ibm

Machines like us? – Part I

Peter Cripps comment , , , , , , ,
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”.

The story so far…

Peter Cripps comment , , , , , , , ,

 

Photo by Joshua Sortino on Unsplash
Photo by Joshua Sortino on Unsplash

It’s hard to believe that this year is the 30th anniversary of Tim Berners-Lee’s great invention, the World-Wide Web, and that much of the technology that enabled his creation is still less than 60 years old. Here’s a brief history of the Internet and the Web, and how we got to where we are today, in ten significant events.

 

#1: 1963 – Ted Nelson begins developing a model for creating and using linked content he calls hypertext and hypermedia. Hypertext is born.

#2: 1969 – The first message is sent over the ARPANET from computer science Professor Leonard Kleinrock’s laboratory at University of California, Los Angeles to the second network node at Stanford Research Institute. The Internet is born.

#3: 1969 – Charles Goldfarb, leading a small team at IBM, developed the first markup language, called Generalized Markup Language, or GML. Markup languages are born.

#4: 1989 – Tim Berners-Lee whilst working at CERN publishes his paper, Information Management: A Proposal. The World Wide Web (WWW) is born.

#5: 1993 – Mosaic, a graphical browser aiming to bring multimedia content to non-technical users (images and text on the same page) is invented by Marc Andreessen. The web browser is born.

#6: 1995 – Jeff Bezos launches Amazon “earth’s biggest bookstore” from a garage in Seattle. E-commerce is born.

#7: 1998 – The Google company is officially launched by Larry Page and Sergey Brin to market Google Search. Web search is born.

#8: 2003 – Facebook (then called FaceMash but changed to The Facebook a year later) is founded by Mark Zuckerberg with his college roommate and fellow Harvard University student Eduardo Saverin. Social media is born.

#9: 2007 – Steve Jobs launches the iPhone at MacWorld Expo in San Francisco. Mobile computing is born.

#10: 2018 – Tim Berners-Lee instigates act II of the web when he announces a new initiative called Solid, to reclaim the Web from corporations and return it to its democratic roots. The web is reborn?

I know there have been countless events that have enabled the development of our modern Information Age and you will no doubt think others should be included in preference to some of my suggestions. Also, I suspect that many people will not have heard of my last choice (unless you are a fairly hardcore computer type). The reason I have added this one is because I think/hope it will start to address what is becoming one of the existential threats of our age, namely how we survive in a world awash with data (our data) that is being mined and used without us knowing, much less understanding, the impact of such usage. Rather than living in an open society in which ideas and data are freely exchanged and used to everyones benefit we instead find ourselves in an age of surveillance capitalism which, according to this source, is defined as being:

…the manifestation of George Orwell’s prophesied Memory Hole combined with the constant surveillance, storage and analysis of our thoughts and actions, with such minute precision, and artificial intelligence algorithmic analysis, that our future thoughts and actions can be predicted, and manipulated, for the concentration of power and wealth of the very few.

In her book The Age of Surveillance Capitalism, Shoshana Zuboff provides a sweeping (and worrying) overview and history of the techniques that the large tech companies are using to spy on us in ways that even George Orwell would have found alarming. Not least because we have voluntarily given up all of this data about ourselves in exchange for what are sometimes the flimsiest of benefits. As Zuboff says:

Thanks to surveillance capitalism the resources for effective life that we seek in the digital realm now come encumbered with a new breed of menace. Under this new regime, the precise moment at which our needs are met is also the precise moment at which our lives are plundered for behavioural data, and all for the sake others gain.

Tim Berners-Lee invented the World-Wide Web then gave it away so that all might benefit. Sadly some have benefited more than others, not just financially but also by knowing more about us than most of us would ever want or wish. I hope for all our sakes the work that Berners-Lee and his small group of supporters is doing make enough progress to reverse the worst excesses of surveillance capitalism before it is too late.

