Biography of Bill Gates

Life story of Bill Gates Free Online Research Papers William (Bill) H. Entryways III is fellow benefactor, director and CEO of Microsoft Company, the universes driving supplier of programming for PCs. Bill Gates was conceived on October 28, 1955. He and his two sisters experienced childhood in Seattle. Their dad, William H. Entryways II, is a Seattle lawyer. Mary Gates, their late mother, was a teacher, University of Washington official and administrator of United Way International. Doors went to open primary school before proceeding onward to the private Lakeside School in North Seattle. It was at Lakeside that Gates started his vocation in PC programming, programming PCs at age 13. In 1973, Gates entered Harvard University as a first year recruit, where he lived a few doors down from Steve Ballmer, who is presently Microsofts president. While at Harvard, Gates built up a form of the programming language BASIC for the principal microcomputer the MITS Altair. Essential was first evolved by John Kemeny and Thomas Kurtz at Dartmouth College in the mid-1960s. In his lesser year, Gates dropped out of Harvard to commit his energies full-an ideal opportunity to Microsoft, an organization he had begun in 1975 with his childhood companion Paul Allen. Guided by a conviction that the PC would be a significant device on each office work area and in each home, they started creating programming for PCs. Doors prescience and vision with respect to individualized computing have been key to the accomplishment of Microsoft and the product business. Doors is effectively engaged with key administration and vital choices at Microsoft, and assumes a significant job in the specialized advancement of new items. Quite a bit of his time is committed to meeting with clients and remaining in contact with Microsoft workers around the globe through email. Under Gates initiative, Microsofts mission is consistently to progress and improve programming innovation, and to make it simpler, more savvy and progressively pleasant for individuals to utilize PCs. The organization is focused on a drawn out view, which is reflected in its venture of some $2.6 billion for innovative work during the current financial year. In 1995 Gates composed The Road Ahead, his vision of where data innovation will take society. Co-created by Nathan Myhrvold, Microsofts boss innovation official, and Peter Rinearson, The Road Ahead held the No. 1 spot on the New York Times hit list for seven weeks, and stayed on the rundown for an aggregate of 18 weeks. Distributed in excess of 20 nations, the book sold in excess of 400,000 duplicates in China alone. In 1996, while deliberately redeploying Microsoft to make the most of the rising open doors made by the Internet, Gates completely reexamined The Road Ahead to mirror his view that intelligent systems are a significant achievement in human correspondence. The soft cover second release additionally has become a smash hit. Doors is giving his returns from the book to a non-benefit subsidize that underpins instructors overall who are consolidating PCs into their homerooms. Notwithstanding his energy for PCs, Gates is keen on biotechnology. He sits on the leading body of the ICOS Corporation and is an investor in Chiroscience Group of the United Kingdom and its entirely possessed auxiliary, Chiroscience RD Inc. (some time ago Darwin Molecular) of Bothell, Wash. He likewise established Corbis Corporation, which is creating perhaps the biggest asset of visual data on the planet a thorough computerized document of workmanship and photography from open and private assortments around the world. Entryways likewise has contributed with cell phone pioneer Craig McCaw in Teledesic, an organization that is taking a shot at an aggressive arrangement to dispatch several low-circle satellites around the Earth to give an overall two-way broadband broadcast communications administration. In the a long time since Microsoft opened up to the world, Gates has given more than $800 million to noble cause, including $200 million to the Gates Library Foundation to help libraries in North America exploit new advancements and the Information Age. In 1994 Gates set up the William H. Entryways Foundation, which bolsters an assortment of activities exceptionally compelling to Gates and his family. The focal point of Gates generosity is in four territories: training; world general wellbeing and populace; non-benefit, municipal and expressions associations; and Puget Sound-region capital crusades. Bill and Melinda French Gates were hitched on January 1, 1994. They have one kid, Jennifer Katharine Gates, who was conceived in 1996. Exploration Papers on Biography of Bill GatesThe Project Managment Office SystemRiordan Manufacturing Production PlanOpen Architechture a white paperNever Been Kicked Out of a Place This NiceBionic Assembly System: A New Concept of SelfPersonal Experience with Teen PregnancyMarketing of Lifeboy Soap A Unilever ProductAnalysis of Ebay Expanding into AsiaDefinition of Export QuotasTwilight of the UAW

Power point presentation on racism Essay

Meaning of Racism rac*ism n (1936) 1 : a conviction that race is the essential determinant of human characteristics and limits and that racial contrasts produce an innate predominance of a specific race 2 : racial preference or separation Webster’s Ninth New Collegiate Dictionary Isolation The partition of gatherings of individuals by custom or by law. It is regularly founded on contrasts of race, religion, riches, or culture. The primary significant difficulties to racial isolation in Canada happened in 1946 when Viola Desmond, a dark businessperson, would not sit in the gallery of a New Glasgow, Nova Scotia, theater yet rather sat ground floor, a zone assigned solely for whites. Viola Desmond’s activity happened nine years before Rosa Parks was captured for declining to surrender her seat to a white man on a transport in Montgomery, Alabama Reasons for isolation Media instances of prejudice What is bigotry? The holding of unfavorable social perspectives or intellectual convictions towards individuals from a specific gathering on the record of their participation to that gathering What is prejudice? The ICERD (International Convention on the Elimination of All Forms of Racial Discrimination) characterizes prejudice as follows: â€Å"Any differentiation, avoidance, limitation, or inclination dependent on race, shading, plunge, or national or ethnic inception which has the reason or impact of invalidating or disabling the acknowledgment, satisfaction, or exercise, on equivalent balance, of human rights and principal opportunities in the political, monetary, social, social, or some other field of open life.† Social capacity of preference and bigotry Preference and bigotry might be an important methods for making substitutes for individual or gatherings that vibe undermined It might be because of socialization (for example tyrant character) It might be the creature sense of territoriality and non domesticated limitation. It might be simple fanaticism and numbness It might be pretention and unyielding ethnocentrism Components of prejudice a hidden confidence in the predominance of one race over another and its entitlement to command. summing one up gathering of individuals by having faith in shortsighted generalizations of that gathering. influences each part of the lives of networks of shading: social, financial, political, wellbeing, and so on. Components of bigotry Preference: A biased, outlandish judgment or assessment of individuals or circumstances. Where preference is negative it frequently brings about hurtful or ominous ramifications for the person in question Bias may have social and organic premise Generalizations and their capacity Generalizations are moderately fixed arrangements of misrepresented convictions about individuals or occasions Generalizations are regularly over speculations about individuals and their accepted characterizing qualities Social subjective hypotheses of preference and generalizing Partiality and segregation may come from the abuse of: 1)cognitive heuristics 2)categorisation and generalizing of in-gatherings and out-gatherings 3)information preparing and the powerlessness to manage complex information (requirement for alternate ways) Prejudice types Singular bigotry alludes to the biased convictions and unfair conduct of people. Institutional prejudice alludes to the strategies that limit the chances of minorities.

