Jonathan Bartlett

Senior Fellow, Walter Bradley Center for Natural & Artificial Intelligence

Jonathan Bartlett is a senior software R&D engineer at Specialized Bicycle Components, where he focuses on solving problems that span multiple software teams. Previously he was a senior developer at ITX, where he developed applications for companies across the US. He also offers his time as the Director of The Blyth Institute, focusing on the interplay between mathematics, philosophy, engineering, and science. Jonathan is the author of several textbooks and edited volumes which have been used by universities as diverse as Princeton and DeVry.

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Autonomous Vehicles Are Catching Up Fast

There are winners and losers. GM's Cruise was kicked out of California over safety issues but Alphabet's Waymo, which emphasizes safety, is still chugging along
Well, some are, anyway, and there is a lesson in that. It’s been a while since I wrote on autonomous vehicles, but there has been quite a lot of action lately in this space, and I thought it was a good time to bring everyone up to speed. Waymo’s Level 4 system continually advances Once upon a time, Tesla was thought to be the industry leader in autonomous vehicles. Their 2016 “Paint It Black” demo convinced most of the world that Tesla was on the verge of having the technology to get people autonomously to and from any destination that could be mapped. Mind Matters News, however, was skeptical. The problem is simple—Tesla was betting on the wrong horse. There are multiple levels of autonomous vehicles, and Tesla was aiming for the top one—Level 5 autonomy—the

Framework for AI Legislation

Unfortunately, current calls for AI legislation seems to be largely motivated by fear of the unknown rather than looking for specific policy goals.
The sudden rise of artificial intelligence (AI) into the Internet landscape has caused many people to be concerned. The people advancing AI seem to have few scruples about where and how it should be applied. This sudden technological change coupled with the fact that those on the forefront seem to be largely amoral opportunists have raised calls for legislation of AI technology. Unfortunately, current calls for AI legislation seems to be largely motivated by fear of the unknown rather than looking for specific policy goals. In this article, I am going to lay the groundwork for what I think good AI legislation will be. However, before I do that, I want to give some cautionary advice about such legislation. It wasn’t too long ago that, during another radical technological shift

Copyright in the Age of Artificial Intelligence

What exactly is a human and how does a human differ from a computer?
On December 27, The New York Times Company sued Microsoft and OpenAI for violations of their copyright. The Times contends that training chatbots on its content in order to create an information competitor is a violation of its copyright. This suit is sure to bring up a number of old copyright issues that were never resolved, plus some new that need to be worked through. The fact is, the big search engines have been violating copyright from the very beginning. All search engines are in fact derivative works of the sites that they crawl, index, and dish out. Most search engines even provide excerpts from the sites they scan. However, most copyright holders have turned a blind eye to this for two main reasons — nobody has deep enough pockets to fight big tech and most of the people

Directed Goals in Living and Evolving Systems

Nearly every action that an organism does is for something.
Teleology is the technical term for goal-directedness, especially when describing living systems. Teleology has been problematic in the sciences because of the amount of hand-waving that teleology has historically allowed. From the outside, it is difficult to tell if something happened because it was intended or if it just happened to be beneficial. Determining the precise goal can be problematic, even if an action is goal-directed. It is easy to construct a story about why an organism does an action, but how do we ascertain whether this story is true? When are attributions of teleology science, and when does it degenerate to mere invention? Additionally, the lack of ability to measure goal-directedness has often placed teleology in the realm of storytelling instead of

Why Build Process Automation Matters

Automated build processes allow for the standardization and systematization of your development pipeline.
One thing that is often overlooked in smaller development organizations is the build process for your software. If you’re not a software developer, the build process is the sequence of steps that takes your source code and creates a final package that is delivered to your customers (or to your servers).  Large organizations have been automating their build process for many years, for the simple reason that the build process has to work for numerous software developers. It has to work on everyone’s machine, every time, and produce reliable results.  Therefore, there is not a large step between the amount of documentation required to communicate this process to everyone and simply automating it. However, in smaller organizations, there is a temptation to bypass build process

Of Infinity and Beyond

What are the problems and solutions with infinity in mathematics?
The concept of infinity has plagued a great many proofs, both formal and informal. I think that there are two foundational problems at play in most people’s thinking about infinity that causes issues. The first problem people have with infinity is that they treat it as if it were a single value. Because infinity is bigger than all possible natural numbers, people assume that it is bigger than any number, and therefore there is nothing beyond infinity. Therefore, people have the concept that if I have two infinities, then I still have the same number.  They believe that 2 * infinity = infinity. However, using that logic can quickly lead to contradictions. This problem is exacerbated by much mathematical notation. People often will use ellipses to indicate that

Why Is Object-Oriented Programming Popular?