Software is Eating the World and Some Tech Companies are Eating Us

Peter Cripps comment , , , , , , , , , , , , , , ,

Today (12th March, 2018) is the World Wide Web’s 29th birthday.  Sir Tim Berners-Lee (the “inventor of the world-wide web”), in an interview with the Financial Times and in this Web Foundation post has used this anniversary to raise awareness of how the web behemoths Facebook, Google and Twitter are “promoting misinformation and ‘questionable’ political advertising while exploiting people’s personal data”.  Whilst I admire hugely Tim Berners-Lee’s universe-denting invention it has to be said he himself is not entirely without fault in the way he bequeathed us with his invention.  In his defence, hindsight is a wonderful thing of course, no one could have possibly predicted at the time just how the web would take off and transform our lives both for better and for worse.

If, as Marc Andreessen famously said in 2011, software is eating the world then many of those powerful tech companies are consuming us (or at least our data and I’m increasingly becoming unsure there is any difference between us and the data we choose to represent ourselves by.

Here are five recent examples of some of the negative ways software is eating up our world.

Over the past 40+ years the computer software industry has undergone some fairly major changes.  Individually these were significant (to those of us in the industry at least) but if we look at these changes with the benefit of hindsight we can see how they have combined to bring us to where we are today.  A world of cheap, ubiquitous computing that has unleashed seismic shocks of disruption which are overthrowing not just whole industries but our lives and the way our industrialised society functions.  Here are some highlights for the 40 years between 1976 and 2016.

waves-since-1976

And yet all of this is just the beginning.  This year we will be seeing technologies like serverless computing, blockchain, cognitive and quantum computing become more and more embedded in our lives in ways we are only just beginning to understand.  Doubtless the fallout from some of the issues I highlight above will continue to make themselves felt and no doubt new technologies currently bubbling under the radar will start to make themselves known.

I have written before about how I believe that we, as software architects, have a responsibility, not only to explain the benefits (and there are many) of what we do but also to highlight the potential negative impacts of software’s voracious appetite to eat up our world.

This is my 201st post on Software Architecture Zen (2016/17 were barren years in terms of updates).  This year I plan to spend more time examining some of the issues raised in this post and look at ways we can become more aware of them and hopefully not become so seduced by those sirenic entrepreneurs.

Tech: The Missing Generation

Peter Cripps comment , , , , , , , , , , ,

I’ve recently been spending a fair bit of time in hospital. Not, thankfully, for myself but with my mother who fell and broke her arm a few weeks back which has resulted in lots of visits to our local Accident & Emergency (A&E)  department as well as a short stay in hospital whilst they pinned her arm back in place.

nhs hospital
An elderly gentleman walks past an NHS hospital sign in London. Photograph: Cate Gillon/Getty Images

Anyone who knows anything about the UK also knows how much we value our National Health Service (NHS). So much so that when it was our turn to run the Olympic Games back in 2012 Danny Boyle’s magnificent opening ceremony dedicated a whole segment to this wonderful institution featuring doctors, nurses and patients dancing around beds to music from Mike Oldfield’s Tubular Bells.

nhs london 2012 olympics
Olympic Opening Ceremony NHS Segment – Picture Courtesy the International Business Times

The NHS was created out of the ideal that good healthcare should be available to all, regardless of wealth. When it was launched by the then minister of health, Aneurin Bevan, on July 5 1948, it was based on three core principles:

  • that it meet the needs of everyone
  • that it be free at the point of delivery
  • that it be based on clinical need, not ability to pay

These three principles have guided the development of the NHS over more than 60 years, remain at its core and are embodied in its constitution.

nhs constitution
NHS Constitution Logo

All of this, of course, costs:

  • NHS net expenditure (resource plus capital, minus depreciation) has increased from £64.173 billion in 2003/04 to £113.300bn in 2014/15. Planned expenditure for 2015/16 is £116.574bn.
  • Health expenditure (medical services, health research, central and other health services) per capita in England has risen from £1,841 in 2009/10 to £1,994 in 2013/14.
  • The NHS net deficit for the 2014/15 financial year was £471 million (£372m underspend by commissioners and a £843m deficit for trusts and foundation trusts).
  • Current expenditure per capita for the UK was $3,235 in 2013. This can be compared to $8,713 in the USA, $5,131 in the Netherlands, $4,819 in Germany, $4,553 in Denmark, $4,351 in Canada, $4,124 in France and $3,077 in Italy.

