This Dot Blog
This Dot provides teams with technical leaders who bring deep knowledge of the web platform. We help teams set new standards, and deliver results predictably.
The Adoption Path for Generative and Predictive AI with Jean Roberts, CTO of DataRobot
Jean Roberts is the field CTO of financial markets at DataRobot and shares her experience on technology adoption, transformation, and AI, both predictive and generative. She has had an unconventional career path, transitioning from investment banking to pursuing a Ph.D. in strategy and applied stats. Her journey led her through various roles in the financial sector, touching on investment banking, equity research, and data science teams, where she leveraged NLP on doctor's notes to predict stock movements. Jean emphasizes the importance of having a non-traditional background, allowing leaders to empathize with clients and build credibility. Rob and Jean talk about the challenges of data adoption, particularly in large companies with outdated technological infrastructures. Modernizing tech stacks is crucial for making informed, data-driven decisions, but it often poses difficulties for established organizations both from a culture perspective and a technology perspective. Jean highlights the cultural shift required to convince leaders to view technology as a profit center rather than a cost center and the changes involved in implementing AI and data science solutions within organizations. The two talk and compare the quick adoption of generative AI compared to predictive AI. Jean attributes this to the accessibility factor which has made generative AI more appealing and easier to understand, thereby making it more appealing for executives to want to integrate and adopt. However, she cautions against blindly accepting generative AI responses, stressing the need for companies to understand the technology's limitations and potential risks. Jean helps explain the difference between predictive and generative AI, and their applications to different problems. Defining the problem and understanding whether the solution requires predictive or generative AI is crucial. Jean recommends experimenting with generative AI cautiously, comparing it to a child's birthday party at a bowling alley with bumpers to prevent undesirable outcomes. The conversation concludes with a reflection on the personification of AI and the potential dangers of assigning human-like qualities to these tools. Jean stresses the importance of understanding the technology's limitations and encourages teams to focus on solving specific problems rather than being swayed by the hype surrounding generative AI. Listen to the full podcast episode here: https://engineeringleadership.podbean.com/e/the-adoption-path-for-generative-and-predictive-ai-with-jean-roberts-cto-of-datarobot/...
Feb 6, 2024
ChatGPT can't solve these problems! with Chris Gardner
Chris Gardner talks neural networks, covering fundamental concepts, historical development, and practical applications. It’s important to understand the difference between artificial intelligence (AI) and machine learning, and the role of neural networks in solving intricate problems with vast datasets. This conversation centers around the intricacies of training neural networks, particularly as they become more complex with multiple layers. Chris and Rob touch on the fascinating yet daunting nature of neural networks, discussing their ability to learn beyond human comprehension. Turning to the practical side of using neural networks, Chris shares the existence of libraries that exist to simplify the process of building a network, enabling users to input data, specify parameters, and entrust the system with the training. Both caution about the biases inherent in the data and the responsibility associated with working on machine learning models. They address challenges related to ethics, highlighting the difficulties in identifying biases and emphasizing the delicate balance between excitement and caution in the evolving field of machine learning. Listen to the podcast here: https://modernweb.podbean.com/e/chris-gardner/...
