Forem: Tracy Lee | ladyleet

Double Click: Jump on the waitlist for Fig.io's new autocomplete terminal add-on!

Tracy Lee | ladyleet — Thu, 23 Sep 2021 16:25:19 +0000

Welcome to the Double Click! This is the weekly blog series that shines a spotlight on emerging technologies, technological concepts, and community projects that enrich the JavaScript Ecosystem!

This week, I’m sharing a really cool, new tool for your existing terminal that I LOVE: Fig!

This add-on provides VSCode-style autocomplete, and is currently being made available for free to those who sign up on their website.

When using the Fig add-on, you get autocomplete options in your terminal, showing you everything you need, such as lists of available directories, folders, git commands, branches, components, and more without taking you out of your flow.

How magical is it to get more time to focus on your code rather than trying to remember shortcuts and commands in terminal? (LIFESAVER!)

Fig is also built to integrate with a number of CLI tools, including npm, Heroku, AWS, GCP, Docker, and more, with plans to add further integrations as the technology develops and new users start including Fig in their tool belt.

Other features of this add-on include keyboard-first functionality, allowing developers to quickly use the auto completes without having to touch their mouse, open-source completion specs, which ensure that the Fig add-on always remains up to date and relevant for users, and the add-on runs locally, offering speed and security.

I’m already using Fig, and am a huge supporter of any tools that work to remove complexities from my development process. I am excited to see this project grow!

This Dot Labs is a modern web consultancy focused on helping companies realize their digital transformation efforts. For expert architectural guidance, training, or consulting in React, Angular, Vue, Web Components, GraphQL, Node, Bazel, or Polymer, visit thisdotlabs.com.

This Dot Media is focused on creating an inclusive and educational web for all. We keep you up to date with advancements in the modern web through events, podcasts, and free content. To learn more, visit thisdot.co.

Double Click: Discover or Be Discovered with Coding Exercises from CodeSignal

Tracy Lee | ladyleet — Thu, 23 Sep 2021 16:20:04 +0000

Welcome to the Double Click! This is the weekly blog series that shines a spotlight on emerging technologies, technological concepts, and community projects that enrich the JavaScript Ecosystem!

This week, I wanted to feature a fun web platform that I have just become aware of that offers coding skills tests and assessments, and also allows developers to make their results available to potential employers: CodeSignal.

Of course, the latter feature is optional, so developers don’t need to worry about endless recruiting e-mails just because they want to test out their skills.

But I think this offers a unique opportunity for junior developers to experience what a code exercise can feel like- and perhaps what questions they might expect to see on a future skills assessment.

While poking through the site, I also learned that the platform offers several unique features for partnered companies aside from connecting with qualified candidates. The platform can also facilitate live, remote skills interviews with questions, features a plagiarism checker, and registers keystrokes to understand the extent to which a developer individually coded their solutions.

For companies with a large hiring program, this can be a fantastic way not only to identify potential candidates, but to increase visibility for their brand among developers working to improve their skills, and manage expectations for candidates across different teams within the same company.

Developers interested in improving their chops, or getting discovered by their next employer should sign up for a free interview practice account, and dev team leaders interested in using Codesignal as part of their hiring program can request a demo here.

Double Click: Vue 3.2 packs stable features & performance improvements!

Tracy Lee | ladyleet — Thu, 23 Sep 2021 16:16:44 +0000

Welcome to the Double Click! This is the weekly blog series that shines a spotlight on emerging technologies, technological concepts, and community projects that enrich the JavaScript Ecosystem!

This week, we turn our attention to the wonderful Vue Core Team, who very recently released Vue 3.2.

According to this statement by VueJS creator, Evan You, this release does include significant performance improvements without subjecting devs to any breaking changes.

New features include stable releases for < script setup > and ‘< style > v-bind ’, which both have use when working with Single File Components. According to the official announcement:

“‘< script setup >’ is a compile-time syntactic sugar that greatly improves the ergonomics when using Composition API inside SFCs.”

This feature will feel very familiar for Svelte users as it allows developers to export individual variables or functions, within their script setup, to their templates, instead of entire objects.

'< style > v-bind' enables component state-driven dynamic CSS values in SFC < style > tags.

This ‘< style > v-bind’ feature automatically passes any value defined in the code directly to the CSS as a binding.

The latest release of Vue 3 also allows expanded options for creating web components using the Vue CLI.

To permit this, the team created a new ‘defineCustomElement’ method which gives developers the ability to create and consume custom web components that can be used across the JavaScript ecosystem, using Vue.

Of course, even if developers are not interested in using one of the handful of new features offered with this 3.2 release, there are still a number of reasons to be excited. This release includes a number of performance enhancements, including (from official announcement):

More efficient ref implementation (~260% faster read / ~50% faster write)
~40% faster dependency tracking
~17% less memory usage

So what are you waiting for? Be sure to update to the latest version of Vue 3, and try out these new features yourself!

This Dot Labs is a development consultancy focused on providing staff augmentation, architectural guidance, and consulting to companies.

We help implement and teach modern web best practices with technologies such as React, Angular, Vue, Web Components, GraphQL, Node, and more.

What Are Smart Contracts? A Brief Overview

Tracy Lee | ladyleet — Thu, 23 Sep 2021 16:04:35 +0000

What are Smart Contracts?

Simply put, smart contracts are distributed blockchain applications that store logic and execute actions when the conditions of a particular agreement are met.

Ex: Imagine you owned a marketplace platform that allows individuals to list items for other users to purchase, but you are tired of having to manually address fraud claims when users purchase items that are not sent. You could code a smart contract that records payment from a buyer, and distributes payment to a seller once a receipt of successful delivery is reflected on the blockchain. In cases where sellers do not meet their obligation to ship within a stated time period, payment can automatically be returned to a user without the time, hassle, or costs of authorizing fraud claims yourself.

Smart contracts, as we understand the term today, originated on the Ethereum network. Ethereum (not to be confused with its cryptocurrency, Ether) is a worldwide blockchain network that allows developers to deploy decentralized applications.

When we say that these applications are “decentralized”, that means that the application, as well as all of the information it receives and outputs, are represented and recorded across a massive network of “nodes” or computers that simultaneously record information. The information is unchangeable, fully transparent, and accessible by all parties involved in the transaction, be it financial or otherwise. This promotes security, automation, and accurate record keeping for both developers and users.

Smart contracts built on the Ethereum network use a language called Solidity. Fortunately, Solidity’s syntax is modeled after JavaScript’s, allowing developers with JS experience to quickly spin-up and deploy these applications with a relatively small learning curve. Additionally, Ethereum serves as the foundational network on which many other blockchain projects, including Chainlink, Aave, Axie, OpenSea, and UniSwap- to name just a few- are built.

Though we have only just begun to exercise the capabilities of smart contracts, many are currently being used to automate interest payments for lenders, simplify and secure the crowdfunding process, and otherwise circumvent third party service providers that either process payments, or verify contracts on behalf of two or more parties.

However, smart contracts have the potential to replace a number of financial and administrative processes that organizations currently outsource, manually process, or process using centralized applications and web architectures that often rely on third-party APIs anyway, and often lack comparable security, speed, and fraud protection.

Deploying Your First Smart Contracts

Deploying your first smart contracts is a critical step towards preparing your development infrastructure to compete in the modern digital marketplace. Teams should take advantage of this period of technological development to create simple smart contracts that demonstrate the advantages of leveraging these revolutionary applications within their organizations.

For example, you could start by creating an employee survey application which anonymously records responses, and waits for a particular volume of responses before distributing them to the department which oversees these surveys. Or, you can create a charitable fundraiser which automatically matches employee contributions up to a certain threshold without ever needing to oversee these payments, or involve a third party financial institution.

