Tag: Artificial Intelligence

An Interesting Review of ChatGPT and Other AIs

I’ve written in the past about limitations of AI from a number of perspectives. In Effects of the Mistruths of Data on Model Output I discuss how the data fed to a model must necessary affect its output in a number of ways, including bias and other unwanted effects. Considering the Four Levels of Intelligence Management tells why it’s not possible for an AI to approach human intelligence today. In Fooling Facial Recognition Software I provide a detailed discussion of why it’s so easy to fool certain types of AI-powered applications. And you learn about why some types of occupations are reasonably safe from AI in Automation and the Future of Human Employment. However, I haven’t really done a detailed investigation of AIs like ChatGPT that seem almost human-like in their understanding, but fall remarkably short in many simple areas. On Artifice and Intelligence is one of the more detailed analysis I’ve found to date on the subject and what it reveals will surprise you. There are a lot of simple problems that ChatGPT and other Large Language Models (LLMs) can’t solve.

What I found interesting in the article is that the author, Shlomi Sher, was able to show that one area where an AI should be strong, math, actually isn’t all that strong at all. He talks about Euclid’s proofs. The artifice is that ChatGPT 4 and other AIs can tell you all about prime numbers and even provide seemingly creative output about them. However, depending on how you ask some basic questions, ChatGPT 4 either gets the answer wrong or right, when the answer is pretty much apparent to any human who knows what prime numbers are. What I liked most about the article is that the author takes time to explain why humans can understand the problem, but the AI can’t.

If it seems as if I have a continuing desire to dissuade others from anthropomorphizing AIs, I most certainly do. When it comes to AI, it’s all about the math and nothing more. However, that doesn’t mean that AIs lack functionality and ability as tools to augment human endeavors. It’s likely that the use of AIs will continue to increase over time. In addition, I think that as we better understand precisely how AIs work, we’ll also come to realize that they’re amazing tools, but most definitely not humans in the making. Let me know your thoughts on ChatGPT at [email protected].

Programming Languages Commonly Used for Data Science

The world is packed with programming languages, each of them proclaiming their particular forte and telling you why you need to learn them. A good developer does learn multiple languages, each of which becomes a tool for a certain kind of development, but even the most enthusiastic developer won’t learn every programming language out there. It’s important to make good choices.

Data Science is a particular kind of development task that works well with certain kinds of programming languages. Choosing the correct tool makes your life easier. It’s akin to using a hammer to drive a screw rather than a screwdriver. Yes, the hammer works, but the screwdriver is much easier to use and definitely does a better job. Data scientists usually use only a few languages because they make working with data easier. With this in mind, here are the top languages for data science work in order of preference:

  • Python (general purpose): Many data scientists prefer to use Python because it provides a wealth of libraries, such as NumPy, SciPy, MatPlotLib, pandas, and Scikit-learn, to make data science tasks significantly easier. Python is also a precise language that makes it easy to use multi-processing on large datasets — reducing the time required to analyze them. The data science community has also stepped up with specialized IDEs, such as Anaconda, that implement the Jupyter Notebook concept, which makes working with data science calculations significantly easier. Besides all of these things in Python’s favor, it’s also an excellent language for creating glue code with languages such as C/C++ and Fortran. The Python documentation actually shows how to create the required extensions. Most Python users rely on the language to see patterns, such as allowing a robot to see a group of pixels as an object. It also sees use for all sorts of scientific tasks.
  • R (special purpose statistical): In many respects, Python and R share the same sorts of functionality but implement it in different ways. Depending on which source you view, Python and R have about the same number of proponents, and some people use Python and R interchangeably (or sometimes in tandem). Unlike Python, R provides its own environment, so you don’t need a third-party product such as Anaconda. However, R doesn’t appear to mix with other languages with the ease that Python provides.
  • SQL (database management): The most important thing to remember about Structured Query Language (SQL) is that it focuses on data rather than tasks. Businesses can’t operate without good data management — the data is the business. Large organizations use some sort of relational database, which is normally accessible with SQL, to store their data. Most Database Management System (DBMS) products rely on SQL as their main language, and DBMS usually has a large number of data analysis and other data science features built in. Because you’re accessing the data natively, there is often a significant speed gain in performing data science tasks this way. Database Administrators (DBAs) generally use SQL to manage or manipulate the data rather than necessarily perform detailed analysis of it. However, the data scientist can also use SQL for various data science tasks and make the resulting scripts available to the DBAs for their needs.
  • Java (general purpose): Some data scientists perform other kinds of programming that require a general purpose, widely adapted and popular, language. In addition to providing access to a large number of libraries (most of which aren’t actually all that useful for data science, but do work for other needs), Java supports object orientation better than any of the other languages in this list. In addition, it’s strongly typed and tends to run quite quickly. Consequently, some people prefer it for finalized code. Java isn’t a good choice for experimentation or ad hoc queries.
  • Scala (general purpose): Because Scala uses the Java Virtual Machine (JVM) it does have some of the advantages and disadvantages of Java. However, like Python, Scala provides strong support for the functional programming paradigm, which uses lambda calculus as its basis. In addition, Apache Spark is written in Scala, which means that you have good support for cluster computing when using this language — think huge dataset support. Some of the pitfalls of using Scala are that it’s hard to set up correctly, it has a steep learning curve, and it lacks a comprehensive set of data science specific libraries.

