alan turing

Enchanting Minds and Machines – Ada Lovelace, Mary Shelley and the Birth of Computing and Artificial Intelligence

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Today (10th October 2023) is Ada Lovelace Day. In this blog post I discuss why Ada Lovelace (and indeed Mary Shelley who was indirectly connected to Ada) is as relevant today as she was then.

Villa Diodati, Switzerland

In the summer of 1816 [1], five young people holidaying at the Villa Diodati near Lake Geneva in Switzerland found their vacation rudely interrupted by a torrential downfall which trapped them indoors. Faced with the monotony of confinement, one member of the group proposed an ingenious idea to break the boredom: each of them should write a supernatural tale to captivate the others.

Among these five individuals were some notable figures of their time. Lord Byron, the celebrated English poet and his friend and fellow poet, Percy Shelley. Alongside them was Shelley’s wife, Mary, her stepsister Claire Clairmont, who happened to be Byron’s mistress, and Byron’s physician, Dr. Polidori.

Lord Byron, burdened by the legal disputes surrounding his divorce and the financial arrangements for his newborn daughter, Ada, found it impossible to fully engage in the challenge (despite having suggested it). However, both Dr. Polidori and Mary Shelley embraced the task with fervor, creating stories that not only survived the holiday but continue to thrive today. Polidori’s tale would later appear as Vampyre – A Tale, serving as the precursor to many of the modern vampire movies and TV programmes we know today. Mary Shelley’s story, which had come to her in a haunting nightmare that very night, gave birth to the core concept of Frankenstein, published in 1818 as Frankenstein: or, The Modern Prometheus. As Jeanette Winterson asserts in her book 12 Bytes [2], Frankenstein is not just a story about “the world’s most famous monster; it’s a message in a bottle.” We’ll see why this message resounds even more today, later.

First though, we must shift our focus to another side of Lord Byron’s tumultuous life and his divorce settlement with his wife, Anabella Wentworth. In this settlement, Byron expressed his desire to shield his daughter from the allure of poetry—an inclination that suited Anabella perfectly, as one poet in the family was more than sufficient for her. Instead, young Ada received a mathematics tutor, whose duty extended beyond teaching mathematics and included eradicating any poetic inclinations Ada might have inherited. Could this be an early instance of the enforced segregation between the arts and STEM disciplines, I wonder?

Ada excelled in mathematics, and her exceptional abilities, combined with her family connections, earned her an invitation, at the age of 17, to a London soirée hosted by Charles Babbage, the Lucasian Professor of Mathematics at Cambridge. Within Babbage’s drawing room, Ada encountered a model of his “Difference Engine,” a contraption that so enraptured her, she spent the evening engrossed in conversation with Babbage about its intricacies. Babbage, in turn, was elated to have found someone who shared his enthusiasm for his machine and generously shared his plans with Ada. He later extended an invitation for her to collaborate with him on the successor to the machine, known as the “Analytical Engine”.

A Model of Charles Babbage’s Analytical Engine

This visionary contraption boasted the radical notion of programmability, utilising punched cards like those employed in weaving machines of that era. In 1842, Ada Lovelace (as she had become by then) was tasked with translating a French transcript of one of Babbage’s lectures into English. However, Ada went above and beyond mere translation, infusing the document with her own groundbreaking ideas about Babbage’s computing machine. These contributions proved to be more extensive and profound than the original transcript itself, solidifying Ada Lovelace’s place in history as a pioneer in the realm of computer science and mathematics.

In one of these notes, she wrote an ‘algorithm’ for the Analytical Engine to compute Bernoulli numbers, the first published algorithm (AKA computer program) ever! Although Babbage’s engine was too far ahead of its time and could not be built using current day technology, Ada is still credited as being the world’s first computer programmer. But there is another twist to this story that brings us closer to the present day.

Fast forward to the University of Manchester, 1950. Alan Turing, the now feted but ultimately doomed mathematician who led the team that cracked intercepted, coded messages sent by the German navy in WWII, has just published a paper called Computing Machinery and Intelligence [3]. This was one of the first papers ever written on artificial intelligence (AI) and it opens with the bold premise: “I propose to consider the question, ‘Can machines think?”.

Alan Turing

Turing did indeed believe computers would one day (he thought in about 50 years’ time in the year 2000) be able to think and devised his famous “Turing Test” as a way of verifying his proposition. In his paper Turing also felt the need to “refute” arguments he thought might be made against his bold claim, including one made by no other than Ada Lovelace over one hundred years earlier. In the same notes where she wrote the world’s first computer algorithm, Lovelace also said:

“It is desirable to guard against the possibility of exaggerated ideas that might arise as to the powers of the Analytical Engine. The Analytical Engine has no pretensions whatever to originate anything. It can do whatever we know how to order it to perform. It can follow analysis, but it has no power of anticipating any analytical relations or truths”.

Although Lovelace might have been optimistic about the power of the Analytical Engine, should it ever be built, the possibility of it thinking creatively wasn’t one of the things she thought it would excel at.

Turing disputed Lovelace’s view because she could have had no idea of the enormous speed and storage capacity of modern (remember this was 1950) computers, making them a match for that of the human brain, and thus, like the brain, capable of processing their stored information to arrive at sometimes “surprising” conclusions. To quote Turing directly from his paper:

“It is a line of argument we must consider closed, but it is perhaps worth remarking that the appreciation of something as surprising requires as much of a ‘ creative mental act ‘ whether the surprising event originates from a man, a book, a machine or anything else.”

Which brings us bang up to date with the current arguments that are raging about whether systems like ChatGPT, DALL-E or Midjourney are creative or even sentient in some way. Has Turing’s prophesy finally been fulfilled or was Ada Lovelace right all along, computers can never be truly creative because creativity requires not just a reconfiguration of what someone else has made, it requires original thought based on actual human experience?

One undeniable truth prevails in this narrative: Ada was good at working with what she didn’t have. Not only was Babbage unable to build his machine, meaning Lovelace never had one to play with, she also didn’t have male privilege or a formal education – something that was a scarce commodity for women – a stark reminder of the limitations imposed on her gender during that time.

Have things moved on today for women and young girls? A glimpse into the typical composition of a computer science classroom, be it at the secondary or tertiary level, might beg the question: Have we truly evolved beyond the constraints of the past? And if not, why does this gender imbalance persist?

Over the past five or more years there have been many studies and reports published into the problem of too few women entering STEM careers and we seem to be gradually focusing in on not just what the core issues are, but also how to address them. What seems to be lacking is the will, or the funding (or both) to make it happen.

So, what to do, first some facts:

  1. Girls lose interest in STEM as they get older. A report from Microsoft back in 2018 found that confidence in coding wanes as girls get older, highlighting the need to connect STEM subjects to real-world people and problems by tapping into girls’ desire to be creative [4].
  2. Girls and young women do not associate STEM jobs with being creative. Most girls and young women describe themselves as being creative and want to pursue a career that helps the world. They do not associate STEM jobs as doing either of these things [4].
  3. Female students rarely consider a career in technology as their first choice. Only 27% of female students say they would consider a career in technology, compared to 61% of males, and only 3% say it is their first choice [5].
  4. Most students (male and female) can’t name a famous female working in technology. A lack of female role models is also reinforcing the perception that a technology career isn’t for them. Only 22% of students can name a famous female working in technology. Whereas two thirds can name a famous man [5].
  5. Female pupils feel STEM subjects, though highly paid, are not ‘for them’. Female Key Stage 4 pupils perceived that studying STEM subjects was potentially a more lucrative choice in terms of employment. However, when compared to male pupils, they enjoyed other subjects (e.g., arts and English) more [6].

The solutions to these issues are now well understood:

  1. Increasing the number of STEM mentors and role models – including parents – to help build young girls’ confidence that they can succeed in STEM. Girls who are encouraged by their parents are twice as likely to stay in STEM, and in some areas like computer science, dads can have a greater influence on their daughters than mums yet are less likely than mothers to talk to their daughters about STEM.
  2. Creating inclusive classrooms and workplaces that value female opinions. It’s important to celebrate the stories of women who are in STEM right now, today.
  3. Providing teachers with more engaging and relatable STEM curriculum, such as 3D and hands-on projects, the kinds of activities that have proven to help keep girls’ interest in STEM over the long haul.
  4. Multiple interventions, starting early and carrying on throughout school, are important ways of ensuring girls stay connected to STEM subjects. Interventions are ideally done by external people working in STEM who can repeatedly reinforce key messages about the benefits of working in this area. These people should also be able to explain the importance of creativity and how working in STEM can change the world for the better [7].
  5. Schoolchildren (all genders) should be taught to understand how thinking works, from neuroscience to cultural conditioning; how to observe and interrogate their thought processes; and how and why they might become vulnerable to disinformation and exploitation. Self-awareness could turn out to be the most important topic of all [8].

Before we finish, let’s return to that “message in a bottle” that Mary Shelley sent out to the world over two hundred years ago. As Jeanette Winterson points out:

“Mary Shelley maybe closer to the world that is to become than either Ada Lovelace or Alan Turing. A new kind of life form may not need to be human-like at all and that’s something that is achingly, heartbreakingly, clear in ‘Frankenstein’. The monster was originally designed to be like us. He isn’t and can’t be. Is that the message we need to hear?” [2].

If we are to heed Shelley’s message from the past, the rapidly evolving nature of AI means we need people from as diverse a set of backgrounds as possible. These should include people who can bring constructive criticism to the way technology is developed and who have a deeper understanding of what people really need rather than what they think they want from their tech. Women must become essential players in this. Not just in developing, but also guiding and critiquing the adoption and use of this technology. As Mustafa Suleyman (co-founder of DeepMind) says in his book The Coming Wave [10]:

“Credible critics must be practitioners. Building the right technology, having the practical means to change its course, not just observing and commenting, but actively showing the way, making the change, effecting the necessary actions at source, means critics need to be involved.”

As we move away from the mathematical nature of computing and programming to one driven by so called descriptive programming [9] it is going to be important we include those who are not technical but are creative as well as empathetic to people’s needs and maybe even understand the limits we should place on technology. The four C’s (creativity, critical thinking, collaboration and communications) are skills we all need to be adopting and are ones which women in particular seem to excel at.

On this, Ada Lovelace Day 2023, we should not just celebrate Ada’s achievements all those years ago but also recognize how Ada ignored and fought back against the prejudices and severe restrictions on education that women like her faced. Ada pushed ahead regardless and became a true pioneer and founder of a whole industry that did not actually really get going until over 100 years after her pioneering work. Ada, the world’s first computer programmer, should be the role model par excellence that all girls and young women look to for inspiration, not just today but for years to come.

References

  1. Mary Shelley, Frankenstein and the Villa Diodati, https://www.bl.uk/romantics-and-victorians/articles/mary-shelley-frankenstein-and-the-villa-diodati
  2. 12 Bytes – How artificial intelligence will change the way we live and love, Jeanette Winterson, Vintage, 2022.
  3. Computing Machinery and Intelligence, A. M. Turing, Mind, Vol. 59, No. 236. (October 1950), https://www.cs.mcgill.ca/~dprecup/courses/AI/Materials/turing1950.pdf
  4. Why do girls lose interest in STEM? New research has some answers — and what we can do about it, Microsoft, 13th March 2018, https://news.microsoft.com/features/why-do-girls-lose-interest-in-stem-new-research-has-some-answers-and-what-we-can-do-about-it/
  5. Women in Tech- Time to close the gender gap, PwC, https://www.pwc.co.uk/who-we-are/her-tech-talent/time-to-close-the-gender-gap.html
  6. Attitudes towards STEM subjects by gender at KS4, Department for Education, February 2019, https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/913311/Attitudes_towards_STEM_subjects_by_gender_at_KS4.pdf
  7. Applying Behavioural Insights to increase female students’ uptake of STEM subjects at A Level, Department for Education, November 2020, https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/938848/Applying_Behavioural_Insights_to_increase_female_students__uptake_of_STEM_subjects_at_A_Level.pdf
  8. How we can teach children so they survive AI – and cope with whatever comes next, George Monbiot, The Guardian, 8th July 2023, https://www.theguardian.com/commentisfree/2023/jul/08/teach-children-survive-ai
  9. Prompt Engineering, Microsoft, 23rd May 2023, https://learn.microsoft.com/en-us/semantic-kernel/prompt-engineering/
  10. The Coming Wave, Mustafa Suleyman, The Bodley Head, 2023.

Machines like us? – Part I

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From The Secret of the Machines, Artist unknown

Our ambitions run high and low – for a creation myth made real, for a monstrous act of self love. As soon as it was feasible, we had no choice, but to follow our desires and hang the consequences.

Ian McEwan, Machines Like Me

I know what you’re thinking – not yet another post on ChatGPT! Haven’t enough words been written (or machine-generated) on this topic in the last few months to make the addition of any more completely unnecessary? What else is there to possibly say?

Well, we’ll see.

