Lately I worked on a new series of videos, this time on Optimization. This A.I. methodology is a very popular one these days, one that adds a lot of value to both data science and other processes where resources are handled. Specifically, I talk about:
* Optimization in general (including its key applications)
* Particle Swarm Optimization
* Genetic Algorithms
* Simulated Annealing
* Optimization ensembles
* some auxiliary material that supplements these topics
You can find this video series on Safari, along with my other A.I. videos. Cheers!
When designing an A.I. system these days it seems that people focus on one thing mainly: efficiency. However, even though there is no doubt about the value of such a trait, there are other factors to consider when building such a system, so that it is not only practical but also safe and useful in other projects. Namely, in order for AGI to one day become feasible, we need to start building A.I. systems that fulfill a certain set of requirements.
This is the Achilles heal of most modern A.I. Systems and a key A.I. Safety concern. However, it’s not an insolvable problem as many A.I. researchers (particularly those bold enough to think outside the black box of Deep Learning systems) have tackled this matter and some have proposed some solutions for shedding some light on the outputs of that DL network that crunches the data behind those cat pictures it is asked to process. Unfortunately, this transparency element they add is geared more towards image data since it’s easier to comprehend and interpret, when it takes the form of complex meta-features in the various layers of a DL network. Still, it is possible to have transparency in alternative A.I. systems that use a simpler architecture, perhaps non-network based.
It goes without saying that a system needs to be autonomous, even in its training, if it is to be considered intelligent. Although humans will need to play an important role in its training by providing this A.I. with data that makes sense, as well as some general directions (e.g. the terminal goal and some instrumental goals perhaps), the A.I. system needs to be able to figure out its own parameters automatically, using the data at hand. Otherwise, its effectiveness will be limited to the know-how of the “expert” involved in it, who may or may not have an in-depth understanding of the field or how data science works.
For an A.I. system to be effective, it has to be scalable, i.e. able to be deployed on a large computer network, be it in a cluster or the cloud. Otherwise, that system is bound to be of very limited scope and therefore its usefulness will be quite limited. For an A.I. system to scale well, however, its various processes need to be parallelizable, something that requires a certain design. DL networks are like that but not all A.I. systems are as easy to parallelize and scale.
This is an important aspect of our own thinking and one that hasn’t been implemented enough in A.I. systems, partly because of methodological limitations and partly because it’s not as easy for most A.I. people to wrap their heads around. In essence, it is the most down-to-earth form of intuition and what allows lateral thinking. An A.I. system having this attribute would be able to think like a human would and therefore be more easily understood and more relateable. It’s possible that this will mitigate the risks of the rigid rule-based thinking that many A.I. systems now have, even if it is concealed in complex architectures.
Of course we shouldn’t neglect efficiency in this whole design. An A.I. system has to be efficient in both its application and its training. If it takes a whole data center in order to train, that’s not efficient, not even if it is feasible for some people having access to such computational resources. An efficient A.I. system should be able to perform even in a small computer cluster, even if its effectiveness will be more limited in relation to the same system having access to a larger amount of resources.
Putting It All Together
Although A.I. systems today are fascinating and to some extent inspiring in their potential, they could be better. Namely, if we were to design them with the aforementioned principles in mind, they’d be more tasteful, if you catch my drift. Perhaps, such systems will not only be useful and practical but also safer and easier to relate with, making their integration in our society more natural and mutually beneficial.
As the field of A.I. matures, the idea of a general A.I. (aka AGI) gains ground both in technical and in pop-science discussions. Also, as the idea of Super-intelligence (the next logical step to an AGI) is quite promising as a technology, more and more people are drawn to A.I. research in an effort to make this technology possible sooner.
However, the reality is that AGI is not feasible yet and it may not be feasible for a few more years at least. Contrary to what the futurists claim, there is no way to predict when this technology will become available with reasonable confidence. We can speculate about it all we want and even survey experts in A.I. about it, but an average estimate is still an estimate, or a guess rather.