This method makes programmers think more systematically about their code
Programming practice has gone through several evolutions in its lifespan. The first phase might be considered the “exploratory” phase, where there were no rules but a lot of imagination. People wrote code that was simultaneously amazing and terrible—amazing at what people got their slow computers to do, but terrible in that no one but the author would ever be able to maintain the programs. The lessons learned from the exploratory phase led to what is known as “structured” programming. The goal of structured programming was to be able to write programs that someone else had a chance of reading and understanding. Structured programming favored having really well-documented inputs and outputs to every function, very clear entry and exit points to each function, and

The Microservices Controversy from a Software Management Perspective

As projects get bigger, so do the reasons for having a microservice architecture
A new report by Amazon has caused a bit of a stir on the Internet. In it, the Amazon Prime video team reported that changing their architecture from a microservice architecture to a monolithic architecture resulted in a 90% cost savings.  While the report itself was very mild (its only claim was that this architecture helped in this specific situation), it has caused the people who disliked the microservice trend to make some noise of their own. Here, I wanted to take a moment to reflect on what I see as the benefits of the microservice approach from a software development management perspective. If you are not familiar with microservice architectures, you can find out more information in my book, Cloud Native Applications with Docker and Kubernetes. First of all, I do

The Raspberry Pi Phenomenon

A Raspberry Pi is a full computer that is not much larger than a credit card, but still packs enough power to be usable as a desktop computer
For the uninitiated, the Raspberry Pi is a single-board computer that runs the Linux operating system. It can be either operated as a desktop computer or as an embedded system (i.e., a custom electronic device), or both. Historically, computer systems were either general-purpose computers or embedded systems. General-purpose computers required too much hardware, too many chips, and too much power to work inside an electronic device. However, as manufacturers packed more and more functionality into less and less space using less and less power, eventually it became possible to have a computer that was small, cheap, powerful, and not especially power-hungry. The Raspberry Pi came about right as this was happening. A Raspberry Pi is a full computer that is not much larger than a

Aren’t US Treasury Bonds Supposed to be Safe?

How can you lose money selling treasury bonds?
For context, read Bartlett’s two previous articles on the fall of SVB and interest rates. Some people are confused as to how you can lose money selling treasury bonds, since they are supposed to be “safe” assets (the government is not expected to default on its loans, and, if it does, the economy probably has bigger problems). Economist Bob Murphy put together a great explainer thread on Twitter, which I will largely follow here. Let’s say that there is an asset that always yields a 1% return every year on however much you have invested, but you never get the principal back except by selling it to someone else. Let us call this asset ABC, and let us say that it is completely riskless. If you buy $1,000 in ABC, you will get $10/year forever. You have essentially

Why Did the Tech Bubble Correspond with Low Interest Rates?

Ultimately, our economy’s deeper problems aren’t so much a result of “money” as they are bad allocations of resources.
For context, read Bartlett’s article explaining the fall of SVB here. I wanted to make a quick note about why tech bubbles tend to correspond with low interest rate environments. Interest rates essentially dictate how long someone can wait before they need to produce something of real value. In a 20% interest rate environment, it will be evident really quickly if you are failing to produce something of value. Since essentially 1/5 of your capital disappears each year, if you aren’t doing something that will generate real profits quickly, you are sunk. You can’t paper over problems with more borrowing because the cost of that borrowing is so high. Additionally, in such an environment, the payoffs for investment need to be large in order for someone to invest. No one is

What’s Going on at Silicon Valley Bank?

The bank's failure is making a lot of people nervous about their money
Many people awoke this morning to news of a bank that suddenly collapsed – Silicon Valley Bank, or SVB. While information is still developing, I thought I would provide some background information on what is known so far. SVB is the go-to bank for Silicon Valley startups. Over the last few years, the tech bubble has been growing and growing and growing, focused especially around Silicon Valley. That meant a lot of banking was happening, and it was happening with SVB. That is where the various companies put their deposits.  How does a bank make money? By lending out deposits. In 2021, at the height of the tech bubble (and, not coincidentally, at a historically low-interest rate environment). The bank did what most banks do and bought very safe

The Need for Accountability in AI-Generated Content

Just because we live in a world of AI does not mean we can escape responsibility
AI-generated content has become increasingly common on the web. However, as we enter this new era, we will need to think through the moral and social ramifications of what we are doing, and how we should negotiate the new ethical landscape. But first, a brief recap of recent history. The first major player to pioneer AI-generated content was the Associated Press. AP realized that many market-oriented articles were pretty monotonous and read like templates anyway, so they decided to fully commit and auto-generate many of them. If you read an AP story about a company’s earnings report and it sounds eerily like every other story about other companies’ earnings reports, there’s a reason for that. Templated content, while annoying, provides window-dressing to raw data, and

