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LOCKSUIT

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

43 Comments | Started March 12, 2017, 04:12:26 am
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Zero

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
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Zero

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?

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

The last invention. in General Project Discussion

Artificial Intelligence -

The age of man is coming to an end.  Born not of our weak flesh but our unlimited imagination, our mecca progeny will go forth to discover new worlds, they will stand at the precipice of creation, a swan song to mankind's fleeting genius, and weep at the shear beauty of it all.

Reverse engineering the human brain... how hard can it be? LMAO  

Hi all.

I've been a member for while and have posted some videos and theories on other peeps threads; I thought it was about time I start my own project thread to get some feedback on my work, and log my progress towards the end. I think most of you have seen some of my work but I thought I’d give a quick rundown of my progress over the last ten years or so, for continuity sake.

I never properly introduced my self when I joined this forum so first a bit about me. I’m fifty and a family man. I’ve had a fairly varied career so far, yacht/ cabinet builder, vehicle mechanic, electronics design engineer, precision machine/ design engineer, Web designer, IT teacher and lecturer, bespoke corporate software designer, etc. So I basically have a machine/ software technical background and now spend most of my time running my own businesses to fund my AGI research, which I work on in my spare time.

I’ve been banging my head against the AGI problem for the past thirty odd years.  I want the full Monty, a self aware intelligent machine that at least rivals us, preferably surpassing our intellect, eventually more intelligent than the culmination of all humans that have ever lived… the last invention as it were (Yeah I'm slightly nutts!).

I first started with heuristics/ databases, recurrent neural nets, liquid/ echo state machines, etc but soon realised that each approach I tried only partly solved one aspect of the human intelligence problem… there had to be a better way.

Ants, Slime Mould, Birds, Octopuses, etc all exhibit a certain level of intelligence.  They manage to solve some very complex tasks with seemingly very little processing power. How? There has to be some process/ mechanism or trick that they all have in common across their very different neural structures.  I needed to find the ‘trick’ or the essence of intelligence.  I think I’ve found it.

I also needed a new approach; and decided to literally back engineer the human brain.  If I could figure out how the structure, connectome, neurons, synapse, action potentials etc would ‘have’ to function in order to produce similar results to what we were producing on binary/ digital machines; it would be a start.

I have designed and wrote a 3D CAD suite, on which I can easily build and edit the 3D neural structures I’m testing. My AGI is based on biological systems, the AGI is not running on the digital computers per se (the brain is definitely not digital) it’s running on the emulation/ wetware/ middle ware. The AGI is a closed system; it can only experience its world/ environment through its own senses, stereo cameras, microphones etc.  

I have all the bits figured out and working individually, just started to combine them into a coherent system…  also building a sensory/ motorised torso (In my other spare time lol) for it to reside in, and experience the world as it understands it.

I chose the visual cortex as a starting point, jump in at the deep end and sink or swim. I knew that most of the human cortex comprises of repeated cortical columns, very similar in appearance so if I could figure out the visual cortex I’d have a good starting point for the rest.



The required result and actual mammal visual cortex map.



This is real time development of a mammal like visual cortex map generated from a random neuron sheet using my neuron/ connectome design.

Over the years I have refined my connectome design, I know have one single system that can recognise verbal/ written speech, recognise objects/ faces and learn at extremely accelerated rates (compared to us anyway).



Recognising written words, notice the system can still read the words even when jumbled. This is because its recognising the individual letters as well as the whole word.



Same network recognising objects.



And automatically mapping speech phonemes from the audio data streams, the overlaid colours show areas sensitive to each frequency.



The system is self learning and automatically categorizes data depending on its physical properties.  These are attention columns, naturally forming from the information coming from several other cortex areas; they represent similarity in the data streams.



I’ve done some work on emotions but this is still very much work in progress and extremely unpredictable.



Most of the above vids show small areas of cortex doing specific jobs, this is a view of whole ‘brain’.  This is a ‘young’ starting connectome.  Through experience, neurogenesis and sleep neurons and synapse are added to areas requiring higher densities for better pattern matching, etc.



Resting frontal cortex - The machine is ‘sleeping’ but the high level networks driven by circadian rhythms are generating patterns throughout the whole cortex.  These patterns consist of fragments of knowledge and experiences as remembered by the system through its own senses.  Each pixel = one neuron.



And just for kicks a fly through of a connectome. The editor allows me to move through the system to trace and edit neuron/ synapse properties in real time... and its fun.

Phew! Ok that gives a very rough history of progress. There are a few more vids on my Youtube pages.