CrowdFlower

CrowdFlower INTRODUCTIONMartin: Today we are in San Francisco in the CrowdFlower office. Hi, Lukas. Who are you and what do you do?Lukas: I am the founder and CEO of CrowdFlower. We help data scientists enriching their data. We make it easy to turn your massive data into clean enriched complete data. That is useful for data scientists because they have to analyze that data or build models.Martin: How did you come up with that idea of CrowdFlower?Lukas: That is simple. I was working as a data scientist. I always felt that the most important part of the process was the collecting and cleaning of the data. In a way, it was my least favorite part of the job but I really wanted to do that, get analysis and build good models. I got interested in building tools to help cleanup data. I found that they were very useful and then I thought this could be useful for other people so I built the company rather helping people clean up their data.Martin: Great! Can you progress through the process once you start ed the company, maybe the first three months or so? What was it like starting the company?Lukas: It was hard. It was a different time. We were little older than a lot of other startups. I remember Y Combinator, it wasnt so clear that that was so important thing. There was a lot less resources for tech entrepreneurs. There wasnt AngelList. AngelList was literally an email list. They would email your company and people would decide to invest or not.It is really somewhat shocking when you go from having a job to starting a company because you have no infrastructure around you to help you. I remember we closed our first deal and we needed to receive a fax and then print it and then send the fax back and so customer was asking: Hey, what is your fax number? I remember we got on our bikes and went to Best Buy, which is a store in America. We literally got a big box. My co-founder, and me we carried it back and we plugged it in and we said okay, you can send us the fax now. There is no inf rastructure and you are not getting a paycheck, which is scary.I think my parents were concerned that maybe I was unemployed. It was super hard. I think it was a lot harder than I was expecting. I was used to building products and having everything else around me taking care of and I think I didnt realize how much work goes into â€" you are doing finances, marketing, sales all those things.Martin: How long did it take you to get the first financing and the first customer?Lukas: The first customer happened early because we needed money. We sold the product long before it was ready. I think if we had had more access to capital we might have waited longer. It took us eighteen months before we raised any financing. Again back then raising seed rounds was hard. There is a lot less interest in doing seed investments. I remember people would laugh at me: You have no business plan, you do not have enough customers. There is no way we re going to invest in you. I think times have changed a l ot.BUSINESS MODEL OF CROWDFLOWERMartin: Let’s talk about business model of CrowdFlower. Did this business model change over time?Lukas: Yes, it changed a lot over time. The way CrowdFlower works is you set up the data cleanup project that you want. We use workforce to go into the jobs and clean them up. Lets say I am a data scientist at eBay and I want to know if a search result is good or bad. It is something that data scientists at eBay are interested in because if you search for iPhone and you get a result as a car with an iPhone adapter in it thats really bad result and you dont buy anything. eBay, they basically write down: Heres my rules, heres what it means for results be relevant, heres what it means for results to be not relevant and then heres a big list of search queries and search results. And I want the crowd to tell me which ones are relevant and not relevant. These are set up in a software and then the crowd does some of the labeling and then we use machine learning to do even more labeling.In early days in the company, we operated as a managed service. You would tell us what you wanted and tell us your requirements and then we would operate the software to get the results for you. We charged per result and weve priced differently based on the types of applications. We switched to model to, now if you are on eBay and you want these results you go into our platform, you set everything up and the platform does all the management. Weve gone through from a managed service model where we price per task and we would take a fixed percentage to now you pay for a platform license and then you can use our platforms as much as you need to. We operate as a SaaS software company now. That is our business model as opposed to managed service.Martin: The example of eBay sounds to me more like you wanted to get this feedback cycle from customers for calibrating the model. In the beginning, you said that you help data scientists to clean their data, which is di fferent â€" can you give us an example of this as well?Lukas: Clean and collect. That is why we are saying ‘enrich’. I think it is the best of what we can think; it means clean and collect. Cleaning is often business data. We work with Autodesk data scientists, for example. They have a huge list of customers and they want to do analytics on their customer base but many times, they have duplicate customers in there. It is even complicated. It is like as YouTube and Google are at the same company. It depends on what youre trying to accomplish. They sent us a big list of customers and then we clean up the records. We say these two records are the same, or we might say this record is mis-categorized, this company isnt a tech company, the more media company or we might say this address looks like it is wrong, this phone number looks like it is wrong. That type of thing.Martin: When we are talking about enrichment you need to get that enrichment right. How do you acquire that people t hat are working and helping data scientists and enriching the data?Lukas: We post a task online and in some cases we pay people directly to do the tasks and in other cases, we have partnered with companies that have big workforces. Weve made deals with companies around the world where we can post tasks on their website and pay people for doing jobs.Martin: How do you control the quality of the work? For example, I am a data scientist and I have tons of data. I need to be sure that the quality I get from you is high because else the analyses will be wrong.Lukas: Our software does it in many different ways. One simple way, simple but effective, is our customers can hide questions where they know what the answer is so they can do some of the labeling themselves. They could say okay this business and this business are the same. If somebody gets that wrong then they didnt understand my instructions. That is the simplest way you can label those. We use that like a test when people come in .We also ask different people the same question and we expect them to agree and if they dont then we worry about that. We also have people look at other peoples’ results and say if theyre good. Our software platform manages all that. Our customer goes and they may write down what they want and then our platform takes care of controlling the quality and building different types of tasks that you need to make sure that results are good.Martin: As I understand, those people are only enriching the data and not modeling features or something like that?Lukas: Just enriching the data. Another thing that our software does is it watches the people label data and it actually builds an algorithm that protects what people are going to do for the label. In some cases, our machine learning software can figure out without even going to human being what the right label should be.Martin: The people, who are enriching the data, are they doing this full time or its just a hobby of