Auto news on Youtube May 7 2018

today I'm building a staircase with a custom handrail

but before we get to the build

let me get you up to speed about what needed to be done on the jobsite

before we can install the stairs and handrail

here's the before picture which the clients felt was a dated design

it was not going to go well with their new

Hickory flooring that they were installing at the top of the stairs

they wanted to update the style of the staircase to an open-ended tread

to do this I needed to modify the wall

so I drew the rise and run of the staircase on the drywall

I then took my sawzall and carefully cut out the shape

unfortunately the treads were dadod into the skirt board so I had to demo them

and cut new stringers

I only demod half the staircase so I'd have something

to stand on while I worked on the other side.

after the demo was done the corners

of the drywall were just flapping in the wind ready to snap off with a little pressure

to solve this I reframe the wall up in sections

then I slid them in between the stringers and drywall nailing them off to the existing studs

but before I demo'd anything I fabricated all the components back at the shop

while I can order custom sized treads the millwork shops in my area didn't offer the customizations I needed

to wrap around the existing walls

so I just fabricated my own

for the treads themselves it was your basic milling to thickness and gluing together operation

but for the nosing I milled up some eight

quarter stock and chucked a stair nose bit in the router

before I ran the stock through the router I clipped off the corners at the table saw

this helped reduce the load of the router especially using a huge stair nosing bit

It also considerably reduced the chatter and tear out on the Hickory which is prone

to having a lot of tear out when routing

then at the router table I used several

feather boards to help keep the stock tight to the fence and tight to the table

This resulted in a perfect bullnose that needed very little sanding

once all the pieces were bullnosed I cut the miters for the open-ended treads on

the table saw

for most of the treads it was your basic edge banding operation with mitered corners

but for the bottom tread I had to make a notch for the new post.

I first attached the center nosing with dominoes and glue

I then went back to the table saw and for safety I cut a mitre on a longer

piece of nosing and then cut the piece to length

I pre-glued the mitered corner before attaching it to the tread

this made installation a bit easier so all I had

to do was tac the return in place with some brads.

I milled the test block the

same width as the new post and did a quick test fit

to be sure I had a nice fit

it's much easier to adjust the edge banding before the glue fully sets

now on to making the new post. I ripped down some eight quarter stock and laminated

two pieces together.

Once the glue is dry I ran it through the planer until it was

down to the thickness I needed

to give the new post illusion that they have

floating panels I milled up some stock and ripped a miter down one edge

I sent up a stop block on the table-saw so I could quickly cut them all to the

same length then I glued the miters together to create the corners for the new post

These were long and skinny, to skinny to clamp,

I used some blue tape to hold them together while the glue dried.

once the glue has set up enough to take the tape off I used a card scraper to

remove the excess glue squeeze-out so the corners would fit tight to the post

Before installing the corners I pre finish the post themselves

this would be a lot easier before the corners are installed and also help prevent any raw

wood from showing along the edges if the wood shrank during seasonal movement

Then as an extra precaution I ran my block plane and down the sides

chamfering the corners to be sure the corner trim would sit tight to the post.

To install the trim I used a headless pin nailer. it was big enough to hold the

trim in place but small enough not to have to putty a bunch of holes

there are three rail elements to the post. One at the bottom one two-thirds up and one at the top

this design element came from the doors in the rest of the house

that had the same panel design

I started out by marking and cutting each piece as I work my way around and up the post

when installing the middle rail I used a spacer block to be sure they would all

be placed in the same spot and to save a bunch of time

not having to measure for each one

The last detail for the new post was to build the cap. There were a few test cuts

involved and a bit of math to get all four sides to meet in the middle at a

nice clean point

I didn't record it because at the time I thought it would

make for a boring video. If there are enough people interested maybe I'll go

back and recreate a video for the future but the operation itself is pretty

straightforward I used my shop made vertical sled to

clamp the workpiece and with the blade raised to the correct height and angle

When I made the cuts I cut the cross grain first as it's more likely to tear out and

then I cut with the grain second removing any tear out from the cross

grain cut

I reset the saw blade to 90 and then cut the decorative shoulders on the top side

the cap will be pinned in place with a brad nailer and a little glue and the

underside will be trimmed out with quarter roun.

I did the same order of operations here

I cut the cross screen first and then I cut with the grain to

cut off any tear-out that may have happened

while the stain and finish was drying on the new posts I moved on to prepping the

stock for the spindles I joined it planed and cut each spindle square there

are 20 spindles in this project plus some extra stock to create the little

cross braces between each spindel so this took some time

next was to cut the little angle of cross braces that were going between the spindles

since my table saw is old-school and doesn't have a proper riving knife

I clamped a shim just so it rubbed the back side of the blade

this way as I cut the little parts

they were pushed away from the blade preventing them from becoming a little kickback bullets

a stop block clamped to the miter gauge made the cuts accurate

and quickly repeatable

While I had the miter gauge set up at the right angle I

cut the tops of all the spindles this angle is going to go against the handrail

then I Re-squared the miter gauge to cut the lower cross braces that

are going between the spindles I set up a stop to make the cuts repeatable but I

took it one step further I set the red arm as the stop so the

metal bar would act as a hold down

this made it a little safer more comfortable

to cut the little parts

now it's time to cut the joinery I know a lot of people

poopoo the Domino because they think it's not real woodworking or it costs

too much or they just love to hate something but for a small custom shop

like mine when I need to get a job finished before the next mortgage

payment is due the Domino is the way to go

there was 72 of these little cross

braces so I think the Domino paid for itself that day

to set up my jig I screwed it down to the table and set up some angled stop blocks to hold the

workpiece in place and a stop block to my right to register the Domino against

this way the mortises would all be in the same place

for the spindles themselves I reset my jig so I'd have something to climb to

then I set a stop block to the left and right to register for both the upper and lower

cross braces

then for the very bottom cross braces I reconfigured the jig one last time to

cut the mortises on both sides

you may have noticed that these parts are

stained and finished. I wanted to pre finish the inside edges before

assembling this as it would be really difficult to stain and finish after they were assembled

to assemble the spindle units I screwed yet another jig to the

table the stop block as the top of the jig is cut at the same angles and rise

and run up the stairs to help me quickly align all the parts at the proper angle

now all there's left is to add Domino's clamps and glue

I clamped it in a way so I could simply lift the assembly off the jig set it aside and start clamping up

the next set

to attach the spindles to the treads I simply doweled them

so once the assembly was dry I routed out a slot in the bottom of the spindles

to receive the doubt to do this

I screwed a jig to the side of my assembly table

to connect the spindle assembly upside down so I could route out an

oblong hole in the bottom of each spindle the reason for the oblong hole is it

gave me a little wiggle room in case one of the dowels in the treads was off

once the glue is set all three dowels would be solid

The reason why I'm blowing

out the hole there is my spiral up cut bit was so dull it made more smoke than sawdust

so I'm using a spiral down cut just to get the job done and it is

driving the chips to the bottom of the hole

I should also mention that big chunk of walnut scrap is only there

to take up the extra space in the clamps

this makes it easier to clamp the workpiece without the bar sticking out in the way

once everything was installed I had one more detail to take care of and

that was the cove on the backside of the treads and risers

my local millwork shop

did not stock Cove and hickory and they charged in the $200 setup fee for a custom run

I only needed a few sticks so I was back at the shop to mill some up

Since Hickory is a splintery wood and a router will often tear out a big chunk of wood

instead of cut it. I did a similar operation as the stair nose to prevent

tear-out and reduce the load on the router

I used the dado blade to remove the bulk of the material then set up feather

boards on the router table to route out the cove

since thin pieces will chatter while milling I used a wider piece of wood than I

needed to make the cove. I'd then rip the cove free at the table saw

this let the router cut a cleaner Cove and is much safer to waste a little wood than to try

to route a little piece

I should mention for the handrail profile I did

have my local mill workshop custom cut it. That was large enough and complicated

enough to justify a custom run over my labor to mill it in-house

so here are some shots of the Finnish staircase and handrail

if you're going to take on a project like this I highly recommend you pick up a code book

in my 20-plus years of working in the trades I've had all kinds of people tell me

what the building codes are and more often than not they're wrong to one degree or another

you'll save yourself all kinds of headaches

if you get your information from the source

For more infomation >> Building a Custom Staircase and Handrail - Duration: 11:57.

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How to build an A.I. brain that can surpass human intelligence | Ben Goertzel - Duration: 11:32.

If you think much about physics and cognition and intelligence it's pretty obvious the

human mind is not the smartest possible general intelligence any more than humans are the

highest jumpers or the fastest runners.

We're not going to be the smartest thinkers.

If you are going to work toward AGI rather than focusing on some narrow application there's

a number of different approaches that you might take.

And I've spent some time just surveying the AGI field as a whole and organizing an

annual conference on the AGI.

And then I've spent a bunch more time on the specific AGI approach which is based on

the OpenCog, open source software platform.

In the big picture one way to approach AGI is to try to emulate the human brain at some

level of precision.

And this is the approach I see, for example, Google Deep Mind is taking.

They've taken deep neural networks which in their common form are mostly a model of

visual and auditory processing in the human brain.

And now in their recent work such as the DNC, differential neural computer, they're taking

these deep networks that model visual or auditory processing and they're coupling that with

a memory matrix which models some aspect of what the hippocampus does, which is the part

of the brain that deals with working memory, short-term memory among other things.

So this illustrates an approach where you take neural networks emulating different parts

of the brain and maybe you take more and more neural networks emulating different parts

of the human brain.

You try to get them to all work together not necessarily doing computational neuroscience

but trying to emulate the way different parts of the brain are doing processing and the

way they're talking to each other.

A totally different approach is being taken by a guy named Marcus Hutter in Australia

National University.

He wrote a beautiful book on universal AI in which he showed how to write a superhuman

infinitely intelligence thinking machine in like 50 lines of code.

The problem is it would take more computing power than there is in the entire universe

to run.

So it's not practically useful but they're then trying to scale down from this theoretical

AGI to find something that will really work.

Now the approach we're taking in the OpenCog project is different than either of those.

We're attempting to emulate at a very high level the way the human mind seems to work

as an embodied social generally intelligent agent which is coming to grips with hard problems

in the context of coming to grips with itself and its life in the world.

We're not trying to model the way the brain works at the level of neurons or neural networks.

We're looking at the human mind more from a high-level cognitive point of view.

What kinds of memory are there?

Well, there's semantic memory about abstract knowledge or concrete facts.

There's episodic memory of our autobiographical history.

There's sensory-motor memory.

There's associative memory of things that have been related to us in our lives.

There's procedural memory of how to do things.

And we then look at the different kinds of learning and reasoning the human mind can

do.

We can do logical deduction sometimes.

We're not always good at it.

We make emotional intuitive leaps and strange creative combinations of things.

We learn by trial and error and habit.

We learn socially by imitating, mirroring, emulating or opposing others.

These different kinds of memory and learning that the human mind has – one can attempt

to achieve each of those with a cutting-edge computer science algorithm, rather than trying

to achieve each of those functions and structures in the way the brain does.

So what we have in OpenCog we have a central knowledge repository which is very dynamic

and lives in RAM on a large network of computers which we call the AtomSpace.

And for the mathematicians or computer science in the audience, the AtomSpace is what you'd

call a weighted labeled hypergraph.

So it has nodes.

It has links.

A link can go between two nodes or a link could go between three, four, five or 50 nodes.

Different nodes and links have different types and the nodes and links can have numbers attached

to them.

A node or link could have a weight indicating a probability or a confidence.

It could have a weight indicating how important it is to the system right now or how important

it is in the long term so it should be kept around in the system's memory.

On this AtomSpace, this weighted labeled hypergraph, we can have a lot of different AI processes

working together cooperatively.

