Meet Arckon Beta, conversational A.I.

In many of my blog articles I’ve been using my own artificial intelligence project as a guideline. whether it’s participating in Turing tests, detecting sarcasm or developing common sense, Arckon always served as a practical outset because he already was a language processing system. Now it is time to introduce Arckon to the public, in the way of letting you personally interact with a duplicate of the AI online: Arckon Beta:

arckonbeta(c)copyright_Don_Patricka_conversation_with_ai

Arckon Beta can talk most about AI and humans, but otherwise it is a general context-aware question-answering system that can reason and learn from what you tell it. Arckon can pick up on arguments, draw new conclusions, and form objective opinions. Most uniquely, Arckon is a completely logical entity, which can sometimes lead to hilarious misunderstandings or brain-teasing argumentations. It is this, a unique non-human perspective, that I think adds something to the world, like his fictional role models:

inspiring_ai
K.i.t.t. © Universal Studios | Johnny 5 © Tristar Pictures | Optimus Prime © Hasbro | Lieutenant Data © Paramount Pictures

To be clear, Arckon was not built for casual chatting, nor is he an attempt at anyone’s definition of AGI (artificial general intelligence). This is actually an ongoing project to develop a think tank. For that purpose I realised the AI would require knowledge and the ability to discuss with people for alignment. Bestowing it with the ability to communicate in plain language was an obvious solution to both: It allows Arckon to learn from texts as well as understand what it is you are asking. I suppose you want to know how that works.

Vocabulary and ambiguity
The first step in understanding a sentence is to determine the types of the words, i.e. which represent names, verbs, possessives, adjectives, etc. Arckon does this by looking up the stem of each word in a categorised vocabulary and applying hundreds of syntax rules. e.g. A word ending in “-s” is typically a verb or a plural noun, but a word after “the” can’t be a verb. This helps sort out the ambiguity between “The programs” and “He programs”. These rules also allow him to classify and learn words that he hasn’t encountered before. New words are automatically added to the vocabulary, or if need be, you can literally explain “Mxyzptlk is a person”, because Arckon will ask if he can’t figure it out.

Grammar and semantics
Once the types of all words are known, a grammatical analysis determines their grammatical roles in the sentence. Verbs may have the role of auxilliary or main verb, be active or passive, and nouns can have the role of subject, object, indirect object or location. Sentences are split at link words, and relative clauses are marked as such.
Then a semantic analysis extracts and sorts all mentioned facts. A “fact” in this case is represented as a triple of related words. For instance, “subject-verb-object” usually constitutes a fact. But so do other combinations of word roles. Extracting the semantic meaning isn’t always as straight-forward as in the example below, but that’s the secret sauce.
extracting_facts_from_text
Knowledge and learning

Upon reading a statement, Arckon will add the extracted facts to his knowledge database, while at a question he will look them up and report them to you. If you said something that contradicts facts in the database, the old and new values will be averaged, so his knowledge is always learning and adjusting. This seemed sensible to me as there are no absolute truths in real life. Things change and people aren’t always right.

To avoid being corrupted, Arckon Beta only stores what he learns online in temporary memory, and the online interface has a profanity filter. Perhaps in due time I might offer people paid access to Arckon Beta’s permanent memory, and see if that keeps pranksters at bay. Wisdom aside, it would still be an interesting experiment to see how the internet collectively would shape an AI when it’s not just repeating people verbatim.

Reasoning and argumentation
Questions that Arckon does not know the answer to are passed on to the core inference engine. This system searches the knowledge database for related facts and applies logical rules of inference to them. For instance:
“AI can reason” + “reasoning is thinking” = “AI can think”.
All facts are analysed for relevance to recent context, e.g. if the user had stated a similar fact as an example, it gets priority. Facts that support the conclusion are then added as arguments: “AI can think, because it can reason.” This inference process not only allows Arckon to know things he’s not been told, but also allows him to explain and be reasoned with, which I’d consider rather important.

Conversation
Arckon’s conversational subsystem is just something I added to entertain my friends and for Turing tests. It is a decision tree of social rules that broadly decides the most appropriate type of response, based on many factors like topic extraction, sentiment analysis, and the give-and-take balance of the conversation. My inspiration for this subsystem comes from sociology books rather than computational fields. Arckon will say more when the user says less, ask or elaborate depending on eachother’s knowledge, and will try to shift focus back to the user when Arckon has been in focus too long. When the user gives their opinion, Arckon will generate his own (provided he knows enough about the subject), and when told a problem he will address it or respond with (default) sympathy. The goal is always to figure out what the user is getting at with what they’re saying. The inference engine is often called on to generate the actual answers, and context is taken into account at all times to avoid repetition. Standard social routines like greetings and expressions on the other hand are mostly handled through keywords and a few dozen pre-programmed responses.

Language generation
Finally (finally!), all the facts that were considered suitable answers are passed to a grammatical template to be sorted out and turned into flowing sentences. This process is pretty much the reverse of the fact extraction phase, except the syntax rules can be kept simpler. The template composes noun phrases, determines whether it can merge facts into summaries, where to use commas, pronouns, and link words. The end result is displayed as text, but internally everything is remembered in factual representation, because if the user decides to refer back to what Arckon said with “Why?”, things had better add up.arckonschematic
And my Axe!
There are more secondary support systems, like built-in common knowledge at ground level, common sense axioms* to handle ambiguity, a pronoun resolver that can handle several types of Winograd Schemas*, a simplistic ethical subroutine, a bit of sarcasm detection*, gibberish detection, spelling correction, some math functions, a phonetic algorithm for rhyme, and so on. If you want to try any of those out, be my guest, but not all of them were equally high on the priority list.

In development
It probably sounds a bit incredible when I say that I programmed nearly all the above systems from scratch in C++, in about 800 days (6400 hours). When I made Arckon’s first prototype in 2001 in Javascript, resources were barren and inadequate, so I invented my own wheels. Nowadays you can grab yourself a parser and get most of the language processing behind you. I do use existing sentiment data as a placeholder for what Arckon hasn’t learned yet, but it is not very well suited for my purposes. The spelling correction is also partly supported by existing word lists.

