"Big Data and Artificial Intelligence: Intelligence Matters" entries
A look at a few ways humans mesh with the rest of our data systems.
Here’s a look at a few of the ways that humans — still the ultimate data processors — mesh with the rest of our data systems: how computational power can best produce true cognitive augmentation.
Deciding betterOver the past decade, we fitted roughly a quarter of our species with sensors. We instrumented our businesses, from the smallest market to the biggest factory. We began to consume that data, slowly at first. Then, as we were able to connect data sets to one another, the applications snowballed. Now that both the front office and the back office are plugged into everything, business cares. A lot.
While early adopters focused on sales, marketing, and online activity, today, data gathering and analysis is ubiquitous. Governments, activists, mining giants, local businesses, transportation, and virtually every other industry lives by data. If an organization isn’t harnessing the data exhaust it produces, it’ll soon be eclipsed by more analytical, introspective competitors that learn and adapt faster.
Whether we’re talking about a single human made more productive by a smartphone-turned-prosthetic-brain, or a global organization gaining the ability to make more informed decisions more quickly, ultimately, Strata + Hadoop World has become about deciding better.
What does it take to make better decisions? How will we balance machine optimization with human inspiration, sometimes making the best of the current game and other times changing the rules? Will machines that make recommendations about the future based on the past reduce risk, raise barriers to innovation, or make us vulnerable to improbable Black Swans because they mistakenly conclude that tomorrow is like yesterday, only more so? Read more…
The history of computing has been a constant pendulum — that pendulum is now swinging back toward distribution.
The trifecta of cheap sensors, fast networks, and distributing computing are changing how we work with data. But making sense of all that data takes help, which is arriving in the form of machine learning. Here’s one view of how that might play out.
Clouds, edges, fog, and the pendulum of distributed computingThe history of computing has been a constant pendulum, swinging between centralization and distribution.
The first computers filled rooms, and operators were physically within them, switching toggles and turning wheels. Then came mainframes, which were centralized, with dumb terminals.
As the cost of computing dropped and the applications became more democratized, user interfaces mattered more. The smarter clients at the edge became the first personal computers; many broke free of the network entirely. The client got the glory; the server merely handled queries.
Once the web arrived, we centralized again. LAMP (Linux, Apache, MySQL, PHP) buried deep inside data centers, with the computer at the other end of the connection relegated to little more than a smart terminal rendering HTML. Load-balancers sprayed traffic across thousands of cheap machines. Eventually, the web turned from static sites to complex software as a service (SaaS) applications.
Then the pendulum swung back to the edge, and the clients got smart again. First with AJAX, Java, and Flash; then in the form of mobile apps, where the smartphone or tablet did most of the hard work and the back end was a communications channel for reporting the results of local action. Read more…
We need to understand our own intelligence is competition for our artificial, not-quite intelligences.
A few days ago, Elon Musk likened artificial intelligence (AI) to “summoning the demon.” As I’m sure you know, there are many stories in which someone summons a demon. As Musk said, they rarely turn out well.
There’s no question that Musk is an astute student of technology. But his reaction is misplaced. There are certainly reasons for concern, but they’re not Musk’s.
The problem with AI right now is that its achievements are greatly over-hyped. That’s not to say those achievements aren’t real, but they don’t mean what people think they mean. Researchers in deep learning are happy if they can recognize human faces with 80% accuracy. (I’m skeptical about claims that deep learning systems can reach 97.5% accuracy; I suspect that the problem has been constrained some way that makes it much easier. For example, asking “is there a face in this picture?” or “where is the face in this picture?” is much different from asking “what is in this picture?”) That’s a hard problem, a really hard problem. But humans recognize faces with nearly 100% accuracy. For a deep learning system, that’s an almost inconceivable goal. And 100% accuracy is orders of magnitude harder than 80% accuracy, or even 97.5%. Read more…
Solutions to a number of problems must be found to unlock PAPI value.
