Primordial Ooze : This blog is about technology, humor, and life. Be sure to check out the Primordia(tm) gaming world.

iTunes Voting

Apple’s iTunes software has a voting feature that works with the iTunes DJ. I have music hooked up to my backyard via iTunes, an Airport Express, and a cheap receiver hooked up to outside speakers.

iTunes DJ works with various remotes such as the “Remote” software available for iPhone/iPod Touch. Using the Remote application, any one of my guests can login to my WiFi, connect to my music library, and begin to vote on songs. The universe of songs is defined by me and this can range from a single playlist to my entire music library. Unfortunately, the system doesn’t allow guests to stream their own music into mine so it can be played from their iPod Touch all the way through and out the speakers. That said, I’ve had a lot of fun with this voting feature.

Here is one of the playlists created by myself and friends as we voted. This is not necessarily the order that the songs were played. Enjoy!

American Pie	Don McLean
Peace Train	Cat Stevens
Soul to Squeeze	Red Hot Chili Peppers
Knockin' On Heaven's Door	Guns N' Roses
Vivi O Preferibilmente Morti - Monty and Ted	Gianni Ferrio
Let's Dance	David Bowie
The Heart Of Rock & Roll	Huey Lewis
Aloha 'Oe	Tia Carrere
Freedom	Wham!
No More Words	Berlin
Ezekiel 25-17	Samuel L. Jackson
Just Can't Get Enough	Depeche Mode
With A Little Help From My Friends	Joe Cocker
Time	Hootie & The Blowfish
Send Me ON My Way	Rusted Root
Thunder Road	Bruce Springsteen
Shake a Leg	AC/DC
The Guitar Man	Bread
Tragedy	Bee Gees
Devil Inside	INXS
Shining Star	Earth Wind & Fire
Lola	The Kinks
Welcome to the Jungle	Guns N' Roses
"A Bunch of Muppets"	Dialogue
Stumblin' In	Suzi Quatro & Chris Norman
Fool In The Rain	Led Zeppelin
Edge Of Seventeen	Stevie Nicks
Mercedes Benz	Janis Joplin
You	Candlebox
The Chain	Fleetwood Mac
She Hates Me	Puddle Of Mudd
Werewolves of London	Warren Zevon
More Than A Feeling	Boston
Talk Dirty to Me	Poison
I Just Want To Be Your Everything	Andy Gibb
Dancing In The Moonlight	King Harvest
Sex and Candy	Marcy Playground
Lightning Crashes	Live
September	Earth Wind & Fire
Here I Go Again	Whitesnake
You're My Wicked Life	Kill Bill Soundtrack
Hungry Heart	Bruce Springsteen
Synchronicity II	The Police
Runaway Romeo	Duane Carleton
Sky High	Jigsaw
Brothers Johnson - Stomp	The Brothers Johnson
Eleanor Rigby	The Beatles
Running On Empty	Jackson Browne
Smooth (Feat. Rob Thomas)	Santana
Again	Duane Carleton
Poison	Alice Cooper
Long Distance Runaround	Yes
Ramblin' Man	The Allman Brothers Band
Creep	Radiohead
Disappear	INXS
Cruel to Be Kind	Nick Lowe
Girls Talk	Dave Edmunds
Minnesota Clay - Minnesota Sky	Piero Piccioni
Promises In The Dark	Pat Benatar
Burnin' For You	Blue Öyster Cult
Shakey Ground	Don Henley
Bawitdaba	Kid Rock
Photograph	Def Leppard
Amazing	Aerosmith
Rio	Duran Duran
Don't Stop 'Till You Get Enough	Michael Jackson
Go All the Way	The Raspberries
Amanda	Boston
Under the Bridge	Red Hot Chili Peppers
Daydream Believer	The Monkees
Behind Blue Eyes	The Who
When I'm With You	Sheriff
Unwell	Matchbox Twenty
Champagne Supernova	Oasis
Hanging On The Telephone	Blondie
Home Town	Joe Jackson
Summer Days	The Partridge Family
Radio GA GA	Queen
Misty Mountain Hop	Led Zeppelin
If It's Love	Train
Bastille Day	Rush
Buddy Holly	Weezer

2010 09 02 Nick Post Comments (0) Permalink

Book Review: On Intelligence by Jeff Hawkins

I spent the last month reading Jeff Hawkins’s On Intelligence. It was one of the better books I’ve read in a while. I’ve read two other treatments on AI in the past and (not surprisingly) both were mentioned in On Intelligence. The first was Bill Joy’s famous Wired article from April 2000. The second was Ray Kurzweil’s Age of Spiritual Machines.

