olympic niblets

To celebrate the start of the Vancouver Olympic Games, I put together a red [strawberries], white [pita triangles] and blue [blueberries] snack tray for the bitty ones. As you can see, there was also yellow [hummus], brown [honey roasted peanuts] and green [Snapea Crisps – the ONLY vegetable twofish will eat].



Onefish and twofish polished off the entire spread during the qualifying rounds of the men’s ski jumping.

  • Laurie says:

    Will you be my mommy, too?

  • Open says:

    This looks like a fun game. Now, I have to play it, even though it’s Saturday now.1. The last thing I ate was LASAGNA and RAVIOLI.2. WHAT HAPPENS IN LONDON BY JULIA QUINN is snhetmiog I recently bought.3. When it rains, IT FLOODS THE ROAD BY MY HOUSE. 4. MY HUBBY was the first person I talked to today.5. Hugs are ALWAYS WELCOME.6. okay, nothing tops Yette’s “extra comfort” comment.7. And as for the weekend, tonight I’m looking forward to ITALIAN FOOD, tomorrow my plans inclue PICNIC AT MY BROTHER’S PLACE. And Sunday, I want to TAKE A NICE LONG NAP.

  • Oumema says:

    Hi Bev i am a follower of Jacguis and your blogs i love the cards and tluoriats you do and you give me such inspiration both of you thank you so much for helping me create cards for my family and friends, i am house bound due to a brain hemorrage. and this is my way being creative noway on the levels you do it, thanks so much for all the hard work you both do everyday curlytops/michelle c

  • Isaias says:

    What I object to is the hyped tone of warridzy that these results are so often presented with.Well, I can agree with you there. And I also agree with you that neither a chess-playing algorithm nor a bayesian algorithm for predicting Supreme Court decisions are examples of “human thinking”. Plainly they are not.On the other hand, when you say…A Bayesian inference engine is good at inferring probabilities from sequences of stochastic events.I think you’re underselling it. Justice Kennedy’s decisions are not random (well, actually …), but we can’t easily know all of the causes of his decisions or how much weight those causes will receive in a particular case. In short, we lack the kind of perfect knowledge that would let us build a deterministic “opinion engine” that perfectly simulates Justice Kennedy.But if we need to predict his decisions, we’ll need some kind of tool that allows us to do so as accurately as possibly. Candidate A is the algorithm Ayers describes. Candidate B is a consensus of expert judgment. You are proposing candidate C, a prediction market where a group of motivated human traders invest according to their best judgments. The choice is an easy one. A has an accuracy of about 75%. Tetlock’s research tells us that B has an accuracy of around 50%. There is no example of C, so we have no idea how well it would do. If I have to make a forecast today, A wins hands down, right?If such a prediction market existed, and was lucrative enough for anyone to care, we’d almost certainly see all the traders equip themselves with some variant of the model in A and try to derive competitive advantage from it. This is basically what has happened on Wall Street, right? And I think that not insignificant effort is put into developing models of NFL games (check out footballoutsiders.com sometime) that would enable one to outperform the Vegas sports books.So when you say…He’s saying that machines beat humans, but it’s sort of like me saying I can beat Mike Tyson – at thumbwrestling. I think you’re really grasping at straws here. Chess, Supreme Court decisions, medical diagnoses, weather forecasts, political election outcomes and financial markets are all areas where statistical models have proven capable of outperforming human judgment. Obviously these models aren’t Frankenstein’s Monster or some sort of Ghost in the CPU, nor are they infallible. They are tools that clever humans employ to obtain the best outcomes in these important areas. Who cares if some of the quants making the models get an overblown Financial Times article once in a while?

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