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Unlocking AI's Potential: Data Strategies with Jan Zirnstein

Published Jun 24, 2024

Updated Dec 11, 2024

2 min read

Jan Zirnstein examines the intricacies of data science and AI, focusing on the challenges and opportunities that lie ahead. As organizations increasingly rely on data to drive decision-making, it's essential to establish clear guidelines and protocols for data management. Jan emphasizes that organizations must take ownership of their data, ensuring its accuracy, security, and ethical use. By implementing robust data governance practices, organizations can build trust, mitigate risks, and fully leverage AI technologies.

Jan also stresses the need for education on AI technologies for leaders. As AI becomes more prevalent across industries, leaders must understand its capabilities, limitations, and potential biases. By fostering a culture of continuous learning and providing leaders with the necessary knowledge, organizations can make informed decisions and effectively use AI tools to drive innovation and growth. Education ensures that leaders are equipped to navigate the complexities of AI and make strategic choices that benefit their organizations.

Along with Rob Ocel, he points out the importance of recognizing and correcting biases in training data, as these can perpetuate and amplify societal inequalities. By implementing rigorous data labeling processes and considering diverse perspectives, organizations can strive for fairness and inclusivity in their AI applications. Tackling bias is essential to creating AI systems that are equitable and credible.

Lastly, Jan underscores the importance of continuous validation and ethical considerations in AI applications. As AI models evolve, it is crucial to regularly validate their performance and ensure they meet ethical standards. Transparency and accountability are key to building trust with users and stakeholders. By prioritizing ethics and embracing transparency, organizations can harness the full potential of AI technologies while minimizing potential risks. The conversations between Rob Ocel and Jan Zirnstein underscore the significance of data ownership, leader education, bias correction in data labeling, and ongoing validation, guiding organizations to navigate the evolving technology landscape and drive innovation, productivity, and societal progress.

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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

1 min

AIMachine LearningChatGPT

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

5 mins

Svelte 5 is Here!

Svelte 5 was finally released after a long time in development. Fortunately, we've been able to test it for some time, and now it has a stable release. Let's dig into its features and why this is such a significant change, even though Svelte 4 code is almost 100% compatible. Svelte syntax everywhere This is one of my favorite additions. Previously, Svelte syntax was limited to a component element. Refactoring code from a component and moving it to a JavaScript file worked differently. Now you can use the .svelte.js or .svelte.ts extension, which allows you to use the same syntax everywhere. It's important to note that it's a way to express that this is not just JS, and what you write may be compiled to something else, just like .svelte files are not just html even though they look very similar. Runes The introduction of runes is one of the most significant changes in Svelte. Many users felt attracted to variables being instantly reactive in previous versions. ` There was, however, a lot of magic underneath and a considerable amount of work from the compiler to make it behave reactively. $state In Svelte 5, a reactive variable has to be explicitly declared using the $state rune, which brings a clear distinction between what is reactive and what isn’t. ` In the previous code, $state is not an actual function being called; it's a hint for the compiler to do something special with this declaration. Rune names always start with a dollar sign ($) and do not need to be imported. However, there’s much more to the changes than just the way we declare reactive variables. Runes bring a lot of new features that make Svelte 5 a great improvement. In Svelte 5, objects or arrays use Proxies to allow for granular reactivity, meaning that individual properties in an object are reactive, even if they are nested objects or arrays. If you modify a property, it will only update that property and will not trigger an update on the whole object. It also supports triggering updates when methods like array.push are called. In previous versions, an assignment was required to trigger an update: ` To have a similar behavior of svelte 4 (no deep reactivity), use the $state.raw() rune. This syntax of .* is common for related features of a rune: $state.raw() will require an assignment to trigger reactivity. ` Because proxies are used, you may need to extract the underlying state instead of the proxy itself. In that case, $state.snapshot() should be used: ` $derived We will use $derived and $derived.by to declare a derived state. The two runes are essentially the same, except one allows us to use a function instead of an expression, allowing for more complex operations to calculate the derived values. ` $effect Effects are useful for running something triggered by a change in state. One of the things you'll note from the $effect rune is that dependencies don't need to be explicit. Those will be picked from reactive values read synchronously in the body of the effect function. ` Something to bear in mind is that these dependencies are picked each time the effect runs. The values read during the last run become the dependencies of the effect. ` Depending on the result of the random method, foo or bar will stop being a dependency of the effect. You should place them outside the condition so they can trigger reruns of the effect. *Variants* of the effect rune are $effect.pre, which runs before a DOM update, and $effect.tracking(), which checks for the context of the effect (true for an effect inside an effect or in the template). $props This rune replaces the export let keywords used in previous versions to define a component's props. To use the $props syntax, we can consider it to return an object with all the properties a component receives. We can use JavaScript syntax to destructure it or use the rest property if we want to retrieve every property not explicitly destructured.. ` $bindable If you want to mutate a prop so that the change flows back to the parent, you can use the $bindable prop to make it work in both directions. A parent component can pass a value to a child component, and the child component is able to modify it, returning the data back to the parent component. ` $inspect The $inspect rune only works in dev mode and will track dependencies deeply. By default, it will call console.log with the provided argument whenever it detects a change. To change the underlying function, use $inspect.with().with): ` $host The host rune gives access to the host element when compiling as a custom element: ` ` Other changes Another important change is that component events are just props in Svelte 5, so you can destructure them using the $props() rune: ` A special children property can be used to project content into a component instead of using slots. Inside the components, use the [@render[(https://svelte.dev/docs/svelte/@render) tag to place them. ` Optional chaining (?.) prevents from attempting to render it if no children are passed in. If you need to render different components in different places (named slots before Svelte 5, you can pass them as any other prop, and use @render with them. Snippets Snippets allow us to declare markup slices and render them conveniently using the render tag (@render). They can take any number of arguments. ` Snippets can also be passed as props to other components. ` ` Conclusion Some exciting changes to the Svelte syntax were introduced while, at the same time, maximum compatibility efforts were made. This was a massive rewrite with numerous improvements in terms of performance, bundle size, and DX. Besides that, a new CLI has been released, making the whole experience of starting a project or adding features delightful. If you haven't tried Svelte before, it's a great time to try it now....

