@T: ALWAYS THE TWAIN

This post shows how different sectors are all thinking about the brain, semantics and how to develop appropriate applications to increase machine intelligence. It also identifies the missing emotion dimension and how female considerations and contributions are needed in application development. Plus we get a glimpse of virtual collaboration and social interaction from MS’s vision for 2019.

According to Hedge Fund Research, AUM (assets under management) increased by more than US$142.5bn over Q2 2009, on the back of some of the best performance since the late 1990s. Whilst following up on this positive news for the alternative assets sector and rooting around the Web for some analysis to structure the fund-of-fund we’re proposing as part of Project ART, I happened across this interview with Richard Peterson, co-manager of the MarketPsy Long-Short Fund:

* http://hedgeweek.com/interviews/detail.jsp?content_id=339372

It was these paragraphs which stood out (source: Hedgeweek):

You can’t help it, but your brain (and everyone else’s) is wired to sabotage your investing. I discovered this through my own experiences in the mid-1990s. As the senior project in my electrical engineering coursework, I chose to develop quantitative neural network-based stock index forecasting software. I found the software to be somewhat predictive of the markets, so I decided to trade the software’s forecasts with a small managed account.

Something unexpected happened. Days when I was most reluctant to take a trade signal were the times that the most profitable trades were made. I measured this effect over three years, and then extended it to my read on ‘the mood of the market’. The pattern was consistent over the years – the market’s mood was inversely correlated with the future direction of prices. High media negativity was correlated with future price gains over the next week.

I realised that an enduring source of alpha lies between our ears. Understanding the workings of the brain, and in particular how investment decisions are made, unlocks a trove of novel investment strategies. And the clues to how and what investors think lies in the unconscious (their feelings) as well as in their conscious minds (what they say in conversation).

If there is alpha in understanding the mood of the market, how can we access the minds of thousands of investors to test this – and potentially profit?

GIVES US PAUSE FOR THOUGHT ON HOW THE GLOBAL FINANCIAL CRISIS AROSE, HMMN? We may have the risk management systems in place but our emotions over-rides its logic:

http://www.nytimes.com/2009/01/04/magazine/04risk-t.html?_r=1&pagewanted=all

Right before I went rooting for hedge fund analysis I was watching Tom Gruber’s presentation on the Siri Virtual Assistant again. Of all the semantic applications and services I’ve trialled or kept up-to-date with from afar, this is the one that has most commercial potential, I think:

KEYNOTE: The Game Changer: Siri, a Virtual Personal Assistant from Semantic Universe on Vimeo.

In Gruber’s presentation he refers to the virtual assistant conducting a CONVERSATION with the user, which would be part of the conscious mind by Peterson’s definitions. The unconscious component is about feelings. It made me wonder how quickly we can get to a virtual assistant that would understand this human instruction:

* I want to trade stock X on exchange Y at best daily price, but I’m concerned this may be too risky. Can you calculate what the likely outcome will be and how much I should trade if I go ahead?

I wonder whether something like Siri in the future will be able to gauge the emotional weights of the words “concerned” and “too”. Also whether it’ll be able to interpret inflexions in our voices that can turn directive statements into queries. Maybe they should trial it on some Australians and test what happens then — LOL. (Readers would understand if they knew the Oz accent.)

Directive statements are how men tend to communicate, btw. So since Siri is programmed to complete directive commands, it’s not surprising when Gruber points out, “It gets things done.” The challenge will be whether instead of a male directive like this, “When’s the next Mets game?” Siri can also understand this female conversation opener, “I feel kind of at a loose end. The kids are all at school now. I really want some funky shoes. Anywhere you can suggest? Nothing too pricey, though. I still have to get them their bikes for Christmas. That’s a few months away, but still………”

If Siri can decipher that chain of emotional reflection and identify a solution — for the shoes and not for the bike — then we’ll be closer to a genuine intelligent machine. After all, the barometer of true intelligence is whether the brain can learn and understand both male and female constructs.

What’s the Twain takeaway? Well, Gruber and the Siri team’s definition of what constitutes a “conversation” may not be what any woman would call a conversation! LOL.

All this considered, having an app like Siri that intends to apply voice-2-text semantics (specifically NLP and constraint satisfaction) and pull+filter multiple information APIs to find users local restaurants, sports events and make flight bookings or send the information to a contact is……. great. It would be really powerful if the virtual assistants of the future can also pick up on the emotions in our voices and determine whether we’re being rational / irrational / indifferent and the risk parameters we’re in whilst we’re trying to make key decisions.

Maybe there will be a time when intelligent machines can prevent excessive risk-taking as per my scenario with the trading instructions (and be more female and nurturing and less egotistical in the process). No offense intended to any male readers, but that’s what countless analysis points us towards: men are nihilistic — whether in war, in business or in relationships………….Hmmn……..

I’ve signed up to join the Siri beta. Meanwhile, back to figuring out the fund-of-fund’s structure.

OUR INTEGRATED FUTURE

Of course, I also recently saw a demo of MS Surface and the Integrated Communicator Office suite. That demo too had voice-2-text recognition and showcased a user accessing their emails via their mobile and being able to instruct a virtual assistant to notify others in the arranged meeting they were going to be late, then dictating another email to the virtual assistant to send before calling to find out where the nearest Starbucks was (answer: on every corner — LOL).

2009 may prove to be a landmark year for technology in terms of setting a new direction towards holistic (aka integrated) virtual and semantic intelligence in applications. Siri and more integrated iPhone apps as well as what Microsoft Labs are cooking up towards 2019:

WOW. XLNT!

LOCATION AWARENESS SERVICES

I should add that I had an idea for a location awareness service over 3 years ago and told a wi-fi entrepreneur. My idea was not to do with being able to upload photos from my mobile and the GPS then tagging it with longitude-latitude points, prior to posting and publication on a Google Maps / flickr / any other photo-sharing site.

My idea was where my mobile could detect when I was within 50 yards of a “to-do” and would send me a voice alert / buzz to remind me. So, for example, imagine that I have an item to mail at the weekend. I’m out and about in town and walk within 50 yards of a mail office. Then my mobile would alert me and up would pop a Google map to show me where it was, how to get there and opening hours.

Ditto reminders like these:

* pick up the dry-cleaning

* buy present for Mother’s Day from store X

* where I parked the car

The alerts would be triggered not by time, as SMS have been up to now. Instead they would cross location with time with the to-do (or as Gruber correctly calls it “intent”).