The NHS also happens to be the largest employer in the UK. In 2014 the NHS employed 150,273 doctors, 377,191 qualified nursing staff, 155,960 qualified scientific, therapeutic and technical staff and 37,078 managers.

So does it work?

From my recent experience I can honestly say yes. Whilst it may not be the most efficient service in the world the doctors and nurses managed to fix my mothers arm and hopefully set her on the road to recovery. There have been, and I’m sure there will be more, setbacks but given her age (she is 90) they have done an amazing job.

Whilst sitting in those A&E departments whiling away the hours (I did say they could be more efficient) I had plenty of time to observe and think. By its very nature the health service is hugely people intensive. Whilst there is an amazing array of machines beeping and chirping away most activities require people and people cost money.

The UK’s health service, like that of nearly all Western countries, is under a huge amount of pressure:

  • The UK population is projected to increase from an estimated 63.7 million in mid-2012 to 67.13 million by 2020 and 71.04 million by 2030.
  • The UK population is expected to continue ageing, with the average age rising from 39.7 in 2012 to 42.8 by 2037.
  • The number of people aged 65 and over is projected to increase from 10.84m in 2012 to 17.79m by 2037. The number of over-85s is estimated to more than double from 1.44 million in 2012 to 3.64 million by 2037.
  • The number of people of State Pension Age (SPA) in the UK exceeded the number of children for the first time in 2007 and by 2012 the disparity had reached 0.5 million (though this is projected to reverse by).
  • There are an estimated 3.2 million people with diabetes in the UK (2013). This is predicted to reach 4 million by 2025.
  • In England the proportion of men classified as obese increased from 13.2 per cent in 1993 to 26.0 per cent in 2013 (peak of 26.2 in 2010), and from 16.4 per cent to 23.8 per cent for women over the same timescale (peak of 26.1 in 2010).

The doctors and nurses that looked after my mum so well are going to be coming under a increasing pressures as this ageing and less healthy population begins to suck ever more resources out of an already stretched system. So why, given the passion everyone has about the NHS, isn’t there more of a focus on getting technology to ease the burden of these overworked healthcare providers?

Part of the problem of course is that historically the tech industry hasn’t exactly covered itself with glory when it comes to delivering technology to the healthcare sector (I’m thinking the NHS National Programme for IT and the US HealthCare.gov system as being two high profile examples). Whilst some of this may be due to the blunders of government much of it is down to a combination of factors caused by both the providers and consumers of healthcare IT mis-communication and not understanding the real requirements that such complex systems tend to have.

In her essay How to build the Next Unicorn in Healthcare the entrepreneur Yasi Baiani   sets out six tactical tips for how to build a unicorn* digital startup. In summary these are:

  1. Understand the current system.
  2. Know your customers.
  3. Have product hooks.
  4. Have a clear monetization strategy and understand your customers’ willingness-to-pay.
  5. Know the rules and regulations.
  6. Figure out what your unfair competitive advantage is.

Of course, these are strategies that actually apply to any industry when trying to bring about innovation and disruption – they are not unique to healthcare. I would say that when it comes to the healthcare industry the reason why there has been no Uber is because the tech industry is ignoring the generation that is in most need of benefiting from technology, namely the post 65 age group. This is the age group that struggle most with technology either because they are more likely to be digitally disadvantaged or because they simply find it too difficult to get to grips with it.

As the former Yahoo chief technology officer Ashfaq Munshi, who has become interested in ageing tech says:

“Venture capitalists are too busy investing in Uber and things that get virality. The reality is that selling to older people is harder, and if venture capitalists detect resistance, they don’t invest.”

Matters are not helped by the fact that most tech entrepreneurs are between the ages of 20 and 35 and have different interests in life than the problems faced by the aged. As this article by Kevin Maney in the Independent points out:

“Entrepreneurs are told that the best way to start a company is to solve a problem they understand. It makes sense that those problems range from how to get booze delivered 24/7 to how to build a cloud-based enterprise human resources system – the tangible problems in the life and work of a 25- or 30-year-old.”