Nov 27, 2024

4 mins

Svelte

Understanding the Difference Between `:focus` and `:focus-visible` in CSS

Understanding the Difference Between :focus and :focus-visible in CSS I have learned my fair share about the importance of keyboard accessibility, so I know that visual indication of the focused element is very important. But the well-known :focus pseudo-class is not always the best fit for this job. That's where :focus-visible comes in. Let's look at the differences between these two pseudo-classes and explore the best practices for using them effectively. What is the :focus Pseudo-Class? The :focus pseudo-class is a CSS selector that applies styles to any element that receives focus, regardless of how that focus was triggered. This includes focus events from keyboard navigation, mouse clicks, and touch interactions. Example Usage of :focus ` In this example, the button will display a blue outline whenever it is focused, whether the user clicks on it with a mouse, taps it on a touchscreen, or navigates to it using the keyboard. What is the :focus-visible Pseudo-Class? The :focus-visible pseudo-class is more specialized. It only applies styles to an element when the browser determines that the focus should be visible. This typically occurs when the user navigates via the keyboard or assistive technologies rather than through mouse or touch input. Example Usage of :focus-visible ` Here, the button will only show a blue outline when focused through keyboard navigation or another input method that usually requires visible focus indicators. Key Differences Between :focus and :focus-visible :focus - Behavior: Applies to any element that receives focus, regardless of the input method. - Use Cases: Ensures that all interactions with the element are visually indicated, whether by mouse, keyboard, or touch. :focus-visible - Behavior: Applies styles only when the focus should be visible, such as using a keyboard or assistive technology. - Use Cases: Ideal for scenarios where you want to provide focus indicators only to keyboard and assistive technology users while avoiding unnecessary outlines for mouse and touch users, typically required by design. Accessibility Implications :focus - Pros: - Guarantees that all users can see when an element is focused, which is critical for accessibility. - Cons: - Can lead to a suboptimal experience for mouse users, as focus styles may appear unnecessarily during mouse interactions. :focus-visible - Pros: - Enhances user experience by showing focus indicators only when necessary, thus keeping the interface clean for mouse and touch users. - Tailors the experience for keyboard and assistive technology users, providing them with clear visual cues. - Cons: - Additional considerations may be required to ensure that focus indicators are not accidentally omitted, especially in older browsers that do not support :focus-visible. - There may be cases where you want to show focus indicators for all users, regardless of input method. Best Practices for Using :focus and :focus-visible To achieve the best accessibility and user experience, combining both :focus and :focus-visible in your CSS is often a good idea. Combining :focus and :focus-visible ` Here is a Stackblitz example of what such styling could look like for you to try out and play with. Additional Tips - Test with Keyboard and Assistive Technology: Ensure that your web application is navigable using a keyboard (Tab, Shift + Tab, etc.) and that focus indicators are visible for those who rely on them. It's never a bad idea to include accessibility testing in your e2e testing suite. - Provide Clear Focus Indicators: Make sure that focus indicators are prominent and easy to see. A subtle or hard-to-spot focus indicator can severely impact accessibility for users who rely on keyboard navigation. Conclusion The :focus-visible pseudo-class offers a more refined way to manage focus indicators, improving accessibility and user experience, particularly for keyboard and assistive technology users. By understanding the differences between :focus and :focus-visible, and applying best practices in your CSS, you can create more accessible and user-friendly web applications. Remember, accessibility should never be an afterthought. By thoughtfully applying focus styles, you ensure that all users, regardless of how they interact with your site, can easily navigate and interact....

Nov 8, 2024

3 mins

AccessibilityCSSPlaywright

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Unlocking AI's Potential: Data Strategies with Jan Zirnstein

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