We’re Excited to Hear What You Have In Store!

Do you have an idea for a game-changing smart contract, but need some extra help developing and deploying it? Reach out to This Dot Labs for a consultation, and we will be thrilled to help you see just how easily you can begin executing business logic on the blockchain!

Supporting Emergency Remote Operations with the PAM Stack

Tracy Lee | ladyleet — Mon, 16 Mar 2020 02:33:36 +0000

On Friday, Donald Trump declared a State of Emergency in response to growing concerns about COVID-19. This declaration follows formal emergency classifications by nearly 30 states, including California, Washington, New York, North Carolina, and Arkansas, which many of the nation’s largest tech, pharmaceutical, and manufacturing/supply chain enterprises call home.

In order to maintain operations while protecting employees, and preventing the continued spread of COVID-19, many large companies, including Google, Amazon, LinkedIn, Spotify, and Airbnb, are asking employees to work remotely for the time being.

This has posed a number of problems for companies that are not equipped to maintain remote operations on a large scale for extended periods of time. Smaller companies that may not have the infrastructure needed to support fully remote operations, and lack the budget needed to make these rapid changes, will be most impacted by these challenges. And this anxiety may be amplified by the possibility that employees in enterprises both small and large may continue working this way indefinitely.

As someone who has managed remote teams for the past few years, I understand many of the challenges that remote workers face, as well as the policies and procedures needed to ensure that remote work remains manageable for employees, and efficient for businesses.

I believe the misconception that any work done exclusively on the computer can transition seamlessly from on to offsite belies the reality that remote structures radically change the culture and management style of businesses.

It is my hope that companies will not only continue to encourage their employees to remain at home for their health and safety, but will also invest in improving their emergency operational procedures. In doing so, they will be able to maintain efficiency, and implement an infrastructure that supports reactionary, company-wide remote work in a way that suits both employee and employer.

SUPPORTING LONG-TERM REMOTE OPERATIONS WITH THE PAMSTACK

If you are familiar with the change management work that I and Rob Ocel, an architect at This Dot have been doing over the past year, you may have already heard of the PAM Stack.

For those who haven’t heard of it, we generally define it as a modern architecture for building sustainable, inclusive development teams. Now, this system wasn’t exclusively built for remote teams, but applying the three guiding principles of the PAM Stack could significantly help leaders building management systems for employees who have recently transitioned to working off-site.

Its three guiding principles are: Process, Abstraction, and Mentorship.

PROCESS

When transitioning to temporary remote work, especially in response to national and international emergencies, company goals are bound to change. And these changes will trickle into every department.

If a company expects its employees to continue working under a drastically different operational culture, it is important that leadership establishes clear expectations and goals for their direct reports, and promotes transparency about those expectations to an appropriate degree. This can be supported by building out physical documentation that outlines departmental and even employee specific responsibilities.

Of course, these procedures will have to be tailored to accommodate each unique circumstance, but should be guided by documentation written ahead of emergencies, without the pressure imposed by reactionary operational changes.

Procedural documentation should also identify common points of error specific to the type of work being carried out, with consideration for what unique error points might arise due to the abrupt change from onsite to remote-only work. The inclusion of regular reviews, checklists, and metric keeping are all essential to maintaining better communication between employees within the same department, as well as positive interdepartmental communication.

ABSTRACTION

When managing a remote team, it is crucial to keep your tech stack as simple as possible. Development teams should only work with as diverse a tech stack as absolutely necessary in order to build and maintain their products, services, and proprietary internal tools.

Companies should also be careful to prevent the use of redundant operational assets, and ensure that all employees are sharing as many technologies as possible. This means sales and marketing teams across a company should use the same suite of products; documents, passwords, and databases should all be accessible through the same interfaces, respectively, and communication should be conducted over as limited a number of channels as possible.

Additionally, by using modern frameworks such as Vue, Angular, or React, teams equip their developers with powerful force-multipliers that abstract away irrelevant complexities, and will reduce the need for peer-to-peer reference due to the support of abundant educational resources, and documentation.

MENTORSHIP

According to a 2018 Spherion study, 35% of developers who do not receive mentorship look for new jobs within twelve months. Oftentimes, this is due to a feeling of isolation and immobility that may be exacerbated by an abrupt transition to remote-only work.

This is a significant problem for leadership, since the cost of recruiting and training a new hire cost as much as double that developer’s annual salary. Unmanageable attrition will slow your team down, increase stress, and trigger panic hiring. All of these problems are tough enough for companies operating within their preferred work cultures, but may be even more difficult if encountered while a company is operating under an emergency procedure.

Despite the additional communicative challenges presented by remote work, companies should maintain strong mentorship programs in order to maintain employee engagement and reduce the feeling of isolation common among even remote workers who elect to work remotely, and may be worse for employees compelled into full time offsite work.

Of course this mentorship can be unstructured and organic- taking the form of natural conversations and friendships between developers on the same team. But teams should also incorporate an intentional program that includes pair programming, code reviews, and lunch & learns, all of which are still maintainable within remote work cultures.

PLANNING FOR AN UNCERTAIN FUTURE

It is my sincere hope that the threat presented by COVID-19 is managed as soon as possible, and I applaud any company that shoulders responsibility for combatting its spread through radically changing their operational structures.

However, I believe that companies not only need to prepare for the possibility that workers may be unable to return to their offices any time soon, but need to implement procedures that encourage a more seamless transition from onsite to offsite work in instances of natural disasters, pandemics, or other states of emergency in the future.

By adopting PAM Stack principles, companies are better able to maintain the health and wellbeing of not only their businesses, but more importantly, the human lives that depend on large, multinational companies being able to radically transform their operations without hesitation.

If there is any way that I or Rob could help you in this transition, please do not hesitate to reach out at hi@thisdot.co.

10 Mobile AR Apps That Are Creating the “New Normal” for Your Customers

Tracy Lee | ladyleet — Tue, 07 Jan 2020 18:46:54 +0000

As developers and businesses leaders, we know that augmented reality is no longer a far-flung futuristic concept. In fact, a 2019 Perkins Coie survey revealed that roughly 90% of tech leaders and consultants believe that the use of immersive technologies will be as ubiquitous as mobile devices by 2025. But more intriguing than this, is the diversity in opinions from respondents when asked which sectors they believed would most invest in AR/VR technologies. Not surprisingly, 54% include gaming in their top picks. But that narrow majority is rivaled by 43% of respondents who include healthcare, and at least 20% who felt strongly about a number of other industries, including education, military and defense, and manufacturing/automotive.

Though the use of AR technologies is expected to explode across the consumer user-base, only 22% of consumers, in a 2019 ARtillery Intelligence survey, were aware of having used mobile AR technologies themselves. Now it may just be one dev’s opinion, but I believe that this number does not accurately reflect the true percentage of those surveyed who have actually used AR- just the percentage that KNOW they’ve used AR.

But is this number important?

I think so.

I imagine that the unknown percentage that claimed not to have used mobile AR apps, but who have unknowingly used them, likely just stumbled upon them, not drawn to the fact that the applications utilize this transformative technology, and being completely oblivious that they used AR. This unknown should terrify, or motivate executives, because this may mean that AR is so quickly and seamlessly integrating with other mobile technologies, that the change is completely inarticulable at best, and unnoticed at worst, by the average consumer.

In other words, AR may be quickly becoming the new normal, with unintegrated technologies possibly being seen by consumers not as a different class of technologies, but, perhaps, as an “inferior” technology.

So let’s check out 10 mobile apps that are already preparing your future customers and users for the “new normal”:

1) Wecasper

Launched in 2015, Wecasper is a mobile social media application that allows its users to store multimedia content in geolocated containers, almost like time capsules, aptly called “caps”. But not only does this app allow users to interact with certain content while in particular locations, it also lets users search for, and see the caps in their environments through their mobile screens!