There are likely other languages that data scientists use, but this list gives you a good idea of what to look for in any programming language you choose for data science tasks. What it comes down to is choosing languages that help you perform analysis, work with huge datasets, and allow you to perform some level of general programming tasks. Let me know your thoughts about data science programming languages at [email protected].

Effects of the Mistruths of Data on Model Output

A number of the books Luca and I have written or I have written on my own, including Artificial Intelligence for Dummies, 2nd Edition, Machine Learning for Dummies, 2nd Edition, Python for Data Science for Dummies, and Machine Learning Security Principles, talk about the five mistruths of data: commission, omission, bias, perspective, and frame of reference. Of these five mistruths, the one that receives the most attention is bias, but they’re all important because they all affect how any data science model you build will perform. Because the data used to create the model isn’t free of mistruths, the model can’t perform as expected in many situations. Consequently, the assertions in the article, LGBTQ+ bias in GPT-3, don’t surprise me at all. The data used to create the model is flawed, so the output is flawed as well.

I chose the article in question as a reference because the author takes the time to point out a problem in ever generating a perfect model, the constant change in human perspective. Words that were considered toxic in the past are no longer considered toxic today, but new words have taken their place. Even if a model were to somehow escape bias today, it would be biased tomorrow due to the mistruth of perspective.

So, why have I been using the term mistruth instead of the term lie? A lie is information that is passed off as true in order to avoid responsibility, to harm others in some way, or to knowingly pass off information that is untrue for personal gain. However, humans use mistruths all of the time to reduce the potential for arguments, to save someone’s ego, or simply because the information that the person has is inaccurate. A mistruth doesn’t have the intent of deceiving another for personal gain, but it’s still not true. So, when someone asks, “Do these pants make me look fat?” and another person states, tactfully, that, “They make you look voluptuous.” the statement could be true or a mistruth, but is done to keep an argument at bay and make the other person feel good about themselves. However, machine learning algorithms have no concept of this interplay and the model created using such statements will be biased.

Anthropomorphizing machine learning models doesn’t change the fact that they’re essentially statistically-based mathematical models of data with no understanding of anything built into them. So, true or not, the model sees all data as being the same—the only solution to the problem of bias is to clean the data. However, the human expectation after getting emotionally attached to their AI is that the AI will somehow just know when something is hurtful. Articles like, What is the Proper Pronoun for GPT-4?, point to the problem of why someone would ask the question at all. The interactions with AI have taken on a harmful aspect because humans are seeing them as sentient when they most certainly aren’t. The issue will come to a head when people try to use the bad advice obtained from their AI as a defense in court. Personally, I see the continued use of “it” as essential to remind people that no matter how human a model may seem, it’s still just a model.

