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GM Is Insourcing Its Data Centers: What’s Your Plan to Leverage High-Value Data?
Recently General Motors announced that they’re building a new, $258 million enterprise data center in Moring, Michigan. With it, they are going from 23 outsourced data centers around the world to 2 data centers that are, in essence, in-sourced. This move of bringing the data centers back to Michigan and back to the full control of GM is a complete reversal of where they were.
So why are they doing this? To reduce costs? Remember that they initially outsourced their data centers to reduce costs. However, with this new move, GM says they can reduce costs by an additional 40%. In other words, they initially outsourced to reduce costs, but now they’re in-sourcing to reduce even more costs, and they’re consolidating and getting all the data in one location. On the surface, this almost doesn’t make sense.
Actually, it makes perfect sense. To better understand why this is a strategic move for GM, we have to look at our three change accelerators—processing power, storage, and bandwidth. The exponential advances that have been taking place in all three areas have reached unprecedented levels. You’ve likely heard the story about what happens when you double a penny every day. Tomorrow you’d have two cents; the next day, four, the next eight, and so on. By the end of the week, you would have a whopping sixty-four cents. By the end of week two, your cache of cash would have grown to $81.92. Not too exciting. But by day twenty-eight, just two weeks later, your pile of pennies would exceed $1 million; on day thirty it would be over $5 million. If this happened to be a thirty-one-day month, you would end the month with more than $10 million.
If doubling a penny and suddenly reaching $10 million seems dramatic, imagine this: what if the next month, you started with that $10 million and kept doubling? That’s the change level we’re approaching with the three accelerators. Consider this: what was considered the world’s fastest super computer two years ago was recently disassembled because it was obsolete. And of course, as the power of those three change accelerators continue to increase dramatically and exponentially, their price continues to drop. So we can do much, much more with much, much less.
But that’s not the only thing driving GM’s decision to in-source their data. The nature of big data and high speed data analytics is changing too. Not only are companies creating more data than ever before, but the data they are creating is much more valuable. Here’s an example.
The latest plug-in electric vehicles produce 25 gigabytes of data an hour. Some of that data is sent to the driver’s smart phone so they know about the car’s battery life, tire wear, vehicle performance, where the nearest plug-in stations are, plus many more things. Thanks to all this data, the driver as well as the service center can do predictive analysis of the car, which is basically being able to predict car troubles before they occur. Now the driver can fix the problem before it manifests, thus eliminating the car from unexpectedly breaking down.
The data the car produces also goes to the car maker so they can track customer satisfaction and vehicle performance, enabling them to make better vehicles in the future. In fact, the car maker can learn what’s happening with the cars in real time, which enhances their ability to continuously innovate. In this sense, data increasingly becomes the company jewels. Because there is an amazing amount of data being generated, and because the data is far more strategic, companies can get active intelligence from it to make better decisions in real time. No wonder GM wants all their data in-house.
Now, this doesn’t mean that every company should have their own data center or copy what GM is doing. Many companies utilize software as a service (SaaS) to lower their software and hardware costs, and hardware as a service (HaaS) for the data storage. Those are valid options for many organizations. There are so many services that can be cloud-enabled and virtualized that we are now seeing everything as a service (XaaS) rapidly emerge, for example collaboration as a service (CaaS).
The key is to do what’s best for your company today, based on the hard trends that are shaping the future and regardless of what may have worked in the past. Therefore, you need to ask yourself:
° What kind of business are we?
° What industries are converging to create new opportunities?
° What is the size and reach of our business?
° What are the ideal short, mid, and long range goals for our organization?
° How much agility do we need to stay ahead of the competition?
° How much data are we producing now and how much do we plan to produce in the near future?
° What is the value of the data we have and are now capable of collecting?
° What kind of competitive advantage can our data help us create?
Not every company generates as much data as GM. And not every company has to track hundreds of thousands of parts and supplies. But every company creates data and will create much more in the future, and that data is increasingly becoming the key to your organization’s growth. Therefore, it’s imperative that you think through your data plan so you can leverage your data to solve problems faster, make smarter decisions, and reach your goals faster.