So the AtomSpace, the memory store, is what we would call neural-symbolic.

That means we can represent nodes and links that are like neurons in the brain which is

fairly low level.

But we can also represent nodes and links that are higher level representing pieces

of symbolic logic expressions.

So we can do explicit logical reasoning which is pretty abstract and low level neural net

stuff in the same hypergraph, the same AtomSpace.

Acting on this AtomSpace we have deep neural networks for visual and auditory perception.

We have a probabilistic logic engine which does abstract reasoning.

We have an evolutionary learning algorithm that uses genetic algorithm type methods to

try to evolve radical new ideas and concepts and look for data patterns.

And we have a neural net type dynamic that spreads activity and importance throughout

the network.

A few other algorithms.

A pattern mining algorithm that just scans through the whole AtomSpace looking for surprising

stuff.

And the trick is all these different cognitive algorithms have to work together cooperatively

to help each other rather than hurt each other.

See, the bottleneck in essentially every AI approach ever taken – be it a neural net,

a logic engine, a genetic algorithm, whatever – the bottleneck in every AI approach ever

taken has been what we call a combinatorial explosion.

And what that means is you have a lot of data items.

You have a lot of perceptions coming into your eye or you have a lot of possible moves

on the chess board or a lot of possible ways to move the wheel of the car.

And there are so many combinations of possible data items and possible things you could do,

sifting through all those combinations becomes an exponential problem.

I mean if you have a thousand things there's two to the one-thousandth way to combine them

and that's way too many.

So how to sift through combinatorial explosions is the core problem everyone has to deal with.

In a deep neural network as currently pursued, it's solved by making the network have a

very specific structure which reflects a structure of visual and auditory streams.

And in a logic engine, you don't have that sort of luxury because a logic engine has

to deal with anything, not just sensory data.

But what we do in OpenCog is we've worked out a system where each of the cognitive processes

can help the other one out when it gets stuck in some combinatorial explosion problem.

So if a deep neural network trying to perceive things gets confused because it's dark or

it's looking at something it never saw before, well maybe the reasoning engine can come in

and do some inference to cut through that confusion.

If logical reasoning is getting confused and doesn't know what step to take next because

there's just so many possibilities out there and not much information about them.

Well, maybe you fish into your sensory-motor memory and you use deep learning to visualize

something you saw before and that gives you a clue of how to pare through the many possibilities

that the logic engine is seeing.

Now you can model this kind of cognitive synergy mathematically using a branch of mathematics

called category theory, which is something I've been working on lately.

But what's really interesting more so is to build a system that manifests this and

achieves general intelligence as a result and that's what we're doing in the OpenCog

project.

We're not there yet to general intelligence but we're getting there step by step.

We're using our open source, OpenCog platform to control David Hanson's beautiful, incredibly

realistic humanoid robots like the Sophia robot which has gotten a lot of media attention

in the last year.

We're using OpenCog to analyze biological data related to the genetics of longevity

and we're doing a host of other consulting projects using this.

So we're proceeding on an R&D track and an application track at the same time.

But our end goal with the system is to use cognitive synergy on our neural-symbolic knowledge

store to achieve initially human level AI but that's just an early stage goal.

And then AI much beyond the human level.

And that is another advantage of taking an approach that doesn't adhere slavishly to

the human brain.

The brain is pretty good at recognizing faces because millions of years of evolution went

into that part of the brain.

But for doing science or math or logical reasoning or strategic planning we're pretty bad.

And these are things that we've started doing only recently in evolutionary time as

a result of modern culture.

So I think actually OpenCog and other AI systems have potential to be far better than human

beings at the sort of logical and strategic side of things.

And I think that's quite important because if you take a human being and upgrade them

to like 10,000 IQ the outcome might not be what you want, because you've got a motivational

system and an emotional system that basically evolved in prehuman animals.

Whereas if you architect a system where rationality and empathy play a deeper role in the architecture

then as its intelligence ramps way up we may find a more beneficial outcome.