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Category Archives: Mind Building
Go (over) easy on the eggs: ‘Egg-cess’ consumption linked to diabetes
Scrambled, poached or boiled, eggs are a popular breakfast food the world over. Yet the health benefits of the humble egg might not be all they’re cracked up to be as new research from the University of South Australia shows that excess egg consumption can increase your risk of diabetes.
Conducted in partnership with the China Medical University, and Qatar University, the longitudinal study (1991 to 2009) is the first to assess egg consumption in a large sample of Chinese adults.
It found that people who regularly consumed one or more eggs per day (equivalent to 50 grams) increased their risk of diabetes by 60 per cent.
With the prevalence of diabetes in China now exceeding 11 per cent — above that of the global average of 8.5 per cent — diabetes has become a serious public health concern.
The economic impact of diabetes is also significant, accounting for 10 per cent of global health expenditure (USD $760 billion). In China, diabetes-related costs have exceeded USD $109 billion.
Epidemiologist and public health expert, UniSA’s Dr Ming Li, says the rise of diabetes is a growing concern, especially in China where changes to the traditional Chinese diet are impacting health.
“Diet is a known and modifiable factor that contributes to the onset Type 2 diabetes, so understanding the range of dietary factors that might impact the growing prevalence of the disease is important,” Dr Li says.
“Over the past few decades China has undergone a substantial nutritional transition that’s seen many people move away from a traditional diet comprising grains and vegetables, to a more processed diet that includes greater amounts of meat, snacks and energy-dense food.
“At the same time, egg consumption has also been steadily increasing; from 1991 to 2009, the number of people eating eggs in China nearly doubled*.
“While the association between eating eggs and diabetes is often debated, this study has aimed to assess people’s long-term egg consumption of eggs and their risk of developing diabetes, as determined by fasting blood glucose.
“What we discovered was that higher long-term egg consumption (greater than 38 grams per day) increased the risk of diabetes among Chinese adults by approximately 25 per cent.
“Furthermore, adults who regularly ate a lot of eggs (over 50 grams, or equivalent to one egg, per day) had an increased risk of diabetes by 60 per cent.”
The effect was also more pronounced in women than in men.
Dr Li says that while these results suggest that higher egg consumption is positively associated with the risk of diabetes in Chinese adults, more research is needed to explore causal relationships.
“To beat diabetes, a multi-faceted approach is needed that not only encompasses research, but also a clear set of guidelines to help inform and guide the public. This study is one step towards that long-term goal.”
Notes to Editors:
- *Between the years 1991-2009, researchers found that the average daily consumption of eggs increased continuously from 16 grams in 1991-93, to 26 grams in 2000-04, and 31 grams in 2009.
- The study population comprised 8545 adults (average age 50 years) participating in the China Health and Nutrition Survey.
- International egg consumption across the same period was:
- 33.65 g/day in Europe
- 28.43 g/day in America
- 20.56 g/day in Asia
- 21.45 g/day in the world
- 18.20 g/day in Oceania (including Australia)
- 5.93 g/day in Africa.
Story Source:
Materials provided by University of South Australia. Note: Content may be edited for style and length.
Desperation Loyalty
Desperation loyalty is remaining loyal to a group or identity based on neediness or clinginess, often in violation more important values. It is a place of stuckness that elevates loyalty to others above the commitment to grow.
It’s relatively easy to spot this in other people. It’s harder to spot this in ourselves.
If you pursue a strong and centered path of personal growth for many years, you will outgrow many friends and social groups along the way. That’s a normal part of a life of growth and change.
But at some point in your journey, especially early on, you may have doubts about taking the next leap because it may feel like a leap into aloneness.
The truth is that you may sometimes leap into aloneness, but these needn’t be leaps into loneliness.
Accepting an invitation to grow is an individual decision. That is your challenge to face, and some challenges in life must be faced alone. Now and then it’s important to get away from social influences to dwell in the fire of your own values and let them burn out of you what no longer feels aligned.
The antidote to desperation loyalty is inspiration loyalty. Feel the call of this fiery invitation, and give it your full attention when it demands this from you.
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Inspiration Doesn’t Run Out
Recently I saw a NaNoWriMo participant complaining of running out of inspiration. Their writing had hit a wall, and the lack of inspiration was to blame.
