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.” 
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    ⇲ 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. 
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      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. 
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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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          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. 

            Start Chapter 1 Now ⇢ 


              How Should My Company Prioritize AIQ™ Capabilities?





                Start With Your AIQ Score

                  3 min read

                  AI & ML: What's the Difference?

                  Featured Image

                  Artificial intelligence (AI) and machine learning (ML) aren't synonymous. It’s a common mistake to use these terms interchangeably, but ML is a subset of AI, not a different word for the same idea. Learning the difference between ML and other types of AI is a step toward tech-literacy worth taking — especially if you’re a business leader, working professional, or student — because you almost certainly encounter them every day. 

                  Artificial Intelligence vs. Machine Learning

                  Artificial intelligence is a sub-discipline of computer science concerned with developing artificial systems capable of performing tasks that typically require human intelligence. We categorize AI into different subsets based on functionality. One of those subsets is machine learning, which involves teaching artificial systems to learn from data and improve their performance over time.  ML-enabled systems can analyze vast amounts of data incredibly quickly and accurately. ML applications are everywhere; in fact, you probably used one to find this blog. Google, Twitter, and LinkedIn leverage ML algorithms to order search results and deliver recommendations.

                  Four Types of Machine Learning

                  You should familiarize yourself with these four common types of machine learning:

                  1. Supervised machine learning requires labeled data, or data that has been classified by a human “supervisor.” It teaches a system to predict outputs from inputs. Your iPhone uses an application of supervised learning, image classification, to recognize when you take a “selfie” and organize your photos accordingly.

                  2. Unsupervised machine learning uses unlabeled data. It’s particularly useful for identifying patterns in large and complex datasets, which are difficult to label. Amazon uses an application of unsupervised learning called “clustering” to group customers by shared characteristics into market segments. [1]

                  3. Semi-supervised learning uses some labeled data alongside lots of unlabeled data. It can improve the accuracy of supervised learning models when labeled data is scarce and unlabeled data is abundant.

                  4. Reinforcement learning trains a system on simulated experiences and feedback. Its objective is to teach a system to make decisions that achieve the best outcome in a dynamic situation. Reinforcement learning enables robots to discover the optimal behaviors for navigating new environments and completing unstructured tasks.


                  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:

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