# Tag Info

28

Here's a snippet from an article by Gary Marcus In particular, they showed that standard deep learning nets often fall apart when confronted with common stimuli rotated in three dimensional space into unusual positions, like the top right corner of this figure, in which a schoolbus is mistaken for a snowplow: . . . Mistaking an ...

19

In theory, most neural networks can approximate any continuous function on compact subsets of $\mathbb{R}^n$, provided that the activation functions satisfy certain mild conditions. This is known as the universal approximation theorem (UAT), but that should not be called universal, given that there are a lot more discontinuous functions than continuous ones,...

18

Siri and co. are AI to some extent. The usual label is "Weak AI" (also called "narrow" or "soft" AI). It turns out the Wikipedia article on Weak AI explicitly refers to Siri: Siri is a good example of narrow intelligence. Siri operates within a limited pre-defined range, there is no genuine intelligence, no self-awareness, no life despite being a ...

18

tl;dr: None of these algorithms are practical for modern work, but they are good places to start pedagogically. You should always prefer to use Alpha-Beta pruning over bare minimax search. You should prefer to use some form of heuristic guided search if you can come up with a useful heuristic. Coming up with a useful heuristic usually requires a lot of ...

15

In our deep learning lecture, we discussed the following example (from Unmasking Clever Hans predictors and assessing what machines really learn (2019) by Lapuschkin et al.). Here the neural network learned a wrong way to identify a picture, i.E by identifying the wrong "relevant components". In the sensitivity maps next to the pictures, we can see that the ...

13

It's all about Return On Investment. If DL is "worth doing", it's not overkill. If the cost of using DL (computer cycles, storage, training time) is acceptable, and the data available to train it is plentiful, and if the marginal advantage over alternative algorithms is valuable, then DL is a win. But, as you suggest, if your problem is amenable to ...

12

Deep learning is powerful but it is not a superior method than bayesian. They work well in what they are designed to do: Use deep learning: Cost for computation is much cheaper than cost of sampling (e.g: natural language processing) If you have highly non-linear problem If you want to simplify feature engineering If you don't have prior distribution (e.g: ...

12

Sophia uses ChatScript. You can read about what ChatScript can do here. ChatScript keeps track of conversations with each user; can record where it is in a conversational flow and what facts it has learned about a user (you have to tell it what facts to try to learn). You can optionally keep logs of the conversations (either on a ChatScript ...

9

There are several examples. For example, one instance of using Statistical AI from my workplace is: Analyzing the behavior of the customer and their food-ordering trends, and then trying to upsell by recommending them the dishes which they might like to order/eat. This can be done through the apriori and FP-growth algorithms. We then, automated the ...

9

I agree that this is too broad, but here's a 1 sentence answer for most of them. The ones I left out (from the bottom of the chart) are very modern, and very specialized. I don't know much about them, so perhaps someone who does can improve this answer. Perceptron: Linear or logistic-like regression (and thus, classification). Feed Forward: Usually non-...

8

I would classify both as having / using elements of AI, yes. But I wouldn't say either represents a truly "intelligent" (in the AGI sense) program. But here's the rub... as you'll see in other questions asking about definitions of AI, there's a sort of memetic thing where anything that AI begins to do successfully, immediately stops being considered "AI". ...

7

N.B The reason why I only chose these three algorithms was due to time I have available in understanding them. From a little research, I found that these algorithms are basically interweaved into the minimax algorithm. So if I can understand one then the other two will just fall into place. Given this context, I would recommend starting out with Minimax. Of ...

6

Adaptive/predictive features are useful in at least some everyday applications. Take text messaging, for instance. All smartphone SMS apps that I know of keep track of the words you use in close proximity and use that information to predict the next word in a message you're typing. (Some are smarter than others. Relevant XKCD.) It can be used to personalize ...

6

Yes, but probably only to a limited degree in the near term. Where people draw the boundaries around 'artificial intelligence' is fuzzy, but if one takes the broad view, where it incorporates any sort of coding of explicitly cognitive functions, then many routine economic tasks can benefit from artificial intelligence. Many search engines, for example, can ...

6

Deep learning allows you to solve complex problems without necessarily being able to specify the important "features" or key input variables for the model in advance. To give an example, a problem that may be easily tackled without deep learning could be predicting the frequency and claim amounts of insurance vehicle claims, given historical claim data ...

