In 2003, CU student Nate Seidle fried a power supply in his dorm room and, in lieu of a way to order easy replacements, decided to start his own company. Since then, SparkFun has been committed to sustainably helping our world achieve electronics literacy from our headquarters in Boulder, Colorado. No matter your vision, SparkFun's products and resources are designed to make the world of electronics more accessible. In addition to over 2,000 open source components and widgets, SparkFun offers curriculum, training and online tutorials designed to help demystify the wonderful world of embedded electronics. We're here to help you start something.

The term "influencer marketing" may call to mind jet-setting travel vloggers on YouTube or cool gamer kids streaming on Twitch. However, there are also many influencers working in the business and marketing realm. Data science influencers educate and inform on the subject of the scientific approach to extracting insights from data for real-world applications. Here are eight of the data science thought leaders topping influencer discovery searches. Andrew Ng's credentials speak for themselves.

While far from being a core feature of most CRMs, Gartner found that artificial intelligence (AI) and predictive analytics have the highest interest from businesses across sales, marketing, customer experience, and digital commerce--the four major CRM services (full content available to Gartner clients). Salesforce is one of the earliest adopters of AI capabilities and technology and the largest customer relationship management (CRM) software vendor, owning 19.5% of the CRM vendor market. In fact, it's one of the first CRM vendors to have AI capabilities; it launched its AI tool, Salesforce Einstein, in September of 2016 for select modules. In this piece, we'll explain what Salesforce Einstein is, as well as provide an overview of the primary features it brings to the table so you can make a more informed decision when purchasing your next CRM. It uses data gathered on every user action to provide predictive analytics, natural language processing (NLP) capabilities, and machine learning to Salesforce customers.

Turning a sketch or photo of an object into a fully realized 3D model so that it can be duplicated using a 3D printer, played in a video game, or brought to life in a movie through visual effects, requires the skills of a digital modeler working from a stack of images. But Nvidia has successfully trained a neural network to generate fully-textured 3D models based on just a single photo. We've seen similar approaches to automatically generating 3D models before, but they've either required a series of photos snapped from many different angles for accurate results or input from a human user to help the software figure out the dimensions and shape of a specific object in an image. Neither are wrong approaches to the problem; any improvements made to the task of 3D modeling are welcome as they make such tools available to a wider audience, even those lacking advanced skills. But they also limit the potential uses for such software.

Thanks to recent advancements in electronics and the programmed logic that provides gadgets with an incipient ability to make decisions and take actions, humans are starting to have meaningful interactions with machines. We are on the cusp of a robotics Golden Age, which I believe will increase our standard of living to the degree that mass production, electricity and transistors did for our ancestors. The term "smart manufacturing" refers to business processes that feature machines in decision making roles. Artificial intelligence (AI) provides the decisions, and machine learning (ML) enables an ongoing refinement of those decisions based on zettabytes of data that the internet of things (IoT) makes available to a global network of devices. Improvements in computer networking technologies are key to realizing autonomy among collaborating devices, cooperative productivity featuring little to no direction from human operators.

Designed to push the frontiers of computing chip and systems performance optimized for AI workloads, an 8 petaflop IBM Power9-based supercomputer has been unveiled in upstate New York that will be used by IBM data and computer scientists, by academic researchers and by industrial and commercial end-users. Installed at the New York State-IBM Research AI Hardware Center in Albany, NY, the system -- called AiMOS (Artificial Intelligence Multiprocessing Optimized System) – was the most powerful to debut on last month's Top500 supercomputer ranking, it's listed as the world's 24th most powerful computer, the most powerful to be housed at a private university and – according to the Green500 listing – the third most energy efficient. It was built using the same IBM Power Systems technology as the Top500's nos. 1 and 2, systems, the US Dept. of Energy's IBM Summit and Sierra supercomputers, based on IBM Power9 CPUs and Nvidia GPUs. AiMOS is the result of a collaboration between IBM, RPI and two New York state programs, Empire State Development (ESD) and NY CREATES. Named for Rensselaer co-founder Amos Eaton, AiMOS will serve as a test bed for computation, modeling and simulation of hardware "designed to push the boundaries of AI performance," IBM said.

The next time your Netflix stream slows to a crawl, it's probably because your data is caught in the digital equivalent of a traffic jam. This is a common occurrence during peak hours, which explains why it may feel as if your download speeds are faster during certain times of the day. Until this issue is addressed by content based companies, the issue will only grow over time, since people are consuming more and more data each year. Improvements leading to 8k video, better smartphone photos, and higher quality music will also magnify the problem.

On Tuesday, career and job site LinkedIn released its annual "Emerging Jobs" list, which identifies the roles that have seen the largest rate of hiring growth from 2015 through this year. No. 1 on the list: Artificial Intelligence Specialist -- typically an engineer, researcher or other specialty that focuses on machine learning and artificial intelligence, figuring out things like where it makes sense to implement AI or building AI systems. Hiring for this role has been tremendous, growing 74% annually in the past 4 years alone. "AI has infiltrated every industry, and right now the demand for people skilled in AI is outpacing the supply for it," Guy Berger, the principal economist at LinkedIn, tells MarketWatch. "This is the third year in a row a role related to machine learning or artificial intelligence has topped the list, and we can only expect demand to increase."

What do Google Assistant, Siri, Alexa, and Cortana have in common? They tell jokes of varying cleverness, most of which are the work of writing teams operating behind the scenes. They're entertaining, but preliminary research suggests they also play a part in making interactions with assistants engaging. In pursuit of assistants capable of tailoring jokes to individual users' tastes, Amazon researchers investigated joke selection methods that tap either a basic natural language processing model or a machine learning model. They say that when tested against production data, both approaches "positively" impacted user satisfaction and potentially improved joke-telling.