DataHammer/scimrc huggingface dataset:
Question: According to the given paragraph, how do convolutional neural networks avoid overfitting in machine learning?
Evidence: In deep learning, a convolutional neural network (CNN) is a class of artificial neural network most commonly applied to analyze visual imagery. CNNs use a mathematical operation called convolution in place of general matrix multiplication in at least one of their layers. They are specifically designed to process pixel data and are used in image recognition and processing. They have applications in image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series.

CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation-equivariant responses known as feature maps. Counter-intuitively, most convolutional neural networks are not invariant to translation, due to the downsampling operation they apply to the input.

CNNs are regularized versions of multilayer perceptrons. Multilayer perceptrons are usually fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The "full connectivity" of these networks make them prone to overfitting data. Typical ways of regularization, or preventing overfitting, include: penalizing parameters during training (such as weight decay) or trimming connectivity (skipped connections, dropout, etc.) Developing robust datasets also increases the probability that CNNs will learn the generalized principles that characterize a given dataset rather than the biases of a poorly-populated set.
Answer: A Convolutional Neural Network (CNN) has a fully connected layer in which each neuron is connected to every neuron in the next layer, this structure helps to avoid overfitting
Question: Why is Lafayette, California such an appealing place to live?
Answer: Lafayette, California is a small that is about a 30-minute drive, east of San Francisco in Contra Costa County. To get to Lafayette from San Francisco, you have to drive through a tunnel in the Sibley Volcanic Preserve. Lafayette has a small downtown area with cheery stores, restaurants and coffee shops. It is home to about 25,000 residents and it is very walkable and easy to access public transportation. The weather in Lafayette is considered to be a Mediterranean climate, so it is generally warm and sunny. There is a beautiful reservoir with a walking trail, paddle boats and a well-known lookout tower. The schools are considered to be some of the top schools in California, which makes the area very appealing for families.
Question: Can you suggest 3 disadvantages  of Genetic modification technologies like CRISPR ?
Answer: 1.Uncontrollable mutations 
2.Uncontrollable population growth
3.Strong ethical concerns about discrimination
Question: What are some of the reasons people like to visit Brazil?
Answer: Brazil is a South American country with a diverse landscape and cultural mix. From subtropical grasslands in the South to drier biomes in the northeast to the Amazon rainforest, many people visit the country to experience one of the world's biodiverse hotspots. Several species are endemic to Brazil, particularly in the Amazon region. The country is also known for a culture that blends European, African and Indigenous influences. Many visitors are intrigued by its diverse culture. The nation received many migrants from all over the world in the 19th and 20th centuries, leading to the largest Japanese community outside of Japan, largest Italian population outside of Italy, and the largest population of  SubSaharan African descent outside of Nigeria. Many of the cultural expressions of Brazil - capoeira, the large Carnaval parades, Brazilian soccer and jiu-jitsu - have found a devotees outside of the country.
Question: List some of the greatest leadership and business books ever written.
Answer: 1. Why Should Anyone Be Led by You? by Rob Goffee
2. Competing Against Luck: The Story of Innovation and Customer Choice.  by Clayton M Christensen, David S. Duncan, Karen Dillon and Taddy Hall
3. Switch: How to Change Things When Change is Hard. by Dan Heath and Chip Heath
4. GOOD STRATEGY/BAD STRATEGY by Richard Rumelt
5. How to Win Friends and Influence People by Dale Carnegie 
6. The Advantage: Why Organizational Health Trumps Everything Else in Business by Patrick M. Lencioni 
7. Crucial Conversation by Joseph Grenny, Kerry Patterson, Ron McMillan, Al Switzler and Emily Gregory 
8. Dare to Lead by Brené Brown (Author)
9. Finish What You Start by Peter Hollins (Author)
10. Radical Candor by Kim Scott (Author)
11. Turn The Ship Around! by L. David Marquet (Author), Stephen R Covey (Foreword)
12. Leaders Eat Last by Simon Sinek (Author)
13. Good To Great by Jim Collins (Author)
14. Decisive by Chip Heath (Author), Dan Heath (Author)
15. Atomic Habits  by James Clear 
16. Thinking, Fast and Slow by Daniel Kahneman (Author)
17. Our Iceberg is Melting by John Kotter (Author), Holger Rathgeber (Author)
18. Multipliers by Liz Wiseman (Author)
19. Extreme Ownership by Jocko Willink (Author)
20. Trillion Dollar Coach by Eric Schmidt (Author), Jonathan Rosenberg (Author), Alan Eagle (Author)
Question: Which is the albino direwolf in Game of Thrones?
Answer: The direwolf, Ghost, is the albino pup and is adopted by Jon Snow
Question: What is the task of Category WikiBio-Systems?
Evidence: Our main experiments are on the WikiBio dataset BIBREF0 , which is automatically constructed and contains many divergent references. In § SECREF47 we also present results on the data released as part of the WebNLG challenge.
We developed several models of varying quality for generating text from the tables in WikiBio. This gives us a diverse set of outputs to evaluate the automatic metrics on. Table TABREF32 lists the models along with their hyperparameter settings and their scores from the human evaluation (§ SECREF33 ). Our focus is primarily on neural sequence-to-sequence methods since these are most widely used, but we also include a template-based baseline. All neural models were trained on the WikiBio training set. Training details and sample outputs are included in Appendices SECREF56 & SECREF57 .