Hawkins argues that traditional approaches to artificial intelligence are wrong. Most or all of traditional thinking about AI has been to think of intelligence as an advanced algorithm. In this way, we build a lot of complicated algorithms that are designed to solve everyday problems and we fail again and again to reproduce what a child would consider mundane. The key argument of the book is to say that traditional approaches think of intelligence as a massively complicated computer system. Hawkins says this is all wrong. We need to understand how out brain works to understand intelligence and the brain is quite plainly a memory system, not a computer system.

He spends a lot of time describing how the brain, specifically the neocortex, is what drives intelligence. He explains how uniform it is in structure and how t here are no special areas of the neocortex for processing vision, or sound, or any of our other senses. There are other parts of the brain that are dedicated to these things and what these other parts do is adapt sensory signals from one sense (that is sight or hearing) and send it to the cortex in a uniform fashion. Essentially, the neocortex has an advanced structure but it has very simple algorithms and these algorithms are applied to all of our senses in the same exact way.

He also talks about how the brain has a hierarchical structure. The neocortex has six layers, about as thick as a credit card.Where lower regions of the hierarchy deal with lower-level information coming right from the senses and higher regions deal with more general information. Everything the brain stores is also stored in something called invariant representations, which is how you can remember what a person looks like even though your senses see them in many different ways (angles, lighting, ages, etc.)

Hawkins also says that sensory signals flood up into the neocortex and the neocortex floods even more information back to the lower regions. This feedback system is a major element of Hawkins theories. He says that the feedback system is the way the brain predicts what its sense will experience next. The brain predicts the future, whether it be a few milliseconds in the future or 10 years. Prediction is the basis of all intelligence. You can read all about the memory prediction framework here.

So, rather than inventing a complicated Calculus algorithm to design a baseball-player robot, where the robot analyzes frames from its video camera and calculates a balls trajectory and physical movements it needs to make in order to catch a ball. Instead, you stick a memory system in the robot and have it learn what it sees as an incoming ball and record what it needs to do in order to catch that ball. It will fail at first, but if the memory system is designed like the one in our human brain, it will slowly learn what works and what won’t over time and eventually master the task. Practice makes perfect.

All of this was new to me and made perfect sense. Then Hawkins spends the last parts of the book explaining how traditional visions of AI are all wrong. This is where I was pissed that I hadn’t thought of all this before. It’s all just common sense. We are not going to build human-like robots at first. There is no use. Why pay 20 million bucks for a robot butler when a human butler will do a better job and for far less cost. Why build a human-like robot at all? It would need to have exactly our senses, exactly our vulnerabilities, and exactly our life experiences in order to come close to being human-like.

Why not instead take advantage of the fact that we can build intelligent machines and equip them with senses beyond what a human could ever have? In this way, if we ever figure out how to create a workable hierarchical memory system which yields an intelligent machine, then we’ll set this AI off to do things that a human robot might be just as ill-equipped to handle as a real human.

Some examples:

Create a feed from global weather stations and make this the vision sense for the AI. It will learn things about global weather patterns that humans cannot since we don’t have these senses.
Equip the AI with traffic monitors and it will learn things about traffic patterns on a grand scale.
Create a computer where CPU, memory, IO, and process thread information flows into the AI and it will learn about how to optimize a system in ways a human could never understand.
Have a computer live in a 10 dimensional world and experience it like we experience our 3D/4D world. The AI will be able to unlock an understanding of these worlds that a human simply cannot.
and more

All in all, it was an easy read and had lots of new ideas. Go pick it up and we can discuss it!

Tagged bookreview

2010 07 29 Nick Post Comments Off Permalink

July is almost over

Somehow, July went and became almost over in the blink of an eye. I did the NYC Triathlon again, but I didn’t beat my time from last year. I finished 3:39, or so and I did it last year almost 10 minutes faster. My only explanation is that I trained a lot more on the running this time around and I ignored the bike and the swim. That’s ok, there is always next year.

This coming weekend is camping, yay! Can’t wait to hang out with the Szinger family and friends. Should be a great weekend. I need to dig up that camping checklist. One of the items I know I have plenty of is propane tanks. I have no less than a dozen of these at various stages of use. Should be plenty for a few weekends.