That strikes me as an odd and hugely misleading way to think about inspiration, like it’s a resource that can run dry. Truthfully it never runs dry.
Saying you’ve run out of inspiration is like saying you’ve run out of sights or sounds. You could become blind or deaf, but the sights and sounds are still present. There are visuals to look at and sounds to be heard, and they don’t run out – or at least they won’t run out during your lifetime.
Inspiration is much the same. It’s always present. It’s a collection of signals that are always broadcasting – all the time and on multiple channels. Inspiration never switches off. This resource is always available to you. For all practical purposes, it is infinite.
Just as you will never run out of sights to see or sounds to hear, you can never run out of inspiration. There is more inspiration available than you can possibly channel, even if you write, speak, or create 24/7.
When people can’t access inspiration, it’s like this scene from The Three Stooges:
Larry: I can’t see! I can’t see!
Moe: What’s the matter?
Larry: I got my eyes closed!
Moe pokes Larry in the eyes.
If you can’t access inspiration, the inspiration itself isn’t the problem. Those signals are broadcasting loud and clear all the time. The problem is with your equipment.
Inspiration and Brain Health
Almost always the problem is physical in nature. It’s a health issue.
You use your eyes and your brain to see light. If you can’t see anything when there is light hitting your eyes, that suggests a problem with your eyes or brain.
You use your ears and your brain to hear sounds. If you can’t hear anything when sounds are entering your ears, that suggests a problem with your ears or brain.
You use other parts of your brain to tune in to the flow of inspired thoughts and ideas. If you cannot perceive anything when you attempt to tune in, that suggests a problem with your brain. Something is preventing you from properly accessing this natural ability.
If you have a problem with your eyes or ears, you can go to a doctor to get diagnosed and treated. If the doctor is competent and the condition treatable, you may be in luck. Unfortunately doctors cannot cure all cases of blindness or hearing loss, and in some cases they cannot even agree on a diagnosis of the cause. Nevertheless, they still typically consider the problem to be physical in nature. Even if the problem is labeled psychological, it’s still a physical problem with the brain instead of the eyes or ears. Some part of the brain is preventing the signals from being interpreted correctly.
Many writers love coffee, tea, and other stimulants. Why? These substances temporarily change the brain’s normal functioning, which can make it easier to tune in to the flow of inspired ideas. It’s like an eye poke to force your eyelids to open. Of course if you rely on this method too much, it can be like getting too many eye pokes, which probably isn’t good for your long-term eye health.
Just as modern society can strain our eyesight and hearing, it can strain our inspiration circuitry even more. That flow of inspiration tends to be more sensitive to degradation when the brain is stressed, especially by poor diet, lack of exercise, and environmental toxins.
We don’t commonly hear laments about lacking inspiration from people who eat super clean diets, such as raw foodists. Hang out with such people for a while, and you’ll generally witness the opposite – an abundant flow of inspired ideas, available at all times.
Treat the Causes
When there’s a lack of inspirational flow, don’t think of it as a psychological or motivational failing. Don’t think of it as a self-discipline problem. See it as a health warning that you should take seriously.
Losing one’s ability to tune in to the flow of inspiration is the canary in the coal mine. It suggests that you’re heading down the wrong path health-wise. Your brain’s loss of ability signals danger. Treat this as seriously as if your eyesight or hearing starts to go. Your lifestyle is degrading your brain’s capabilities.
If I want to reduce my sensitivity to the flow of inspired ideas, that’s relatively easy. I can just eat more processed food , fewer whole foods, and fewer fruits and veggies. If I want to increase my sensitivity, I can stick with whole foods and eat lots of fresh fruits and veggies.
For a short-term boost, one or two green smoothies a day is great. A 45-minute cardio workout is also great because it rebalances hormones and neurotransmitters.
One of the most powerful habits for a high-functioning brain, especially when it comes to tuning in to inspiration reliably, is daily cardio exercise. A good minimum is 45 minutes.
If you ever run into writer’s block, try doing a one-hour cardio workout. Then drink a green smoothie (or sip on one while you write). Can’t do an hour-long cardio workout? That’s probably why you have writer’s block. If your body is that out of shape, so is your brain. Cardio doesn’t just exercise the body – it exercises and strengthens the brain too.
