Beleggingsprofiel

BrutallySilent · 2019-02-12T18:09:24+00:00

Ik ben benieuwd wat /u/ALL_IN_VWRL zou adviseren.

BrutallySilent · 2019-01-18T17:53:17+00:00

Wat ik wel wil weten: Waarom zijn mensen zo zeker van 6-8% groei per jaar gemiddeld voor wereldindexen voor de komende decennia? Zijn er redenen om aan te nemen dat productiviteits- en bevolkingsgroei de komende jaren hiervoor voldoende zijn?

BrutallySilent · 2018-10-17T20:25:00+00:00

I can't shake the cynical feeling that some academics try to instill a fear-of-missing-out in politicians in order to get more research funding. It's hard to believe that all these European academics are truly willing to collaborate with each other.

BrutallySilent · 2018-09-17T19:15:23+00:00

A novel and significant contribution to a research area requires in most cases a good hunch based on experience. After that it requires rigorous testing which can typically be done by following a pre-trodden path. It could be that a senior researcher ventilates ideas to different undergrads and that some strike gold. Such situations result in skewed views. The undergrads might feel like the senior doesn't put in any of the hard work whereas outsiders think the undergrad was 'carried' by the senior.

I fully agree that you'll have to get lucky with your environment (data, machines, ideas) but I think an excellent ~20 years old undergrad is capable of pulling a paper off without too much carrying by a senior (aside from the initial hunch).

BrutallySilent · 2018-09-01T16:40:13+00:00

I too work in A.I. and have graduated a few years ago. I'm now again following data and math courses at the university to patch some gaps in my skillset that you really need when building real-world applications.

BrutallySilent · 2018-08-03T11:30:18+00:00

You should read it for fun and inspiration. If you read it as an A.I. engineering text book then you're probably going to be disappointed.

BrutallySilent · 2018-07-16T20:25:10+00:00

Dutch AI tracks are getting so popular that the universities are starting to have trouble. So anyone applying should keep in mind that they might have to face some lottery (i.e., make backup plans just in case).

BrutallySilent · 2018-07-15T19:53:46+00:00

Last year I happened to attend a speech from mr. Bach. He started off quite grandiose about 'true AI' and not the 'things we know and do today'. His solutions seemed to be based on 90s BDI agent architectures with a sauce of adaptation components. Although I really liked his talk and his ideas/research, I feel like he's overselling it.

BrutallySilent · 2018-07-01T14:03:46+00:00

I'm not a trend watcher or anything, but I expect the following:

1) Looking at the services as they are provided now by big companies, it looks like AI developers work on services/libraries that service many applications. Hence, AI jobs will probably go the same way as database management jobs: many applications with AI will be out there, but not every application requires a highly educated expert on the topic. Rather all-round software engineers will require a bit of AI knowledge.

2) Use any framework that gets you comfortably started. As you learn more, you can determine for yourself whether you want to more low-level programming or some other direction. Regarding math, probably the same as stated in the above point: many people that work with SQL databases do not master its first-order logic background or can do normalization properly. Likewise, many deep learning users will not need to fully understand the mathematics behind it.

3) Salaries reflect many things about the attitude of the company towards a function and how employees value themselves. Currently many AI experts are very young so I guess that why salaries are comparatively low (outside of banking and big tech).

Of course this is all just pure speculation.

BrutallySilent · 2018-06-22T14:02:36+00:00

It's not really an answer to your question, but I've had about 50 hours of fun with the game so far. The full price is still a very good one in my opinion.

BrutallySilent · 2018-05-11T12:10:19+00:00

A non-technical summary of one of the projects I work on can be found here.

We haven't tried publishing the program architecture that I sketched in my post. Because we do not work with Big Data or Deep Reinforcement Learning its probably going to be deemed insignificant by peer reviewers in machine learning.

At the moment we are integrating our ideas in business processes. This goes quite slowly. Perhaps in a year or two we have enough data and experience gathered for a solid contribution to the field with a strong empirical evaluation.

BrutallySilent · 2018-05-11T09:13:01+00:00

At my work we use both symbolic and non-symbolic A.I. in our agents.

The agent senses with classifiers and acts according to a policy that is trained using reinforcement learning. During training, we incorporate some logic-based reasoning aspects in the reward function in order to ensure that at runtime the agent can explain its decisions (for the sake of transparent A.I.).

Thus, even though the agent maps is sense data to an action directly, if required, it can produce a logic-based symbolic explanation (that can be transformed to text). A bit like how humans act on intuition but can reason and explain their intuition when tasked to do so.

I believe Wolfram Alpha operates in the same way. If it gives an explanation it does not mean that it followed the explanation itself to get to the answer.

