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AI Careers: Kesha Williams, Software Engineer, Continues Her Exploration

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Helping information technology to diversify and especially to help women of color achieve in technology and business, has been a personal goal for Kesha Williams, software engineer, author and speaker. (GETTY IMAGES)

By John P. Desmond, AI Trends Editor

We recently had a chance to catch up on the career of Kesha Williams, software engineer, author, speaker and instructor. AI Trends published an Executive Interview with Kesha in June 2018. At the time she was in the Information Technology department at Chick-fil-A, the restaurant chain, with responsibility to lead and mentor junior software engineers, and deliver on innovative technology.

She decided to move on from Chick-fil-A after 15 years in June 2019. Now she works at A Cloud Guru, an online education platform for people interested in cloud computing. Most of the courses prepare students for certification exams. The company was established in Melbourne, Australia in 2015.

“I wanted a role that allowed me to be more hands on with the latest, greatest technology,” she said in a recent interview. “And I wanted to be able to help people on a broader scale, on a more global level. I always felt my part of being here on the planet is to help others, and more specifically to help those in tech.”

Kesha Williams, software engineer, author, speaker and instructor

A Cloud Guru offers certifications for Amazon Web Services (AWS), Microsoft Azure and Google Cloud. It also has what Williams calls “cloud adjacent” courses including on Python programming and machine learning. “These courses will help you ‘skill up’ in the cloud and prepare for certification exams,” she said.

Kesha’s role is as a training architect, focusing on online content around AWS, specifically in the AI space. “Many people have taken this time being at home, to work on skills or learn something new. It’s a great way to spend time during the lockdown,” she advised. A true techie.

AWS DeepComposer Helps Teach About Generative AI and GANs

Most recently, she has been using AWS DeepComposer, an educational training service through AWS that allows the user to compose music using generative AI and GANs (generative adversarial networks, a class of machine learning frameworks). “I have been learning about that, so I can teach others about machine learning and music composition,” she said.

Using music samples, the user trains a music genre model. That model learns how to create new music, based on studying the music files you upload to it. The user plays a melody on a keyboard, gives it to the model, the model composes a new song by adding instruments. She is working on a web series to teach students about that process.

“It’s a fun way to teach some of the more complex topics of GANs and machine learning,” she said. Fortunately she can fall back on youth choir days playing the piano. “I’m remembering things,” she said.

Amazon makes it easy to start out, not charging anything for up to 500 songs. A student can buy the keyboard for $99, or use a virtual keyboard available on the site. Behind the scenes, Amazon SageMaker is working. That will cost some money if the student continues. (SageMaker is a cloud machine-learning platform, launched in November 2017. It enables developers to create, train and deploy machine-learning models in the cloud, or on edge devices.)

So far, Williams has done about 30 songs. “I have used my machine learning skills to train my own genre model. I trained a reggae model; I love reggae.”

Kesha’s Korner is a blog on A Cloud Guru where Williams introduces people to machine learning, offering four to six-minute videos on specific topics. The videos are free to watch; pricing for the A Cloud Guru courses come with membership priced from $32/mo to $49/mo depending, “It’s been a fun series to demystify machine learning,” she said. “It generates a lot of conversations. I often receive feedback from students on which topics to talk about.”

Woman Who Code Planning Virtual Conference

Women Who Code is another interest. The organization works to help women be represented as technical leaders, executives, founders, venture capitalists, board members and software engineers.

The Connect Digital 2020 is the organization’s first entirely virtual conference, to be held on three successive Fridays in June, with Williams scheduled for Friday, June 19. At that meeting, she will deliver a talk about using machine learning for social good, then kick off a “hackathon” to start the following week. The hackathon will start with three technical workshops, the first an introduction to machine learning tools, the second about preparing data, the third about building models. “Their challenge is to take everything they have learned and use machine learning to build a model to help battle the spread of the Covid-19 virus,” she said. “They will have a month to go off and build it, then present it to a panel of judges.” The winner receives a year of free access to the A Cloud Guru platform.

“There are a lot of software engineers that want to make a transition to data science and machine learning,” she said.

Asked what advice she would have for young people or early-career people interested in exploiting AI, Williams said, “Whenever I try to demystify machine learning for people, I tell them it’s complex, but not as complex as most people make it out to be. I thought at first you needed a PhD and to work in a research lab to grasp it. But there are many tools and services out there, especially from AWS, that make these complex technologies approachable and affordable to play around with.

“When you are first learning, you will make a lot of mistakes,” she said. “Don’t beat yourself up. Just stay at it.”

