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An AI founder’s struggle to be seen in the age of Black Lives Matter 




I’m founder of AI4US. We build high-performing AI teams with majority Black women scientists to help companies overcome the tech gender and diversity talent shortage.

Our flagship program is RAPIDS. Here’s how it works: In Fall 2020, students will be placed in virtual cohorts of 20-25 students based on computing proficiency. These students take Nvidia’s Fundamentals of Accelerated Data Science. The course is given as a three semester-credit course. (Our first cohort went through the program last year, but they were taught by White male instructors. 2020 is our first cohort with Black women instructors.)

We are uniquely using culturally responsive instruction; that’s “a pedagogy that empowers students intellectually, socially, and emotionally.” This allows us to incorporate Breonna Taylor into a class on Logistic Regression.

The goal is not for students to just complete the class; they commit to becoming certified to teach it (and other courses in the NVIDIA Deep Learning Institute curriculum). So we can offer training to the federal government, or provide diverse on-site employee development training, or training for other potential customers. Our students can also move on to fill data-science/AI related roles, as interns or employees, giving companies access to a new talent pool.

VB Transform 2020 Online – July 15-17. Join leading AI executives: Register for the free livestream.

The goal

I’ve been doing substantial outreach for the program across the tech and business community in an effort create job-pipeline partnerships for our alumni and to obtain funding for our teachers so that we can keep tuition free for students. (The plan is to become self-funding via subcontracting opportunities with these companies.) But I’m having a hard time being heard.

Google announced their “Google for Startups Accelerator: Black Founders” this month as part of their commitment to racial equity. It’s a three-month digital accelerator program for high potential Seed to Series A tech startups based in the U.S. “The accelerator program is designed to bring the best of Google’s programs, products, people, and technology to Black founder communities across the U.S. In addition to mentorship and technical project support.” I also found their initiative “Google for Startups Accelerator for Women Founders.” I thought I had access to two perfect opportunities! They both are interested in startups that leverage AI/ML technology in their product. Unfortunately, they both require companies to have a minimum of 10 employees. I have a team of three. (As entrepreneur James Norman recently explained, most Black entrepreneurs don’t match the expected pattern of having several technical cofounders they’ve known for years and so frequently miss out on accelerator opportunities.)

I attended Spelman College. Spelman, Howard, and Hampton College (HBCUs) are not producing more than 10 Black CS/Math/or Stats graduates a year (according to 2015 figures) and certainly do not produce 10 Black women in computing in a given year (let’s not add in what happens to those numbers when we see the full impact of Covid on HBCUs). 

The purpose of founding AI4US was so that I — and all the women who go through our training program — can name 10 Black women, descendants of the brutal institution of chattel slavery, working in AI. I am a powerful promising Black woman entrepreneur. Yet I am locked out of the very opportunities created to advance racial equity for Black communities.

In 2018, Melinda Gates partnered with McKinsey to collect data directly from tech companies to understand their philanthropic and CSR initiatives. The 32 tech companies collectively spent more than $500 million on philanthropic giving in 2017, but only around 5% of that went toward programs aimed at correcting the gender imbalance. Less than 0.1% of philanthropic investing —  $335,000 — was directed at removing barriers hindering women of color from pursuing careers in tech.

What does that $335,000 represent? It includes both programs that are exclusive to women of color, as well as programs open to a larger group of students that makes a deliberate and successful effort to attract and serve women of color.

Struggling to be heard

Since the death of George Floyd unleashed protests across the United States, tech executives have been speaking out forcefully against racial violence in the U.S., with some promising millions of dollars in contributions to organizations pursuing justice. Facebook CEO Mark Zuckerberg announced that the company will contribute $10 million to “groups working on racial justice,” and told followers that he’s working with advisers and employees to find organizations that “could most effectively use this right now.”