We divide these models into two categories and measure correlation separately for both the categories. The first category, WikiBio-Systems, includes one model each from the four families listed in Table TABREF32 . This category tests whether a metric can be used to compare different model families with a large variation in the quality of their outputs. The second category, WikiBio-Hyperparams, includes 13 different hyperparameter settings of PG-Net BIBREF19 , which was the best performing system overall. 9 of these were obtained by varying the beam size and length normalization penalty of the decoder network BIBREF23 , and the remaining 4 were obtained by re-scoring beams of size 8 with the information extraction model described in § SECREF4 . All the models in this category produce high quality fluent texts, and differ primarily on the quantity and accuracy of the information they express. Here we are testing whether a metric can be used to compare similar systems with a small variation in performance. This is an important use-case as metrics are often used to tune hyperparameters of a model.
The first category, WikiBio-Systems, includes one model each from the four families listed in Table 1. This category tests whether a metric can be used to compare different model families with a large variation in the quality of their outputs.
Answer: This category tests whether a metric can be used to compare different model families with a large variation in the quality of their outputs.
Question: what is the difference between a tale and a tail
Answer: tale is a noun referencing narration events vs a tail could be rear end of an animal
Question: Are they the first to experiment with deep learning architectures for the hate speech detection task?
Evidence: With the massive increase in social interactions on online social networks, there has also been an increase of hateful activities that exploit such infrastructure. On Twitter, hateful tweets are those that contain abusive speech targeting individuals (cyber-bullying, a politician, a celebrity, a product) or particular groups (a country, LGBT, a religion, gender, an organization, etc.). Detecting such hateful speech is important for analyzing public sentiment of a group of users towards another group, and for discouraging associated wrongful activities. It is also useful to filter tweets before content recommendation, or learning AI chatterbots from tweets.
The manual way of filtering out hateful tweets is not scalable, motivating researchers to identify automated ways. In this work, we focus on the problem of classifying a tweet as racist, sexist or neither. The task is quite challenging due to the inherent complexity of the natural language constructs – different forms of hatred, different kinds of targets, different ways of representing the same meaning. Most of the earlier work revolves either around manual feature extraction BIBREF0 or use representation learning methods followed by a linear classifier BIBREF1 , BIBREF2 . However, recently deep learning methods have shown accuracy improvements across a large number of complex problems in speech, vision and text applications. To the best of our knowledge, we are the first to experiment with deep learning architectures for the hate speech detection task.
In this paper, we experiment with multiple classifiers such as Logistic Regression, Random Forest, SVMs, Gradient Boosted Decision Trees (GBDTs) and Deep Neural Networks(DNNs). The feature spaces for these classifiers are in turn defined by task-specific embeddings learned using three deep learning architectures: FastText, Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs). As baselines, we compare with feature spaces comprising of char n-grams BIBREF0 , TF-IDF vectors, and Bag of Words vectors (BoWV).
Main contributions of our paper are as follows: (1) We investigate the application of deep learning methods for the task of hate speech detection. (2) We explore various tweet semantic embeddings like char n-grams, word Term Frequency-Inverse Document Frequency (TF-IDF) values, Bag of Words Vectors (BoWV) over Global Vectors for Word Representation (GloVe), and task-specific embeddings learned using FastText, CNNs and LSTMs. (3) Our methods beat state-of-the-art methods by a large margin ( INLINEFORM0 18 F1 points better).
To the best of our knowledge, we are the first to experiment with deep learning architectures for the hate speech detection task.
Answer: Yes.
Question: Name two cabinet members mentioned in the following paragraph.  Provide these as a bulleted list in order of appearance.
Evidence: Washington played an indispensable role in adopting and ratifying the Constitution of the United States, which replaced the Articles of Confederation in 1789 and remains the world's longest-standing written and codified national constitution to this day. He was then twice elected president by the Electoral College unanimously. As the first U.S. president, Washington implemented a strong, well-financed national government while remaining impartial in a fierce rivalry that emerged between cabinet members Thomas Jefferson and Alexander Hamilton. During the French Revolution, he proclaimed a policy of neutrality while sanctioning the Jay Treaty. He set enduring precedents for the office of president, including use of the title "Mr. President" and taking an Oath of Office with his hand on a Bible. His Farewell Address on September 19, 1796, is widely regarded as a preeminent statement on republicanism.
Answer: * Thomas Jefferson
* Alexander Hamilton
Question: Who is the next superpower country?
Evidence: Currently, only the United States fulfills the criteria to be considered a superpower. However, the United States is no longer the only uncontested foremost superpower and the world's sole hyperpower to dominate in every domain (i.e. military, culture, economy, technology, diplomatic).