Williams has concerns about AI going forward. “I have always been concerned about the lack of diversity in AI, about the bias issues and the horror stories we have seen when it comes to certain bad-performing models that are used to make decisions about people. It’s still an issue; we need to continue to talk about it and solve it.”

Being in information technology for 25 years has been and continues to be a good career. “It’s still exciting for me. Every day there is something new to learn.”

Learn more at Kesha’s Korner and Women Who Code.

Source: https://www.aitrends.com/ethics-and-social-issues/ai-careers-kesha-williams-software-engineer-continues-her-exploration/

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XBRL: scrapping quarterlies, explaining AI and low latency reporting

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Here is our pick of the 3 most important XBRL news stories this week.

1 FDIC considers scrapping quarterly bank reports

The Federal Deposit Insurance Corp. is moving to boost the way it monitors for risks at thousands of U.S. banks, potentially scrapping quarterly reports that have been a fixture of oversight for more than 150 years yet often contain stale data.

The FDIC has been one of the cheerleaders and case studies for the efficiency increasing impact of XBRL based reporting forever. Therefore it will be fascinating to observe this competition and its outcome.

2 XBRL data feeds explainable AI models

Amongst several fascinating presentations at the Eurofiling Innovation Day this week was an interesting demonstration on how XBRL reports can be used as the basis of explainable AI for bankruptcy prediction.

The black box nature of many AI models is one biggest issues of applying AI in regulated environments, where causal linkages are the bedrock of litigation etc. Making them explainable would remove a major headache for lots of use cases.

3 Low latency earnings press release data

Standardized financials from Earnings Press Release and 8-Ks are now available via the Calcbench API minutes after published.  Calcbench is leveraging our expertise in XBRL to get many of the numbers from the Income Statement, Balance Sheet and Statement of Cash Flows from the earnings press release or 8-K.  

The time lag between the publication of earnings information and its availability in the XBRL format continues to be a roadblock for the wholesale adoption of XBRL by financial markets until regulators require immediate publication in the XBRL format in real time. The Calcbench API is a welcome stop gap measure. 

 

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Christian Dreyer CFA is well known in Swiss Fintech circles as an expert in XBRL and financial reporting for investors.

 We have a self-imposed constraint of 3 news stories each week because we serve busy senior leaders in Fintech who need just enough information to get on with their job.

 For context on XBRL please read this introduction to our XBRL Week in 2016 and read articles tagged XBRL in our archives. 

 New readers can read 3 free articles.  To  become a member with full access to all that Daily Fintech offers,  the cost is just USD 143 a year (= USD 0.39 per day or USD 2.75 per week). For less than one cup of coffee you get a week full of caffeine for the mind.

Source: https://dailyfintech.com/2020/07/02/xbrl-scrapping-quarterlies-explaining-ai-and-low-latency-reporting/

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AI- hot water for insurance incumbents, or a relaxing spa?

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Frog-in-boiling-water

The parable of the frog in the boiling water is well known- you know, if you put a frog into boiling water it will immediately jump out, but if you put the frog into tepid water and gradually increase the temperature of the water it will slowly boil to death.  It’s not true but it is a clever lede into the artificial intelligence evolution within insurance.  Are there insurance ‘frogs’ in danger of tepid water turning hot, and are there frogs suffering from FOHW (fear of hot water?)

image source

Patrick Kelahan is a CX, engineering & insurance consultant, working with Insurers, Attorneys & Owners in his day job. He also serves the insurance and Fintech world as the ‘Insurance Elephant’.

The frog and boiling water example is intuitive- stark change is noticed, gradual change not so much.  It’s like Ernest Hemmingway’s quotation in “The Sun Also Rises”- “How did you go bankrupt?  Gradually, and then suddenly!”  In each of the examples the message is similar- adverse change is not always abrupt, but failure to notice or react to changing conditions can lead to a worst-case scenario.  As such with insurance innovation.

A recent interview in The Telegraph by Michael Dwyer of Peter Cullum, non-executive Director of Global Risk Partners (and certainly one with a CV that qualifies him as a knowing authority), provided this view:

“Insurance is one business that is all about data. It’s about numbers. It’s about the algorithms. Quite frankly, in 10 years’ time, I predict that 70pc or 80pc of all underwriters will be redundant because it will be machine driven.

“We don’t need smart people to make what I’d regard as judgmental decisions because the data will make the decision for you.”

