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Why I Became a Believer in Artificial Intelligence
My opinion is that IBM’s Watson computer is able to answer questions, and so, in my subjective view, that qualifies as intelligence.
I’ve been asked periodically for a couple of decades whether I think artificial intelligence is possible. And I taught the artificial intelligence course at Columbia University. I’ve always been fascinated by the concept of intelligence. It’s a subjective word. I’ve always been very skeptical. And I am only now newly a believer.
Now this is subjective: my opinion is that IBM’s Watson computer is able to answer questions, and so, in my subjective view, that qualifies as intelligence. I spent six years in graduate school working on two things. One is machine learning and that’s the core to prediction – learning from data how to predict. That’s also known as predictive modeling. And the other is natural language processing or computational linguistics.
Working with human language really ties into the way we think and what we’re capable of doing and that does turn out to be extremely hard for computers to do. Now playing the TV quiz show Jeopardy means you're answering questions – quiz show questions. The questions on that game show are really complex grammatically. And it turns out that in order to answer them Watson looks at huge amounts of text, for example, a snapshot of all the English speaking Wikipedia articles. And it has to process text not only to look at the question it’s trying to answer but to retrieve the answers themselves. Now at the core of this it turns out it’s using predictive modeling. Now it’s not predicting the future but it’s predicting the answer to the question.
The core technology is the same. In both cases it involves learning from examples. In the case of Watson playing the TV show Jeopardy it takes hundreds of thousands of previous Jeopardy questions from the TV show having gone on for decades and learns from them. And what it’s learning to do is predict whether this candidate answer to this question is likely to be the correct answer. So it’s going to come up with a whole bunch of candidate answers, hundreds of candidate answers, for the one question at hand at any given point in time. And then amongst all these candidate answers it’s going to score each one. How likely is it to be the right answer? And, of course, the one that gets the highest score as the highest vote of confidence – that’s ultimately the one answer it’s going to give.
So it’s not a yes/no thing. It’s trying to choose between a huge number of candidate answers and it has to choose the one correct answer to the question in order to be correct. And it is correct a great deal of the time. It knows how to assess its own confidence, buzz in on the game show if it feels it has a high confidence. And when it does, it is correct. It’s correct, I believe, about 90 or 92 percent of the time that it actually buzzes in to intentionally answer the question. The way it does this is by looking through all of this text to try to find all kinds of little evidence is this the right answer. And then once again, just like predicting a human’s behavior where you know a bunch of things about the person and you want to pull them all together – in this case you know a bunch of pieces of evidence.
Some of them are arcane, some of them are simplistic, some of them are grammatically deep but brittle, so there’s lots of errors – it’s not very trustable. But there’s a lot of them and you bring them all together with a predictive model that’s been derived automatically over the hundreds of thousands of learning examples from prior questions from prior TV episodes of this quiz show and by doing that, by driving it with all of that information, right. So it’s this sort of merging of many examples. Here’s a question and here’s what turned out to be the right answer and all the textual data. And it’s merging this together and creating – it emerges from that process the ability to sort of rattle off answers to questions.
You can go on YouTube and you can watch the episode where they aired the competition between IBM’s computer Watson and the all time two human champions of Jeopardy. And it just rattles off one answer after another. And it doesn’t matter how many years you’ve been looking at this. In fact, maybe the more years you’ve studied the ability or inability of computers to work with human language, the more impressive it is. It’s just rattling off one answer after another. I never thought that in my lifetime I would have cause to experience that the way I did which was, “Wow, that’s anthropomorphic. This computer seems like a person in that very specific skill set. That’s incredible. I’m gonna call that intelligent.”
Eric Siegel's chapter detailing how IBM's Watson works is in his new book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.
In Their Own Words is recorded in Big Think's studio.
Image courtesy fo Shutterstock.
Why mega-eruptions like the ones that covered North America in ash are the least of your worries.
- The supervolcano under Yellowstone produced three massive eruptions over the past few million years.
- Each eruption covered much of what is now the western United States in an ash layer several feet deep.
- The last eruption was 640,000 years ago, but that doesn't mean the next eruption is overdue.
