You created a machine learning application. Now make sure it's secure.

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In a recent post, we described what it would take to build a sustainable machine learning practice. By "sustainable," we mean projects that aren't just proofs of concepts or experiments. A sustainable practice means projects that are integral to an organization's mission: projects by which an organization lives or dies. These projects are built and supported by a stable team of engineers, and supported by a management team that understands what machine learning is, why it's important, and what it's capable of accomplishing. Finally, sustainable machine learning means that as many aspects of product development as possible are automated: not just building models, but cleaning data, building and managing data pipelines, testing, and much more. Machine learning will penetrate our organizations so deeply that it won't be possible for humans to manage them unassisted. Organizations throughout the world are waking up to the fact that security is essential to their software projects. Nobody wants to be the next Sony, the next Anthem, or the next Equifax. But while we know how to make traditional software more secure (even though we frequently don't), machine learning presents a new set of problems. Any sustainable machine learning practice must address machine learning's unique security issues.


Privacy Attacks on Machine Learning Models

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Machine learning is an exciting field of new opportunities and applications; but like most technology, there are also dangers present as we expand the machine learning systems and reach within our organizations. The use of machine learning on sensitive information, such as financial data, shopping histories, conversations with friends and health-related data, has expanded in the past five years -- and so has the research on vulnerabilities within those machine learning systems. In the news and commentary today, the most common example of hacking a machine learning system is adversarial input. Adversarial input, like the video shown below, are crafted examples which fool a machine learning system into making a false prediction. In this video, a group of researchers at MIT were able to show that they can 3D print an adversarial turtle which is misclassified as a rifle from multiple angles by a computer vision system.


Harnham hiring Machine Learning Engineer in New York, New York, United States LinkedIn

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Based in NYC, a specialized data driven firm is growing the opportunity to create data-driven products in the E-Commerce space. The company is one of the largest and most profitable consumer product companies in the Amazon ecosystem while building an amazing place to work.The team there is utilizing applied intelligence to unlock the full potential of an e-commerce market. The team is creating innovative and actionable algorithms to reveal research-based secrets pulled from massive data sources, in order to get ahead of today's market. They've recently received funding for a Series A round, and is ALREADY profitable. This role is part individual contributor, part manager, where you will work with business stakeholders to design data driven products and built the internal tools that will help ensure data quality and create opportunity for team members to quickly access data and make decisions related to growth of the brands.


Novateur Research Solutions hiring Machine Learning Scientist in New York City Metropolitan Area LinkedIn

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We value creativity, vision, collaboration, and above all, ambition to innovate. We are looking for Scientists and Mathematicians to join our research team and help us solve challenging scientific and computational problems in machine learning, computer vision, distributed computing, and related areas. As part of the Novateur Team, you will actively collaborate with world-renowned researchers in academia and industry to develop cutting-edge technologies for smart systems. You will have opportunities and professional freedom to create novel research and technical directions in your areas of interest, attend major scientific conferences and seminars, and publish research papers. Novateur offers competitive pay and benefits comparable to Fortune 500 companies that include a wide choice of healthcare options with generous company subsidy, 401(k) with generous employer match, paid holidays and paid time off increasing with tenure, and company paid short-term disability, long-term disability, and life insurance.


The Peggy Smedley Show: Are Voice Assistants Creepy?

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Are privacy and security a top concern with voice assistants? Peggy answers, indicating how many people find it creepy when they get ads based on something they have talked about around a voice assistant. She explains while most people enjoy the benefits of personalization of voice assistants, we still need to be asking: What are we giving away when we talk to our voice assistants?


Frontend.ai – – UI/UX for AI and AI for UI/UX

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AI-based customized learning experiences will be one the biggest revolutions since the Internet. The world has almost eight billion people, with an unimaginable capacity to create, invent, and transform things.


7 Ways Machine Learning Can Optimize DevOps Plow

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By streamlining and optimizing processes and workflows and improving tools, this methodology is essential for the efficiency of the software delivery process and bringing value to businesses. This set of practices heavily relies on a tremendous amount of data for orchestrating and monitoring the processes, as well as identifying faults and bugs in a timely manner. While all this data is vital for DevOps, it's also one of its downsides.


Google Teaches AI To Play The Game Of Chip Design

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If it wasn't bad enough that Moore's Law improvements in the density and cost of transistors is slowing. At the same time, the cost of designing chips and of the factories that are used to etch them is also on the rise. Any savings on any of these fronts will be most welcome to keep IT innovation leaping ahead. One of the promising frontiers of research right now in chip design is using machine learning techniques to actually help with some of the tasks in the design process. We will be discussing this at our upcoming The Next AI Platform event in San Jose on March 10 with Elias Fallon, engineering director at Cadence Design Systems.


Why 2020 will be the Year of Automated Machine Learning

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As the fuel that powers their ongoing digital transformation efforts, businesses everywhere are looking for ways to derive as much insight as possible from their data. The accompanying increased demand for advanced predictive and prescriptive analytics has, in turn, led to a call for more data scientists proficient with the latest artificial intelligence (AI) and machine learning (ML) tools. But such highly-skilled data scientists are expensive and in short supply. In fact, they're such a precious resource that the phenomenon of the "citizen data scientist" has recently arisen to help close the skills gap. A complementary role, rather than a direct replacement, citizen data scientists lack specific advanced data science expertise.


Should We Trust Algorithms? · Harvard Data Science Review

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There is increasing use of algorithms in the health care and criminal justice systems, and corresponding increased concern with their ethical use. But perhaps a more basic issue is whether we should believe what we hear about them and what the algorithm tells us. It is illuminating to distinguish between the trustworthiness of claims made about an algorithm, and those made by an algorithm, which reveals the potential contribution of statistical science to both evaluation and'intelligent transparency.' In particular, a four-phase evaluation structure is proposed, parallel to that adopted for pharmaceuticals. When on holiday in Portugal last year, we came to rely on'Mrs.