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Need to Build Trustworthy AI Systems Gains Importance as AI Progresses

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As AI systems take on more responsibility, the strengths and weaknesses current AI systems need to be recognized to help build a foundation of trust. (GETTY IMAGES)

By John P. Desmond, Editor, AI Trends

The push is on to build trusted AI systems with an eye toward instilling confidence that results will be fair, accuracy will be sufficient, and safety will be preserved.

Gary Marcus, the successful entrepreneur who sold his startup Geometric Intelligence to Uber in 2016, issued a wakeup call to the AI industry as co-author with Ernest Davis of “Rebooting AI,” (Pantheon, 2019) an analysis of the strengths and weaknesses of current AI, where the field is going, and what we should be doing.

Marcus spoke about building trusted AI in a recent interview with The Economist. Here are some highlights:

“Trustworthy AI has to start with good engineering practices, mandated by laws and industry standards, both of which are currently largely absent. Too much of AI thus far has consisted of short-term solutions, code that gets a system to work immediately, without a critical layer of engineering guarantees that are often taken for granted in other fields. The kinds of stress tests that are standard in the development of an automobile (such as crash tests and climate challenges), for example, are rarely seen in AI. AI could learn a lot from how other engineers do business.”

AI developers, “can’t even devise procedures for making guarantees that given systems work within a certain tolerance, the way an auto part or airplane manufacturer would be required to do.”

“The assumption in AI has generally been that if it works often enough to be useful, then that’s good enough, but that casual attitude is not appropriate when the stakes are high.”

IBM Team Identifies Four Pillars of Trusted AI

Support for building trust in AI systems was furthered in a recent paper by an IBM team suggesting Four Pillars to Trusted AI, as described in a recent account in Towards Data Science from Jesus Rodriguez, chief scientist and managing partner at Invector Labs.

“The non-deterministic nature of artificial intelligence(AI) systems breaks the pattern of traditional software applications and introduces new dimensions to enable trust in AI agents,” Rodriquez states. Trust in software development has been built through procedures around testing, auditing, documentation, and many other aspects of the discipline of software engineering. AI agents execute behavior based on knowledge that evolves over time. It’s difficult to understand.

Rodriguez suggests the Four Pillars from IBM are a viable idea for establishing the foundation of trust in AI systems. The foundations are:

  • Fairness: AI systems should use training data and models that are free of bias, to avoid unfair treatment of certain groups.
  • Robustness: AI systems should be safe and secure, not vulnerable to tampering or compromising the data they are trained on.
  • Explainability: AI systems should provide decisions or suggestions that can be understood by their users and developers.
  • Lineage: AI systems should include details of their development, deployment, and maintenance so they can be audited throughout their lifecycle.

To help identify whether an AI system is built consistent with the four pillars of trust AI, IBM proposes a Supplier’s Declaration of Conformity (SDoC, or factsheet, for short) that helps to provide information. It should answer basic questions, including this selection:

  • Does the dataset used to train the service have a data sheet or data statement?
  • Was the dataset and model checked for biases? If “yes” describes bias policies that were checked, bias checking methods, and results.
  • Was any bias mitigation performed on the dataset? If “yes” describes the mitigation method.
  • Are algorithm outputs explainable/interpretable? If yes, explain how is explainability achieved (e.g. directly explainable algorithm, local explainability, explanations via examples).
  • Describe the testing methodology.

Human Observers Need to Understand the AI System

Trust in an AI system is built by repeated correct performance of the system, making it highly reliable, and also a system that human observers can understand. When working with resilient, intelligent robotic systems, for example in the military, that are built to adapt and evolve to yield increasingly improved performance, it is challenging to understand the system. The human observers need to be able to understand how the system is improving through experience, suggests Nathan Michael, CTO of Shield AI, writing recently in National Defense. Shield develops AI for national security and defense applications.

“One of the greatest challenges with artificial intelligence is that there is an overwhelming impression that magic underlies the system. But it is not magic, it’s mathematics.

What is being accomplished by AI systems is exciting, but it is also simply theory and fundamentals and engineering. As the development of AI progresses, we will see, more and more, the role of trust in this technology, Michael stated.

Read the source articles in The Economist, Towards Data Science and in National Defense.

Source: https://www.aitrends.com/ethics-and-social-issues/need-to-build-trustworthy-ai-systems-gains-importance-as-ai-progresses/

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Is It Worth Investing in a Website Builder?

