That sound…. Ah, that sound… I don’t about know you, but I am almost 40 and the father of two young boys, and for me that sound reminds me with a touch of nostalgia of the cry of a newborn. It makes you smile as fond memories have overshadowed the dirty nappies.
To a certain extent, that sound was the cry of a baby internet, when browsing was a commitment and a flaming logo the paramount of creativity. The world wide web has since grown up. It’s left home and got a job. It’s a young adult now, doing the brilliant, creative, inventive things that young adults are so capable of.
Looking back, it’s fair to say that the dial-up buzz was literally the first signal in your digital footprint.
You signed on.
And in that moment big data spun to life.
What were we learning from those early signals? Well, we knew how many people were online and roughly where they were. That’s not much.
As the web of documents developed, search engines launched onto the scene and suddenly our ability to understand you skyrocketed.
We knew what you wanted.
We understood that your inputs into a search engine were, on a personal level, an expression of your desire. And on a global level, an expression of the world’s consciousness.
This is heady stuff.
You will tell a search engine things you wouldn’t tell the person you are closest to.
My name is Cedric Chambaz. I work for Microsoft and for the last 8 years I have contributed to the development of Bing, our search engine. My focus is on the advertising side, with Bing Ads and most of my career has been spent in digital marketing.
But today we’re going to focus less on search marketing per se, and more on the information infrastructure and machine learning that Bing is part of, looking at how this is influencing your future. What are we doing with your data footprint? This is the question we’ll be answering today.
We’re using Bing Pulse, Microsoft’s audience engagement technology, to get your input. I am conscious of the irony of asking you to create data on a presentation on data, but what a better way to make real?
Here’s how it works: On your mobile device, go to aka.ms/ConnectedCows.
[Pause a bit for your audience to find the site – maybe look for it yourself on your own phone. You could also have a big plasma screen off to the side that’s hooked up to a computer. Have the Pulse results showing live there.]
Over the course of our time together, we’ll be asking you some specific poll questions so we can see what you think.
Let’s go through our first question together: Does the idea of data collection make you uncomfortable?
But let’s get back to our story. So that you understand the complexity and depth of the data infrastructure that you are part of, I’m going to walk you through what has changed since the emergence of the first search engines. With every change I mention here, you need to visualize a growing mountain – that’s the big data.
First is your search habit.
You’ve progressed from a few searches per day to multiple searches per hour, and not just you but also the billions of people who got online in the recent decade.
Second is your search access.
Most of us had our own desktop or laptop computer 20 years ago. But just one. And we certainly couldn’t put it in our pocket and take it with us to a party. And it is not just computers, think about all the devices which are hosting computing power that you currently own: laptop, tablet, smartphones, TV, but also your car and soon enough your fridge.
The third big change is your search complexity.
You have gone from using commands to using more human a language.
You’ve gone from asking “what” to asking “why…” and “how to…” You’ve layered sequential searches on top of these, in a complex web of intent.
The integration of search with other infrastructure components has also changed. It used to be that a search engine was an isolated service. Now it’s plugged into the social graph.
This means that several points of contact are linked and with them a flurry of new signals, millions of them that only a few super-computers are able to capture, organize, model and render.
Search engines are the database of intent. And social networks are the depository of sentiments. We have developed the ability to process, analyze and understand these two humongous, historical and real-time information sets together.
We can understand your sentiment for certain events or entities, estimate popularity trends, as well as predict outcomes of future events.
But that is only a few voices in a teepee… How about looking at the opinion of 22m British searchers/581 Bing users worldwide?
We have a program called Bing Predicts which combines and models all the data signals we can find, and comes up with incredibly accurate predictions.
We initially explored popularity-based contests like American Idol, for which the web and social signals are very strong and highly correlate with popularity voting patterns.
Bing Predicts could accurately project who would be eliminated each week during American Idol and who the eventual winner would be. Just by using all of the signals that are out there.
Getting more complex, we turned to sporting events and even world political challenges.
During the World Cup in Brazil, our team predicted accurately with 100% accuracy the winners of the final elimination round. During the Rugby World cup, we had 80% accuracy across the tournament. Surprised? In order to successfully predict a sporting event outcome, the number and type of signals we incorporated quadrupled from what we used to predict a basic popularity event like American Idol.
This is because we recognize that popularity alone does not predict whether a team will win – Sorry for the fans. A fan base has special insight into the abilities of their teams, and those fans are having constant discussions about their team. This is called the “wisdom of the crowd.” We weighted their knowledge against player and team stats, tournament trends, game history, location and even weather conditions. This is how we were successful in our predictions.
We finally turned our attention to political events, and in particular the Scottish referendum of last year.
This is fantastic stuff. We’re predicting the future. Think about that for a minute. We’re predicting the future.
Can you imagine a business need that this kind of prediction can answer? Of course you can!
We’re experimenting right now with predicting the upcoming trends in fashion, in automobile popularity, in technology – so we can help our advertisers make smarter business decisions. Imagine Marks & Spencer knowing what next spring’s most popular shoe will be? Not just guessing it, but knowing it without a doubt. How might this affect their business decisions?