2) WallaMe

Not so unlike Wecasper, WallaMe have given users the ability to hide messages in the real world, and share those messages with their friends, and the general public. Users can draw pictures, or paint messages on physical walls around them- almost like virtual graffiti- and then alert their friends about the message’s location! Friends are then able to see the messages they leave for each other, and even share those messages with other users!

3) Pottery Barn’s 3D Room View App

Ever been on the verge of buying that stylish chair, or perfect coffee table, but just don’t know how it would look in your living room? Pottery Barn is one of a number of furniture retailers, including my favorite, Ikea, that have launched AR powered retail platforms. With their 3D Room View App, users are now able to place spatially accurate renderings of Pottery Barn’s catalog of furniture to see how the pieces fit their rooms. Customers never need to doubt their online purchases, and Pottery Barn will undoubtedly see an increase in engagement with their ecommerce platforms.

4) Sherwin William’s ColorSnap Visualizer

It seems Pottery Barn isn’t the only home decor and improvement company that has caught the AR bug. Industrial paint company, Sherwin Williams is giving their customers the ability to try out colors without having to pick up swatches, or dab unsightly (and let’s be honest, unhelpful) brush strokes on to their walls. Instead, customers can actually project colors onto their very own walls through a mobile interface, giving them the ability to try out hundreds of different colors in just minutes.

5) Star Walk 2

Developed by Vito Technologies, Star Walk 2 is a fun educational application that guides users through the night’s sky. All a user has to do is point their phone toward the sky, and the app’s AR feature will not only tell the user what stars, constellations, and other astronomical features are above, but will show them as well! No more guessing at what constellations are what, or if that little light above is a star or an airplane- users can trust Sky Walk 2’s AR feature to tell them everything they want to know, and probably a little more! Sappy high school dates will never be the same!

6) MeasureKit

Now I know that I’m not the only one who is CONSTANTLY misplacing the one measuring tape we keep around the house. That’s where MeasureKit swoops in for the rescue. MeasureKit uses advanced AR technologies to allow its users to quickly and accurately measure lengths, distances, trajectories, and angles with nothing more than the camera on their mobile devices. I would download this is a second if it didn’t make me so afraid of misplacing my Smart Phone too!

7) The Safety Compass

Now let’s take a quick diversion from consumer apps, and look at a mobile app being used to help keep industrial work sites safer for employees. According to the app’s publisher, an employee dies on a worksite every 15 seconds, globally. The Safety Compass is hoping to decrease that number significantly with its mobile app that gives workers the ability to scan their worksites for potential hazards, and shows them relevant information on standard safety protocols with which to approach the hazards. This takes the guess-work out of not only whether something is hazardous, but what that hazard is called, equipping workers with relevant information that will help keep them safe.

8) Janus Health AR

Janus Health is changing the future of dentistry and orthodontics with its mobile compatible AR application. The app scans a patient’s teeth, and shows them, in real-time, what their teeth could look like after a procedure. This not only boosts patient confidence, but may also expediate the consultation process for providers, and increase patient engagement with the practice. I wonder if the app can make your teeth look worse too? Maybe if my orthodontist had this app when I was a teenager, I would have actually worn my retainer!

9) Google Lens

Google Lens, for Android (sorry iOS users), brings the power of advanced image recognition software to the palm of its user’s hand. Have you ever taken a walk through the park, and wanted to identify what kind of tree or flower is in front of you? Now all you need to do is point your mobile camera toward it, and Google Lens will identify it for you! But that’s not all it does! Google advertises that users can also translate text from signs or packages, scan restaurant menus to find pictures and reviews of the items, and even find out more information about landmarks just by pointing their mobile cameras!

10) Quiver

Quiver is an AR enhanced edutainment mobile app that helps bring kids’ drawings to life! Users simply need to print off the pages offered through the company’s web platform or app, color in the photos, and use a mobile device to see lifelike 3-D renderings of the pictures right in front of them! Quiver has also expanded, releasing new product lines, including a version of their app that provides all of the fun of coloring, and AR, with a greater focus on education!

SO WHAT'S THE TAKE AWAY?

According to Perkins Coie, the North American market is expected to see the most significant growth in AR investment over the next 5 years. While the language of AR is not necessarily on the forefronts of consumers’ minds, the pressure to provide these more natural connections between the digital world and consumers will increase as more users come to expect AR integrated application functionalities.

Now is a perfect time to invest in AR technology! Whether you are looking to engage more with your customers, improve the lives of your employees, or simply create a product that will enrich users' lives through play and learning, there is space for AR integrated mobile applications within your company. By starting your transformation now, you are affording your company the opportunity to identify the areas of your business that can best be enhanced by this and other transformative technologies, and make mindful, well considered steps to developing these technologies for yourself.

Do not wait until the pressure from your competitors eventually forces you to hurry a product to market. Begin the process now, and you will thank yourself later.

Don’t know how to start?

Contact This Dot Labs to speak with leaders in enterprise-level digital transformation. This Dot Labs is a web development firm that helps some of the world’s leading enterprises, including American Express, ING, Groupon, and many others, reach their development goals through consulting, mentorship, and training. With our diverse team of seasoned senior developers and mentors, we can help you harness the power of advanced modern tools to keep you ahead of the AR curve!

The Future (of AI) is Female: How Hiring Bias Mitigation in NLP Can Be Great for Women Now and in the Future

Tracy Lee | ladyleet — Wed, 13 Nov 2019 15:48:40 +0000

INTRODUCTION

AI technology is often billed as an answer to the physical, and mental shortcomings of the human brain, and productive capacity. We think of their processes as being wholly objective-- separate from human bias, and prejudice, without considering that machines are only able to learn from the data that we provide them. As AI powered technologies continue to permeate every industry, organization, and social structure, we are seeing the negative influence which our history of deeply encoded gender bias has had on contemporary digital innovation.

These problems, at times, seem so insurmountable, that some are even led to question whether Artificial Intelligence will actually damage our progress toward a more equitable society. The intent of this article is to show why Artificial Intelligence may suffer from the prejudice ingrained in human language, especially when it comes to evaluative software like applicant scoring programs, but also why realizing this issue and taking action could be great for women in tech.

Human language, especially written language, has always reflected the biases of historically platformed groups. In the English language, literary history has assigned feminine connotations to many negative terms like “chattering” and “bitchy”, while similar behaviors in men might be described as “gregarious” and “assertive”, respectively. In turn, our machines are teaching themselves this very same bias through Natural Language Processing. As we teach ourselves to deprogram socially ingrained prejudices within ourselves, will we be as diligent for our machines?

Of course, this is a pressing issue, but I believe AI can be one of the greatest tools for combating social inequality moving forward. However, the only way to do this is to begin mitigating the bias inherent to NLP through reevaluating the way that algorithms interact with language that might reflect unconscious or intentional bias in human speech. And the best way to do this is to begin balancing the ratio of men and women who are contributing to the development of these technologies.

Currently, only 1 in every 4 computing positions is held by a woman. And when we look even closer, we see that, among the women who are working as developers, there is a significant disparity in their seniority when compared to their male colleagues. Some might point to this as a reflection of disinterest in STEM among women, but the numbers show us that this is not true. In both 2017 and 2018, women made up roughly 40% of all coding bootcamp graduates. However, we know that a significant proportion of these graduates aren’t always able to bridge the gap from formal education to their first junior development role, and even if women are successful in launching development careers, their male colleagues, at this time, are 3.5 times more likely to be in senior level positions by the age of 35.