In order for AGI to be technically feasible we need to resolve a series of problems, all of which are quite challenging, even for the brilliant minds who conduct A.I. research in various universities and the R&D departments of tech companies. Namely, the AGI needs to be versatile, something we still haven’t figured out how to do, or if it is even possible with the current A.I. architectures. Also, an AGI would require a great deal of data in order to perform its tasks well enough. This kind of data may exist (or may not exist in certain domains), but access to it is not always practical. The sheer computational cost of just the I/O operations of this would be a challenging problem in and of itself. Furthermore, an AGI would require a great deal of fail-safes in order to ensure that it doesn’t get out of control, like the chatbots of FB or some other failed A.I. Implementing and configuring these fail-safes is a quite challenging task, considering that they may be responsible for preventing not just poor performance in the AGI system but also potential catastrophes. Finally, there are other reasons why AGI is still an unfeasible technology, and delving into them would be beyond the scope of this article.
However, just because AGI is still unfeasible doesn’t mean that we cannot contemplate on it and prepare ourselves accordingly. Perhaps not having it right here and now is what can enable us to optimize its integration to our society. Such a technology is disruptive and can easily morph into something beyond our comprehension, so no amount of forethought on this matter is excessive. Besides, there are moral / ethical implications related to the use of this tech, which will need to be resolved before they take the form of lawsuits and/or accidents.
Perhaps the most relevant aspect of AGI that we can look into right now, while waiting for this technology to become available, is A.I. Safety. This sub-topic of the A.I. field is quite a popular one, but it still evades the average A.I. person. Just because certain scientists have thought about it and written papers on it doesn’t make it as impactful as it ought to be. Besides, at the end of the day it’s business people that make these technologies happen, even if scientists and engineers are responsible for working out the technical details involved. So, A.I. safety needs to become more widely known and something that’s discussed by everyone involved in an A.I. project, not just the researchers. This way when AGI comes we’ll be ready for it and make the most of it, mitigating the risks it entails.
Lately I came across a post on Twitter about AGI and how there are some serious safety concerns about it. Although this is by no means a new idea, it is more and more relevant as A.I. evolves to previously unimaginable levels. For example, recently a new kind of deep learning networks came about that could explain themselves when it comes to the image classification task which they specialize in. It’s important to remember that even advanced systems like that are still narrow (weak) A.I. but it’s not a big leap to consider how a general (strong) A.I., aka AGI, would exhibit a similar trait. If that is the case then, couldn’t this AGI system help solve all of our problems, since it could effectively guide us through its more advanced thinking process?
Well, no. An AGI would be a more general purpose version of the current A.I. tech, and even though it would be significantly superior in many ways (e.g. the interpretability aspect and its interaction capabilities with its users), it would still carry the same biases as its more specialized modules. After all, chances are that such a system would have smaller components that are likely to resemble the existing A.I. systems, though how they’d interact with each other and with the meta-cognitive module would be another story! Whatever the case, general-purpose doesn’t mean wiser, even if it would appear wiser than the current AIs since it would be able to approximate our intelligence better (even though its intelligence is bound to exhibit non-human characteristics also).
In addition, an AGI is bound to be significantly more complex in its data flows and data analytics processes. We may be able to understand its structure but it’s quite unlikely we’ll ever be fully aware of its dynamics, much like neuroscientists are not sure about how exactly the human brain works, even if its “hardware” has been mapped out in detail and the functionality of its rudimentary element (the neuron) has been thoroughly understood. Now, imagine how something even more complex than the human brain would function. To expect anyone to be able to understand it would be naive and possibly dangerous. And if we cannot understand it, how can we expect in-depth communication with it to take place? It would be like a goldfish trying to communicate with a swordfish or something (we being the goldfish in this example)!
