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Open Source AI For Everyone: Three Projects to Know

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Abstract Brain

We look at three open source AI projects aimed at simplifying access to AI tools and insights.

At the intersection of open source and artificial intelligence, innovation is flourishing, and companies ranging from Google to Facebook to IBM are open sourcing AI and machine learning tools.

According to research from IT Intelligence Markets, the global artificial intelligence software market is expected to reach 13.89 billion USD by the end of 2022. However, talk about AI has accelerated faster than actual deployments. According to a detailed McKinsey report on the growing impact of AI, “only about 20 percent of AI-aware companies are currently using one or more of its technologies in a core business process or at scale.” Here, we look at three open source AI projects aimed at simplifying access to AI tools and insights.

TensorFlow

Google has open sourced a software framework called TensorFlow that it spent years developing to support its AI software and other predictive and analytics programs. TensorFlow is the engine behind several Google tools you may already use, including Google Photos and the speech recognition found in the Google app.

Google has also released two new AIY kits that let individuals easily get hands-on with artificial intelligence. Focused on computer vision, and voice assistants, the two kits come as small self-assembly cardboard boxes with all the components needed for use. The kits are currently available at Target in the United States, and, notably, are both based on the open source Raspberry Pi platform—more evidence of how much is going on at the intersection of open source and AI.

Sparkling Water

H2O.ai, formerly known as OxData, has carved out a niche in the machine learning and artificial intelligence arena, offering platform tools as well as Sparkling Water, a package that works with Apache Spark. H2O.ai’s tools, which you can access simply by downloading, operate under Apache licenses, and you can run them on clusters powered by Amazon Web Services (AWS) and others for just a few hundred dollars. Never before has this kind of AI-focused data sifting power been so affordable and easy to deploy.

Sparkling Water includes a toolchain for building machine learning pipelines on Apache Spark. In essence, Sparkling Water is an API that allows Spark users to leverage H2O’s open source machine learning platform instead of or alongside the algorithms that are included in Spark’s existing machine-learning library. H2O.ai has published several use cases for how Sparkling Water and its other open tools are used in fields ranging from genomics to insurance, demonstrating that organizations everywhere can now leverage open source AI tools.

H2O.ai’s Vinod Iyengar, who oversees business development at the company, says they are working to bring the power of AI to businesses. “Our machine learning platform features advanced algorithms that can be applied to specialized use cases and the wide variety of problems that organizations face,” he notes.

Just as open source focused companies such as Red Hat have combined commercial products and services with free and open source ones, H2O.ai is exploring the same model on the artificial intelligence front. Driverless AI is a new commercial product from H2O.ai that aims to ease AI and data science tasks at enterprises. With Driverless AI, non-technical users can gain insights from data, optimize algorithms, and apply machine learning to business processes. Note that, although it leverages tools with open source roots, Driverless AI is a commercial product.

Acumos

Acumos is another open source project aimed at simplifying access to AI. Acumos AI, which is part of the LF Deep Learning Foundation, is a platform and open source framework that makes it easy to build, share, and deploy AI apps. According to the website, “It standardizes the infrastructure stack and components required to run an out-of-the-box general AI environment. This frees data scientists and model trainers to focus on their core competencies and accelerates innovation.”

The goal is to make these critical new technologies available to developers and data scientists, including those who may have limited experience with deep learning and AI. Acumos also has a thriving marketplace where you can grab and deploy applications.

“An open and federated AI platform like the Acumos platform allows developers and companies to take advantage of the latest AI technologies and to more easily share proven models and expertise,” said Jim Zemlin, executive director at The Linux Foundation. “Acumos will benefit developers and data scientists across numerous industries and fields, from network and video analytics to content curation, threat prediction, and more.” You can learn more about Acumos here.

7 Axioms for Calm Technology

By Blog
Amber Case

Amber Case

By 2020, 50 billion devices will be online. That projection was made by researchers at Cisco, and it was a key point in Amber Case’s Embedded Linux Conference keynote address, titled “Calm Technology: Design for the Next 50 Years” which is now available for replay.

Case, Author and Fellow at Harvard University’s Berkman Klein Center, referred to the “Dystopian Kitchen of the Future” as she discussed so-called smart devices that are invading our homes and lives, when the way they are implemented is not always so smart. “Half of it is hackable,” she said. “I can imagine your teapot getting hacked and someone gets away with your password. All of this just increases the surface area for attack. I don’t know about you, but I don’t want to have to be a system administrator just to live in my own home.”

Support and Recede

Case also discussed the era of “interruptive technology.” “It’s not just that we are getting text messages and robotic notifications all the time, but we are dealing with bad battery life, disconnected networks and servers that go down,” she said. “How do we design technology for sub-optimal situations instead of the perfect situations that we design for in the lab?”

“What we need is calm technology,” she noted, “where the tech recedes into the background and supports us, amplifying our humanness. The only time a technology understands you the first time is in Star Trek or in films, where they can do 40 takes. Films have helped give us unrealistic expectations about how our technology understands us. We don’t even understand ourselves, not to mention the person standing next to us. How can technology understand us better than that?”

Case noted that the age of calm technology was referenced long ago at Xerox PARC, by early ubiquitous computing researchers, who paved the way for the Internet of Things (IoT). “What matters is not technology itself, but its relationship to us,” they wrote.

7 Axioms

She cited this quote from Xerox researcher Mark Weiser: “A good tool is an invisible tool. By invisible, we mean that the tool does not intrude on your consciousness; you focus on the task, not the tool.”

Case supplied some ordered axioms for developing calm technology:

  1.    Technology shouldn’t require all of our attention, just some of it, and only when necessary.
  2.    Technology should empower the periphery.
  3.    Technology should inform and calm.
  4.    Technology should amplify the best of technology and the best of humanity.
  5.    Technology can communicate, but it doesn’t need to speak.
  6.    Technology should consider social norms.
  7.    The right amount of technology is the minimum amount to solve the problem.

In summing up, Case said that calm technology allows people to “accomplish the same goal with the least amount of mental cost.” In addition to her presentation at the Embedded Linux Conference, Case also maintains a website on calm technology, which offers related papers, exercises and more.

Watch the complete presentation below: