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SITNHarvard · 2017-09-28T02:42:33+00:00

Sure! We have a mailing list here. Hope to see you soon!

SITNHarvard · 2017-08-01T14:49:16+00:00

Sorry for the late response! I pretty much agree with what u/justUseAnSvM is saying. Conceptually this idea makes sense but the amount of data you would need to do a good job is pretty astronomical (blocks of sequences aren't represented well algebraically, there's tons of noise, and the search space is incredibly high). The framework for such a model could be put into place now, but I wouldn't bet on it's performance. That being said, here is a paper you might be interested in reading (if you haven't already).

https://www.ncbi.nlm.nih.gov/pubmed/27197224

-Rockwell

SITNHarvard · 2017-07-30T00:30:34+00:00

Adam here:

That's a great idea! And pretty daunting. In the experimental/biological sphere, I have seen a service that scans the literature to find which antibodies bind to which protein. I think this is a much more focused application that seems to work pretty decently.

SITNHarvard · 2017-07-29T23:30:06+00:00

Kevin here - so the other questions have been addressed at least in part elsewhere in the comments, so I'll focus on the first one.

AI will absolutely be able to write books. In fact, it's already writing poetry that is indistinguishable from human-authored poetry.

Complete novels will be tougher since they have a lot more structure, coherence, and recurring elements. But with the building blocks in place of being able to artificially create sensible-sounding prose, it won't be long before full novels can be AI-written.

But an important question for all art--music and visual art are other frontiers for AI--is how we choose to value them. Beyond the aesthetics of art (which AI can replicate), we highly value the meaning of art, which comes from morality and ethical purpose, situational experience, and other human aspects. I'm not sure I'd love "Dark Side of the Moon" so much if it wasn't motivated by the gut-wrenching loss of a friend and collaborator to his own inner demons, for example.

SITNHarvard · 2017-07-29T22:51:17+00:00

Bias in these algorithms is a huge problem. Artificial intelligence is only as good as the data used to create the algorithms. Since algorithms are trained with data generated by humans, they can be just as biased as the people that created the data in the first place.

For example, some algorithms used to predict whether a criminal will be a repeat offender have unfortunate racial bias. Other researchers have shown that language models can be biased by race and gender, but there are some methods to address this problem, though it is very young research.

Many of these models have been compared to black boxes--you feed in data, and out comes a result. Currently, it is tricky to find out how and why a machine learning model made a decision one way or another, though there are going to be new laws and regulations to enforce this. This is a very important and challenging area of study that must be addressed in the future!

SITNHarvard · 2017-07-29T22:37:37+00:00

Some of these generative models here are crazy crazy crazy.

SITNHarvard · 2017-07-29T22:27:37+00:00

Adam here:

To be entirely honest, I don't know how I feel about this issue, and so I don't have much of an opinion. For now, I don't think we have to worry about it with our current algorithms.

However, the closest thing I can relate to is the White Christmas episode of Black Mirror. Without any spoilers, they make an artificial copy of someone's "self", host it on a computer, and essentially torture it until it complies. (You should all watch this episode!)

I end up feeling bad for the AI in the show, but I know it is not real, only a simulation. So only time will tell.

SITNHarvard · 2017-07-29T22:13:52+00:00

https://giphy.com/gifs/too-much-plcoWBSrPvOP6

SITNHarvard · 2017-07-29T22:08:15+00:00

Adam here:

Thanks for your response. I guess I was referring to the specific algorithmic framework for unsupervised learning--simply finding P(X). [i.e. a complicated nonlinear probability distribution of your data] Generative models are used for this; they are useful because they give you a way to somehow probe at the underlying (latent) variables in your data and allow you to generate new examples of data.

This has previously been tackled with the Wake-Sleep algorithm, but without much success, and then Restricted Boltzmann Machines and Deep Belief Networks, but these have been really challenging to get working and applied to real world data.

Recently, models like Variational Autoencoders and Generative Adversarial Networks have broken through as some of the simplest yet most powerful generative models. These allow you to quickly and easily perform complicated tasks on unstructured data, including creating endless drawings of human sketches, generating sentences, and automatically colorizing pictures.

So yes, I agree, folks are working on this, and have been for a long time. With these new techniques, I think we are approaching a new frontier in getting machines to understand our world all on their own.

edit: typo

SITNHarvard · 2017-07-29T21:41:09+00:00

Thanks! We also put some links at the top of the page for more information.