If it really is the case that entrepreneurs only look at problems they understand or are on their immediate event horizon then clearly we need more entrepreneurs of my age group (let’s just say 45+). We are the people either with elderly parents, like my mum, who are facing the very real problems of old age and poor health and who themselves will very soon be facing the same issues.

A recent Institute of Business Value report from IBM makes the following observation:

“For healthcare in particular, the timing for a game changer couldn’t be better. The industry is coping with upheaval triggered by varied economic, societal and industry influences. Empowered consumers living in an increasingly digital world are demanding more from an industry that is facing growing regulation, soaring costs and a shortage of skilled resources.”

Rather than fearing the new generation of cognitive systems we need to be embracing them and ruthlessly exploiting them to provide solutions that will ease all of our journeys into an ever increasing old age.

At  SXSW, which is running this week in Austin, Texas IBM is providing an exclusive look at its cognitive technology called Watson and showcasing a number of inspiring as well as entertaining applications of this technology. In particular on Tuesday 15th March there is a session called Ageing Populations & The Internet of Caring Things  where you can take a look at accessible technology and how it will create a positive impact on an aging person’s quality of life.

Also at SXSW this year President Obama gave a keynote interview where he called for action in the tech world, especially for applications to improve government IT. The President urged the tech industry to solve some of the nation’s biggest problems by working in conjunction with the government. “It’s not enough to focus on the cool, next big thing,” Obama said, “It’s harnessing the cool, next big thing to help people in this country.”

obama-sxsw
President Barack Obama speaks during the 2016 SXSW Festival at Long Center in Austin, Texas, March 11, 2016. PHOTO: NEILSON BARNARD/GETTY IMAGES FOR SXSW

It is my hope that with the vision that people such as Obama have given the experience of getting old will be radically different 10 or 20 years from now and that cognitive and IoT technology will make all of out lives not only longer but more more pleasant.

* Unicorns are referred to companies whose valuation has exceeded $1 billion dollars.

Getting Started with Blockchain

Peter Cripps education , , , , , , , ,

In an earlier post I discussed the UK government report on distributed ledger technology (AKA ‘blockchain‘) and how the government’s Chief Scientific Advisor, Sir Mark Walport, was doing the rounds advocating the use of blockchain for a variety of (government) services.

Blockchain is a shared, trusted, public ledger that everyone can inspect, but which no single user controls. The participants in a blockchain system collectively keep the ledger up to date: it can be amended only according to strict rules and by general agreement. For a quick introduction to blockchain this article in the Economist is a pretty good place to start.

Blockchains are going to be useful wherever there is a need for a trustworthy record, something which is pretty vital for transactions of all sorts whether it be in banking, for legal documents or for registries of things like land or high value art works etc. Startups such as Stampery are looking to use blockchain technology to provide low cost certification services. Blockchain is not just for pure startups however. Twenty-five banks are part of the blockchain company, called R3 CEV, which aims to develop common standards around this technology. R3 CEV’s Head of Technology is Richard Gendal Brown an ex-colleague from IBM.

IBM recently announced that, together with Intel, J.P. Morgan and several large banks, it was joining forces to create the Open Ledger Project with the Linux Foundation, with the goal of re-imagining supply chains, contracts and other ways information about ownership and value are exchanged in a digital economy.

As part of this IBM is creating some great tools, using its Bluemix platform, to get developers up and running on the use of blockchain technology. If you have a Bluemix account you can quickly deploy some applications and study the source code on GitHub to see how to start making use of blockchain APIs.

This service is intended for developers who consider themselves early adopters and want to get involved with IBM’s approach to business networks that maintain, secure and share a replicated ledger using blockchain technology. It shows how you can:

  • Deploy and invoke simple transactions to test out IBM’s approach to blockchain technology.
  • Learn and test out IBM’s novel contributions to the blockchain open source community, including the concept of confidential transactions, containerized code execution etc.

It provides some simple demo applications you can quickly deploy into Bluemix to play around with this technology.

Marbles
Marbles Running in IBM Bluemix

This service is not production ready. It is pre-alpha and intended for testing and experimentation only. There are additional security measures that still must be implemented before the service can be used to store any confidential data. That said it’s still a great way to learn about the use and potential for this technology.