The end of the world as we know it
Panoramic view of Yellowstone National Park
Image: Heinrich Berann for the National Park Service – public domain
Of the many freak ways to shuffle off this mortal coil – lightning strikes, shark bites, falling pianos – here's one you can safely scratch off your worry list: an outbreak of the Yellowstone supervolcano.
As the map below shows, previous eruptions at Yellowstone were so massive that the ash fall covered most of what is now the western United States. A similar event today would not only claim countless lives directly, but also create enough subsidiary disruption to kill off global civilisation as we know it. A relatively recent eruption of the Toba supervolcano in Indonesia may have come close to killing off the human species (see further below).
However, just because a scenario is grim does not mean that it is likely (insert topical political joke here). In this case, the doom mongers claiming an eruption is 'overdue' are wrong. Yellowstone is not a library book or an oil change. Just because the previous mega-eruption happened long ago doesn't mean the next one is imminent.
Ash beds of North America
Ash beds deposited by major volcanic eruptions in North America.
Image: USGS – public domain
This map shows the location of the Yellowstone plateau and the ash beds deposited by its three most recent major outbreaks, plus two other eruptions – one similarly massive, the other the most recent one in North America.
The Huckleberry Ridge eruption occurred 2.1 million years ago. It ejected 2,450 km3 (588 cubic miles) of material, making it the largest known eruption in Yellowstone's history and in fact the largest eruption in North America in the past few million years.
This is the oldest of the three most recent caldera-forming eruptions of the Yellowstone hotspot. It created the Island Park Caldera, which lies partially in Yellowstone National Park, Wyoming and westward into Idaho. Ash from this eruption covered an area from southern California to North Dakota, and southern Idaho to northern Texas.
About 1.3 million years ago, the Mesa Falls eruption ejected 280 km3 (67 cubic miles) of material and created the Henry's Fork Caldera, located in Idaho, west of Yellowstone.
It was the smallest of the three major Yellowstone eruptions, both in terms of material ejected and area covered: 'only' most of present-day Wyoming, Colorado, Kansas and Nebraska, and about half of South Dakota.
The Lava Creek eruption was the most recent major eruption of Yellowstone: about 640,000 years ago. It was the second-largest eruption in North America in the past few million years, creating the Yellowstone Caldera.
It ejected only about 1,000 km3 (240 cubic miles) of material, i.e. less than half of the Huckleberry Ridge eruption. However, its debris is spread out over a significantly wider area: basically, Huckleberry Ridge plus larger slices of both Canada and Mexico, plus most of Texas, Louisiana, Arkansas, and Missouri.
This eruption occurred about 760,000 years ago. It was centered on southern California, where it created the Long Valley Caldera, and spewed out 580 km3 (139 cubic miles) of material. This makes it North America's third-largest eruption of the past few million years.
The material ejected by this eruption is known as the Bishop ash bed, and covers the central and western parts of the Lava Creek ash bed.
Mount St Helens
The eruption of Mount St Helens in 1980 was the deadliest and most destructive volcanic event in U.S. history: it created a mile-wide crater, killed 57 people and created economic damage in the neighborhood of $1 billion.
Yet by Yellowstone standards, it was tiny: Mount St Helens only ejected 0.25 km3 (0.06 cubic miles) of material, most of the ash settling in a relatively narrow band across Washington State and Idaho. By comparison, the Lava Creek eruption left a large swathe of North America in up to two metres of debris.
The difference between quakes and faults
The volume of dense rock equivalent (DRE) ejected by the Huckleberry Ridge event dwarfs all other North American eruptions. It is itself overshadowed by the DRE ejected at the most recent eruption at Toba (present-day Indonesia). This was one of the largest known eruptions ever and a relatively recent one: only 75,000 years ago. It is thought to have caused a global volcanic winter which lasted up to a decade and may be responsible for the bottleneck in human evolution: around that time, the total human population suddenly and drastically plummeted to between 1,000 and 10,000 breeding pairs.
