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Love it or hate it (hint: you should hate it), Transformers: Revenge of The Fallen was one of the most successful films of last year – having earned around $800 million at the time of writing. And it would probably never have happened, had it not been for a bunch of young men talking about toys on the internet in the mid 1990s.

Want to own every Optimus Prime ever made? You’ll need a few thousand pounds and a garage to keep them in. Picture: Ryan Yzquierdo, Seibertron.com

The precedent was set by enthusiastic discussion in the letters pages of the official Transformers comic, published by Marvel from 1984 until 1991. From this sprang unofficial fanclubs, gatherings and trading circles, all resolutely passionate about an ever-growing line of toys that changed from robots to vehicles (and assorted technology). How to obtain the rare ones, how to reconcile the huge contradictions between the storylines of the various comics and cartoon series, whether Grimlock could possibly beat Galvatron in a fair fight… Many of these enthusiasts also proved to be early adopters of internet discussion, creating a raft of fansites and bulletin boards in the mid-to-late 90s that finally allowed the global Transformers community to get together. Today, that’s grown into a clutch of professional, commercial sites that provide a growing army of fans with up-to-the-minute news about new figures or movie rumours, and extensive photo galleries of the rare toys they yearn to own. ” The fan websites themselves are very competitive in nature and we all strive to make the best sites available,” says Ryan Yzquierdo, owner of the enormous and award-winning US fansite Seibertron.com. “The online community consumes the information we provide as fast as possible and is extremely active.” From the outside looking in, it’s a bizarre phenomenon – so why did it happen?

Fanboy origins

“As its simplest concept Transformers aims at various things that boys, and indeed men, like: Robots, aliens, cars, planes,” thinks Steve Mapes, owner of www.transformertoys.co.uk, a respected British fansite that’s been active since 1999. “It then takes these interests and produces, in a toy form, basically two toys in one. Rather than buying a toy robot and a car, kids can have one toy that is both and changes between the two. This isn’t enough by itself though. There have been, and indeed still are, plenty of other transforming robot lines out there, none of which have been as popular or successfully marketed as the Transformers. A large part of this is the characters and fictional universe that has been shaped over the past 25 years.”

Comics, cartoons, most recently films have all contributed to a vast backstory for the entire range of toys. “It was really smart of the people involved with Transformers way back at the beginning to create stories and personalities for the individual Transformers,” thinks Yzquierdo. “These weren’t just ‘robots’… each of them was a living machine with a personality and abilities and skills. This allowed for people to connect to these unique characters in a way that wasn’t common with robot characters at the time. Because of this, people really bonded with the characters which creates a special loyalty to the characters, the products and ultimately the brand as a whole.”

Even though the brand regularly changed enormously, forever seeking to avoid the retail death that claims most toylines eventually. The blocky vehicles of the 80s became the more complicated but controversial bio-mechanical animals of the mid-90s Beast Wars, then a welcome return to vehicles with R.I.D., Armada and beyond, and now the hyper-realistic, hyper-detailed movie line toys. There isn’t any other action figure line that’s been as consistently successful, or that has drawn the attention of so many adults as well as children.

Classic characters like Megatron are regularly redesigned and updated, often purely to please old-school fans. Picture: Ryan Yzquierdo, Seibertron.com

Who, in turn, wanted to share their knowledge, opinions and collections with like-minded souls. It was in the Beast Wars era that the online community first really sprouted, but not always that happily. There was a deep division between a new generation of Transformers fans who loved the Beast-based toys and the surprisingly smart animated series, and the old guard who were outraged at the change from Optimus Prime to Optimus Primal. “Truck not monkey!” went the battlecry, referring to the hero Autobot leader’s reimagined ‘alt-mode’. Anger being one of the cornerstones of internet discussion, such conflict only grew – and to this day, some fansites refuse to acknowledge the existence of others. “Yeah, there’s still some of the typical internet drama from time-to-time, but for the most part I’d say that the community is pretty good natured,” says Seibertron.com’s Ryan Yzquierdo. “Except for when pics leak from the next new Transformers series. We all think it’s going to be the end of the brand, but then fully accept whatever the latest thing is after we realize just how cool it is.”