Since the 1990s, China, the European Union, India, and Russia have been commonly described as potential superpowers. Japan was formerly considered a potential superpower due to its high economic growth. However, its status as a potential superpower has eroded since the 1990s due to an aging population and economic stagnation.
Answer: A nation with the capacity to have great power and influence on the international stage is considered a prospective superpower. These nations have the ability to significantly influence the world due to their combination of economic, political, and military might. While the United States and the Soviet Union were generally considered superpowers during the Cold War, the phrase has now come to refer to other nations as well.

Because of its fast expanding economy and military might, China is sometimes mentioned as a potential superpower. The nation's Belt and Road Initiative is viewed as a major step toward enhancing its worldwide influence. India, on the other hand, is a possible threat because of its enormous population, quickly expanding economy, and potent military.

Due to its combined economic and political might, the European Union is another prospective superpower. Despite having a small military, it has a substantial amount of influence and soft power. Another nation that has been mentioned as a prospective superpower is Russia, which has a sizable military and abundant natural resources. Yet, recent economic troubles could hinder its ability to rise to superpower status.

Brazil's enormous population, wealth of natural resources, and expanding economy are other factors that make it a prospective powerhouse. The nation's participation in international negotiations on climate change as well as its leadership in Latin America have contributed to an increase in its impact on the global scene.
Question: Given a reference text about Lee Kuan Yew, tell me when they were born, when they were president, and what their most famous accomplishment was in office.
Evidence: Lee was born at home on 16 September 1923, the first child to Lee Chin Koon, a Semarang born Singaporean, and Chua Jim Neo, at 92 Kampong Java Road in Singapore, then part of the British Empire. Both of Lee's parents were English-educated third-generation Straits Chinese, with his paternal side being of Hakka descent from Dabu County. He was named 'Kuan Yew', meaning 'light and brightness', alternately meaning 'bringing great glory to one's ancestors'. Lee's paternal grandfather Lee Hoon Leong, who was described as "especially westernised", had worked on British ships as a purser, and hence gave Lee the Western name 'Harry'