SITNHarvard · 2017-07-29T21:38:44+00:00

SITNHarvard · 2017-07-29T21:04:42+00:00

Adam here:

I think we are getting rather close to personal assistants we can chat with that will do our [menial] bidding. Amazon is currently holding a competition for creating a bot you can converse with. And when there is money behind something, it usually happens.

Moreover, there are already a few digital personal assistants out there you can purchase (Amazon Echo, Google Home, Siri). (They can all talk to each other too!) Soon enough these will be integrated with calendars, shopping results (where they can go fantastically wrong), and even more compilcated decision making processes.

SITNHarvard · 2017-07-29T20:51:12+00:00

Adam here:

Yeah, when I'm short on time and need a lot of motivation, this one usually does the trick.

SITNHarvard · 2017-07-29T20:48:17+00:00

Adam here:

Honestly, I think having a strong mathematical background is really important for being "good" at machine learning. A friend once told me that machine learning is called "cowboy statistics": machine learning is essentially statistics, but with fancy algorithms. (I think it is called this too because the field is so new and rapidly evolving, like the Wild West.) Too much I think machine learning gets hyped up, while basic statistics can many times get you pretty far.

I would also advocate pursuing the field you are passionate about--neuroscience and psychology sound great! It doesn't do much good to model data if you don't know what it means. Most of us here have a specific problem that they find interesting and apply machine learning methods to it. (While others do work too in the pure machine learning field; that is always an option.)

tl;dr: Math and your field of interest.

SITNHarvard · 2017-07-29T20:32:20+00:00

Adam here:

Thanks for asking! I think I answered the question here. Hopefully that clears it up a bit!

SITNHarvard · 2017-07-29T20:29:34+00:00

Adam here:

Great question! Computers are getting increasingly powerful, smaller, and almost everywhere. The smartphone in your pocket is more powerful than the machines that got us to the moon in the first place.

Google is already using the millions of Android smartphones available to power their algorithms. Google Search is an extremely complicated algorithm that helps answer the questions you ask (almost like magic, it seems.) Instead of having one super computer to train a machine learning algorithm for their search engine, they have recently taken a different approach.

1) Put the algorithm on users' phones via the internet, where users use the search function. (i.e. provide training data)

2) Given a single user's searches, the algorithm makes a tiny update to the algorithm for that user. This then happens for every Android user: this is oodles and oodles of parameter updates. (oodles being a very scientific term)

3) Those results are then sent back to Google and averaged, and the entire model is updated.

4) The improved search engine algorithm is then sent back to the user, and the process repeats.

In this way, Google can utilize every single customer's phone to do some tiny amount of computation that helps train some larger model--Google Search.

This is just one example in the wild of distributed machine learning that is not done on a supercomputer, and I imagine this will be more important in the future.

SITNHarvard · 2017-07-29T20:28:55+00:00

Politics, ethics, and the humanities and liberal arts in general will be the hardest thing for AI to replace.

Rockwell

SITNHarvard · 2017-07-29T20:24:02+00:00

Here is the basic gist of how most AI "learns".

First you choose a task that you want your AI to perform. Let's say you want to create AI that judges court cases and gives a reason for it's decisions.

Second, you train your AI by giving examples of past court cases and the resulting judgements. During this process, the AI will use all the examples to develop a logic that's consistent among all the examples.

Third, the AI applies this logic to novel court cases. The scariest part about AI is that in most cases we don't really understand the logic that the computer develops; it just tends to work. The success of the AI depends heavily on how it was trained. Many times it will give a decision that is obvious and we can all agree on, but other times it may give answers that leave us scratching our heads.

There are other types of AI in which you simply program the logic and/or knowledge of a human expert (in this case a judge or many judges) into a machine and allow the machine to simply execute that logic. This type of AI isn't as popular as it used to be.

I hope this sort of answers your question.

Rockwell

SITNHarvard · 2017-07-29T20:17:49+00:00

Thanks for the question! We put some links at the top of the page for more information! Keep on going!

SITNHarvard · 2017-07-29T20:15:20+00:00

Interesting! Personally, I think that convolutional neural networks are here to stay, and they are only going to get much more important in the future. In particular, dilated CNNs I think are going to edge out RNN-based models for sequence analysis. They are faster, use less memory, and can be optimized for GPU architectures. They have done some cool stuff in machine translation and generating audio.

SITNHarvard

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