them?Lukas: Typica lly, part time.Martin: This means only when I have some kind of job to solve which is bigger I can hire flexible workforce via CrowdFlower?Lukas: Exactly.Martin: How did you acquire the first customers?Lukas: It was hard. We saw the market need and we asked all our friends: Hey, do you know anyone that needs this kind of data cleanup? Luckily, we were able to get to some people that were willing to try us and we took good care of the early customers. Some of them are still with us. LinkedIn was very, very early. I remember this guy, DJ Patil he did our first deal and hes become very famous data scientist. He signed the first contract.In fact, one of our very early customers became an Angel investor, actually two. It is interesting, one of our early customers, Gary Kremen, was the founder of Match.com and he is used us and invested. Then Travis Kalanick, now is is famous as the CEO of Uber, but at the time, he was actually just an engineer working on data problems. He was a very earl y user of a platform member. He called in and he wanted to meet in person. He angel invested in CrowdFlower too. These are early people, who helped us long before Uber.Martin: When you entered the market, did you only provide high discount or did you say try this for free, and if you love it, we can provide you more with a decent pricing?Lukas: We never offered it for free because the problem is I think free is little too easy for people. We felt like you need to pay some things that we know that you actually care about the results and youre not just doing this as a favor for us.Martin: Currently, are you using any distribution partners or are you having only direct sales or more inbound or outbound sales?Lukas: We are mainly direct sales. Our leads mainly come from inbound sources. One of the advantages we have is that we really sell data scientists. I dont think that a lot of companies have figured out how to reach data scientists well. I think Kaggle has done a good job and some others but because we have narrow focus on a specific kind of customer, we find that inbound works well. We just try to make stuff that data scientists are going to be interested in. We put out data. That is what data scientists like. We will post data sets that we think are interesting or useful, we will survey data science industry. We have just run a conference last week for data scientists. I think it was very successful because it was specific. We love data scientists. I was a data scientist. It is easy for us to talk to them.I think we will probably start doing outbound but in my experience I think inbound, its harder to scale in a way, but it is more the way people want to be sold to. I believe in content marketing, for example. Create content that is interesting. People can come to our website and they can learn about the state of the data science industry. Thats useful for them and then optionally they can take a look at CrowdFlower. We dont force you to put in your email address or anything like that. We make the content available and then if people are interested and they want to collect data they can try that.Martin: What is your competitors’ advantage over other platforms where you can also have crowd sources working for you?Lukas: I think the simple answer is quality. Were the biggest platform in terms of volume. The only one thats close in terms of volume is called Mechanical Turk. You have maybe heard of it. They have an advantage because they dont charge any fee to use their marketplace. They have a very low barrier to entry. My experience is that quality is often very bad or at least uneven. What we try to do is make sure the quality is good. I think that we are the highest quality data cleanup provider out there.Martin: Is there a reason for this that you have a kind of mechanics in place or that you have higher qualified workforce?Lukas: One is that our software is better. We are focused on data cleanup for data scientists. That means th at our software is very specific for our application. We have good templates that worked really well for the types of things data scientists want to do. We have good accuracy measurements and sophisticated tools. It is not for everyone. It is for people that really care about data equality and really want to make sure they get good results. What that means on, what we call a contributor side of the marketplace, is people know that theyre being measured and they believe theyre being treated fairly. When we see someone do good work for us for a long time we get them access to more and more work, which is what they want. That means that the people that have been in our system long time you can really trust because theyve proven over and over that they can do high quality work and they like coming back every day. I think better software and better market place means that when customers use us they get high quality results.Martin: Imagine, I am a data scientist. I have several jobs to do . I have some kind of one-time analyzes to do, I have some kind of predictive analytics and algorithms I want to build. Are you only focusing on the first one which is some kind of pattern analyzes to do data enrichment for this, or you have some kind of maintenance data enrichment for live products?Lukas: We love to do maintenance data enrichment for live products. We have tools to help with that. If you want to, you can use machine-learning tools to continuously label your data and you can even set things up so that you send it to the machine learning first and if the machine learning is confident and its answer you get back, and if it is not confident it gets labeled. Then those labels get fed back into the algorithm as a training data. You can use our tools. Many of our customers are advanced data scientists and they choose to use their own tools, their own custom stuff. Thats great too.I think we are especially strong in predictive analytics because it requires so much training data. It is something that many people dont realize. I would say that industry about predictive analytics is the best way to make your models effective. It has given lots and lots training data. Thats a great market for us and we love to help people with that.Martin: Imagine, I am a market place. I would have a job for you and say: Hey, we have one million search results. How much would it cost to label the search results, those one million on search list?Lukas: It depends on many things that you can set. There is a kind of a cost, quality and speed tradeoff. If you are very price-sensitive you just want to get it done as cheap as possible and you are willing to wait you can post a job at a low price and just kind of wait until it finishes. If you really want to get results back faster and you want high quality results than you have to pay a lot to get those results back. We do not mind any of these strategies. We make our money giving you the platform and tell you license for that and then you can pick your tradeoffs. We have a wide range of templates that you can use to get those results back. We will have simple templates that might cost, if it takes a few seconds maybe it only cost you a few cents to say if it is relevant result or not. If you have complicated taxonomy, you have complicated rules or if you want only to target our best contributors then you might have paid a $1 per record or something like that.Martin: Is this auction based? Imagine I would put in the job description and say okay, people can bid or I can set a price â€" how does it work?Lukas: The way our stuff works today is you set the price and then people can choose to do it or not.ADVICE TO ENTREPRENEURS FROM LUKAS BIEWALD In San Francisco (CA), we meet Founder CEO of CrowdFlower, Lukas Biewald. Lukas talks about his story how he came up with the idea and founded CrowdFlower, how the current business model works, as well as he provides some advice for young