This is very similar to the California list put on sale last week. This is put out for any group of entrepreneurs, court reporting businesses, or others that might need a list of lawyers for cold calling operations in Texas. The development of sales & marketing strategies in our field is essential. Having information like this in one simple spot can be a game changer. There are over 1,600 entries on this list, so it’s priced at about $0.08 per entry. The format is xlsx, which can be opened via Google Sheets, Microsoft Excel, and if I’m not mistaken, Apache Open Office.
So, if you are in need of a cold calling spreadsheet for Texas, look no further.
I plan to release one or two more lists like this and then move into more educational materials that help make use of information like this, so if you’re generally interested and don’t have a use for this yet, sit tight, there is more to come. If you have an interest in a specific state, feel free to write me at contact@stenonymous.com. I’ll see what I can do.
A reader asked whether this list shows the city and e-mail address. This list has an address listed for most firms, with many located in Irving, Austin, Houston, and Dallas, but it does not have a great e-mail listings. There are services that provide more comprehensive lists, but they also tend to have a higher price point.
A series of 2019 predictions by Gartner were reported on by Venture Beat on June 28, 2021. As explained in a priorpost, “AI”, or machine learning, relies on datasets and algorithms. If the data is imperfect or incomplete, a computer has a chance of giving bad output. If the algorithm that tells the computer what to do with the data is imperfect, the computer has a chance of giving bad output. It’s easy to point to anecdotal cases where “AI” makes a bad call. There have been reports of discrimination in facial recognition technology, driverless cars killing people, or Amazon’s algorithm deciding to fire drivers that are doing their job. I’ve seen plenty of data on the failings of overhyped technology and commercial ASR. What I hadn’t seen prior to today was somebody willing to put a number on the percentage of AI solutions that succeed. Today, we have that number, and it’s an abysmal 15%.
Perhaps this will not come as a surprise to my readers, considering prior reports that automatic speech recognition (ASR), an example of machine learning, is only 25 to 80 percent accurate depending on who’s speaking. But it will certainly come as a surprise to investors and companies that are dumping money into these technologies. Now there’s a hard number to consider. And that 15% itself is misleading. It’s a snapshot of the total number of implementations, not just ASR. ASR comprises a percentage of the total number of implementations out there. And it’s so bad that some blogs are starting to claim word error rate isn’t really that important.
Judge, I know I botched 20 percent of the words. But word error rate really isn’t that important.
That 15% is also misleading in that it’s talking about solutions that are implemented successfully. It is not talking about implementations that provide a positive return on investment (ROI). So imagine having to go to investors and say “our AI product was implemented with 100% success, but there’s still no money in this.”
The Venture Beat article goes on to describe several ways to make AI implementation a success, and I think it’s worth examining them briefly here.
Customizing a solution for each environment. No doubt that modeling a solution for every single business individually is bound to make that solution more successful, but it’s also going to take more staff and money. This would be almost like every court reporting company having their own personal software development staff to build their own CaseCAT or Eclipse. Why don’t they do that? It’s hopelessly expensive.
Using a robust and scalable platform. The word robust doesn’t really mean anything in this context. Scalability is tied to modular design — the ability to swap out parts of the program that don’t work for specific situations. For this, you need somebody bright and forward thinking. They have to have the capability to design something that can be modified to handle situations they may not even be aware exist. With the average software engineer commanding in the ballpark of $90,000 a year and the best of them making over $1 million a year, it’s hopelessly expensive.
Staying on course once in production. This involves reevaluating and sticking with something that may appear to be dysfunctional. This would be almost like the court reporter coming to the job, botching the transcript, and the client going “yes, I think I’ll use that guy again so that I can get a fuller picture of my operational needs.” It’s a customer service nightmare.
Adding new AI use cases over time. Piggybacking on number 3, who is going to want to continue to use AI solutions to patch what the first solution fails to address? This is basically asking businesspeople to trust that it will all work out while they burn money and spend lots of time putting out the fire. It’s a customer service nightmare.
I really respect Venture Beat trying to keep positive about AI in business, even if it’s a hopelessly expensive customer service nightmare.
With some mirth, I have to point out to those in the field that believe the stenographer shortage is an insurmountable problem that we now know machine learning in the business world has a failure rate that’s right up there with stenographic education’s failure rate. Beyond the potential of exploiting digital reporters or stealing investor money, what makes this path preferable to the one that has worked for the last hundred years? As I wrote a week ago, the competition is going to wise up. Stenographic court reporters are the sustainable business model in this field, and to continue to pretend otherwise is nothing short of fraud.
