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Deep Learning: Intelligence from Big DataTue Sep 16, 2014 6:00 pm - 8:30 pmStanford Graduate School of BusinessKnight Management Center – Cemex Auditorium641 Knight Way, Stanford, CAA 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. ModeratorSteve Jurvetson, Partner, DFJ Ventures PanelistsAdam Berenzweig, Co-founder and CTO, ClarifaiNaveen Rao, Co-founder and CEO, Nervana SystemsElliot Turner, Founder and CEO, AlchemyAPIIlya Sutskever, Research Scientist, Google Brain Demo Companies**:Clarifai | SkyMind | Ersatz Labs | AlchemyAPI ** Follow (@VLAB) on Twitter and Event Hashtag #VLABdl
tis guy speaks too fast! i suggest you to smoke some pot before watching this .
But that sir all in black is a gravedigger?
fuuuuck, so fast english speach, cannot understand it
We need to Identify that financial transactions were made by people hacking and or pinging my phone. I've searched this issue in searches and found over 8,000 issues
can you do a lecture on how to speak like you ? Amazing video. Thank you
+ had me at Wolfram alpha... remarkable insight, thanks for sharing... how thoughts become things and moments in time. The math of life is thought
:) .... it's the information AGE....get smart pplz....It's not that important how fast or slow he is talking.....please judge people according to the quality of knowledge he/she holds and delivers to the people.
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.
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.
This guy used the word Segway so many times that i don’t wanna hear it for the next 100 years
Normally I watch at 2x but not this one. This is already 5x.
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?
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.
What terrible camera work. I so wanted to see the slides, but they're only shown for a couple of seconds each.
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!
God is worlds ahead of these fucking atheists like me
Step into analysis please.
Big Data, AI, ML, IoT: Strategy, Monetization and Future: https://www.slideshare.net/ishmelev/datamoney
Those grids pictures and filters at around 36:00 are eerily similar to visions you get with migraines .
Christopher Walken look alike..