Jan 16, 2024
Let Purpose Drive Your Artificial Intelligence Transformation
From administrative and analytics tasks present in nearly every industry, to niche processes that serve only a handful of businesses, the diversity of how we apply Artificial Intelligence grows, seemingly by the moment. And with it, the size of the market is exploding. In fact, less than half a decade ago, this market was valued at $644 million, and half a decade from now, it is expected to be worth nearly $118 billion. When discussing the growth of AI, many have a hard time separating it from the advancement of AI. While the latter will naturally follow the former, the vast majority of companies that are integrating, and expanding the size of this market, will be in a constant state of catching up to some of the world’s foremost leaders in AI advancement. And I’m here to tell you, that’s okay. Because, as the growth of the AI market continues, the diversity in humans that will use it, and human demand that will be met by it, will grow too. And your responsibility as an executive or leader is not necessarily to push the envelope of AI, or discover the next game changing function at which the world can marvel; it is to meet the needs of the humans, be they your employees or customers, who will benefit from your integration. Of course, this is a very exciting time in the history of advanced digital technologies. Despite how far we have come, when we consider the potentially unending future of this transformative technology, we realize that AI is still in its infancy. As such, we often approach from a performance advancement mindset. However, if you confine your definition of performance to objective technical metrics, you may find yourself perpetually pursuing faster load times, smaller bundle sizes, and more impressive features. This may work out for your team if you have an unending line of investors, or get incredibly lucky. But even some of the biggest companies that neglected to first think of their user experience above all else have found themselves victims of Icarian pitfalls that have shelved their projects, depleted their financial resources, and denied users of products that they need. I do want to note, however, that the focus of this article isn’t to suggest that we shouldn’t strive to advance our understanding of the “nuts and bolts” of AI and other transformative digital technologies. But it’s time to make tough decisions about your company’s place in the AI marketplace. Do you have the budget, the demand, or a responsibility that requires you to implement the most cutting edge technologies available to the market? The answer for most companies is, no. For many, their future with AI is a continual, and incremental process of meeting the minimal needs of their customers, employees, or processes. And I am here to say that not only is this okay, but it’s a good thing. In this article, I implore you to look at AI as a tool for promoting human excellence and experience, and rethink what it means for AI technology to be “performant”. Ultimately, I want you to feel inspired and empowered to begin your digital transformation voyage by placing people and purpose at the helm. RETHINKING PERFORMANCE 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 normative social conventions in a manner that impacts their daily lives. Due to advancements in our understanding of autism, it’s now typically diagnosed in early childhood, tasking many parents with the responsibility of helping their children navigate a world that better accommodates neurotypical behaviors. Laura Krieger is one of these parents. Matthew, her eight year old son, has autism, and struggles to read others’ emotions. The pair were featured in a PBS Newshour segment on Brain Power, a company founded by award winning neuroscientist and entrepreneur, Ned Sahin. Brain Power’s product is a Google Glass aided software that utilizes both AI and augmented reality technologies to help kids with learning differences build skills such as identifying emotions, and maintaining eye contact, through play. When Krieger plays one of these skill building games with her son, she can’t help but break out in tears, saying she feels like he can truly see her for the first time. Krieger is not responding to just how low the latency of the codebase powering the software is, or whether its deploying the latest architectural concepts. She likely doesn’t care whether the software was created using Amazon Web Services, TensorFlow, or a proprietary system. What she does care about is getting the chance to connect with her son in a way she never thought possible. And when we look at Brain Power through the eyes of children who use it, we see its strength in its mode of education. Rather than running users through drills or lesson plans, it rewards them through collaboration, natural interaction, and a gamified points system. When you see the children using the system, they aren’t marveling at the natural feel of the interface that betrays its complexity. They’re simply having fun. This is the type of performance for which we should strive. THE CASE OF WATSON FOR ONCOLOGY In 2013, IBM launched a partner project with The University of Texas MD Anderson Cancer Center to create a “Watson for Oncology” software that would help doctors identify and prescribe courses of action for cancer treatment. After pumping $62 million dollars into the project, MD Anderson officially shelved it in 2017, halting their pursuit of a cure for cancer. But the problem wasn’t a technical one. That’s to say, it was not found within the codebase. It was with the data being processed by it. The reason that MD Anderson pulled the plug on the project, which would eventually be revealed by StatNews after reviewing internal slide decks from MD Anderson, was that the program was prescribing “unsafe and incorrect” treatment plans to real patients after the product had been sold to hospitals around the world. Anderson sourced the problem back to IBM engineers and New York City-based Memorial Sloan Kettering Cancer Center, who were responsible for training Watson for Oncology. It was later discovered that their ML training process relied on a relatively small collection of hypothetical oncological cases rather than using actual patient data. Of course, we cannot presuppose intent. I am of the opinion that IBM and Kettering truly wanted to provide a product that would revolutionize oncology treatment, and save lives. But it is hard to imagine a reason why a company creating a product meant to help doctors treat their patients in the field, would not have trained their AI software with data produced by doctors working with actual cancer patients. So after indefinitely stopping this project, MD Anderson had exchanged $62 million, and four years of its time, to create a product that may have reflected some of the most advanced technical concepts available at that time, but is completely useless, in its current form, for the purpose it was meant to serve. Imagine where we could be, how many lives could have been saved or prolonged, and how much money would have been saved, in the nearly three years since this product was pulled, if developers had placed equal focus on their users and the purpose of their product as they did the technical elements. MEETING NEEDS *“As researchers, we make decisions about what our AI systems can do. It may not necessarily be optimal. It may not be necessarily efficient if you look at all of the metrics… but it may be optimal with respect to the human… which means that the system works.”