Category Archives: artificial-intelligence

Whatever happened to artificial intelligence?

At a recent business analytics event, Lennart Frantzell demonstrated how (at least at a practical level) there has been a shift in business computing from Artificial Intelligence (AI) to Business Analytics:

Using a healthcare example, Lennart explained how 20 years ago AI was used to form a diagnostic method for treating snakebites in Australia. The approach was to look at the cognitive process doctors go through when treating snakebites and build a system of complex algorithms to mimic this process. The emphasis was on the algorithm – not the underlying dataset. Any sub-optimal decisions made by doctors (say as a result of bias in their individual experience) would also reflected in the system.

Fast forward 20 years. In order to treat HIV in Ethiopia, business analytics is being used to crawl over 41,000 HIV treatment histories. The EuResist system takes data from a new patient and matches this against patients who have been successfully treated in the past, so determining the most appropriate treatment. The treatment consists of a cocktail of drugs, in which the proportion of each drug in the cocktail can affect how successful the treatment will be. This obviously adds a layer of complexity to determining the ideal solution. What success are they seeing on this project? Over 78% accuracy, outperforming 9 out of 10 human experts. 

The key difference here compared to the snakebite project is the focus on data. The EuResist project pulls data from disparate databases into a flexible DB2 platform that can be analyzed using business analytics. The algorithms are simpler than those used in AI, but the results can be impressive because the reliance is on exposing trends in the data.

The separation of the algorithms and the data also makes it easier to create products that can be implemented with minimal customization, compared to large AI systems that need to be custom-built. Eg. the underlying technology and methodology for treating HIV in Ethiopia can be applied to looking at Asthma in Western Europe.

As we continue to produce more data (just take a look at the 389,000 datasets the US government makes publicly available), business analytics can play a significant role in turning this data into insight and solve problems that were previously out of the reach of artificial intelligence systems.

See more on this business analytics presentation.

Learn about IBM’s Business Analytics solutions.

IBM Watson: counting down to the Jeopardy challenge

I’ve covered this before, but there is palpable excitement in the air as there are literally minutes before an IBM computer competes against Jeopardy all-time champions Ken Jennings and Brad Rutter (see here for local US show times).

Delaney Turner over on the IBM Software Blog does an excellent job of running through the different ways you can connect and learn more about every aspect of IBM Watson and this fascinating project.

For those interested in some of the specs of Whatson, check out this technical post on Wikibon.

On February 9, Nova aired this breathy backgound piece on Watson’s four year build-up to this event with an in-depth look at technology used and the team that created it.

I thought it’s worth sharing the chapters:

Chapter 1: smartest machine on earth – preview

Watch the full episode. See more NOVA.

Chapter 2: the challenge

Watch the full episode. See more NOVA.

Chapter 3: programming intelligence

Watch the full episode. See more NOVA.

Chapter 4: Watson’s audition

Watch the full episode. See more NOVA.

Chapter 5: machine learning

Watch the full episode. See more NOVA.

Chapter 6: playing the game

Watch the full episode. See more NOVA.

For more information, follow IBM Watson on Twitter.  

IBM Watson: is artificial cleverness the same as AI?

Let’s start with the obvious: this is the opinion of one mere human. Someone who would fail miserably at the US quiz show Jeopardy: it’s that ‘start-with-the-answer’ approach that just screws me up every time. Not being a native of this soil, I claim it’s just not part of my DNA.

But an IBM supercomputer called Watson (which was indeed conceived on US soil) appears to be performing awfully well at the contest and as such is causing a lot of media attention, much of it centered around the whole field of artificial intelligence (AI) and IBM’s involvement in this area.

As PC World reports, Watson overcame two Jeopardy all-time champs in a practice round recently. How does it do this? The silicon contestant has read countless encyclopedias and other tomes, contains natural language processing capabilities and can even determine how confident it is in its response. Couple this with industry-leading computational power and you have one efficient competitor.

IBM has a history in the development of pitting computers against humans on the cerebral battlefield. In the late ‘nineties, Deep Blue defeated chess grandmaster Gary Kasparov (although Kasparov disputes that he was indeed beaten). However the team behind the Watson project are quick to point out that the level of computing required to deal with the high-level semantic reasoning they are up against is different to the logic-bound nature of chess. Chess is a game of limited moves on an 8×8 grid; Jeopardy a game of infinite words.

I can’t help but think back to my Philosophy of the Mind classes where we studied the Turing test – that black box approach to measure AI proposed by Alan Turing in the 50s. Sometimes called the ‘imitation game’, the concept was that if someone could ask questions to a black box and not discern whether a computer or a person was inside, you could attribute intelligence to the machine on a par to that which us humans enjoy. This Stanford article does a good job of discussing the Turing Test and its objections in some detail.

One objection that stands out is that of origination: could a computer do more than just perform tasks (or deal with questions) set by humans? In the case of Watson, it was a team of people within IBM Research that came up with the idea to build a supercomputer to compete in Jeopardy. The motivations? Showcase technology. A fun work-related project. Team-building. The question is whether a computer could have had the ‘wisdom’ (foolhardiness) to come up with the idea of the project in the first place.

I’d suggest this level of decision-making is a quantum leap beyond the semantic analysis of IBM Watson.

Jonah Lehrer, in the provocatively-titled Proust was a Neuroscientist, uses the filter of art to illustrate what neuroscience is uncovering about the complexity of our intelligence. Within the poetry of Walt Whitman you find the idea that feelings and emotions are born in our bodies, not our minds:

"Antonio Damascio, a neuroscientist who has done extensive work on the etiology of feeling, calls this process the body loop. In his view the mind stalks the flesh; from our muscles we steal our moods."

You can’t separate our thought process from our bodily existence. This could be a problem for a computer lacking flesh and bones.

I don’t just bring this up in the vein of being a contrarian or mean-spirited towards what is quite an astounding piece of computing. I think there is a message here that relates to the technology at the core of Watson: business analytics.

Decision-making within the enterprise happens at different levels and business analytics doesn’t necessarily apply at all of those. For instance, business analytics is ideal at helping a marketer pinpoint prospects who might be interested in a particular offer. It’s less good at determining whether that same marketer should run a conference program if they’ve never run one before. We’re still not close to being able to automate that intuitive part of the decision-making process in business.

Last year I sat in a discussion around decision management and heard from a product marketing manager that a barrier to adoption of business analytics systems is the fear from decision-makers that this technology will take away their jobs (the very same people who normally sign the check on these kinds of purchases). This would suggest we in the field of business analytics need to do a better job of explaining that there are some decisions that can be automated and others that cannot. Business analytics consists of a set of tools that us humans can use to make smarter decisions, but like all tools, it has limits.

So whilst IBM Watson shows what computers can achieve in the human realm, it’s worth bearing in mind (no pun intended) that computers pose little threat to the human realm. The Jeopardy contest that is coming up on February 14 is a battle of one computer against 2 humanoids. If Watson wins, we’re not talking about the dawn of a new era where Jeopardy is played out by tin robots bearing the IBM insignia. We are talking about a triumph of a technology that has applications in healthcare and customer service and beyond – a technology that remains a tool in the hands of us mere humans.

More about IBM Watson, including some wonderful videos on its construction

(Image courtesy of The Doctor Fun Archive)