entrepreneurs.INTRODUCTIONMartin: Today we are in San Francisco in the CrowdFlower office. Hi, Lukas. Who are you and what do you do?Lukas: I am the founder and CEO of CrowdFlower. We help data scientists enriching their data. We make it easy to turn your massive data into clean enriched complete data. That is useful for data scientists because they have to analyze that data or build models.Martin: How did you come up with that idea of CrowdFlower?Lukas: That is simple. I was working as a data scientist. I always felt that the most important part of the process was the collecting and cleaning of the data. In a way, it was my least favorite part of the job but I really wanted to do that, get analysis and build good models. I got interested in buildi ng tools to help cleanup data. I found that they were very useful and then I thought this could be useful for other people so I built the company rather helping people clean up their data.Martin: Great! Can you progress through the process once you started the company, maybe the first three months or so? What was it like starting the company?Lukas: It was hard. It was a different time. We were little older than a lot of other startups. I remember Y Combinator, it wasnt so clear that that was so important thing. There was a lot less resources for tech entrepreneurs. There wasnt AngelList. AngelList was literally an email list. They would email your company and people would decide to invest or not.It is really somewhat shocking when you go from having a job to starting a company because you have no infrastructure around you to help you. I remember we closed our first deal and we needed to receive a fax and then print it and then send the fax back and so customer was asking: Hey, what is your fax number? I remember we got on our bikes and went to Best Buy, which is a store in America. We literally got a big box. My co-founder, and me we carried it back and we plugged it in and we said okay, you can send us the fax now. There is no infrastructure and you are not getting a paycheck, which is scary.I think my parents were concerned that maybe I was unemployed. It was super hard. I think it was a lot harder than I was expecting. I was used to building products and having everything else around me taking care of and I think I didnt realize how much work goes into â€" you are doing finances, marketing, sales all those things.Martin: How long did it take you to get the first financing and the first customer?Lukas: The first customer happened early because we needed money. We sold the product long before it was ready. I think if we had had more access to capital we might have waited longer. It took us eighteen months before we raised any financing. Again back then raisin g seed rounds was hard. There is a lot less interest in doing seed investments. I remember people would laugh at me: You have no business plan, you do not have enough customers. There is no way we re going to invest in you. I think times have changed a lot.BUSINESS MODEL OF CROWDFLOWERMartin: Let’s talk about business model of CrowdFlower. Did this business model change over time?Lukas: Yes, it changed a lot over time. The way CrowdFlower works is you set up the data cleanup project that you want. We use workforce to go into the jobs and clean them up. Lets say I am a data scientist at eBay and I want to know if a search result is good or bad. It is something that data scientists at eBay are interested in because if you search for iPhone and you get a result as a car with an iPhone adapter in it thats really bad result and you dont buy anything. eBay, they basically write down: Heres my rules, heres what it means for results be relevant, heres what it means for results to be not r elevant and then heres a big list of search queries and search results. And I want the crowd to tell me which ones are relevant and not relevant. These are set up in a software and then the crowd does some of the labeling and then we use machine learning to do even more labeling.In early days in the company, we operated as a managed service. You would tell us what you wanted and tell us your requirements and then we would operate the software to get the results for you. We charged per result and weve priced differently based on the types of applications. We switched to model to, now if you are on eBay and you want these results you go into our platform, you set everything up and the platform does all the management. Weve gone through from a managed service model where we price per task and we would take a fixed percentage to now you pay for a platform license and then you can use our platforms as much as you need to. We operate as a SaaS software company now. That is our business mo del as opposed to managed service.Martin: The example of eBay sounds to me more like you wanted to get this feedback cycle from customers for calibrating the model. In the beginning, you said that you help data scientists to clean their data, which is different â€" can you give us an example of this as well?Lukas: Clean and collect. That is why we are saying ‘enrich’. I think it is the best of what we can think; it means clean and collect. Cleaning is often business data. We work with Autodesk data scientists, for example. They have a huge list of customers and they want to do analytics on their customer base but many times, they have duplicate customers in there. It is even complicated. It is like as YouTube and Google are at the same company. It depends on what youre trying to accomplish. They sent us a big list of customers and then we clean up the records. We say these two records are the same, or we might say this record is mis-categorized, this company isnt a tech company, the more media company or we might say this address looks like it is wrong, this phone number looks like it is wrong. That type of thing.Martin: When we are talking about enrichment you need to get that enrichment right. How do you acquire that people that are working and helping data scientists and enriching the data?Lukas: We post a task online and in some cases we pay people directly to do the tasks and in other cases, we have partnered with companies that have big workforces. Weve made deals with companies around the world where we can post tasks on their website and pay people for doing jobs.Martin: How do you control the quality of the work? For example, I am a data scientist and I have tons of data. I need to be sure that the quality I get from you is high because else the analyses will be wrong.Lukas: Our software does it in many different ways. One simple way, simple but effective, is our customers can hide questions where they know what the answer is so they can do some o f the labeling themselves. They could say okay this business and this business are the same. If somebody gets that wrong then they didnt understand my instructions. That is the simplest way you can label those. We use that like a test when people come in.We also ask different people the same question and we expect them to agree and if they dont then we worry about that. We also have people look at other peoples’ results and say if theyre good. Our software platform manages all that. Our customer goes and they may write down what they want and then our platform takes care of controlling the quality and building different types of tasks that you need to make sure that results are good.Martin: As I understand, those people are only enriching the data and not modeling features or something like that?Lukas: Just enriching the data. Another thing that our software does is it watches the people label data and it actually builds an algorithm that protects what people are going to do for t he label. In some cases, our machine learning software can figure out without even going to human being what the right label should be.Martin: The people, who are enriching the data, are they doing this full time or its just a hobby of them?Lukas: Typically, part