365 Data Science

Artificial Intelligence and Business with Neil Sahota

Lessons on AI from the author of Own the AI Revolution.

Continue reading on Becoming Human: Artificial Intelligence Magazine »

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Demystifying the AI Infrastructure Stack

AI tools and services are expanding at a rapid clip, and keeping a handle on this evolving ecosystem is crucial for the success of your AI projects. This framework will help you build your technical stack to deploy AI projects faster and at scale.

Originally from KDnuggets https://ift.tt/3f60z7n

source https://365datascience.weebly.com/the-best-data-science-blog-2020/demystifying-the-ai-infrastructure-stack

Optimize Response Time of your Machine Learning API In Production

This article demonstrates how building a smarter API serving Deep Learning models minimizes the response time.

Originally from KDnuggets https://ift.tt/2WuWst5

source https://365datascience.weebly.com/the-best-data-science-blog-2020/optimize-response-time-of-your-machine-learning-api-in-production

Which Face is Real? Applying StyleGAN to Create Fake People

This post explains using a pre-trained GAN to generate human faces, and discusses the most common generative pitfalls associated with doing so.

Originally from KDnuggets https://ift.tt/2y62xUB

source https://365datascience.weebly.com/the-best-data-science-blog-2020/which-face-is-real-applying-stylegan-to-create-fake-people

Natural Language Processing Recipes: Best Practices and Examples

Here is an overview of another great natural language processing resource, this time from Microsoft, which demonstrates best practices and implementation guidelines for a variety of tasks and scenarios.

Originally from KDnuggets https://ift.tt/3bXbPRe

source https://365datascience.weebly.com/the-best-data-science-blog-2020/natural-language-processing-recipes-best-practices-and-examples

KDD 2020 Invites Top Data Scientists To Compete in 24th Annual KDD Cup

This year’s KDD Cup features four distinct tracks that welcome participants to tackle challenges in e-commerce, generative adversarial networks, automatic graph representation learning (AutoGraph) and mobility-on-demand (MoD) platforms. Winners will be recognized at KDD 2020, the leading interdisciplinary conference in data science, in San Diego on August 23-27, 2020.

Originally from KDnuggets https://ift.tt/3f5Hpym

source https://365datascience.weebly.com/the-best-data-science-blog-2020/kdd-2020-invites-top-data-scientists-to-compete-in-24th-annual-kdd-cup

Outbreak Analytics: Data Science Strategies for a Novel Problem

You walk down one aisle of the grocery store to get your favorite cereal. On the dairy aisle, someone sick from COVID-19 coughs. Did your decision to grab your cereal before your milk possibly keep you healthy? How can these unpredictable, near-random choices be included in complex models?

Originally from KDnuggets https://ift.tt/2VNHnDE

source https://365datascience.weebly.com/the-best-data-science-blog-2020/outbreak-analytics-data-science-strategies-for-a-novel-problem

AI for CFD: Intro (part 1)

Thank you for stopping by! This is the first part, a kind of introductory story to a series where I will be describing my company’s efforts and progress in bringing AI (Artificial Intelligence) to the world of CFD (Computational Fluid Dynamics). We believe it will be a complete game-changer for many industries, not only bringing speedup and cutting TCO (Total Cost of Ownership) but most importantly, offering entirely new possibilities for the CFD users. These 3 elements are also the goals for us as a company, and I will be referring to them in my subsequent stories while sharing the progress of this fascinating research project at byteLAKE. So let me say, welcome to the AI for CFD, season premiere. Follow us on a journey to bring your CFD work to a very new level. Also, you are very welcome to join the discussions and actively participate in the groups I linked below. We’re really looking forward to hearing your comments, and hopefully, our work will inspire you to open up your thoughts with us.

AI for CFD will be a complete game-changer for many industries, not only bringing speedup and cutting TCO but most importantly, offering entirely new possibilities for the CFD users.

I am pretty sure that the audience of this story is very well familiar with what the CFD is and I bet most of you bring tons of precious experience in the field. But just in case you landed here by chance or out of sheer curiosity or simply wonder how to pave your way to getting started with fluid dynamics, let me start by saying a few words of definition. In that context, and I will explain it in more detail in my future posts, we’re also thinking of lowering the entry barrier for those who want to get started with CFD simulations to solve particular engineering problems. In other words, part of our job is to make the CFD learning curve much steeper: straight forward tools you need to rapidly solve complex problems.

CFD, Computational Fluid Dynamics tools combine numerical analysis and algorithms to solve fluid flows problems. They are used to model fluids density, velocity, pressure, temperature, and chemical concentrations in relation to time and space.

A range of industries such as automotive, chemical, aerospace, biomedical, power and energy, and construction rely on fast CFD analysis turnaround time. It is a key part of their design workflow to understand and design how liquids and gases flow and interact with surfaces.

Typical applications include weather simulations, aerodynamic characteristics modeling and optimization, and petroleum mass flow rate assessment.

As a company, we’ve been working in the CFD field for many years now. We are not your typical CFD user or adopter. Our take on the subject is more from the algorithmics perspective and its adaptation or optimization (if you like) to various, heterogeneous HPC configurations.

For instance, one of the projects we did was about moving weather simulation engine from Fortran-based desktop implementation to a fully scalable, MPI-enabled C++ & NVIDIA CUDA optimized version. Needless to say, the work included a tremendous effort to mathematically redesign the computations, as well as reorganize the data to efficiently deploy it in many-CPU and many-GPU cluster environments. One such was Piz Daint supercomputer in Switzerland (still the fastest supercomputer in Europe). We’ve summarized some of the key parts of that project in the case study on our website: www.byteLAKE.com/en/MPDATA (CFD acceleration with GPU).

