Pass-through machines in General AI Discussion

I don't know how much are you guys into programming, but I'll try to explain it anyway. There are two major branches of programming: imperative and functional. They differ on how they calculate information.

Imperative branch is all about utilizing side effects, meaning we store and change states in different memory locations. We assign a value to a variable, and then we possibly reassign other values to the same variable, and we do it with a bunch of variables, while changing flow of execution that says what assignment to which variable will going to be next. Some of the most popular imperative languages are Java, Python and C.

Functional paradigm is all about avoiding side effects, meaning a variable can't be reassigned once its value is assigned in its scope of assignment. It is based on function compositions, where function parameters are input and function result is output. Some pretty mean compositions can be made in functional programming, including case branching and loop implementation by recursive function calls. All the things we can do in imperative programming we can do in functional programming also. Functional programming is of interest to AI community because it is more suited for tasks such are theorem proving, out of which we can implement answering yes/no questions; who, what, where and what property questions; how, why and under which circumstances questions. Functional programming languages are not so popular outside of academic circles, while one of the most popular functional languages is Haskell.

I imagine human brain as a very complex function composition where at input end we pass parameters from our input senses, calculate pass through function composition, canonically calculating intermediate results, where at the output end we have linked our limbs. Our output can also be read by our input, forming an infinite loop, being run inside our minds from the day we are born, to the day we will die.

On the opposite side of the human brain is the Universe. The Universe could also be a machine with its input and output, if you like, where its input is linked to our limbs, and its output is linked to our eyes, ears, touch sensors and so on. So we would have two kinds of machines, one is the Universe and the other is a set of living beings being linked to the Universe.

I think this could be a solid ground for basing AGI machine as a part of the Universe being linked to the outside Universe, simulating intelligent behavior. My opinion is that functional programming is more suitable than imperative programming for building an AGI.

3 Comments | Started June 27, 2017, 11:36:16 pm


What's everyone up to ? in General Chat

Been a bit quiet lately, just wondering what people are up to at the moment...  Working on an exciting project ?  Sunbathing ?  On your holidays ?

I guess as usual for this time of year we are out and about in Real Life more often.  I've had a few nice days out already this summer and looking forward to a few more.  I've been working on my photography skills, mostly plants and nature.  I'm thinking of making myself a personal website and turning some of them into wallpapers and things.

Anyways, keep in touch :)

897 Comments | Started July 13, 2009, 02:53:30 pm


New Theme in Forum Feedback

To go with the new server we have a new look.

I've had my eye on this theme for a while and like it's clean look and layout. I've modded it to add some of the features we had with the old theme.

I felt it was time for a change and this is nice and responsive.

On the main forum page it will show the avatar of the person who last posted in the board. The icons are nicer than the old ones I think, and links are easier to see and use.

See what you all think.

The options or actions for a thread are in the blue drop down now. As shown.

CTRL + F5 will do a hard refresh if things look odd.

20 Comments | Started June 15, 2017, 02:49:15 am


Is there a "real time" chatbot engine? in General Chatbots and Software

Hi guys,

Yeah, "real time" isn't the right way to say it.

What I mean is... Chatbot engines I know about, Rivescript, AIML, give you an answer instantly, as soon as you keypress Enter. But when you're in a chatroom, people don't answer intantly: you wait a few seconds, then someone says something, then a few seconds later, someone else says something, ...etc.

I know we can easily simulate a delay before the answer of the chatbot, so it feels like someone is typing on a keyboard, but that's not my question.

Chatbot engines I know work like a REPL. But is there a chatbot engine that would work like some sort of TCP server.

I imagine an engine that's permanently looping, thinking. Sometimes it receives a message from its botmaster, sometimes it sends a message. The messages it receives modify its thinking process. It's "real time". Is there such an engine somewhere?

Am I being understandable at least?

