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ChatGPT can't solve these problems! with Chris Gardner

Published Jan 16, 2024

Updated Jan 16, 2024

1 min read

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/

This Dot is a consultancy dedicated to guiding companies through their modernization and digital transformation journeys. Specializing in replatforming, modernizing, and launching new initiatives, we stand out by taking true ownership of your engineering projects.

We love helping teams with projects that have missed their deadlines or helping keep your strategic digital initiatives on course. Check out our case studies and our clients that trust us with their engineering.

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

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!...

Jan 7, 2020

7 mins

AIMachine Learning

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

Introduction to Zod for Data Validation

As web developers, we're often working with data from external sources like APIs we don't control or user inputs submitted to our backends. We can't always rely on this data to take the form we expect, and we can encounter unexpected errors when it deviates from expectations. But with the Zod library, we can define what our data ought to look like and parse the incoming data against those defined schemas. This lets us work with that data confidently, or to quickly throw an error when it isn't correct. Why use Zod? TypeScript is great for letting us define the shape of our data in our code. It helps us write more correct code the first time around by warning us if we are doing something we shouldn't. But TypeScript can't do everything for us. For example, we can define a variable as a string or a number, but we can't say "a string that starts with user_id_ and is 20 characters long" or "an integer between 1 and 5". There are limits to how much TypeScript can narrow down our data for us. Also, TypeScript is a tool for us developers. When we compile our code, our types are not available to the vanilla JavaScript. JavaScript can't validate that the data we actually use in our code matches what we thought we'd get when we wrote our TypeScript types unless you're willing to manually write code to perform those checks. This is where we can reach for a tool like Zod. With Zod, we can write data schemas. These schemas, in the simplest scenarios, look very much like TypeScript types. But we can do more with Zod than we can with TypeScript alone. Zod schemas let us create additional rules for data parsing and validation. A 20-character string that starts with user_id_? It's z.string().startsWith('user_id_').length(20). An integer between 1 and 5 inclusive? It's z.number().int().gte(1).lte(5). Zod's primitives give us many extra functions to be more specific about *exactly* what data we expect. Unlike TypeScript, Zod schemas aren't removed on compilation to JavaScript—we still have access to them! If our app receives some data, we can verify that it matches the expected shape by passing it to your Zod schema's parse function. You'll either get back your data in exactly the shape you defined in your schema, or Zod will give you an error to tell you what was wrong. Zod schemas aren't a replacement for TypeScript; rather, they are an excellent complement. Once we've defined our Zod schema, it's simple to derive a TypeScript type from it and to use that type as we normally would. But when we really need to be sure our data conforms to the schema, we can always parse the data with our schema for that extra confidence. Defining Data Schemas Zod schemas are the variables that define our expectations for the shape of our data, validate those expectations, and transform the data if necessary to match our desired shape. It's easy to start with simple schemas, and to add complexity as required. Zod provides different functions that represent data structures and related validation options, which can be combined to create larger schemas. In many cases, you'll probably be building a schema for a data object with properties of some primitive type. For example, here's a schema that would validate a JavaScript object representing an order for a pizza: ` Zod provides a number of primitives for defining schemas that line up with JavaScript primitives: string, number, bigint, boolean, date, symbol, undefined, and null. It also includes primitives void, any, unknown, and never for additional typing information. In addition to basic primitives, Zod can define object, array, and other native data structure schemas, as well as schemas for data structures not natively part of JavaScript like tuple and enum. The documentation contains considerable detail on the available data structures and how to use them. Parsing and Validating Data with Schemas With Zod schemas, you're not only telling your program what data should look like; you're also creating the tools to easily verify that the incoming data matches the schema definitions. This is where Zod really shines, as it greatly simplifies the process of validating data like user inputs or third party API responses. Let's say you're writing a website form to register new users. At a minimum, you'll need to make sure the new user's email address is a valid email address. For a password, we'll ask for something at least 8 characters long and including one letter, one number, and one special character. (Yes, this is not really the best way to write strong passwords; but for the sake of showing off how Zod works, we're going with it.) We'll also ask the user to confirm their password by typing it twice. First, let's create a Zod schema to model these inputs: ` So far, this schema is pretty basic. It's only making sure that whatever the user types as an email is an email, and it's checking that the password is at least 8 characters long. But it is *not* checking if password and confirmPassword match, nor checking for the complexity requirements. Let's enhance our schema to fix that! ` By adding refine with a custom validation function, we have been able to verify that the passwords match. If they don't, parsing will give us an error to let us know that the data was invalid. We can also chain refine functions to add checks for our password complexity rules: ` Here we've chained multiple refine functions. You could alternatively use superRefine, which gives you even more fine grained control. Now that we've built out our schema and added refinements for extra validation, we can parse some user inputs. Let's see two test cases: one that's bound to fail, and one that will succeed. ` There are two main ways we can use our schema to validate our data: parse and safeParse. The main difference is that parse will throw an error if validation fails, while safeParse will return an object with a success property of either true or false, and either a data property with your parsed data or an error property with the details of a ZodError explaining why the parsing failed. In the case of our example data, userInput2 will parse just fine and return the data for you to use. But userInput1 will create a ZodError listing all of the ways it has failed validation. ` ` We can use these error messages to communicate to the user how they need to fix their form inputs if validation fails. Each error in the list describes the validation failure and gives us a human readable message to go with it. You'll notice that the validation errors for checking for a valid email and for checking password length have a lot of details, but we've got three items at the end of the error list that don't really tell us anything useful: just a custom error of Invalid input. The first is from our refine checking if the passwords match, and the second two are from our refine functions checking for password complexity (numbers and special characters). Let's modify our refine functions so that these errors are useful! We'll add our own error parameters to customize the message we get back and the path to the data that failed validation. ` Now, our error messages from failures in refine are informative! You can figure out which form fields aren't validating from the path, and then display the messages next to form fields to let the user know how to remedy the error. ` By giving our refine checks a custom path and message, we can make better use of the returned errors. In this case, we can highlight specific problem form fields for the user and give them the message about what is wrong. Integrating with TypeScript Integrating Zod with TypeScript is very easy. Using z.infer<typeof YourSchema> will allow you to avoid writing extra TypeScript types that merely reflect the intent of your Zod schemas. You can create a type from any Zod schema like so: ` Using a TypeScript type derived from a Zod schema does *not* give you any extra level of data validation at the type level beyond what TypeScript is capable of. If you create a type from z.string.min(3).max(20), the TypeScript type will still just be string. And when compiled to JavaScript, even that will be gone! That's why you still need to use parse/safeParse on incoming data to validate it before proceeding as if it really does match your requirements. A common pattern with inferring types from Zod schemas is to use the same name for both. Because the schema is a variable, there's no name conflict if the type uses the same name. However, I find that this can lead to confusing situations when trying to import one or the other—my personal preference is to name the Zod schema with Schema at the end to make it clear which is which. Conclusion Zod is an excellent tool for easily and confidently asserting that the data you're working with is exactly the sort of data you were expecting. It gives us the ability to assert at runtime that we've got what we wanted, and allows us to then craft strategies to handle what happens if that data is wrong. Combined with the ability to infer TypeScript types from Zod schemas, it lets us write and run more reliable code with greater confidence....

Nov 15, 2024

7 mins

JavaScriptZod

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

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ChatGPT can't solve these problems! with Chris Gardner

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Let's innovate together!

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