AI Recruitment - How Businesses Are Winning the Race for the Talent
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About the webinar Have you ever faced this situation wherein your recruitment team didn’t get enough time to build a stellar candidate experience and faced a hard time sifting through thousands of resumes and scheduling calls? According to a survey by HR.com, in today's time one in ten recruiters use AI and nearly half expect to adopt it in their recruitment process within the next 5 years to keep up with changing market pace. Over the course of 45 minutes, you will gain insights into how AI is changing recruitment and giving companies a competitive edge. What you'll learn: - How organizations are leveraging AI to accelerate the search for top talent - Live Demo of smart resume search using Natural language processing - Best practice to automate machine learning models in hours not months To explore more, visit: https://skyl.ai/form?p=start-trial
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AI Recruitment - How Businesses Are Winning the Race for the Talent
- 1. AI Recruitment How businesses are winning the race for talent?
- 2. Technology leader with 20+ years expertise in Product Development, Business strategy and Artificial Intelligence acceleration. Active contributor in the New York AI community Extensively worked with global organizations in BFSI, Healthcare, Manufacturing, Human Resources, Retail and Ecommerce to define and implement AI strategies Nisha Shoukath Co-founder, People10 & Skyl.ai The Speaker
- 3. Shruti Tanwar Lead - Data Science Extensive experience building future tech products using Machine Learning and Artificial Intelligence. Areas of expertise includes Deep Learning, Data Analysis, full stack development and building world class products in ecommerce, travel and healthcare sector. The Speaker
- 4. CTO & Software Architect with 15 years of experience working at the forefront of cutting-edge technology leading innovative projects Areas of expertise include Architecture design, rapid product development, Deep Learning and Data Analysis The Panelist Bikash Sharma CTO and Co-founder at Skyl.ai
- 5. All dial-in participants will be muted to enable the presenters to speak without interruption Getting familiar with ‘Zoom’ Questions can be submitted via Zoom Questions chat window and will be addressed at the end during Q&A The recording will be emailed to you after the webinar Finally, please familiarize yourself with the Zoom ‘Control Panel’ on your screen
- 6. Live Demo of smart resume search using Natural language processing ...In the next 45 minutes How organizations are leveraging AI to accelerate the search for top talent Best practice to automate machine learning models in hours not months 1 2 3
- 7. A quick intro about Skyl.ai Machine Learning automation platform for unstructured data Build & deploy ML models faster on unstructured data Guided machine learning workflow Collaborative Data Collection and Labelling Easy-to-use & scalable AI SaaS platform.
- 8. POLL #1 At what stage of Machine learning adoption your organization is at? ⊚ Exploring - Curious about it ⊚ Planning - Creating AI/ML strategy ⊚ Experimenting - Building proof of concepts ⊚ Scaling up - Some departments are using it ⊚ In production - Using it in product features ⊚ Transforming - AI/Ml driven business
- 9. How organizations are leveraging AI to accelerate the search for top talent 0 1
- 10. Artificial intelligence (AI) is the ability of a computer to think and learn like a human Understanding the fundamentals Select best resumes through automatic keyword match Apply the most suitable algorithms that are trained to process the resumes Machine learning (ML) is an application of AI with algorithms that provide systems the ability to automatically learn and improve from data without being explicitly programmed. Natural Language Processing (NLP) is the branch of machine learning that helps computers understand, interpret and manipulate human language. Keyword extraction from resume with NLP
- 11. Hence, Medical imaging is treated as Computer Vision problems in the AI world AI is here to enhance our decision making, improve efficiency and simplify jobs. How can it help you in Recruitment? ⊚ ⊚ ⊚ Analyze large number of resumes in less time Provide intelligent recommendations to match relevant job profiles & candidates Channelize efforts to build stellar hiring experience instead of focusing on paperwork It will help recruitment in following:
