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Use Hard Trends to Create Your Company’s New Cash Cow
Most companies begin with a flash of foresight that leads to an innovation. They come out with a new product or service that satisfies an unmet need, or better yet, a need that customers can’t live without, and then focus on growing their “cash cow.” Of course, a cash cow is a company’s major source of money. They then “milk” the cash cow for all it’s worth. If they’re smart, they create some additional cash cows, but that isn’t always the case.
We saw much of this scenario play out with Google, a company that was founded with a strong focus on innovation. Their initial cash cow was built on a new way to bring in advertising dollars around search. And one of the great things that Google did was to keep the pipeline of innovation going by encouraging the Google engineers to spend 20% of their time coming up with new ideas. They even provided resources for the engineers to be creative. The result? It yielded lots of great stuff from Google, including Gmail, Chrome, and many other advances.
Falling Behind Can Happen Fast When You Focus On Your Competition
Predictably, based on hard trends, we can see that the main computer people use has been shifting from a laptop/desktop to a smart phone or tablet. And even though that shift started happening just a few short years ago with Apple’s launch of the iPhone, the reality is that it was very predictable. For example, increasing and predictable advances in processing power, storage, and bandwidth have been in play for many decades.
So what did Google do when Apple changed the game by launching the iPhone? They reacted by innovating, and copying to a degree, and came out with the Android operating system that could work on a variety of manufacturer’s smart phones. Unfortunately, Google didn’t create a game-changing innovation as Apple did; they were instead playing Apple’s game, and that’s never a good place to be for a company that wants to lead.
At the same time there was another predictable hard trend, social media, that was not being addressed by Google’s innovation engine, and that gave Facebook time to rapidly become a giant in that market. And this was where it looks like some bigger mistakes started to occur, mistakes that many companies that have a great cash cow make. Google shifted their focus from “innovation” to “beating the competition.”
One of the problems of focusing on the competition is that you end up competing with them. In contrast, when you focus on innovation, you become the competition. That’s a big difference.
Realize that when you try to copy someone, you can never really catch up, because the leader is constantly innovating. Unless you manage to jump ahead in a big way, you’re always behind. And that’s what happened when Google released Google+, their counter to Facebook. It’s very good, but there is too much copying and trying to catch up with Facebook and not enough game-changing innovation.
Unfortunately, the company was so focused on winning the social media game that all of the engineers were told to put their innovation around social. In other words, they were told to spend 20% of their time focused on innovation, so long as that innovation was aimed at social media. This mandate, of course, diluted their innovation engine. A better approach would have been to jump ahead—to use hard trends to look where social media is going and innovate there to create a new bouncing baby cash cow.
Using Hard Trends to Jump Ahead
Where is the web and social media going? Well, it started with search, what has been called Web 1.0. Of course, Yahoo started that long before Google, giving us access to information. Then Web 2.0 came along with the key focus being content sharing and social media.
Back in 1993 I wrote about this shift in my book Technotrends, and I said that when our devices (phones and computers) become true communication age devices, so that we can use them for informing and communicating (think smart phone), then we’d have another major revolution. And, of course, that’s exactly what Apple helped to spur when they came out with the iPhone and gave us a true communication/information age device. They combined the information age and communication age.
What’s next? If you use the predictability of hard trends to look ahead, which is what I’d like Google to do, you’ll see that we’re embarking on Web 3.0, which is all about immersion. It’s the 3D experience. But I’m not talking about 3D as we’ve known it for years, where you have to put on special glasses. That’s too cumbersome.
I’m talking about using our primary personal computers—our tablets and smart phones—and having a fully-immersed 3D experience where you go into environments (think X-Box gaming), as well as having things stick out at you, like when you wear the 3D glasses. As it turns out, you can have that experience on some hand-held gaming devices right now, without having to wear glasses.
So let’s turn this around to Google. What innovation is waiting for them to seize? How about a 3D web browser? That would be a game-changing innovation. That could create a platform for a big new cash cow!
Why? Because web pages right now are like a flat piece of paper, except they have a hyperlink and perhaps an embedded video. So we can watch a video, but it’s a flat video—it’s not 3D. But what if we had a 3D browser and didn’t just look at a web page, but actually went into it and experienced it? Now that changes the game.
Let’s then look ahead even more. After Web 3.0 is Web 4.0, which is all about intelligence—the personal assistant. Apple has already started this with Siri, where you can talk to your smart phone and your intelligent agent tells you the answers. And, of course, Siri will get smarter every year.
Could Google have done what Apple did before Apple? Yes. In fact, they already had the ability to do so with their Google search App. In fact, most iPhone users already loved it, where you could type or say, “Where is a restaurant in Del Mar, California?” and then Google would send you to a website. Imagine if they would have added the Siri capability of responding to you in voice before Apple. It would have helped them jump ahead rather than once again copy Apple.
The point is that Apple used the predictability of hard trends to innovate outside of their core. Because they were focused not just on one thing—not just on computers or smart phones or tablets—but rather on innovation, they were able to jump ahead. They were not focusing on what the competition was doing. They were looking in front of them rather than at what everyone else was doing.
By the way, Google did come out with their e-personal assistant shortly after Siri was launched. Agility, the ability to react fast is good, but it keeps you behind, playing the catch-up game, and that’s a hard game to win..
Crank Up the Innovation Engine Using Hard Trends
What I’d like to see Google and all companies do is to get back on the innovation bandwagon. Yes, social is big and will continue to grow, but there’s far more ways to create game-changing innovations than that.
So here’s the moral to all this: Don’t just milk your cash cow. True success comes when you focus on innovating versus imitating, anticipating versus reacting. So use the hard trends to create some new bouncing baby cash cows. We’re in a new world of technology-driven transformational change. The playing field has been leveled, and the game is changing fast. It’s time to stop playing the old game, or someone else’s game, and start defining the new one.
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).
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."