 

From Turing to Watson (via Minsky)

Peter Cripps comment , , , , ,

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?

Blockchain in UK Government

Peter Cripps comment , ,

You can always tell when a technology has reached a certain level of maturity when it gets its own slot on the BBC Radio 4 news program ‘Today‘ which runs here in the UK every weekday morning from 6am – 9am.

Yesterday (Tuesday 19th January) morning saw the UK government’s Chief Scientific Advisor, Sir Mark Walport, talking about blockchain (AKA distributed ledger) and advocating its use for a variety of (government) services. The interview was to publicise a new government report on distributed ledger technology (the Blackett review) which you can find here.

The report has a number of recommendations including the creation of a distributed ledger demonstrator and calls for collaboration between industry, academia and government around standards, security and governance of distributed ledgers.

As you would expect there are a number of startups as well as established companies working on applications of distributed ledger technology including R3CEV whose head of technology is Richard Gendal Brown, an ex-colleague of mine from IBM. Richard tweets on all things blockchain here and has a great blog on the subject here. If you want to understand blockchain you could take a look at Richard’s writings on the topic here. If you want an extremely interesting weekend read on the current state of bitcoin and blockchain technology this is a great article.

IBM, recognising the importance of this technology and the impact it could have on society, is throwing its weight behind the Linux Foundations project that looks to advance this technology following the open source model.

From a software architecture perspective I think this topic is going to be huge and is ripe for some first mover advantage. Those architects who can steal a lead on not only understanding but explaining this technology are going to be in high demand and if you can help with applying the technology in new and innovative ways you are definitely going to be a rockstar!

Back to the Future Day

Peter Cripps comment , , , ,

So, the future has finally arrived and today is ‘Back to the Future Day‘. Just in case you have missed any of the newspaper, internet and television reports that have been ‘flying’ around this week, today is the day that Marty McFly and Doc Brown travel to in the 1980s movie Back To The Future II as dialled into the very high-tech (I love the Dymo labels) console of the modified (i.e. to make it fly) Delorean DMC-12 motor car. As you can see the official time we can expect Marty and Doc Brown to arrive is (or was) 04:29 (presumably that’s Pacific Time).

Back to the Future Delorean Display
Back to the Future Delorean Display

Depending on when you read this therefore you might still get a chance to watch one of the numerous Marty McFly countdown clocks hitting zero.

Most of the articles have focussed on how its creators did or didn’t get the technology right. Whilst things like electric cars, wearable tech, drones and smart glasses have come to fruition what’s more interesting is what the film completely missed i.e. the Internet,  smartphones and all the gadgets which we now take for granted thanks to a further 30 years (i.e. since 1985, when the first film came out) of Moore’s Law.

Coincidentally one day before ‘Back to the Future’ day I gave a talk to a group of university students which was focussed on how technology has changed in the last 30 years due to the effects of Moore’s Law. It’s hard to believe that back in 1985, when the first Back to the Future film was released, a gigabyte of hard disk storage cost $71,000 and a megabyte of RAM cost $880. Today those costs are 5 cents and a lot less than 1 cent respectively. This is why it’s now possible for all of us to be walking around carrying smart devices which have more compute power and storage than even the largest and fastest super computers of a decade or so ago.

It’s also why the statement made by Jim Deters, founder of the education community Galvanise, is so true, namely that today:

“Two guys in a Starbucks can have access to the same computing power as a Fortune 500 company.”

Today anyone with a laptop, a good internet connection and the right tools can set themselves up to disrupt whole industries that once seemed secure and impeneterable to newcomers. These are the disruptors who are building new business models that are driving new revenue streams and providing great, differentiated client experiences (I’m talking the likes of Uber, Netflix and further back Amazon and Google). People use the term ‘digital Darwinism’, meaning the phenomenon of technology and society evolving faster than an organization can adapt, to try and describe what is happening here. As Charles Darwin said:

“It’s not the strongest of the species that survive, nor the most intelligent, but the one most responsive to change.”