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There are many different ways to build a website these days. There’s the timeless method of building your site code in Adobe Dreamweaver and exporting it to the web.

You can build a site in WordPress with a bit of CSS knowledge, or you can just outsource everything to a website design agency. Then there’s also the option of using a website builder, which is perhaps the easiest solution of all.

“Website builders are a popular way for people to easily and quickly set up a website with as little hassle as possible.” 

They’re great for small retail businesses, whether you’re selling handmade crafts or drop shipping products from Amazon, but larger companies can effectively use website builders as well. They certainly aren’t for everyone, but let’s take a look at whether or not investing in a website builder is the right choice for you.

How much do website builders actually cost?

Website builders are always going to be cheaper than custom website design, and to an extent WordPress, but there are some variables. The thing is that some people (design agencies) like to point out that website builders cost a little more in things like domain hosting, SSL certificates, and other little monthly fees, compared to DIY hosting or a WordPress domain host.

So it becomes a question of upfront costs versus long-term costs in monthly fees, but there are several catches people don’t like to mention. Let me try to explain it succinctly.

Cost of a website builder

If you use a website builder to create, for example, a small eCommerce website. You’re probably going to pay around $200 ~ $500 upfront. This will include your domain name, any premium themes and add-ons (like a shopping cart module), and monthly hosting (which you’ll probably pay as an annual subscription upfront). It’s kind of like an “all-inclusive” vacation package, where everything is included in the total upfront cost.

So you’ll pay a small upfront fee which is mostly the annual hosting subscription, followed by monthly fees for the additional customizations you add to your website. Hosting plans on website builder platforms average around $9 to $75 per month, depending on your plan.

Again, it really depends on your plan, as website builders aren’t just for eCommerce websites. For example, there are a number of platforms which are built for specific industries, such as real estate, as this guide describes. Ultimately if you are going to use a website builder, it’s best to find one that is best suited to the industry you are operating in. 

Cost of a WordPress website

If you use WordPress, you can expect to pay around $500 – $1,000 upfront for a similar small eCommerce website, with lower monthly fees. This is because you can shop around for a domain name and domain hosting from sites like HostGator, BlueHost, etc. to get the best subscription-based pricing available, but you’ll also be paying additionally for WordPress themes, mobile design plug-ins, shopping cart plug-ins, etc.

Using the vacation package analogy again, WordPress is like you’re paying for your own drinks, meals, and WiFi access at the resort.

This means that you’ll be spending a bit more upfront on piecing together the different elements of your website, but you’ll pay on average around $11 – $40 per month for domain hosting. Of course, you could also pay monthly for plug-in subscriptions, website maintenance, etc.

Cost of custom website design

So in the vacation package analogy, custom website design is like flying first-class to a resort, and you own the resort. Custom website design is going to cost a minimum of around $5,000 and could go much higher, depending on your web project.

“Website designers are paid around $50 – $100 per hour, and custom website design takes around 14 weeks on average, from beginning to launch.” 

Now some website design agencies are going to be mad at me for saying this, but when they like to point out the “higher monthly costs” of a website builder, take a look at their fine print. Many website design agencies can lock you into monthly maintenance contracts, which can range from an additional $500 up to $3,000 per month or more, depending on the size of your site.

It’s kind of like if you have a contract with a car mechanic to inflate your tires and change your oil every month, except they keep billing you for a clutch assembly replacement. I’m not saying that website design agencies are dishonest, but you do need to be aware of what kind of monthly maintenance your website actually needs.

Conclusion

When we compare all three options (website builder, WordPress, and custom website design), it’s quite clear that website builders are the most affordable option. However, you’ll also be limited in customization options with a website builder, as you’re really piecing together templates and blocks, so you won’t get the exclusive customization and brand appeal you would with custom web design or a WordPress website. So you’ll have to consider what’s best for your long-term business plan.

Also, Read Tips to Automate Your Ecommerce

Source: https://www.aiiottalk.com/business/investing-in-a-website-builder/

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Amazon EC2 Inf1 instances featuring AWS Inferentia chips now available in five new Regions and with improved performance

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Following strong customer demand, AWS has expanded the availability of Amazon EC2 Inf1 instances to five new Regions: US East (Ohio), Asia Pacific (Sydney, Tokyo), and Europe (Frankfurt, Ireland). Inf1 instances are powered by AWS Inferentia chips, which Amazon custom-designed to provide you with the lowest cost per inference in the cloud and lower barriers for everyday developers to use machine learning (ML) at scale.

