Category Archives: AI

I.J. Good on Super Intelligence

Source: Gizmodo, Oct 2013

I. J. Good happened to invent the idea of an intelligence explosion, and if it really was possible. The intelligence explosion was the first big link in the idea chain that gave birth to the Singularity hypothesis.

In the 1965 paper “Speculations Concerning the First Ultra-intelligent Machine,” Good laid out a simple and elegant proof that’s rarely left out of discussions of artificial intelligence and the Singularity:

Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an “intelligence explosion,” and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make . . .

Thus the first ultraintelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control (emphasis mine).

In a 1996 interview with statistician and former pupil David L. Banks, Good revealed that he was moved to write his essay after delving into artificial neural networks. Called ANNs, they are a computational model that mimics the activity of the human brain’s networks of neurons. Upon stimulation, neurons in the brain fire, sending on a signal to other neurons. That signal can encode a memory or lead to an action, or both. Good had read a 1949 book by psychologist Donald Hebb that proposed that the behavior of neurons could be mathematically simulated.

In 1998, Good was given the Computer Pioneer Award of the IEEE (Institute of Electrical and Electronics Engineers) Computer Society. He was eighty-two years old. As part of his acceptance speech he was asked to provide a biography. He submitted it, but he did not read it aloud, nor did anyone else, during the ceremony. Probably only Pendleton knew it existed.

[The paper] “Speculations Concerning the First Ultra-intelligent Machine” (1965) . . . began:

“The survival of man depends on the early construction of an ultra-intelligent machine.” Those were his [Good’s] words during the Cold War, and he now suspects that “survival” should be replaced by “extinction.” He thinks that, because of international competition, we cannot prevent the machines from taking over. He thinks we are lemmings. He said also that “probably Man will construct the deus ex machina in his own image.”

Free (Online) Deep Learning Textbook

Source: DeepLearning book website, 2016

Lectures and Slides Available
http://www.deeplearningbook.org/lecture_slides.html 

Economics of Machine Intelligence

Source: HBR, Nov 2016

Technological revolutions tend to involve some important activity becoming cheap, like the cost of communication or finding information. Machine intelligence is, in its essence, a prediction technology, so the economic shift will center around a drop in the cost of prediction.

The first effect of machine intelligence will be to lower the cost of goods and services that rely on prediction. This matters because prediction is an input to a host of activities including transportation, agriculture, healthcare, energy manufacturing, and retail.

When the cost of any input falls so precipitously, there are two other well-established economic implications. First, we will start using prediction to perform tasks where we previously didn’t. Second, the value of other things that complement prediction will rise.

Lots of tasks will be reframed as prediction problems

As machine intelligence lowers the cost of prediction, we will begin to use it as an input for things for which we never previously did. As a historical example, consider semiconductors, an area of technological advance that caused a significant drop in the cost of a different input: arithmetic. With semiconductors we could calculate cheaply, so activities for which arithmetic was a key input, such as data analysis and accounting, became much cheaper. However, we also started using the newly cheap arithmetic to solve problems that were not historically arithmetic problems. An example is photography. We shifted from a film-oriented, chemistry-based approach to a digital-oriented, arithmetic-based approach. Other new applications for cheap arithmetic include communications, music, and drug discovery.

The same goes for machine intelligence and prediction. As the cost of prediction falls, not only will activities that were historically prediction-oriented become cheaper — like inventory management and demand forecasting — but we will also use prediction to tackle other problems for which prediction was not historically an input.

Consider navigation. Until recently, autonomous driving was limited to highly controlled environments such as warehouses and factories where programmers could anticipate the range of scenarios a vehicle may encounter, and could program if-then-else-type decision algorithms accordingly (e.g., “If an object approaches the vehicle, then slowdown”). It was inconceivable to put an autonomous vehicle on a city street because the number of possible scenarios in such an uncontrolled environment would require programming an almost infinite number of if-then-else statements.

Inconceivable, that is, until recently. Once prediction became cheap, innovators reframed driving as a prediction problem. Rather than programing endless if-then-else statements, they instead simply asked the AI to predict: “What would a human driver do?” They outfitted vehicles with a variety of sensors – cameras, lidar, radar, etc. – and then collected millions of miles of human driving data. By linking the incoming environmental data from sensors on the outside of the car to the driving decisions made by the human inside the car (steering, braking, accelerating), the AI learned to predict how humans would react to each second of incoming data about their environment. Thus, prediction is now a major component of the solution to a problem that was previously not considered a prediction problem.

Judgment will become more valuable

When the cost of a foundational input plummets, it often affects the value of other inputs. The value goes up for complements and down for substitutes. In the case of photography, the value of the hardware and software components associated with digital cameras went up as the cost of arithmetic dropped because demand increased – we wanted more of them. These components were complements to arithmetic; they were used together.  In contrast, the value of film-related chemicals fell – we wanted less of them.

All human activities can be described by five high-level components: data, prediction, judgment, action, and outcomes. For example, a visit to the doctor in response to pain leads to: 1) x-rays, blood tests, monitoring (data), 2) diagnosis of the problem, such as “if we administer treatment A, then we predict outcome X, but if we administer treatment B, then we predict outcome Y” (prediction), 3) weighing options: “given your age, lifestyle, and family status, I think you might be best with treatment A; let’s discuss how you feel about the risks and side effects” (judgment); 4) administering treatment A (action), and 5) full recovery with minor side effects (outcome).