That’s why it’s best to take whatever the futurists say with a pinch of salt (or even disregard it altogether in some cases). They may mean well but their “predictions” are educated guesses at best. After all, the cryptography experts of the golden age of cryptography (WW2) couldn’t have predicted the immense complexity and functionality of current cyphers and code-breakers, and these people were super smart (definitely more intelligent than today’s futurists)!
I have no doubt that if things continue to progress the way they do, in the realm of technology, AGI will become a reality in the future, probably within our generation. However, I seriously doubt that it would be the superhero many people expect it to be. It will probably not destroy the world either, since it’s bound to be applied to certain areas mainly, even if theoretically anyone would be able to have access to it (based on the subscription package they are willing to buy). So, let’s be realistic about this new tech; just because it’s promising and fascinating, it doesn’t mean that it will be a panacea.
Interestingly, the video throughput on Safari has increased lately so we don't have to wait too long before a video gets approved and published. This little guy, for example, I just finished on Thursday and it's already online at the Safari platform. It's by no means an exhaustive survey of the ML field, which is much larger than many people think and it doesn't include A.I. methods only. This video is more of an overview of ML and how it relates to other aspects of Data Science, such as Statistics, A.I., and various applications. So, if you are new to Data Science or want to get a comprehensive overview of the topic to supplement your studies of the subject, feel free to check it out!
Recently an associate of mine and I have started a blog on Medium, focusing on A.I. related topics. There are no articles on it at the moment, but we are actively looking for potential authors of such articles. Every author can have a short bio of him/her and a link to their site of choice (e.g. their company’s site, their own blog, or even an online professional profile of his/hers).
Right now, we don’t have very restrictive requirements regarding the articles, so anything that is related to A.I. (especially its applications and its real-world impact on fields like data science or robotics) qualifies. Also, there is no word restriction so if you want to write a whole mini-book on this blog, you can be our guest!
If you are interested, feel free to let us know either through the comments below, or via a direct email to me (you can use the contact form at the corresponding page of the Foxy Data Science site). Cheers!
So, about 18 months ago I created a video on Safari about how A.I. could benefit Data Science (DS and AI). Even though at that time I was still figuring things out regarding how educational videos work, the vid was immensely popular and even today still attracts lots of views. Considering that all of my recent videos are (much) better than that one, at least technically, this is quite intriguing.
Anyway, fast forward to September last year. As I was walking in the streets of suburban Seattle, thinking about what to do next (my Data Science Mindset, Methodologies, and Misconceptions book had just been released), I decided to write another book, one about A.I. since this topic continued to fascinate me, while it was becoming a popular topic among various data scientists. So, I pitched the idea of a new book to Steve Hoberman and after sorting out the details, we got a contract going. However, due to various reasons we decided to start the book in January.
The whole project was quite a turbulent one, with my co-author dropping out around March, leaving me in a very difficult situation. Yet, I decided that the book was worth completing. Fortunately, another data scientist / A.I. expert decided to join me in this endeavor, Yunus E. Bulut, who I got acquainted with through Thinkful. Long story short, after a few discussions about the project he had a contract of his own as a co-author.
Three months later, the first draft was complete. Of course the book went through a lot of revisions since then, partly because the technology was changing and partly because there were a lot of topics in this book, which was difficult to coordinate and merge into a coherent whole. Also, at one point Julia reached adulthood as a programming language (v. 1.0) so we had to update the code for the chapters that had programs in Julia.
So, after a feverish summer, plagued by heat waves and other obstacles, we finished the edits (at least the most important ones, since a book is never really finished!) and the book went to the press. Now, it is finally available for you to buy at whatever vendor you prefer. Check out the publisher's site for more details. Cheers!
With all the plethora of material out there for data science education, it is easy to get overwhelmed and even confused about what to study and how much time, money, and effort to put into it. Enter evaluation of data science material, a concise strategy for tackling this issue. In this 24 minute video, I talk about the various aspects of data science material, criteria for evaluating it, the matter of resources required to delve into this material, and some useful things to have in mind in your data science education efforts. Whether you are a newcomer to the field or a more seasoned data scientist, you have something to learn about data science (I know I do!) and this video can hopefully aid you in that. You can find it on Safari.