Remember, too, that because the three change accelerators of processing power, storage, and bandwidth are still growing and will continue to do so, you need to re-evaluate where you are often. Even though GM is bringing their data centers back home, they’ll have to look at their current strategy again in just a few years.
Times are changing fast, and the rate of change will only increase as times goes on. So what works today may not work two years from now. Therefore, whatever your company does or decides is best for today, re-evaluate that strategy often. Look at your data and where your competitive advantage is coming from so you can take advantage of the newest technologies and not be trapped in the past.
If you keep doing what you’ve always done in the midst of rapid change, you’ll lose your competitive advantage. You either change with the times, or you get left behind. Which option makes the most sense for your company?
The father of all giant sea bugs was recently discovered off the coast of Java.
- A new species of isopod with a resemblance to a certain Sith lord was just discovered.
- It is the first known giant isopod from the Indian Ocean.
- The finding extends the list of giant isopods even further.
Humanity knows surprisingly little about the ocean depths. An often-repeated bit of evidence for this is the fact that humanity has done a better job mapping the surface of Mars than the bottom of the sea. The creatures we find lurking in the watery abyss often surprise even the most dedicated researchers with their unique features and bizarre behavior.
A recent expedition off the coast of Java discovered a new isopod species remarkable for its size and resemblance to Darth Vader.
The ocean depths are home to many creatures that some consider to be unnatural.
According to LiveScience, the Bathynomus genus is sometimes referred to as "Darth Vader of the Seas" because the crustaceans are shaped like the character's menacing helmet. Deemed Bathynomus raksasa ("raksasa" meaning "giant" in Indonesian), this cockroach-like creature can grow to over 30 cm (12 inches). It is one of several known species of giant ocean-going isopod. Like the other members of its order, it has compound eyes, seven body segments, two pairs of antennae, and four sets of jaws.
The incredible size of this species is likely a result of deep-sea gigantism. This is the tendency for creatures that inhabit deeper parts of the ocean to be much larger than closely related species that live in shallower waters. B. raksasa appears to make its home between 950 and 1,260 meters (3,117 and 4,134 ft) below sea level.
Perhaps fittingly for a creature so creepy looking, that is the lower sections of what is commonly called The Twilight Zone, named for the lack of light available at such depths.
It isn't the only giant isopod, far from it. Other species of ocean-going isopod can get up to 50 cm long (20 inches) and also look like they came out of a nightmare. These are the unusual ones, though. Most of the time, isopods stay at much more reasonable sizes.
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During an expedition, there are some animals which you find unexpectedly, while there are others that you hope to find. One of the animal that we hoped to find was a deep sea cockroach affectionately known as Darth Vader Isopod. The staff on our expedition team could not contain their excitement when they finally saw one, holding it triumphantly in the air! #SJADES2018
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What benefit does this find have for science? And is it as evil as it looks?
The discovery of a new species is always a cause for celebration in zoology. That this is the discovery of an animal that inhabits the deeps of the sea, one of the least explored areas humans can get to, is the icing on the cake.
Helen Wong of the National University of Singapore, who co-authored the species' description, explained the importance of the discovery:
"The identification of this new species is an indication of just how little we know about the oceans. There is certainly more for us to explore in terms of biodiversity in the deep sea of our region."
The animal's visual similarity to Darth Vader is a result of its compound eyes and the curious shape of its head. However, given the location of its discovery, the bottom of the remote seas, it may be associated with all manner of horrifically evil Elder Things and Great Old Ones.
If computers can beat us at chess, maybe they could beat us at math, too.
- Most everyone fears that they will be replaced by robots or AI someday.
- A field like mathematics, which is governed solely by rules that computers thrive on, seems to be ripe for a robot revolution.
- AI may not replace mathematicians but will instead help us ask better questions.
The following is an excerpt adapted from the book Shape. It is reprinted with permission of the author.
Will machines replace us? Since the origin of artificial intelligence (AI), people have worried that computers eventually (or even imminently!) will surpass the human cognitive capacity in every respect.
