What shall we do tonight? You ask. Friend A is flexible — You choose — while Friend B has a strong opinion and lets you know it. Which friend is being kinder to you?
In “Algorithms to Live by” , the authors argue that Friend B is the more generous of the two:
“Seemingly innocuous language like ‘Oh, I’m flexible’ […] has a dark computational underbelly that should make you think twice. It has the veneer of kindness about it, but it does two deeply alarming things. First, it passes the cognitive buck: ‘Here’s a problem, you handle it.’ Second, by not stating your preferences, it invites the others to simulate or imagine them.”
If we allow that deciding on a plan for the evening is mostly a matter of computation, this computational kindness principle has evident merits and I, for one, am nodding in agreement. And so are also the big tech companies, all furiously competing to be your best Friend B. The last campaign by Google — Make Google do it. — makes this plain: The ambition is that of thinking for us — giving us directions, telling us what to buy, what to watch, where to go, whom to date, and so on.
The amount of cognitive offload from humans to AI-powered apps is an evident, and possibly defining, trend of our times. As pointed out by James Bridle in “New Dark Age: Technology and the End of the Future“, this process is accompanied by the spreading “belief that any given problem can be solved by the application of computation”, so that
“Computation replaces conscious thought. We think more and more like machine, or we do not think at all.”
A typical way to justify our over-reliance on machines as surrogates for our own cognitive capacities is to point to the complexity of the modern world, which has been compounded by the inter-connectivity brought about by the Internet. This view echoes this prescient 1926 passage by H. P. Lovecraft as cited by Bridle:
“The most merciful thing in the world, I think, is the inability of the human mind to correlate all its contents. We live on a placid island of ignorance in the midst of black seas of infinity, and it was not meant that we should voyage far. The sciences, each straining in its own direction, have hitherto harmed us little; but some day the piecing together of dissociated knowledge will open up such terrifying vistas of reality, and of our frightful position therein, that we shall either go mad from the revelation or flee from the light into the peace and safety of a new dark age.”
But the effects of this unprecedented cognitive offload, even on our health, are at best unclear. Ominously, Vivienne Ming warns that, as the use of apps is depriving our brains of the exercise they have becomes used to over millions of years, we might see widespread early-onset dementia within a single generation.
Some find the (relatively) new tools a liberation from the time-consuming task of acquiring domain knowledge and advanced analytical skills. For this camp, machine learning shifts the engineering problem from the traditional model-based optimization of individual functionalities to an overall data-driven end-to-end design.
Others hold the reduction of communication engineering to the choice of objective functions and hyperparameters of general-purpose machine learning algorithms to be disturbing and demeaning. This second camp views machine learning as an excuse to cop out on the engineering responsibilities of ensuring reliable and verifiable performance guarantees.
And yet, to continue with the initial paraphrase: Where is the research group that has not attempted to apply machine learning to well-studied communication problems? Where is the communication researcher who has not tweaked hyperparameters so as to reproduce known engineering solutions?
Some perspective seems needed in order to find a beneficial synthesis between the two extreme viewpoints that may open new opportunities rather than transform communication engineering into a branch of computer science. To this end, I would propose that the use of machine learning in communications is justified when:
1) a good mathematical model exists for the problem at hand, and:
- an objective function is hard to define or to optimize mathematically (even approximately);
- it is easy to draw data samples from the model;
- it is easy to evaluate desired pairs of well-specified input and output pairs (supervised learning), or to provide feedback on sequentially selected outputs (reinforcement learning);
- the task does not required detailed explanations or performance guarantees;
or 2) a good mathematical model does not exist, and:
- it is easy to collect real-world data;
- the phenomenon under study does not change rapidly over time;
- it is easy to evaluate desired pairs of well-specified input and output pairs (supervised learning), to assess the quality of the generated output (unsupervised learning), or to provide feedback on sequentially selected outputs (reinforcement learning);
- the task does not required detailed explanations or performance guarantees.
Even when the use of machine learning methods is justified, the adoption of a data-driven approach does not entail the complete rejection of domain knowledge and engineering principles. As a case in point, the decoding of a dense channel code via efficient message passing schemes is known to fail in general. Nevertheless, the structure of a message passing algorithm can still be leveraged to define a parametrized scheme that can be tuned based on input-output examples.
More broadly, engineering a modern communication system is likely to call for a macro-classification of subtasks for which either model- or data-driven approaches are most suitable, followed by a micro-design of individual subproblems and by a possible end-to-end fine-tuning of parameters via learning methods.
Computer scientists and communication engineers of all countries, unite?
The novel “Snow Crash” published in 1992 by Neal Stephenson, is often invoked as a prime example of fiction that has shaped, and not merely predicted, today’s technological landscape. The following lightly commented list of quotes from the first one hundred pages offer ample justification for the novel’s reputation. [Comments are in brackets.]