Image: USGS – public domain
So, what are the chances of something that massive happening anytime soon? The aforementioned mongers of doom often claim that major eruptions occur at intervals of 600,000 years and point out that the last one was 640,000 years ago. Except that (a) the first interval was about 200,000 years longer, (b) two intervals is not a lot to base a prediction on, and (c) those intervals don't really mean anything anyway. Not in the case of volcanic eruptions, at least.
Earthquakes can be 'overdue' because the stress on fault lines is built up consistently over long periods, which means quakes can be predicted with a relative degree of accuracy. But this is not how volcanoes behave. They do not accumulate magma at constant rates. And the subterranean pressure that causes the magma to erupt does not follow a schedule.
What's more, previous super-eruptions do not necessarily imply future ones. Scientists are not convinced that there ever will be another big eruption at Yellowstone. Smaller eruptions, however, are much likelier. Since the Lava Creek eruption, there have been about 30 smaller outbreaks at Yellowstone, the last lava flow being about 70,000 years ago.
As for the immediate future (give or take a century): the magma chamber beneath Yellowstone is only 5 percent to 15 percent molten. Most scientists agree that is as un-alarming as it sounds. And that its statistically more relevant to worry about death by lightning, shark, or piano.
Strange Maps #1041
Got a strange map? Let me know at email@example.com.
Measuring a person's movements and poses, smart clothes could be used for athletic training, rehabilitation, or health-monitoring.
In recent years there have been exciting breakthroughs in wearable technologies, like smartwatches that can monitor your breathing and blood oxygen levels.
But what about a wearable that can detect how you move as you do a physical activity or play a sport, and could potentially even offer feedback on how to improve your technique?
And, as a major bonus, what if the wearable were something you'd actually already be wearing, like a shirt of a pair of socks?
That's the idea behind a new set of MIT-designed clothing that use special fibers to sense a person's movement via touch. Among other things, the researchers showed that their clothes can actually determine things like if someone is sitting, walking, or doing particular poses.
The group from MIT's Computer Science and Artificial Intelligence Lab (CSAIL) says that their clothes could be used for athletic training and rehabilitation. With patients' permission, they could even help passively monitor the health of residents in assisted-care facilities and determine if, for example, someone has fallen or is unconscious.
The researchers have developed a range of prototypes, from socks and gloves to a full vest. The team's "tactile electronics" use a mix of more typical textile fibers alongside a small amount of custom-made functional fibers that sense pressure from the person wearing the garment.
According to CSAIL graduate student Yiyue Luo, a key advantage of the team's design is that, unlike many existing wearable electronics, theirs can be incorporated into traditional large-scale clothing production. The machine-knitted tactile textiles are soft, stretchable, breathable, and can take a wide range of forms.
"Traditionally it's been hard to develop a mass-production wearable that provides high-accuracy data across a large number of sensors," says Luo, lead author on a new paper about the project that is appearing in this month's edition of Nature Electronics. "When you manufacture lots of sensor arrays, some of them will not work and some of them will work worse than others, so we developed a self-correcting mechanism that uses a self-supervised machine learning algorithm to recognize and adjust when certain sensors in the design are off-base."
The team's clothes have a range of capabilities. Their socks predict motion by looking at how different sequences of tactile footprints correlate to different poses as the user transitions from one pose to another. The full-sized vest can also detect the wearers' pose, activity, and the texture of the contacted surfaces.
The authors imagine a coach using the sensor to analyze people's postures and give suggestions on improvement. It could also be used by an experienced athlete to record their posture so that beginners can learn from them. In the long term, they even imagine that robots could be trained to learn how to do different activities using data from the wearables.
"Imagine robots that are no longer tactilely blind, and that have 'skins' that can provide tactile sensing just like we have as humans," says corresponding author Wan Shou, a postdoc at CSAIL. "Clothing with high-resolution tactile sensing opens up a lot of exciting new application areas for researchers to explore in the years to come."
The paper was co-written by MIT professors Antonio Torralba, Wojciech Matusik, and Tomás Palacios, alongside PhD students Yunzhu Li, Pratyusha Sharma, and Beichen Li; postdoc Kui Wu; and research engineer Michael Foshey.