- 13. Resume Screening Shortlist resumes by matching and ranking based on relevancy Matching and categorizing job profile phrases with resume content to shortlist candidates Autofill information like skills, experience, candidate details by parsing resume Resume ranking based on relevancy of skill set and job profile Automatic generation of profile summary based on candidate’s resume Source: Naukri Resume Quality score too, Linkedin profile summary
- 14. Intelligent Recommendations For candidates: Identifying the most relevant jobs where they are top applicants Source: Linkedin feature For recruiters: Identifying the most relevant resumes from applicant tracking system
- 15. Job Discovery Suggesting jobs based on the profile and skill set of the candidate Recommend job postings based on the profile Source: Linkedin & Naukri job recommendation feature
- 16. Interview Assessment Creating QnA from a given set of resumes to help interviewers with relevant questions and suggestions Helping recruiters assess candidates based on facial expression, body language and communication during video interview Help interviewers with contextual data
- 17. Diversity Hiring Writing inclusive job adverts without any human preferences or bias AI can help eliminate bias which can be a result of human preference & assumptions Find and attract new hires of all sexes, ages, and ethnicities
- 18. POLL #2 State your role in the AI initiatives/ projects in your organization ⊚ We don’t have any AI projects yet ⊚ Practitioner - Data Science/ Engineering background ⊚ Sponsor / Executive ⊚ Product Manager ⊚ Project Manager ⊚ Student ⊚ Others
- 19. Live Demo of smart resume search using Natural language processing0 2
- 20. 8 stages of Machine Learning workflow
- 21. Live Demo on how to extract keywords to shortlist resume using Natural language processing
- 22. Advantages of a unified platform Speed, Visibility, Quality, Collaboration, Flexibility 03
- 23. POLL #3 Some challenges that you are facing while implementing AI & Machine Learning ⊚ Not started yet, so no challenges` ⊚ Data collection ⊚ Data Labeling ⊚ Large volumes of data ⊚ Identifying the right data set to train ⊚ Lack of knowledge of ML tools ⊚ Lack of end to end platform ⊚ Lack of expertise ⊚ Choosing the right algorithms
- 24. Data Collection - Flexible options (CSV bulk upload, APIs, Mobile capture, Form based…)
- 25. Data Labeling - Simple 4 steps process (collaboration jobs, guided workflow…)
- 26. Data Labeling - Real-time early visibility (class balance, missing data…)
- 27. Data Labeling - Early Visibility (data frequency, data intuition, outliers, trends, labeling accuracy…)
- 28. Data Labeling with Effective Collaboration (Job allocation, trend, statistics, interactive messaging…) Manage collaborator progress, activity, interactive messaging Analyse trends and progress of your data labeling job in real time with statistics and interactive visualizations
- 29. Data Visualization in building strong data intuition ( visuals for data composition, data adequacy)
- 30. Machine Learning - One click training at scale (Easy feature sets, out of the box algorithms, API integration, hyper parameter tuning, auto scaling…) ● Train, Deploy and Version your models by creating feature-sets in no time with our easy feature selection provision. ● Choose from state-of-art neural network algorithms, tune hyperparameters and see logs for your training in real time. ● Integrate our powerful inference API with your application for AI- driven actionable intelligence. ● Auto scaling of model training based on data and hyperparameters
- 31. Model Monitoring of metrics in real-time (inference count, execution time, accuracy…) ● Monitor your deployed models and analyse inference count, accuracy and execution time. ● See how your models are performing in real-time. No black boxes here.
- 32. Model Evaluation - Release Confidently (Accuracy, Precision, Recall, F1 Score) ● Monitor your deployed models and analyse inference count, accuracy and execution time. ● See how your models are performing in real-time. No black boxes here.
- 33. No upfront cost in Infrastructure set up (no DevOps needed, auto-deploy, SaaS & On-prem models…) No DevOps required - Incorporates automatic deployment and dockerization Scalable tech with latest stack Domain agnostic build by data type Scalable on demand On premise and saas models
- 34. Skyl.ai - as ML automation platform
- 35. Try out 15 days free trial with complimentary consultation on pilot project Register https://skyl.ai/form?p=start-trial
- 37. 85 Broad Street, New York, NY, 10004 +1 718 300 2104, +1 646 202 9343 contact@skyl.ai We hope to hear from you soon Thank you for joining!