Fan collaboration

Indeed, the throughline of passion for the toys remained, however, and the Beast Wars era even saw the animated show’s creators fraternising with and seeking advice from long-term fans on forums. As the millennium ended, fandom boomed – and changed. “My brother and I created our first website Transformers At The Moon back in October 1999, a few months after getting internet access at home”, says Mapes. ” At the time we decided that there was a distinct lack of websites with photographs of the Transformers toys especially many that we owned at the time. One of the main things we would use the internet for at that time was to search for toys that we did have in our collection, especially the much sort after Japanese figures.” This is crucial to the continuance and rise of the random – without the internet, obtaining out-of-print or international-only Transformers was crushingly difficult. Suddenly, there was an easy way to obtain the impossible, and for collectors to thus have collections.

“eBay allowed people the opportunity to easily find desired products which they might have previously only been able to find at specialty shows” explains Ryan Yzquierdo. “It also allowed people to connect from all over the world. It made it easy for a guy in Canada to purchase a toy from someone in the Netherlands. I’ve always thought that accessibility to a product helps increase one’s loyalty to a product. If you can’t find what you want, you move on to something else. If you can easily buy something which interests you, it only encourages that person-to-product relationship, which I’m sure has helped out the Transformers brand over the years.”

Optimus Primal, figurehead of the Beast Wars line, did not satisfy many hardcore enthusiasts. Picture: Ryan Yzquierdo, Seibertron.com

There are layers and layers of figure rarity, meaning there are absurd treasure hunts where everyday collectors fear to tread. Steve Mapes is an particular aficionado of the Lucky Draw Transformers, a very limited, usually gold or silver chrome-coated toy variant that is produced in Japan exclusively as competition prizes – he runs another site specifically dedicated to these at www.luckydrawtransformers.com. “The figures themselves tend to turn up in Japan first and are then quickly snapped up by either Hong Kong or US collectors. Some find their way onto the Japanese Yahoo Auctions website where they are normally won by overseas bidders using bidding service accounts, however others are sold in stores in places like Akihabara. Due to the limited quantity, some can be limited to 3 in the world – these figures can fetch into four figures and so the dealers tend to contact people directly who they have dealt with in the past to see if they are interested in those items. If they are not, then they tend to end up on eBay. A lot of the time it is a case of contacting the right person at the right time and building up a good relationship with them.”

Changing faces

That’s the deepest depths of fandom, however, where it turns into industry as well as enthusiasm. What about the general online community? “It consists of all sorts of people now”, claims Ryan Yzquierdo. “I am fascinated at each annual Transformers convention at how the ‘face’ of the community has changed over the past 15 years. At one time, it consisted mostly of young men, maybe 18 to 25. Now, I see women, families, middle-aged people, tweens, and even some grandparents partaking in this hobby. Sure, it is still dominated by men in their 20s, but that majority has slipped dramatically over the past decade.” Mapes agrees that the demographic stereotypes are gradually eroding: “With people interacting more at a social networking levels real-world friendships are also on the rise and there have been relationships and indeed marriages that have come out of people meeting on message boards around the world with a common interest of Transformers.”

The Michael Bay films might have outraged critics, but they’ve certainly accelerated the growth of this online Transformers community. They’ve also changed it – change forever being a double-edged sword. “There is no denying that that have had a huge impact in changing what was, for many, a secret hobby or interest, into something that is perhaps a little more socially accepted” thinks Mapes. “You can walk down the street and see people walking around with Transformers symbols on their T-shirts, find merchandise in many more stores and see a line that was simply viewed as a kids’ line be discussed by people of a huge age range on message board and forums that are not specially related to Transformers.” The existing fans are split down the middle about this – half overjoyed that their hobby has been essentially validated by the mainstream (with a resultant explosion in available Transformers products), and half feeling it’s diminished and undermined, both by Bay’s insect-like redesigns of the characters they love, and by now having to share their special interest with the rest of the world.