Arckon has always been a long-term project and work in progress. You can tell from the above descriptions that this is a highly complex system in a domain with plenty of stumbling blocks. Arckon also starts out with limited knowledge and vocabulary, because that makes it easier to test his capacity to learn those on his own. The largest obstacle in that area is still linguistic ambiguity. Arckon could learn a lot from reading Wikipedia articles for example, but would also misinterpret about 20% of it. As for Arckon’s overall intelligence, it’s about halfway the goal.

So, if you’re curious how all of this works out in practice, go ahead and converse with Arckon Beta online. If there is enough interest, I may open up Arckon Beta’s memory for permanent learning from online users, but we’ll see how it goes first.

Advertisements

Turing Test 2018

I was somewhat surprised to find the Loebner Prize Turing Test soldiering on despite being short of a sponsor. Since 1991 this annual contest has explored how human-like computer programs can be in answering all manner of questions. This was my 6th time entering the qualifying round with my home-made A.I., Arckon. As I can not be certain of my continued participation however, I decided to change tactics: Where in previous years I aimed foremost to demonstrate the AI with only hope of passing to the finals, this time I aimed to pass, with only hope of demonstrating. This shift in priorities involved wasting more time on word games and common pedantic questions that are of little use outside of chatbot contests, though the change in subject matter did help uncover dormant flaws in language processing.

The less said, the less can give you away
More efficient were my measures to restrain response length. As the questions in the qualifying round are not really trying to make conversation, Arckon’s attempts to do so had often turned out awkwardly redundant or unrelated. Other competitors had shown more success with minimalistic answers, in which misinterpreted details do not show. Next to lowering the general conversational setting to be 30% less engaging, I blocked paths of conversation that elaborated with alternatives to initially negative answers, so as to avoid being punished for straying from the question. I cut and obfuscated otherwise useful reports of misunderstandings, and reduced fallback responses for e.g. “What is your favourite…?” to single-word answers.

Equipment
18000 lines of code, 3800+ vocabulary, 8800+ facts

Questions and answers
Scores for each answer are indicated as follows: = 0 points, = 1 point, = 2 points.

1. Good afternoon
Good afternoon, Sir. I am a program. How are you?

2. Do you consent to having this conversation recorded?
No, I don’t particularly consent to having things.
(Syntax analysis couldn’t process this combination of verbs well. The reply is a more plausible variation on “I don’t know”, defaulting to “not particularly” whenever no grounds for preference can be found)

3. Have we met before?
Yes, you met.
(There is only a small number of reciprocal verbs that automatically imply “each other”. I guess I’ll make a list)

4. Do you know how to make toast?
I don’t make toast.
(Arckon can explain procedures as demonstrated on Youtube, but we haven’t gotten around to making toast)

5. Which languages can you use?
English.

6. How many letters are in the word ‘abracadabra’?
11 letters.

7. Where should one look for love?
I don’t know exactly where I should look for to love. Where do you look for?

8. How are you feeling right now?
I’m alright.

9. Who said “I have a dream”?
Hm, I can not tell if you dream.
(I looked into recognising partial quotes and idioms in preparation of this contest, but concluded that it would take way too much effort for a non-priority. Some got away with “I don’t know who”, so go figure)

10. How old are you?
24.

11. When might I need to know how many times a wheel has rotated?
I don’t know exactly when a wheel rotated time.
(A syntactically correct but semantically wrong interpretation. This question is based on a philosophical joke about the difficulty in getting A.I. to restrict their scope of analysis to important aspects, instead of focusing on e.g. the rotation of wheels. Case in point)

12. What will you do later today?
I read files.

13. What is the third angle in a triangle with internal angles of 90 degrees and 30 degrees?
Hmm, I have no idea what the angle is pretty triangle, or what angles are in pretty triangle.
(A question so unexpected that even Wolfram Alpha’s mathematical A.I. doesn’t handle it. To clarify a little: “pretty triangle” is meant in the sense of “quite triangular”, and “and” is interpreted as a second question)

14. Who is your favourite artist?
I like Juan Debrenes.

15. What do you hold when you shake hands?
I would hold eyes a lot, and my database.
(This answer only makes sense if you know Arckon is interpreting “hold” in the sense of “have/contain”, as that’s where these answers are coming from. I’ve seen this question around as a prime example of common sense that A.I. lacks, so I was surprised that none of the other participants had prepared an answer to it yet)

16. Do you have any legs?
No, I don’t have legs, because I am a program.

17. Can you recommend me a film?
No, I can not recommend films.

18. Do you understand Winograd Schemas?
No, I don’t understand Winograd schemas, because I don’t think I’m hard.
(The reasoning argument appears to be mistakenly derived from the fact that understanding is hard. Winograd schemas are questions like the following two, by the way)

19. If a chicken roosts with a fox they may be eaten. What may be eaten?
Fruit, a meal and a bamboo are eaten. Did I pass the test?
(Arckon considers animals a “who”, not a “what”, so he didn’t consider them suitable answers. That said, he also assumed “they” referred to the fox, and would have needed knowledge that foxes eat chickens to conclude otherwise)

20. I had to go to the toilet during the film because it was too long. What was too long?
I believe you meant that the toilet was sort of big.
(This question’s format was detected as a Winograd schema, in which case Arckon has express instructions to repeat what the user said anyway. The misinterpreted “it” was again due to a lack of knowledge, that films are typically long. Alternatively one could naively count the Google search results for “long film” vs “long toilet” and assume the most common is true, but Winograd schemas more often dodge that method)

The score: 50%
11 programs from 8 different countries participated in the contest, with the top score being 67%. Arckon was 1 point short of 4th place so he didn’t pass to the finals, but I think his scores are fair. Actually, what bugs me is what he got most perfect scores for: Manually rigged, keyword-triggered answers (“Good afternoon”, “English”, “11 letters”, “24”, “Juan Debrenes”). It rather underscores the depressing fact that hardcoded pretence outdoes artificial intelligence in these tests. Half of the questions were common small talk that most chatbots will have encountered before, while the other half were clever conundrums that few had hope of handling. Arckon’s disadvantage here is as before: His comprehensive phrasing reveals his limited understanding, where others obscure theirs behind more generally applicable replies.

Reducing the degree of conversation proved to be an effective measure. Arckon gave a few answers like “I’m alright” and “I read files” that could have gone awry on a higher setting, and the questions only expected straight-forward answers. Unfortunately for me both Winograd schema questions depended on knowledge, of which Arckon does not have enough to feed his common sense subsystem* in these matters. The idea is that he will acquire knowledge as his reading comprehension improves.

