Well, like most things of a certain level of sophistication, the answer is it depends. But before we delve into this matter, let’s start with defining what DS research is exactly. By this term, I refer to with the advancement of the field through the experimentation around new ideas, methods, techniques, and even the development and testing of new algorithms applicable to data science. Sounds like a lot but in practice there is a great deal of specialization so it’s not as overwhelming. For example, someone may do research in the data science technology focusing on distributed computing, while someone else focus on the design of a new supervised learning technique or a heuristic.

But don’t you need funding for all this? Well, in the traditional approach to research funding, usually in the form of grants sponsored by a government or some large organization, is something essential. After all, scientific research requires a great deal of resources and people who although passionate about the subject, may not work for free. Nevertheless, the expenses of research in data science are minimal, meaning that you don’t need a huge grant in order to get the ball rolling.

In essence, when you do DS research your key expenses are your time and the cloud computing rental. After all, Amazon and Microsoft need to make some money too when you are using their cloud services for your projects. Still, the prototyping is something you can do on your own computer so the cloud bill doesn’t have to be very high, unless you are working with a particularly large dataset, one that qualifies as big data.

I’m not saying that everyone can do data science research on his own. However, nowadays it’s easier than ever before to experiment without a lot of facilities or some sponsorship for a research project. People have been publishing papers on their own for years now and unless you want to do some large-scale research project, you can work with limited resources. And who knows, maybe this idea of yours can morph into a business product or service that can be a data science start-up. It doesn’t hurt to try!

A good tool for doing data science research is Julia, particularly through the Jupyter IDE. You can learn more about the language through the corresponding website, while my book on it can be a great resource for delving into it deeper. Note that the book was written for an earlier version of the language so the code may not be compatible with the latest version (v. 1.0) of Julia. Cheers!

Trinary Logic is not something new. It’s been around for decades, though it was more of a mathematical / high-level framework. I should know, as I did my Masters thesis on this subject and how it applies to GIS. I even wrote code implementing the corresponding model I came up with, though in today’s programming world it seems like legacy code... Anyway, bottom line is that Trinary Logic is useful and could have a place in modern Information Systems, including data analytics projects. The question is, could it be applicable to A.I. too?

The answer is, as usual, “it depends.” Trinary Logic on its own is quite limited and unless you are familiar with its 700+ gates, it may be like any novel idea: interesting but not exactly something worth delving into. After all, just like any system of reasoning, Trinary Logic is meaningless without an in-depth understanding of its key contribution to the thorny issue we always tackle through reasoning: handling uncertainty effectively.

Uncertainty, oftentimes modeled as noise or randomness (depending on who you ask), is everywhere. Since we cannot eliminate it without damaging the signal too, we find ways to cope with it. Trinary Logic offers an interesting way of doing that through the 3rd value of its variables, namely the “indifferent” state. Something can be True, False, or Indifferent, the latter being something in-between. These are the states of those intermediate values in the membership functions of fuzzy variables, in Fuzzy Logic. The latter is a well-known and quite established A.I. framework with lots of applications in data science. Do you see where I’m going with this?

So Trinary Logic is a framework for reasoning, much like Fuzzy Logic, but the latter is an A.I. framework too, so Trinary Logic is A.I. also, right? Well, no. Trinary Logic is a mathematical construct, so unless it is applied to A.I. programmatically, and as a well-defined process, it is yet another concept that can’t even fetch an academic publication! But if it were to manifest as a heuristic of sorts and add value to a process in the A.I. sphere, things would be different.

Enter the Trinary Curve, a heuristic (or meta-heuristic, depending on how you use it) that encapsulates Trinary Logic in a simple yet not simplistic way, turning an input signal into something that an A.I. agent can understand and work with. Namely, it can engineer a new variable in the [-1, 1] interval (notice the closed brackets in this case), that enables the corresponding module to have the in-between state of uncertainty more evident. As a result, the A.I. agent is allowed to be unsure about something and examine it more closely, given the right architecture, instead of working with what it has and hope for the best. Note that the Trinary Curve can be customized, while its output can be normalized to a different interval (always closed) if needed. The Trinary Curve is differentiatable throughout the space it is defined, while it’s easy to use programmatically (at least in Julia).

Perhaps the Trinary Curve is a novelty and an A.I. system can evolve adequately without it. However, it is something worth considering, instead of just experimenting with the countable parameters of existing A.I. systems solely. After all, Trinary Logic is compatible with existing A.I. frameworks so if it’s not utilized, it’s primarily because of some people’s unwillingness to think outside the box, and that’s something that doesn’t have any uncertainty about it...