A clever insurance innovation colleague, Craig Polley, recently posed Peter’s insurance scenario for discussion and the topic generated lively debate- will underwriting become machine driven, or is there an overarching need for human intuition?  I’m not brave enough to serve as arbiter of the discussion, but the chord Craig’s question struck leads to the broader point- is the insurance industry sitting in that tepid water now, and are the flames of AI potentially leading to par boiling?

I offered a thought recently to an AI advocate looking for some insight into how the concept is embraced by insurance organizations.  In considering the fundamentals of insurance, I recounted that insurance as a product thrives best in environments where risk can be understood, predicted, and priced across populations with widely varied individual risk exposures as best determined by risk experience within the population or application of risk indicators.  Blah, blah, blah. Insurance is a long-standing principle of sharing of the ultimate cost of risk where no one participant is unduly at a disadvantage, and no one party is at a financial advantage- it is a balance of cost and probability.

Underwriting has been built on a model of proxy information, on the law of large numbers, of historical performance, of significant populations and statistical sampling.  There is not much new in that description, but what if the dynamic is changed, to an environment where the understanding of risk factors is not retrospective, but prospective?

Take commercial motor insurance for example.  Reasonably expensive, plenty of human involvement in underwriting, high maximum loss outcomes for occurrences.  Internal data are the primary source of rating the book of business.  There are, however,  new approaches being made in the industry that supplant traditional internal or proxy data with robust analysis of external data.  Luminant Analytics is an example of a firm that leverages AI in providing not only provide predictive models for motor line loss frequency and severity trends, but also analytics that help companies expanding into new markets, where historical loss data is unavailable.  Traditional underwriting has remained a solid approach, but is it now akin to turning the heat up on the industry frog?

The COVID-19 environment has by default prompted a dramatic increase in virtual claim handling techniques, changing what was not too long ago verboten- waiver of inspection on higher value claims, or acceptance of third party estimates in lieu of measure by the inch adjuster work.  Yes, there will be severity hangovers and spikes in supplements, but carriers will find expediency trumps detail- as long as the customer is accepting of the change in methods.  If we consider the recent announcement by US P&C carrier Allstate of significant staff layoffs as an indicator of the inroads of virtual efforts then there seemingly is hope for that figurative frog.

Elsewhere it was announced that the All England Club has not had its Wimbledon event cancellation cover renewed for 2021 (please recall that the Club was prescient in having cancellation cover in force that included pandemic benefits).  The prior policy’s underwriters are apparently reluctant to shell out another potential $140 million with a recurrence of a pandemic, but are there other approaches to pandemic cover?  The consortium of underwriting firms devised the cover seventeen years ago; can the cover for a marquee event benefit from AI methodology that simply didn’t exist in 2003?  It’s apparent the ask for cover for the 2021 event attracted knowledgeable frogs that knew to jump out of hot water, but what if the exposure burner is turned down through better understanding of the breadth of data affecting the risk, that there is involvement of capital markets in diversifying the risk perhaps across many unique events’ outcomes and alternative risk financing, and leveraging of underwriting tools that are supported by AI and machine learning?  Will it be found in due time that the written rule that pandemics cannot be underwritten as a peril will have less validity because well placed application of data analysis has wrangled the risk exposure to a reasonable bet by an ILS fund?

There are more examples of AI’s promise but let us not forget that AI is not the magic solution to all insurance tasks.  Companies that invest in AI without a fitting use case simply are moving their frog to a different but jest as threatening a pot.  Companies that invest in innovation that cannot bridge their legacy system to meaningful outcomes because there is no API functionality are turning the heat up themselves.  Large scale innovation options that are coming to a twenty-year anniversary (think post Y2K) may have compounding legacy issues- old legacy and new legacy.

The insurance industry needs to consider not just individual instances of the gradual heat of change being applied.

What prevents the capital markets from applying AI methods (through design or purchase) in predicting or betting on risk outcomes?  The more comprehensive and accurate risk prediction methods become the more direct the path between customer and risk financing partner also becomes.  Insurance frogs need not fear the heat if there are fewer pots to work from, but no pots, no business.

The risk sharing/risk financing industry has evolved through application of available technology and tools, what’s to say AI does not become a double-edged sword for the insurance industry- a clever tool in the hands of insurers, or a clever tool in the hands of alternative financing that serves to cut away some of the insurers’ business?  If asked, Peter Cullum might opine that it’s not just underwriting that AI will affect, but any other aspect of insurance that AI can effectively influence.  Frogs beware.