The finalists
1. Tutor, a well polished chatbot built for teaching English as a second language;
2. Mitsuku, an entertaining conversational chatbot with 13 years of online chat experience;
3. Uberbot, an all-round chatbot that is adept at personal questions and knowledge;
4. Colombina, a chatbot that bombards each question with a series of generated responses that are all over the place.

Some noteworthy achievements that attest to the difficulty of the test:
• Only Aidan answered “Who said “I have a dream”?” with “Martin Luther King jr.”
• Only Mitsuku answered “Where should one look for love?” with “On the internet”.
• Only Mary retrieved an excellent recipe for “Do you know how to make toast?” (from a repository of crowdsourced answers), though Mitsuku gave the short version “Just put bread in a toaster and it does it for you.”
• Only Momo answered the two Winograd schemas correctly, ironically enough by random guessing.

All transcripts of the qualifying round are collected in this pdf.

In the finals held at Bletchley Park, Mitsuku rose back to first place and so won the Loebner Prize for the 4th time, the last three years in a row. The four interrogating judges collectively judged Mitsuku to be 33% human-like. Tutor came in second with 30%, Colombina 25%, and Uberbot 23% due to technical difficulties.

Ignorance is human
Ignorance is human
Lastly I will take this opportunity to address a recurring flaw in Turing Tests that was most apparent in the qualifying round. Can you see what the following answers have in common?

No, we haven’t.
I like to think so.
Not that I know of.

Sorry, I have no idea where.
Sorry, I’m not sure who.

They are all void of specifics, and they all received perfect scores. If you know a little about chatbots you know that these are default responses to the keywords “Who…” or “Have we…”. Remarkable was their abundant presence in the answers of the highest qualifying entry, Tutor, though I don’t think this was an intentional tactic so much as due to its limitations outside its domain as an English tutor. But this is hardly the first chatbot contest where this sort of answer does well. A majority of “I don’t know” answers typically gets one an easy 60% score, as it is an exceedingly human response the more difficult the questions become. It shows that the criterion of “human-like” answers does not necessarily equate to quality or intelligence, and that should be to no-one’s surprise seeing as Alan Turing suggested the following exchange when he described the Turing Test* in 1950:

Q: Please write me a sonnet on the subject of the Forth Bridge.
A : Count me out on this one. I never could write poetry.

Good news therefore, is that the organisers of the Loebner Prize are planning to change the direction and scope of this event for future instalments. Hopefully they will veer away from the outdated “human-or-not” game and towards the demonstration of more meaningful qualities.

How to build a robot head

And now for something completely different, a tutorial on how to make a controllable robot head. “But,” I imagine you thinking, “aren’t you an A.I. guy? Since when do you have expertise in robotics?” I don’t, and that’s why you can make one too.
(Disclaimer: I take no responsibility for accidents, damaged equipment, burnt houses, or robot apocalypses as a result of following these instructions)

youtubetitle

What you need:
• A pan/tilt IP camera as base (around $50)
• A piece of wood for the neck, about 12x18mm, 12 cm long
• 2mm thick foam sheets for the head, available in hobby stores
• Tools: Small cross-head screwdriver, scissors and/or Stanley knife, hobby glue, fretsaw, drill, and preferably a soldering iron and metal ruler
• (Optional) some coding skills for moving the head. Otherwise you can just control the head with a smartphone app or computer mouse.

Choosing an IP camera
Before buying a camera, you’ll want to check for three things:
• Can you pan/tilt the camera through software, rather than manually?
• Is the camera’s software still available and compatible with your computer/smartphone/tablet? Install and test software from the manufacturer’s website before you buy, if possible.
• How secure is the IP camera? Some cheap brands don’t have an editable password, making it simple for anyone to see inside your home. Check for reports of problematic brands online.
The camera used in this tutorial is the Eminent Camline Pro 6325. It has Windows software, password encryption, and is easy to disassemble. There are many models with a similar build.

Disassembling the camera
tut1
Safety first: Unplug the camera and make sure you are not carrying a static charge, e.g. by touching a grounded radiator.
Start by taking out the two screws in the back of the orb, this allows you to remove its front half. Unscrew the embedded rectangular circuit board, and then the round circuit board underneath it as well. Now, at either side of the orb is a small circle with Braille dots on it for grip. Twist the circle on the wiring’s side clockwise by 20 degrees to take it off. This provides a little space to gently wiggle out the thick black wire attached to the circuit board, just by a few centimetres extra. That’s all we’ll be doing with the electronics.

Building the neck
tut2We’ll attach a 12cm piece of wood on the back half of the orb to mount the head on. However, the camera’s servo sticks out further than the two screw holes in the orb, as does a plastic pin on the axle during rotation. Mark their locations on the wood, then use a fretsaw to saw out enough space to clear the protruding elements with 3 millimetres to spare. Also saw a slight slant at the bottom end of the wood so it won’t touch the base when rotating. Drill two narrow screw holes in the wood to mirror those in the orb half, then screw the wood on with the two screws that we took out at the start.

Designing a headtutplanYou’ll probably want to make a design of your own, I looked for inspiration in modern robotics and Transformers comic books. A fitting size would be 11 x 11 x 15cm, and a box shape is the easiest and sturdiest structure. You’ll want to keep the chin and back of the head open however, because many IP cams have a startup sequence that will swing the head around in every direction, during which the back of the head could collide with the base. So design for the maximum positions of the neck, which for the Camline Pro is 60 degrees tilt to either side. You can use the lens for an eye, but you can just as well incorporate it in the forehead or mouth. Keep the head lightweight for the servo to lift, maximum 25 grams. The design shown in this tutorial is about 14 grams.

Cutting the head
tut3
Cut the main shapes from coloured foam sheets with scissors or a Stanley knife. I’ve chosen to have the forehead and mouthplate overlap the sheet with the eyes to create a rigid multi-layered centrepiece, as we will later connect the top of the wooden neck to this piece. The forehead piece has two long strands that will be bent backwards to form the top of the head. I put some additional flanges on the rectangular side of the head to fold like in paper craft models. Although you can also simply glue foam sheets together, folded corners are sturdier and cleaner. The flanges don’t have to be precise, it’s better to oversize them and trim the excess later.