It’s important to understand that I see AI as an amazing tool that will only get more amazing as data scientists, developers, and others add to it’s potential by doing things like adding more memory. Although I don’t agree that any machine learning model can be someone’s friend, articles like How to Use ChatGPT in Daily Life? do make a strong argument for using machine learning models as tools to enhance human capability. However, it is a person who thought up these uses, not the machine learning model. Machine learning models will remain limited because they can’t understand the data they manipulate. In fact, articles like Why ChatGPT Won’t Replace Coders Just Yet point out just how limited machine learning models remain. However, I do think that machine learning models will get expanded by humans to perform new tasks as described in articles like How To Build Your Own Custom ChatGPT With Custom Knowledge Base. Of course, if that custom knowledge base is biased in any way, then the output from the new model will also be biased.

It’s important to know that AI is moving forward and that it will be extended to do even more in assisting humans to realize their full potential. However, it’s also important to know that the five mistruths will continue to be a problem because machine learning models are unable to understand the data used to train them and to provide knowledge bases for their output. Realistic expectations will help improve AI as a tool that augments human capabilities and helps us achieve amazing things in the future. Let me know your thoughts on machine learning bias at [email protected].

Giving Hackers an Exciting Target

Hackers will attack anyone, any organization, or anything that seems to offer the promise of something in exchange for the time spent: money, resources, revenge…the list goes on. However, for many hackers the kicker for choosing somewhere to hit is some level of challenge, some sort of excitement. After all, why attack a boring site when there is one out there literally begging you to attack it? Such is the case with GrapheneOS, which bills itself as:

The private and secure mobile operating system with Android app compatibility.

GrapheneOS Website

According to Multiple DDoS Attacks at GrapheneOS — What’s Going On Behind the Scenes?, GrapheneOS has recently endured multiple attacks. I verified the story on Twitter from a post by GrapheneOS. Such an attack can happen to anyone at any time. Keeping a low profile seems prudent, but not always possible (as is the case here). One of the things I stressed when writing Machine Learning Security Principles is that anything an organization can do to make attacks harder and less attractive will only reduce the security burden of the organization in the long run. Keeping a low profile tends to make an attack less attractive.

The reason that I was attracted to this particular DDoS attack is that GrapheneOS is using Synapse, an AI-based product. The article, Synapse Technology Corporation: Using AI to Take a Good Look at Airport Security, tells you a bit more about the history of this product. In looking at the Synapse website, you can see that they have some interesting customers, including the military and government. Oddly enough, I’m not seeing any other reports of major problems with Synapse. The problem must be with the GrapheneOS security setup.

The bottom line is that if a hacker decides to break into your organization, it’ll happen at some point no matter how good your security systems are, which means that it’s essential to combine security with monitoring and analysis of attack vectors. Keeping a low profile is essential too because hackers, like the most of the rest of us, love a good challenge. Reviewing attacks like the ones targeted at GrapheneOS can help you improve your own security setup. Let me know your thoughts on AI-based security at [email protected].

Considering the Four Levels of Intelligence Management

One of the reasons that Luca and I wrote Artificial Intelligence for Dummies, 2nd Edition was to dispel some of the myths and hype surrounding machine-based intelligence. If anything, the amount of ill-conceived and blatantly false information surrounding AI has only increased since then. So now we have articles like Microsoft’s Bing wants to unleash ‘destruction’ on the internet out there that espouse ideas that can’t work at all because AIs feel nothing. Of course, there is the other end of the spectrum in articles like David Guetta says the future of music is in AI, which also can’t work because computers aren’t creative. A third kind of article starts to bring some reality back into the picture, such as Are you a robot? Sci-fi magazine stops accepting submissions after it found more than 500 stories received from contributors were AI-generated. All of this is interesting for me to read about because I want to see how people react to a technology that I know is simply a technology and nothing more. Anthropomorphizing computers is a truly horrible idea because it leads to the thoughts described in The Risk of a New AI Winter. Another AI winter would be a loss for everyone because AI really is a great tool.