Artificial intelligence pioneer Oliver Selfridge, in a television interview from the early 1960s, said, "I am convinced that machines can and will think in our lifetime" — though with the proviso, "I don't think my daughter will ever marry a computer." (Apparently, there is no technical advance so abstract that people can't feel sexual anxiety about it.)
Let's make the relevant question more personal: will machines replace me? I'm a mathematician; my profession is often seen from the outside as a very complicated but ultimately purely mechanical game played with fixed rules, like checkers, chess, or Go. These are activities in which machines have already demonstrated superhuman ability.
Some people imagine a world where computers give us all the answers. I dream bigger. I want them to ask good questions.
But for me, math is different: it is a creative pursuit that calls on our intuition as much as our ability to compute. (To be fair, chess players probably feel the same way.) Henri Poincaré, the mathematician who re-envisioned the whole subject of geometry at the beginning of the 20th century, insisted it would be hopeless
"to attempt to replace the mathematician's free initiative by a mechanical process of any kind. In order to obtain a result having any real value, it is not enough to grind out calculations, or to have a machine for putting things in order: it is not order only, but unexpected order, that has a value. A machine can take hold of the bare fact, but the soul of the fact will always escape it."
But machines can make deep changes in mathematical practice without shouldering humans aside. Peter Scholze, winner of a 2018 Fields Medal (sometimes called the "Nobel Prize of math") is deeply involved in an ambitious program at the frontiers of algebra and geometry called "condensed mathematics" — and no, there is no chance that I'm going to try to explain what that is in this space.
Meet AI, your new research assistant
What I am going to tell you is the result of what Scholze called the "Liquid Tensor Experiment." A community called Lean, started by Leonardo de Moura of Microsoft Research and now open-source and worldwide, has the ambitious goal of developing a computer language with the expressive capacity to capture the entirety of contemporary mathematics. A proposed proof of a new theorem, formalized by translation into this language, could be checked for correctness automatically, rather than staking its reputation on fallible human referees.
Scholze asked last December whether the ideas of condensed mathematics could be formalized in this way. He also wanted to know whether it could express the ideas of a particularly knotty proof that was crucial to the project — a proof that he was pretty sure was right.
When I first heard about Lean, I thought it would probably work well for some easy problems and theorems. I underestimated it. So did Scholze. In a May 2021 blog post, he writes, "[T]he Experiment has verified the entire part of the argument that I was unsure about. I find it absolutely insane that interactive proof assistants are now at the level that within a very reasonable time span they can formally verify difficult original research."
And the contribution of the machine wasn't just to certify that Scholze was right to think his proof was sound; he reports that the work of putting the proof in a form that a machine could read improved his own human understanding of the argument!
The Liquid Tensor Experiment points to a future where machines, rather than replacing human mathematicians, become our indispensable partners. Whether or not they can take hold of the soul of the fact, they can extend our grasp as we reach for the soul.
Slicing up a knotty problem
That can take the form of "proof assistance," as it did for Scholze, or it can go deeper. In 2018, Lisa Piccirillo, then a PhD student at the University of Texas, solved a long-standing geometry problem about a shape called the Conway knot. She proved the knot was "non-slice" — this is a fact about what the knot looks like from the perspective of four-dimensional beings. (Did you get that? Probably not, but it doesn't matter.) The point is this was a famously difficult problem.
A few years before Piccirillo's breakthrough, a topologist named Mark Hughes at Brigham Young had tried to get a neural network to make good guesses about which knots were slice. He gave it a long list of knots where the answer was known, just as an image-processing neural net would be given a long list of pictures of cats and pictures of non-cats.
Hughes's neural net learned to assign a number to every knot; if the knot were slice, the number was supposed to be 0, while if the knot were non-slice, the net was supposed to return a whole number bigger than 0. In fact, the neural net predicted a value very close to 1 — that is, it predicted the knot was non-slice — for every one of the knots Hughes tested, except for one. That was the Conway knot.
For the Conway knot, Hughes's neural net returned a number very close to 1/2, its way of saying that it was deeply unsure whether to answer 0 or 1. This is fascinating! The neural net correctly identified the knot that posed a really hard and mathematically rich problem (in this case, reproducing an intuition that topologists already had).