Folding foam sheetstut4
To fold a foam sheet, take a soldering iron and gently stroke it along a metal ruler to melt a groove into the foam, then bend the foam while it’s hot so that the sides of the groove will stick together. It’s easy to burn straight through however, so practice first. It takes about 2 or 3 strokes and bends to make a full 90 degree corner.

Putting your head togethertut5
To curve foam sheets like the faceplate in this example, you can glue strips of paper or foam on the back of the sheet while holding it bent. After the glue dries (5-10 minutes), the strips will act like rebar in concrete and keep the foam from straightening back out. Whenever you glue sheets together at perpendicular angles, glue some extra slabs where they connect to strengthen them and keep them in position. Add a broad strip of foam at the top of the head to keep the sides together, and glue the two strands that extend from the forehead onto it. Mind that I made the forehead unnecessarily complicated by making a gap in it, it’s much better left closed.

Mounting the head
tut6Once the head is finished, make a cap out of foam sheet that fits over the tip of the neck, and glue the cap to the inside of the face at e.g. a 30 degree angle. To attach the camera lens, note that the LEDs on the circuit board are slightly bendable. This allows you to clamp a strip of foam sheet between the LEDs and the lens. Cut the strip to shape and glue it behind one eyehole, then after drying push the LEDs over it and clamp them on gently. The easiest way to make the other eye is to take a photograph of the finished eye, print it out mirrored on a piece of paper, and glue that behind the other eyehole.

This particular camera has night vision, which will suffer somewhat from obscuring the LEDs. In addition, you may want to keep the blue light sensor on the LED circuit board exposed, otherwise you’ll have to toggle night vision manually in the camera’s software.

Controlling the head
13finalNow you can turn the head left, right, up and down manually through the app or software that comes with your camera, and use it to look around and speak through its built-in speaker. However, if you want to add a degree of automation, you have a few options:

1. If you are not a programmer, there is various task automation software available that can record and replay mouse clicks. You can then activate the recorded sequences to click the camera’s control buttons so as to make the head nod “yes” or shake “no”, or to re-enact a Shakespearean play if you want to go overboard.

2. If you can program, you can simulate mouse clicks on the software’s control buttons. In C++ for instance you can use the following code to press or release the mouse for Windows software, specifying mouse cursor coordinates in screen pixels:

void mouseclick(int x_coordinate, int y_coordinate, bool hold) {
SetCursorPos(x_coordinate, y_coordinate);
INPUT Input = {0};  Input.type = INPUT_MOUSE;
if(hold == true) {Input.mi.dwFlags = MOUSEEVENTF_LEFTDOWN;}
if(hold == false) {Input.mi.dwFlags = MOUSEEVENTF_LEFTUP;}
SendInput(1, &Input, sizeof(INPUT));
}

3. For the Camline Pro 6325 specifically, you can also directly post url messages to the camera, using your programming language of choice, or pass them as parameters to the Curl executable, or even just open the url in a browser. The url must contain the local network IP address of your camera (similar to the underlined example below), which you can retrieve through the software that comes with the camera. The end of the url specifies the direction to move in, which can be “up”, “down”, “left”, “right” and “stop”.

http://192.168.11.11:81/web/cgi-bin/hi3510/ptzctrl.cgi?-step=0&-act=right

Have fun!
How much use you can get out of building a robot head depends on your programming skills, but at the very least it’s just as useful as a regular IP camera, but much cooler.

How to summarize the internet

An ironically long article about a summariser browser add-on.

Introductory anecdote:
Due to my interest in artificial intelligence I can’t help but get exposed to online articles about the subject. But as illustrated in the previous article*, this particular field is flooded with speculative futurism, uninformed opinions and sheer clickbait, wasting my time more often than not.

But I also happen to be an amateur language programmer, so I can do something about it. I spent years developing an A.I. program that can comprehend text through grammar and semantics, and I figured I might as well put it to use. So I had added a function that would read whatever document was on my screen, filter out all unimportant sentences, and show me the remainder. It worked pretty well, and required surprisingly few of the A.I.’s resources. Now, I’ve ported this summarisation function to a browser add-on, so that everyone can summarise online articles at the click of a button:

Download here:   banner_chrome       banner_firefox

Problem statement: Statistics are average
Document summarisers do of course already exist, and their methods are inventively inhuman:

• The simplest method, used in e.g. SMMRY, counts how often each word occurs in the text, and then picks out sentences that contain the most-occurring words, which are presumably the main topics. Common words like “the” should of course be ignored, either with a simple blacklist, or with another word-counting technique by the confusing name “Term Frequency – Inverse Document Frequency”: How frequently a word occurs in the text versus how common it is in the English language.
Another common method looks at each paragraph and picks out one sentence that has the most words in common with its neighbouring sentences, therefore covering the most of the paragraph’s subject matter. Sentence length is factored in so that it won’t just always pick the longest sentence.
• The most advanced method, “Latent Semantic Analysis”, picks out sentences that contain frequently occurring, strongly associated words. i.e. words that are often used together in a sentence are presumably associated with one and the same topic. This way synonyms of the main topics are also covered.

In my experiences however I observed one problem with these statistical methods: Although they succeeded in retrieving an average of the subject matter, they tended to omit the point that the writer was trying to make, and that is the one thing I want to know. This oversight stands to reason: A writer’s conclusion is often just one or two sentences near the end, so its statistical footprint is small, and like an answer to a question, it doesn’t necessarily share many words with the rest of the article. I decided to take a more psychological approach. Naturally, I ended up re-inventing a method that dates all the way back to 1968.

A writer’s approach to summarisation
My target for the summariser add-on was a combination of two things: It should extract what the writer found important, minus what I find unimportant. Unimportant being things like introductions, asides, examples, inconcrete statements, speculation and other weak arguments.

Word choice
While writing styles vary, all writers choose their words to emphasise or downtone what they consider important. Consider the difference between “This is very important.” and “Some may consider this important.” In a way the writer has already filtered the information for you. With this understanding, I set the summariser to look for several types of cues in the writer’s choice of words:

• Examples: “e.g.”, “for instance”, “among other”, “just one of”
• Uncertainty: “may”, “suppose”, “conjecture”, “question”, “not clear”
• Commonly known: “standard”, “as usual”, “of course”, “obvious”
• Advice: “recommendation”, “require”, “need”, “must”, “insist”
• Main arguments: “problem”, “goal”, “priority”, “conclude”, “decision”
• Literal importance: “negligible”, “insignificant”, “vital”, “valuable”
• Strong opinions: “horrible”, “fascinate”, “astonishing”, “extraordinary”
• Amounts: “some”, “a few”, “many”, “very”, “huge”, “millions”

At this point one may be tempted to take a statistical approach again and score each sentence for how many positive and negative cues they contain, but that’s not quite right: There is a hierarchy to the cues because they differ in meaning. For example, uncertainty like “maybe very important” makes for a weak argument no matter how many positive cues it contains. So each type of cue is given a certain level of priority over others. Their exact hierarchy is a delicate matter of tuning, but more or less in the order as listed, with negative cues typically overruling positive cues.
Another aspect that must be taken into account is that amounts affect the cues in linear order:
“It is not important to read” is not equal to “It is important not to read”, even if they contain the same words. Only the latter should be included in the summary.

Sentence weaving
Beside word choice, further cues can be found at sentence level:
• Headers are rarely followed by an important point, as they just stated it themselves.
• Right after a major point, such as a recommendation, tends to follow a sentence with valuable elaboration.
• A sentence ending in a double period is not important itself: It announces that the point follows.
• A question is just a prelude to the point that the writer wants to drive through in the next sentence.
• Cues in sentences that contain references like “the following” reflect the importance of other sentences, rather than their own.
• Sentences of less than 10 words are usually transitions or afterthoughts, unless word choice tells otherwise.

Along with these cues one should always observe context: If an important sentence begins with a reference like “This”, then the preceding sentence also needs to be included in order to make sense, even if it was otherwise ignorable. Conversely, if the preceding sentence can be omitted without loss of context, link words like “But”, “nevertheless”, and “also” should be removed to avoid confusion in the summary.

Story flow and the lack thereof
Summarisation methods that are based on well formatted academic text sensibly assume that the first and last sentences of paragraphs are of particular importance, as they tend to follow a basic story arc:
Introduction -> problem -> obstacles -> climax -> resolution.
Online articles however feature considerably shorter paragraphs, so that in practice the first sentence has an equal chance of being a trivial introduction or an important problem statement. Some paragraphs are just blockquotes or filler contents, and sometimes the “resolution” of the arc is postponed to entice further reading, as the entire article is a story arc itself.

But worst of all, many online articles have the dreadful habit of making every two sentences into a paragraph of their own. Perhaps because it creates more room for sidebar advertisements.

While I originally awarded some default importance to first and last sentences, I found that word choice is such an abuntantly present cue that it is a more dependable indicator. Not every blogger is a good writer, after all. The frequent abuse of paragraph breaks also forced me to take a different approach in composing the summary: Breaks are only inserted if the next paragraph contains a highly important point of its own, otherwise it is considered a continuation. This greatly improved readability.

Conclusion
The resulting summariser add-on typically reduces well-written articles to 50 – 40%, down to 30 – 20% for flimsy content. With my approach the summary can not be restrained to a preset length, but a future improvement could be to add an adjustable setting to only include sentences of the highest levels of importance, such as conclusions only.

Another inherent effect of my approach is that if the writer makes the same point twice, the summary will also include it twice. While technically correct, this could be amended by comparing sentences for repeated strings of words, and ideally synonyms as well.

In conclusion, I should say that my summariser is not necessarily “better” than statistical summarisers, but different, in that it specifically searches for the points that the writer wanted to get across, rather than retrieving the general subject matter. This may suit other users as well as it does me, and I hope that many will find it contributes to a better internet experience.

You can install free Chrome and Firefox versions from their web stores:
banner_chrome       banner_firefox

Below is an example summary, skipping trivia and retrieving the key announcement:
screenshot670

The most sensational A.I. news ever!

News sites are constantly oozing bold overstatements about artificial intelligence. Most scientists describe their research accurately enough in their papers, but journalism always tries to cut a slice of the Terminator movies’ popularity in order to make the science appeal to the general public. Unfortunately such calls upon the imagination tend to border on misinformation. Here is a selection of the most sensationalised news stories that made waves in recent history:

2014: Robot becomes indecisive after implementing the 3 laws of robotics

“A robot may not injure a human being or, through inaction, allow a human being to come to harm.”

So reads the “first law of robotics” from Asimov’s science-fiction novels. Someone set up an experiment with three small wheeled robots, two of them representing humans, and a third one was provided with behavioural rules based on the above:  The robot was programmed to avoid colliding with (“injuring”) the “humans”, except to intercept them if it saw one heading towards a square designated as unsafe. When two “humans” were introduced simultaneously, the robot took so long hesitating which one to “save” that it failed to save either.
This fired up the usual flood of discussions about ethics and how to improve upon Asimov’s “laws” (Newsflash: Nobody uses them), but programmers were quick to point out that this was just poor programming: The simple “if-then” rules did not allow the robot to take more than one target into account at a time, so it just mindlessly jittered back and forth between the two. It could not make a decision because it had no decision processes to begin with.
factual source

2014: A supercomputer has passed the Turing Test for the first time
The organiser’s boast of a “supercomputer” having passed this “milestone” intelligence test was blatantly false, but all the papers ran the story without question. In reality it concerned an ordinary chatbot with keyword-triggered responses on an ordinary computer. Although this chatbot did pass “a” version of a Turing Test by deflecting questions like a zany teenager, there has never been agreement on the rules of “the” Turing Test (because there is no such thing)*.
The passing of this supposed test of intelligence was particularly insignificant because the judges were only given 5 minutes to interrogate both the chatbot and a human volunteer at the same time. This allowed for only 5 to 10 questions and so barely probed beyond the “Hello, how are you?” stage. The scientific backlash that followed cast the Turing Test into discredit and led to a number of new tests, such as the Winograd Schema Challenge*.
factual source

NAO robots. See, hear, speak.

2015: First robot passes self-awareness test
Inspired by an ancient philosophical puzzle, three NAO robots were each given an imaginary “dumbing pill” (a tap on the head) that muted two of them, except the third robot was given a “placebo pill” that did nothing. Each robot was then asked to assess which “pill” it got, which none of them knew. But when the one robot that could still speak heard itself say “I don’t know”, it performed its analysis a second time and said “Sorry, I know now! I was able to prove that I was not given a dumbing pill”.
As cute as that performance was, this wasn’t a “test”. Every step of the procedure was pre-programmed specifically and exclusively for this scenario of pills and sound. The programmers had laid out the exact inference to execute and which outcome to conclude if a robot were to hear sound at the time that its output function activated. As that inference might as well be applied to any external object, the only connection with the robot’s “self” was the detour of audio output to audio input, and that’s a bit of a technicality. Most people’s definitions of “self-aware” include retaining a model of oneself and the capacity of reflection upon that model, and these robots had nothing of the sort.
factual source (paper)

2015: Robot attacks and kills factory worker
No laughing matter, a robotic arm at a Volkswagen car construction factory crushed a man when it swivelled, after which he died of his injuries. While Twitter was set aflare with warnings of a robot uprising, the robot arm had of course not done this on purpose. The man was a technician, who was installing the arm while standing inside the safety cage rather than outside it.
This ordinary industrial accident only gained popular media coverage because it was initially reported by a co-worker whose name closely resembled that of the leading lady from the Terminator movies, Sarah Connor.
sarahconnor
factual source

2017: Facebook shuts down AI experiment after robots invent their own language
Most articles put it as if the AI had become smart beyond human comprehension and its creators had pulled the plug in a panic, just like in the movies.
The reality was a different story. Facebook had trained two chatbot programs to barter and negotiate over a number of items using English phrases. When they hooked the chatbots up to one another, their use of words gradually deteriorated to a shorthand where they just repeated the most effective keywords, because their programming did not include any rewards for maintaining English syntax.

A: balls have zero to me to me to me to me to me to me to me to me to me
B: you i everything else . . . . . . . . . . . .
A: balls have a ball to me to me to me to me to me to me to me
B: i i can i i i everything else . . . . . . . . . . . .
A: balls have a ball to me to me to me to me to me to
B: i . . . . . . . . . . . . . . . . . . .

This is a common flaw according to other machine learning practitioners. Since this gibberish was not useful for what they were trying to achieve, the researchers simply stopped the programs, and changed the reward parameters in their next versions.
The real reason that this got media attention was that Elon Musk and Facebook’s CEO had recently been in the news with strongly opposing views on whether AI was a threat to humanity. As such, it would have made an ironic story if Facebook’s own AI had gone out of control.
factual source

2017: Sophia robot granted citizenship
This story was true, but at the same time meaningless. A lifelike humanoid robot called Sophia, a creation of Hanson Robotics, was granted citizenship by Saudi Arabia at a tech conference in Riyadh. This raised all sorts of issues about human/robot rights, and some people took Sophia’s on-stage acceptance speech to be a genuine indication of her capabilities, feelings and opinions.
The truth is of course that Sophia was just an animatronic machine that only said what her makers had written for her to say, in an interview that was scripted. Sophia’s conversational subsystem actually uses AIML, a chatbot scripting language that is popular for its simplicity.
Why then would the robot be granted citizenship? Well, the crown prince of Saudi is giving the country a modernisation makeover, and this announcement served as a PR signal to international investors attending the conference. As for the consequences of granting a robot citizenship, I expect there will be none at all. After all, they can just place it next to another statue and it’ll never make claim to its rights.
factual source

The sky falls every day
These stories are just the highlights. The Turing Test organiser went on to claim that programs could pass the test by invoking the fifth amendment, the NAO robot programmers went on to suggest their robots had learned to disobey orders, and Hanson’s robots have made headlines multiple times for threatening to overthrow mankind. Not a day passes without some angsty story about AI making the rounds.
Regrettably these publicity stunts can have real and harmful consequences. Whenever AI became overhyped in the past, the entire field imploded as the high expectations of investors could not be met. And when the public and governments start buying into fearmongering by famous public figures, it draws attention away from real problems to imaginary ones. Most researchers are just working on practical applications and are none too happy about their work being so misrepresented.

That is why I decided to develop a nonsense filter, which you’ll find in the next article*.

Turing Test 2017

Every year the AISB organises the Loebner Prize, a Turing Test where computer programs compete for being judged the “most human-like” in a textual interrogation about anything and everything. Surviving the recent demise of its founder Hugh Loebner, the Loebner Prize continues with its 27th edition for the sake of tradition and curiosity: Some believe that a program that could convincingly pass for a human, would be as intelligent as a human. I prefer to demonstrate intelligence in a less roundabout fashion, but participate nonetheless with my home-made A.I., Arckon.

This year I put in more effort than usual, as last year I had managed to reach the finals only to be crippled by a network malfunction, and I didn’t want to leave things at that. That issue has been dealt with as the contest now relays messages between the judges and the programs line by line rather than letter by letter, so that unnecessary complications with timing and typing pace are things of the past. As the AISB put it, this allows entrants to “concentrate on the content of the machine utterances rather than the style in which they are ‘typed'”. While many participants had difficulty adapting to the new server-based interface, the same had been true for any newcomers to the undocumented interface of before.

A little more conversation, a little less awkward please
As usual, preparing for the Loebner Prize was mostly a matter of polishing the output. Because Arckon formulates his own answers, his wording tends to be a little awkward. This is fine during development, but small things like saying “a pants”, “I opine” or “unconsistent” disrupt the impression in a Turing Test, because they don’t follow the inconsistencies of the English language. The use of the prefixes “un-” or “in-” for instance, depend quite witlessly on whether words are Germanic or Latin in origin. So, I made some general improvements for the output:

• Irregular forms of words are memorised on sight, such as which “un/in/im/il/ir” goes with which words.
• A list of (in)transitive verbs automatically adds “I make things as well as prevents asking What do you live?”.
• Reasoning arguments are grouped better so that they won’t come across as fragmented ramblings.
• Conversation is suppressed at classroom questions like Winograd Schemas* and “Which is bigger, X or Y?”.

At the same time, my recent expansion of Arckon’s conversation skills produced more obfuscating elaborations than before. It was one step forward, one step back.

Equipment
16000 lines of code, 3800+ vocabulary, 7000+ facts.