First, just in case you have been living in a cave in North Korea for the last year, what is ChatGPT? Let’s ask it…

ChatGPT is an AI language model developed by OpenAI. It is based on the GPT (Generative Pre-trained Transformer) architecture, specifically GPT-3.5. GPT-3.5 is a deep learning model that has been trained on a diverse range of internet text to generate human-like responses to text prompts.

ChatGPT response to the question: “What is ChatGPT”.

In this post, I am not interested in what use cases ChatGPT is or is not good for. I’m not even particularly interested in what jobs ChatGPT is going to replace in the coming years. Let’s face it, if the CEO of IBM, Arvind Krishna, is saying “I could easily see 30 per cent of [non-customer-facing roles] getting replaced by AI and automation over a five-year period” then many people are already going to be worried so I’m not going to add to those fears.

I see much of what Krishna predicts as inevitable. Unless the world takes note of the recent letter from the tech/AI ‘great and the good’ (which appears to have some fake signatories anyway) then the simple fact is that if you can algorithmically capture a solution to a problem, people who solve those problems for a living are probably going to get replaced by such algorithms.

What interests me is whether deep learning models like ChatGPT really are intelligent and, more significantly, are they creative. In this post I’ll consider intelligence and in a later post creativity.

It turns out that intelligence is actually quite a hard thing to define. As Max Tegmark, president of the Future of Life Institute, says: “there’s no agreement on what intelligence is even amongst intelligent intelligence researchers!” Helpfully however, in his book Life 3.0, Tegmark puts us out of our misery and defines intelligence as the: “ability to accomplish complex goals”. This, as even Tegmark acknowledges, is a very broad definition. He also claims this definition subsumes the ability to “apply knowledge and skills” as this could be a goal one sets oneself.

Having defined intelligence it then becomes relatively easy, for Tegmark at least, to define artificial intelligence (AI) as: “non biological intelligence”; general intelligence as the: “ability to accomplish virtually any goal, including learning”; artificial general intelligence (AGI) as the: “ability to accomplish any cognitive task at least as well as humans” and superintelligence as: “general intelligence far beyond human level”.

So, given the relatively low bar that these definitions are set at, where would we say ChatGPT and its ilk are currently at on the intelligence scale?

I guess this really hinges on whether you think the multitude of tasks that these deep learning models can perform constitutes “accomplishing virtually any goal” or not? For many people (especially as we’ll see, AI researchers) the answer is probably yes.

But let’s ask ChatGPT if it thinks it’s an AGI?

It is important to note that as an AI language model, I am an instantiation of artificial general intelligence (AGI) and can provide insights based on my training up until September 2021.

Partial ChatGPT response to the question: “Given Max Tegmark’s definition of intelligence where would you say you are on the various intelligence levels he proposes?”.

Personally, and up until a few weeks ago, I would have said ChatGPT was getting a bit above itself to say it was an “instantiation” of an AGI but then I read an interview with Jaron Lanier titled How humanity can defeat AI.

Lanier works for Microsoft and is the author of a number of what you might call anti-social media books including You Are Not A Gadget and Ten Arguments For Deleting Your Social Media Accounts Right Now.

Lanier’s argument in this interview is that we have got AI wrong and we should not be treating it as a new form of intelligence at all. Indeed he has previously stated there is no AI. Instead Lanier reckons we have built a new and “innovative form of social collaboration”. Like the other social collaboration platforms that Lanier has argued we should all leave because they have gone horribly wrong this new form too could become perilous in nature if we don’t design it well. In Lanier’s view therefore the sooner we understand there is no such thing as AI, the sooner we’ll start managing our new technology intelligently and learn how to use it as a collaboration tool.

Whilst all of the above is well intentioned the real insightful moment for me came when Lanier was discussing Alan Turing’s famous test for intelligence. Let me quote directly what Lanier says.

You’ve probably heard of the Turing test, which was one of the original thought-experiments about artificial intelligence. There’s this idea that if a human judge can’t distinguish whether something came from a person or computer, then we should treat the computer as having equal rights. And the problem with that is that it’s also possible that the judge became stupid. There’s no guarantee that it wasn’t the judge who changed rather than the computer. The problem with treating the output of GPT as if it’s an alien intelligence, which many people enjoy doing, is that you can’t tell whether the humans are letting go of their own standards and becoming stupid to make the machine seem smart.