Then, we shifted our gears towards FPGAs, a story that led us to explore the potential of datacenter accelerator cards named Alveo. In collaboration with Xilinx, we designed from scratch the Advection algorithm and adapted it heavily to the U250 and later U280 Alveo boards. Again, straight-forward porting was not an option due to hardware specifics and our ambition to make the most of it. We decided to go with OpenCL though. However, in that particular example, our code reached almost 99% of the attainable performance. Hence, we saw no reason for moving to C++. Eventually, as in the previous case, we saw some interesting acceleration numbers. What is different though, we achieved these within extremely low energy budgets, positioning the Alveo-based configurations as a preferred accelerator if Green Computing is on your agenda. We’ve described all the technical details at www.byteLAKE.com/en/FPGA as well as in a linked technical blog post.

Both of these experiences (and many other, similar in scope) led us to a conclusion that:

CFD adaptation to hardware accelerator cards, while promising in many cases, certainly does not bring substantially new value to the industries.

The speedup offered by GPUs and FPGAs is definitely of value. Energy efficiency concerns seem to be gaining a wider audience, pushing the engineers further towards more efficient designs. However, we need to remember that CFD codes have primarily been designed for and still are heavily being customized for CPU-only machines. Therefore adaptation to heterogeneous architectures comes at a cost. A substantial one to be honest. And while such efforts are indeed interesting and just referring to the case studies I linked above, “CFD on steroids” makes sense in many cases. However, we as a company see it as an intermediate step, leading to a completely new, refreshed approach to the engineering simulations as a whole.

Beyond HPC, it is AI that we as a team always strive to leverage everywhere we possibly can to augment the intelligence of humans, machines, and even algorithms. Therefore we have been experimenting with applied machine learning for CFD for quite some time. The first time we successfully used it, was probably in combination with a mixed-precision arithmetic to further increase the efficiency of calculations in terms of shortening the time to results and lowering the energy consumption. Then we modeled a simplified advection of sin(x) * cos(y) by predicting its result with a custom-designed AI model, based on machine learning and 2D convolution. We trained the model with 1000 time steps. As a result, it needed only 3 time steps of simulation to predict the next 100 time steps. And the accuracy loss was under 0.001. Quite a simple scenario to start with and we are now tackling on a way more complex simulations, details of which I will be covering in the next parts of this series.

Don’t forget to give us your ? !

AI for CFD: Intro (part 1) was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

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How AI Can Determine Which Coronavirus Patients Require Hospitalization

While the COVID-19 threat continues to grip the world and people are falling prey to this invisible enemy, AI has come up with a tool that will help in finding out the severity of the patients suffering from this disease. Artificial Intelligence has been around for quite some time, but it’s only recently that we have seen a growing inclination towards its implementation in different walks of life. The ever increasing demand for artificial intelligence training and artificial intelligence certification courses is an indication of how important this technology is.

It is said that the data is the king, and if used properly, it can be very profitable. The same concept is being used by one of the AI companies to assess COVID-19 patients and find the severity of the case. In this blog, we will find out about how AI can come as a helpful tool in combating COVID-19.

What’s the latest development?

COVID-19 has brought a lot of sadness around us, while the number of patients is increasing at a breakneck pace; the facilities to cure them are not sufficient. In such a case, the biggest challenge for everyone is to find out and assess which patient needs what kind of healthcare treatment and what is the seriousness of their case.

Computer scientists at the University of Copenhagen are developing a computer model that is AI-enabled. These models are equipped to assess the risk of an individual patient’s need for a ventilator or ICU. This study is being done in association with Rigshospitalet and Bispebjerg Hospital.

How does this tool work?

The primary objective of this to help the hospitals find out which patients can be sent home and who all need beds and breathing equipment. The tool made use of data on 53 patients from two hospitals in China who were tested positive for COVID-19 in January.

ARDS is a condition wherein the lungs get infected by a severe infection like pneumonia, and the body organs don’t get enough oxygen. In this condition, the fluid leaks into the air sacs of the lungs, thus making breathing difficult.

With the help of the AI tool, the data of the previous patients would be useful for creating a meaningful interpretation that will eventually help in interpreting the condition of the patient.

While the work is going on to assess how fruitful this tool can be for the prediction of the severity patient, it will be really helpful for those who are looking for medical assistance and whether or not they would need a ventilator or not. With the help of this tool, it will become easier for healthcare professionals to manage the resources so that the optimum allocation of medical aid can be done.

This is just one of the applications of AI in real-life. In the times to come, AI will find many such uses across different spectrums of life. When we are talking about AI, then it becomes important to mention that this is also a lucrative career option.

The future of Artificial Intelligence:

Artificial intelligence training and artificial intelligence course are not just a career option; rather, many companies are also looking for hiring individuals who have knowledge about this technology and its implementation.

Conclusion-

Artificial Intelligence is one of the most influencing technologies of the modern-day; it is the hottest skills desired by companies who are working in this domain. The above-mentioned use of AI is just one of the many examples where AI can prove to be beneficial for humanity.

If you are planning to make a career in this field, and are willing to find a well-paying job, artificial intelligent certification will be a great tool for the same. It is expected that by 2022 AI will create 58 million new jobs, so you can assume how much opportunity this technology holds for you. There is no better time than today to join the online certificate course in Artificial Training and let your professional graph grow.