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Current and future challenges in data science
- 1. Current and Future Challenges in Data Science Nathaniel Shimoni
- 2. First a bit about myself⌠⢠Married to Yonit & father of Yahav & Arbel ⢠Lecturer & researcher @ Ben Gurion University ⢠Worked as a data science researcher @ ⢠Co-founded the non-profit âDeep-Learning-Boot-Campâ ⢠Co-founder of Dudes (still in stealth) ⢠Ranked (at peak) #175 of over 80,000 active Kaggle competitors ⢠Self taught and very(!) curious
- 3. Data is everywhere Nature monitoring Automatic x-ray analysis Seizure prediction Fashion simulations Video analysis Speech 2 Text 2 Speech
- 4. But what is data science? Well⌠the answer largely depends on who do we ask⌠We will use some loose/partial definition most can agree upon: Data science is: Both academic and practical research field that is aimed to programmatically extract insights and knowledge from data in a reproduceable & generalizable manner Training data New unseen data
- 5. The enablers! or âThese are the things that made such abilities possibleâ
- 6. Current state â enablers Hugh increase in compute ability mainly GPU compute and RAM up to 48GB RAM on a single card & 11GB for most consumer cards Highly available and scalable through cloud services
- 7. Current state â enablers Large open and labeled datasets âBookCorpusâ dataset (11,038 books) MSCOCOcelebsAImageNet Yelp Open Datasets
- 8. Current state â enablers Open source code and large model zoo UNet Efficient Net Faster RCNN
- 9. Current state â enablers Good ability to perform transfer-learning and fine tuning between datasets transfer-learning
- 10. Current tasks and common applications
- 11. Current state tasks and applications â visual domain (partial list) visual domain Classification Detection Segmentation Pose Estimation Style transfer Generation (GANs)Document Authentication Deep Fake & Face-swap Adversarial attacks Depth estimation Tracking Search Within Images Object & Crowd Counting Regression Similarity & metric learning Image based search Severity Estimation Object price Estimation Super resolution Image colorization Image captioning Visual question answering OCR Image-Text cross learning Disease classification Activity classification Medical imagery segmentation Risky objects segmentation Route segmentation auto. vehicle Object Relation Inference
- 12. Current tasks and applications â language domain (partial list) language domain Classification Machine Translation Free text to structured Sentiment analysis Question Answering summarization Inappropriate content Semantic Text Similarity (e.g. for search relevance) Research papers legal medical Call centers Automatic Rating Stress detection Named Entity Recognition Part of Speech Tagging Semantic Role Labeling Conversational Bots Free Text querying Online Translation Auto Language Classification
- 13. Current tasks and applications - other domains (extremely partial list) Other domains Classification Time series sound Tabular Data Graph based learning Speech 2 Text Text 2 Speech Speaker Separation Background Noise removal classification forecasting Signal disaggregation segmentation Anomaly detection regression clustering Content recommendation Node / Graph Embeddings Graph Relation Inference Partial Graph Completion
- 15. Current challenges ⢠Dirty data (garbage in garbage out problem)
- 16. Current challenges ⢠Industry talent gap Job postings at top companies Increase in job postings since 2012
- 17. Current challenges ⢠Accelerating rate of change
- 18. Current challenges ⢠Limited or lack of monitoring in production systems In many production systems monitoring is either lacking or limited Training data Production data On deployment Production data 2 month later Production data 4 month later
- 20. Future challenges ⢠Bias detection and bias correction professional hairstyle for work: unprofessional hairstyle for work: Gender bias in word embeddings: Man -> doctor woman -> nurse
- 21. Future challenges ⢠Learning from very few samples So, how many data samples do you have???
- 22. Future challenges ⢠Explainability of results We need to operate. urgently! But why???
- 23. Future challenges ⢠Cross modality learning The dog is about to chase a cat Voice Image / video text Complementary inputs to the same model
- 24. Future challenges ⢠Function learning A chair is an object you can seat on A parking spot is a place to leave the car when not driving
- 25. Future challenges ⢠Wide adoption of content generation Great potential in: ⢠Film making ⢠Transcribing ⢠Content candidate generation ⢠Art ⢠Simulation
- 26. Future challenges ⢠Authenticity of documents, content and digital records
- 27. Future challenges ⢠AutoML and End2End applications data Task (?) Desired results
- 28. Future challenges ⢠Continuous monitoring and validation
- 29. With great power comes great responsibility https://github.com/daviddao/awful-ai Well⌠these are powerful tools â we must use them carefully
- 30. Future challenges Data science involves many other positions Communication with business is crucial for success Management must become data-literate ⢠Metrics ⢠Tasks ⢠Validation ⢠Findings to actions ⢠Engineering ⢠Analysts ⢠Business ⢠IT ⢠Results & goals ⢠Assumptions ⢠Domain knowledge ⢠Capabilities ⢠Needs (both sides)