Our AI Impact

 for the health of people



 Our AI Impact

 for the health of planet



 Our AI Impact

 for the health of business



“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.” 
Read the Case Study ⇢ 


    ⇲ Implement & Scale
    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. 
    Read the Case Study ⇢ 


      Thwart errors, relieve in-take form exhaustion, and build a more accurate data picture for patients in chronic pain? Those who prefer the natural albeit comprehensive path to health and wellness said: sign me up. 
      Read the Case Study ⇢ 


        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. 
        Read the Case Study ⇢ 


          man-wong-aSERflF331A-unsplash (1)-1
          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. 
          Read the Case Study ⇢ 



            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. 

            Start Chapter 1 Now ⇢ 


              How Should My Company Prioritize AIQ™ Capabilities?





                Start With Your AIQ Score

                  3 min read

                  Generative AI & Predictive AI: What's the Difference?

                  Featured Image

                  Artificial Intelligence (AI)

                  We tend to talk about “using” and “adopting” artificial intelligence like it is a tangible thing. But, actually, AI is a collection of algorithms, data, and computational techniques that enable machines to perform tasks that typically require human intelligence. Think of AI as a field of study, like computer science or mathematics, comprised of an ever-changing array of specialized branches. Generative AI and predictive AI are two such branches.

                  Generative AI

                  AI vs. Generative AI (alternate colors)-1

                  Generative AI specializes in generating new content, such as text and images. Generative AI applications employ techniques like natural language processing and deep learning to extract patterns from large, complex datasets of human-generated content. They extrapolate upon these learned patterns to create synthetic content.

                  For example, large language models (LLMs) are a class of generative AI applications that excel in understanding and generating text and code. LLMs like OpenAI's ChatGPT and Google's Bard are trained on sequences of tokens (basic units of text and code) to generate human-like text and code across a wide range of topics.

                  Predictive AI

                  AI vs. Predictive AI-1

                  Predictive AI specializes in making informed guesses about unknown values. Predictive AI applications use techniques such as regression analysis, time series analysis, and other supervised and unsupervised learning algorithms to identify underlying relationships or trends in observed data. They apply this knowledge to make predictions about unobserved data.

                  For example, recommendation systems used by e-commerce platforms like Amazon and streaming services like Netflix are a class of predictive AI applications fine-tuned to anticipate what products or content will appeal to any given user. They analyze historical data such as previous purchases, browsing history, and preferences to predict with high accuracy which products or content a user is likely to enjoy next, offering personalized recommendations.



                  humankind of ai


                  About Synaptiq

                  Synaptiq is an AI and data science consultancy based in Portland, Oregon. We collaborate with our clients to develop human-centered products and solutions. We uphold a strong commitment to ethics and innovation. 

                  Contact us if you have a problem to solve, a process to refine, or a question to ask.

                  You can learn more about our story through our past projects, blog, or podcast

                  Additional Reading:

                  BETTER Customer Review Sentiment Analysis: A Business Case for N-grams

                  Sentiment analysis is a useful tool for organizations aiming to understand customer preferences, gauge public...

                  Smart and Safe Innovation: Synthetic Data for Proof-of-Concept Projects

                  In the ever-evolving landscape of technology, innovation and experimentation are key drivers of success. However, the...

                  Customer Review Sentiment Analysis: A Business Case for Tokenization

                  Sentiment analysis is a must-have for organizations with a business-to-consumer (B2C) business model. This natural...