As part Python for Data Science for Dummies and Machine Learning for Dummies, 2nd Edition Luca and I considered issues like the seven kinds of intelligence and how an AI can only partially express most of them. We even talked about how the five mistruths in data can cause issues such as skewed or even false results in machine learning output. In  Machine Learning Security Principles I point out the manifest ways in which humans can use superior intelligence to easily thwart an AI. Still, people seem to refuse to believe that an AI is the product of clever human programmers, a whole lot of data, and methods of managing algorithms. Yes, it’s all about the math.

This post goes to the next step. During my readings of various texts, especially those of a psychological and medical variety, I’ve come to understand that humans embrace four levels of intelligence management. We don’t actually learn in a single step as might be thought by many people, we learn in four steps with each step providing new insights and capabilities. Consider these learning management steps:

  1. Knowledge: A person learns about a new kind of intelligence. That intelligence can affect them physically, emotionally, mentally, or some combination of the three. However, simply knowing about something doesn’t make it useful. An AI can accommodate this level (and even excel at it) because it has a dataset that is simply packed with knowledge. However, the AI only sees numbers, bits, values, and nothing more. There is no comprehension as is the case with humans. Think of knowledge as the what of intelligence.
  2. Skill: After working with new knowledge for some period of time, a human will build a skill in using that knowledge to perform tasks. In fact, very often this is the highest level that a human will achieve with a given bit of knowledge, which I think is the source of confusion for some people with regard to AIs. Training an AI model, that is, assigning weights to a neural network created of algorithms, gives an AI an appearance of skill. However, the AI isn’t actually skilled, it can’t accommodate variations as a human will. What the AI is doing is following the parameters of the algorithm used to create its model. This is the highest step that any AI can achieve. Think of skill as the how of intelligence.
  3. Understanding: As a human develops a skill and uses the skill to perform tasks regularly, new insights develop and the person begins to understand the intelligence at a deeper level, making it possible for a person to use the intelligence in new ways to perform new tasks. A computer is unable to understand anything because it lacks self-awareness, which is a requirement for understanding anything at all. Think of understanding as the why of intelligence.
  4. Wisdom: Simply understanding an intelligence is often not enough to ensure the use of that intelligence in a correct manner. When a person makes enough mistakes in using an intelligence, wisdom in its use begins to take shape. Computers have no moral or ethical ability—they lack any sort of common sense. This is why you keep seeing articles about AIs that are seemingly running amok, the AI has no concept whatsoever of what it is doing or why. All that the AI is doing is crunching numbers. Think of wisdom as the when of intelligence.

It’s critical that society begin to see AIs for what they are, exceptionally useful tools that can be used to perform certain tasks that require only knowledge and a modicum of skill and to augment a human when some level of intelligence management above these levels is required. Otherwise, we’ll eventually get engulfed in another AI winter that thwarts development of further AI capabilities that could help people do things like go to Mars, mine minerals in an environmentally friendly way in space, cure diseases, and create new thoughts that have never seen the light of day before. What are your thoughts on intelligence management? Let me know at [email protected].

Fooling Facial Recognition Software

One of the points that Luca and I made in Artificial Intelligence for Dummies, 2nd Edition, Algorithms for Dummies, 2nd Edition, Python for Data Science for Dummies, and Machine Learning for Dummies, 2nd Edition is that AI is all about algorithms and that it can’t actually think. An AI appears to think due to clever programming, but the limits of that programming quickly become apparent under testing. In the article, U.S. Marines Outsmart AI Security Cameras by Hiding in a Cardboard Box, the limits of AI are almost embarrassingly apparent because the AI failed to catch even one of them. In fact, it doesn’t take a Marine to outsmart an AI, the article, This Clothing Line Tricks AI Cameras Without Covering Your Face, tells how to do it and look fashionable at the same time. Anthropomorphizing AI and making it seem like it’s more than it is is one sure path to disappointment.