AI has the capacity to be one of the most integral tools in eliminating human bias. It is our responsibility to ensure that learning algorithms are not teaching themselves the same sorts of problematic ways of thinking that make objective, evaluative software so valuable to human progress. This however, is not simply a problem of addressing technical shortcomings, but an opportunity to empower femme developers who will bring not only their technical talent, but their experiences as women, to be the arbiters of how NLP is succeptable to negative gender bias. It is time for project managers, and C-level executives to take a step back and evaluate whether or not their teams are demographically balanced, and if their team’s structure is built for uplifting junior developers, where an overwhelming proportion of women find themselves perpetually stuck.

GENDER BIAS IN LANGUAGE

Human language is perhaps the most critical way that gender bias is perpetuated and reinforced within culture. Antiquated Western stereotypes about the roles of men and women inform the unconscious associations we make between words and gender. Words that reflect communal or collaborative values have become associated with women, while words reflecting industrious traits are often assigned to men.

This wouldn’t necessarily be a problem if it weren’t for the fact that deeply ingrained social imbalances between men and women, reflected in the way that different types of work, and the way we describe work, have been sexed, and subsequently valued against other types of work or traits.

This has created semi-conscious value differentiations between words that describe behaviors that society associates with femininity, and those that describe masculinity. It is a textbook example of the Whorfian Hypothesis, which states that language is a reflection of our social values.

LANGUAGE AS DATA

When thinking about how machines learn, I am reminded of an Introduction to Philosophy class I took while I was in school. I don’t know if this is a common thought exercise for college underclassmen, but one of the essay prompts asked us to make an argument for whether or not it is ethical to “kill” a computer.

Of course, I’m sure the professor would have accepted any compelling argument, but she seemed to be partial to the idea that a computer is not so unlike a human mind. Computers, like humans, receive input, reference the functions and processes that make meaning out of that input, and produce output- whether those processes are encoded by a scientist, or by our lived experiences, is maybe not as important as we might believe it to be. The difference between these encoded processes is narrowed even further by machine learning, such that AI technologies could theoretically learn in such a way that is so similar to how a human does, that its being, so to speak, could be indistinguishable from that of a human.

Artificial technology is an often misunderstood science. We aren’t creating vacuumous robots that are “born” with some apriori ability to objectively analyze data points. Not unlike a human brain, a machine must also learn through observing the data available to it. So the problem of AI technologies internalizing the same prejudices that permeate society is a completely realistic, and observable, phenomenon.

When we discuss gender bias in AI, we are often referring to a problem that arises within a class of artificial intelligence known as Natural Language Processing (NLP). NLP is a subsection of AI technology that deals with the extraction and analysis of data from unstructured human language. Some might argue that computers can’t “understand” what words mean with quite the same subjectivity and nuance as a human being, but the reality is that what computers are able to do with language is not far off from what we do. They can extract values from words through a slew of different identifiers and context clues, including, but not limited to grammatical, syntactical, and lexical features, as well as the complex contexts and connotations implied by the relationship between words as they appear in human writing or speech. They then use these words to form context analyses about the data in question, and like us, come to conclusions about that input.

HOW LANGUAGE DATA HURTS WOMEN

Since 2014, Amazon.com had been building an artificial intelligence powered recruitment software to help them quickly find the best talent. The system worked by reviewing resumes for specific keywords informed by over ten years of the company’s hiring data, and ranking those resumes based on their similarity to past hires.

It did not take long for Amazon to realize that this algorithm penalized resumes submitted by women. In fact, those who worked on the project reported that resumes of applicants from women’s colleges, or those whose resumes even contained the word “women” were given less preference by the software. This, of course, is because the overwhelming majority of Amazon’s technical workforce is male, with 2017 stats showing that women make up only 40% of its total workforce. Amazon attempted to mitigate this problem by neutralizing terms that denote demographic information, but discluding select words cannot address the issue of gender encoding within all forms of language. Recognizing the inability to account for all the manners in which such technology could possibly discriminate against certain groups by assigning different levels of value to words with possibly prejudicial cultural imprints, the company eventually discarded the software in early 2017.

THE FUTURE (OF AI) IS FEMALE

There is no easy way to address the problem of gender bias in NLP based Artificial Intelligence. If there were, I think it’s pretty safe to say that some of the world’s largest tech based companies, if only for the sake of public perception (though I would like to have more faith in humanity), would have already implemented these fixes. The problem is much deeper. We need to look at our entire labor culture and ask ourselves, why is it that over half of university graduates are women and yet only 5% of the S&P 500 CEOs are women? Women are pursuing STEM education and are finding themselves pursuing increasingly more diverse professional areas. Yet, by looking at just the tech industry- the very industry that could create software that would mitigate hiring bias with AI- women are not being placed in roles at a rate that reflects the pipeline created by bootcamp education, and when they are, they are not rising in those roles like their male colleagues.

Achieving a solution will not come overnight, but it is not an impossible feat either. Currently, we know that NLP technology needs to be able to draw data from unstructured language without giving value to biases that, through the course of human history, have been deeply encoded onto the language of which the data, referenced by such technologies, is comprised.

The technical solution to this has yet to be discovered. However, it is crucial that, in the pursuit of developing these technologies, women play a commanding role. In many ways, driving the direction of our digital tools is as much a social science as it is engineering. We need to internalize the value that lived experience brings to conversations about mitigating bias through technology. To approach redesigning applicant scoring and recruitment software without creating more inclusive workplace environments for women in tech is placing the cart in front of the horse. Too many C-level leaders focus on achieving diversity metrics rather than fostering inclusive environments. If we start by supporting the success of women in tech, who are historically disadvantaged in this industry, we can begin to create the environments where technological solutions can be born of deeper appreciation for the ways that our past traditions inform the language that we use, the ideas that we propagate, and the workplaces we build.

SO WHERE DO WE START

These bias mitigation technologies will be amazing tools for our children, grandchildren, and all of the talented women who will be entering the workforce over the next decades and beyond. But as of now, NLP software is not developed enough to significantly prevent the same highly insidious biases to which non-augmented hiring processes tend to fall victim.

The truth is that these technologies are not the answer to creating a more equitable tech space. They can be a great tool, but we need to work harder to help women overcome the obstacles that prevent them from accessing necessary educational and work opportunities, and make this industry into a space to which women want to contribute, in which they want to be, and where they feel that not only their talent, but their unique experiences are valued.

It will require companies to contribute more of their time, energy, and resources into making their businesses places where women feel they can receive positive, constructive mentorship, and meaningful routes for advancement. Despite being a web development consultancy, This Dot Labs is doing all that it can to begin giving back to the women who make this industry so great by creating avenues through which companies can invest in uplifting developers from historically underrepresented demographic. As of Summer of 2019, we have launched our Open Source Apprenticeship Program, partnering with several wonderful companies, who recognize the value of connecting talented women with paid opportunities to contribute to their open-source projects.

This solution, however, will require the work of our entire industry to internalize the belief that when our industry is more equitable, our technologies will become more equitable. We owe it to ourselves, to the future of this industry, and to all the millions of people who are using and will use NLP and other AI technologies to enrich their lives.

This Dot Inc. is a consulting company which contains two branches : the media stream and labs stream. This Dot Media is the portion responsible for keeping developers up to date with advancements in the web platform. In order to inform authors of new releases or changes made to frameworks/libraries, events are hosted, and videos, articles, & podcasts are published. Meanwhile, This Dot Labs provides teams with web platform expertise using methods such as mentoring and training.

Alternative Interfaces Are Nothing New, But the Time to Adapt is Now

Tracy Lee | ladyleet — Mon, 04 Nov 2019 20:47:16 +0000

Last year marked the 50th anniversary of Stanley Kubrick’s space-race era epic, “2001: A Space Odyssey”, prompting several outlets to do side-by-side comparisons on how the film’s depiction of digital technologies matched up against what we really had around the turn of the century.