In November, the first International Conference on Predictive APIs and Apps will take place in Barcelona, just ahead of Strata Barcelona. This event will bring together those who are building intelligent web services (sometimes called Machine Learning as a Service) with those who would like to use these services to build predictive apps, which, as defined by Forrester, deliver “the right functionality and content at the right time, for the right person, by continuously learning about them and predicting what they’ll need.”
This is a very exciting area. Machine learning of various sorts is revolutionizing many areas of business, and predictive services like the ones at the center of predictive APIs (PAPIs) have the potential to bring these capabilities to an even wider range of applications. I co-founded one of the first companies in this space (acquired by Salesforce in 2012), and I remain optimistic about the future of these efforts. But the field as a whole faces a number of challenges, for which the answers are neither easy nor obvious, that must be addressed before this value can be unlocked.
In the remainder of this post, I’ll enumerate what I see as the most pressing issues. I hope that the speakers and attendees at PAPIs will keep these in mind as they map out the road ahead. Read more…
How neuroscience is benefiting from distributed computing — and how computing might learn from neuroscience.
When we think about big data, we usually think about the web: the billions of users of social media, the sensors on millions of mobile phones, the thousands of contributions to Wikipedia, and so forth. Due to recent innovations, web-scale data can now also come from a camera pointed at a small, but extremely complex object: the brain. New progress in distributed computing is changing how neuroscientists work with the resulting data — and may, in the process, change how we think about computation. Read more…
True artificial intelligence will require rich models that incorporate real-world phenomena.
In my last post, we saw that AI means a lot of things to a lot of people. These dueling definitions each have a deep history — ok fine, baggage — that has massed and layered over time. While they’re all legitimate, they share a common weakness: each one can apply perfectly well to a system that is not particularly intelligent. As just one example, the chatbot that was recently touted as having passed the Turing test is certainly an interlocutor (of sorts), but it was widely criticized as not containing any significant intelligence.
Let’s ask a different question instead: What criteria must any system meet in order to achieve intelligence — whether an animal, a smart robot, a big-data cruncher, or something else entirely? Read more…
Step-by-step instruction on training your own neural network.
When I first became interested in using deep learning for computer vision I found it hard to get started. There were only a couple of open source projects available, they had little documentation, were very experimental, and relied on a lot of tricky-to-install dependencies. A lot of new projects have appeared since, but they’re still aimed at vision researchers, so you’ll still hit a lot of the same obstacles if you’re approaching them from outside the field.
In this article — and the accompanying webcast — I’m going to show you how to run a pre-built network, and then take you through the steps of training your own. I’ve listed the steps I followed to set up everything toward the end of the article, but because the process is so involved, I recommend you download a Vagrant virtual machine that I’ve pre-loaded with everything you need. This VM lets us skip over all the installation headaches and focus on building and running the neural networks. Read more…
Announcing a new series delving into deep learning and the inner workings of neural networks.
Editor’s note: this post is part of our Intelligence Matters investigation.
When I first ran across the results in the Kaggle image-recognition competitions, I didn’t believe them. I’ve spent years working with machine vision, and the reported accuracy on tricky tasks like distinguishing dogs from cats was beyond anything I’d seen, or imagined I’d see anytime soon. To understand more, I reached out to one of the competitors, Daniel Nouri, and he demonstrated how he used the Decaf open-source project to do so well. Even better, he showed me how he was quickly able to apply it to a whole bunch of other image-recognition problems we had at Jetpac, and produce much better results than my conventional methods.
I’ve never encountered such a big improvement from a technique that was largely unheard of just a couple of years before, so I became obsessed with understanding more. To be able to use it commercially across hundreds of millions of photos, I built my own specialized library to efficiently run prediction on clusters of low-end machines and embedded devices, and I also spent months learning the dark arts of training neural networks. Now I’m keen to share some of what I’ve found, so if you’re curious about what on earth deep learning is, and how it might help you, I’ll be covering the basics in a series of blog posts here on Radar, and in a short upcoming ebook. Read more…