My book, Machine Learning Security Principles, points out a wealth of specific examples of the AI being fooled as part of an examination of machine learning-based security. Some businesses rely on facial recognition now as part of their security strategy with the false hope that it’s reliable and that it will provide an alert in all cases. As recommended in my book, machine learning-based security is just one tool that requires a human to back it up. The article, This Simple Technique Made Me Invisible to Two Major Facial Recognition Systems, discusses just how absurdly easy it is to fool facial recognition software if you don’t play by the rules; the rules being what the model developer expected someone to do.

The problems become compounded when local laws ban the use of facial recognition software due its overuse by law enforcement in potentially less than perfect circumstances. There are reports of false arrest that could have possibly been avoided if the human doing the arresting made a check to verify the identity of the person in question. There are lessons in all this for a business too. Using facial recognition should be the start of a more intensive process to verify a particular action, rather than just assume that the software is going to be 100% correct.

Yes, AI, machine learning, and deep learning applications can do amazing things today, as witnessed by the explosion in use of ChatGPT for all kinds of tasks. It’s a given that security professionals, researchers, data scientists, and various managerial roles will increasingly use these technologies to reduce their workload and improve overall consistency of all sorts of tasks, including security, that these applications are good at performing. However, even as the technologies improve, people will continue to find ways to overcome them and cause them to perform in unexpected ways. In fact, it’s a good bet that the problems will increase for the foreseeable future as the technologies become more complex (hence, more unreliable). Monitoring the results of any smart application is essential, making humans essential, as part of any solution. Let me know your thoughts about facial recognition software and other security issues at [email protected].

Making Algorithms Useful

This is an update of a post that originally appeared on December 2, 2015.

Writing about machine learning and deep learning in my various books has been interesting because it turns math into something more than a way to calculate. Machine learning is about having inputs and a desired result, and then asking the machine to create an algorithm that will produce the desired result from the inputs. It’s about generalization. You know the specific inputs and the specific results, but you want an algorithm that will provide similar results given similar inputs for any set of random inputs. This is more than just math. In fact, there are five schools of thought (tribes) regarding machine learning algorithms that Luca and I introduce you to in books such as Machine Learning Security Principles, Algorithms for Dummies, 2nd Edition, Python for Data Science for Dummies, and Machine Learning for Dummies, 2nd Edition:

  • Symbolists: The origin of this tribe is in logic and philosophy. This group relies on inverse deduction to solve problems.
  • Connectionists: The origin of this tribe is in neuroscience. This group relies on backpropagation to solve problems.
  • Evolutionaries: The origin of this tribe is in evolutionary biology. This group relies on genetic programming to solve problems.
  • Bayesians: This origin of this tribe is in statistics. This group relies on probabilistic inference to solve problems.
  • Analogizers: The origin of this tribe is in psychology. This group relies on kernel machines to solve problems.

Of course, the problem with any technology is making it useful. I’m not talking about useful in a theoretical sense, but useful in a way that affects everyone. In other words, you must create a need for the technology so that people will continue to fund it. Machine learning and deep learning are already part of many of the things you do online. For example, when you go to Amazon and buy a product, then Amazon makes suggestions on products that you might want to add to your cart, you’re seeing the result of machine learning. Part of the content for the chapters of our book is devoted to pointing out these real world uses for machine learning.

As I’ve written new books and updated existing ones, I’ve seen an almost magical progression in the capabilities of machine learning and deep learning applications such as ChatGPT, Chat Generative Pre-Trained Transformer, which can produce some pretty amazing output.

Some of these applications, such as Siri and Alexa, continue to learn as you use them. The more you interact with them, the better they know you and the better they respond to your needs. The algorithms that these machine learning systems create get better and better as the database of your specific input grows. The algorithms are tuned to you specifically, so the experience one person has is different from an experience another person will have, even if the two people ask the same question.

Machine learning is a big mystery to many people today, while other people have gained enough experience to have strong opinions about it. Because I continue to write new machine learning/deep learning books and update others, it would be interesting to hear your questions about machine learning and deep learning. After all, I’d like to tune the content of my books to meet the most needs that I can. Where do you see this technology headed? What confuses you about it? Talk to me at [email protected].