* - Ayanna Howard, Ph.D Chair, School of Interactive Computing Georgia Institute of Technology Kaden Bowen of Lincoln, Nebraska shares his father’s passion for cars. Though he is non-verbal due to cerebral palsy, he asks his father, James, to “go for a ride”, multiple times a day with the help of a digital talkboard. James dreamed of taking his son on a roadtrip in a vehicle that was more stylish and fun than their wheelchair-accessible van. He searched high and low for a sporty car that he could modify to at least allow him to store a foldable wheelchair, until he realized that the hatchback of a standard Corvette might just be big enough for one. Two weeks later, the duo took a 728 mile round trip to the Corvette museum in Bowling Green, Kentucky in their brand-new (to them) Corvette, which came standard with a trunk big enough for Kaden’s chair. The Bowens don’t seem to over-complicate the accommodations that they have put in place for their son. To help increase his level of independence, they outfitted their home with Amazon Echo devices that are sensitive enough to understand the commands that are programmed into Kaden’s talkboard. Using a combination of the two technologies, Kaden can do a lot on his own. He can call his parents on the phone, stream videos on Netflix, and operate lights. It seems so obvious, but it really is quite inventive. Sure, one day we will have widely accessible neural link technology that will allow Kaden to circumvent the talkboard, and do so much more than what is permitted by an Echo. But just like his dad’s Corvette, that comes standard with enough storage space, sometimes, the best fixes are the best fixes because they are available, and they work. The technologies that Kaden uses may not boast the best metrics, feature the most jaw dropping functions, or offer the most direct route to helping him achieve increased independence. I’m sure that the communication between the talkboard and the Echo device is not always perfect. But the combination of these two relatively affordable, and accessible technologies have given Kaden more autonomy with reliable, usable services, and that, in and of itself, *is* high performance. AI INITIATIVES ARE FAILING These past few years have proven to be extremely exciting for AI technologies, and businesses are responsive, with a 2019 Gartner report showing that 37% of the nation’s leading enterprises are or are shortly planning to integrate some form of AI into their products or processes. This percentage reflects a 270% growth in that statistic when compared to research conducted in 2015. And this proportion bumps up to 62% when we look at Supply Chain and Logistics, and reaches nearly 80% when discussing the Healthcare industry. But these awe-inspiring stats come with troubling predictions that, through 2020, roughly 80% of enterprise AI programs will remain in a limbotic state of development due to their inability to properly scale with their organizations. In essence, we may be creating fabulous algorithms, with wildly impressive features and functions that may become little more than multi-million dollar proof-of-concepts. And this might be okay for some businesses who have the resources to pursue multiple avenues and digital transformation. But I am of the belief that most companies that have yet to enter AI space by now will depend on a significant ROI from the programs that they start over the next few years. For these companies, it is imperative not only to balance resources, and create realistic time-frames for integrating and/or shipping their products, but to stay diligent against the propensity for AI programs to become isolated within an organization. With all of the buzz around AI, and the seemingly endless supply of jaw dropping products and services coming out of the world’s foremost information technology companies, it’s natural that smaller programs want to keep up with the Joneses. It is, therefore, the responsibility of executives, and others in business development roles, to remain involved with their AI development programs to ensure that products and services do not outpace the demands and capacities of their customers or businesses. WORKING SYSTEMS *“The current wave of Artificial Intelligence is going to hit a peak inside of the enterprise. But when it does, it’s not going to be a monumental revolution of technology, but rather a monumental revolution of people.”* - Traci Gusher Partner, US Leader- Artificial Intelligence Analytics and Engineering, KPMG There is a lot of chatter about the need for enterprises to integrate AI powered technologies into their workflows, and products. Anxiety about the need to introduce this transformative technology is warranted. Products like Alexa, Siri, and Echo Dot are changing consumer expectations, while Forrestor predicts that, in 2020, 25% of Fortune 500 companies will include AI building blocks in their automated processes. From product to process, AI is infiltrating nearly every sect of business. That being said, it is a mistake for companies to rush an AI program without first internalizing and developing concepts of how the resulting products will better the lives or capacities of those who will use it. I get it. It’s so tempting to want to patent the next game changing system or algorithm, but if your customers, users, or employees, could be equally, if not better served by a simpler system, or another company’s proprietary tools, what is the point? Is it 100% necessary that your business be on the cutting edge of Artificial Intelligence? Will it truly better your products, services, workflows, or customers? AI has such an amazing capacity to enrich the human experience, be that personal, or professional. We overemphasize minute technical details without giving that same attention to the aspects of user needs, and experience at our own peril. Don’t feel the need to push the boundaries of our understanding of transformative technologies simply to implement your own products and services. All you need is something that sees your user, empowers them, strengthens their skills, and most importantly, works. Ready to begin your digital transformation journey, but don’t know how to start? Don’t hesitate to reach out to the team at This Dot Labs by emailing firstname.lastname@example.org....
