# Tag Info

17

No, with a but. We can have creative yet ethical problem-solving if the system has a complete system of ethics, but otherwise creativity will be unsafe by default. One can classify AI decision-making approaches into two types: interpolative thinkers, and extrapolative thinkers. Interpolative thinkers learn to classify and mimic whatever they're learning ...

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Informally, AI-complete problems are the most difficult problems for an AI. The concept is not mathematically defined yet, as e.g. NP-complete problems. However, intuitively, these are the problems that require a human-level or general intelligence to be solved. Real natural language understanding is believed to be an AI-complete problem (this is also ...

6

As @nbro has already said that Hill Climbing is a family of local search algorithms. So, when you said Hill Climbing in the question I have assumed you are talking about the standard hill climbing. The standard version of hill climb has some limitations and often gets stuck in the following scenario: Local Maxima: Hill-climbing algorithm reaching on the ...

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Hill climbing is not an algorithm, but a family of "local search" algorithms. Specific algorithms which fall into the category of "hill climbing" algorithms are 2-opt, 3-opt, 2.5-opt, 4-opt, or, in general, any N-opt. See chapter 3 of the paper "The Traveling Salesman Problem: A Case Study in Local Optimization" (by David S. Johnson and Lyle A. McGeoch) for ...

4

I might be wrong, but I do not believe that something of the scope you describe would be possible with the current state of technology. It would require a lot of things which are still in relatively early stages of research. For one, just extracting relevant information from text is a huge task by itself. Doubly so with a novel which contains a large amount ...

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 ...

3

The "objective function" is the function that you want to minimise or maximise in your problem. The expression "objective function" is used in several different contexts (e.g. machine learning or linear programming), but it always refers to the function to be maximised or minimised in the specific (optimisation) problem. Hence, this expression is used in ...

3

Fuzzy logic seems to have multiple of applications historically in Automotive Engineering. I found an interesting article on the subject from 1997. This excerpt provides an interesting rationale: The key reason for fuzzy logic’s success in automotive engineering lies in the implications of its paradigm shift. Previously, engineers spent much time ...

3

This is basically the problem of commonsense knowledge. It is AI-complete. If we knew how to solve it, Siri and Cortana wouldn't be as limited as they are.

2

Generally agree with @Inquisitive Lurker, but I think we also have a wide range of potential abilities/requirements. As with computer chess or Go, where there's a big difference between "beating an honest novice human " and "beating all humans"; there's a big difference between solving a simple kids' mystery and a complex adult novel. So I don't think there ...

2

Ethics involves the relationships of needs between two or more parties. As Matthew Graves said, if the AI lacks the sufficient human context (understanding of needs), it will produce seemingly perverse ethical behavior. And let's be honest, some people would cut of other people's arms and put them on pressure plates. Even the best of us will not be able to ...

2

It's currently just too complex The different sources of information are too varied, in economics this is often referred to as a local knowledge problem, which hampers many large scale plans. Humans can react to slight differences like respecting local traditions, landscapes, history but an artificial intelligence would (currently at least) struggle not to ...

2

You are mixing up lots of things here. Specifically, you seem to be lacking a basic understanding of artificial neural networks and what they can do (e.g. what type of articifial neural networks are linear classifiers/regressors and which can model non-linear relationships). Therefore, I'd take a step back and start with understanding the basics of AI. The ...

2

The site FuzzyTECH lists an array of applications: Industrial Automation Monitoring Glaucoma Coal Power Plant Complex Chilling Systems Refuse Incineration Plant Fuzzy Logic Design Practical Design Water Treatment System Truck Speed Limiter Medical Shoe Fuzzy in Appliances Automotive Engineering Antilock Braking System ...

2

Though there is no universal method which can be blindly used for all datasets, but here is what i usually do; Fill missing values using interpolation or mean, if missing values are less than 10-15 percent of number of rows else drop the column. Encode categorical data using some kind of encoding, e.g. one hot, etc. Then normalize/rescale columns. Now look ...