Whatever You Do, Don’t Ask GPT for Sources

The chatbot will give you a lot of links that don't necessarily direct you where you want to go
One of the more amusing things I’ve found from OpenAI’s GPT-3 and ChatGPT is the fact that it will very confidently provide you with sources on anything you ask—and they will often be completely made up. It will even provide fake (but real-looking) URLs for you! I stumbled across this feature when researching a previous GPT-3 article about how well it could write blog posts compared to real authors. I initially tried asking GPT-3 to include sources, and it generated complete nonsense for the sources. I decided that, for that article, sources were not the main question, so I left it out of the final queries. However, in response to my latest article about ChatGPT not being a Google replacement, someone commented that, in the future, it would be simple to expand

Why ChatGPT Won’t Replace Google

With Google, the algorithm eventually leads you to content made by real people. With ChatGPT, you never leave the algorithm
To some extent, ChatGPT is a newer, easier-to-use interface than Google.  Unlike Google, it doesn’t make you waste time by visiting those pesky websites.  It not only looks into its database for content, but it also summarizes it for you as paragraphs. There is a problem lurking in there, however.  Being computers, neither Google nor ChatGPT cares about the truth.  They are algorithms, and they merely do as they are told.  Additionally, you can’t code the human mind into algorithms.  However, there is a fundamental difference between what ChatGPT does and what Google does that will prevent content generators like ChatGPT from displacing search engines like Google: Google eventually lets you out of its system. Ultimately, the goal of search engines like

GPT-3 Versus the Writers at Mind Matters

How does the AI fare when it is asked to write on topics covered in Mind Matters articles?
In order to give a real-world comparison of the output of GPT-3 to human-written writing, I decided it would be a fun activity to see how OpenAI’s GPT-3 compares to Mind Matters on a variety of topics that we cover.  Here, we are using OpenAI’s direct API, not ChatGPT, as there is a lot of evidence that ChatGPT responses have a human-in-the-loop.  Therefore, we are going to focus on the outputs from their API directly. I used several criteria for article selection in order to even the playing field as much as possible.  For instance, I only chose articles that did not depend on recent events.  This way, GPT-3 is not disadvantaged for not having up-to-date material.  However, I also chose recent articles so the articles themselves were less likely to be

How are Developers Using OpenAI’s Tools in their Software?

There are several interesting uses of the new AI tools, but time will tell which ones take off
OpenAI has released two major tools for developers to make use of GPT-3 and DALL-E.  GPT-3 is the radical new text generation tool, which generates large or small amount of texts from simple prompts.  It can also classify text into categories  GPT-3’s text-generation system forms the core of OpenAI’s new chatbot, ChatGPT.  DALL-E is an image generation tool, which creates images from text prompts.  Together, these two tools provide today’s state-of-the-art in AI-based content generation. So how are developers making use of these new features?  Today we are looking at several ways that these tools have been put to use. Basic Content Generation The core of GPT-3 is generating content from prompts.  Whether for making blog posts, writing

Musk’s Tyrannical Turn With Twitter

While the political bias is gone, the ego bias seems to have just begun
I was hopeful when Elon Musk took control of Twitter. As a longtime Musk skeptic in many areas, I thought that his move into Twitter would actually be a good thing. First of all, it matches his background better than Tesla. Twitter is a software play, and Musk’s actual expertise is in building software. Second, Twitter is just about software, not artificial intelligence, which tends to be where Musk gets into trouble. Finally, Musk has at least claimed to be a libertarian, though this seems to be limited to situations where he simply decides that he doesn’t want to do what is required of everyone else. Elon Musk When Musk first took control, it looked positive. Despite the incessant screaming of the media, Musk’s first actions were fantastic. He unbanned a lot of accounts

FTX Implosion: There Are Warning Signs from Binance

The CEO of Binance fundamentally misrepresented the character of the firm's engagement of the Mazars auditing firm
Since the implosion of FTX, many have been watching cryptocurrency exchange Binance to see if it might be the next crypto firm to face a liquidity crunch. While the CEO of Binance, known in the crypto world as CZ (Changpeng Zhao), has assured investors and depositors that the firm is financially sound, many of its recent moves have left observers wondering if there is more to the story. The Audit that Wasn’t The first major red flag was the “audit.” Binance has long been criticized for failure to provide audited financial statements. Because it is not a public company, an audit is not a legal requirement. Nonetheless, many crypto watchers view the failure to have an external audit as evidence of concern about possible outcomes. On December 7, Binance put out