How to Safely Get Started with Large Language Models
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“The work [with Synaptiq] is unprecedented in its scale and potential impact,” Mortenson Center’s Managing Director Laura MacDonald MacDonald said. “It ties together our center’s strengths in impact evaluation and sensor deployment to generate evidence that informs development tools, policy, and practice.”
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DATA STRATEGY
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A startup in digital health trained a risk model to open up a robust, precise, and scalable processing pipeline so providers could move faster, and patients could move with confidence after spinal surgery.
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PREDICTIVE ANALYTICS
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Using a dynamic machine vision solution for detecting plaques in the carotid artery and providing care teams with rapid answers, saves lives with early disease detection and monitoring.
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INTELLIGENT AUTOMATION
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This global law firm needed to be fast, adaptive, and provide unrivaled client service under pressure, intelligent automation did just that plus it made time for what matters most: meaningful human interactions.
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Mushrooms, Goats, and Machine Learning: What do they all have in common? You may never know unless you get started exploring the fundamentals of Machine Learning with Dr. Tim Oates, Synaptiq's Chief Data Scientist. You can read and visualize his new book in Python, tinker with inputs, and practice machine learning techniques for free. |
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Generative AI is AI, but not all AI is generative.
We tend to anthropomorphize artificial intelligence as if it were a physical entity. It's common to encounter references to businesses "using" or "adopting" AI — language that suggests an ability to engage with AI as if it were an object, akin to how we "use" a hammer or "adopt" a pet dog.
AI, in reality, is not a tangible thing. It's a collection of algorithms, data, and computational techniques that enable artificial systems (i.e., machines) to perform tasks that we typically associate with human intelligence, such as pattern recognition and problem-solving.
Don’t believe us? Take it from the legendary mathematician and computer scientist John McCarthy, one of the "founding fathers" of artificial intelligence:
“[AI is] The science and engineering of making intelligent machines”
We can conceptualize AI as a field of study, much like mathematics. Businesses can’t “adopt” math itself, but they can incorporate mathematical techniques into their operations. Similarly, businesses can’t “adopt” AI, but they can incorporate AI techniques like machine learning.
If AI is a field of study, then generative AI is a specialized branch of that field. It focuses on the development of artificial systems capable of generating content like text and images. Generative AI applications employ AI techniques to extract patterns from large, complex datasets of human-generated content. They extrapolate upon these learned patterns to create synthetic content that closely resembles what a human might produce.
For example, large language models (LLMs) are a class of generative AI applications that excel in understanding and generating text and code. To put it simply, LLMs like ChatGPT and Bard are trained on sequences of tokens (basic units of text and code), allowing them to generate human-like text and code across a wide range of topics.
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