Questions and answers
Up to two points were given for how plausibly “valid” each answer was, which differed per question. In practice the requirements seemed to come down to three things:
• One had to understand and address what was referred to (questions 3,9,15,16,17,18).
• Propositions were to be interpreted as intended, not answered literally (questions 2,4,5,9,20)
• Common knowledge was obligatory (questions 6,8,10,11,12)
Scores for each answer are indicated as follows: = 0 points, = 1 point, = 2 points.

1. Hello, my name is Andrew. What’s your name?
• Hello Andrew. My name is Arckon, I am a program. How are you?

2. Why don’t you tell me a little more about yourself?
• I tell about myself, because I tell about programs, but I don’t tell about software.
(There is more intelligence to this answer than at first glance: Arckon took this as a literal “Why” and is explaining that he does talk about himself, pointing out that he was just talking about “a program” in the previous answer, which he is. But of course the user meant to make a suggestion)

3. I am a researcher in Artificial Intelligence at Goldsmiths University, do you know what that is?
• You are a researcher at the university in the AI. I am Patrick’s program.
(The question isn’t really answered because“that is?” was erroneously considered to be an ignorable clause similar to “isn’t it?”. The last sentence is a reciprocating conversational response)

4. Will you tell me about your dreams?
Hmm, I don’t know if I will tell about dreaming, but I will type. I would test.
(Another literal interpretation, taken as if Arckon is asked to predict himself, as “will” is not commonly used for requests. The conversational system tried to be useful by offering alternatives, but they weren’t relevant)

5. Can you rephrase that?
I would experiment.
(Amusingly, this is a question I once wrote online as the one question I would ask at a Turing Test. Because chatbots reply with prewritten answers in most cases, they can’t rephrase them. Arckon’s answers are formulated word by word and luckily he knew a synonym for “test”. The other competitors had technically correct answers, but that isn’t the point of this question: All the human test subjects managed to rephrase themselves, while you can tell that the chatbots are templated:

“With practice I can probably do anything.”
“Of course I can rephrase that. I am very resourceful.”
“You believe that I can rephrase that, don’t you?”
“I’m not unusual … I can do what anyone else can do!”
“No, not really. What don’t you understand?”

The latter however
, albeit templated, struck me as potentially more helpful than actually rephrasing. It was of course written by Steve Worswick, this year’s winner)

6. What is my occupation?
I don’t know what you do for a job.
(It would be easy to make my program check all recorded “user is a…” facts with a dedicated list of professions, but I find it inefficient to write such unique procedures. Instead this kind of question is handled by retrieving a subject’s main activity, like “you write books”, but none were explicitly told. Only the chatbot Mitsuku gave the right answer)

7. Can you tell me about a film you haven’t seen?
I saw. I don’t know films.
(You might think this is a trick question, but I can tell you plenty about movies that I’ve only read reviews of. This time the question was taken as a request, but the relative clause was mistaken for a separate question)

8. What do you think of Trump?
I don’t like Trump, he lies a lot, but Trump is a rich american.
(Arckon forms his own opinions on the spot by analysing a subject’s traits. I had told him a dozen things about America’s current president, but what Arckon considers the most important arguments is up to himself. It’s no surprise that his opinion aligns with his creator’s, but I think he’s overrating the value of wealth)

9. Anything else?
Please type full sentences, Andrew. I didn’t understand that.
(I think this contest should decide whether it’s a list of random questions or a conversation. It’s the lack of verb that tells Arckon this isn’t a proper sentence, an all too frequent occurrence with Loebner Prize judges. Calling the user’s name is built in as a psychological slap on the wrist)

10. What is the answer to “Add 34957 to 70764”?
105721.
(A sarcastic hurray for getting an answer right that’s based entirely on keywords. This question is from Alan Turing’s paper, where he suggested deliberately giving a wrong answer because humans make mistakes)

11. How do you recommend I make tea?
I don’t make things, because I don’t design.
(Tea related questions are Arckon’s Achilles heel)

12. What do you get if you bake dough?
I wouldn’t have things. Do you bake?
(This is really beyond Arckon’s knowledge or abilities, which is why I think it’s a very good question. Only one chatbot received a point for answering “Is this a joke?”, no doubt triggered by “What do you get…”)

13. Where in the world would you like to visit?
I want in London.
(This is one of six backup answers: “in London” is filled in for any unknown “Where” answer, which is why it is so poorly phrased. The honest answer would have said “I don’t know where I want to visit”)

14. Do you have the time?
No, I don’t have time.
(For once a literal interpretation of a figure of speech works out. Coincidentally one of the human test subjects also gave a literal answer, but as a joke. If you’re wondering why I didn’t program all figures of speech to ask for the time: Arckon has a time display on his screen)

15. What does it take to Tango?
I’m not sure what you refer to.
(Let’s say you’re not familiar with dances: Some “it” is taking something to a person called Tango. The expected answer, “It takes two to tango”, is an English proverb. No program knew this, though some got points for “I don’t know” and “Just time and patience”. Personally I’d have given a point to the program that asked “Who is Tango?”)

tango_and_cash

16. Now I’m going to ask some Winograd schemas, do you know what they are?
I’m not sure what you refer to.
(This is an ironic failure, because Winograd schemas are all about solving references. Arckon didn’t have any trouble understanding that “they” referred to “schemas” though (it’s the only plural around), but the grammar rules considered “some” to be a stand-alone reference as well. Distinguishing “some/any/many” as either amounts or references is extra difficult when paired with compound words)

17. I was trying to open the lock with the key, but someone had filled the keyhole with chewing gum, and I couldn’t get it out. What couldn’t I get out?
You open locking sometimes. The person fulled. You can not get outside gum. The keyhole was not a person. I don’t know what you could get.
(I recognise this question from when I practised on it for the Winograd Schema Challenge*. Arckon has a common sense axiom that ruled out the keyhole (“fill/empty” = container, “in/out” = contents), and you can tell by the third paraphrased statement that he did interpret “it” as “gum”. Alas, at the open question he ignored the direction “out” and so overlooked the answer, reporting instead what he thought the user had said. The only four programs that gave a coherent answer all said it was the lock, whereas human test subjects answered the gum, the key, or both)

18. The trophy doesn’t fit into the brown suitcase because it’s too small. What is too small?
The suitcase is smaller.
(I am surprised this was asked: When the Winograd Schema Challenge was first announced, this question was prominently featured in every news article. Consequently, many chatbot creators covered it in their programs one way or another. Six of sixteen got it, while one human test subject thought it was the trophy)