I answered their call! I shared the mission of AI4US with the 32 tech companies from the Gates/McKinsey report. I wrote Facebook, Google, Clarifai, Duolingo, Netflix, Zoom, Hulu, AWS, Uber, Lyft, Amazon, Asana, Github, Salesforce, PagerDuty, YouTube, Fastly, Kleiner Perkins, Sequoia, Away, Twilio, Square, Twitter, Medium, Box, Shopify, Intel, The ChanZuckerberg Initiative, BAE, Cisco, LinkedIn, The Gates Foundation, Snap, Dropbox, Omnisci, Redhat, Walmart, Mercury, Pure storage, Carahsoft, GDIT, Talentseer, Elementai, Splunk, Zoox, Lockheed Martin, Alion, Modzy, Goldman Sachs, Verizon, Pinnacle, Niantic, Apple, IBM, Go Daddy, Dell, NetApp, EA, Adobe, PayPal, Best Buy, Workday, Chase, Charles Schwab, Vista Equity, The Business Roundtable, AT&T, Bosch, T-Mobile, Fitbit, Capital One, Accenture, Hyundai, Subaru, Booz Allen Hamilton, General Dynamics, Bank of America, BP,  Chevron, PepsiCo, Associate Resource Group, Comcast, IBM, OmniSci, The Foundry Group, ServiceNow, the Fourmation ERG, Sofi, Synnex, SoftBank, Hypergiant, Andreessen Horowitz, Okta, Humu  … should I go on? And then I emailed them again!

I have sent hundreds of letters. 99% were ignored. All of these companies have positions for data scientists and machine learning engineers. And most have few (if any) Black women occupying these roles. I kept reading that corporate America had pledged over a billion dollars for Justice. Not only have I not been able to tap into the less than 0.1% of 2017 philanthropic spending, I can barely get a meeting.

When I contacted the Chief Diversity and Inclusion officer at Intel, who was included in the Gates/McKinsey report, I was ignored. I secured a meeting with Intel only after contacting the CEO! This was also the case with Bank of America. The CEO of Bank of America read my letter and connected me with the right people! I have not yet created a partnership with BofA, but I am hopeful. I have also been able to obtain one, hour long meeting with a tech firm, but that was because I had already volunteered my services to the company for free.

I am discouraged. But I will persist. My resilience comes from the spirit of my ancestors.

Hagar Murrell founded Garnett Training School around the year 1900 in Pollocksville, North Carolina. At the time, there were four schools for White students, but no school for Black students. So, Hagar stepped up and stepped in!

Black students were educated there until integration allowed students to attend the White school in 1968. My letters to the CEO of Intel — my letter to you — are inspired by Hagar’s letters and legacy.

She probably raised at least half a million dollars by today’s standards. You can find her funding requests in newspapers across the country. I have found articles from Texas to New York, from 1888 to 1928 in which she attempted to raise money for teachers and dorms.

Her story is one of a woman, born into slavery, but who is STILL freeing generations of students. One of them is me, Andrea Roberson, descendant of Garnett students and teachers, who became the first Black woman to receive a PhD in Applied Math from Stony Brook University. Me writing this today bears witness to the truth that her program worked. The success of AI4US is encoded in my DNA.

If tech companies give me a chance, a meeting, a portion of that less than 0.1%, imagine what we could do together. How many students can we free to realize their wildest dreams?

Will you respond? I struggle with the anxiety of being ignored, unheard and unseen. Invisible. But Grandma Hagar already broke ground for me, and I will persevere to break ground for other Black women.

Andrea Roberson is CEO of AI4US. She was previously a Machine Learning Researcher in the Economic Statistical Methods Division (ESMD) of the U.S. Census Bureau for over a decade.  Her work includes a variety of design strategies to build and operationalize predictive text analytics solutions. She has authored papers and presentations for conferences including the Association for Computational Linguistics (ACL), the Symposium on Data Science & Statistics (SDSS), New Techniques and Technologies for Statistics (NTTS), and FCSM. 



Is It Worth Investing in a Website Builder?




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.


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


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




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.


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Argonne National Labs Using AI To Predict Battery Cycles




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:

“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).


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