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Source: https://dailyfintech.com/2020/07/02/ai-hot-water-for-insurance-incumbents-or-a-relaxing-spa/

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MIT takes down 80 Million Tiny Images data set due to racist and offensive content

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Creators of the 80 Million Tiny Images data set from MIT and NYU took the collection offline this week, apologized, and asked other researchers to refrain from using the data set and delete any existing copies. The news was shared Monday in a letter by MIT professors Bill Freeman and Antonio Torralba and NYU professor Rob Fergus published on the MIT CSAIL website.

Introduced in 2006 and containing photos scraped from internet search engines, 80 Million Tiny Images was recently found to contain a range of racist, sexist, and otherwise offensive labels such as nearly 2,000 images labeled with the N-word, and labels like “rape suspect” and “child molester.” The data set also contained pornographic content like non-consensual photos taken up women’s skirts. Creators of the 79.3 million-image data set said it was too large and its 32 x 32 images too small, making visual inspection of the data set’s complete contents difficult. According to Google Scholar, 80 Million Tiny Images has been cited more 1,700 times.

Above: Offensive labels found in the 80 Million Tiny Images data set

“Biases, offensive and prejudicial images, and derogatory terminology alienates an important part of our community — precisely those that we are making efforts to include,” the professors wrote in a joint letter. “It also contributes to harmful biases in AI systems trained on such data. Additionally, the presence of such prejudicial images hurts efforts to foster a culture of inclusivity in the computer vision community. This is extremely unfortunate and runs counter to the values that we strive to uphold.”

The trio of professors say the data set’s shortcoming were brought to their attention by an analysis and audit published late last month (PDF) by University of Dublin Ph.D. student Abeba Birhane and Carnegie Mellon University Ph.D. student Vinay Prabhu. The authors say their assessment is the first known critique of 80 Million Tiny Images.

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Both the paper authors and the 80 Million Tiny Images creators say part of the problem comes from automated data collection and nouns from the WordNet data set for semantic hierarchy. Before the data set was taken offline, the coauthors suggested the creators of 80 Million Tiny Images do like ImageNet creators did and assess labels used in the people category of the data set. The paper finds that large-scale image data sets erode privacy and can have a disproportionately negative impact on women, racial and ethnic minorities, and communities at the margin of society.

Birhane and Prabhu assert that the computer vision community must begin having more conversations about the ethical use of large-scale image data sets now in part due to the growing availability of image-scraping tools and reverse image search technology. Citing previous work like the Excavating AI analysis of ImageNet, the analysis of large-scale image data sets shows that it’s not just a matter of data, but a matter of a culture in academia and industry that finds it acceptable to create large-scale data sets without the consent of participants “under the guise of anonymization.”

“We posit that the deeper problems are rooted in the wider structural traditions, incentives, and discourse of a field that treats ethical issues as an afterthought. A field where in the wild is often a euphemism for without consent. We are up against a system that has veritably mastered ethics shopping, ethics bluewashing, ethics lobbying, ethics dumping, and ethics shirking,” the paper states.

To create more ethical large-scale image data sets, Birhane and Prabhu suggest:

  • Blur the faces of people in data sets
  • Do not use Creative Commons licensed material
  • Collect imagery with clear consent from data set participants
  • Include a data set audit card with large-scale image data sets, akin to the model cards Google AI uses and the datasheets for data sets Microsoft Research proposed

The work incorporates Birhane’s previous work on relational ethics, which suggests that the creators of machine learning systems should begin their work by speaking with the people most affected by machine learning systems, and that concepts of bias, fairness, and justice are moving targets.

ImageNet was introduced at CVPR in 2009 and is widely considered important to the advancement of computer vision and machine learning. Whereas previously some of the largest data sets could be counted in the tens of thousands, ImageNet contains more than 14 million images. The ImageNet Large Scale Visual Recognition Challenge ran from 2010 to 2017 and led to the launch of a variety of startups like Clarifai and MetaMind, a company Salesforce acquired in 2017. According to Google Scholar, ImageNet has been cited nearly 17,000 times.

As part of a series of changes detailed in December 2019, ImageNet creators including lead author Jia Deng and Dr. Fei-Fei Li found that 1,593 of the 2,832 people categories in the data set potentially contain offensive labels, which they said they plan to remove.

“We indeed celebrate ImageNet’s achievement and recognize the creators’ efforts to grapple with some ethical questions. Nonetheless, ImageNet as well as other large image datasets remain troublesome,” the Birhane and Prabhu paper reads.

Source: http://feedproxy.google.com/~r/venturebeat/SZYF/~3/knS0Ix3IHxA/

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