time.Martin: This means only when I have some kind of job to solve which is bigger I can hire flexible workforce via CrowdFlower?Lukas: Exactly.Martin: How did you acquire the first customers?Lukas: It was hard. We saw the market need and we asked all our friends: Hey, do you know anyone that needs this kind of data cleanup? Luckily, we were able to get to some people that were willing to try us and we took good care of the early customers. Some of them are still with us. LinkedIn was very, very early. I remember this guy, DJ Patil he did our first deal and hes become very famous data scientist. He signed the first contract.In fact, one of our very early customers became an Angel investor, actually two. It is interesting, o ne of our early customers, Gary Kremen, was the founder of Match.com and he is used us and invested. Then Travis Kalanick, now is is famous as the CEO of Uber, but at the time, he was actually just an engineer working on data problems. He was a very early user of a platform member. He called in and he wanted to meet in person. He angel invested in CrowdFlower too. These are early people, who helped us long before Uber.Martin: When you entered the market, did you only provide high discount or did you say try this for free, and if you love it, we can provide you more with a decent pricing?Lukas: We never offered it for free because the problem is I think free is little too easy for people. We felt like you need to pay some things that we know that you actually care about the results and youre not just doing this as a favor for us.Martin: Currently, are you using any distribution partners or are you having only direct sales or more inbound or outbound sales?Lukas: We are mainly direct sales. Our leads mainly come from inbound sources. One of the advantages we have is that we really sell data scientists. I dont think that a lot of companies have figured out how to reach data scientists well. I think Kaggle has done a good job and some others but because we have narrow focus on a specific kind of customer, we find that inbound works well. We just try to make stuff that data scientists are going to be interested in. We put out data. That is what data scientists like. We will post data sets that we think are interesting or useful, we will survey data science industry. We have just run a conference last week for data scientists. I think it was very successful because it was specific. We love data scientists. I was a data scientist. It is easy for us to talk to them.I think we will probably start doing outbound but in my experience I think inbound, its harder to scale in a way, but it is more the way people want to be sold to. I believe in content marketing, for exampl e. Create content that is interesting. People can come to our website and they can learn about the state of the data science industry. Thats useful for them and then optionally they can take a look at CrowdFlower. We dont force you to put in your email address or anything like that. We make the content available and then if people are interested and they want to collect data they can try that.Martin: What is your competitors’ advantage over other platforms where you can also have crowd sources working for you?Lukas: I think the simple answer is quality. Were the biggest platform in terms of volume. The only one thats close in terms of volume is called Mechanical Turk. You have maybe heard of it. They have an advantage because they dont charge any fee to use their marketplace. They have a very low barrier to entry. My experience is that quality is often very bad or at least uneven. What we try to do is make sure the quality is good. I think that we are the highest quality data cle anup provider out there.Martin: Is there a reason for this that you have a kind of mechanics in place or that you have higher qualified workforce?Lukas: One is that our software is better. We are focused on data cleanup for data scientists. That means that our software is very specific for our application. We have good templates that worked really well for the types of things data scientists want to do. We have good accuracy measurements and sophisticated tools. It is not for everyone. It is for people that really care about data equality and really want to make sure they get good results. What that means on, what we call a contributor side of the marketplace, is people know that theyre being measured and they believe theyre being treated fairly. When we see someone do good work for us for a long time we get them access to more and more work, which is what they want. That means that the people that have been in our system long time you can really trust because theyve proven over and over that they can do high quality work and they like coming back every day. I think better software and better market place means that when customers use us they get high quality results.Martin: Imagine, I am a data scientist. I have several jobs to do. I have some kind of one-time analyzes to do, I have some kind of predictive analytics and algorithms I want to build. Are you only focusing on the first one which is some kind of pattern analyzes to do data enrichment for this, or you have some kind of maintenance data enrichment for live products?Lukas: We love to do maintenance data enrichment for live products. We have tools to help with that. If you want to, you can use machine-learning tools to continuously label your data and you can even set things up so that you send it to the machine learning first and if the machine learning is confident and its answer you get back, and if it is not confident it gets labeled. Then those labels get fed back into the algorithm as a training data. You can use our tools. Many of our customers are advanced data scientists and they choose to use their own tools, their own custom stuff. Thats great too.I think we are especially strong in predictive analytics because it requires so much training data. It is something that many people dont realize. I would say that industry about predictive analytics is the best way to make your models effective. It has given lots and lots training data. Thats a great market for us and we love to help people with that.Martin: Imagine, I am a market place. I would have a job for you and say: Hey, we have one million search results. How much would it cost to label the search results, those one million on search list?Lukas: It depends on many things that you can set. There is a kind of a cost, quality and speed tradeoff. If you are very price-sensitive you just want to get it done as cheap as possible and you are willing to wait you can post a job at a low price and just kind of wait until it f inishes. If you really want to get results back faster and you want high quality results than you have to pay a lot to get those results back. We do not mind any of these strategies. We make our money giving you the platform and tell you license for that and then you can pick your tradeoffs. We have a wide range of templates that you can use to get those results back. We will have simple templates that might cost, if it takes a few seconds maybe it only cost you a few cents to say if it is relevant result or not. If you have complicated taxonomy, you have complicated rules or if you want only to target our best contributors then you might have paid a $1 per record or something like that.Martin: Is this auction based? Imagine I would put in the job description and say okay, people can bid or I can set a price â€" how does it work?Lukas: The way our stuff works today is you set the price and then people can choose to do it or not.ADVICE TO ENTREPRENEURS FROM LUKAS BIEWALDMartin: Let†™s talk about your learnings over the last year. What has been the major learnings from your side?Lukas: One thing thats really served us that we didnt do in the beginning that I wish we have done earlier is to focus on one particular kind of customer. I think for