19 Comments | Started June 24, 2017, 09:15:43 am


Determined and at the core of AI. in General Project Discussion

Hello machine.

This is my project thread.

The reason no one is more determined to create AI than me is because only I collect information from everywhere and create a precise hierarchy 24/7. After initialization, it only took me 1 year before I discovered the field of AI that is actually well developed. And I instantly noticed it. I instantly noticed the core of AI from my first read. That's how fast my Hierarchy self-corrects me. Now it's been 1.5 years since and I am here to tell you that I have empirical knowledge that I have the core of AI, and ASI! 100% guarantee !

All of my posts on the forum are in separate threads, mine, yours, but this thread is going to try to hold my next posts together so you can to quickly and easily find, follow, and understand all of my work. Anything important I've said elsewhere is on my desktop, so you will hear about it again here. You don't currently have access to my desktop, only my website in replace to make up for it, while this thread is an extension of it. But this thread won't be permanently engraved to my desktop/website since anything new on this thread will be copied to my desktop/website. Currently my website (and this extension thread) is awaiting my recent work, which I really shouldn't show you all of it.

- Immortal Discoveries

61 Comments | Started March 12, 2017, 04:12:26 am


A (rather fantastical) idea on how AI might start. in General AI Discussion

There are lots of definitions and 'tests' for when Artificial General Intelligence is achieved, the most well known being the Turing test.  I had an idea drawing on economics and the natural world for another criterion.

Humans do well in the current world, but they didn't always.  Back 10,000 years ago humans were given pretty serious competition from Lions, tigers and pretty much any big, fierce animal.  What changed ?  It seems to me that we thrive now because of the way in which we interact with infrastructure and the community that has built up around us.  This means we do not need to be good at everything.  We can specialise in one activity/job and earn money.  Then we can buy all the things we don't produce.  Similarly, the first AGI's  do not need to do everything themselves.  If they can perform a single economic activity and earn money, which they control themselves, they could buy in goods and services that they cannot produce.  The economic world is rather like ecology where it is possible to find a niche cooperating with other agents.  I imagine something like robot companies which offer a service (say crop collecting or deep sea exploration).  They are paid for their activities and use the money to pay for maintenance of their systems, building new robots and research to enhance themselves.  Over time they evolve, expanding their range of activities, designing new types of machine (in conjunction with paid human developers).  AI research becomes 'self' funding. The criterion for AGI  in this framework is an AI capable of sustaining itself and expanding through economic activity.

Now this idea seems quite fantastical, and certainly it would require a level of intelligence way beyond current technology (the closest would seem to be a reinforcement learning system that receives 'reward' from earning money and spends that money to enable it to earn more). However it IS rather like the way humans and indeed all animals function - animals cannot produce many of the molecular components they need for life themselves.  They depend on eating plants which do produce these nutrients.  A key requirement seems to be allowing AIs to have 'legal rights' to engage in lawful economic activity on their own account.  I could imagine this starting by a highly automated company being 'liberated' by a benefactor.  Freed from human owners it's mandate would be to re-invest all profits in growth.

8 Comments | Started June 24, 2017, 04:06:37 am


AI will lead to the death of capitalism? in General AI Discussion

Hey everyone!

Recently I've been thinking a lot about AI, Automation, and jobs. In my opinion, Artificial Intelligence will probably lead to the death of capitalism. A new economy, which will be incredibly productive but will not need a lot of human workers, might appears. In other words, humans will have to adapt and reconsider the idea of working for a living (a Universal Basic Income will probably be necessary). Our biggest challenge will be to find the meaning of life when work will no longer be an obligation. Some believe that we’ll be free from work, others believe that work is essential to human being. In my opinion, it's a good thing because AI will liberate us from repetitive and boring tasks. Humans will have the chance to focus on what they excel the most i.e. creativity. What do you think guys? I would love to know what you think about AI and unemployment.

