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AI MIT NLP - Coggle Diagram
AI MIT NLP
KEY BENEFITS
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Enables very early detection years before clear symptoms, allowing timely interventions.
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TAKEAWAYS
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Human-AI collaboration is critical - experts guide what to monitor, AI handles scalable analysis.
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AI automates initial screening, while experts focus on confirming/addressing confirmed issues.
Concepts
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Augmented Workers - AI taking over routine tasks, humans providing higher cognition
VALUABLE INSIGHTS
Simply having more sensor data is not enough, domain expertise is required to identify relevant signals.
AI can automate routine screening tasks, freeing human experts for more complex decision-making.
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AI-driven monitoring has applications across healthcare, manufacturing, and other domains.
TAKEAWAYS
Early AI systems aimed to build machines that could converse and understand language like humans, but this proved extremely challenging.
NLP today can robustly handle certain tasks like spam detection, named entity recognition, sentiment analysis, and machine translation for some language pairs.
However, open-ended dialogue, question-answering for non-trivial queries remain very difficult for current NLP systems.
The performance depends heavily on the training data - NLP models perform well on domains they were trained on but struggle with out-of-domain texts.
While NLP systems can sometimes appear to exhibit human-level understanding, their capabilities are still narrow and opaque.
There is often a mismatch between perceived AI capabilities versus actual capabilities limited by training data. / Simple language tasks that are trivial for humans can stump state-of-the-art NLP models due to their data limitations. / Human-level open-ended language understanding remains an elusive goal for current NLP technology.
KEY CONCEPTS
Syntactic Parsing: Identifying grammatical structure like verbs, subjects in sentences - a relatively solved problem.
Machine Translation: Automatically translating between natural languages, reasonably accurate for some language pairs.
Out-of-Domain Performance: NLP models struggle with text genres/domains very different from their training data.
Training Data Limits: NLP capabilities are constrained by the datasets used to train the underlying statistical models.
TAKEAWAYS
Major current applications of NLP include search engines, information extraction, machine translation, text generation, and sentiment analysis.
NLP systems can excel at tasks perceived as difficult for humans (like retrieving specific facts) but struggle with simple tasks trivial for humans.
Ambiguity in human language is a core challenge - sentences can have multiple valid interpretations at syntactic, semantic, and discourse levels.
Dangers of blindly using NLP tools without understanding their limitations, e.g. stock trading mistakes due to misinterpreting word senses.
Fatcs
The perceived performance of NLP often doesn't match reality - systems narrowly excel on their training domains but fail on simple tasks.
Human language is fundamentally ambiguous at multiple levels that current NLP methods struggle with.
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Critical to deeply understand the strengths and limitations of NLP systems before deploying them in real-world applications.
TRAINING DATA
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Training data includes annotated examples (e.g., sentences highlighting customer complaints).
If you have a large corpus of relevant data, solving specific tasks becomes easier.
However, related tasks (e.g., extracting product drawbacks from Amazon reviews) require dedicated annotations due to different word distributions.
NLP
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Features relevant to the task (e.g., customer plan information or complaint length) must be included as inputs to the model. / Humans play a crucial role in feature selection and annotation.
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Syntactic Ambiguity: A single sentence can have multiple syntactic parse trees representing different grammatical interpretations.
Semantic Ambiguity: Words can have multiple meanings/senses that need to be disambiguated based on context (e.g. "mother" as female parent vs bacterial culture)
Anaphora Resolution: Determining what pronouns like "she/he/it" refer to across sentences in a discourse.
Word Segmentation Issues: Some languages make it non-trivial to identify word boundaries compared to English.
KEY CONCEPTS
Recommendation Systems - Algorithms that suggest relevant items (movies, products, content) to users based on their preferences and behavior.
Collaborative Filtering - Using patterns across many users to identify similarities and make personalized recommendations.
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NETFLIX SAMPLE
Netflix uses machine learning personalization algorithms to customize each user's homepage thumbnails and recommendations.
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PERSONLIZATION
Personalization drives differentiation by creating a tailored, engaging customer experience.
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However, the "human-in-the-loop" is vital to incorporate qualitative nuances.
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Success metrics include customer satisfaction, repeat purchases, subscription renewals.
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However, customization requires high recommender accuracy to be cost-effective.
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INSIGHTS NLP
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NLP systems often generate mistakes, but some applications can still be useful despite these errors.
For example, Google Search provides relevant links even though it may return some irrelevant information.
Machine translation, while imperfect, can still convey the gist of a document.
However, for critical tasks (e.g., legal document translation), existing machine translation falls short due to inaccuracies.
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