Of course, no one can do this without mentioning the film’s central antagonist, HAL 9000, a sentient computer who interacts with the doomed characters through a verbal interface, reminiscent of the way that contemporary users deploy Amazon’s Alexa to order pizza.

So, while some might point to this similarity as proof of Kubrick’s intuition, the reality is that we have been pushing towards zero U/I, or interfaces that do not rely on screens, for decades.

The concept of zero U/I was first defined by then Fjord Design Director, now frog Creative Director, Andy Goodman, during a 2015 speech at San Francisco’s Solid Conference. He describes it as a natural approach to user-interface interactions, which abandons the abruptness of the screen in favor of a more natural environment, through which users can communicate with devices using speech, motion, and even thought.

Sound familiar?

One of the earliest arguable examples of this concept, however elementary when compared to the technologies of today, predated even Kubrick’s film. In 1952, Bell Laboratories invented “Audrey” a rudimentary speech recognition software that was able to “understand” single digit numbers. Considering the fact that touch screen technology was not patented, let alone produced successfully, until over two decades later, zero U/I technologies are less the fantastical subject of speculative sci-fi films, than they are an example of alternative interfaces that have been developing right beside more traditional screen and/or touch interfaces for years.

Of course, we know why screen interfaces became the gold standard for consumer technology. They limit the way that users can possibly interact with applications. Imagine having to account for any possible variation in user input during the age of punch card programming! Being able to tailor your code to a restrictable set of user inputs makes our lives as developers easier, but doesn’t provide the absolute best user experience that we now know is possible.

Artificial intelligence will prove to be the most impactful technology in the push away from screens. Without the ability to control for linear interactions, developers will need to rely heavily on machine learning to allow programs to interpret zero U/I input. One will logically progress alongside the other, and the technological landscape is preparing itself.

Don’t just take my word for it, according to last year's AI Index, the number of AI-centric start-ups had increased 110% from 2015 against only 30% for all other start-ups. While it’s still too early to draw any absolute conclusions about the long-term growth rate of artificial intelligence, it is probable that both investments in, and demands for these technologies will continue to grow exponentially.

With a larger move toward data and artificial intelligence integration into common technologies, users are going to expect to be able to use more, and more diverse zero U/I technologies in their daily lives. This isn’t only an amazing challenge for us to accept as developers and business owners, but also the standard we have an obligation to meet for differently-abled users who are challenged by screen interfaces, or who are unable to interact with applications in the linear fashion that programs are typically coded to expect.

Interface diversification is not a new technology, or a new demand. Technological innovators have been pushing towards it while artists and storytellers have been capitalizing on our desire for these technologies for years. Users have never been slow to adapt to more obtuse versions of these technologies, and demand will only continue to grow for more advanced forms.

Zero U/I, as well as other AI reliant consumer technologies, are the absolute most performant interfaces we can imagine at this moment in history. The sooner companies adapt, the more time they will give themselves to affordably, and strategically integrate data and AI reliant interfaces into the software they create.

It isn’t a question of if, but of when, and how quickly?

Will your business or career lead this digital revolution, or find itself playing catch up?

It's a Very Scary This Dot Halloween: LEGACY CODE

Tracy Lee | ladyleet — Wed, 23 Oct 2019 23:01:58 +0000

WHAT IS LEGACY CODE?

This is a very fitting question for the Halloween season, because if you ask anyone on your business’ web development team, their immediate response might be to run out of the room, screaming, as if being chased by a masked killer. Like Freddy Krueger from A Nightmare on Elm Street, it’s a remnant of a company’s past that comes back to haunt the dreams of present day developers. It’s the Necronomicon Ex-Mortis from Evil Dead- the ancient text, written by nameless, faceless beings, that releases terror when opened.

9...10...never sleep again...

Alright, that might be an over-exaggeration. But trust me, the topic of legacy code has long been memeified in the web development community because of how challenging it can be to work with. That being said, it really is less Leatherface from The Texas Chainsaw Massacre than it is Sloth from The Goonies. Sure, it’s a little scary at first glance, but ultimately legacy code is highly misunderstood, and can be an amazing asset to your team.

Legacy Code has a number of definitions, but it’s generally safest to describe it as code that was primarily authored by developers who are no longer working within the company or project, and which often utilizes superseded technologies, custom built elements, and parts that are either totally or partially inactive. Sometimes it’s difficult to convey how a codebase grows and can deteriorate over time, and how that can create future problems for developers, but for the sake of this spooky themed blog post, let’s use the analogy of the Frankenstein Monster.

IT’S ALIVE

If you’re familiar with the old James Whale Frankenstein movie from the 30’s, you would remember how excited our friend, Henry, was when he finally got his monster to open its eyes!

TFW you deploy on a Friday afternoon, and everything works.

Now let’s imagine that, after a long day of his monster running amok, Dr. Frankenstein notices that his creation is limping a bit. Upon closer inspection, he notices some wear on the monster’s ACL. Knowing that he needs to get him up and running as soon as possible, Henry chooses to patch up the ligament with a fast fix rather than the best conceived repair. He could take some time in his lab to test out a better anatomy, or really open the monster up to ensure that perhaps another problem isn’t contributing to the monster’s limp, but he doesn’t have the time to do that. There are villagers to terrify!

A few years of monster maintenance go by, and a lot of these workable, but maybe not optimal fixes have piled up, or have been discarded, or have been replaced. When Henry finally passes the project on to Fritz, his assistant, Fritz will have a tough time making sense of all of the monster’s features, and how to best begin working with it when he has no basis for making sense of how all of the repairs, optimizations, additions, etc have impacted the Monster’s function.

Similarly, it’s very tough for developers to make sense of a highly intricate codebase that they have never worked on. It’s not always easy for non-technical professionals to understand how code can decay, just like a house, car, or a creature made of the piecemeal limbs of excavated bodies. But challenges such as scale, feature demands, and technical limitations often force developers to create highly convoluted codebases that work for them as their architects, but are hard to make sense of from the more objective perspective of a fresh set of eyes.

For the next part of this analogy, we’ll fast forward to what I think we can all agree was an especially… interesting… time for cinema: the mid 2000’s.

IF ANYONE FINDS THIS, IT MEANS MY CODE DIDN’T WORK AND I’M ALREADY DEAD

I apologize in advance if you haven’t seen this movie, but fortunately, what I then thought was an earth-shattering, deep look into the far reaching consequences of our choices, was really just an opportunity to capitalize on That 70’s Show, and give the people of 2004 what they really wanted: shirtless Ashton Kutcher.

Yeah, it’s not that complicated, you’ll be fine.

MFW I showed up to the “spaghetti code” party, and there was no garlic bread.

In the “psychological thriller” The Butterfly Effect, Kutcher’s character finds that even the smallest decisions can have devastating impacts on himself, and his friends.

This is honestly a great analogy for what is often known in coding as “The Jenga Effect”, which is a particularly pressing anxiety for people working with legacy code. In many legacy codebases, different elements of the functionality might depend on code referenced in other parts of the base- sometimes these connections can be so confused and convoluted, that developers refer to it as “spaghetti code”. For these new developers, there is a constant fear that making even a tiny change to the functionality of one element of the codebase might lead to the complete obsoletion of countless other features, with no clear recourse to fix the problem. And unfortunately, closing your eyes and willing 2000’s era Ashton Kutcher to save you from making those keystrokes probably won’t work for you (trust me, I’ve already tried).

YOU WILL DEPLOY IN SEVEN DAYS

Legacy code is like Samara, the little girl who kills you if you watch that creepy VHS tape in The Ring- the better you understand it, the less likely it is to hurt you.

When my PM tells me we have a week to deploy.