As machine intelligence improves, the value of human prediction skills will decrease because machine prediction will provide a cheaper and better substitute for human prediction, just as machines did for arithmetic. However, this does not spell doom for human jobs, as many experts suggest. That’s because the value of human judgment skills will increase. Using the language of economics, judgment is a complement to prediction and therefore when the cost of prediction falls demand for judgment rises. We’ll want more human judgment.

For example, when prediction is cheap, diagnosis will be more frequent and convenient, and thus we’ll detect many more early-stage, treatable conditions. This will mean more decisions will be made about medical treatment, which means greater demand for the application of ethics, and for emotional support, which are provided by humans. The line between judgment and prediction isn’t clear cut – some judgment tasks will even be reframed as a series of predictions. Yet, overall the value of prediction-related human skills will fall, and the value of judgment-related skills will rise.

Interpreting the rise of machine intelligence as a drop in the cost of prediction doesn’t offer an answer to every specific question of how the technology will play out. But it yields two key implications: 1) an expanded role of prediction as an input to more goods and services, and 2) a change in the value of other inputs, driven by the extent to which they are complements to or substitutes for prediction. These changes are coming. The speed and extent to which managers should invest in judgment-related capabilities will depend on the how fast the changes arrive.

Word (Thought) Vectors

Source: Acolyer.Org, Apr 2016

What is a word vector?

At one level, it’s simply a vector of weights. In a simple 1-of-N (or ‘one-hot’) encoding every element in the vector is associated with a word in the vocabulary. The encoding of a given word is simply the vector in which the corresponding element is set to one, and all other elements are zero.

Suppose our vocabulary has only five words: King, Queen, Man, Woman, and Child. We could encode the word ‘Queen’ as:

Using such an encoding, there’s no meaningful comparison we can make between word vectors other than equality testing.

In word2vec, a distributed representation of a word is used. Take a vector with several hundred dimensions (say 1000). Each word is representated by a distribution of weights across those elements. So instead of a one-to-one mapping between an element in the vector and a word, the representation of a word is spread across all of the elements in the vector, and each element in the vector contributes to the definition of many words.

If I label the dimensions in a hypothetical word vector (there are no such pre-assigned labels in the algorithm of course), it might look a bit like this:

Such a vector comes to represent in some abstract way the ‘meaning’ of a word. And as we’ll see next, simply by examining a large corpus it’s possible to learn word vectors that are able to capture the relationships between words in a surprisingly expressive way. We can also use the vectors as inputs to a neural network.

Thought Vectors

Source: InfoProc, Feb 2017

The implications of this for document processing are very important. If we convert a sentence into a vector that captures the meaning of the sentence, then Google can do much better searches; they can search based on what’s being said in a document.

Also, if you can convert each sentence in a document into a vector, then you can take that sequence of vectors and [try to model] natural reasoning. And that was something that old fashioned AI could never do.

If we can read every English document on the web, and turn each sentence into a thought vector, you’ve got plenty of data for training a system that can reason like people do.

Now, you might not want it to reason like people do, but at least we can see what they would think.

What I think is going to happen over the next few years is this ability to turn sentences into thought vectors is going to rapidly change the level at which we can understand documents.

To understand it at a human level, we’re probably going to need human level resources and we have trillions of connections [in our brains], but the biggest networks we have built so far only have billions of connections. So we’re a few orders of magnitude off, but I’m sure the hardware people will fix that.

MSFT CEO Nadella: Need AI to Drive Global Growth

Source: Silicon Review, Jan 2017

Speaking at the World Economic Forum in Davos, Nadella disproves the claims that AI would speed up wealth inequality and in its place said that it could be a very important driver of growth. “One of the things I am grounded in is the overall world GDP (gross domestic product) growth is not stellar,” he said at a panel at the event in Switzerland recently.

“It’s not like we actually have economic growth today. So we actually need technological breakthrough, we need AI,” he added.

“Our responsibility is to have the AI augment the human ingenuity and augment the human opportunity. I think that’s the opportunity in front of us and that’s what we have got to go to work on”

Elon Musk: Cyborg Interface between Human Brains and Machines

Source: CNBC, Feb 2017

“Over time I think we will probably see a closer merger of biological intelligence and digital intelligence,” Musk told an audience at the World Government Summit in Dubai, where he also launched Tesla in the United Arab Emirates (UAE).

“It’s mostly about the bandwidth, the speed of the connection between your brain and the digital version of yourself, particularly output.”

Musk explained what he meant by saying that computers can communicate at “a trillion bits per second”, while humans, whose main communication method is typing with their fingers via a mobile device, can do about 10 bits per second.

In an age when AI threatens to become widespread, humans would be useless, so there’s a need to merge with machines, according to Musk.

“Some high bandwidth interface to the brain will be something that helps achieve a symbiosis between human and machine intelligence and maybe solves the control problem and the usefulness problem,” Musk explained.