Founder & CEO of Hacker Noon
by our weekly sponsor Discover.bot, an Amazon Registry Services Inc. creation
Voice recognition and deep learning are paving the way for bots to replace humans in customer service. Learn about the challenges facing bot developers today.
Chatbots, or bots, with customer service skills, feel like a thing of the future, but increasingly are things of the present. And voice recognition is making them even better at their jobs.
How many times have you called the bank, pharmacy, or power company and tried to interact with their voice recognition technology? And how many of those times have you given up and used the keypad to repeatedly hit “0” — until you’re eventually put on hold for a human agent?
That’s a mouthful of a title right there. Don’t let it scare you away. What it boils down to is rather simple. We want the best of both worlds. The SEO boost server-side rendering provides, and the speed of a Single Page Application. All this while hosted basically for free in a serverless environment on AWS Lambda.
When you’re first starting out, everything is a mistake. Everything is a failure and life sucks. But, the mistakes are actually a personal godsend, because you learn from them. You overcome challenges. You make a better thing, or service, because you made mistakes that helped you to grow.
You may be asking yourself, “Well Mr. McGruff, what are the Heikin-Ashi candles?” Well, they are a powerful candleset that utilizes the average prices of candlesticks to smooth out the trend. If you’d like to see the technical formula for it, it is available here on Investopedia.
The earliest horizontal scaling was just running duplicates of the web server. However recently with the advancement of the cloud, microservice architecture has rapidly begun to dominate the playground. The idea is to split up a huge application into individual components, called services, that each perform a specialized task. So instead of having a single web server process a request from start to finish, developers break the application into services like a user authenticator, page server, api server, database model service, etc.
We also saw that Blockchain developers are in high demand — with related jobs being the fastest growing in today’s labor market: “At the moment there are now 14 job openings for every one blockchain developer.” With money pouring into the space and more than $6 billion being raised through ICOs in the first half of this year alone, blockchain startups, large tech firms, banks, payments and professional services firms are all aggressively hiring.
Studying development pipelines evidences how different ways of handling the same problem can have drastically different results and how interconnected every decision is. As well as how improvements in the methods used can generate exponentially better performances. Optimizing pipelines is a lot like fine tuning an engine, small screw adjustments here and there and the engine’s performance greatly changes while letting you know by how it sounds.
It can be helpful to look at today’s programming languages across 3 different levels — “build fast” programming languages used for prototyping applications as quickly as possible, “infrastructure” programming languages which help with performance-centric/use-case specific portions of your application and finally, “systems” programming languages useful for embedded hardware and use cases where you need absolute control of memory usage.
Blockchains got it all started but they’re only the beginning of decentralized money. They’re the first solution, the breakthrough that made it all possible, but they’re just that, the first solution. And first solutions rarely last. Blockchains are the cave man’s fire of decentralized consensus technology. They’re the Model T. To understand why, you just have to know there’s a massive difference between an iteration of technology and a category of technology. In the long run categories of tech succeed but iterations fail.
What books / research / models / frameworks might you recommend for change agents with low/no positional authority hoping to coax their orgs in a new direction?
I went to Google Play, and after a bit of research, I looked at what were the apps offering and what features they were missing. This was the first mistake I made. You always think that you can do better, but is that “better” necessary for your future customers? Some functionality that I think it’s good to have might not be useful for the users.
So, buying foreign currencies are discouraged by the governments and banks, there isn’t enough gold volume for everyone-how will the citizens of Turkey store their wealth? The answer is digital gold: Bitcoin. It seems we are seeing the beginning of the Bitcoin trend: while most cryptocurrency exchanges have lost significant amount of volume after January, exchanges in Turkey have started to gain volume in the last few months. While the Turkish banks are displeasing customers, Turkish cryptocurrency exchanges are offering them a way out.
For customers, SaaS just works™. They don’t have to install anything, and paying a smaller recurring fee is a lot more palatable on the balance books. For us, SaaS has two benefits. First, having recurring revenue makes our finances predictable. Second, it cuts down development overhead, since new products can be rolled out simultaneously to all clients.
In assessing new technologies and ideas, our minds rely on old models of understanding. When it comes to AI possibilities (intelligence at a level we cannot truly understand), we model AI as a potential extension of the things we already know (data analysis getting more powerful and not impacting us as individuals that much). Respondents to the survey believe that AI (or data science) will not impact their lives, but AI is already enabling companies to harness optimal levels of operational efficiency that create unbelievably personalized experiences and, consequently, generate margins and revenues that we couldn’t fathom just a few years ago (Trillion Dollar market cap companies!?!!).
It’s important to note that more creative freedom begets more trial and error to reach the solution — which is fine. Some people think that you can just know the perfect solution ex-ante (i.e. in advance) before writing a single line of code. I contend instead that, for creative activities, the process of discovering a solution to a given problem (not only software) is a process of tinkering: you can’t have perfect knowledge in advance, but instead you learn by doing, iteratively trying new things and keeping what works, and by refining your solution (and possibly shipping it to your customers if you follow are into lean/agile) until you are satisfied with it.