[S5E3] Measure Of Intelligence
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[S5E3] Measure of Intelligence
Externally Daleks resemble human-sized pepper pots with a single mechanical eyestalk mounted on a rotating dome, a gun-mount containing an energy-weapon ("gunstick" or "death ray") resembling an egg-whisk, and a telescopic manipulator arm usually tipped by an appendage resembling a sink-plunger. Daleks have been known to use their plungers to interface with technology, crush a man's skull by suction, measure the intelligence of a subject, and extract information from a man's mind. Dalek casings are made of a bonded polycarbide material called "Dalekanium" by a member of the human resistance in The Dalek Invasion of Earth and the Dalek comics, as well as by the Cult of Skaro in "Daleks in Manhattan."
A rel is a Dalek and Kaled unit of measurement. It was usually a measurement of time, with a duration of slightly more than one second, as mentioned in "Doomsday", "Evolution of the Daleks" and "Journey's End", counting down to the ignition of the reality bomb. (One earth minute most likely equals about 50 rels.) However, in some comic books it was also used as a unit of velocity. Finally, in some cases it was used as a unit of hydroelectric energy (not to be confused with a vep, the unit used to measure artificial sunlight).
If you have an artificial pancreas, it will measure your glucose and insulin levels and deliver insulin as needed to maintain them appropriately. This kind of closed loop system is beginning to come into medicine. Another place you see it would be an Automatic Positive Airway Pressure (APAP) machine. If you wear it at night for sleep apnea, that machine will measure your breathing and will adjust itself to accommodate what you need. But most medicine is still reactive rather than proactive. It's an open loop rather than a closed loop.
MH: Well, help me understand how you build a digital twin, because I can understand the concept of a digital twin on a factory floor. You've got Internet of Things (IoT) sensors for the assembly line, you've got cameras that monitor individuals, you've got an incredible amount of data that's coming into an artificial intelligence machine learning system to build that digital twin. What are the data points that allow you to build a digital twin from the human body? Are you going to have to put machinery in me?
But if you ask what it was like 30 years ago, basically you could predict the weather a day or two in advance. Now they could go out for about a week. I think the thing to think about is what we can do to predict short times and then ask the question, how do you extend the time you can predict? And the other key questions, as you point out, will be instrumentation. What can you measure?
A third thing, which is important, is what kind of model you use to do the prediction. You mentioned machines, machine learning, artificial intelligence, forward prediction. And that works very well in many cases, and it's used a lot today. But if you look at industrial digital twins and you look at the history of digital twins, for example, General Electric, they started out with detailed mechanistic models of how the devices that they were going to predict worked.
And the reason for that is that one of the things in engineering as a principle is that when you're going to do control, you want to measure as close to the point of control as possible, and you want to apply the control as close to the outcome as possible. In medicine, especially with drugs, you're applying a molecular perturbation. And modern molecular biology is tremendous. We know all sorts of things about the molecular states of cells.
And that means that direct prediction is very difficult. I'm not promising that you'll have medical digital twins or immune digital twins in the next five years, I'm afraid. It's a hard problem and you've identified, I think, one of the killers, which is instrumentation. At the moment, we can't measure cytokine levels. If you're in the hospital, you're lucky if they do a cytokine profile once a week. If you ask the question about infectious diseases, by the time you have symptoms of the flu, your viral load is already peaking. That's why antivirals generally aren't useful. They work very well, but by the time you know you need an antiviral, it's too late to take them.
JG: That's a great question. If you look at measurements today, we run this National Institute of Health (NIH) working group and do these seminars, and we try to get industrial representation. And the other day, we had a talk with a company that's essentially doing monitoring, biosensor monitoring, attached, a glue on biosensor. They are measuring heart rate, body temperature, blood oxygenation. The simple things that a Fitbit can measure essentially, although it's glued on so it's a little bit better than a Fitbit.
And interestingly, they started with mechanistic models, and they went to a pure AI model, because the kind of prediction they're trying to make doesn't need a bigger model and it's faster. The key thing that they try to do is they're trying to back out the unmeasurable things about the body from the things that you can measure. For example, if I look at body temperature, every human being's body temperature is different. Doing a population average on body temperature doesn't tell me much.
And I think the place you're going to see this kind of thing done first will probably be in intensive care wards. You're going to see people who have a central line. You're going to see the development of sensors that can measure cytokine panels that can be put in there, and that will give real-time measurement of the immune state to warn you when you're having a septic shower and these kinds of things.
Now, is it fast enough that a typical VC in an era of high interest rates will want to fund it today? Maybe not. Three years ago, maybe in the era of low interest rates. But what do we need to be able to do it? We need to be able to measure the immune state. We need to be able to have models that take the immune state and predict what the immune state will be going forward. And that won't be a single model. That's going to be like weather forecasting, where you'll have an ensemble forecast of probabilities of the forward state.
Once you have that forward state, the question is how far you can predict forward. You need to decide whether the model prediction and the measured state agree or not. That sounds trivial. With temperature, the thermostat could say, "It should be this temperature. The actual temperature is this." We could make a difference. With things like the immune state, it's a little bit harder, because we don't know how to combine our signals in a way that gives us what's called a residual, a measurement of the error of our result.
A 2006 study conducted by psychology professor Keith Oatley at the University of Toronto found that people who read a lot of fiction tend to score better on tests that measure empathy and emotional intelligence. While researchers are still working to better understand the link between empathy and regular reading, we can reasonably conclude that our favorite books stir something within us, something that feels uniquely human.
At the same time the dialogue between Roose and LF was brilliant, the mis-step is on purpose, Roose is formidable and this is pointing to him having the measure of LF + it is hinting at the fact LF is slightly out of his depth in the north
This is all to show Roose has the measure of him on one hand, but it is all hinting at the fact that LF is out of his depth. Not subtle but this is a TV medium so they have to be kind of obvious with so many things
For young people to be able to lead and succeed in the data-driven economy, a strong understanding of this ever-evolving technology is paramount. In order to engage students with this topic, lessons should not only highlight the many forms that artificial intelligence can take in the real world, but also offer tangible experience of and interactions with the technology. Here are just some of the angles from which this topic can be approached, and suggestions for resources that can complement them.
With the recent proliferation of smart speakers and virtual assistants, this technology can be a useful framework for an initial discussion around the key tenets of artificial intelligence. Most young people will have been exposed to these devices in some form; fewer, however, are likely to identify them as an example of AI. You could ask students:
There is a wealth of resources that we can draw on to assist students in forming their own opinions in this debate. The Turing test, for example, is an important concept for students to understand and remains a useful benchmark against which to measure the capabilities of AI technology. Encourage your students to read aloud some of the transcripts for entries to the most recent Loebner Prize, an annual Turing test competition. Would any of these convince them that they were speaking to a real human?
Meanwhile, developments in self-driving vehicle technology presents a unique opportunity for students to explore ethics in the context of computer science. Moral Machine, developed by the Massachusetts Institute of Technology, is an interactive tool that asks the user to judge the most acceptable outcomes of a series of moral dilemmas faced by a driverless car. By engaging with this modern take on the classic trolley problem, students develop a deeper and more personal understanding of the ethical challenges surrounding artificial intelligence. 041b061a72