As prime minister from 1959 to 1990, Lee presided over many of Singapore's advancements. He oversaw Singapore's transformation from an island nation with a high illiteracy rate and no natural resources into a developed country with a high-income economy within a single generation, commonly termed (from his autobiography) as 'From the third world to the first world'. Singapore's gross national product per capita (GNP) rose from $1,240 in 1959 to $18,437 in 1990. The unemployment rate in Singapore dropped from 13.5% in 1959 to 1.7% in 1990. External trade increased from $7.3 billion in 1959 to $205 billion in 1990. In other areas, the life expectancy at birth for Singaporeans rose from 65 years at 1960 to 74 years in 1990. The population of Singapore increased from 1.6 million in 1959 to 3 million in 1990. The number of public flats in Singapore rose from 22,975 in 1959 (then under the Singapore Improvement Trust) to 667,575 in 1990. The Singaporean literacy rate increased from 52% in 1957 to 90% in 1990. Telephone lines per 100 Singaporeans increased from 3 in 1960 to 38 in 1990. Visitor arrivals to Singapore rose from 100,000 in 1960 to 5.3 million in 1990.
Answer: Lee Kuan Yew was born on 16 September 1923. 
He served as the inaugural Prime Minister of Singapore from 1959 to 1990.
His most famous accomplishment in office was overseeing Singapore's transformation from an island nation with a high illiteracy rate and no natural resources into a developed country with a high-income economy within a single generation, commonly termed (from his autobiography) as 'From the third world to the first world.
Question: What the five love languages?
Evidence: According to Chapman, the five "love languages" are: words of affirmation (compliments), quality time, receiving gifts, acts of service, and physical touch.

Examples are given from his counseling practice, as well as questions to help determine one's own love languages. According to Chapman's theory, each person has one primary and one secondary love language.

Chapman suggests that to discover another person's love language, one must observe the way they express love to others, and analyze what they complain about most often and what they request from their significant other most often. He theorizes that people tend to naturally give love in the way that they prefer to receive love, and better communication between couples can be accomplished when one can demonstrate caring to the other person in the love language the recipient understands.

An example would be: if a husband's love language is acts of service, he may be confused when he does the laundry and his wife does not perceive that as an act of love, viewing it as simply performing household duties, because the love language she comprehends is words of affirmation (verbal affirmation that he loves her). She may try to use what she values, words of affirmation, to express her love to him, which he would not value as much as she does. If she understands his love language and mows the lawn for him, he perceives it in his love language as an act of expressing her love for him; likewise, if he tells her he loves her, she values that as an act of love.
Answer: The five love languages include words of affirmation, quality time, receiving gifts, acts of service, and physical touch.
Question: Write a review of Brandon Sanderson's book, The Hero of Ages
Answer: Sanderson neatly ties together loose ends in a gripping end to the trilogy. Overall, the book was largely plot driven. By now Vinn and co are well developed characters, and don't change as much. Sazed has an existential crisis though, which is interesting. Instead, Sanderson focuses on their struggle against a crazy antagonist and slowly dribbles out the last few mysteries of the world they live in.
Question: From the passage provided, extract the population of Bainbridge Island
Evidence: Bainbridge Island is a city and island in Kitsap County, Washington. It is located in Puget Sound. The population was 23,025 at the 2010 census and an estimated 25,298 in 2019, making Bainbridge Island the second largest city in Kitsap County.

