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Foundations Built for a General Theory of Neural Networks

When we design a skyscraper we expect it will perform to specification: that the tower will support so much weight and be able to withstand an earthquake of a certain strength.

Increasingly, neural networks are moving into the core areas of society: They determine what we learn of the world through our social media feeds, they help doctors diagnose illnesses, and they even influence whether a person convicted of a crime will spend time in jail.

Yet “the best approximation to what we know is that we know almost nothing about how neural networks actually work and what a really insightful theory would be,” said Boris Hanin, a mathematician at Texas A&M University and a visiting scientist at Facebook AI Research who studies neural networks.

Within the sprawling community of neural network development, there is a small group of mathematically minded researchers who are trying to build a theory of neural networks — one that would explain how they work and guarantee that if you construct a neural network in a prescribed manner, it will be able to perform certain tasks.

Complexity of thought, in this view, is then measured by the range of smaller abstractions you can draw on, and the number of times you can combine lower-level abstractions into higher-level abstractions — like the way we learn to distinguish dogs from birds.

(The neurons in a neural network are inspired by neurons in the brain but do not imitate them directly.) Each neuron might represent an attribute, or a combination of attributes, that the network considers at each level of abstraction.

In 1989, computer scientists proved that if a neural network has only a single computational layer, but you allow that one layer to have an unlimited number of neurons, with unlimited connections between them, the network will be capable of performing any task you might ask of it.

Researchers today describe such wide, flat networks as “expressive,” meaning that they’re capable in theory of capturing a richer set of connections between possible inputs (such as an image) and outputs (such as descriptions of the image).

More recently, researchers have been trying to understand how far they can push neural networks in the other direction — by making them narrower (with fewer neurons per layer) and deeper (with more layers overall).

So maybe you only need to pick out 100 different lines, but with connections for turning those 100 lines into 50 curves, which you can combine into 10 different shapes, which give you all the building blocks you need to recognize most objects.

(These are just equations that feature variables raised to natural-number exponents, for example y = x3 + 1.) They trained the networks by showing them examples of equations and their products.

And while multiplication isn’t a task that’s going to set the world on fire, Rolnick says the paper made an important point: “If a shallow network can’t even do multiplication then we shouldn’t trust it with anything else.” Other researchers have been probing the minimum amount of width needed.

More specifically, Johnson showed that if the width-to-variable ratio is off, the neural network won’t be able to draw closed loops — the kind of loops the network would need to draw if, say, all the red sheep were clustered together in the middle of the pasture.

So while the theory of neural networks isn’t going to change the way systems are built anytime soon, the blueprints are being drafted for a new theory of how computers learn — one that’s poised to take humanity on a ride with even greater repercussions than a trip to the moon.

What is AI?

Artificial intelligence (AI) is currently the source of much hype, both positive and negative, with it being predicted to be the most disruptive technology of all time.

On a more controversial level, it's also being developed to make much harder decisions, where the loss of life can be minimised, but not avoided, by choosing who to kill when there's certainly someone will have to die in order for others to survive.

This has been the most successful application of the technology within an industry, allowing systems to make recommendations based on past behaviour, ingesting huge quantities of data to make more accurate predictions or suggestions.

While general AI is generating a lot of excitement and research, it's still a long way off - perhaps thankfully, because this is the type of AI sci-fi writers discuss when talking about the singularity - a moment when powerful AI will rise up and subjugate humanity.

Given the focused nature of applied AI, systems have been developed that not only replicate human thought processes, but are also capable of learning from the data they process - known widely as 'machine learning'.

A system may be designed to manipulate pre-scripted routines that analyse shapes, colours and objects in a picture, scanning millions of images in order to teach itself how to correctly identify an image.

However, as this process developed it quickly became clear that machine learning relied far too much on human prompting and created wide margins of error if an image was blurry or ambiguous.

In basic terms, deep learning effectively involved the creation of an artificial neural network, which is essentially a computerised take on the way mammal brains work.  The human brain for example is made up of neurons that are connected together with synapses in a massive network.

This network will take in information, say what a person is viewing, and dissect with nuggets of data flowing to each neuron which works to figure out what it is viewing, say if part of an image contains a certain object or colour.

For example, in the case of an AI system designed to combat bank fraud, a first layer may analyse basic information such as the value of a recent transaction, while the second layer may then add location data to inform the analysis.

If not, then that data is then sent back through the network in a process called backpropagation whereby the network readjusts the values each nose has given to the data segment it looked at until is effectively comes up with the best possible answer;

In the case of Google's AlphaGo, the system that defeated a champion Go player in 2016, the deep learning neural network is comprised of hundreds of layers, each providing additional contextual information.

Even threats like email phishing scams, which today are fairly unsophisticated, are likely to be far more nuanced, particularly if AI and Internet of Things devices are used together to target a vast number of victims.

Everything at CES 2019 Is Here!

Open-Ended Trailblazer (POET), combines these ideas to push this line of research explicitly towards generating new tasks, optimizing solutions for them, and transferring agents between tasks to enable otherwise unobtainable advances. (Uber

As a solution, we propose a novel neural architecture, \textit{Transformer-XL}, that enables Transformer to learn dependency beyond a fixed length without disrupting temporal coherence. (arXiv)

using enhanced super-resolution technology are giving classic video games of the past incredible, texture-rich visual makeovers.

The team has released ‘remastered’ versions of Return to Castle Wolfenstein, Doom, The Elder Scrolls III: Morrowind, and most recently — a visually enhanced version of 2001 third-person shooter game Max Payne.​ (Synced)

system can be deployed on a smartphone, achieves 91 percent top-10-accuracy in identifying over 215 different genetic syndromes, and has outperformed clinical experts in three separate experiments. The FDNA team’s research paper, Identifying facial phenotypes of genetic disorders using deep learning, has been published in Nature Medicine. (Synced)

Stanford Intelligent Systems Laboratory (SISL) research group has announced it is open-sourcing its NeuralVerification.jl project, which helps verify deep neural networks’ training, robustness and safety results. (Synced)

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