a lot of entrepreneurs that come out with a new tool like a really new approach, a new kind of thing you can get lots of different people that are interested in using it. When we first launched CrowdFlower, we had many different kinds of people saying wow this is a cool tool. It is for surveys; I can do site usability testing with it and all other amazing things I can do with it. What that does is it feels good because you have all these options but it makes it impossible to do marketing. It was a scary hard decision for us to say hey were going to focus on data science. It was really difficult and I think a lot of the team was worried because data scientists were less than a quarter of our customer base. However, I felt t hat the data scientists were the happiest customers. I knew that if we focused on them we would be able to grow that market.I think one of the skills of entrepreneurs to say is not how is things now but how could things change. Back in 2012, it looked like a data science market was small. Some of our investors and the management were as if this is too small. We cannot only focus on this market. I think if you look at the trends if youre in it, you are thinking wow this markets going to grow a lot so. Having some patience like the one we are going to focus on this now because it is going to set us up for success later. It is going to help us make a good decision. That decision is one of the best decisions that we have ever made. In retrospect, it looks like an obvious decision but at the time, it was not obvious. We had two executives leave because they were not on board. The decision seemed too risky.Martin: What other lessons did you learn over the years?Lukas: I think another unde rrated piece of running a business, and I have actually seen this in many of my friends companies’ too, is it is important to really like your customers. Everyone says: It is true. In many ways, your customer is your most important constituent. As a founder, you really want to like the people that you serve. One of the things that have made running this company fun is that I like data scientists. When I go to the data science conference I am interested I love hearing what they are up to. I feel much comfortable hanging out with the data scientists than often like C-level executives of the companies that were in. I find their problems much more interesting than like managing thousand persons team. I think that there is definitely an effective strategy with the business when you are going to the top and target the C-suite. But I think for us, as far as it looks for us as a business and our DNA is serving data scientists and making them successful.I see that in many people that have gotten frustrated. Sometimes people go into business thinking they are going to serve one market that they like and then they end up serving a different market. Sometimes it works great but often it fails because they do not like that market. I have friends sell to HR, some of them love HR conferences. And then I have other friends that have discovered that HR is a great place to sell to but you know I dont like these people that much. You cannot succeed if you dont really enjoy your customers.That is something to think about when I look at people and they ask me hey, I want to start a company. One way of looking at it is working backwards from whom you want to serve. Whom do you like? You maybe like entrepreneurs. Then you can start with that. Okay. I like entrepreneurs. What do they really want? I think like back into like that is a real recipe for success because you are going to make something you want; it’s going to be fun. If you are running a company, you spend so much of y our time with customers. This is going to be most of your life.Martin: Lukas, let’s talk about the growth options because you described that when the data science market was still small, you said: Okay, I bet on that the market will grow, data enrichment provider. But at some point the market is saturated with this kind of service. What other growth options do you perceive for your company?Lukas: I would say we are far from selling every data scientists in the world. I think that we are going to grow with that for a good long time. In the back of my mind, there are so many cool things to try but I do not ever bring that up because we need to focus on saturating the data science market before we start to worry about expansion opportunities. I even think that there are ways to serve our market better so that we can actually increase the value that were making for our customers beyond just what were doing today. You look at the average sales price today; I think that could actually g o up a lot in a way that everyone looks good about it because we can make our software more useful and sell more modules to our customer base. I guess for me, in my situation, I look more at how do we actually saturate the market because we actually havent done it yet and then how do we expand within the market that we are in.For example, we have launched a new AI module recently. It was interesting experience for me because it was the biggest launch that we have had since we launched CrowdFlower. The original CrowdFlower did not have a machine-learning piece. Every record that you got back then was done by the Crowd. Recently we launched a thing where it is ok to say now you can have it done by artificial intelligence module. When we first built the CrowdFlower I think the hardest thing as an entrepreneur is to get feedback on what you are doing because people are busy and if theyre not using it, it is really hard to get peoples attention.These books like The Lean Startup, The Four Steps to the Epiphany, they are excellent. They tell you: Hey, run everything by customers before you make it. That is easier said than done because you cannot just call up a potential user and they take your phone call. You really have to hustle to get in front of them. It was interesting to build this kind of second module for our data scientists, to do data enrichment because we actually had hundreds of people everyday that are logging on the CrowdFlower trying to enrich the data, using the tools. So Hey, here I is what I am thinking. I think we built machine learning, do you have any feedback on it? Of course, they have tons of feedback because they are so excited that were going to make the tool even more useful for them. It was a much faster customer development process that we were able to run because we had his existing customer base â€" they were all trying to do the same thing.Martin: Just for clarification, the new AI module basically tries to do what the humans have bee n doing based on the machine learning that data enrichment is done by an algorithm which improves over time?Lukas: Exactly.Martin: The feedback loop is then done by your customers so the data scientists or is it done also by the Crowd?Lukas: The feedback loop is be done by the Crowd. In a sense that if the data scientist is controlling everything, the data scientist might say: If the model is under 90% confident in the answer, I want a human to actually look at it. We automatically feed that back in the oven so it could get smarter. If we get 85% confident then we get a human to label it, then the algorithm can see if: I was right in which case it gets little more confident, or maybe I am wrong in which case it gets less confident and sort of retrain the parameters.Martin: Thereby you can reduce the cost for data enrichment because they can only focus on the last 10%.Lukas: Exactly. A good question. I think I explained it well.Martin: Lukas, thank you so much for sharing your knowle dge.Lukas: Thank you very much.Martin: If you are a data scientist you know data is the key for building some really awesome data products. If you want to enrich your data you have to focus on the cool machine-learning stuff then maybe you should think about CrowdFlower.