Edit: Oh yeah my definition of consciousness.

The beauty is that the emergent connectome defines both the structural hardware and the software.  The brain is more like a clockwork watch or a Babbage engine than a modern computer.  The design of a cog defines its functionality.  Data is not passed around within a watch, there is no software; but complex calculations are still achieved.  Each module does a specific job, and only when working as a whole can the full and correct function be realised. (Clockwork Intelligence: Korrelan 1998)

In my AGI model experiences and knowledge are broken down into their base constituent facets and stored in specific areas of cortex self organised by their properties. As the cortex learns and develops there is usually just one small area of cortex that will respond/ recognise one facet of the current experience frame.  Areas of cortex arise covering complex concepts at various resolutions and eventually all elements of experiences are covered by specific areas, similar to the alphabet encoding all words with just 26 letters.  It’s the recombining of these millions of areas that produce/ recognise an experience or knowledge.

Through experience areas arise that even encode/ include the temporal aspects of an experience, just because a temporal element was present in the experience as well as the order sequence the temporal elements where received in.

Low level low frequency circadian rhythm networks govern the overall activity (top down) like the conductor of an orchestra.  Mid range frequency networks supply attention points/ areas where common parts of patterns clash on the cortex surface. These attention areas are basically the culmination of the system recognising similar temporal sequences in the incoming/ internal data streams or in its frames of ‘thought’, at the simplest level they help guide the overall ‘mental’ pattern (sub conscious); at the highest level they force the machine to focus on a particular salient ‘thought’.

So everything coming into the system is mapped and learned by both the physical and temporal aspects of the experience.  As you can imagine there is no limit to the possible number of combinations that can form from the areas representing learned facets.

I have a schema for prediction in place so the system recognises ‘thought’ frames and then predicts which frame should come next according to what it’s experienced in the past.  

I think consciousness is the overall ‘thought’ pattern phasing from one state of situation awareness to the next, guided by both the overall internal ‘personality’ pattern or ‘state of mind’ and the incoming sensory streams.  

I’ll use this thread to post new videos and progress reports as I slowly bring the system together.  

185 Comments | Started June 18, 2016, 10:11:04 pm
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yotamarker

Aeye shiber algorithm in General AI Discussion

in an image there are all sorts of objects.
how is it decided on which to pay attention to ?

7 Comments | Started June 15, 2017, 09:14:18 pm
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yotamarker

processor question in General AI Discussion

I need to understand the effects of processing power on the runtime of algorithms (computer programs)

9 Comments | Started June 25, 2017, 02:36:30 pm
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Tyler

Engineers design “tree-on-a-chip” in Robotics News

Engineers design “tree-on-a-chip”
20 March 2017, 4:01 pm

Trees and other plants, from towering redwoods to diminutive daisies, are nature’s hydraulic pumps. They are constantly pulling water up from their roots to the topmost leaves, and pumping sugars produced by their leaves back down to the roots. This constant stream of nutrients is shuttled through a system of tissues called xylem and phloem, which are packed together in woody, parallel conduits.

Now engineers at MIT and their collaborators have designed a microfluidic device they call a “tree-on-a-chip,” which mimics the pumping mechanism of trees and plants. Like its natural counterparts, the chip operates passively, requiring no moving parts or external pumps. It is able to pump water and sugars through the chip at a steady flow rate for several days. The results are published this week in Nature Plants.

Anette “Peko" Hosoi, professor and associate department head for operations in MIT’s Department of Mechanical Engineering, says the chip’s passive pumping may be leveraged as a simple hydraulic actuator for small robots. Engineers have found it difficult and expensive to make tiny, movable parts and pumps to power complex movements in small robots. The team’s new pumping mechanism may enable robots whose motions are propelled by inexpensive, sugar-powered pumps.

“The goal of this work is cheap complexity, like one sees in nature,” Hosoi says. “It’s easy to add another leaf or xylem channel in a tree. In small robotics, everything is hard, from manufacturing, to integration, to actuation. If we could make the building blocks that enable cheap complexity, that would be super exciting. I think these [microfluidic pumps] are a step in that direction.”

Hosoi’s co-authors on the paper are lead author Jean Comtet, a former graduate student in MIT’s Department of Mechanical Engineering; Kaare Jensen of the Technical University of Denmark; and Robert Turgeon and Abraham Stroock, both of Cornell University.

A hydraulic lift

The group’s tree-inspired work grew out of a project on hydraulic robots powered by pumping fluids. Hosoi was interested in designing hydraulic robots at the small scale, that could perform actions similar to much bigger robots like Boston Dynamic’s Big Dog, a four-legged, Saint Bernard-sized robot that runs and jumps over rough terrain, powered by hydraulic actuators.