6

Ontology learning is a relatively new field that aims to automatically (or semi-automatically) learn or create ontologies (using machine learning, text mining, knowledge representation and reasoning, information retrieval and natural language processing techniques) from some text or corpus. Ontology learning can be divided into different phases or tasks ...

5

Deep Learning these days mean a lot of things to a lot of people, its quickly becoming a buzz-word. But so far it still retains two very important conceptual properties: Does away with most feature engineering work. This was mentioned in the answer above, but this is very important. It really saves a lot of work. Allows you to make maximal use of ...

5

This is fairly boilerplate advice, but, since you're brand new to AI, I'd personally suggest writing a classical Tic-Tac-Toe AI, ideally using minimax. I suggest this because minimax is fundamental to AI, and there are many webpages devoted to this subject, such as How to make your Tic Tac Toe game unbeatable by using the minimax algorithm and Tic Tac Toe: ...

5

I would guess no, because if the language is unknown (no data available on it), then we would not have training data with which the machine learning algorithm could learn from. If it is related to some known language, then some statistical analysis can lead to a guess at decipherment (assuming certain similarities among the two languages). If interested on ...

5

I say yes it definitely could be. But i agree with Skim you need some information as a starting point. Egyptian hieroglyphs were only (recently) understood following the discovery of the Rosetta Stone (https://en.m.wikipedia.org/wiki/Rosetta_Stone). With the same message in both known and unknown language the program could find the/a correlation. Without ...

5

Easy answer: utility. The strength and applicability of "black box" NNs has been regularly validated in the past few years, and business is concerned with results. (i.e. they don't care how the sausage is made, so long as it gets made.)

5

One application I know of being used in industry is of image classification, by only training the last layer of one of the inception models released by Google, with the desired number of classes. I can't provide specific details. Transfer learning is useful when: 1) You do not have the resources (time, processing power etc.) to train a DL model from ...

4

I'm not sure about "emulating the brain" per-se, but in a more general sense there has been some thought given to using analog computing for AI/ML. It seems clear that analog computers do have certain advantages over digital computers. For one, they can (depending on the application) be faster, albeit at the cost of some loss of precision. But that's OK, ...

4

Chieko Asakawa (wiki, TED, IBM) is a major researcher in this area, and the linked TED talk is probably a good introduction to the state of the art as of 2015. Here's a link to a 2016 paper on a smartphone navigation system. Guide animals perform manipulation tasks as well as identification tasks, and so it's not clear if those could be replaced well at all....

4

There are many online services that use statistical neural networks for recommendations. For example, we have a well known service here in Russia that could give it's users recommendations for movies and shows to watch and books to read. Its recommendation core is based on many things known about a user: what movies/books he or she loves and what not, ...

4

There is no doubt that AI has the potential to pose an existential threat to humanity. The greatest threat to mankind lies with superintelligent AI. An artificial intelligence that surpasses human intelligence will be capable of exponentially increasing its own intelligence, resulting in an AI system that, to humans, will be completely unstoppable. At ...

4

There are projects out there attempting to apply Machine Learning / AI to cyber-security in different ways. One that I'm familiar with is Apache Metron. Another related project is Apache Spot. I think if you read over the docs for these two projects respectively, they will probably give you some good insights on this subject.

4

You've obviously never heard of fuzzy logic washing machines. ● Typically, fuzzy logic controls the washing process, water intake,water temperature, wash time, rinse performance, and spin speed. This optimises the life span of the washing machine. More sophisticated machines weigh the load (so you can’t overload the washing machine), advise on the ...

4

Well, what I can think about at the moment is the following scenarios : Decision Maker : If you have any problem making a decision, chatbots can be used to weight evidence and give you statistics to rule out bad decisions. Online Teacher : In the far future, chatbots may acquire human-like skills, they maybe used to teach different students (from different ...

4

There is an academic paper here that studies a neural approach to deciphering ancient languages:-- (https://arxiv.org/pdf/1906.06718.pdf) "In this paper we propose a novel neural approach for automatic decipherment of lost languages. To compensate for the lack of strong supervision signal, our model design is informed by patterns in language change doc-...

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