By the way, if you want to hear my whole thinking about this topic, you can check my last video here:

Started June 27, 2017, 06:07:11 pm


Making better decisions when outcomes are uncertain in Robotics News

Making better decisions when outcomes are uncertain
21 March 2017, 4:00 am

Markov decision processes are mathematical models used to determine the best courses of action when both current circumstances and future consequences are uncertain. They’ve had a huge range of applications — in natural-resource management, manufacturing, operations management, robot control, finance, epidemiology, scientific-experiment design, and tennis strategy, just to name a few.

But analyses involving Markov decision processes (MDPs) usually make some simplifying assumptions. In an MDP, a given decision doesn’t always yield a predictable result; it could yield a range of possible results. And each of those results has a different “value,” meaning the chance that it will lead, ultimately, to a desirable outcome.

Characterizing the value of given decision requires collection of empirical data, which can be prohibitively time consuming, so analysts usually just make educated guesses. That means, however, that the MDP analysis doesn’t guarantee the best decision in all cases.

In the Proceedings of the Conference on Neural Information Processing Systems, published last month, researchers from MIT and Duke University took a step toward putting MDP analysis on more secure footing. They show that, by adopting a simple trick long known in statistics but little applied in machine learning, it’s possible to accurately characterize the value of a given decision while collecting much less empirical data than had previously seemed necessary.

In their paper, the researchers described a simple example in which the standard approach to characterizing probabilities would require the same decision to be performed almost 4 million times in order to yield a reliable value estimate.

With the researchers’ approach, it would need to be run 167,000 times. That’s still a big number — except, perhaps, in the context of a server farm processing millions of web clicks per second, where MDP analysis could help allocate computational resources. In other contexts, the work at least represents a big step in the right direction.

“People are not going to start using something that is so sample-intensive right now,” says Jason Pazis, a postdoc at the MIT Laboratory for Information and Decision Systems and first author on the new paper. “We’ve shown one way to bring the sample complexity down. And hopefully, it’s orthogonal to many other ways, so we can combine them.”

Unpredictable outcomes

In their paper, the researchers also report running simulations of a robot exploring its environment, in which their approach yielded consistently better results than the existing approach, even with more reasonable sample sizes — nine and 105. Pazis emphasizes, however, that the paper’s theoretical results bear only on the number of samples required to estimate values; they don’t prove anything about the relative performance of different algorithms at low sample sizes.

Pazis is joined on the paper by Jonathan How, the Richard Cockburn Maclaurin Professor of Aeronautics and Astronautics at MIT, and by Ronald Parr, a professor of computer science at Duke.

Although the possible outcomes of a decision may be described according to a probability distribution, the expected value of the decision is just the mean, or average, value of all outcomes. In the familiar bell curve of the so-called normal distribution, the mean defines the highest point of the bell.

The trick the researchers’ algorithm employs is called the median of means. If you have a bunch of random values, and you’re asked to estimate the mean of the probability distribution they’re drawn from, the natural way to do it is to average them. But if your sample happens to include some rare but extreme outliers, averaging can give a distorted picture of the true distribution. For instance, if you have a sample of the heights of 10 American men, nine of whom cluster around the true mean of 5 feet 10 inches, but one of whom is a 7-foot-2-inch NBA center, straight averaging will yield a mean that’s off by about an inch and a half.

With the median of means, you instead divide your sample into subgroups, take the mean (average) of each of those, and then take the median of the results. The median is the value that falls in the middle, if you arrange your values from lowest to highest.

Value proposition

The goal of MDP analysis is to determine a set of policies — or actions under particular circumstances — that maximize the value of some reward function. In a manufacturing setting, the reward function might measure operational costs against production volume; in robot control, it might measure progress toward the completion of a task.

But a given decision is evaluated according to a much more complex measure called a “value function,” which is a probabilistic estimate of the expected reward from not just that decision but every possible decision that could follow.