Our brains simply do not function well without regular exercise.
You may notice a difference in inspirational flow just from taking a day or two off from exercise. Take a week or more off from exercise, and the degradation of this natural ability will likely be significant.
For a really powerful long-term boost, eat 100% raw for a month or longer. The difference is undeniable. The cleaner your brain, the better it functions.
Maintain Your Inspiration Interface
Your brain is your interface for tuning in to the flow of inspiration. If you don’t maintain that interface, it’s predictable that you’ll run into problems with degraded performance. And if you’re already running into problems, there’s your invitation to permanently upgrade your health habits.
If your lifestyle involves feeding your body low-quality ingredients or disregarding your body’s need for regular exercise, creative blocks will serve to remind you that there’s a price to be paid.
Brain degradation is often cumulative. The longer you maintain habits that degrade your mental functioning, the more trapped you may become. You still have to use that same brain to work your way out of that trap, so don’t bury yourself so deeply that you can’t climb back up again.
Inspiration is a valuable personal resource. It’s a source of opportunity. It’s a way to connect with people. It’s readily convertible into other forms of abundance, including plenty of money if you so desire. But you need a properly functioning brain to intelligently leverage this resource.
One of the best decisions you can make is to permanently raise your health standards, and decide to never go back to a degraded level of functioning. Going vegan was a key turning point for me. Committing to regular exercise was another. I made both of those lifestyle improvements back in the 1990s. Those prior commitments are why my one-year blogging challenge for 2020 has been pretty easy. It’s straightforward to access the flow of inspiration every day – much like seeing and hearing – so I’m really just doing a typing challenge. 🙂
Struggling with creative blocks is like straining to see or hear. Step back and fix the underlying health issues. More strain isn’t a wise solution.
If your brain is healthy enough, you need never deal with writer’s block or other creative blocks. Or at least if they do arise, you know how to fix them. Imagine if you could just create, create, create as much as you want, whenever you want. Instead of trying to come up with ideas, you can enjoy the endless flow and dance with it as you please.
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System brings deep learning to ‘internet of things’ devices
Deep learning is everywhere. This branch of artificial intelligence curates your social media and serves your Google search results. Soon, deep learning could also check your vitals or set your thermostat. MIT researchers have developed a system that could bring deep learning neural networks to new — and much smaller — places, like the tiny computer chips in wearable medical devices, household appliances, and the 250 billion other objects that constitute the “internet of things” (IoT).
The system, called MCUNet, designs compact neural networks that deliver unprecedented speed and accuracy for deep learning on IoT devices, despite limited memory and processing power. The technology could facilitate the expansion of the IoT universe while saving energy and improving data security.
The research will be presented at next month’s Conference on Neural Information Processing Systems. The lead author is Ji Lin, a PhD student in Song Han’s lab in MIT’s Department of Electrical Engineering and Computer Science. Co-authors include Han and Yujun Lin of MIT, Wei-Ming Chen of MIT and National University Taiwan, and John Cohn and Chuang Gan of the MIT-IBM Watson AI Lab.
The Internet of Things
The IoT was born in the early 1980s. Grad students at Carnegie Mellon University, including Mike Kazar ’78, connected a Cola-Cola machine to the internet. The group’s motivation was simple: laziness. They wanted to use their computers to confirm the machine was stocked before trekking from their office to make a purchase. It was the world’s first internet-connected appliance. “This was pretty much treated as the punchline of a joke,” says Kazar, now a Microsoft engineer. “No one expected billions of devices on the internet.”
Since that Coke machine, everyday objects have become increasingly networked into the growing IoT. That includes everything from wearable heart monitors to smart fridges that tell you when you’re low on milk. IoT devices often run on microcontrollers — simple computer chips with no operating system, minimal processing power, and less than one thousandth of the memory of a typical smartphone. So pattern-recognition tasks like deep learning are difficult to run locally on IoT devices. For complex analysis, IoT-collected data is often sent to the cloud, making it vulnerable to hacking.
“How do we deploy neural nets directly on these tiny devices? It’s a new research area that’s getting very hot,” says Han. “Companies like Google and ARM are all working in this direction.” Han is too.