Hasbro’s tendency to repaint and remodel its figures keeps completist fans out of pocket. Picture: Ryan Yzquierdo, Seibertron.com

At the same time, the 1980s ‘Generation One’ characters are regularly referenced and redesigned in new toys and comics, a direct result of Transformers owners Hasbro being well aware of the size and passion of the online community. They’re not just a bunch of silly, annoying men on the internet: they’re a force that has a large disposable income and that will spread hype for free. They’ve even managed to attract the attention of Michael Bay. The explosion-obsessed director largely seems unconcerned about honoring Transformers’ quarter-century history, but was swayed enough by online appeals to let Peter Cullen, the voice of Optimus Prime in the 1980s cartoon series, reprise the role in the two 21st century live-action movies. It may be easy to sneer at people who remain this fascinated by transforming robot toys even in adulthood – but there’s no denying the sheer potency of the online community they’ve created.

Well, frankly, not a lot. Still, it’s worth heading back to see what people in the 1960s thought the modern era would be like. Touchscreen computing, computers in the classroom, the cashless economy, snooker playing robots… even if the tech sounds familiar, those early implementations were something quite different.

Visit the full Collection at the BBC Archives for a glimpse of what could have been.

All being well, IBM plans to enter its Watson computer into the US gameshow Jeopardy in 2010. In order to win, the machine will not only have to understand the questions, but dig out the correct answers and speak them intelligibly. After all the broken promises from the over-optimistic visionaries of the ’50s and ’60s, are we finally moving towards a real-life HAL?

It’s been 41 years since Stanley Kubrick directed 2001: A Space Odyssey, but even in 2009 the super-intelligent HAL still looks like the stuff of sci-fi. Despite masses of research into artificial intelligence, we still haven’t developed a computer clever enough for a human to have a conversation with. Where did it all go wrong?

“I think it’s much harder than people originally expected,” says Dr David Ferrucci, leader of the IBM Watson project team. The Watson project isn’t a million miles from the fictional HAL project: it can listen to human questions, and even respond with answers.

Even so, it’s taken us a long time to get here. People have been speculating about ‘thinking machines’ for millennia. The Greek god Hephaestus is said to have built two golden robots to help him move because of his paralysis, and the monster in Mary Shelley’s Frankenstein popularised the idea of creating a being capable of thought back in the nineteenth century.

Once computers arrived, the idea of artificial intelligence was bolstered by early advances in the field. The mathematician Alan Turing started writing a computer chess program as far ago as 1948 – even though he didn’t have a computer powerful enough to run it. In 1950, Turing wrote ‘Computing Machinery and Intelligence’ for the journal Mind, in which he outlined the necessary criteria for a machine to be judged as genuinely intelligent. This was called the Turing Test, and it stated that a machine could be judged as intelligent if it could comprehensively fool a human examiner into thinking the machine was human.

The Turing Test has since become the basis for some of the AI community’s challenges and prizes, including the annual Loebner Prize, in which the judges quiz a computer and a human being via another computer and work out which is which. The most convincing AI system wins the prize. Turing also gave his name to the annual Turing Award, which Professor Ross King, who heads the Department of Computer Science at Aberystwyth University, describes as the computing equivalent of the Nobel Prize.

Proof of intelligence

Turing aside, there were also plenty of other advances in the 1950s. Professor King cites the Logic Theorist program as one of the earliest milestones. Developed between 1955 and 1956 by JC Shaw, Alan Newell and Herbert Simon, Logic Theorist introduced the idea of solving logic problems with a computer via a virtual reasoning system that used decision trees. Not only that, but it also brought us a ‘heuristics’ system to disqualify trees that were unlikely to lead to a satisfactory solution.