Notas del editor
- She is a technology leader who wears multiple hats. From defining product strategy , developing product , accelerating AI adoption to scaling businesses, she knows it all. She is based in New york
- She is a versatile person who builds scalable , high-performance solutions and shares expertise through blogs and is currently building future tech products using ML and AI
- Innovator, problem solver and creator
- Change Medical imaging image - different types of scans /CTs
- Exploring - Curious about it Planning - Creating AI/ML strategy Experimenting - Building proof of concepts Scaling up - Some departments are using it In production - Using it in product features Transforming - AI/Ml driven business
- Speed up the recruitment process by automating time-consuming & repetitive tasks
- automatically opens the resumes and parses the content. If this were to be done manually it would take a lot of time. Automating the detection of abnormalities in commonly-ordered imaging tests, such as chest x-rays, could lead to quicker decision-making and fewer diagnostic errors. For example, when a patient enters the emergency department with a complaint such as shortness of breath, “the chest radiograph is often the first imaging study that is available,” ACR DSI says. “It can be used as a quick initial screening tool for cardiomegaly, which in and of itself can be used as a marker for heart disease. A quick visual assessment by a radiologist is sometimes inaccurate.” 3. Pneumonia and pneumothorax are two conditions that require quick reactions from providers. Both may also be prime targets for artificial intelligence algorithms. Pneumonia, either acquired in the community or after a medical procedure, can be life threatening if left untreated. Radiology images are often used to diagnose pneumonia and distinguish the condition from other lung conditions, such as bronchitis. Yet radiologists may not always be available to read images – and even if radiologists are present, they may have difficulty identifying pneumonia if the patient has pre-existing lung conditions, such as malignancies or cystic fibrosis. In addition, “subtle pneumonias, such as those projecting below the dome of the diaphragms on front chest radiographs, can easily be overlooked and lead to unnecessary CT scans, which AI could help reduce,” ACR DSI says. An AI algorithm could assess x-rays and other images for evidence of opacities that indicate pneumonia, then alert providers to the potential diagnoses to allow for speedier treatment. 4. Fractures and musculoskeletal injuries can contribute to long-term, chronic pain if not treated quickly and correctly. Injuries such as hip fractures in elderly patients are also tied to poor overall outcomes due to reductions in mobility and associated hospitalizations. Using artificial intelligence to identify hard-to-see fractures, dislocations, or soft tissue injuries could allow surgeons and specialists to be more confident in their treatment choices. Images obtained by MRI machines, CT scanners, and x-rays, as well as biopsy samples, allow clinicians to see the inner workings of the human body. However, these images often contain large amounts of complex data that can be difficult and time consuming for human providers to evaluate. AI tools can augment the workflow of radiologists and pathologists, acting as clinical decision support and enhancing care delivery.
- Use images and expand the points Automating the detection of abnormalities in commonly-ordered imaging tests, such as chest x-rays, could lead to quicker decision-making and fewer diagnostic errors. For example, when a patient enters the emergency department with a complaint such as shortness of breath, “the chest radiograph is often the first imaging study that is available,” ACR DSI says. “It can be used as a quick initial screening tool for cardiomegaly, which in and of itself can be used as a marker for heart disease. A quick visual assessment by a radiologist is sometimes inaccurate.” 3. Pneumonia and pneumothorax are two conditions that require quick reactions from providers. Both may also be prime targets for artificial intelligence algorithms. Pneumonia, either acquired in the community or after a medical procedure, can be life threatening if left untreated. Radiology images are often used to diagnose pneumonia and distinguish the condition from other lung conditions, such as bronchitis. Yet radiologists may not always be available to read images – and even if radiologists are present, they may have difficulty identifying pneumonia if the patient has pre-existing lung conditions, such as malignancies or cystic fibrosis. In addition, “subtle pneumonias, such as those projecting below the dome of the diaphragms on front chest radiographs, can easily be overlooked and lead to unnecessary CT scans, which AI could help reduce,” ACR DSI says. An AI algorithm could assess x-rays and other images for evidence of opacities that indicate pneumonia, then alert providers to the potential diagnoses to allow for speedier treatment. 4. Fractures and musculoskeletal injuries can contribute to long-term, chronic pain if not treated quickly and correctly. Injuries such as hip fractures in elderly patients are also tied to poor overall outcomes due to reductions in mobility and associated hospitalizations. Using artificial intelligence to identify hard-to-see fractures, dislocations, or soft tissue injuries could allow surgeons and specialists to be more confident in their treatment choices. Images obtained by MRI machines, CT scanners, and x-rays, as well as biopsy samples, allow clinicians to see the inner workings of the human body. However, these images often contain large amounts of complex data that can be difficult and time consuming for human providers to evaluate. AI tools can augment the workflow of radiologists and pathologists, acting as clinical decision support and enhancing care delivery.
- Examples of traits —— (write an example - what you are training the machine on - and output)
- In the future, assessments themselves will evolve. For example, today’s ability tests typically include yes/no or true/false statements, which are easily analysed. Future AI-powered ability tests could include open-ended questions, such as ‘Tell me what you see when you look at this graphic’.
- Change pic
- We don’t have any AI projects yet Practitioner - Data Science/ Engineering background Sponsor Product Manager Project Manager Student Others
- How
- 5 minutes intro - 10 industry awareness - 15 min demo - 20 minutes QnA Define problem - Features model - How this model is built using skyl.ai Add slide of Pneumonia detection
- Benefit
- Not started yet, so no challenges Data collection Data Labeling Data Bias Large volumes of data Identifying the right data set to train Lack of knowledge of ML tools Lack of end to end platform Lack of expertise Choosing the right algorithms Monitoring the model performance
- Now, we
- Confidently - to be charge of / control of your AI projects. Script: https://docs.google.com/document/d/1NWGBbMg1SpePzvaFiO0gy_vkgoyAGGcwe1D2_OUcQIE/edit#