Some people imagine a world where computers give us all the answers. I dream bigger. I want them to ask good questions.
Dr. Jordan Ellenberg is a professor of mathematics at the University of Wisconsin and a number theorist whose popular articles about mathematics have appeared in the New York Times, the Wall Street Journal, Wired, and Slate. His most recent book is Shape: The Hidden Geometry of Information, Biology, Strategy, Democracy, and Everything Else.
Laughing gas may be far more effective for some than antidepressants.
- Standard antidepressant medications don't work for many people who need them.
- With ketamine showing potential as an antidepressant, researchers investigate another anesthetic: nitrous oxide, commonly called "laughing gas."
- Researchers observe that just a light mixture of nitrous oxide for an hour alleviates depression symptoms for two weeks.
The usual antidepressants don't work for everyone. That's what makes a new study of the antidepressant properties of nitrous oxide so intriguing. It looks like just a single low dose of what your dentist may call "laughing gas" can help alleviate symptoms of depression for weeks afterward.
The study, from researchers at University of Chicago and Washington University-St. Louis, is published in the journal Science Translational Medicine.
Resistance to anti-depression medications
Nitrous oxide: two atoms of nitrogen, one of oxygenCredit: Big Think
According to the senior author of the study, Charles Conway, "A significant percentage — we think around 15 percent — of people who suffer from depression don't respond to standard antidepressant treatment."
"These 'treatment-resistant depression' patients," Conway says, "often suffer for years, even decades, with life-debilitating depression. We don't really know why standard treatments don't work for them, though we suspect that they may have different brain network disruptions than non-resistant depressed patients. Identifying novel treatments, such as nitrous oxide, that target alternative pathways is critical to treating these individuals."
"There is a huge unmet need," says lead author Peter Nagele. "There are millions of depressed patients who don't have good treatment options, especially those who are dealing with suicidality."
If ketamine can help, can nitrous oxide?
Credit: sudok1 / Adobe Stock
The researchers wondered if some of the anti-depression properties seen in ketamine might also apply to nitrous oxide. Nagele explains, "Like nitrous oxide, ketamine is an anesthetic, and there has been promising work using ketamine at a sub-anesthetic dose for treating depression."
The researchers conducted a one-hour session — they describe it as a "proof-of-principle" trial — in which 20 individuals with depression were administered an air mixture with 50 percent nitrous oxide. Twenty-four hours later, the researchers found a significant reduction in the participants' symptoms of depression versus a control group.
However, the individuals also suffered the unpleasant side effects that laughing gas often causes in dental patients: headache, nausea, and vomiting.
Smaller dose, longer effect
Credit: sudok1 / Adobe Stock
"We wondered if our past concentration of 50 percent had been too high," recalls Nagele. "Maybe by lowering the dose, we could find the 'Goldilocks spot' that would maximize clinical benefit and minimize negative side effects."
In a new trial, 20 people with depression were given a lighter nitrous oxide mix, just 25 percent, and the individuals tested reported a 75 percent reduction in side effects compared to the a control group given an air/oxygen placebo. This time, the researchers also tracked the effect of nitrous oxide on symptoms of depression for a far longer period, two weeks instead of just 24 hours.
"The reduction in side effects was unexpected and quite drastic," reports Nagele, "but even more excitingly, the effects after a single administration lasted for a whole two weeks. This has never been shown before. It's a very cool finding."
Nagele also notes that, despite its popular renown as laughing gas, even a light 25 percent mix of nitrous actually causes people to nod off. "They're not getting high or euphoric; they get sedated."
Delivering help to people with depression
Nagele cautions, "These have just been pilot studies. But we need acceptance by the larger medical community for this to become a treatment that's actually available to patients in the real world. Most psychiatrists are not familiar with nitrous oxide or how to administer it, so we'll have to show the community how to deliver this treatment safely and effectively. I think there will be a lot of interest in getting this into clinical practice."
After all, Nagele adds, "If we develop effective, rapid treatments that can really help someone navigate their suicidal thinking and come out on the other side — that's a very gratifying line of research."