In fact, legacy code, when approached with the correct mindset, can be an awesome tool to improve your company’s codebase, set precedents for your development team’s culture, and better understand the challenges that past developers encountered when working with your technologies. Whether you choose to work within the old codebase, or use it as a roadmap for creating a full migrated system, legacy code is a gift. It is an asset that reflects the talent, knowledge, and beliefs of teammates who contributed to the past success of your company. It has proven its worth. It's been in production, has been used by real users, often times for up to years, and has been patched and fixed after countless bug reports and user research sessions.

As digital technology advances, the worst thing that we can do as tech professionals, is to mock, chastise, or discredit the work of past teammates, or superceded technologies. The tech labor pipeline has exploded with new developers who may have never seen the technologies that they started with fall out of relevance, but with the speed at which digital technologies are transforming, and new areas of web development are being explored, it’s pretty safe to say that everyone’s code will someday look foreign and overly convoluted to the next generation of fresh developers.

What teams should do is take time to examine the code- audit and document its features, run tests, figure out what elements are actively contributing to the functionality, and why. Developers should consider why remnants of past functionality might still exist- is there a possibility that the developers were exploring features that reflected past company goals/values? Are there systems in place to mitigate unique challenges to the successful function of the codebase? Maybe there are deeply embedded processes, unique to the real world functionality of certain applications, that can be identified and simplified through teamwork.

I SEE DEAD FEATURES

I’m ready to tell you my secret now. I Google EVERYTHING.

One of the most difficult, but most rewarding challenges or approaching legacy code, is sustaining dialogue between the development team, and non-tech management, to determine what features are important to the future function of the codebase, and what aren’t essential. Whether you are updating the legacy code without migrating, or you are completely rewriting the codebase, legacy code can be a foundation on which a conversation about what features management wants to keep, and what features they are interested in parting with, can be built. Having this dialogue will not only improve communication between the C-suite and the development team, but will also instill a sense of direction and mutual understanding about how your company’s technical progress relates to its goals, and values.

This is obviously easier said than done. Approaching legacy code with not only technical expertise, but purpose-driven strategy requires significant experience working with numerous technologies, as well as a deep understanding of proper code documentation, and feature migration. This complete skill package may not exist on every team, and if it does, organizational structures and hierarchies might make progress especially arduous. Many companies might find that bringing in outside resources to guide their teams with not only expert level technical training and augmentation, but also team building mentorship, and a heightened capacity to unite c-level management with their web development teams, might help them better reach their technical goals, and establish better strategies for their future updates.

Annual codebase maintenance can cost up to 10% of the initial cost of development. For out of date codebases, these costs can create long term budgetary problems for your organization, which will only grow as the utilized technologies grow more and more antique. Though you have a number of options when approaching legacy code, This Dot Labs encourages many companies to consider either migrating most of the major functionality components, or rebuilding the entire codebase in order to reduce long term maintenance costs, afford more opportunities to employ cutting edge technologies, and reduce the onboarding speed of new developers.

If there is ever anything that This Dot Labs can do to help you translate your legacy code into a tool for strengthening your teams, leveraging the resources of past expertise, and creating a roadmap by which we can strengthen or rebuild your code, please don’t hesitate to reach out.

I promise, we don’t bite.

What We Can Learn From These 10 Companies That Are Implementing AI/ML in Unique and Exciting Ways

Tracy Lee | ladyleet — Tue, 08 Oct 2019 23:57:33 +0000

The Facts

According to a 2019 Gartner survey of three thousand CIOs across a variety of industries, the number of enterprises that utilize AI technology has grown roughly 230% over the past four years. This is a particularly startling number for companies who have yet to integrate this transformative technology, since its growth has placed the estimated percentage of organizations that employ some form of AI at 37%. Some executives, leading companies within the other 63%, may brush these stats off, believing that they don’t stand to benefit enough from AI right now to justify investing in significant digital transformation.

These companies, however, may find themselves playing catch up with competitors who have found creative ways to leverage this technology within their specific industries. Just take a look at these 10 trailblazing companies who are teaching us the value of modern technical innovation through the fascinating ways that they use AI!

Blue River Tech: Shrinking Chemical Use in Agriculture

It has been nearly 60 years since the publication of Rachel Carlson’s Silent Spring, which decried the indiscriminate use of pesticides and herbicides in commercial agriculture. Though we have seen different chemical and policy changes attempt to address the issues raised in the text, none may be as revolutionary as Blue River Tech’s AI driven farming equipment.

Founded in 2011, the company has since been incorporated by leading agricultural machinery company, John Deere, in an effort to grow its flagship “See & Spray” product. This device uses deep learning algorithms, similar to those used in facial recognition software, to discern weeds from crops, and apply appropriate amounts of chemical herbicides. Not only does this decrease the need for farm managers to employ, and train multiple workers to spot and correctly treat invasive, and highly threatening plants, but it can, according to Business Development VP, Ben Chostner, “save up to 90% of the amount of herbicide that you would spray if you were spraying the entire field”.

Cinalytic: Modeling the Financial Success of Big Budget Movies

As the ways that audiences consume film advertisements continues to fragment by the day, the cost for marketing films-- one of the largest areas of investment for production companies-- is growing exponentially.

However, this troubling news comes with a silver lining. According to 2018 data compiled by IBIS Worldwide, the compound annual growth rate for the American film industry is roughly 2%, which outpaces the nation’s overall economic growth. This saddles production companies with the task of finding new ways to optimize ROI at a time where there has never been so much money to make, but where it also requires such a financial investment to make a movie.

This is where Cinalytics steps in. The LA based startup’s AI powered platform considers fifteen unique attributes that can predict the potential success of a film. Using historical data about the performance of thousands of films, the program is able to identify relationships between the financial success of films, and factors like casting, genre, and ratings, to forecast the revenue that producers might be able to expect from any given project. Users can then make changes, such as swapping out one actor for another, or changing the scale of the film’s release, to see how different pre-production decisions may impact their performance metrics.

Not looking to remove the human element from filmmaking, CEO Tobias Queisser believes his company’s tool can “supplement the creative process”, and help producers think through artistic decisions with the added confidence provided by predictive data.

Brain Power: Augmenting Reality for People on the Autism Spectrum

According to leading educational non-profit Autism Speaks, 1 in every 59 children born today will fall somewhere on the autism spectrum. Although symptoms are diverse, many individuals with this diagnosis struggle with typical social conventions in a manner that impacts their daily lives. As autism is typically diagnosed in early childhood, many families are tasked with the responsibility of helping their children navigate a world that better accommodates neurotypical behaviors.

Brain Power is using transformative technologies, including AI and AR (augmented reality) to provide autistic children, and adults, with learning experiences that help them with daily tasks and life skills. Having consulted a diverse array of families, healthcare providers, and counselors, and ran clinical trials, the company has created software that allows users to build skills such as identifying emotions based on facial cues, and maintaining eye contact.

Utilizing Google Glass, the software’s zero UI interface reacts to the user’s eye motion while simultaneously reading their environment to detect not only the presence of other people, but also another person’s facial expressions. In one demo, featured on a PBS News Hour, Laura Krieger, the mother of an eight year old child with autism, plays a game with her son where she emotes surprise, which Brain Power’s software reads and then prompts her son to identify among two possible options. This, however, is just one of the wide array of skill building tools and games that Brain Power has delivered through its cutting edge software suite.

Stanford Computational Policy Lab: Standardizing Judicial Practices

Bias mitigation is a significant concern in modern policing. When many think about AI implementation in police and judicial processes, they shudder at the potential risks. Because AI/ML relies on prior data, some suggest that implementing AI software will increase bias given the historical over-policing of certain groups in America. Though they haven’t created a predictive software, Stanford’s Computational Policy Lab has pursued a unique way to combat racial prejudice in the judicial, and very well may be laying the groundwork for creating more equitable crime predicting AI in the future.