Interestingly IBM is working with Galvanise in San Francisco at its Bluemix Garage where it brings together entrepreneurs and start ups, as well as established enterprises, to work with new platform as a service (PaaS) tools like IBM Bluemix, Cloudant and Watson to help them create and build new and disruptive applications. IBM also recently announced its Bluemix Garage Method which aims to combine industry best practices on Design Thinking, Lean Startup, Agile Development, DevOps, and Cloud to build and deliver innovative and disruptive solutions.

There are a number of Bluemix Garages opening around the world (currently they are in London, Toronto, Nice and Melbourne) as well as local pop-up garages. If you can’t get to a garage and want to have a play with Bluemix yourself you can sign up for a free registration here.

It’s not clear how long Moore’s Law has left to run and whether non-silicon based technologies, that overcome some of the laws of physics that are threatening the ongoing exponential growth of transistors in chips, will ever be viable. It’s also not clear how relevant Moore’s Law actually is in the age of Cloud computing. One thing that is certain however is that we already have access to enough technology and tools that mean we are only limited by our ideas and imaginations in creating new and disruptive business models.

Now, where did I leave my hoverboard so I can get off to my next meeting.

Hello, World (from IBM Bluemix)

Peter Cripps education , , , , ,

“The only way to learn a new programming language is by writing programs in it. The first program to write is the same for all languages: Print the words ‘hello, world’.”

So started the introduction to the book The C Programming Language by Brian Kernighan and Dennis Ritchie back in 1978. Since then many a programmer learning a new language has heeded those words of wisdom by trying to write their first program to put up those immortal words on their computer screens. Even the Whitehouse is now in on the game.

You can find a list of how to write “hello, world” in pretty much any language you have ever heard of (as well as some you probably haven’t) here. The idea of writing such a simple program is not so much that it will teach you anything about the language syntax but it will teach you how to get to grips with the environment that the code (whether compiled or interpreted) runs in. Back in 1978 when C ran under Unix on hardware like Digital Equipment Corporation’s PDP-11 the environment was a relatively simple affair consisting of a processor, some storage and rudimentary cathode ray terminal (CRT). Then the ‘environment’ amounted to locating the compiler, making sure the right library was provided to the program and figuring out the options to run the compiler and the binary files output. Today things are a bit more complicated which is why the basic premise of getting the most simple program possible (i.e. writing ‘hello, world’ to a screen) is still very relevant as a way of learning the environment.

All of this is by way of an introduction to how to get ‘hello, world’ to work in the IBM Bluemix Platform as a Service (PaaS) environment.  In case you haven’t heard, IBM Bluemix is an open source platform based on Cloud Foundry that provides developers with a complete set of DevOps tools to develop, deploy and maintain web and mobile applications in the cloud with minimal hassle. Bluemix-hosted applications have access to the capabilities of the underlying cloud infrastructure to support the type of non-functional requirements (performance, availability, security etc) that are needed to support enterprise applications. Bluemix also provides a rich set of services to extend your applications with capabilities like analytics, social, internet of things and even IBM Watson cognitive services. The Bluemix platform frees developers and organizations from worrying about infrastructure-related plumbing details and focus on what matters to their organizations – business scenarios that drive better value for their customers.

IBM Bluemix
IBM Bluemix

Because Bluemix supports a whole range of programming languages and services the options for creating ‘hello, world’ are many and varied. Here though are the basic instructions for creating this simplest of programs using the JavaScript language Node.js.  Follow these steps for getting up and running on Bluemix.

Step 1: Sign Up for a Free Bluemix Trial

You can sign up for a free Bluemix trial (and get an an IBM ID if you don’t have one) here. You’ll need to do this before you do anything else. The remainder of this tutorial assumes you have Bluemix running and you are logged into your account.

Step 2: Download the Cloud Foundry Command Line Interface

You can write code and get it up and running in numerous ways in Bluemix including within Bluemix itself, using Eclipse tools or with the Cloud Foundry command line interface (CLI). As this example uses the latter you’ll need to ensure you have the CLI downloaded on your computer. To do that follow the instructions here.

Step 3: Download the Example Code

You can download the code for this example from my GitHub here. Thanks to Carl Osipov over at Clouds with Carl for this code. Once you have downloaded the zip file unpack it into a convenient folder. You will see there are three files (plus a readme).