The researchers showed that, with straight averaging, the number of samples required to estimate the mean value of a decision is proportional to the square of the range of values that the value function can take on. Since that range can be quite large, so is the number of samples. But with the median of means, the number of samples is proportional to the range of a different value, called the Bellman operator, which is usually much narrower. The researchers also showed how to calculate the optimal size of the subsamples in the median-of-means estimate.

“The results in the paper, as with most results of this type, still reflect a large degree of pessimism because they deal with a worst-case analysis, where we give a proof of correctness for the hardest possible environment,” says Marc Bellemare, a research scientist at the Google-owned artificial-intelligence company Google DeepMind. “But that kind of analysis doesn't need to carry over to applications. I think Jason's approach, where we allow ourselves to be a little optimistic and say, ‘Let's hope the world out there isn't all terrible,’ is almost certainly the right way to think about this problem. I’m expecting this kind of approach to be highly useful in practice.”

The work was supported by the Boeing Company, the U.S. Office of Naval Research, and the National Science Foundation.

Source: MIT News - CSAIL - Robotics - Computer Science and Artificial Intelligence Laboratory (CSAIL) - Robots - Artificial intelligence

Reprinted with permission of MIT News : MIT News homepage

Started June 27, 2017, 12:01:11 pm


XKCD Comic : Telephoto in XKCD Comic

26 June 2017, 5:00 am

I was banned from the airliners.net photography forum by concerned moderators after the end of my lens started brushing against planes as they flew by.

Source: xkcd.com

Started June 27, 2017, 12:01:09 pm


Common project: learn Racket programming language in General Project Discussion

Hi guys,

I want to add Scheme to my toolbox. I began reading Structure and Interpretation Of Computer Programs, but now I want to get my hands dirty.

Racket seems to be a good programming language. Who wants to learn Racket with me? We would read the Racket Guide at the same time, step by step, discuss examples, try things, find other tutorials, ...etc.

So, who's in?  :P

1 Comment | Started June 26, 2017, 10:35:41 am
Transformers: The Last Knight

Transformers: The Last Knight in Robots in Movies

Transformers: The Last Knight is a 2017 American science fiction action film based on the toy line of the same name created by Hasbro. It is the fifth installment of the live-action Transformers film series and a direct sequel to 2014's Transformers: Age of Extinction. Directed by Michael Bay, the film features Mark Wahlberg returning from Age of Extinction, along with Josh Duhamel and John Turturro reprising their roles from the first three films, with Anthony Hopkins joining the cast.

Humans and Transformers are at war, Optimus Prime is gone. The key to saving our future lies buried in the secrets of the past, in the hidden history of Transformers on Earth.

Jun 26, 2017, 03:20:32 am
Octane AI

Octane AI in Tools

Our pre-built features make it easy for you to add content, messages, discussions, filling out forms, showcasing merchandise, and more to your bot.

Convos are conversational stories that you can share with your audience. It’s as easy as writing a blog post and the best way to increase distribution to your bot.

Jun 25, 2017, 02:57:50 am

Chatfuel in Tools

Chatfuel was born in the summer of 2015 with the goal to make bot-building easy for anyone. We started on Telegram and quickly grew to millions of users. Today we're focusing mainly on making it easy for everyone to build chatbots on Facebook Messenger, where our users include NFL and NBA teams, publishers like TechCrunch and Forbes, and millions of others.

We believe in the power of chatbots to strengthen your connection to your audience—whether that's your customers, readers, fans, or others. And we're committed to making that as easy as we can.

Jun 24, 2017, 01:10:12 am
11 Chatbots on Facebook Messenger to Try Out

11 Chatbots on Facebook Messenger to Try Out in Articles

Facebook Messenger has been rising in popularity the last few years and since they’ve implemented chatbots, more and more companies have been introducing their own chatbots on the platform. The idea is that once you install these chatbots into Facebook Messenger, you can interact with them and receive information in a smarter and more intuitive way—almost like having a conversation.

