I don't mind being tested.

"I've tested it on the data from the KDD 2006 cup a contest in the KDD conference whose goal was to identify Pulmonary Embolism based on data generated from CT scans and it out scored the cup winners by a 50% margin."

"Upon qualifying, as described above, the Participant is required to submit within one (1) week for judging a description of their algorithm along with all source code. The Participant warrants that the source code is either fully or substantially developed and functions or will function as represented by the description. Failure to deliver both the description and source code within one (1) week will disqualify that entry and additional qualifying entries will be considered."

It's better to use a new technology to create a complete solution for people.

There is just too much science involved in feature selection etc...

Then you give people enough to get enticed, but don't give everything away. You can then pursue multiple applications with a parallel effort.

Oh yes, they do. That I do know.

It is not like "here is one transaction, is it a fraud?" but "here are 2^20 transactions, what are the frauds?".

You could do this by pipelining, but I guess Banks want a zero downtime system and I personally would not trust an API in terms of reliability.

Another point is, that banks will not give you the original data. They will have to "pseudonymize" several entries, such as credit card numbers, names, ... This would force them to preprocess the data which gives every transaction a very little + O(n) and which might decrease the speed even more.

(I'm not saying it's technically impossible, but I'd say there are better ways, such as releasing it closed source or just using it to predict financial data - which as we all know is possible and being done by hedge fonds, so this should be the best way IF you have that algorithm ;)

Performance is of the essence in these situations; any clever trick you can think of to speed things up should be used in such a situation (but keep it fairly simple; lookup tables and so forth, for example).

I'm not sure I buy the web service idea, either: wouldn't typical applications require a lot of input data (training set + test set) in order to be effective? Uploading all that data could be annoying, compared with just running the algorithm locally at the customer's site.

I'll put it this way: if you can consistently beat the market by a few percentage points, you can be a billionaire.

Or maybe people are _wasting_ way too much money to talk about it openly.

But I don't mean to sound too snarky. Sorry about that. So go ahead and try to get a job at Machine Insight or another such firm to find out what they're all about. http://www.machineinsight.com/

http://en.wikipedia.org/wiki/Quantitative_analyst

http://en.wikipedia.org/wiki/Algorithmic_Trading_Platforms

http://www.iht.com/articles/2006/11/23/business/trading.php?...

Typically this is not a question of "find the secret strategy and make billions!", but rather finding a good execution algorithm to break an order of $billions down into a series of smaller orders for a good average price, or finding and exploiting minute arbitrage opportunities, etc. Algorithmic trading is responsible for many billions of dollars of trades daily; I don't know offhand of particular people who have become billionaires off it, but there are definitely lots of folks who have made a lot of money.

Or maybe people are _wasting_ way too much money to talk about it openly?

PS: Many machine learning systems can trade off accuracy for efficiency so you might look into increasing efficiency vs. accuracy for some existing application.

Math should not be patentable. It's a crime.

On the other hand imagine a world without patents and you'd probably get a world fill with trade secrets and that is even worse, at least a patent guarantees that the idea will pass to the public domain after a certain period. Would the world benefit if technology was segmented and kept secret like the coca-cola formula?

But seriously that is a great discussion for different thread.

If you've got awesome prediction technology, that's an easy $50k, and maybe an easy $1 million.

Symbolic addition is exactly the wrong kind of problem for this algorithm as the symbols don't have any relationships with each other which is exactly the insight this algorithm adds, and which almost every other dataset has.

I've proven that the algorithm works to my complete satisfaction. I've tested it almost every dataset in UCI machine learning repository and it outperforms the best published results on almost all of them. I've tested it on the data from the KDD 2006 cup a contest in the KDD conference whose goal was to identify Pulmonary Embolism based on data generated from CT scans and it out scored the cup winners by a 50% margin.

I know the algorithm works, I am just not sure how to monetize it...

That was my thinkng exactly. I am glad it is confirmed by an expert...

E-mail me and maybe we can figure out what it would be good for. I'm thinking you should patent the technology, show proof of concept comparison tests, and shop application specific licenses around.

Essentially, it'd be the virtual embodiment of my conscience.

Going along those lines, let's say you're a brilliant CEO or hacker. You could license copies of your wisdom to others who need guidance.

Talks about some similar ideas -- applying ML techniques to make information workers more productive. Cool stuff.

There are a zillion ways to monetize it. The real question is can you successfully do so. What about selling an appliance similar to google search, but instead is your predictive technology. Make a public api for people to test it, and then sell the hardware option.

My algorithm scored 2.043 19 on tasks 1 & 2 Top 3 places 1: 1.35 1.28 1.27 Top 3 places 2: 13.58 13.56 13.44 Note these scores represent 5 different teams.

I don't want them to take my word for it, just supply me with more test data and I could send them my results.

I'm sure an opportunity to prove your algorithm will pop up. Its the sort of field where you can fool yourself sometimes by not being careful enough with training and testing data, so random people off the internet generally don't seem worth the trouble to an established researcher.

http://en.wikipedia.org/wiki/No_free_lunch_in_search_and_opt...

In response to your question I say that it should be easy to monetize your algorithm if it is truly good at solving some class problems. Just find problems where the solutions are valuable to some group of people and sell the solutions you create.

That is what I am trying to find in this thread ideas on where the solutions are most valuable and how to pitch them.

Anything else?

break CAPTCHA

There are plenty of people who will pay for that, and a successful implementation of Hawkins/George system will be able to do so.

Don't want the spam in the worlds Inboxes to be on my hands.

The users would input their to do list and the AI would suggest the best next task for them to complete. You could have the user input some information about their lifestyle - married? family? when do they work? etc - and the AI would take into consideration these factors when determining the next best task.

You're really wasting your time with the stock market though. Technical analysis is just silly.

That was also my first reaction, but maybe the commodities market (which he's targeting) is more inefficient than equities.

check out http://evolvedmachines.com

those guys might like to help you out.

also there is http://www.numenta.com/

that might be interested in some good algorithyms

When it comes to what middleware game AI developers want, they're far more interested in a framework that helps them organize all the domain specialization rather than improved algorithms. And game middleware is an increasingly well-trodden and difficult market. Again, forget it.