1 I NTRODUCTION Insights as Stories Ganes Kesari Co-founder & - - PowerPoint PPT Presentation

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1 I NTRODUCTION Insights as Stories Ganes Kesari Co-founder & - - PowerPoint PPT Presentation

1 I NTRODUCTION Insights as Stories Ganes Kesari Co-founder & Head 100+ Clients of Analytics Simplify Data Science for all Help apply & adopt Analytics Our data science platform, Gramex is now open- sourced! 2 Smart


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INTRODUCTION

Ganes Kesari Co-founder & Head

  • f Analytics

“Simplify Data Science for all” 100+ Clients Insights as Stories Help apply & adopt Analytics

Our data science platform, Gramex is now open- sourced!

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Smart Contract Ris isk k Id Identif ification wit ith AI

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AI VS LAWYERS: THE ULTIMATE SHOWDOWN

https://hackernoon.com/20-top-lawyers-were-beaten-by-legal-ai-here-are-their-surprising-responses-5dafdf25554d https://www.lawgeex.com/resources/AIvsLawyer/

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CHALLENGES WITH CONTRACT REVIEW Voluminous Deliberate Verbosity Tedious Error-prone Lower cognition

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HOW CAN TECHNOLOGY HELP?

Images: Gmail; Grammarly; Skype

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HOW DO THESE WORK? “Programs that solve the problem” “Programs that learn to solve the problem” vs Machine Learning

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HOW DO MACHINES LEARN?

New Input Desired Outcome

Machine learning how to do the job

Known Input Known Outcome

…210.5 AAPL 207.4, 207.1, $204.5, 205.2, …

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ANALYTICS IS MORE THAN MAGIC WITH NUMBERS

”What a nice sunny …day?

Icons: Watercolor vector by starline on freepik; flaticon on freepik; all-free-download.com

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WHY DEEP LEARNING?

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Input Output

Identify features to teach model

Traditional Machine Learning Deep Learning

Person Name

Input Output Model automatically identifies features to learn

Person Name

https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf

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NOT VERY DIFFERENT FROM HOW WE LEARN

Training ..Versatile Detection!

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AI AROUND US - READING TEXT

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AI AROUND US – TRANSLATING TEXT

Google Translate

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AI AROUND US – GENERATING TEXT

OpenAI: https://openai.com/blog/better-language-models/

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OUR WORK ON CONTRACT RISK IDENTIFICATION

✔︐ Categorize ✔︐ Assess Risk ✔︐ Simplify Reviews

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PREPARING DATA TO TRAIN THE AI

100+ Documents 6000+ Clauses

Icon by Smashicons on Freepik

Categories: Agreement Liability-Indemnity Confidentiality Others Dispute Resolution Payment Terms IP Rights Scope Terms And Conditions Sub Contract Warranty

Data:

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  • Term. This Agreement shall be in force for a period of two (2) years from the Effective Date. This

Agreement may be terminated by either Party at any time upon thirty (30) days written notice to the

  • ther Party. Provided however that the termination of this Agreement shall not relieve any Party of its
  • bligations with respect to Confidential Information disclosed under this Agreement for a period of five

(5) years after the expiry/termination of this Agreement.

PREPARING DATA TO TRAIN THE AI

Categories: Agreement Liability-Indemnity Confidentiality Others Dispute Resolution Payment Terms IP Rights Scope ✔︐ Terms And Conditions Sub Contract Warranty Criticality: Non-Critical ✔︐ Critical

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BUILDING THE MODEL

Data Preparation Gathering Structuring Labeling Modeling ULM Fit Language model Built a Classifier – Criticality & Clause type Transfer Learning Pretrain on WikiText Train on Legal text User Workflow Interactive UI Human augmentation Start End

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LANGUAGE MODEL PRIMED ON OUR CONTRACTS

Gramener Technology will be…. 1. Gramener Technology will be… the sole and exclusive owner of Gramener ’s Confidential Information and the Project Plan is for the sole purpose of exploring the possibility of Data Analytics , Visualization and Consulting Services . xxbos This Master Services Agreement ( “ Agreement ” ) is made on 16th February , 2015 ( “ Effective Date ” ) xxbos The Software Services and Maintenance Services Guidelines will be provided by the Client. 2. Gramener Technology will be… a company incorporated under the provisions of the Companies Act , 1956 or having its registered office at Plot No.9 / 2 , 2nd Floor , Sy . No.64 , HUDA Techno Enclave , Phase – II , Madhapur , Hyderabad - 500081 , Telangana , India ( “ Gramener ” ) , ( " Vendor " ) and the place of supply to Client ( hereinafter referred to as " the Receiving Party " ).

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DEMO

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TAKEAWAYS

Acquire right Data Structure & Clean Data Plan for labelling Factor business dynamics Augment with human inputs Build into workflow Sensitize on accuracy Plan for refresh

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HUMAN + AI

Machines will partner and cooperate with humans rather than become mankind’s biggest enemy.

  • Jack Ma, Founder of Alibaba
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@kesaritweets gramener.com @kesari

Presentation deck available at

gkesari.com/LawAI

Thank You!