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How will data be managed and transferred in autonomous cars?

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As the development of autonomous cars continues, the challenges around how data from those vehicles is managed needs to be addressed, according to Dell Technologies' Florian Baumann. There's a lot of buzz around the development of autonomous cars, from discussions about the software that goes into them to the time it will take to have fully autonomous vehicles on the road. However, an area less commonly discussed in relation to autonomous vehicles is the data involved in autonomous cars. The sheer amount of data storage they require highlights questions around how that data will be safely managed, held and transferred when self-driving cars start appearing on our roads. Florian Baumann is the global CTO for automotive and AI in Dell Technologies.


Docker: Containerization for Data Scientists - KDnuggets

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Data scientists come from different backgrounds. In today's agile environment, it is highly essential to respond quickly to customer needs and deliver value. Faster value provides more wins for the customer and hence more wins for the organization. Information Technology is always under immense pressure to increase agility and speed up delivery of new functionality to the business. A particular point of pressure is the deployment of new or enhanced application code at the frequency and immediacy demanded by typical digital transformation.


IBM publishes wide-ranging free data sets to assist AI developers

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IBM has published a number of notebooks, datasets and more resources from IBM Research on their Data Asset eXchange (DAX) for developers to use free of cost. DAX is an online hub with curated, free data sets that AI developers and data scientists can use under open data licenses. The resources on DAX take the form of open-source code, security and compliance information, specialist learning tools and even expert support. Among the recent additions are three Watson Studio projects. IBM Watson Studio is an enterprise-focused AI developer tool which helps data scientists and researchers to build models and prepare data at scale across any cloud.


Data Science complete guide on Linear Algebra - DeepLearning

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Mathematical intuition required for Data Science and Machine Learning. The linear algebra intuition required to become a Data Scientist. Then, this course is for you. The Common mistake by a data scientist is Applying the tools without the intuition of how it works and behaves. Having the solid foundation of mathematics will help you to understand how each algorithms work, its limitations and its underlying assumptions.


Build AI you can trust with responsible ML

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As AI reaches critical momentum across industries and applications, it becomes essential to ensure the safe and responsible use of AI. AI deployments are increasingly impacted by the lack of customer trust in the transparency, accountability, and fairness of these solutions. Microsoft is committed to the advancement of AI and machine learning (ML), driven by principles that put people first, and tools to enable this in practice. In collaboration with the Aether Committee and its working groups, we are bringing the latest research in responsible AI to Azure. Let's look at how the new responsible ML capabilities in Azure Machine Learning and our open-source toolkits empower data scientists and developers to understand ML models, protect people and their data, and control the end-to-end ML process.


How Easy Is It To Deploy Explainable Machine Learning Methods?

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There has been a lot of talk about making machine learning more explainable so that the stakeholders or the customers can shed the scepticism regarding the traditional black-box methodology. So, in order to find out how it is being implemented, a group of researchers conducted a survey. In the next section, we look at a few findings and practices for deploying as recommended by the researchers at Carnegie Mellon University, who published a work in collaboration with top institutes. During their survey, the researchers have come across some concerns such as model debugging, model monitoring and transparency among many others during the interviews that they have conducted with organisations as part of their work. The study found that most data scientists struggle with debugging poor model performance.


Top 9 languages for Data Science in 2020

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Data Science has been a big deal for quite some time now. In the rapidly expanding technological world of today, when humans tend to generate a lot of data, it is quintessential that we know how to analyze, process, and use that data for further knowledgable business insights. There has been enough said on Python vs R for Data Science but I am not talking about it here. We need both of them and that's about it. The languages made to the list on the basis of their popularity, number of Github mentions, the pros and the cons, and their relevancy to Data Science in 2020.


Artificial Intelligence Startup Accern Raises $13 Million In Series A To Help Enterprises Adopt AI More Easily

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When Kumesh Aroomoogan was working in the public finance department at Citigroup, he spent hours looking at financial statements, copying and pasting from one document to the next, arranging and rearranging his Excel sheet, and performing a lot of repeated manual tasks. "They were scaling on labor versus technology," Aroomoogan says. "I thought there has to be a better way to automate this entire process." So in 2014, Aroomoogan, who had an idea for a product, teamed up with Anshul Vikram Pandey, a data visualization PhD student at NYU at the time, and the two started working in Aroomoogan's basement in Queens, NY. What came out of the effort of the two entrepreneurs is Accern, an enterprise whose AI Platform contains ready-made solutions for the financial service industry.


Seven Ways to Avoid Bias in Your Data

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AI is taking off in all areas of business and in our daily lives – from improving agriculture and predicting where forest fires might erupt to determining who is likely to return to a hospital after discharge. With advanced GPUs that can crunch more data faster and growing demand from companies looking to increase competitive advantage, machine learning and other forms of AI are expected to become more pervasive. Today, many companies are relying on smart apps to provide the insight needed to make decisions that can affect people's lives, such as who qualifies for a mortgage or who will be insured. Because of this responsibility, it's more important than ever that data professionals don't inadvertently automate any biases into the AI algorithm because of the data they use or don't use, and how they use it. While AI should be regulated to ensure the fair and ethical use of data, particularly as it impacts decision-making and people's lives, unfortunately, we still have a long way to go before this happens.


Towards A More Transparent AI

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One cornerstone of making AI work is machine learning - the ability for machines to learn from experience and data, and improve over time as they learn. In fact, it's been the explosion in research and application of machine learning that's made AI the recent hot bed of interest, investment, and application that it is today. Fundamentally, machine learning is all about giving machines lots of data to learn from, and using sophisticated algorithms that can generalize from that learning for data that the machine has never seen before. In this manner, the machine learning algorithm is the recipe that teaches the machine how to learn, and the machine learning model is the output of that learning that can then generalize to new data. Regardless of the algorithm used to create the machine learning model, there is one fundamental truth: the machine learning model is only as good as its data. In many cases, these bad models are easy to spot since they perform poorly.