As you scale your use of deep learning across new applications, you may be bound by the high cost of running trained ML models in production. In many cases, up to 90% of the infrastructure spent on developing and running an ML application is on inference, making the need for high-performance, cost-effective ML inference infrastructure critical. Inf1 instances are built from the ground up to support ML inference applications and deliver up to 30% higher throughput and up to 45% lower cost per inference than comparable GPU-based instances. This gives you the performance and cost structure you need to confidently deploy your deep learning models across a broad set of applications.

Customers and Amazon services adopting Inf1 instances

Since the launch of Inf1 instances, a broad spectrum of customers, such as large enterprises and startups, as well as Amazon services, have begun using them to run production workloads. Amazon’s Alexa team is in the process of migrating their Text-To-Speech workload from running on GPUs to Inf1 instances. INGA Technology, a startup focused on advanced text summarization, got started with Inf1 instances quickly and saw immediate gains.

“We quickly ramped up on AWS Inferentia-based Amazon EC2 Inf1 instances and integrated them in our development pipeline,” says Yaroslav Shakula, Chief Business Development Officer at INGA Technologies. “The impact was immediate and significant. The Inf1 instances provide high performance, which enables us to improve the efficiency and effectiveness of our inference model pipelines. Out of the box, we have experienced four times higher throughput, and 30% lower overall pipeline costs compared to our previous GPU-based pipeline.”

SkyWatch provides you with the tools you need to cost-effectively add Earth observation data into your applications. They use deep learning to process hundreds of trillions of pixels of Earth observation data captured from space every day.

“Adopting the new AWS Inferentia-based Inf1 instances using Amazon SageMaker for real-time cloud detection and image quality scoring was quick and easy,” says Adler Santos, Engineering Manager at SkyWatch. “It was all a matter of switching the instance type in our deployment configuration. By switching instance types to AWS Inferentia-based Inf1, we improved performance by 40% and decreased overall costs by 23%. This is a big win. It has enabled us to lower our overall operational costs while continuing to deliver high-quality satellite imagery to our customers, with minimal engineering overhead.”

AWS Neuron SDK performance and support for new ML models

You can deploy your ML models to Inf1 instances using the AWS Neuron SDK, which is integrated with popular ML frameworks such as TensorFlow, PyTorch, and MXNet. Because Neuron is integrated with ML frameworks, you can deploy your existing models to Amazon EC2 Inf1 instances with minimal code changes. This gives you the freedom to maintain hardware portability and take advantage of the latest technologies without being tied to vendor-specific software libraries.

Since its launch, the Neuron SDK has seen dramatic improvement in performance, delivering throughput up to two times higher for image classification models and up to 60% improvement for natural language processing models. The most recent launch of Neuron added support for OpenPose, a model for multi-person keypoint detection, providing 72% lower cost per inference than GPU instances.

Getting started

The easiest and quickest way to get started with Inf1 instances is via Amazon SageMaker, a fully managed service for building, training, and deploying ML models. If you prefer to manage your own ML application development platforms, you can get started by either launching Inf1 instances with AWS Deep Learning AMIs, which include the Neuron SDK, or use Inf1 instances via Amazon Elastic Kubernetes Service (Amazon EKS) or Amazon Elastic Container Service (Amazon ECS) for containerized ML applications.

For more information, see Amazon EC2 Inf1 Instances.


About the Author

Michal Skiba is a Senior Product Manager at AWS and passionate about enabling developers to leverage innovative hardware. Over the past ten years he has managed various cloud computing infrastructure products at Silicon Valley companies, large and small.

Source: https://aws.amazon.com/blogs/machine-learning/amazon-ec2-inf1-instances-featuring-aws-inferentia-chips-now-available-in-five-new-regions-and-with-improved-performance/

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Argonne National Labs Using AI To Predict Battery Cycles

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Researchers at the Argonne National Laboratory are exploring the use of AI to decrease the testing time of batteries for demanding grid applications. (GETTY IMAGES)

By Allison Proffitt, Editorial Director, AI Trends

Thanks to the cost reductions that have come from global electric vehicle adoption, lithium ion batteries now have an important role to play in grid storage, Susan Babinec, Argonne National Laboratory, told audiences last week at the International Battery Virtual Seminar and Exhibit. But making full use of them is going to require a bit of help from artificial intelligence.

