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Articles related to "machine"


An Introduction to Machine Learning and AI in the Legal Industry

  • The most exciting aspect of applying AI in the legal profession lies in the automation of repetitive and straightforward tasks, like eDiscovery or judicial bill review, while enabling human experts to improve results beyond what machines or people could do alone.
  • As an example: A restaurant could, for example, better cater to their customers by building a machine learning model that analyzes their busiest periods, the most popular menu items, and estimated waiting times to more accurately schedule service staff and schedule stock supplies for improved customer experience.
  • When machine learning is implemented for legal operations, the data from invoices, matters, other legal records “trains” the AI to recognize patterns, while the expertise of the legal department staff provides feedback that allows the AI to improve results over time.
  • Through utilizing machine learning and AI, legal and claims departments see that compliance with their billing guidelines — an issue many companies struggle with — can be improved significantly across outside counsel relationships.

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An Introduction to Machine Learning and AI in the Legal Industry

  • The most exciting aspect of applying AI in the legal profession lies in the automation of repetitive and straightforward tasks, like eDiscovery or judicial bill review, while enabling human experts to improve results beyond what machines or people could do alone.
  • As an example: A restaurant could, for example, better cater to their customers by building a machine learning model that analyzes their busiest periods, the most popular menu items, and estimated waiting times to more accurately schedule service staff and schedule stock supplies for improved customer experience.
  • When machine learning is implemented for legal operations, the data from invoices, matters, other legal records “trains” the AI to recognize patterns, while the expertise of the legal department staff provides feedback that allows the AI to improve results over time.
  • Through utilizing machine learning and AI, legal and claims departments see that compliance with their billing guidelines — an issue many companies struggle with — can be improved significantly across outside counsel relationships.

save | comments | report | share on


An Introduction to Machine Learning and AI in the Legal Industry

  • The most exciting aspect of applying AI in the legal profession lies in the automation of repetitive and straightforward tasks, like eDiscovery or judicial bill review, while enabling human experts to improve results beyond what machines or people could do alone.
  • As an example: A restaurant could, for example, better cater to their customers by building a machine learning model that analyzes their busiest periods, the most popular menu items, and estimated waiting times to more accurately schedule service staff and schedule stock supplies for improved customer experience.
  • When machine learning is implemented for legal operations, the data from invoices, matters, other legal records “trains” the AI to recognize patterns, while the expertise of the legal department staff provides feedback that allows the AI to improve results over time.
  • Through utilizing machine learning and AI, legal and claims departments see that compliance with their billing guidelines — an issue many companies struggle with — can be improved significantly across outside counsel relationships.

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Google's AI Chief Wants to Do More With Less (Data)

  • WIRED: You gave a research talk about building new kinds of computers to power machine learning.
  • We've been playing with a bunch of different internal Google chips, things like TPUs [Google’s custom machine learning chips].
  • We have a process by which product teams thinking of using machine learning in some way can get early opinions before they have designed the entire system, like how should you go about collecting data to ensure that it's not biased or things like that.
  • W: Mustafa Suleyman, a cofounder of DeepMind, the London AI startup that’s part of Alphabet and a major player in machine learning research, recently moved over to Google.
  • He's been pretty involved in Google’s AI principles and review process as well, so I think he’s going to focus most of his time on that: AI ethics and policy related work.

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An Introduction to Machine Learning and AI in the Legal Industry

  • The most exciting aspect of applying AI in the legal profession lies in the automation of repetitive and straightforward tasks, like eDiscovery or judicial bill review, while enabling human experts to improve results beyond what machines or people could do alone.
  • As an example: A restaurant could, for example, better cater to their customers by building a machine learning model that analyzes their busiest periods, the most popular menu items, and estimated waiting times to more accurately schedule service staff and schedule stock supplies for improved customer experience.
  • When machine learning is implemented for legal operations, the data from invoices, matters, other legal records “trains” the AI to recognize patterns, while the expertise of the legal department staff provides feedback that allows the AI to improve results over time.
  • Through utilizing machine learning and AI, legal and claims departments see that compliance with their billing guidelines — an issue many companies struggle with — can be improved significantly across outside counsel relationships.

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Project Or No Project?

  • While I do agree that if you exclusively spend your time working on projects and neglect to interview prep and network, then yes you will have a really hard time landing a job (this is true for any field, not just data science).
  • Let me provide a few thoughts that will hopefully convince you why working on projects is worth your time and how best to present your findings in a way that might catch the eye of employers (and other interested parties).
  • Nothing revolutionary here but I tried as best I could with my very limited graphic design skills to show something rolling down from a high place to a low place (that’s what gradient descent is trying to do in spirit — find the minimum).

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JVM vs DVM

  • The question was why java bytecode can run on any machine or platform and how it is different from the other code?.
  • For example, If you have a Hello.java class and when you run this class file then javac compiler turns your source code to bytecode and creates Hello.class file which means javac compiler does not convert Java code directly to machine code like other compiler does.
  • As you can see from the image above, once you have .class file ready then you can give this file to any platform and it will convert it to native machine code.
  • JVM will work based on byte code and the DVM will work based on optimized bytecode, it is optimised for mobile platforms because mobile devices have less memory, low process and low power that’s why it is using the linux kernal.

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Project Or No Project?

  • While I do agree that if you exclusively spend your time working on projects and neglect to interview prep and network, then yes you will have a really hard time landing a job (this is true for any field, not just data science).
  • Let me provide a few thoughts that will hopefully convince you why working on projects is worth your time and how best to present your findings in a way that might catch the eye of employers (and other interested parties).
  • Nothing revolutionary here but I tried as best I could with my very limited graphic design skills to show something rolling down from a high place to a low place (that’s what gradient descent is trying to do in spirit — find the minimum).

save | comments | report | share on


JVM vs DVM

  • The question was why java bytecode can run on any machine or platform and how it is different from the other code?.
  • For example, If you have a Hello.java class and when you run this class file then javac compiler turns your source code to bytecode and creates Hello.class file which means javac compiler does not convert Java code directly to machine code like other compiler does.
  • As you can see from the image above, once you have .class file ready then you can give this file to any platform and it will convert it to native machine code.
  • JVM will work based on byte code and the DVM will work based on optimized bytecode, it is optimised for mobile platforms because mobile devices have less memory, low process and low power that’s why it is using the linux kernal.

save | comments | report | share on


Project Or No Project?

  • While I do agree that if you exclusively spend your time working on projects and neglect to interview prep and network, then yes you will have a really hard time landing a job (this is true for any field, not just data science).
  • Let me provide a few thoughts that will hopefully convince you why working on projects is worth your time and how best to present your findings in a way that might catch the eye of employers (and other interested parties).
  • Nothing revolutionary here but I tried as best I could with my very limited graphic design skills to show something rolling down from a high place to a low place (that’s what gradient descent is trying to do in spirit — find the minimum).

save | comments | report | share on