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Deep Learning: Intelligence from Big Data
Tue Sep 16, 2014 6:00 pm – 8:30 pm
Stanford Graduate School of Business
Knight Management Center – Cemex Auditorium
641 Knight Way, Stanford, CA

A machine learning approach inspired by the human brain, Deep Learning is taking many industries by storm. Empowered by the latest generation of commodity computing, Deep Learning begins to derive significant value from Big Data. It has already radically improved the computer’s ability to recognize speech and identify objects in images, two fundamental hallmarks of human intelligence.

Industry giants such as Google, Facebook, and Baidu have acquired most of the dominant players in this space to improve their product offerings. At the same time, startup entrepreneurs are creating a new paradigm, Intelligence as a Service, by providing APIs that democratize access to Deep Learning algorithms. Join us on September 16, 2014 to learn more about this exciting new technology and be introduced to some of the new application domains, the business models, and the key players in this emerging field.

Steve Jurvetson, Partner, DFJ Ventures

Adam Berenzweig, Co-founder and CTO, Clarifai
Naveen Rao, Co-founder and CEO, Nervana Systems
Elliot Turner, Founder and CEO, AlchemyAPI
Ilya Sutskever, Research Scientist, Google Brain

Demo Companies**:
Clarifai | SkyMind | Ersatz Labs | AlchemyAPI

** Follow (@VLAB) on Twitter and Event Hashtag #VLABdl



George Kozub says:

His mentor Hinton has already admitted that deep learning is a dead end. Stop peddling BS. Look for an AI tech that is based on non linear concepts from the start and can learn faces etc with only 2 cells. It's out there but H's buddies keep suppressing it. Shame even google and IBM couldn't see the opportunity. Search the web, it's there.

Scuba Steve says:

Whose to say in the distant future that all this data wouldn't be turned against us, for example certain patterns of behavior are deemed unhealthy for the society at large. How would a society whose evermore becoming more dehumanized choose to deal with these social deviants. You may gain great strides in understanding the fundamentals of our world as we evolve with it, but caution should be used when simplifying a human spirit to a digitized entity as a resource of information.

Silvio Lima says:

This guy used the word Segway so many times that i don’t wanna hear it for the next 100 years

boron says:

fame whores!

Being Skilled says:

Normally I watch at 2x but not this one. This is already 5x.

Boris Köster says:

9:15 As far as I remember, unsupervised learning (if you are classifying) gives you a class/group, not the definition of the cat. The data is unlabeled. On 28:52 we see some connected nodes, the speaker talks about deep learning, but the gfx has no clear definition about any input layer, hidden layer or output layer but seems to be able to classify some images (supervised learning). Finally I don't want to know if or how this technology is used to identify targets/people to kill them with weapon systems. Who will be responsible for this? The people who invented the algorithms or the people who used them in their weapons?

omegapointil says:

World Peace … I'm only kidding ,partially. I was encouraged by the application to cancer that IBMS Watson undertook. Then I was discouraged at learning that IBM was using Watson for tasks that might fit the priorities of a 16 year old girl — like sorting thru clothes; finding a dress they like that resembles one they saw. The examples of discouragement don't stop there. Do I need to talk to my refrigerator — really fast? The expiration dates are right on the jar. But after viewing the 60 Minutes episode I thought of the heroic uses for Watson and scaling Watson for them at the exclusion of the more superficial applications that I guess it needs to do to pay the bills. I'm not investing a lot of faith in the profit motive as the catalyst for the kinds of future computing power Kurweil's take on Moores Law predicts. While a necessary means of funding, it doesn't answer; what would be the best, most responsible use of it. The World is a basket case, if you're interested. Gates can't even keep up with remedying the myriad biologically based ills in this World. I'm not sure enough attention is being placed on what goals should be pursued. If that computing power isn't pointed at curing cancer for instance then all you've got is a really fast calculator. The problems are as important as the solutions. How about a full court press pile on , scaled like it was WWII towards prioritizing putting that researching power at the fingertips of every doctor on the planet. Then there's the Planet, ourselves.

Rob Gannon says:

What terrible camera work. I so wanted to see the slides, but they're only shown for a couple of seconds each.

Sophrosynicle says:

Don't be discouraged by the 'actually engaging in this field is hard' comments and statements in this talk. ML is incredibly easy if you are patient enough with the documentation. You don't even need to understand the math behind it (but it does help tremendously if you work your way through the papers). Just start with simple problems and work your way up. Some examples provided below.

0. Get to know the libraries, tools and algorithms you'll be working with (plenty of ebooks and papers are freely available, as well as open source code from major ML companies, youtube has great tutorials, lectures and examples). You should have some programming skills and be familiar with (or motivated enough to learn about) processing data or handling a database system. There are also plenty of API's that are useful for the generation of data sets.

1. Get some equations of moderate difficulty to solve and train a small NN to solve them for you. Run through different strategies to optimize.

2. Design and build a model to put images through a convolutional network to detect features (detecting sunrises and sunsets should be easy, there's a clear gradient and a bright light in the middle, easy to detect features. you could go one further and differentiate between rising and setting sun based on the colorspace, morning fog/dew, tides if they are beach pictures, etc).

3. Predict tomorrow's weather based on the past 30 days and/or historical weather data for your locality and day of the year.

4. Classify MIDI files (by genre, f.e). Just like with image classification, but a bit harder.

5. Use a movie or book dataset (api, db, …) and let it suggest a movie or book based on what you've already seen and liked.

6. Perform sentiment analysis on an inbox of your choosing (e-mail, fb, yt, twitter?) or another data set.

7. Develop an AI (or ghost player) for an open source game (no matter how trivial it is) or a triple A game if you are up to it.

8. Lastly, develop a trading bot, based on sentiment analysis coupled with an ensemble of indicators. Let the statisticians inside you all come out!

Roland Hall says:

God is worlds ahead of these fucking atheists like me

sephiroth x says:

Step into analysis please.

Ivano Sporitus says:

Big Data, AI, ML, IoT: Strategy, Monetization and Future:

cucking funt says:

Those grids pictures and filters at around 36:00 are eerily similar to visions you get with migraines .

Paul Dacus says:

Christopher Walken look alike..

SuperMantaraya says:

Let's Masonic sing along everyone!

Dr mosfet says:

So neural networks started in the 80's? in the 80's they started getting back into a forgotten technology from the 50's "neural network", because digital computer ≠ 42

Mc Cine says:

Google's money not withstanding, this by and large goes nowhere… dorks were teaching "neural networks" in the early 80s… computing power is much better, but the silicon chip is reaching its limit…. unless progress is made – no go;
We are not as smart as we think… for awhile it seems that imitating nature has more bang…. organoids such as neurons in a dish go further… that is barely what could be done, growing them and interfacing them; some of them pilot flight simulators… the rest of the talk is verbose rubbish !

YS Sun says:

I thought it was 2x speed.

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