The School Of Prison Pipeline Presents The Intersection Of...

Chapter One: Introduction Background of the Study: The School-to-Prison Pipeline presents the intersection of a K-12 educational system and a juvenile system, which too often fails to serve our nations at risk youth. For most students, the pipeline begins with inadequate resources in public schools. Overcrowded classrooms, a lack of qualified teachers, and insufficient funding for extras such as counselors, special education services, even textbooks, lock students into second-rate educational environments. This failure to meet educational needs increases disengagement and dropouts, increasing the risk of later court involvement (Bennett-Haron, Fasching-Varner, Martin, Mitchell 2014). Even worse, schools may actually encourage dropouts in response to pressures from test-based accountability regimes such as the No Child Left Behind Act, which create incentives to push out low-performing students to boost overall test scores (Cramer, Gonzales, Lafont-Pellegrini 2014). Lacking resources, facing incentives to push out low-performing st udents, and responding to a handful of highly-publicized school shootings, schools have embraced zero-tolerance policies that automatically impose severe punishment regardless of circumstances. Under these policies, students have been expelled for bringing nail clippers or scissors to school (Christle, Jolivette, Nelson 2005). Rates of suspension have increased dramatically in recent years from 1.7 million in 1998 to 3.1 million in 2010

Lottery Ticket Case Ii Solution Essay - 1036 Words

Five-Step Approach to Unstructured Problems 1. Succinct Statement of the Financial Reporting Issue(s) Provide a brief statement of the accounting issue that includes the characteristics of the transaction that introduce uncertainty about how to record it. How should an expenditure, in this instance to purchase a lottery ticket, which has a risk of providing no future cash flows be reported? 2. Brief Summary of the Economic Purpose of the Transaction State the reason corporate management has entered into the transaction, or, alternatively, summarize the event that has led to the reporting controversy. (This can be difficult in some practice cases but is usually obvious in the FASB concepts cases.) Phil N. Tropic bought†¦show more content†¦4. Neutral Discussion of the Major Alternatives, Citing Relevant Authoritative Literature and Theoretical Concepts Discuss the merits for and against each of the alternative ways to report the transaction listed in the previous step. Cite authoritative accounting rules (from the conceptual framework or practice literature) and specific facts of the case that help you apply the rules. If you have developed a long list of alternatives in step 3, you may be able to eliminate some of them without a detailed analysis (but state reasons). This is the longest section of your analysis. Alternatives a and b (from step 3) are closely related so I will discuss them together in applying the recognition criteria. A critical aspect in determining whether the $150 is an asset or contribution expense is whether the benefit is viewed as i) the chance to receive $100-$100,000 or ii) the right to participate in the drawing. These alternatives assume that Phil plans to keep the ticket and participate in the lottery. Under view i), the probability of receiving $100-$100,000 is a probable future economic event since the chances of winning a prize are greater than 50%. Although the FASB doesn’t require a 50% chance to be probable, the fact that the odds are greater than 50% is favorable. With regard to control, he has paid in full for the ticket but he has no control over the outcome of the drawing. Control is thereby questionable. Finally, since Phil hasShow MoreRelatedLottery Ticket Case II Solution991 Words   |  4 Pagesin this instance to purchase a lottery ticket, which has a risk of providing no future cash flows be reported? 2. Brief Summary of the Economic Purpose of the Transaction State the reason corporate management has entered into the transaction, or, alternatively, summarize the event that has led to the reporting controversy. (This can be difficult in some practice cases but is usually obvious in the FASB concepts cases.) Phil N. 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Command vs Market Economy Free Essays

Command Economic System: When we talk about the term â€Å"command† in historical context; whether it relates to economic, political or warfare, command has always been vested in the hands of the few. If we relate â€Å"these few† to a group of people who exercise power in terms of making decisions (be it economic/social/political etc) for ALL the people they govern, we call this process or system a â€Å"Government†. In a command economic system, this government basically owns and controls most of the economic resources of the country. We will write a custom essay sample on Command vs Market Economy or any similar topic only for you Order Now This â€Å"Command† economic system is also known as â€Å"socialism† or â€Å"communism† (McConnell – Economics) In any economic system decisions have to be made regarding production of goods and services, price setting, education, expenditure on infrastructure, resource allocation, resource/property ownership, resource distribution, establishment of industries and businesses, salaries for individuals etc. In a Command Economic System, all these decisions are taken by the Centre/Government. Public in general/ individuals in general do not have any â€Å"say† in such government decisions. Practically speaking, â€Å"Absolute† command economy doesn’t exist in this world, even near perfect â€Å"command economy† of Soviet Union/Russia had private/market influences in its system. McConnell states North Korea and Cuba as near to perfect Command Economic Systems. Pakistan too took a step towards socialism/nationalization in Zulfiqar Ali Bhutto’s era in 1970s, which later had to be discontinued in wake of emerging capitalist economic forces at that time. Market Economic System As opposed to Command Economic System, Market Economic System is characterized by near to minimal role of Government in governing and directing economic activity of the country. In other words, it is simply the opposite of a command economic system discussed above. The salient features of a market economic system includes â€Å"Private† ownership of economic resources (i. e. , land, labor, capital and entrepreneur), coordination of economic activity through markets, production and distribution decisions aken by private businesses and firms, determination of