This week I'm away, as I prepare for my talk at the Consumer Identity World EU 2018 conference in Amsterdam (the same conference takes place in a couple of other places, but I'll be attending just the one in Europe). So, if you are in the Dutch capital, feel free to check it out. More information on my talk here. Cheers!

Dichotomy: a binary separation of a set into two mutually exclusive subsets

Data Science: the interdisciplinary field for analyzing data, building models, and bringing about insights and/or data products, which add value to an organization. Data science makes use of various frameworks and methodologies, including (but not limited) to Stats, ML, and A.I.

After getting these pesky definitions out of the way, in an effort to mitigate the chances of misunderstandings, let’s get to the gist of this fairly controversial topic. For starters, all this information here is for educational purposes and shouldn’t be taken as gospel since in data science there is plenty of room for experimentation and someone adept in it doesn’t need to abide to this taxonomy or any rules deriving from it.

The inaccurate dichotomy issues in data science, however, can be quite problematic for newcomers to the field as well as for managers involved in data related processes. After all, in order to learn about this field a considerable amount of time is required, something that is not within the temporal budget of most people involved in data science, particularly those who are starting off now. So, let’s get some misconceptions out of the way so that your understanding of the field is not contaminated by the garbage that roams the web, especially the social media, when it comes to data science.

Namely, there are (mis-)infographics out there that state that Stats and ML are mutually exclusive, or that there is no overlap between non-AI methods and ML. In other words, ML is part of AI, something that is considered blasphemy in the ML community. The reason is simple: ML as a field was developed independently of AI and has its own applications. AI can greatly facilitate ML through its various network-based models (among other systems), but ML stands on its own. After all, many ML models are not AI related, even if AI can be used to improve them in various ways. So, there is an overlap between ML and AI, but there are non-AI models that are under the ML umbrella.

Same goes with Statistics. This proud sub-field of Mathematics has been the main framework for data analytics for a long time before ML started to appear, revolting against the model-based approach dictated by Stats. However, things aren’t that clear-cut. Even if the majority of Stats models are model-based, there are also models that are hybrid, having elements of Stats and ML. Take Bayesian Networks for example, or some variants of the Naive Bayes model. Although these models are inherently Statistical, they have enough elements of ML that they can be considered ML models too. In other words, they lie on the nexus of the two sets of methods.

What about Stats and AI? Well, Variational AutoEncoders (VAEs) are an AI-based model for dimensionality reduction and data generation. So, there is no doubt that it lies within the AI set. However, if you look under the hood you’ll see that it makes use of Stats for the figuring out what the data generated by it would be like. Specifically, it makes use of distributions, a fundamentally statistical concept, for the understanding and the generation of the data involved. So, it wouldn’t be far-fetched to put VAEs in the Stats set too.

From all this I hope it becomes clear that the taxonomy of data science models isn’t that rigid as it may seem. If there was a time when this rigid separation of models made sense, this time is now gone as hybrid systems are becoming more and more popular, while at the same time the ML field expands in various directions outside AI. So, I’d recommend you take those (mis-)infographics with a pinch of salt. After all, most likely they were created by some overworked employee (perhaps an intern) with a limited understanding of data science.

I've been writing a lot about A.I. lately and AGI has been a recurring topic lately. Although the possibility of this technology becoming a reality is still a bit futuristic, we can still ponder on the possibility and explore how such an A.I. system would affect us. Hence this fiction book I wrote in the past few months and published this week on Amazon (Kindle version only). Feel free to check it out when you have a minute.

The book is dedicated to researchers of A.I. Safety.

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!

Introduction
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.

Transparency
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.

Autonomy
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.

Scalability
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.

Transduction
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.

Efficiency
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.

Although lately I've been writing about the infeasibility of AGI in our current time and how an AGI can pose great threats, it is still useful to consider what would happen if an AGI actually existed and how it would see and interact with our world. Hence this novel, which through the first-person perspective of an AGI system, explores how the advent of such a technology could have noticeable consequences to our world, transcending even its creator's expectations. After all, the difference between an AGI with our level of intelligence and a super-intelligent AGI is not as large, though for the purpose of the  plot of this novel, it has been shown to take place over a period of several months.

In any case, if you are into science fiction and wish to contemplate on the matter of AGI and A.I. Safety, this novel may be for you. Feel free to check it out on Amazon (currently only in Kindle format). Thanks!

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.