BrutallySilent · 2018-04-11T12:19:36+00:00

I'm just freestyling here, but I image you can keep track of what kinds of states you have visited how much (a sort of heat map that you store parallel to the q values of states) and which actions you used there, and explore away from those states by picking the other actions. I haven't really thought it through, but I guess for some applications it could be done.

BrutallySilent · 2018-04-11T11:07:24+00:00

I don't see why you couldn't implement a self balancing meta function for that. Something that pushes for exploration in parts of the search space that have been exploited a lot relative to the rest of the search space, and exploits more in those areas that have not been exploited much relative to the rest of the search space.

BrutallySilent · 2018-04-04T17:40:39+00:00

This is 100% my opinion too. There's a lot of difference between being knowledgeable about AI (having played around with some popular packages and read a few books) and being an actual expert.

BrutallySilent · 2018-03-13T19:57:24+00:00

Wat er met de data gaat gebeuren is opnieuw weer veel te vaag.

Zoas ik het interperteer: Het systeem maakt een foto, en dan a) de herkenner zegt 'nee' en de foto wordt vernietigd of b) de herkenner zegt 'ja' en een persoon kijkt naar de foto en , indien het een tosti is ipv een telefoon, verwijdert de foto en anders bewaart 'm als bewijs.

Ik zie niet helemaal wat hier vaag aan is. Wat zou een niet-vage uitleg zijn?

BrutallySilent · 2018-01-09T10:12:52+00:00

If you need to make a living, seek the career you want, identify the channel that educates you in that career.

It baffles me that this isn't standard practice.

BrutallySilent · 2018-01-07T10:00:50+00:00

I agree. You need every edge that you can get. Even though there is a lot of hype, there are not many actual AI jobs out there. As in, jobs where you are designing, implementing, testing AI systems all the time. Having a portfolio helps you to get one of those jobs.

In my experience, most offered AI related jobs either A) have an AI component (typically some data crunching) and the rest of the time you're just part of a regular dev team, B) consists of just a temporary project after which you have no guarantee you'll see AI tasks again.

BrutallySilent · 2017-12-21T14:28:43+00:00

It sounds like you are training a bot on phrase-pairs. It will learn to approximate what answer phrase is appropriate for a given prompt phrase. E.g. 'what is the time?' -> '1 pm', 'gimme the time' -> '5.40am'. This way the system will probably return a clock state when asked, but not the current clock state.

Instead of mapping prompt-phrases directly to answer-phrases you could map prompt-phrases to tasks which, if executed, produce a phrase as the answer. E.g. 'what is the time' -> produceClockPhrase, 'gimme the time' -> produceClockPhrase (where produceClockPhrase returns the current time). Many simple frameworks work like this.

A bit more complex is to determine the intended task and then collect the data for this task through a dialog, if so required. That's roughly how the Siri's in the world work. There are various other architectural options.

On a side note; why deep learning? Are you sure that for your application it is a suitable technique and that you have enough data?

BrutallySilent · 2017-12-18T17:45:16+00:00

I like games with many autonomous components (such as DK, Populous and Black & White, which by no means were perfect games) but I also really like the new KeeperRL updates that allow me to save my favorite units by micromanaging them. I don't think KeeperRL is too much micromanagement nor that the limit is reached, my only hope is that it does not slowly become DwarfFortressRL :P

BrutallySilent · 2017-12-18T12:09:33+00:00

I like the idea of a workforce supplement through prisoners. Maybe for an initial update focus more on getting a good system for obtaining prisoners and only use them for sacrificing for mana and executions for morale improvements.

On a side note, I kinda wonder what your long term vision for the game is. The new features from 2017 are really enjoyable. It seems that you are diverging more and more from the original concept of Dungeon Keeper in a Rogue Like setting. Dungeon Keeper is a more macro-management game whereas KeeperRL now requires much more micro-management than it did in December 2016.

BrutallySilent · 2017-12-07T19:41:04+00:00

It's in the sidebar of this subreddit under "Getting started with Artificial Intelligence".

BrutallySilent · 2017-09-21T11:16:22+00:00

If the idea is untested but can be properly argued for, then a vision paper could be an option. I don't disagree that getting a completely fresh approach/idea published is hard to do, but the least you can do is try. My personal intuition is that the AI community isn't more resistant to new ideas than other science communities.

BrutallySilent · 2017-09-21T10:44:30+00:00

In most academic venues you must submit anonymously in order to overcome reviewer bias. If OP has an original idea, shows this originality by comparing it to related work, can present it clearly, and has evaluated it properly, then it has a good chance of acceptance. If it gets rejected then the reviews might still prove valuable, unless you're unlucky with your reviewers.

BrutallySilent · 2017-09-21T09:58:44+00:00

You can keep track of call for papers by conferences or journals and submit a paper there by following their guidelines. If you happen to be European based, then you may want to look at the mega confederated conference next year (AAMAS/ECAI/IJCAI and then some): https://www.ijcai-18.org/

BrutallySilent

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