13 766 tweets in total, roughly dating from February to March 2016. However, these tweets contained a lot of non-textual content which we filtered out automatically by removing tweets consisting solely of links or images. We also only considered original tweets, as retweets or replies to other tweets might only be clearly understandable when reading both tweets together. In addition, we removed duplicates and near-duplicates by discarding tweets that had a normalised Levenshtein edit distance smaller than .85 to an aforementioned tweet. A first inspection of the remaining tweets indicated that not all search terms were equally suited for our needs. The search term #Pack (vermin or lowlife) found a potentially large amount of hate speech not directly linked to the refugee crisis. It was therefore discarded. As a last step, the remaining tweets were manually read to eliminate those which were difficult to understand or incomprehensible. After these filtering steps, our corpus consists of 541 tweets, none of which are duplicates, contain links or pictures, or are retweets or replies.
To find a large amount of hate speech on the refugee crisis, we used 10 hashtags that can be used in an insulting or offensive way.
Answer: The answers are shown as follows:
* 10 hashtags that can be used in an insulting or offensive way
Question: Identify which instrument is string or percussion: Ngoma drums, Zhuihu
Answer: Zhuihu is string, Ngoma drums is percussion.
Question: Who was involved with the epic poker league?
Evidence: The Epic Poker League was a series of poker tournaments which took place in 2011, organised by Federated Sports + Gaming. Former World Series of Poker commissioner Jeffrey Pollack served as Executive Chairman, professional poker player Annie Duke was Commissioner, and Matt Savage was Tournament Director. The three events held took place at the Palms Casino Resort in Las Vegas, Nevada. Season One received television coverage on CBS and Velocity Network.

The League was exclusionary in that it was only open to poker players who met certain qualification criteria for multimillion-dollar lifetime earnings, multiple wins and/or cashes at high level tournaments, and who were supposed to maintain adherence to a specifically defined Code of Conduct.

On 29 February 2012, Federated Sports + Gaming announced that it had filed for Chapter 11 bankruptcy. The Epic Poker League brand was among the assets acquired by Pinnacle Entertainment, Inc., at a bankruptcy auction in June 2012.
Answer: The following people were involved with the Epic Poker League:
- Former World Series of Poker commissioner Jeffrey Pollack (Executive Chairman)
- Professional poker player Annie Duke (Commissioner)
- Matt Savage (Tournament Director)