market prices and quantity through forces of demand and supply (rather than government) etc. The concept of market is fundamental in understanding the captioned subject. Market is a place where buyers and sellers of products come together and through their buying and selling behaviour, price and output for the economy is determined. The sellers seek to maximise their objectives (primarily profit) thro ugh engaging in practices that may compromise societal benefits at large (self interest). To keep profitable, businesses innovate/invest in RD to achieve economies of scale to minimise cost and this lust for market power often leads to competition/inter rivalry amongst firms which leads to production of goods and services at less than socially optimum level. Though practically speaking a perfect market economy can’t exist (government intervention is required in certain areas) Hong Kong, United States and Ireland (ref McConnell) are nearest examples of free market economies in today’s world, where Government’s intervention is minimal. How to cite Command vs Market Economy, Essay examples

Legal Studies Adult Parole of Victoria Australia †Free Samples

Question: Discuss about the Legal Studies The Adult Parole of Victoria Australia. Answer: Benefits Community Safety-The Law Institute of Victoria (LIV) is very much supportive of the parole scheme. This parole arrangement permits for supervision, management and supported reintegration of all the prisoners back into the society (Liv.asn.au 2017). In addition to this, there are empirical evidence that reflect that parole lessens rates of recidivism and defers the beginning of reoffending, hence benefitting the broader community. Parole issued based on good behaviour or else determination of the board that a particular convict has sufficiently reformed and can be allowed to re-enter the community. This provides the convict fresh opportunity as well as the opportunity to start afresh for all the convicted criminals (Callinan 2013).Justice Callinan recognizes fact parole system generates creates hope, self-reverence and the motivation to reform as well as rehabilitate throughout the population of the prison (Liv.asn.au 2017). In addition to this, Justice Callinan also mentions the fact that parole system benefits the entire community from essentially the rehabilitation of all the offenders. On the whole, the LIC supports the suggestions of Justice Callinans for better resourcing for particular Parole Board as well as Corrections Victoria to make certain the best results for the concerned people (Callinan 2013). Essentially, strengthening the Victorias parole system is said to strengthen the overall community sa fety. 98% of offenders will thus be in due course be released from prison and around 5500 prisoners are unconfined into the Victorian community each year (Liv.asn.au 2017). However, it is essential for the parole board to make the correct decisions regarding granting parole as well as cancellation of parole. In essence, the Victorian Government is by now creating alterations to enhance parole. Saves money- The prisons are very expensive and the legal authorities need to bear huge amount of costs for running the same. Huge number of prisoners therefore increases the costs of the authority. Thus, parole can help in saving money as prisoners are released prior to conclusion of the statement. Again, this is beneficial to the public and this can reduce the total number of people who are incarcerated (Bartels 2013). This can essentially cost huge sums of money per prisoner each year. Moreover, reduction of the incarcerated rates can be considered to be conducive to a free as well as democratic community. Reduces overcrowding in prisons- the release of the prisoners prior to the completion of the sentence also helps in reduction of the overcrowding of convicts in the prison. In Australia the nationwide imprisonment rate is 168 prisoners for every 100,000 adults. Therefore, more number of prisoners can overcrowd the prison. As per reports, the rate of imprisonment in Australia has increased each year and since 2002, the population of prison has increased by nearly 31% (Fitzgerald et al. 2016). Limitations Paroles cannot be monitored all the time: Parole involves a huge risk in which the parolee might perhaps become a repeat offender. This becomes a huge risk that the convict needs to be able to survive on their own upon release and can also be remain unemployed, homeless and can face social maladjustments or else substance abuse (Heraldsun.com.au 2017). Therefore, paroles need to be monitored properly by the criminal justice system. Issues with administration of parole: there are three different key agencies that is essentially involved in the Australian parole system (Bartels 2013). Again, Adult Parole Board is accountable for granting as well as cancellation of parole, along with overseeing parolee improvement in the community. Department of Justice Regulation is responsible for preparing prisoners for essentially the parole. Again Victoria Police also plays an important role in the system by notifying both DJR as well as APB. However, there are several issues as there are few prisoners that receive parole and as an outcome there are more offenders who are not receiving the support. Insufficient information as well as communications technology (ICT) schemes at DJR augments the overall risk of error and create inadequacies. Issues with resources of supervisions and enforcement of conditions of parole: Precise monitoring as well as assessment is crucial to allow agencies to recognize areas for enhancement and to determine the magnitude to which the parole system is raising community safety (Fitzgerald et al. 2016). These insufficient ICT systems obstruct the monitoring as well as assessment of the impact of the alterations on the parole scheme. References Bartels, L., 2013. Parole and Parole Authorities in Australia: A System in Crisis?. Fitzgerald, R., Freiberg, A., Cherney, A. and Buglar, S., 2016. How does the Australian public view parole? Results from a national survey on public attitudes towards parole and re-entry.CriminalLaw Journal,40(6), pp.307-324. Callinan, I., 2013. Review of the parole system in Victoria. Liv.asn.au. 2017. [online] Available at: https://www.liv.asn.au/getattachment/3c3b7020-e37a-48a3-bf64-167f44d2fe82/Review-of-the-Parole-System-in-Victoria [Accessed 1 May 2017]. Heraldsun.com.au. 2017. Strong parole system will benefit all. [online] Available at: https://www.heraldsun.com.au/news/opinion/strong-parole-system-will-benefit-community/news-story/614fb011dc8ea02658993e35d643afb2 [Accessed 1 May 2017].