The island is separated from the Kitsap Peninsula by Port Orchard, with Bremerton lying to the southwest. Bainbridge Island is a suburb of Seattle, connected via the Washington State Ferries system and to Poulsbo and the Suquamish Indian Reservation by State Route 305, which uses the Agate Pass Bridge.
Answer: The population was 23,025 at the 2010 census
Question: What was Tyson Fury's fight record as an amateur?
Evidence: As an amateur, Fury represented both England and Ireland. Fury represented Ireland three times at international level. He was based out of the Holy Family Boxing Club in Belfast, Northern Ireland, and later switched to the Smithboro Club in County Monaghan, Ireland. In a double international match against an experienced Polish team in 2007, the Irish team lost 12–6 overall; Fury, however, was victorious in both his fights in Rzeszów and Białystok. In another Irish match against the US, Fury won his bout by knockout. He won bronze at the AIBA Youth World Boxing Championships in 2006.

In England, while representing Jimmy Egan's Boxing Academy in Wythenshawe, Manchester, he participated in the senior national championships in 2006 but was beaten by David Price 22–8. In May 2007, he won the EU Junior Championship, defeating Istvan Bernath in the final. In July 2007 he won silver at the European Junior Championship, losing to Maxim Babanin in the final.

As a junior, Fury was ranked number three in the world behind the Russians Maxim Babanin and Andrey Volkov, but did not get the chance to represent Great Britain at the 2008 Olympics because each country is restricted to one boxer per weight division and David Price was selected. Price came up through the amateur Olympic programme. Fury also unsuccessfully tried to qualify for Ireland. Speaking in 2011, Fury said "I should have gone to the Olympic games in 2008 and won a gold medal for Ireland, but I was denied the chance to go and do it" He was also forced to withdraw from the Irish national championships after officials from the Holy Trinity Boxing Club in West Belfast, the club of the then Irish amateur heavyweight champion, submitted a protest regarding his eligibility as he was not born in Ireland.

Fury won the ABA super-heavyweight title in 2008 by defeating Damien Campbell 19:1. He turned professional later that year. Feeling disillusioned with amateur boxing, he decided not to wait for the 2012 Olympics. He finished with an amateur record of 31–4 (26 KOs).
Answer: He finished with an amateur record of 31–4 (26 KOs
Question: What is Scala?
Answer: Scala is a strong statically typed high-level general-purpose programming language that supports both object-oriented programming and functional programming.
Question: How can a balance be drawn between capitalism and economic equality?
Answer: Capitalism, an economic system that promotes the private generation and ownership of wealth and resources, helps promote innovations and economic growth. However, when left unchecked, it can also be a factor in economic inequality, as people are generally unequal in terms of ideas, drive/grit to push through, and access to capital resources. These provide some dis-proportional advantages of the economic wealth and resources to a few. In an ideal world, most players are expected to be considerate and mindful of equitability and balanced economic development. However, this is seldom the case, hence an approach for drawing a balance is by having government policies and laws, that protect the interest of the general citizenry and prevent bad players from taking advantage of the system. This is a very difficult problem, and a lot of countries are still struggling with this dilemma of striking a balance.
Question: What are five different things you could use a ruler to do?
Answer: You could use a ruler to measure the length of things, to draw a straight line, to check if something is straight, to mark a spot in a book or to hold down a stack of papers
Question: Given a reference text about Adventfjorden, how wide and long is it?
Evidence: Adventfjorden (Advent Bay) is a 7 km long and 4 km wide bay running south-eastwards from the southern side of Isfjorden, on the west coast of Spitsbergen in Svalbard. The name represents a corruption of Adventure Bay - probably named after the Hull-built whaleship Adventure, which operated in Isfjorden in 1656. The fjord was originally[when?] known as Klass Billen Bay. At the head of Adventfjorden is Adventdalen (Advent Valley).

The former mining-camp of Longyearbyen (now Svalbard's main town) lies on the fjord's southwestern shore, while another former mining-camp, Hiorthhamn, is located on its northeastern shore.
Answer: Adventfjorden is 4km wide and 7km long.