So we saw how predictions can play a role in entertainment, sport or business, fine. Fine, until we find a way to make this kind of data infrastructure even more meaningful, at a society and mankind level. What can we do with this capability that goes beyond entertainment and the novelty factor? Can we use our big data infrastructure to make a meaningful impact on society? Here’s Rani again:
Isn’t that wonderful? I’ve got another Pulse question for you:
All of this is exciting on a global or country level. When we’re talking about millions of inputs, it’s no wonder you can make predictions and have an impact like this. It is just a massive sample size…
What about bringing this big data infrastructure to a personal level?
Is it possible for a machine to learn so much about you that it can accurately predict your next move? Or predict when you will need something, and provide it?
Maybe you’ve heard about Cortana. She’s our digital personal assistant and she is brilliant. Cortana is not only on Windows Phone but also Android and iPhone. And with the release of Windows 10, she’s even on your desktop.
You set up Cortana with some basic info about yourself, then use her to help you with things like scheduling and reminders and web searches.
Before you know it, Cortana is spontaneously sending you an alert to inform you that you should leave the office now to be on time for your next appointment in Farringdon, because she found some congestion on your normal route.
It doesn’t take Cortana long to learn so much about you that she can predict your next move and offer assistance.
This video will show you a tiny bit of her capabilities.
While our mobile phones aren’t exactly wearables, we sometimes behave as if they are, keeping them on our body no matter where we go.
With wearables, two important things converge: big data infrastructure and your expectations.
You might think of this when you hear “wearables”
But these are just the first baby steps towards the full potential of wearable and how that technology will be able to enhance our capabilities, as individuals or as professionals. Think about it: Wearables can capture and communicate signals about your location, your manner of travel – whether you’re on foot or in a car – time of day, most recent queries, usual route home from work, your physiological state, what the weather is… You name it.
So if your wearable identifies that your hydration is low, it could respond with an answer that factors in your location, whether you’re moving, what time of day it is and therefore whether the nearby branch of your favourite coffee shop is open. It could even cross-reference this with your earlier interest in gingerbread lattes, and the fact that it is raining, and direct you to the nearest open coffee shop with plenty of indoor seating and gingerbread lattes on the holiday menu.
Your wearable might even send you an alert for a coupon the coffee shop is offering.
As the wearable technology grows, your expectations for your experience with technology in general will change.
As you can tell from my accent, I am not from this country. I am actually from the French Alps where I spent most of my summers walking the mountains with my grandmother. She used to herd cattle in these alpine pastures and she was telling me stories about how much each of her cows were almost like members of her family. They had names, and she could tell when something was wrong with any of them…
Well, these days are gone, and nowadays a farm is no longer taking care of a small dozen of cows, but hundreds. And the personal relationship of each animal is no longer an option. The story of the connected cows started with a farmer in Japan who was exhausted with the effort of figuring out the exact time his cows were fertile – because it is a very short window, only 12-18 hours every 21 days, and it happens usually between 10pm and 8am. And of course knowing this precise time of estrus would give the farmers a chance to artificially inseminate the cows and have a successful pregnancy. These are farms with hundreds of cows – you can image what a nightmare this would be.
Could technology help? This farmer in Japan asked Fujitsu for help. Fujitsu consulted with some university researchers and they came up with this idea of putting wearables – pedometers – on the cows, and providing the data to Microsoft Azure, in the cloud, for analysis and alerts that go straight to the farmer’s smartphone.
It turns out that when a cow is in estrus, she paces. The number of steps she is taking increases tremendously, and this data alerts the farmer to the right moment for fertilization. The connected cow project has been 95% accurate – and that 5% where it misses the mark turns out to be when the cow actually skips the farm and goes missing.
Not only is this wearable incredibly accurate, it also helped the researches discover that there is an optimum window for fertilization if you’d like a female or if you’d like a male. With 70% probability, a farmer should fertilize in the first half of the estrus window if he needs more milk cows or if he needs more bulls. But it does not stop there… The Fujitsu researchers were able to also correlate pacing patterns with increased risks of genetic diseases and pathology.
That is amazing what data can tell you, if you know how to look at it. Sometimes creatively!
This is the joy of data infrastructure.
We can do wonderful things in the world when we collect, analyze and render the data that’s available to us. Microsoft is on the leading edge of this, with products like Power BI, Azure, our cloud platform but also Bing our search engine and its machine learning capabilities which can make sense of the millions other data points that come together to make big data smart, useful, creative and – yes – joyful. In our vision for the future, Microsoft will lean heavily on this infrastructure. Let’s take a look:
Cristal Festival 2015 - "Why are the cow connected?" - Cédric Chambaz - Microsoft - Bing Ads
Uncovering the joy of big data infrastructure
Why are the
how to make a baby stop
how to make a baby stop crying
how to make a baby stop screaming
how to make a baby stop biting
how to make a baby stop howling
how to make a baby stop shrieking
how to make a baby stop making me insane