Feb 21, 2020
Financial Data Security 2020: How Banks Can Leverage AI to Detect and Prevent Fraud
As the volume of data grows and becomes more integral to daily and long term enterprise operations, the need to secure that data, and identify potential threats to it has never been more important. Cybercrimes are on the rise, with supply chain attacks up 79%, and reports of malicious PowerShell scripts increasing 1000%, in 2019 alone. A snapshot of 2018 fraud statistics presented by the FTC shows that the total identified fraud losses for individual Americans was about $1.48 billion, with the median loss per reportant equaling $375. And these statistics only reflect a portion of the 1.4 million total known instances of fraud, 75% of which did not result in any financial loss. For institutions, fraud threats can be financially devastating, resulting in losses due to theft, misrepresented financial reports, settlement costs and legal fees, operational disruption, and damaged customer and business relationships. Fraudsters are leveraging powerful, and widely available tech tools to mine businesses and individuals for important data with malicious intent, and it is incumbent that financial institutions apply the most advanced tech tools for combatting this. Promoting Customer Security with AI Most financial institutions use rule-based algorithms for alerting and blocking the potentially fraudulent transactions within all customer accounts. While this method has long been a sufficient mode of identifying fraudulent spending, the advancement of the digital age, and the expansion of mobile banking, means that individuals are keeping their money in a growingly diverse number of places, and routinely make transactions all over the world from single locations. This change has made it difficult for companies to employ one-size-fits-all predictive models to secure all customer accounts, since the diversity in user profiles and legitimate financial behaviors has outgrown them. Artificial Intelligence, however, is making it possible for financial institutions to create dynamic user profiles that track the behavior of individual account holders, and tailor fraud markers to their unique spending patterns. Kount, one company that has deployed its own proprietary AI model, for example, has found that their payments fraud detection accuracy has doubled when compared to predictive models, all the while maintaining a response rate of less than half a second. Implementing a successful AI fraud detection solution not only increases the volume of accurate detection, but decreases the instances of false positives, saving financial institutions the time and resources expended on human fraud analysis, falsely frozen accounts, and the need to constantly update predictive models. Protecting Data in the New Digital Age In late 2019, RiskedBased Security released a mid year report claiming that 2019 was on track to be the worst year for breach activity, with over 4.1 billion records compromised in the first 6 months. When we look at the total number of exposed records that same year, 61.7% belonged to financial institutions, including American Express, Suntrust, Capital One, Discover, and Lincoln Financial. Although falling victim to only 6.5% of all reported security breaches, financial companies tend to be most devastated by attacks due to the volume, variance, and sensitivity of their records when compared to that of other businesses. In the advanced digital age, data is not only crucial to protect for the interest of your customers, but also to prevent the distribution and corruption of information used to propel crucial operational decisions. Companies do not only have the responsibility to protect their customers against identity theft and fraudulent spending, but must also promote their own interests by safeguarding their most valuable digital assets. Financial institutions are massive, and as newer technologies are introduced alongside more antique, but nonetheless useful software and hardware, the challenge of protecting sensitive information at scale grows with every passing day. And just as our technologies are diversifying, so too are the threats against it. By implementing AI technologies, companies no longer need to develop seemingly endless and rapidly evolving solutions to potential security threats. Instead, companies can mobilize their existing data to create or implement intuitive softwares, able to identify real time security threats, and take appropriate action both with and without human intervention. How to Start Your AI Journey in 2020 According to IDC predictions, worldwide AI spending in 2019 was expected to be 44% higher when compared to data from the previous year. Further analysis also suggests that the banking sector is the second highest AI investor, with total spending believed to fall around $5.6 billion. With the emergence of blockchain technology threatening to dramatically revolutionize the banking sector, financial services companies will need to maintain their appeal