With MCUNet, Han’s group codesigned two components needed for “tiny deep learning” — the operation of neural networks on microcontrollers. One component is TinyEngine, an inference engine that directs resource management, akin to an operating system. TinyEngine is optimized to run a particular neural network structure, which is selected by MCUNet’s other component: TinyNAS, a neural architecture search algorithm.
System-algorithm codesign
Designing a deep network for microcontrollers isn’t easy. Existing neural architecture search techniques start with a big pool of possible network structures based on a predefined template, then they gradually find the one with high accuracy and low cost. While the method works, it’s not the most efficient. “It can work pretty well for GPUs or smartphones,” says Lin. “But it’s been difficult to directly apply these techniques to tiny microcontrollers, because they are too small.”
So Lin developed TinyNAS, a neural architecture search method that creates custom-sized networks. “We have a lot of microcontrollers that come with different power capacities and different memory sizes,” says Lin. “So we developed the algorithm [TinyNAS] to optimize the search space for different microcontrollers.” The customized nature of TinyNAS means it can generate compact neural networks with the best possible performance for a given microcontroller — with no unnecessary parameters. “Then we deliver the final, efficient model to the microcontroller,” say Lin.
To run that tiny neural network, a microcontroller also needs a lean inference engine. A typical inference engine carries some dead weight — instructions for tasks it may rarely run. The extra code poses no problem for a laptop or smartphone, but it could easily overwhelm a microcontroller. “It doesn’t have off-chip memory, and it doesn’t have a disk,” says Han. “Everything put together is just one megabyte of flash, so we have to really carefully manage such a small resource.” Cue TinyEngine.
The researchers developed their inference engine in conjunction with TinyNAS. TinyEngine generates the essential code necessary to run TinyNAS’ customized neural network. Any deadweight code is discarded, which cuts down on compile-time. “We keep only what we need,” says Han. “And since we designed the neural network, we know exactly what we need. That’s the advantage of system-algorithm codesign.” In the group’s tests of TinyEngine, the size of the compiled binary code was between 1.9 and five times smaller than comparable microcontroller inference engines from Google and ARM. TinyEngine also contains innovations that reduce runtime, including in-place depth-wise convolution, which cuts peak memory usage nearly in half. After codesigning TinyNAS and TinyEngine, Han’s team put MCUNet to the test.
MCUNet’s first challenge was image classification. The researchers used the ImageNet database to train the system with labeled images, then to test its ability to classify novel ones. On a commercial microcontroller they tested, MCUNet successfully classified 70.7 percent of the novel images — the previous state-of-the-art neural network and inference engine combo was just 54 percent accurate. “Even a 1 percent improvement is considered significant,” says Lin. “So this is a giant leap for microcontroller settings.”
The team found similar results in ImageNet tests of three other microcontrollers. And on both speed and accuracy, MCUNet beat the competition for audio and visual “wake-word” tasks, where a user initiates an interaction with a computer using vocal cues (think: “Hey, Siri”) or simply by entering a room. The experiments highlight MCUNet’s adaptability to numerous applications.
“Huge potential”
The promising test results give Han hope that it will become the new industry standard for microcontrollers. “It has huge potential,” he says.
The advance “extends the frontier of deep neural network design even farther into the computational domain of small energy-efficient microcontrollers,” says Kurt Keutzer, a computer scientist at the University of California at Berkeley, who was not involved in the work. He adds that MCUNet could “bring intelligent computer-vision capabilities to even the simplest kitchen appliances, or enable more intelligent motion sensors.”
MCUNet could also make IoT devices more secure. “A key advantage is preserving privacy,” says Han. “You don’t need to transmit the data to the cloud.”
Analyzing data locally reduces the risk of personal information being stolen — including personal health data. Han envisions smart watches with MCUNet that don’t just sense users’ heartbeat, blood pressure, and oxygen levels, but also analyze and help them understand that information. MCUNet could also bring deep learning to IoT devices in vehicles and rural areas with limited internet access.
Plus, MCUNet’s slim computing footprint translates into a slim carbon footprint. “Our big dream is for green AI,” says Han, adding that training a large neural network can burn carbon equivalent to the lifetime emissions of five cars. MCUNet on a microcontroller would require a small fraction of that energy. “Our end goal is to enable efficient, tiny AI with less computational resources, less human resources, and less data,” says Han.