“… all of the information got converted into machine-readable form, which is to say, ones and zeroes. And as the number of media grew, the material became more up to date, and the methods for searching the Library became more up to date […] Millions […] are uploading millions of other fragments at the same time. […] clients, mostly large corporations and Sovereigns, rifle through the Library looking for useful information, and if they find a use for something that Hiro put into it, Hiro gets paid.”
[Mosaic, the first popular web browser, was introduced only in 1993, and the first widely known search engine, WebCrawler, came out in 1994. Rather than relying on systems of payments as envisaged in the novel, the world wide web was built on an advertisement-based model. This system is currently being challenged by the changing legal and political climate, while new technologies for micro-payments are emerging.]
“By drawing a slightly different image in front of each eye, the image can be made three-dimensional. By changing the image seventy-two times a second, it can be made to move. By drawing the moving three-dimensional image at a resolution of 2K pixels on a side, it can be as sharp as the eye can perceive, and by pumping stereo digital sound through the little earphones, the moving 3-D pictures can have a perfectly realistic soundtrack. So Hiro’s not actually here at all. He’s in a computer-generated universe that his computer is drawing onto his goggles and pumping into his earphones. In the lingo, this imaginary place is known as the Metaverse.”
“It was, of course, nothing more than sexism, the especially virulent type espoused by male techies who sincerely believe that they are too smart to be sexists.”
[Sadly still relevant today.]
“There is something new: A globe about the size of a grapefruit, a perfectly detailed rendition of Planet Earth, hanging in space at arm’s length in front of his eyes. […] It is a piece of […] software called, simply, Earth. It is the user interface that CIC uses to keep track of every bit of spatial information that it owns — all the maps, weather data, architectural plans, and satellite surveillance stuff. ”
[This ideas has reportedly inspired the development of Google Earth.]
““For the most part I write myself,” explains an automated online search engine. “That is, I have the innate ability to learn from experience. But this ability was originally coded into me by my creator.”
[A good self-definition of a machine learning algorithm.]
- Human imitation: AI should behave in a way that is indistinguishable from that of a human being.
- Intelligence Augmentation (IA): AI should augment human capacity to think, communicate, and create.
- Intelligence Infrastructure (II): AI should manage a network of computing and communicating agents, markets, and repositories of information in a way that is efficient, supportive of human and societal needs, as well as economically and legally viable.
Human-imitative AI is often singled out by technologists, academicians, journalists and venture capitalists as the real aspiration and end goal of AI research. However, most progress in “AI” to date has not concerned high-level abstract thinking machines, but rather low-level engineering solutions with roots in operations research, statistics, pattern recognition, information theory and control theory.
In fact, as argued by Jordan, the single-minded focus on human-imitative AI has become a distraction from the more useful endeavor of addressing IA and II. To this end, the post calls for the founding of a new engineering discipline building on
“ideas that the preceding century gave substance to — ideas such as “information,” “algorithm,” “data,” “uncertainty,” “computing,” “inference,” and “optimization.” Moreover, since much of the focus of the new discipline will be on data from and about humans, its development will require perspectives from the social sciences and humanities.”
The development of such a discipline would call not only for large-scale targeted research efforts, but also for new higher education programs. As proposed in “Robot-proof“, the new programs should impart data literacy, technological literacy, and human literacy:
“Students will need data literacy to manage the flow of big data, and technological literacy to know how their machines work, but human literacy–the humanities, communication, and design–to function as a human being. Life-long learning opportunities will support their ability to adapt to change.“
[According to Google’s Talk to Books]
“Information theory is a branch of applied mathematics providing a framework allowing the quantification of the information generated or transmitted through a communication channel.” from The Manual of Photography
“Information theory is a mathematical theory dealing with highly precise aspects of the communication of information in terms of bits through well-defined channels. The theory of international politics developed in this volume is non-mathematical and non-precise.” from System and Process in International Politics
“Information theory is a branch of science, mathematics, and engineering that studies information in a physical and mathematical context rather than a psychological framework.” from Assessing and Measuring Environmental Impact and Sustainability
“Information theory is the science of message transmission developed by Claude Shannon and other engineers at Bell Telephone Laboratories in the late 1940s. It provides a mathematical means of measuring information.” from The Creation Hypothesis: Scientific Evidence for an Intelligent Designer
“Information theory is a branch of statistics and probability theory dealing with the study of data, ways to manipulate it (for instance, cryptography and compression) and communicate it (for instance, data transmissions and communication systems)”. from AI Game Development: Synthetic Creatures with Learning and Reactive Behaviors
“Information theory provides a means to quantify the complexity of information that can be used in the design of communication systems (Shannon 1948). It originated during World War II as a tool for assuring the successful transmission…” from Oceanography and Marine Biology: An Annual Review
A revised version of my notes on machine learning can be found here. I am grateful for all the comments received on the first version, and I welcome further feedback.