Stanford’s Computational Policy Lab recently partnered with the San Francisco Police Department to develop a tool that strips arrest records of information that might evoke such conscious or unconscious biases when being presented to the District Attorney’s Office. The software uses name-entity recognition technology to identify and remove not only an arrestee’s race, but also descriptive factors that might allude to a suspect’s demographic information from unstructured text. These include physical descriptions like eye, and hair color, names, locations, and neighborhoods where the subject lives or was arrested. The software will also remove information alluding to the identity of involved officers, including names, and badge numbers to even further prevent the DA’s office from making unfair inferences. The goal of this is to reduce or eliminate the probability that racial bias might impact what charges are levied against a suspect.

Though San Francisco commissioned the non-profit to develop this software, Stanford has delivered this software to the city at no charge, and intends to release the software widely for any city to use.

IBM and McCormick & Company: Enhancing Human Creativity

Remember in 2011, when IBM’s Watson annihilated champion contestants Ken Jennings and Brad Rutter on a special episode of Jeopardy? Well Watson has since stepped away from the podium and into the kitchen.

Through a partnership with McCormick & Company, IBM has deployed “Chef Watson” to generate AI conceived spices that will be sold under the iconic seasoning and condiment company’s branding. The company hopes to eliminate the need for consulting often dozens of developers to create a product by leveraging their decades of recipes in order to invent standardized metrics for understanding flavor as data. This removes the issue of developer preference and bias by assigning objective metrics to ingredients that then can be combined to create highly original recipes that are less conventionally referential, and thus less easily replicated.

This technology will not totally remove the human element of product development, but may lead to a 70% reduction in labor according to Hamed Faridi, the company’s Chief Science Officer. Since not all elements of the flavors can be easily and totally objectifiable, there still remains a need for human testing and augmentation. However, as McCormick believes, using IBM’s technology will allow even less experienced product developers to work as efficiently as developers with twenty years of experience. And the company believes its first AI generated product will be released by the end of 2019.

Nauto: Uniting with Human Drivers to Create Safer Roads

Though Stanford researchers have suggested that there will be 10 million self driving cars on the road by the year 2020, concerns still abound regarding the safety of full automated vehicles. Even though a McKinsey study has shown that 90% of driving related deaths could have been avoided had the drivers been using AI powered vehicles, there is something unnerving, for some, about placing their lives in the hands of a computer, and at this time, the technology is prohibitively expensive for most. And this concern multiplies for fleet companies, whose vehicles are both extremely expensive to own as it is, and capable of doing considerably more damage in an accident scenario.

Nauto, the commercial fleet safety company, has created Prevent, an AI-powered device that employs deep learning algorithms to identify when drivers are distracted based upon their facial expressions, and eye lines, as well as their spatial relations to other cars, and whether they commit traffic violations. Then, the system is able to notify the driver, as well as the driver’s employer, in real time, about the nature of their risky behavior. However, CEO Stefan Heck stressed, in an interview with Tech Crunch, that the technology’s intention is, “to be really focused on keeping the driver safe without being intrusive… We want to help human drivers, not just rat them out to their boss.”

As of the publication of this article, Nauto reports that it has detected nearly 49 million high risk driving events, and have saved commercial fleet companies an estimated 976 million dollars.

Inspirata: Driving Precision Medicine

For individuals who develop cancer, early detection greatly improves their chances of being able to successfully treat the illness both in the short and long-term. However, accurate and timely detection is not always easy when the signs that pathologists need to identify in order to diagnose and treat cancers consists of such a wide breadth of information.

Inspirata, an oncological informatics company, is helping us realize a future where our doctors can utilize the pinpoint accuracy of artificial intelligence to pull critical data from not only our physical sample slides, but also our radiology narratives, as well as unstructured clinical text. These softwares rely on deep learning algorithms that collect user input and are able store that data in to make inferences about future input that it encounters. For example, if a pathologist identifies a cluster of cancerous cells in a sample slide and reports this finding to the software, the software can use that information to help future pathologists identify similar cases. This, of course, also relies on the software’s astounding capacity to map the structure of human cells and quantify that data in a manner that can be understood and reapplied in the future.

Similarly, Inspirata has continued to expand its suite of workflow tools to include natural language processing that extracts data from millions of clinical texts deriving from a network of over 400 global healthcare providers. In this sense, doctors are not only able to benefit from the forward progression of medical technology, but this implementation of machine learning, in the words of Inspirata Founder and Executive VP, Dr. Mark Lloyd, “gives pathologists the power to not only contribute to precision medicine, but to drive precision medicine.”

LeadGenius: Automating the B2B Sales Pipeline

In modern sales, there are generally two broadly defined strategies: old school, reliant on cold calls, handshakes, and remembering the names of your clients’ kids, and new school, where representatives connect over Linkedin, and hook potential clients by pushing material relevant to their product or service. But is there something even newer than “new school”?

Not too unlike the way that Inspirata generates and leverages data by relying on doctors to teach its AI technology to identify diagnostic data, LeadGenius benefits from the talent and know-how of over 500 researchers from 40 countries to train its software to do much of the legwork involved in sales.

LeadGenius uses crawlers to search the web for potential clients, and is able to identify and isolate roughly thirty data points about a company, including its name, industry, revenue, and the technologies it uses, in order to determine the business’ suitability as a customer. Since much of this information is not contextualized or well structured, LeadGenius’ software must not only rely on language processing, but must also be trained by seasoned salespeople who know how to find, and identify this crucial data. The software is even capable of emailing prospective clients using natural language as if developed by a salesperson themselves.

In a 2015 talk at a Silicon Valley Data Engineering Meetup, LeadGenius Co-Founder and Chief Scientist, Anand Kulkari made the startling claim that machines will replace salespeople in 10 years. But this doesn’t mean that salespeople will be out of work- what it means is that professionals will have to spend far less time searching out companies, analyzing their buying patterns, and initiating contact. This will allow representatives the ability to get in front of more potential customers and simply focus on closing.

Solutions 4 Health: Tailoring Support for Cigarette Cessation

According to the CDC, 68% of US smokers report wanting to quit. However, in the same year that these statistics were obtained, less than 8% of adult smokers reported being able to successfully give up smoking that same year. For many smokers, the support of tailored counseling and monitoring can be a significant motivator to maintain their cessation journey. That being said, lack of access to these services, which are often cost prohibitive or otherwise difficult to access, can leave many looking to lead healthier lives without any support.

Leading health technology company, Solutions 4 Health, is on a mission to address such healthcare inequalities with its line of various AI powered innovations. One of these technologies is “Quit with Bella”, an iOS mobile application that allows users to text and even speak with an intelligent chatbot, who offers responsive, personalized support to help them quit smoking.

The tech team behind the app utilized Microsoft’s JavaScript Object Notation (JSON) to transmit data via human-readable text. This allows users to freely speak with “Bella”, who can process their language input, and source helpful advice on a range of behavioral and product solutions from a vast databank. Unlike a conventional, human counselor, Bella is accessible at any time, day or night, and is undoubtedly any early iteration of the sort of life-like AI healthcare providers that the healthcare industry will utilize to better meet patients’ needs.

Inturn: Strategizing Supply for Retailers

Overstock is one of the most crippling sources of waste for millions of global retailers, with the current market creating a 170 billion dollar annual problem. Many, if not all, major retailers depend on being able to sell their overstock to discount and resale companies to reduce the loss in revue. However this can be a weeks long process for retailers, which often involves massive excel spreadsheets, and moving their inventory from showroom to showroom. The time and difficulty of this practice not only results in increased labor costs, and decreased human productivity, but also places companies under immense pressure to move their products before products and fashions become out of date.

