Скачать или смотреть Industry Survey Analysis - The Industry Landscape Of Natural Language Use Cases in 2020

💻 Abstract:
Industry Survey Analysis - The Industry Landscape Of Natural Language Use Cases in 2020. We recently conducted an industry survey of firms that have natural language systems in production. This includes an organization that has a history of leveraging NLP systems as well as those which are just beginning to plan their approach. A "dramatic shift" would be an understatement: since 2018, the field of natural language has undergone a sea change. Breakthroughs in the usage of deep learning, as well as the availability of more sophisticated hardware and cloud resources, led to sudden advances in natural language. The results are pervasive across technology subcategories within the field of natural language: parsing, natural language understanding, sentiment detection, entity linking, speech recognition, abstractive summarization, and so on.
While the tech unicorns and their proxies have conducted almost an "arms race" since early 2018, sometimes publishing papers twice monthly to outdo their competitors' most recently published benchmarks -- how are these advances diffusing into practical use cases, and becoming adopted by mainstream businesses for their needs? Our survey results explore both the contours of the evolving landscape as well as the industry adoption and business trends for NLP.

🔊 Speaker bio:
Paco Nathan Computer Scientist, Derwen, Inc.
Paco Nathan is known as a "player/coach", with core expertise in data science, natural language, machine learning, cloud computing; 38+ years of tech industry experience, ranging from Bell Labs to early-stage start-ups. Advisor for Amplify Partners, IBM Data Science Community, Recognai, KUNGFU.AI, Primer. Lead committer PyTextRank. Formerly: Director, Community Evangelism @ Databricks, and Apache Spark. Cited in 2015 as one of the Top 30 People in Big Data and Analytics by Innovation Enterprise.

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Timestamps:

0:00 Intro
0:13 Getting to know Paco Nathan
0:35 The NLP survey
2:55 2020 NLP industry survey report
4:15 Survey background
5:10 Stages of Maturity
5:32 NLP summit 2020
6:58 Feedback from expert NLP practitioners
9:30 Budgets are growing
9:52 Comparing to 2019 budgets
10:08 Consistent with AI funding trends in 2019
11:39 Stark reality: an accelerating divide
12:15 Top use cases
14:13 Popular libraries
14:44 Biome: integrations of popular libraries
16:08 PyTextRank: graph-based entity extraction
16:38 Cloud services
19:43 Challenges for NLP cloud services
23:15 Business driver
23:29 Caveat: define the word "optimize"
25:12 Tools that add engineering rigor to NLP apps
25:39 Transformers uber alles?
26:45 Hyper-growth of model parameters
27:17 Stark reality: diminishing returns
28:02 US basketball vs. EU futbol
29:55 Knowledge as an abstraction layer
30:02 Looking ahead
30:19 Responsible AI in NLP

❓ Q&A section ❓

31:36 When people say they use NER, when do they use entity linking along with that? and when do they not? Is there any information about this in the survey results?
33:46 Is the list in descending order of the number of applications of popularity?
34:04 Having the conversational use case, especially the spread of chatbots, on the list
35: 46 How many of the respondents use multiple NLP tasks in their projects?
36:42 Do you know how many of the respondents use multiple NLP tasks in their projects?
37:03 What mechanism do you use (if any) to ensure the sample of respondents is representative?
38:29 How many of the respondents work on non-English NLP?
40:15 What do you see as the emerging tools/techniques for integrating the search part of AI with deep learning models?
43:10 Any thoughts on the AI platform cube flow ml flow?
44:39 What do you see as the emerging tools/techniques for integrating the search part of AI with deep learning models? E.g. semantic web, MCTS, logic programming, probabilistic programming, etc . . .
48:21 What are the inference rules? If I were to take this deep learning model, and now come back and say, Can I distill it down into a set of rules to apply?

53:18 Closing remarks