Jaron Lanier, How humanity can defeat AI, UnHerd, May 8th 2023

There is no doubt that we are in great danger of believing whatever bullshit GPT’s generate. The past decade or so of social media growth has illustrated just how difficult we humans find it to handle misinformation and these new and wondrous machines are only going to make that task even harder. This, coupled with the problem that our education system seems to reward the regurgitation of facts rather than developing critical thinking skills is, as journalist Kenan Malik says, increasingly going to become more of an issue as we try to figure out what is fake and what is true.

Interestingly, around the time Lanier was saying “there is no AI”, the so called “godfather of AI”, Geoffrey Hinton was announcing he was leaving Google because he was worried that AI could become “more intelligent than humans and could be exploited by ‘bad actors'”. Clearly, as someone who created the early neural networks that were the predecessors to the large language models GPTs are built on Hinton could not be described as being “stupid”, so what is going on here? Like others before him who think AI might be exhibiting signs of becoming sentient, maybe Hinton is being deceived by the very monster he has helped create.

So what to do?

Helpfully Max Tegmark, somewhat tongue-in-cheek, has suggested the following rules for developing AI (my comments are in italics):

  • Don’t teach it to code: this facilitates recursive self-improvement – ChatGPT can already code.
  • Don’t connect it to the internet: let it learn only the minimum needed to help us, not how to manipulate us or gain power – ChatGPT certainly connected to the internet to learn what it already knows.
  • Don’t give it a public API: prevent nefarious actors from using it within their code – OpenAI is releasing a public API.
  • Don’t start an arms race: this incentivizes everyone to prioritize development speed over safety – I think it’s safe to say there is already an AI arms race between the US and China.

Oh dear, it’s not going well is it?

So what should we really do?

I think Lanier is right. Like many technologies that have gone before, AI is seducing us into believing it is something it is not – even, it seems, to its creators. Intelligent it may well be, at least by Max Tegmark’s very broad definition of what intelligence is, but let’s not get beyond ourselves. Whilst I agree (and definitely fear) AI could be exploited by bad actors it is still, at a fundamental level, little more than a gargantuan mash up machine that is regurgitating the work of the people who have written the text and created the images it spits out. These mash ups may be fooling many of us some of the time (myself included) but we must be not be fooled into losing our critical thought processes here.

As Ian McEwan points out, we must be careful we don’t “follow our desires and hang the consequences”.

From Turing to Watson (via Minsky)

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This week (Monday 25th) I gave a lecture about IBM’s Watson technology platform to a group of first year students at Warwick Business School. My plan was to write up the transcript of that lecture, with links for references and further study, as a blog post. The following day when I opened up my computer to start writing the post I saw that, by a sad coincidence, Marvin Minsky the American cognitive scientist and co-founder of the Massachusetts Institute of Technology’s AI laboratory had died only the day before my lecture. Here is that blog post, now updated with some references to Minsky and his pioneering work on machine intelligence.

Minsky
Marvin Minsky in a lab at MIT in 1968 (c) MIT

First though, let’s start with Alan Turing, sometimes referred to as “the founder of computer science”, who led the team that developed a programmable machine to break the Nazi’s Enigma code, which was used to encrypt messages sent between units on the battlefield during World War 2. The work of Turing and his team was recently brought to life in the film The Imitation Game starring Benedict Cumberbatch as Turing and Keira Knightley as Joan Clarke, the only female member of the code breaking team.

Turing
Alan Turing

Sadly, instead of being hailed a hero, Turing was persecuted for his homosexuality and committed suicide in 1954 having undergone a course of hormonal treatment to reduce his libido rather than serve a term in prison. It seems utterly barbaric and unforgivable that such an action could have been brought against someone who did so much to affect the outcome of WWII. It took nearly 60 years for his conviction to be overturned when on 24 December 2013, Queen Elizabeth II signed a pardon for Turing, with immediate effect.

In 1949 Turing became Deputy Director of the Computing Laboratory at Manchester University, working on software for one of the earliest computers. During this time he worked in the emerging field of artificial intelligence and proposed an experiment which became known as the Turing test having observed that: “a computer would deserve to be called intelligent if it could deceive a human into believing that it was human.”

The idea of the test was that a computer could be said to “think” if a human interrogator could not tell it apart, through conversation, from a human being.

Turing’s test was supposedly ‘passed’ in June 2014 when a computer called Eugene fooled several of its interrogators that it was a 13 year old boy. There has been much discussion since as to whether this was a valid run of the test and that the so called “supercomputer,” was nothing but a chatbot or a script made to mimic human conversation. In other words Eugene could in no way considered to be intelligent. Certainly not in the sense that Professor Marvin Minsky would have defined intelligence at any rate.