Watch any episode of Mad Men and you are likely to be struck by the casual way in which cigarettes were lit and smoked in quick succession in the 60s: How could everyone be so oblivious to the health risks? A new report by the National Toxicology Program of the US Department of Health and Human Services raises concerns that we may have been similarly nonchalant about the use of wireless devices in the last twenty years. While the findings are not conclusive, there appears to be enough evidence to recommend a reconsideration of a pervasive use of wearable wireless devices around the human body. For further discussion, I recommend this interview.
One often hears the following two statements made in the same breath: that the current resurgence of artificial intelligence research will fundamentally transform the way in which we live, and that we are on the verge of mastering general intelligence. The frequent conflating of these two strikingly different assessments has led to predictions ranging from an evolutionary catastrophe for the human race to the onset of a downward cycle in AI of innovation brought about by overhype. In order to make sense of these claims, it is useful to take a deeper look at the current state of the art and to walk a few steps back in history for some perspective.
One of the most publicized successes of modern AI is given by programs, developed most notably by DeepMind, that have mastered complex games such as Go, obtaining super-human performance. The topic is considered to be of sufficient dramatic heft to justify a Netflix production. An aspect that has particularly captured the attention of commentators is the capability of these algorithms to learn from a blank slate, not requiring even an initial nudge by the programmers towards strategies that have been found to be effective by human players.
The engine underlying these programs is reinforcement learning, which builds on the idea of using feedback from a large number of simulated experiences to slowly gather information about the environment and/or the optimal strategies to be adopted. The specific algorithms employed date from the 70s and can be eloquently explained by a cartoon. What the new AI wave has brought to the table is hence, by and large, not a novel understanding of intelligence, but rather a bag of clever tricks that allows old algorithms to make an effective statistical use of unprecedented computational resources. Today’s algorithms would look very familiar to a researcher of the 80s. But they would also be useless: with her computing technology, the researcher would have to wait a few million years to obtain the same results that now take just a few days on modern processors.
In fact, even the most straightforward black-box optimization schemes, such as evolution strategies, have been shown to provide state-of-the-art results. These schemes merely test many random perturbations of the current strategy by leveraging computing parallelism, and they modify the current solution according to the feedback received from simulations of the environment. As such, these methods merely rely on the capability of the computing system to simulate the effect of many variations of the actions on the environment across test runs.
The fact that sheer computing power is to be credited for the most visible successes of AI should give us pause when commenting on our understanding of intelligence. The key principles at play in the current AI wave are in fact still the same postulated by Norbert Wiener’s cybernetics, namely information processing and feedback. Whether these are the right principles on which to build a theory of intelligence appears to be a valid open question.
As nicely summarized in Jessica Riskin’ essay, our understanding of intelligent behavior has over the centuries shifted to concentrate on different manifestations of intelligence, such as motion or programmability. For example, following Aristotle’s definition of living beings as things that can move at will, hydraulic automata that can make water travel upward, against gravity, would qualify as intelligent. In light of the apparent limitations of our current understanding of intelligence, artificial or otherwise, will some new principle of intelligence emerge that will make our current established framework appear as quaint as Aristotle’s?
As any quantitative researcher knows all too well, visualizing data facilitates interpretation and extrapolation, and can be a powerful tool for solving problems and motivating decision and actions. Visualization allows one to leverage our intuitive sense of space in order to grasp connections and relationships, as well as to notice parallels and analogies. (It can, of course, also be used to confuse).
Modern machine learning algorithms operate on very high dimensional data structures that cannot be directly visualized by humans. In this sense, machines can “see”, and “think”, in spaces that are inaccessible to our eyes. To put it with Richard Hamming’s words:
“Just as there are odors that dogs can smell and we cannot, as well as sounds that dogs can hear and we cannot, so too there are wavelengths of light we cannot see and flavors we cannot taste. Why then, given our brains wired the way they are, does the remark ‘Perhaps there are thoughts we cannot think’, surprise you?”
In an era of smart homes, smart cities, and smart governments, methods that visualize high-dimensional data in two dimensions can allow us to bridge, albeit partially, the understanding gap between humans and algorithms. This explains the success of techniques such as t-SNE, especially when coupled with interactive graphical interfaces.
As virtual reality and mixed technologies vie with standard two-dimensional interfaces as the dominant medium between us and the machines, data visualization and interactive representation stand to gain another dimension. And the difference may not be merely a quantitative one. As suggested by Jaron Lanier:
“People think differently when they express themselves physically. […] Having a piano in front of me makes me smarter by applying the biggest part of my cortex, the part associated with haptics. […] Don’t just think of VR as the place where you can look at a molecule in 3-D, or perhaps handle one, like all those psychiatrists in Freud avatars. No! VR is the place where you become a molecule. Where you learn to think like a molecule. Your brain is waiting for the chance.”
As such, VR may allow humans “to explore motor cortex intelligence”. Can this result in a new wave of innovations and discoveries?