I bought an arctic air cold water evaporator. It work well for near field personal cooling.

So even though its summer time. I wonder. Could you heat a room or create near field warming. This with hot water instead of cold.

Perhaps the water temperature only need to be 35 degrees celsius and not boiling. Since this would break the device. Then the evaporation cools it down a bit. But it should still be warm. A 10 degree drop I guess.

Could be the most efficient way to save money on electricity. USB powered heater. But I think there needs to be a special version of the device.

Solved a stiff diff equation using the chainer python module. Taken from wikipedia. The way I did it was to iterate with rng() random as the input with a while loop. That is. Iterate until the model(rng()) outputs the correct number.

Some problems were that dt could not be too small or it would get zero for update(). So I put it at 0.01 which I think is pretty high.

The loop for the initial model outputs.

y0 = model0(rng()) # previous value, y0
y1 = model1(rng()) # next value, y1
yp = (y1-y0)/dt # derivate
loss_eq = F.mean_squared_error(yp,-15*y0) # first error loss equation
loss_initial = F.mean_squared_error(init,y0) # inital error loss
loss = loss_eq + loss_initial
model0.cleargrads()
model1.cleargrads()
loss.backward()
optimizer0.update() # update each model0, model1
optimizer1.update()

source: diff eq y0 = model(rng),y1 = model(rng),dt = model(rng)(1)

dt could not be a model since it was so picky. Had to set it dt = 0.01

A quick idea.

I wonder. Could you capture heat and press it along the plate. If so then maybe this would be an ?efficient wooden stove. Anyway its a good imagination exercise.

“In computability theory, the halting problem is the problem of determining, from a description of an arbitrary computer program and an input, whether the program will finish running (i.e., halt) or continue to run forever.” – wikipedia

I think this is possible and quite important. If we rewrite the halting problem to a practical, still useful problem. Then “from a description” becomes from a small number of initial steps.

Then the problem could be fed to another machine learning model(). With indata like the model parameters, data from inital step calculations. Like the input, output and error.

Then the idea is that the practical halting model() will output a number indicating the probability of success.

If this works. The lessons is that even if something seems impossible you can rewrite the rules a bit but still get something useful back. Do we need to change the approach of the model().

Air Cooler Innovation Guess – Could Metal Bands Enhance The Function Of Evaporation Coolers In The Filter? In Some Cooling Optimizing Configuration.

Like blood vessels transport heat. Metal also have heat properties.

A quick idea.

To adjust sunlight and heat. Why not install window blinds in the greenhouse? Just an idea.

A not so bad idea. If you want to make money I guess.

Refrigerator interior decorations. Why only food items when you can have plastic flowers and other decorations. The woman likes it so I thought I share.

A quick peace idea.

Could we have company contracts for greening dried out areas? I mean. The government might not have the best ideas. So I think companies can compete and come up with the best course of actions.

Since food is peace. Maybe greening could be partly food plants?

A quick peace idea.

I think we now have access to education in peace.

Peace is not easy. However common grounds are many. Therefor to get people interested in peace from an ?early age. Why not invent peace games.

The idea is simple.

Why not invent something of a replacement for cubic splines. I’m thinking of using a machine learning model() as smart interpolation.

The idea is that a cubic spline might resemble one type of events where as a ML model() could learn to interpolate any type of curve.

So it would work a little like super resolution. You have a limited numbers of samples and you apply the ML-spline-model(). It then looks more like the true signal.

Why battery? Running on battery means you can move the air cooler to where your at. Then using a foldable solar charger to charge the battery. Means that those without reliable power or no power at all also could have a small air cooler. For rural Africa perhaps.

The USB powered Artic Air had three speed settings. So the running time with a powerbank will vary. It came with a usb charger but it could run on computer usb. So the manual said. So I assumed a powerbank could do the same job. The powerbank was rated at 2.1A.