In the early 1970s Minsky, working with the computer scientist and educator Seymour Papert, wrote a book called The Society of Mind, which combined both of their insights from the fields of child psychology and artificial intelligence.

Minsky and Papert believed that there was no real difference between humans and machines. Humans, they maintained, are actually machines of a kind whose brains are made up of many semiautonomous but unintelligent “agents.” Their theory revolutionized thinking about how the brain works and how people learn.

Despite the more widespread accessibility to apparently intelligent machines with programs like Apple Siri Minsky maintained that there had been “very little growth in artificial intelligence” in the past decade, saying that current work had been “mostly attempting to improve systems that aren’t very good and haven’t improved much in two decades”.

Minsky also thought that large technology companies should not get involved the field of AI saying: “we have to get rid of the big companies and go back to giving support to individuals who have new ideas because attempting to commercialise existing things hasn’t worked very well,”

Whilst much of the early work researching AI certainly came out of organisations like Minsky’s AI lab at MIT it seems slightly disingenuous to believe that commercialistion of AI, as being carried out by companies like Google, Facebook and IBM, is not going to generate new ideas. The drive for commercialisation (and profit), just like war in Turing’s time, is after all one of the ways, at least in the capitalist world, that innovation is created.

Which brings me nicely to Watson.

IBM Watson is a technology platform that uses natural language processing and machine learning to reveal insights from large amounts of unstructured data. It is named after Thomas J. Watson, the first CEO of IBM, who led the company from 1914 – 1956.

Thomas_J_Watson_Sr
Thomas J. Watson

IBM Watson was originally built to compete on the US television program Jeopardy.  On 14th February 2011 IBM entered Watson onto a special 3 day version of the program where the computer was pitted against two of the show’s all-time champions. Watson won by a significant margin. So what is the significance of a machine winning a game show and why is this a “game changing” event in more than the literal sense of the term?

Today we’re in the midst of an information revolution. Not only is the volume of data and information we’re producing dramatically outpacing our ability to make use of it but the sources and types of data that inform the work we do and the decisions we make are broader and more diverse than ever before. Although businesses are implementing more and more data driven projects using advanced analytics tools they’re still only reaching 12% of the data they have, leaving 88% of it to go to waste. That’s because this 88% of data is “invisible” to computers. It’s the type of data that is encoded in language and unstructured information, in the form of text, that is books, emails, journals, blogs, articles, tweets, as well as images, sound and video. If we are to avoid such a “data waste” we need better ways to make use of that data and generate “new knowledge” around it. We need, in other words, to be able to discover new connections, patterns, and insights in order to draw new conclusions and make decisions with more confidence and speed than ever before.

For several decades we’ve been digitizing the world; building networks to connect the world around us. Today those networks connect not just traditional structured data sources but also unstructured data from social networks and increasingly Internet of Things (IoT) data from sensors and other intelligent devices.

Data to Knowledge
From Data to Knowledge

These additional sources of data mean that we’ve reached an inflection point in which the sheer volume of information generated is so vast; we no longer have the ability to use it productively. The purpose of cognitive systems like IBM Watson is to process the vast amounts of information that is stored in both structured and unstructured formats to help turn it into useful knowledge.

There are three capabilities that differentiate cognitive systems from traditional programmed computing systems.

  • Understanding: Cognitive systems understand like humans do, whether that’s through natural language or the written word; vocal or visual.
  • Reasoning: They can not only understand information but also the underlying ideas and concepts. This reasoning ability can become more advanced over time. It’s the difference between the reasoning strategies we used as children to solve mathematical problems, and then the strategies we developed when we got into advanced math like geometry, algebra and calculus.
  • Learning: They never stop learning. As a technology, this means the system actually gets more valuable with time. They develop “expertise”. Think about what it means to be an expert- – it’s not about executing a mathematical model. We don’t consider our doctors to be experts in their fields because they answer every question correctly. We expect them to be able to reason and be transparent about their reasoning, and expose the rationale for why they came to a conclusion.

