• Smarter Everything with ML & the IoT

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    ML applications have never been easier to build. Whether cloud or edge-based, developers have a number of tools at their disposal for adding machine intelligence to any connected product. But as our systems become more sophisticated and capable and a dependence on the cloud increases, the need for privacy grows, and every embedded system should be designed with privacy in mind.

    Thankfully, there are advances in the ML and communications space that prioritize privacy, by default, including edge-based ML inferencing and cellular for cloud connectivity. With edge-based ML, decisions happen on devices instead of the cloud. And with cellular connectivity, edge systems send only the results of ML processing to the cloud, as opposed to potentially private or sensitive raw data. When used together, these two approaches help developers build applications that are secure by default.

    This month we are joined by Brandon Satrom - VP of Developer Experience & Engineering at Blues Wireless. Brandon is a driven technologist and experienced leader with a background in product management, strategy, architecture, software development, developer advocacy, and community management. He is a technologist first and loves to use what he knows and learn to teach others how to build things and solve problems with the latest technologies and platforms.

    Hope you can join us for this fascinating presentation and discussion at the intersection of Machine Learning and the Internet of Things!

  • The Third Wave of AI: The Fast Track to Artificial General Intelligence

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    For the first time, there is a methodology, a “cognitive chemistry,” which endows computers with practical, scalable common sense knowledge in the same sense that humans have knowledge. Knowledge is in-turn the key to genuine comprehension of human language. Now the computers can grasp what words mean. Thinking machines, the company calls them sapiens.

    This month we are joined by Bryant Cruse. Bryant has been a pioneer in the application of AI technology to difficult real-world problems. He graduated from St. John’s College in Annapolis, Maryland where he acquired his lifelong interest in the philosophy of Epistemology (EPISSTAMOLOGY) ; or “how we know what we know.” After serving for eight years as a Naval Aviator he returned to school for an MS in Space Systems Engineering from Johns Hopkins. While on the Mission Operations team for the Hubble Telescope he found a personal mission to change the way spacecraft were operated by seeking a way to capture human knowledge in computers. This work led him to a six-month residency at the Lockheed AI Center in Palo Alto. He went on to found two successful AI companies, both of which were ultimately acquired by public corporations. New Sapience is his third technology company. The patented technology represents more than 15 years of development and a lifetime of thinking from first principles.

  • Where the Rubber Meets the Road: How to Get Business Value From Your AI Models

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    AI is expected to deliver US$13 trillion of economic impact worldwide by 2030. With that high of an impact, it’d be easy to expect every AI project to be a guaranteed goldmine. However, 85% of AI projects fail to deliver their intended results to the business. So how can both things be true?

    The answer lies in the execution. Most organizations view AI as evolutionary rather than revolutionary – they look at their existing business processes and think about how to fit AI into them. Instead, the organizations that realize the majority of the $13 trillion value are the ones who are able to take the output of AI models and use it to reimagine the way that they do business.

    This month, we are joined by Fraser Gray-Smith – Senior Consultant at Slalom Consulting. He is a problem-solver, self-professed geek, and lifelong learner who has spent the last 8 years building and leading analytics teams across a wide array of industries. His focus is on teaching organizations how to implement AI in ways that drive business value.

  • Feminist Data Set: Using Art, Design, and Technology to Combat Bias

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    What is feminist data inside of social networks, algorithms, and big data? How can we queer data, the archive, and the internet? How can a data set act as a form of protest, of a creation of bias mitigation? This talk looks at ways of intervention, from art, design, and technology that combat and challenge bias. How can we create data to be an act of protest against algorithms? Part of this talk will focus on Caroline's research and current art project, Feminist Data Set.

    Feminist Data Set acts as a means to combat bias and introduce the possibility of data collection as a feminist practice, aiming to produce a slice of data to intervene in larger civic and private networks. Exploring its potential to disrupt larger systems by generating new forms of agency, her work asks: can data collection itself function as an artwork?

    Our Presenter: Caroline Sinders

    Caroline Sinders is a critical designer and artist. For the past few years, she has been examining the intersections of artificial intelligence, abuse, and politics in digital conversational spaces.

    She has worked with the United Nations, Amnesty International, IBM Watson, the Wikimedia Foundation, and others. Sinders has held fellowships with the Harvard Kennedy School, Google's PAIR (People and Artificial Intelligence Research group), the Mozilla Foundation, the Weizenbaum Institute Pioneer Works, Eyebeam, Ars Electronica, the Yerba Buena Center for the Arts, the Sci Art Resonances program with the European Commission, and the International Center of Photography.

    Some of her research fellowships and funded research work have focused on dark patterns, community health, online harassment, AI inequity, and the labor and systems in AI and platforms. Currently, she is a fellow with Ars Electronica AI Lab with the Edinburgh Futures Institute. Her work has been featured in the Tate Exchange in Tate Modern, Victoria and Albert Museum, MoMA PS1, LABoral, Wired, Slate, Quartz, the Channels Festival, and others. Sinders holds a Masters from New York University's Interactive Telecommunications Program.

  • Hamilton: An Open Source Python Micro-Framework for Data / Feature Engineering

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    In this talk, we will be joined by Stefan Krawczyk, Manager & Lead ML Platform/Data Platform as he shares with us Hamilton, an Open Source Python Micro-Framework for Data / Feature Engineering

    At Stitch Fix, we have 130+ “Full Stack Data Scientists” who, in addition to doing data science work, are also expected to engineer and own data pipelines for their production models.

    One data science team, the Forecasting, Estimation, and Demand team, was in a bind. Their feature generation process was causing them iteration & operational frustrations in delivering time-series forecasts for the business. In this talk, I’ll present Hamilton, a novel open-source Python micro framework, that solved their pain points by changing their working paradigm.

    Specifically, Hamilton enables a simpler paradigm for Data Science & Data Engineering teams to create, maintain, execute, and scale code for feature/data transforms, especially when there is a chain of them. Hamilton does this by building a DAG of dependencies directly from Python functions. Tune in to hear what Hamilton is, what it looks like to use it, what benefits it provides, and where it's going.