2

Yes, that's possible. I am working on a project in which I have to detect text in images. I did a quick search and found these two algorithms: 1. EAST: (Efficient and Accurate Scene Text Detector) I am not sure if it is based on Machine Learning. Here are some links link1 link2 explaining how to use it with an example and using tesseract to extract the ...

1

A successor function is a function that generates a next state from the current state, plus the choices that affect state changes. In e.g. the 8 queens problem, a state might be the location of 5 queens so far, the choice might be where to put the next queen, and the successor function would return the resulting state with 6 queens on the board. Typically a ...

1

For example, from among house size, lot size, age of house and asking price, what formula best predicts selling price? There is no general formula for this. Search for neural network regression and you can get started. The AI technique or any prediction algorithm in general will learn a function that maps from the input feature vector $(x_1, ...,x_n)$, ...

1

It’s seems like quite challenging problem; at least you would need quite a lot of annotated data and computational power. The approaches/optimizations you could consider: To make scene change detection and take short piece out of each To introduce some kind of “novelty” metric and try to maximize it to get most different parts of video To convert video ...

1

As an example of local/global minima, imagine being on a rugged, mountainous landscape, and you want to find the lowest point within some area. For a greedy search, every step you take will take you downhill. If you go downhill long enough, you'll eventually find a flat spot, which is a minimum - from here, there's no step you can take that will get you any ...

1

This is more of a comment and philosophical opinion, but I don’t believe that there are any problems an AI couldn’t solve, that a human can. Being new to this forum, I cannot make it a comment on the question (and it would probably be too long) — I preemptively ask for your forgiveness. AI Eventually Will Mimic Humans (and surpass them) Humans by nature ...

1

SAT problems are decision problems that can be categorised as NPC. This informally means although there has not been any solution that can solve these problem in the polynomial order, the solutions of such problems can be satisfied in $O(n^c)$. About your question, first, you should see your problem has exponential space and cannot be solved in polynomial ...

1

In general, the process of modelling a problem as a search problem consists in creating a graph which contains nodes, which represent the possible states in your problem, and edges, which represent the relations between these states (that is, you will have an edge between nodes $A$ and $B$ if it is possible to go from state $A$ to state $B$, and vice-versa, ...

1

In human-computer-interaction, the objective function directs to constraints from the outside. It is an in-between layer which communicates between the needs of the system itself and the operator who want's to do useful things with the system. For example, the operator want's to walk with the avatar to the right of the screen. The objective function is the ...

1

I think all your answers are wrong or I miss a crucial part of information. For example with the digit recognition, I would say the input is a pixel image, the output is one of ten digits and the dataset could be MNIST. To be more specific: $X=R^{28 \times 28}$, $Y=\{0, 1, 2, 3, 4, 5, 6, 7, 8, 9\}$ e)A problem of interest to you for which there is no ...

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The first step is to increase the abstraction level of the game. Instead of storing the game characters with absolute position on a pixel level, a text-adventure-like layer above the game has to be established. In the literature this concept is called “knowledge containers” and is described under the topic of case-based reasoning. Now, it is possible to ...

1

I spent some time on the Future of Work in UK government. If you want to look at the impact of AI on work you need to have good definitions of soft skills that you can measure and you can track progress against. So the definitions sometimes need to change and decomposed. Take "Coaching" from that list: there are now bots (such as Wysa.ai) that coach you. ...

1

It sounds like you're looking at the Partition Problem. https://en.wikipedia.org/wiki/Partition_problem The task of slicing one set into N sets so that each set is equal or as close to equal as possible. Obtaining an exact solution is NP-hard (you can't do much better than trying all combinations), however you can get an approximate answer in polynomial ...

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There are a variety of possible things that could be wrong, but to answer the short question specifically: relu networks are turing complete (well, if you put them in an RNN so they can compute indefinitely, anyway). for any computation, you can devise an rnn that will perform it. as a proof of this, here is a relu neuron that implements nor, which with ...

1

While I have not determined if there are problems which cannot be solved with ReLU, I have found ample documentation in the literature that XOR is solvable with as few as 1 hidden node. Therefore, I must assume there is something wrong with my implementation. Edit: The solution is simpler than I thought. The output layer needs connections, not just to the ...

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