Monday, March 10, 2014

The next big transformation

If you revisit my earlier blog entitled “Prognosticatingthe Future[1], you will see that a key technique in prognostication is the ability to identify the various technology trends whose trajectories combine in novel and synergistic ways.

So what are the most significant technology trends today?  What will be the next big transformation effort?

Recently, Gartner identified their top ten trends for 2014[2].  Unsurprisingly, mobile technologies and the Cloud are mentioned, but a brand new entry on their list is the advent of the so-called Smart Machines.

Gartner defines Smart Machines as contextually aware, intelligent personal assistants, and smart advisors (such as IBM’s Watson). I feel this definition is somewhat narrow. After all, self-driven cars and recent advancements in robotics are also representative of the advent of these Smart Machines. But whether one envisions a Knight Rider Pontiac, Siri on steroids, or HAL from Space Odyssey, Smart Machines are ultimately the outcome of the convergence of several emerging technologies. This is where the action is taking place. The focus must be on the underlying technologies needed to accelerate the next transformation effort:

Machine Learning (ML): Bill Gates recently listed “Getting ahead in Machine Learning” as one of the three things he wishes he had started doing earlier[3]. Indeed, as of late, interest in ML has been nothing short of explosive. A Google search of Machine Learning returns 175 million results, and Stanford University’s Professor Andrew Ng free course on Machine Learning has attracted over half a million visitors for the first lecture, and retained a healthy 20,000+ viewers in his last.  ML is making strides thanks to the application of both unsupervised and supervised learning algorithms, including traditional ones like the Bayes Theorem and more recent ones such as Sparse Distributed Memory. Indeed, trying to predict the possible applications for ML would be like trying to predict the applications for the Internet. ML is a set of core technologies sure to revolutionize many of the IT processes in your company today.

Big Data:  If you are in a major financial institution, an insurance conglomerate, a large travel reservation system or any other large company with access to millions of loyalty or credit records, you are in the happy position of being able to gather and exploit massive amounts of data. If not; don’t fret. Even normal-size companies would be lost in a data desert were it not for the emergence of the Internet. Efforts on the Semantic Web front with global ontologies such as OWL (Web Ontology Language, which should be abbreviated WOL if you ask me), and open semantic databases such as Freebase[4], containing information on millions of structured topics contributed by a broad community, make it possible even for small companies to benefit from “Big Data” aggregations.  Additionally, the Web has a wealth of raw information that can be mined and re-purposed via the use collective intelligence gathering tools.

Big Data practitioners highlight that 80% of the effort goes into preparing the data and 20% into analyzing it. There are several vendor supported ETL (Extract-Transform-Load) tools you could use (IBM’s Datastage, Microsoft’s SSIS, Oracle’s Data Integrator, etc.) for this purpose. However, they apply mostly to mappings from one structured data format to another. When mining the Web, you will need natural language parsers. To assist in this effort, I suggest you check the University of Washington’s Open Information Extraction project code named ReVerb. This project has mined the Web to automatically build Subject-Predicate-Object relationships from English sentences, yielding data bases with millions of entries[5].

Toolkits & Languages.  Apache’s Mahout[6] is an open source toolkit consisting of classification, clustering, and other scalable machine learning algorithms.  Also openly available are a number of non-SQL data base products such as Hadoop, MongoDB, and Cassandra that can be leveraged to help you build and exploit your own Teradata-sized data extractions. On the Natural Language Processing front, there are a variety of freely available lexical databases (WordNet), corpora, and toolkits (NLTK).  Obviously, the 800-pound gorillas (Google, Microsoft, IBM) are making significant investments to offer products and services in this area. IBM has recently opened its Watson Cognitive Computer API to external developers[7]; so you are sure to see a continued emergence of start-up companies with products targeting this area.

The Environment: Clearly the other identified trends such as mobility and the Cloud will also help make pervasive access to Smart Machines a reality. Mobility will evolve further via wearable computers (think Google Glass or smart-watches), and the much touted “Internet of Things” will fuse with mobile computing to better allow universal access to the services provided by these smart systems.  

The Business Transformation Impact.