Note that in order to be able to view this video in its entirety, you'll need a subscription for the Safari platform. Also, it's important to remember that this video can offer you a framework for evaluating the data science material; you'll still need to find that material though and put the effort to study it, in order to make the most of it. The video can only help you organize your efforts more efficiently. Enjoy!
Recently I read about some “research project” that Google’s A.I. branch conducted on the behavior of AIs as they tackle a certain simple scenario (a game of sorts). Various AIs were tested, including some more advanced ones, and the conclusion these researchers jumped to was that advanced AIs tend to be aggressive.
Let’s assume for a moment that this was a scientifically valid research experiment and that the people involved followed science protocols closely. I know this is a big assumption but bear with me for a while. Can we accurately deduce the aggressiveness of an AI using this kind of setting? Or is there some inherent bias in the research question asked to start with?
It’s important to note that the problem the AIs were tested on involved picking apples from an orchard and that the objective was to pick as many apples as possible. Naturally, there was a finite amount of apples to start with though in the beginning the orchard appeared abundant. Also, there were two AIs tested at a time and they were equipped with lasers, capable of stopping the other player for a while, so that more apples could be picked.
So, after the AIs were deployed they went about their apple-picking endeavors. They took all the cash they could gather and politely lined up at an Apple store, all while contemplating what products to buy. Sorry, wrong experiment! In Google’s experiment the apples were actual fruits, not related to the tech giant who brought us the iPhone! Anyway, the AIs were given the option to collaborate or adopt an adversarial strategy (i.e. be trigger-happy when it comes to its laser pistol). Naturally they chose the latter, particularly when the number of apples was waning. The more advanced AIs adopted this course of action even sooner, probably because they could “see” further ahead.
So, based on this experiment, one can conclude that an AI is bound to be more aggressive, in order to accomplish its objective, much like an animal would (e.g. a dog that feels that its territory is being threatened by some other dog that decided to pee there for some reason). In other words, intelligence can advance all it wants, but at the end of the day, its bearer is bound to act like an animal, since it only cares about winning its game (i.e. optimizing its objective function). This is sound reasonable, right?
Well no. This is a particular case where an AI is given only two options and a very rigid objective, while its perception is limited to the two dimensional data of the game and a score. So, one could argue that the whole scenario is oversimplified and unrealistic. Plus what would the AI do with all these apples? Does it account for the fact that some of them may go bad or that if it decides to sell them in some form (e.g. an apple pie), there is the law of diminishing returns in the ROI of this whole endeavor? What about AI politics? What would other AIs think if it exhibits such aggressive behavior? Would anyone ever want to collaborate with it for another project? Naturally, the AIs involved in Google’s experiment don’t think about these things (like a human would probably do), since they have a one-track mind, caring only about the number of apples they collect. In such a scenario, no matter how advanced the AI is, it’s bound to seek actions that optimize the corresponding objective function, attacking anything that comes in its way, much like a short-sighted beast.
Perhaps instead of taking the word of some “expert” as gospel, it would be more fruitful for someone to ponder on this matter himself. Also, if so inclined, one can build her own AI experiments and explore other alternatives in the AIs’ pursuit of apples (or some other measurable objective). After all, things are not so simple when it comes to AI, so it makes sense to examine this matter with sufficient depth of thought, unless of course we just opt for some sensational result to drive home a point, which may or may not bear any scientific validity.
So, recently I decided to make a couple of videos on niche topics, namely the Business Aspect of A.I. in Data Science and Extreme Learning Machines (ELMs). These vids are now available on Safari (here and here). Enjoy!
Note that in order to view these vids in their entirety you'll need a subscription to the platform. The latter enables you to view other materials, including a large variety of technical books as well as all my other videos. Cheers!
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.