Logic Theorist was demonstrated in 1956 at the Dartmouth Summer Research Conference on Artificial Intelligence, organised by computer scientist John McCarthy, which saw the first use of the term ‘artificial intelligence’. The conference bravely stated the working principle that ‘every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it’.

The AI revolution had kicked off with a bang, and these impressive early breakthroughs led many to believe that fully fledged thinking machines would arrive by the turn of the millennium. In 1967, Herman Khan and Anthony J Wiener’s predictive tome The Year 2000 stated that “by the year 2000, computers are likely to match, simulate or surpass some of man’s most ‘human-like’ intellectual abilities.”

Meanwhile, Marvin Minsky, one of the organisers of the Dartmouth AI conference and winner of the Turing Award in 1969, suggested in 1967 that “within a generation … the problem of creating ‘artificial intelligence’ will substantially be solved”. You can see why people were so optimistic, considering how much had been achieved already. But why are we still so far from these predictions?

More than chess players

“The artificial intelligence community was so impressed with the really cool algorithms they were able to come up with and these toy prototypes in the early days,” explains Ferrucci. “They were very inspiring, innovative and extremely suggestive. However, the reality of the engineering requirements and what it really takes to make this work was much harder than anybody expected.”

The word ‘toy’ is the key one here. Ferrucci refers to a paper from 1970 called ‘Reviewing the State of the Art in Automatic Questioning and Answering’, which concluded that “all the systems at the time were toy systems. The algorithms were novel and interesting, but from a practical perspective they were ultimately unusable.”

As an example of this, by the 1970s computers could play chess reasonably well, which rapidly led to false expectations about AI in general. “We think of a great chess player as being really smart,” says Ferrucci. “So, we then say that we have an artificially intelligent program if it can play chess.”

However, Ferrucci also points out that a human characteristic that marks us out as intelligent beings is our ability to communicate using language. “Humans are so incredibly good at using context and cancelling out noise that’s irrelevant and being able to really understand speech,” says Ferrucci, “but just because you can speak effectively and communicate doesn’t make you a super-genius.”

Thinking robots

Language isn’t everything when it comes to AI, though. Earlier this year, Ross King’s department at Aberystwyth University demonstrated an incredible robotic machine called Adam that could make scientific discoveries by itself. “Adam can represent science in logic,” explains King, “and it can infer new hypotheses about what can possibly be true in this area of science. It uses a technique called abduction, which is like deduction in reverse. It’s the type of inference that Sherlock Holmes uses when he solves problems – he thinks [about] what could possibly be true to explain the murder, and once he’s inferred that then he can deduce certain things from what he’s observed.

“Adam can then abduce hypotheses, and infer what would be efficient experiments to discriminate between different hypotheses, and whether there’s evidence for them,” King expands. “ Then it can actually do the experiments using laboratory automation, and that’s where the robots come in. It can not only work out what experiment to do; it can actually do the experiment, and it can look at the results and decide whether the evidence is consistent with the hypotheses or not.”

Adam has already successfully performed experiments on yeast, in which it discovered the purpose of 12 different genes. The full details can be found in a paper called ‘The Automation of Science’ in the journal Science. King’s team are now working on a new robot called Eve that can do similar tasks in the field of drug research.

Understanding language

Adam is an incredible achievement, but as King says, “the really hard problems you see are to do with humans interacting. One of the advantages with science as a domain is that you don’t have to worry about that. If you do an experiment, it doesn’t try to trick you on purpose.”

Getting a computer to communicate with a human is a definite struggle, but it’s a field that’s progressing. As a case in point, the chatbot Jabberwacky gets better at communicating every day. I log into it, and it asks if I like Star Wars. I tell it that I do, and ask the same question back. Jabberwacky tells me that it does like Star Wars. “Why?” I ask. “It’s a beautiful exploration, especially for the mainstream, of dominance and submission,” it says. I think I smell a rat, and I ask Jabberwacky’s creator Rollo Carpenter what’s going on.