Contemplating the Issue of Bias in Data Science

When Luca and I wrote Python for Data Science for Dummies we tried to address a range of topics that aren’t well covered in other places. Imagine my surprise when I saw a perfect article to illustrate one of these topics in ComputerWorld this week, Maybe robots, A.I. and algorithms aren’t as smart as we think. With the use of AI and data science growing exponentially due to the fact it can help improve a company’s marketing tenfold, you might think that computers can think. They can’t. You can learn about the role data science has in marketing here but for now, I think it’s important to reiterate the fact that computers can emulate or simulate the thinking process, but they don’t actually think. A computer is a machine designed to perform math quite quickly. If we want thinking computers, then we need a different kind of a machine. It’s the reason I wrote the Computers with Common Sense? post not too long ago. The sort of computer that could potentially think is a neural network and I discuss them in the Considering the Future of Processing Power post. (Even Intel’s latest 18 core processor, which is designed for machine learning and analytics isn’t a neural network-it simply performs the tasks that processors do now more quickly.)

However, the situation is worse than you might think, which is the reason for mentioning the ComputerWorld article. A problem occurs when the computer scientists and data scientists working together to create algorithms that make it appear that computers can think forget that they really can’t do any such thing. Luca and I discuss the effects of bias in Chapter 18 of our book. The chapter might have seemed academic at one time-something of interest, but potentially not all that useful. Today that chapter has taken on added significance. Read the ComputerWorld article and you find that Flickr recently released a new image recognition technology. The effects of not considering the role of bias in interpreting data and in the use of algorithms has has horrible results. The Guardian goes into even more details, describing how the program has tagged black people as apes and animals. Obviously, no one wanted that particular result, but forgetting that computers can’t think has caused precisely that unwanted result.

AI is an older technology that isn’t well understood because we don’t really understand our own thinking processes. It isn’t possible to create a complete and useful model of something until you understand what it is that you’re modeling and we don’t truly understand intelligence. Therefore, it’s hardly surprising that AI has taken so long to complete even the smallest baby steps. Data science is a newer technology that seeks to help people see patterns in huge data sets-to understand the data better and to create knowledge where none existed before. Neither technology is truly designed for stand-alone purposes yet. While I find Siri an interesting experiment, it’s just that, an experiment.

The Flickr application tries to take the human out of the loop and you see the result. Technology is designed to help mankind achieve more by easing the mundane tasks performed to accomplish specific goals. When you take the person out of the loop, what you have is a computer that is only playing around at thinking from a mathematical perspective-nothing more. It’s my hope that the Flickr incident will finally cause people to start thinking about computers, algorithms, and data as the tools that they truly are-tools designed to help people excel in new ways. Let me know your thoughts about AI and data science at [email protected].

 

Computers with Common Sense?

The whole idea behind products, such as Siri, is to give computers a friendlier face. Much like the computer on the Enterprise in Star Trek, you converse with the machine and get intelligent answers back much of the time. The problem is that computers don’t currently have common sense. A computer really doesn’t understand anything anyone says to it. What you’re seeing is incredibly complex and clever programming. The understanding is in the math behind the programming. Computers truly are machines that perform math-related tasks with extreme speed and perfection.

It was with great interest that I recently read an article on the Guardian, Google a step closer to developing machines with human-like intelligence. The opening statement is misleading and meant to bedazzle the audience, but then the article gets into the actual process behind computers that could emulate common sense well enough that we’d anthropomorphize them even more than we do now. If the efforts of Professor Geoff Hinton and others are successful, computers could potentially pass the Turing Test in a big way. In short, it would become hard to tell a computer apart from a human. We very well could treat them as friends sometime in the future (some people are almost there now).

Articles often allude to scientific principles, but don’t really explain them. The principle at play in this case is the use of sentiment analysis based on words and word n-grams. You can build a sentiment analysis by using machine learning and multiclass predictors. Fortunately, you don’t have to drive yourself nuts trying to understand the basis for the code you find online. Luca and I wrote Python for Data Science for Dummies to make it easier to understand the science behind the magic that modern applications seemingly ply. Let me know your thoughts about the future of computers with common sense at [email protected].