The work was partially funded by Toyota Research Institute.
How imagining the worst case scenario can help calm anxiety.
- Stoicism is the philosophy that nothing about the world is good or bad in itself, and that we have control over both our judgments and our reactions to things.
- It is hardest to control our reactions to the things that come unexpectedly.
- By meditating every day on the "worst case scenario," we can take the sting out of the worst that life can throw our way.
Are you a worrier? Do you imagine nightmare scenarios and then get worked up and anxious about them? Does your mind get caught in a horrible spiral of catastrophizing over even the smallest of things? Worrying, particularly imagining the worst case scenario, seems to be a natural part of being human and comes easily to a lot of us. It's awful, perhaps even dangerous, when we do it.
But, there might just be an ancient wisdom that can help. It involves reframing this attitude for the better, and it comes from Stoicism. It's called "premeditation," and it could be the most useful trick we can learn.
Broadly speaking, Stoicism is the philosophy of choosing your judgments. Stoics believe that there is nothing about the universe that can be called good or bad, valuable or valueless, in itself. It's we who add these values to things. As Shakespeare's Hamlet says, "There is nothing either good or bad, but thinking makes it so." Our minds color the things we encounter as being "good" or "bad," and given that we control our minds, we therefore have control over all of our negative feelings.
Put another way, Stoicism maintains that there's a gap between our experience of an event and our judgment of it. For instance, if someone calls you a smelly goat, you have an opportunity, however small and hard it might be, to pause and ask yourself, "How will I judge this?" What's more, you can even ask, "How will I respond?" We have power over which thoughts we entertain and the final say on our actions. Today, Stoicism has influenced and finds modern expression in the hugely effective "cognitive behavioral therapy."
Helping you practice StoicismCredit: Robyn Beck via Getty Images
One of the principal fathers of ancient Stoicism was the Roman statesmen, Seneca, who argued that the unexpected and unforeseen blows of life are the hardest to take control over. The shock of a misfortune can strip away the power we have to choose our reaction. For instance, being burglarized feels so horrible because we had felt so safe at home. A stomach ache, out of the blue, is harder than a stitch thirty minutes into a run. A sudden bang makes us jump, but a firework makes us smile. Fell swoops hurt more than known hardships.
What could possibly go wrong?
So, how can we resolve this? Seneca suggests a Stoic technique called "premeditatio malorum" or "premeditation." At the start of every day, we ought to take time to indulge our anxious and catastrophizing mind. We should "rehearse in the mind: exile, torture, war, shipwreck." We should meditate on the worst things that could happen: your partner will leave you, your boss will fire you, your house will burn down. Maybe, even, you'll die.
This might sound depressing, but the important thing is that we do not stop there.
Stoicism has influenced and finds modern expression in the hugely effective "cognitive behavioral therapy."
The Stoic also rehearses how they will react to these things as they come up. For instance, another Stoic (and Roman Emperor) Marcus Aurelius asks us to imagine all the mean, rude, selfish, and boorish people we'll come across today. Then, in our heads, we script how we'll respond when we meet them. We can shrug off their meanness, smile at their rudeness, and refuse to be "implicated in what is degrading." Thus prepared, we take control again of our reactions and behavior.
The Stoics cast themselves into the darkest and most desperate of conditions but then realize that they can and will endure. With premeditation, the Stoic is prepared and has the mental vigor necessary to take the blow on the chin and say, "Yep, l can deal with this."
Catastrophizing as a method of mental inoculation
Seneca wrote: "In times of peace, the soldier carries out maneuvers." This is also true of premeditation, which acts as the war room or training ground. The agonizing cut of the unexpected is blunted by preparedness. We can prepare the mind for whatever trials may come, in just the same way we can prepare the body for some endurance activity. The world can throw nothing as bad as that which our minds have already imagined.
Stoicism teaches us to embrace our worrying mind but to embrace it as a kind of inoculation. With a frown over breakfast, try to spend five minutes of your day deliberately catastrophizing. Get your anti-anxiety battle plan ready and then face the world.
A study on charity finds that reminding people how nice it feels to give yields better results than appealing to altruism.