This weekend’s camping trip kicks off a week off work, which is something I’m looking forward to. I’m hoping to go to the beach a few times, go to our local waterpark out in Riverhead, maybe go to New Jersey and play Magi Quest, possibly a Zoo, etc. The kids are excited and we hope to be in the car a lot and out and about. While I love our pool and the serenity of our backayrd, we all need to get out and have some fun.

2010 07 26 Nick Post Comments Off Permalink

del.icio.us links for Today

Drawing graphs with dot

(tags: graphviz graphs)

2010 07 24 Nick del.icio.us Comments Off Permalink

Crazy Double Rainbow Guy

I love this guy and I want what he is on!

2010 07 07 Nick Post Comments Off Permalink

Wish I had an AT-AT

Thanks @briangerhardt

2010 06 29 Nick Post Comments Off Permalink

Cool robot

Check out the terrain navigation of this cool robot.

2010 06 26 Nick Post Comments Off Permalink

RSA Animate – Drive: The surprising truth about what motivates us

Simply gorgeous!

2010 06 16 Nick Post Comments Off Permalink

Ragnar Relay

Me running my first leg of the race.

Last weekend, 12 of my co-workers and I ran in the Ragnar Relay New York 2010. This was a 180-mile relay race from Woodstock to New York City. The race was a fantastic experience and posed a good challenge. The hardest part is dealing with very little sleep as you never have more than 3-4 hours to relax and although we did rent a hotel room, it was mainly to clean up our outsides (and insides) before our final legs. The race was a fantastic team-building experience as you got to interact with your team a lot more than any single-day experience would let you.

The way this works is the 12 runners are divided into two sub-teams. These are generally labeled “van1″ and “van2″ since that accurately describes how we got around. The relay is broken up into 36 legs, each runner runs 3 separate legs. Distances range from 12-16 miles. Typical legs are from 3.5 to 8.5 miles. My legs were 4.0, 3.6, and 6.0 miles. Elevation gains range from 100 ft. to 700 ft. across that distance. Your team runs non-stop, physically handing off a bracelet at each exchange. Van 1 runs 6 legs, one for each runner, and then hands off to van2 who runs the next 6 legs. This gives each van a quiet period to go along with a busy period. The whole event went from 19 hours for the fastest teams to about 36 hours.

My midnight run was probably the most miserable. It was about 4 miles, in the dark, and I had to contend with this monster hill and the bugs. At one point I thought I was lost. There were no cars, no support, and no other runners. These were country roads, so there were no lights. I was lit up like a firefly, though, as all night racers have to have reflective gear, a flashing but-lamp, and either a flashlight or a headlamp.

I was looking forward to a nice beer at the finish line, but all they had was Heineken and Miller Light. Prices were reasonable ($4 and $3 respectively) but the line was long and I was pretty damn tired. Before my last leg I think I started to hallucinate. I had to volunteer a shift at one of the exchanges and I could barely speak.

Picture of the Liquor Nuts team!

We had one of the best names, IMHO, but the team who won Nom de Plume (Best Team Name) was Team #157, I’m Woodstocking Your Girlfriend. Lame. Here are the race results, which you might find interesting.

Fastest team:

1 Team, # 183 North American Distance Squad 19:28:05

Our team:

133 Team, # 10 Liquor Nuts 29:06:03

Slowest team:

199 Team, # 170 Ragnar It’s so easy a Caveman can do it. 34:16:21

Note: Some teams are called “Ultra” and they only sport 6 runners, so the runners on those teams run twice as long.

Everyone is pretty jazzed about doing this next year.I hope I can finally break the mythical 10 minute mile mark; I’m still running 12-13 minute miles depending on elevation. Just recently I started to show I can run 11:30 minute miles so there has been some progress on that end.

Here is a quick snapshot of my training in the last three months. Certainly more than I trained last year. Blue is running, red is cycling. I still haven’t started swimming and I have my first triathlon of the season in 5 weeks!

March

April

May

See you next time!

2010 05 20 Nick Post Comments Off Permalink

Primordial Ooze

iTunes Voting

Book Review: On Intelligence by Jeff Hawkins

July is almost over

del.icio.us links for Today

Crazy Double Rainbow Guy

Wish I had an AT-AT

Cool robot

RSA Animate – Drive: The surprising truth about what motivates us

Ragnar Relay

Recommended Reading

Primordia(tm) Gaming World

nickcoding

Subscribe

Archives

Original Ooze

Blogroll