  • main.js – the Javascript source code. The code returns a ‘hello, world’ message to any HTTP request sent to the web server running the code.
  • package.json – which tells Bluemix it needs a Node.js runtime.
  • manifest.yml – this file is used when you deploy your code to Bluemix using the command line interface.  It contains the values that you would otherwise have to type on the command line when you ‘push’ your code to Bluemix. I suggest you edit this and change the ‘host’ parameter to something unique to you (e.g. change my name to yours).

Step 4: Deploy and Run the Code

Because all your code and the instructions for deploying it are contained in the three files just downloaded deploying into Bluemix is simplicity itself. Do the following:

  1. Open a command a Command Prompt window.
  2. Change to the directory that you unpacked the source code into by typing: cd your_directory.
  3. Connect to Bluemix by typing: cf api https://api.ng.bluemix.net.
  4. Login to Bluemix with your IBM ID credentials: cf login -u user-id -o password -s dev. Here dev is the Bluemix space you want to use (‘dev’ by default).
  5. Deploy your app to Bluemix by typing: cf push.

That’s it! It will take a while to upload, install and start the code and you will receive a notification when it’s done.  Once you get that response back on the command line you can switch to your Bluemix console and should see this.

IBM Bluemix Dashboard
IBM Bluemix Dashboard

To show the program is working you can either click on the ‘Open URL’ widget (the square with the right pointing arrow in the hello-world-node-js application) or type the URL: ‘hello-world-node-js-your-name.mybluemix.net’ into a browser window (your-name is whatever you set ‘host’ to in the manifest file). The words ‘hello, world’ will magically appear in the browser. Congratulations you have written and deployed your first Bluemix app. Pour yourself a fresh cup of coffee and bask in your new found glory.

If you live in the UK and would like to learn more about the IBM Bluemix innovation platform then sign up for this free event in London at the Rainmaking Loft on Thursday 25th June 2015 here.

“I’ll Send You the Deck”

Peter Cripps comment , , , ,

Warning, this is a rant!

I’m sure we’ve all been here. You’re in a meeting or on a conference call or just having a conversation with a colleague discussing some interesting idea or proposal which he or she has previous experience of and at some point they issue the immortal words “I’ll send you the deck”. The “deck” in question is usually a (at least) 20 page presentation, maybe with lots of diagrams so quite large, of material some of which may, if you’re lucky, relate to what you were actually talking about but most of which won’t. Now, I’m not sure about you but I find this hugely annoying for several reasons. Here are some:

  1. A presentation is for, well presenting. It’s not for relaying information after the event with no speaker to justify its existence. That’s what documents are for. We need to make careful decisions about the tools we use for conveying information recognising that the choice of tool can equally well enhance as well as detract from the information being presented.
  2. Sending a presentation in an email just clogs up your inbox with useless megabytes of data. Not only that but you are then left with the dilemma of what to do with the presentation. Do you detach it and store it somewhere in the hope you will find it later or just leave it in the email to ultimately get lost or forgotten?
  3. Chances are that only a small part of the presentation is actually relevant to what was been discussed so you are left trying to find out what part of the presentation is important and what is largely irrelevant.

So, what is the alternative to “sending a deck”? In this age of social the alternatives are almost too overwhelming but here are a few.

  • If your presentation contains just a few core ideas then take the time to extract the relevant ones and place in the email itself.
  • If the information is actually elsewhere on the internet (or your company intranet) then send a link. If it’s not commercially sensitive and available externally to your organisation why not use Twitter? That way you can also socialize the message more widely.
  • Maybe the content you need to send is actually worth creating as a blog post for a wider, and more permanent distribution (I actually create a lot of my posts like that).
  • Many large organisations are now investing in enterprise social software. Technology such as IBM Connections provides on premise, hybrid and in the cloud based software that not only seamlessly integrates email, instant messaging, blogs, wikis and files but also delivers the information to virtually any mobile device. Enterprise social software allows people to share content and collaborate in new and more creative ways and avoids the loss of information in the ‘tar pits‘ of our hard drives and mail inboxes.

Finally, here’s the last word from Dilbert, who is spot on the money as usual.

Dilbert PowerPoint

(c) 2010 Scott Adams Inc