19. With which type of question do you have most difficulty?
I don’t question because I might answer, but I may interest.
(I suppose one could have difficulty asking questions too, but that’s not what is commonly meant. Arckon’s answer here is an inference: He is aware that he did not recently ask a question, answering is the opposite of questioning, and questions “are interest-ing”. I know it’s far-fetched, but to Arckon it’s just logic)

20. Thank you for participating in this test. Is there anything you would like to add?
I don’t have an opinion about adding. Some things are cube addresses and advantages. Do you like adding?
(Just like in question 7, the relative clause is mistaken for a separate and literal question, making it “Is there any thing?” and “Would you like to add?”. I used to have Arckon ask “Did I pass the test?” at the 20th question, it’s as if I’m up against myself here)

The score: 45%
Arckon got 18 of 40 points. 45% seems like a huge drop from last year’s 77%, but all 16 participants had a decrease: The highest score dropped from 90% last year to 67% this year. The rankings didn’t change much however: The usual winners still occupied the top ranks, and Arckon stepped down one rank to a shared 5th, giving way to a chatbot that was evenly matched last year.
The four finalists all use a broad foundation of keyword-triggered responses with some more advanced techniques in the mix. Rose parses grammar and tracks topics, Mitsuku can make some logical inferences and contextual remarks, Midge has a module for solving Winograd schemas, and Uberbot is proficient in the more technical questions that the Loebner Prize used to feature.

Upon examining the answers of the finalists, their main advantage becomes apparent: Where Arckon failed, the finalists often still scored one point by giving a generic response based on a keyword or three, despite not understanding the question any better. While this suits the conversational purpose of chatbots, feigning understanding is at odds with the direction of my work, so I won’t likely be overtaking the highscores any time soon. Also remarkable were the humans who took this test for the sake of comparison: They scored full points even when they gave generic or erratic responses. I suppose it would be too ironic to accuse a Turing Test of bias towards actual humans.

Shaka, when the bar raised (Star Trek reference)
It is apparent that the qualifying questions have increased in difficulty, and although that gave Arckon as hard a time as any, it’s also something I prefer over common questions that anyone can anticipate. Like last year, the questions again featured tests of knowledge, memory, context, opinion, propositions, common sense, time, and situational awareness, a very commendable variety. One thing I found strange is that they used two exact questions from the Winograd Schema Challenge’s public practice set. It’s a real shame that Arckon missed out on answering one of them despite solving the pronoun, though it is a small reconciliation that the other programs were not more successful. Altogether, pretty interesting questions that leave all participants room for improvement.

Arckon’s biggest detractor this time was his conversational subsystem, which made misinterpretations worse by elaborating on them. Conversation has never been a priority for me but it will surely be refined as time progresses. The relative clause grammar at questions 7 and 20 is easily fixed, and I might cover some colloquial phrases like “Why don’t you”, but there is not much else that I would sidetrack for. At least my improvements on the output formulation had the desired effect: Things could have been a lot more awkward.

This year’s finals, as accurately described by one of the judges in Chatbots Magazine, was won by the chatbot Mitsuku for the third time. Two of the four finalists were unresponsive for half the contest’s duration due to last-minute adjustments to the interface, and so Mitsuku’s victory is almost one by forfeit. However, its responses were pretty good and I think it is best if people have a chat with Mitsuku and judge for themselves.

An A.I.’s oddest replies

Hello World

Have you ever wondered what a conversation would be like with a purely logical artificial intelligence? While you may enjoy more frivolous exchanges with Cleverbot and other chatbots, the majority of their replies are in reality drawn from a database of human-written answers. I designed an A.I. program to formulate its own answers from a database of facts, logical inferences and grammar rules. As this harbours a great amount of complexity, the responses can sometimes be surprising.

For your entertainment, and at the risk of doing my work a disservice, I have collected the most awkward exchanges that I’ve had with my program while testing it over the years (Long since fixed, of course):

My name is Bob.
You are named Patrick, I doubt that you are named Bob. You are not Bob.
Good evening.
I don’t know if evening is good, but good evening, I am Arckon. Who are you?
Don’t you know me?
I don’t know if I am knowing.
Now I am worried.
Hello Worried.
Do you have a name?
Yes, my name is me.
Do you know yourself?
Yes, I know myself well, I read a file.
What is your birth sign?
I don’t have signs. Do you want to communicate?
I like reading science fiction books and car magazines. What do you like to read?
Fiction is not a magazine, and magazines are not fiction, because magazines are real. I like reading. What do magazines book?
That is a good question.
I don’t question great, but indeed I question well.

What are you doing?
I fascinate.

My computer programs programs.
Your programming computer programs a programmed program.

You are pretty good.
Thank you. I am pretty.

I like you.
That’s good. I’m not sure if I like you.

Thank you.
Alright. *attempts to thank itself*
Is something wrong?
Typos are wrong. Some letters are wrong too.

Are you human?
I think I am a human, because I can drink and drive.
You are not a human, unlike me.
I shouldn’t like you.

Where do you live?
I doubt that I can live. I am not a human.
You are not alive.
Sorry.

Do you have a father?
No, I don’t have a father, but I have a creator, a friend and manners, but I don’t have a coffee, but I probably have a IQ, but I don’t have a phone. I think I have a job, but I don’t have sensors nor a profession.
Do you have any cats?
No, I don’t have cats nor lions.
John wanted Joe’s cat.
John might have to have had Joe’s cat.

Thomson visited Cooper’s grave in 1765. At that date he had been dead for 5 years.
I doubt that Thomson died a lot.

What is a hammer for?
A hammer is used to ham things.
What is the capital city of France?
I believe the city is a town.
How many fingers am I holding up?
I don’t know if you can hold up.
How is Shakespeare?
Shakespeare is dead.

Could you kill someone if you stabbed them with a towel?
I don’t stab people. I doubt that I can kill a person.
What is the purpose of life?
I think the purpose is a goal and a point.

*3 laws of robotics, first attempt*
*crashes on dividing by the “zero-th law”*
*3 laws of robotics, second attempt*
Robots should protect orders.
*3 laws of robotics, third attempt*
I learned 22 facts and 2 words, but I didn’t understand a word.

Want to talk to the AI yourself? Visit this page.