An old adage states that if you are a big company you want to appear to be a Mom-and-Pop shop, and if you are a small company you want to look like a global conglomerate. Achieving these goals for either size company is made easier through the use of Data Science. The emerging field of Data Science is a direct result of the convergence of Big Data, Cloud, and Machine Learning. However, while today’s Data Science is geared primarily toward predictive analytics applications, it would be a good idea if, together with your business team, you began to evaluate these and other potential applications:

  •  Collective Intelligence Analytics. Explore what data is available on the Web that could be analyzed to the advantage of your business. This includes sentiment analysis[8] for your company and its products from social web sites, as well as competitive pricing, and future demand analysis.
  • Recommendation Engines. Like Amazon or Netflix recommendation engines, your company could more effectively execute cross-selling, up-selling, or targeted promotions based on specific machine learning customer associations. This involves linking your current CRM system with available industry data, yielding behavior patterns based on demographics and explicit and implicit preferences.
  • Information filters/hunters. This includes the more traditional spam-filters, but will also  lead to modern automatic information topic detectors, and automated news and reports summarizers.
  • Correlation Analytics. Not all applications need to be customer-facing. Data mining based on pattern matching and other ML regression techniques can be applied to examine operational failure logs against prior symptoms. This type of approach can also aid in quality control in manufacturing.
  • Shopping/Search Avatars. You will be able to unleash your electronic avatar to continuously search and shop for the best deals or information items. The avatar will know enough about your preferences and cost constraints to alert you to opportunities and even act on your behalf.
  • Security. Recent security breach incidents have raised the need for additional levels of security. Facial and voice recognition systems are bound to benefit greatly from ML advances.

Does this mean that Artificial Intelligence has finally arrived?

Ever since the dawn of the computer age it was foreseen that machines would one day match and even exceed the intelligence of humans. This strong view of Artificial Intelligence (i.e. “Strong AI”) was envisioned to occur “within decades” and, as a result, it became a strong field of study in leading universities worldwide.

Inventors had tried for years to replicate a bird’s fight by devising machines consisting of flapping wings and artificial feathers. In the end they only succeeded after the Wright Brothers invented a machine that resembled more a bicycle than a bird’s body.  Analogously, building a chess program able to defeat a chess Grandmaster was achieved at the end of the twentieth century when IBM’s Deep Blue beat Garry Kasparov, but it used techniques that do not resemble how we humans actually think. So, impressive as that feat was, no one tried to claim Deep Blue was proof AI had finally arrived.

Still, recent strides with Machine Learning have once again opened the old Artificial Intelligence debate. . .

Yes, it is conceivable that we will soon have a machine able to pass the Turing test with flying colors by “fooling” a human into believing the machine is human. Even then, once we look under the covers, the ML advances will look more like the type of ‘brute force’ approaches used in chess playing software than the structured thought answers originally contemplated as “Strong Artificial Intelligence”. ML is like a video-camera; intelligence is still the eye. Yes, we might one day develop something as complex and seemingly sentient as Samantha from the movie “Her”, but the essential question as to whether such an “entity” is truly intelligent in a human sense will boil down to asking whether the software truly has consciousness.  And debating the issue of consciousness is something that can quickly devolve (or evolve) into a more philosophical and religious debate with profound existential implications. Are we humans the mere biological machines neuroscientists say we are,  endowed with the illusion of ‘consciousness’ that is a by-product of our brain, or is there something more transcendent going on?

I myself used to believe that anything in our brains could be reproduced computationally, but after reading about the Penrose-Hameroff model of consciousness[9], which proposes consciousness is a quantum physics related phenomena, I am not so sure anymore.

As we sort out this doozy of a dilemma, I prefer to use the term “Simulated Intelligence” to refer to the new algorithms and ‘smart machines’ of the future. After all, there is no question that a form of ‘weak’ AI is actually occurring now with ML and that computers may soon appear to have true intelligence traits, especially in narrow-domain applications.

Even as philosophers debate the true meaning of life and the possibility that artificial consciousness might exist, there is no reason why you and your company cannot begin to plan a point-by-point transformation plan based on how these exciting developments can be made to work to the benefit of your business.