to individuals and enterprises by leveraging their data to create more secure, performant products, services, and experiences. AI’s use in promoting data security is only one of the myriad applications of this transformative asset, expected to drive global trade operations as we enter a new era of advanced digital technologies. AI and Machine Learning are both tools that financial institutions must use to improve customer experiences, uncover novel business opportunities, and drive the direction of their future operations. The journey to full digital transformation is one of small, incremental steps. Financial services companies interested in AI integration do not have to start by creating full fledged, proprietary security systems, but can invest in future success by preparing their data, and implementing a simple AI based foundation for handling internal functions before ramping their technologies up to handle more critical aspects of their enterprise operations. Take this opportunity to set your company up for success as our digital capabilities grow both in capacity and necessity for modern global trade. By working with This Dot Labs, financial institutions learn more about how AI can support their unique operational needs, plan a pathway for its place in their future technical programs, and even start implementing some of the world’s most cutting edge technologies into your daily workflows....
Jan 29, 2020
10 Mobile AR Apps That Are Creating the “New Normal” for Your Customers
Check out these 10 AR integrated mobile apps that are changing not only the way consumers interact with the physical world, but also their expectation for mobile app performance....
Jan 7, 2020
What We Can Learn From These 10 Companies That Are Implementing AI/ML in Unique and Exciting Ways
Oct 31, 2019
A Symbiotic Approach to Breaking the Labor Barrier: How One AI Company is Standing to Support Rising Web Developers
Last June, leading web development firm, This Dot Labs, formally announced their Open Source Apprentice Program. This program provides a pathway for companies to invest in diversifying the demographic ratio of tech professionals. Despite data on tech bootcamp enrollment, which has shown significant interest from prospective developers from underrepresented groups, a large proportion of graduates are not able to find employment within a timely manner. This leaves a massive pool of talent that will remain unutilized unless companies step up to give these rising developers opportunities to apply what they already know and learn more about what they don’t. Many companies realize that creating culturally diverse workspaces is not only an ethical obligation, but also has the potential to enhance quality-of-work, as well as offer numerous financial benefits. One of these companies is Applitools. Its revolutionary titular product, a visual UI testing and monitoring software, leverages the power of AI to replicate the function and preferences of the human eye. This provides users with a zero-calibration testing software that immediately troubleshoots and enhances web and mobile app user interfaces. However, A/B testing is not the only area of the tech world that Applitools is committed to improving. During the pre-publicized development phase of This Dot’s Open Source Apprentice Program, Applitools partnered with the tech consultancy to sponsor thirteen talented women who joined the program as apprentice engineers. Applitools' support provided a pipeline of meaningful, paid, open source projects for the participants to complete with the support of This Dot’s seasoned senior developers. One of these apprentices was Blessing Mbonu, who didn’t know how to go about contributing to open source projects prior to her apprenticeship. Over the three months she spent in the program, she contributed three pull requests to Applitools' project, and learned, “how to work with contributors in the open source community." Other participants, including Taryn Li, felt most empowered by the mentorship offered by the host company, This Dot Labs. Many bootcamp graduates find themselves having to bridge the gaps in their knowledge by themselves, before feeling confident enough to apply to their first junior level position. As Li stated, “Being able to get answers that googling couldn’t provide was super beneficial.” The future success of the tech industry, as well as companies that benefit from developer talent, relies on the push toward democratizing access to the enterprise-level labor pipeline. As of now, the supply of quality developer talent is not meeting the demand presented by rapidly transforming digital technology, and this disparity will only grow worse with each passing day. It is up to forward thinking companies, such as Applitools and This Dot, to recognize the vastly underestimated pool of talent that lies within the bridge between education and paid experience, and utilizing that talent in a responsible, symbiotic manner....
Oct 28, 2019
Accessibility Work and Its Impact on Future AI
Oct 28, 2019