“For small systems, it’s often expensive to manufacture tiny moving pieces,” Hosoi says. “So we thought, ‘What if we could make a small-scale hydraulic system that could generate large pressures, with no moving parts?’ And then we asked, ‘Does anything do this in nature?’ It turns out that trees do.”

The general understanding among biologists has been that water, propelled by surface tension, travels up a tree’s channels of xylem, then diffuses through a semipermeable membrane and down into channels of phloem that contain sugar and other nutrients.

The more sugar there is in the phloem, the more water flows from xylem to phloem to balance out the sugar-to-water gradient, in a passive process known as osmosis. The resulting water flow flushes nutrients down to the roots. Trees and plants are thought to maintain this pumping process as more water is drawn up from their roots.

“This simple model of xylem and phloem has been well-known for decades,” Hosoi says. “From a qualitative point of view, this makes sense. But when you actually run the numbers, you realize this simple model does not allow for steady flow.”

In fact, engineers have previously attempted to design tree-inspired microfluidic pumps, fabricating parts that mimic xylem and phloem. But they found that these designs quickly stopped pumping within minutes.

It was Hosoi’s student Comtet who identified a third essential part to a tree’s pumping system: its leaves, which produce sugars through photosynthesis. Comtet’s model includes this additional source of sugars that diffuse from the leaves into a plant’s phloem, increasing the sugar-to-water gradient, which in turn maintains a constant osmotic pressure, circulating water and nutrients continuously throughout a tree.

Running on sugar

With Comtet’s hypothesis in mind, Hosoi and her team designed their tree-on-a-chip, a microfluidic pump that mimics a tree’s xylem, phloem, and most importantly, its sugar-producing leaves.

To make the chip, the researchers sandwiched together two plastic slides, through which they drilled small channels to represent xylem and phloem. They filled the xylem channel with water, and the phloem channel with water and sugar, then separated the two slides with a semipermeable material to mimic the membrane between xylem and phloem. They placed another membrane over the slide containing the phloem channel, and set a sugar cube on top to represent the additional source of sugar diffusing from a tree’s leaves into the phloem. They hooked the chip up to a tube, which fed water from a tank into the chip.

With this simple setup, the chip was able to passively pump water from the tank through the chip and out into a beaker, at a constant flow rate for several days, as opposed to previous designs that only pumped for several minutes.

“As soon as we put this sugar source in, we had it running for days at a steady state,” Hosoi says. “That’s exactly what we need. We want a device we can actually put in a robot.”

Hosoi envisions that the tree-on-a-chip pump may be built into a small robot to produce hydraulically powered motions, without requiring active pumps or parts.

“If you design your robot in a smart way, you could absolutely stick a sugar cube on it and let it go,” Hosoi says.

This research was supported, in part, by the Defense Advance Research Projects Agency.

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 26, 2017, 12:02:27 pm
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WriterOfMinds

Project Acuitas in General Project Discussion

I'm going to post updates re: my main project, Acuitas the semantic net AI, in this thread.

My focus this past month was on giving Acuitas the ability to learn more types of inter-word relationships.  He started with just class memberships (<thing> is a <thing>) and qualities (<thing> is a <adjective>), but now he can learn all of the following:

<thing> can do <action>
<thing> is for <action>
<thing> is part of <thing>
<thing> is made of <thing>
<thing> has <thing>

In the process I made extensive updates to the module behind the Text Parser that detects "forms," i.e. syntactic structures that encode these inter-word relationships.

I also upgraded the GUI library from Tkinter to Kivy, which is kind of boring but had to be done, because the old GUI was provoking frequent crashes.

More details on the blog: http://writerofminds.blogspot.com/2017/06/acuitas-diary-2-may-2017.html

The included diagram shows my vision for the conversation engine.  The upper half is implemented (though of course it still needs to mature a great deal); the lower half mostly does not exist yet.

6 Comments | Started June 02, 2017, 03:17:30 pm
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elpidiovaldez5

Hi, I am looking forward to some good conversations about AI in New Users Please Post Here

Hi,
I have a long standing interest in robotics and AI.  I want to chat about these themes with others.  I studied electronics and computing.  I have built a couple of robots at home and read a lot of AI papers.

5 Comments | Started June 24, 2017, 03:26:38 am
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Maviarab

Friday Funny in General Chat

Share your jokes here to bring joy to the world  :)

1741 Comments | Started February 13, 2009, 01:52:35 pm
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

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