Inturn is leveraging the power of AI to help companies sell their overstock more quickly and strategically. Founded in 2013, the company offers a suite of different tools to automate what is often the impossible manual task of identifying which products are not selling at appropriate volumes, and expediting their liquidation to discount retailers. The software relies on analyzing large volumes of legacy data across multiple sources to cross reference the sales patterns of a retailer’s products based on multiple categorical factors such as size, color, style, fabric. Not only allowing companies to better strategize when they move inventory to the discount supply market, the software can also aggregate all of the information contained across multiple inventory databases, and present that information on a single platform for easy use by interested buyers. This allows companies to reach more potential resellers, secure better deals, and ultimately move their overstock more quickly, increasing margins by an average of 23%.

The Future

The purpose of this article is not just to highlight 10 companies that are utilizing AI to disrupt and advance their particular industries. The creative implementation of AI, Machine Learning, and other transformative technologies is on the rise, and with it, comes their demystification. The technological capabilities of AI are vast and have yet to be fully explored, however, our baseline understanding of its innumerable capacities, and the ways to unlock these functions is there.

In putting together this list, we attempted to find companies who are each implementing digital transformation in unique ways. However, when you explore the technologies that power their products and services, you realize the concepts behind them aren’t all that dissimilar. Both Blue River Technologies, and Nauto have leveraged video-based object-recognition to address devastating problems unique to their industries. Solutions 4 Health, and the Stanford Computational Policy Lab operate in wholly unrelated spaces with drastically different goals. However, both are using machines to isolate, identify, and extract data from uncontextualized human speech and writing. Even Cinalytic, the film pre-production advisor, is utilizing very similar data analytics concepts that McCormick has employed to help them develop the next big food seasoning!

If you or the company you work for has not yet investigated how AI and machine learning can help you better automate your workflow and services, while drastically reducing waste and maximizing profit, you are not alone. Remember that an estimated 63% of major enterprises have yet to successfully integrate digital transformation into their operations! That being said, the International Data Corporation (IDC) predicts that companies will spend roughly $1.25 trillion dollars by the end of 2019 on their transformative digital journeys.

It is clear that major corporations are recognizing the long-term investment value for AI and ML integration. As these companies and organizations continue to invest, they are likely to see significant improvements not only in the quality of their products, services, and work processes, but will be setting themselves up for long-term economic growth that we can not possibly imagine at this time. Continued investment also gives these companies a great foundation off of which they can grow their technologies through further technical integration as new problems arise, and new capabilities are imaginable.

Beginning your process of digital integration doesn’t have to start with a multi-million dollar overhaul of your technical stacks. It can start with small improvements to the functionality of your existing technologies, or creating simple software to better automate services you already provide. Where you do not want to find yourself, as a business, is being in the position where, in order to compete with companies in your own industry, you are forced to make significant investments over a short period of time, to meet the new standards created by these revolutionary technologies. There has never been a better time to start your journey. So many companies have done the legwork of not only creating the multiple, diverse, open source technologies that are leveraged to achieve these advancements, but they have also done the equally difficult work of imagining ways to effectively apply these technologies to challenges that impact their businesses and industries. As an unintegrated business, you have a unique opportunity to expand your technical program at a time where the science and concepts abound, but where the features offered by AI and ML are not yet the baseline standard for enterprises in the modern world. It now falls upon C-level executives to make the often difficult, but obvious choice to begin transforming their digital technologies now.

The future of your business depends on it.

5 Simple Ways Enterprise Engineering Teams Can Increase Productivity, Performance, and Developer Happiness

Tracy Lee | ladyleet — Thu, 03 Oct 2019 18:17:20 +0000

At This Dot Labs, we work alongside Fortune 500 engineering teams as development partners every day, focusing on how to help reduce deployment delays, decrease regression risks, improve application performance, maximize productivity, increase user engagement, and provide standards that allow for rapid iteration across design, engineering, and other disciplines.

Here are 5 simple ways you can achieve success multipliers within your own organization.

Resilient Development with Testing

As software development increases, so does our complexity. One of the root causes of defects is deficient testing and code inspection. Empowering developers with testing helps decrease regression risk, and reduce deployment delays.

-Enables developers to develop and test components in isolation to understand component behavior under different conditions with ease.
Decrease visual development iterations by at least 50% through testing multiple component views without the need for a data connection with tools like Storybook.

-Capture 20-30% more regressions and UI bugs in pre-release cycle with automated visual testing, using tools like Applitools.2
Ensure scalability of performance at all stages of development, from the render layer to the API request layer by defining a constant web and API performance testing layer.

Efficiencies and Improved Performance with GraphQL

GraphQL increases application performance by allowing the reduction of complex data to achieve more performant deliverables. This also provides more productive options to deliver requirements faster without new APIs being developed.

-Improve data enrichment performance and render speed by reducing unused field retrieval that can create an estimated 50-60% reduction of API payloads.

-Adoption of GraphQL allows you access to features for free such as “out of the box” push based event design systems.

-Separation of team concerns: Allow your API team to migrate to a microservices approach without impacting the deliverables of the UI teams.

-Increase team efficiency by decreasing the need for coordination around architecture decisions between API and developer teams with decoupling,

Improving Developer Onboarding and Productivity with Standard Tooling

Bringing developers onto an existing or new project can often be difficult, resulting in lost project time handling start up procedures. Creating and/or improving standard development procedures will improve onboarding and developer spin-up time, regardless of the project. Here are a few simple ways to do so.

-Create components faster that follow the organization standard by using template repositories, and existing build tooling created by architects.

-Allows for scalability in growing development teams in any region by onboarding them into a system that automatically handles that spin-up of new or existing work.

-Allows cross-functional movement of development resources between teams and easier integration of less expensive resources, gaining upwards of 62% greater productivity.

-Reduce production defects through tightly coupled development processes that do not allow the commit of unverified or uninspected code.

Increasing Brand Consistency with Design Systems

-Design systems are becoming a standard that enterprises are adopting to allow for rapid iteration using a shared vocabulary across design, engineering, and other disciplines.

-Allows for developers, and designers, to create and build components that follow the agreed upon standard and philosophy, increasing brand equity by 39%.

-Adhere to DRY development principles through a unified, customizable component library, such as inputs, buttons, modals, tables, tooltips, charts, icons, charts, snackbars.

-Scale design standards seamlessly with the guarantee of a constant brand, and theme for all IP being released by your organization.

Replacing Dedicated Development Resources with Low Code Solutions

Companies with superior technologies or processes will invariably unseat established ones. Old ways of doing things must be replaced by better ones. Low code solutions help organizations achieve internal efficiencies through effectively outsourcing work previously completed by dedicated development resources, and building low maintenance technology solutions instead.

-Low code solutions are force multipliers, and allow organizations to scale easily.

-These technology enhancements increase the ability for internal teams to deliver business applications faster, and improve internal processes going forward.

-Building in-house low-code solutions will reduce the need for ongoing, dedicated development resources, thereby decreasing overhead costs.

Enjoy this article? Head on over to This Dot Labs and check us out! We are a tech consultancy that does all things javascript and front end. We specialize in open source software like, Angular, React and Vue.

I'm Tracy Lee, a RxJS Core Team member and Google Developer Expert, ask me anything![FINISHED]

Tracy Lee | ladyleet — Wed, 21 Mar 2018 17:42:57 +0000

My name is Tracy and I am a Google Developer Expert, RxJS Core Team member, a Women Techmakers Lead, and a frequent keynote speaker at conferences. I also host the Modern Web podcast, organize This.JavaScript, Contributor Days, Google Developer Group Silicon Valley and Triangle, and RxWorkshop. Oh yeah and I'm Co-Founder of This Dot Labs, an elite consultancy helping teams build front end applications. You can find me on Twitter @ladyleet or at http://thisdot.co/labs.