While EVs prize high energy density, and only need to last about eight years, grid applications require more cycles, more calendar life—20 to 30 years—and more safety at a lower cost.

“Grid economics requires precise life data, which is very time and resource intensive to generate,” Babinec said. “We are using approximations that create risk, limit our design creativity, and increase cost.” The solution? Of course, in today’s day and age the solution is always artificial intelligence, Babinec quipped. “In this case, we’re going to use AI to massively reduced time to cycle life prediction.”

Sue Babinec, Program Lead, Grid Storage at Argonne National Laboratory

Babinec’s team categorized the variables impacting lithium ion batteries for grid applications—acknowledging that adjusting any one variable will always mean changes in others. “For grid storage, first and foremost, low cost is always the most important,” Babinec said. But others include state-of-charge swing, C-rate, average state-of-charge, and temperature.

“Today we handle this variability by estimating the cycle life, but those estimates do not really allow us to push these cells to the limits of what they can really do,” Babinec said. “We just simply don’t have enough information on the cycle life and we are limited by the information that is provided by the cell manufacturer, which is really all about them making sure they can live up to their warranty.”

Babinec is prioritizing overall cost per cycle (levelized cost of storage, LCOS). This is a better metric than capital cost because grid storage batteries are durable goods, she explained. The Department of Energy’s target for LCOS is $0.02/kWh, a target for which we currently fall far short.

“No matter how you look at it, we are not there today with any combination of capital and cycles,” Babinec said. “We need to bring the capital down, but right here and now we need to bring the number of cycles up.”

Looking to AI to Decrease Testing Time from Two Years to Two Weeks

Argonne is applying artificial intelligence to the problem. Babinec’s group is developing rapid cycle life evaluations using AI to decrease testing from the current two years to a goal of two weeks. Argonne is the right spot for this research, Babinec argues. As the DOE’s battery hub, Argonne has plenty of data, a team of AI experts, and a new supercomputer up to the task. Aurora, created in partnership with Argonne, Cray and the DOE, will be the first exascale computer in the U.S.

The scope of the project is broad. They are using several AI approaches—from physics-based tools to deep neural nets. “We want to see which AI approach is the best for this problem,” Babinec said. All of the Li-ion chemistries will be tested deliberately and sequentially, and the current, voltage, and time will be recorded for every second, of every cycle, for every cell.

Babinec describes the basic AI process as encoding data from one cell running one cycle. Each cell cycle generates 150 features. Narrowing in on one feature from many cells, you determine correlations and relationships and decode for one behavior: cycles to failure.

To test their plan, the group used public data published last year in Nature Energy (DOI: 10.1038/s41560-019-0356-8). They compared the capacity at a certain voltage in cycle one to the capacity at the same voltage in cycle 20 and generated correlations and relationships then predictions from there. The results: the experimental cycles to failure and the predicted cycles to failure aligned.

Her presentation at Florida Battery was the first presentation of Argonne’s experimental results, and Babinec shared that the approach seems to be working. When testing many chemistries, like cells self-organize by chemistry and cycles to failure. When run on real cells, predictions match observed. So far, Babinec says it looks like it will take as few as 40-60 cycles to predict cycle life—more for high cycle life, less for low cycle life.

The key to a high-quality prediction, she emphasized, is using training data from cells with a cycle life that is similar to your goal cycle life. For example, cells that failed at 150 cycles will not accurately train an algorithm to predict 2,000 cycles.

While work on the cycle life predictions continues, Babinec says Argonne is also focused on cleaning up more than 20 years’ worth of spreadsheets, databases, and machine files containing battery data. “The data is wonderful, but it has to be cleaned up. It’s a major effort, which we are working on,” she said. The team is working toward machine learning-ready training data including, for example, capacity vs. cycle comparisons and discharge curves. Some data are available on Github: https://github.com/materials-data-facility/battery-data-toolkit

“There is promise for this,” Babinec said. Testing timelines will decrease, which she says may open up assessments of complex and changing use scenarios, eventually enhancing deployment flexibility while minimizing risk.

Learn more at Nature Energy (DOI: 10.1038/s41560-019-0356-8).

Source: https://www.aitrends.com/ai-research/argonne-national-labs-using-ai-to-predict-battery-cycles/

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