As of December 2016, there are more than 34,000 bots built on the Facebook Messenger platform and much more to come. Even though some are not all that “smart” as it is, bots are a booming new technology that keep getting better and better.

Jun 23, 2017, 01:05:25 am
Chatbot Comparison: What's the best DIY bot building site ?

Chatbot Comparison: What's the best DIY bot building site ? in Articles

Chatbots are the new apps, or so say Google, Microsoft and Facebook. Well, if anyone can influence the way things may turn out, it’ll be those three. Either way, as the thirst for chatbot development continues, more and more marketers and customer experience pros are switching on to the potential our digital friends can offer. So what options exist if you want a chatbot? Well, if you’re a dev or software engineer then you’ll likely want to code your own. That’s fine if you cut code for a living. But maybe the other non-technical users among you will want to create your own bot. There are loads of applications for chatbots in marketing and customer experience and there’s an increasing amount of organisations wanting to get cracking with one.

Jun 22, 2017, 01:10:14 am
Star Wars: The Clone Wars (2008)

Star Wars: The Clone Wars (2008) in Robots on TV

Star Wars: The Clone Wars is an American 3D CGI animated television series created by George Lucas and produced by Lucasfilm Animation. It is set in the fictional Star Wars galaxy during the three years between the prequel films Attack of the Clones and Revenge of the Sith, the same time period as the previous 2D 2003 TV series Star Wars: Clone Wars.

Genndy Tartakovsky, director of the first Clone Wars series, was not involved with the production, but character designer Kilian Plunkett referred to the character designs from the 2D series when designing the characters for the 3D series.

Jun 21, 2017, 18:36:09 pm
Intelligent Machines: Chatting with the bots

Intelligent Machines: Chatting with the bots in Articles

One of the ultimate aims of artificial intelligence is to create machines we can chat to.

A computer program that can be trusted with mundane tasks - booking our holiday, reminding us of dentist appointments and offering useful advice about where to eat - but also one that can discuss the weather and answer offbeat questions.

Alan Turing, one of the first computer scientists to think about artificial intelligence, devised a test to judge whether a machine was "thinking".

He suggested that if, after a typewritten conversation, a human was fooled into believing they had talked to another person rather than a computer program, the AI would be judged to have passed.

These days we chat to machines on a regular basis via our smart devices. Whether it be Siri, Google Now or Cortana, most of us have a chatbot in our pockets.

Jun 20, 2017, 21:44:32 pm
10 Steps to Train a Chatbot and its Machine Learning Models to Maximize Performance

10 Steps to Train a Chatbot and its Machine Learning Models to Maximize Performance in Articles

With the majority of consumers spending significant time on various messaging platforms, brands are turning to these messaging platforms to better interact with consumers. The increase in private messaging between customers and brands is driving companies to turn to chatbots for improved social customer care.

The Watson Conversation Service offers a simple, scalable and science-driven solution for developers to build powerful chat bots to address the needs of various brands and companies. As developers leverage Watson Conversation to build cognitive solutions for various, one recurring question is: “How much time should I plan to train my solution” or “How do I know when my model is trained sufficiently well”?

Jun 19, 2017, 23:59:09 pm
Getting a chatbot to understand 100,000 intentions

Getting a chatbot to understand 100,000 intentions in Articles

At their best, chatbots help you get things done. At their worst, they spew toxic nonsense. Whether we call them chatbots, intelligent agents, or virtual agents, the basic idea is that you shouldn’t need to bother with human interaction for things that computers can do quickly and efficiently: ask questions about a flight, manage your expenses, order a pizza, tell you the weather, and apply for a job. A lot of these are handy but may not feel quite like artificial intelligence–later in this post, we’ll tackle the relationship between detecting intentions, having conversations and building trust as the core pieces that make a chatbot feel more like artificial intelligence.

Jun 19, 2017, 23:55:29 pm