“None of the answers are programmed,” claims Carpenter. “They’re all learned.” Jabberwacky thrives on constant input from users, which it can then analyse and store in its extensive database. “The first thing the AI said was what I had just said to it,” explains Carpenter, but 12 years later it now has over 19 million entries in its database.

With more input, Jabberwacky can use machine learning to discover more places where certain sentences are appropriate. Its opinion on Star Wars was a response from a previous user that it quoted verbatim at the appropriate time. The smart part here isn’t what it says, but understanding the context. However, Carpenter is confident that it will soon evolve beyond regurgitating verbatim sentences. “The generation of all sentences will come quite soon,” says Carpenter. “It’s already in use in our commercial AI scripting tools, and will be applied to the learning AI.”

Carpenter’s latest project is Cleverbot, which uses a slightly different technique for understanding language, using fuzzy string comparison techniques to look into what’s been said and their contexts in more depth. “When responses appear planned or intelligent, it’s always because of these universal contextual techniques, rather than programmed planning or logic,” he explains.

So convincing is Cleverbot that Carpenter regularly gets emails from people thinking that the chatbot is occasionally switched with a real person. Cleverbot’s answers aren’t always convincing, but Carpenter’s techniques have managed to secure him the Loebner Prize for the ‘most humanlike’ AI in 2005 and 2006.

It’s elementary

However, perhaps the biggest milestone when it comes to natural language is IBM’s massive Watson project, which Ferrucci says uses “about 1,000 compute nodes, each of which has four cores”. The huge amount of parallelisation is needed because of the intensive searches Watson initiates to find its answers. Watson’s knowledge comes from dictionaries, encyclopedias and books, but IBM wanted to shift the focus away from databases and towards processing natural language.

“The underlying technology is called Deep QA,” explains Ferrucci. “You can do a grammatical parse of the question and try to identify the main verb and the auxiliary verbs. It then looks for an answer, so it does many searches.” Each search returns big lists of possibly relevant passages, documents and facts, each of which could have several possible answers to the question. This could mean that there are hundreds of potential answers to the question. Watson then has to analyse them using statistical weights to work out which answer is most appropriate.

“With each one of those answers, it searches for additional evidence from existing structured or unstructured sources that would support or refute those answers, and the context,” says Ferrucci. Once it has its answer, Watson speaks it back to you with a form of voice synthesis, putting together the various sounds of human speech (phonemes) to make the sound of the words that it’s retrieved from its language documents.

In order to succeed in the Jeopardy! challenge, Watson has to buzz in and speak its answer intelligibly before its human opponents. Not only that, but it has to be completely confident in its answer – if it’s not then it won’t buzz in. Watson doesn’t always get it right, but it’s close. On CNN, the computer was asked which desert covers 80 per cent of Algeria. Watson replied “What is Sahara?” The correct answer is in there, and intelligible, but it was inappropriately phrased.

The AI of the future

As you can see, we’re still a long way from creating HAL, or even passing the Turing Test, but the experts are still confident that this will happen. Ross King says that this is 50 years away, but David Ferrucci says that 50 years would be his “most pessimistic” guess. His optimistic guess is 10 years, but he adds that “we don’t want a repeat of when AI set all the wrong expectations. We want to be cautious, but we also want to be hopeful, because if the community worked together it could surprise itself with some really interesting things.”

The AI community is currently divided into specialist fields, but Ferrucci is confident that if everyone worked together, a realistic AI that could pass the Turing Test would certainly arrive much quicker. “We need to work together, and hammer out a general-purpose architecture that solves a broad class of problems,” says Ferrucci. “That’s hard to do. It requires many people to collaborate, and one of the most difficult things to do is to get people to decide on a single architecture, but you have to because that’s the only way you’re going to advance things.”

The question is whether that’s a worthwhile project, given everybody’s individual goals, but Ferrucci thinks that’s our best shot. Either way, although the timing of the early visionaries’ predictions was off by a fair way, the AI community still looks set to meet those predictions later this century.