NVIDIA Unveils Amazing Open Source Machine Learning Tools Every Data Scientist Must Check Out


NVIDIA has emerged as one of the leading organizations in the machine learning and deep learning space. We have previously seen some breakthrough software from them in this field – from a robot that can copy and execute human actions to an open source Python library that makes anyone an artist. And now they have announced a slew of machine learning tools at the Computer Vision and Pattern Recognition Conference (CVPR) in Utah. CVPR is an annual machine learning conference which sees the top minds in the ML and DL industry come together to discuss and present the latest tools and research to the community. These latest tools by NVIDIA include TensorRT 4, Apex, NVIDIA DALI (data loading library) and Kubernetes on NVIDIA's GPUs.

Machine Learning and Mobile: Deploying Models on The Edge - Algorithmia Blog


Machine Learning is emerging as a serious technology just as mobile is becoming the default method of consumption, and that's leading to some interesting possibilities. Smartphones are packing more power by the year, and some are even overtaking desktop computers in speed and reliability. That means that a lot of the Machine Learning workloads that we think of as requiring specialized, high priced hardware will soon be doable on mobile devices. This post will outline this shift and how Machine Learning can work with the new paradigm. Unsurprisingly, this trend is starting to impact Machine Learning as well.

Neura Moments uses AI and IoT data to personalize app experiences


Neura, a personalization platform for app developers, today announced the launch of Moments, which aims to synthesize a smartphone user's situation within a specific context, place, and time. "It delivers personalization that is based on the real world," Neuro CEO Amit Hammer told VentureBeat in a phone interview. "It discovers the preferences and needs of people so it can serve them better." Here's how it works: Neura taps a well of data from devices like smartwatches, door locks, body weight scales, appliances, home security systems, and more, partnering with internet of things (IoT) manufacturers like Philips. Its "hybrid" AI engine ingests the data and learns users' sleep schedules and daily routines, which it uses to populate cloud-hosted profiles that Neura calls True Personas.

Machine Learning Best Algorithms: Gradient Boosting Machines (GBM)


We'll have a main talk (30 mins) and 3 excellent lightning talks about the machine learning algorithm that usually achieves the best accuracy on structured/tabular data (e.g. in industry/business applications or in Kaggle competitions): Abstract: With all the hype about deep learning and "AI", it is not well publicized that for structured/tabular data widely encountered in business applications it is actually another machine learning algorithm, the gradient boosting machine (GBM) that most often achieves the highest accuracy in supervised learning tasks. In this talk we'll review some of the main GBM implementations available as R and Python packages such as xgboost, h2o, lightgbm etc, we'll discuss some of their main features and characteristics, and we'll see how tuning GBMs and creating ensembles of the best models can achieve the best prediction accuracy for many business problems. Bio: Szilard studied Physics in the 90s and obtained a PhD by using statistical methods to analyze the risk of financial portfolios. He worked in finance, then more than a decade ago moved to become the Chief Scientist of a tech company in Santa Monica doing everything data (analysis, modeling, data visualization, machine learning, data infrastructure etc). He is the founder/organizer of several meetups in the Los Angeles area (R, data science etc) and the data science community website

Microsoft Acquires AI Specialist Bonsai


The startup claims large industrial companies as early customers, including those seeking to improve operations via "dynamic control systems" spanning robotics, wind turbines and machine tuning. "To realize this vision of making AI more accessible and valuable for all, we have to remove the barriers to development, empowering every developer, regardless of machine learning expertise, to be an AI developer," Microsoft noted in a blog post announcing the deal. Terms of the acquisition were not disclosed.

Driving Robotics and Artificial Intelligence from the C-Suite


C-3PO and R2-D2 are an odd couple in the Star Wars universe. C-3PO is a cowardly droid who obeys pre-defined protocols and routine tasks, while R2-D2 is a curious and adventurous robot who learns from previous problems, uses logical thinking and larger concepts to solve new problems. But together they do things they could not do alone. Similarly, RPA (Robotic Process Automation) and Advanced Analytics are an odd but very complementary combination of new business technologies. Like the diligent but unimaginative C-3PO, RPA follows precise rules to execute repetitive business processes; and like the curious and adaptable R2-D2, Advanced Analytics learns to make complex judgments when faced with new situations.

What Every Engineer Should Know About Open Source Software Licenses and IP Uber Engineering Blog


Does the name of the project conflict with either a trademark or the name of another open source project? A trademark is generally a name, phrase, or symbol intended to distinguish a source of goods or services, and so it is important to avoid a project name that conflicts with another company's trademark. For example, using names like "adobe", "amazon", or "oracle" in the context of software could be problematic, even though they are well-known words that predate the companies using them, because doing so could create confusion in people's minds as to whether such software is being provided by those companies. In the open source community, many software projects relate to each other, so it is tempting to use similar names. However, those names should not be so similar as to cause confusion between the projects.

GitHub: Changes to EU copyright law could derail open source distribution


Could the imposition of content filters, mandated by the European Union for use by all Internet content distributors, wreck the distribution systems on which the entire open source ecosystem now depends? The largest public open source repository, GitHub -- in the midst of its being acquired by Microsoft in a friendly deal -- warns that, should new legislation be passed by the European Parliament, the systems with which open source applications are distributed and maintained, would effectively fall apart. "Automated upload filtering of code would require entirely new technology," stated GitHub Policy Director Mike Linksvayer, in a note to ZDNet Wednesday, "and would result in either vast numbers of false positives -- causing software to become much more fragile, literally breaking builds -- or vast numbers of false negatives -- because most software, including proprietary software, includes some open source components." Parliament's Legal Affairs Committee voted 14-9-2 Wednesday, Brussels time, to approve the latest draft of a directive to impose sweeping changes to the continent's copyright protections. Ostensibly, the purpose of this Parliamentary Directive would be to ensure the accessibility of all forms of content to "cultural heritage institutions" (mainly libraries and museums).

SIOS Technology Opens R&D Facility at University of South Carolina to Advance Artificial Intelligence and Application Availability Technologies through Collaboration with Faculty and Students - SIOS


SAN MATEO, CA and COLUMBIA, SC – June 21, 2018 – SIOS Technology Corp., the industry pioneer in the application of artificial intelligence (AI) to help enterprises lower costs and ensure resilience of their critical information technology infrastructures, today announced the opening of the SIOS R&D facility at the M. Bert Storey Engineering and Innovation Center at the University of South Carolina's College of Engineering and Computing in Columbia. The new facility will serve as the SIOS R&D center for product development and is strategically located at the University for the purpose of advancing collaborative research in AI and machine learning through collaboration with students and faculty. With the new R&D facility located on-campus, students will have an opportunity to work with the latest AI technologies on projects addressing real-world problems alongside senior research engineers at SIOS. In turn, SIOS has the unique opportunity to participate deeply in a vibrant and rich academic community, tapping into academic programs, intern programs, Capstone projects, and helping to design meaningful research projects. To support the fostering of leading-edge research in AI, SIOS has also awarded the University a $475,000 grant for the use of its SIOS iQ software.