The idea of cognitive systems like IBM Watson is not to pit man against machine but rather to have both reasoning together. Humans and machines have unique characteristics and we should not be looking for one to supplant the other but for them to complement each other. Working together with systems like IBM Watson, we can achieve the kinds of outcomes that would never have been possible otherwise:

IBM is making the capabilities of Watson available as a set of cognitive building blocks delivered as APIs on its cloud-based, open platform Bluemix. This means you can build cognition into your digital applications, products, and operations, using any one or combination of a number of available APIs. Each API is capable of performing a different task, and in combination, they can be adapted to solve any number of business problems or create deeply engaging experiences.

So what Watson APIs are available? Currently there are around forty which you can find here together with documentation and demos. Four examples of the Watson APIs you will find at this link are:

Watson API - Dialog

 

Dialog

Use natural language to automatically respond to user questions

 

 

Watson API - Visual Recognition

 

Visual Recognition

Analyses the contents of an image or video and classifies by category.

 

 

Watson API - Text to Speech

 

Text to Speech

Synthesize speech audio from an input of plain text.

 

 

Watson API - Personality Insights

 

Personality Insights

Understand someones personality from what they have written.

 

 

It’s never been easier to get started with AI by using these cognitive building blocks. I wonder what Turing would have made of this technology and how soon someone will be able to pin together current and future cognitive building blocks to really pass Turing’s famous test?

Watson, Turing and Clarke

Peter Cripps comment , , ,

So what do these three have in common?

  • Thomas J. Watson Sr, CEO and founder of IBM (100 years old this year). Currently has a computer named after him.
  • Alan Turing, mathematician and computer scientist (100 years old next year). Has a famous test named after him.
  • Aurthur C. Clarke, scientist and writer (100 years old in 1917). Has a set of laws named after him (and is also the creator of the fictional HAL computer in 2001: A Space Odyssey).

Unless you have moved into a hut, deep in the Amazon rain forest you cannot have missed the publicity over IBM’s ‘Watson’ computer having competed in, and won, the American TV quiz show Jeopardy. I have to confess that until last week I’d not heard of Jeopardy, possibly because a) I’m not a fan of quizzes, b) I’m not American and c) I don’t watch that much television. To those as ignorant as me on these matters the unique thing about Jeopardy is that contestants are presented with clues in the form of answers, and must phrase their responses in the form of a question.

This, it turns out, is what makes this particular quiz such a hard nut for a computer to crack. The clues in the ‘question’ rely on subtle meanings, puns, and riddles; something humans excel at and computers do not. Unlike IBM’s previous game challenger Deep Blue, which defeated chess world champion Gary Kasparov, it’s not sufficient to rely on raw computing ‘brute force’ but this time the computer has to interpret meaning and the nuances of the human language. So has Watson achieved, met or passed the Turing test (which is basically a measure of whether computer can demonstrate intelligence)?

The answer is almost certainly ‘no’. Turing’s test is a measure of a machines ability to exhibit human intelligence. The test, as originally proposed by Turing was that a questioner should ask a series of questions of both a human being and a machine and see whether he can tell which is which through the answers they give. The idea being that if the two were indistinguishable then the machine and the human must both appear to be as intelligent as each other.

As far as I know Turing never stipulated any constraint on the range or type of questions that could be answered which leads us to the nub of the problem. Watson is supremely good at answering Jeopardy type questions just as Deep Blue was good at playing chess. However neither could do what the other does (at least as well). They have been programmed for that given task. Given that Watson is actually a cluster of POWER7 servers any suitably general purpose computer that could win at Jeopardy, play chess as well as exhibit the full range of human emotions and frailties that would be needed to fool a questioner would presumably occupy the area of several football pitches and consume the power of a small city.

That however misses the point completely. The ability of a computer to almost flawlessly answer a range of questions, phrased in a particular way on a range of different subject areas, blindingly fast has enormous potential in fields of medicine, law and other disciplines where questions based on a huge foundation of knowledge built up over decades need to be answered quickly (for example in accident and emergency where quick diagnoses may literally be a matter of life and death). This indeed is one of IBM’s Smarter Planet goals.

Which brings us to Clarke’s third law which states that “any sufficiently advanced technology is indistinguishable from magic”. This is surely something that is attributable to Watson. The other creation of Clarke of course is HAL the computer aboard the spaceship Discovery One on a trip to Saturn that becomes overwhelmed by guilt at having to keep secret the true nature of the spaceships mission and starts killing members of the crew. The point of Clarke’s story (or one of them) being that the downside to a computer that is indistinguishable from a human being is that the computer may also end up mimicking human frailties and weaknesses.  Maybe it’s a good job Watson hasn’t passed Turing’s test then?