Anyway 30+ degrees at home without an air conditioner. Just fans. Is hard. This air cooler worked by near field personal cooling. Sitting infront of the cooler is what I mean.

I think I imagined a new business. The energy truck driver.

So the idea is for a large foldable solar panel park on wheels. Where the operator goes to place to place on demand. Partly because the energy grid is not developed. So it could be for emergencies, refugee camps, black outs etc. Or maybe just as a business for earning money.

Anyhow this is a very important idea for a resilient world.

You split a problem to solve it more easily. Therefor I wonder if the  universe is really a big energy problem. Splitted into many small solvable objects. I mean. Would this mean it stores energy in many small singularities. Since a singularities could have high value. Like divided by zero. This would be a managable way to store lots of energy in a safe way.

So the problem of the universe is safe energy storage?

To cool yourself during these very hot temperatures why not mimic the situation when you were at the lake. Just fill a bucket with mildly cold water for a foot bath.

A quick idea.

Scan or show your washable fabric shopping bag and get discount on selected items. Like you get from being a member of various stores anyway.

I wonder if this learning technique could work. The idea is to split the data into two or more model() groups. Each belonging to a separate model().

So the way I would test this would be to look at some sort of ?confidence score for each classification the two models could do. Like in the long vector binary encoding. Where you want 1.0 and the rest to be zeros.

So then the idea is to move the sample data into the model group with the highest confidence. This way the two models compete to have data in its group.

Then if I get 100% confidence for each model for training data within its group. Then the sample data transformation output from the test set would be selected from the model with the highest confidence score.

My first try (jupyter notebook) Sort Data into Model() Groups

98.11% Accuracy score

A quick idea.

Could information be an energy carrier? As if a network of potential energy could be larger than its sum.

As a cool idea. I suspect gaming can still be done locally over a LAN network. I remember playing Doom over the school network. Much fun.

So the suggestion is to see if maybe ?retro gaming or new games could be played on mobiles for group of people in a LAN network.

I think this is possible. With some ?12 volts motor drive a spinning disk for clay forming.

So the idea is for solar panel charged battery powered pottery.

I managed to fix some photos that were blurry simply using a super resolution machine learning algorithm.

The idea is simple. Down scale the photo. Until you don’t see the blurriness. Then apply super resolution. Its as simple as that. The result is not any way perfect but its better.

Super resolution should be in gimp I think.

Since machine learned super-resolution worked. Why should not do the same training for images with visible blockiness.

The idea is to degrade or hard compress images using some image compression algorithm to blockiness level. Use those images as the training and then as the target use the original images.

If this works than it would be possible to compress images to near ?the maximum.

The maximum ?would be unsupervised labeling of small objects in the image to text form and then machine learning to imagine what the image would look like.

So now you have a machine learning layer on top of chosen algorithm.

Perhaps this could work on youtube also. It would be possible to compress video on quality, not just on resolution. Then use the machine learning layer to correct the blockiness and resolution.

I think this is the way to do it. You have a standard algorithm in the back and a machine learning layer on top of it.

The idea is for emission free cities. Why not have environmental bonuses. For cities to work hard to become an all electric car city. So the idea is to split the problem.

A quick idea.

If Hot Water Freeze Faster Than Cold. Do Pretreated Air Also Cool Faster?

The reason for this effect I believe is optimization for life. The fluid will ?try to get an acceptable mean temperature. By cooling fast the mean will be lower if then the temperature sky rockets again.

As the weather gets very hot. One invention that is missing is an air cooling device. Run by solar. Small enough that it does not cost that much.

For such climate inventions to be built I wonder if we need to connect engineers online.

So the idea is for an online engineer volunteering site with connected online prototype manufacturing site.

Just because we need it.

I wonder. My little laptop goes super hot every time I use something like LSTM. I guess it could be the cache. However. For many processors in a room this could make a substantial difference I guess.

So the idea is for optimize algorithms with machine learning for low energy consumption or heat dissipation. Could there be a “lagom” speed for the amount of heat?