AIML (Artificial Intelligence Markup Language) is an XML-compliant language that's easy to learn, and makes it possible for you to begin customizing an Alicebot or creating one from scratch within minutes.
Its goal is to enable pattern-based, stimulus-response knowledge content to be served, received and processed on the Web and offline in the manner that is presently possible with HTML and XML. AIML has been designed for ease of implementation, ease of use by newcomers, and for interoperability with XML and XML derivatives such as XHTML.
A beginner's guide to AIML can be found in the AIML Primer. The free A.L.I.C.E. AIML includes a knowledge base of approximately 41,000 categories.
Nice to see XML in action. In this case, it is used as a way to store knowledge the bot uses.
The number of Web 2.0 applications are exploding. Several mashups are being built. Components for Google, Live.com arw on the rise. While keeping track of all these innovative products is pretty tough, we have help.
del.icio.us, technorati, slashdot let you discover them
TechCrunch, Emily Chang, Jon Udell, Robert Scoble cover them
Seomoz classifies them
Some of them are great utilities. Some of them have terrific ideas. So what can you do with them all?
How about taking the most interesting ideas and some of the implementations and applying them to prototype your next generation enterprise applications? It is possible since most of them provide programmatic interface for you to work with.
One of the important aspects of building successful products is an extreme sense of awareness. Awareness about your product(s), your customers, your customer's customers, your competition, your segment of the industry and many others. In addition, you may also want to track short term and long term trends, new companies being funded and how they relate to your products and services (long term and short term). You also need to be aware of big trends caused by the major players in your industry, their partners.
Awareness involves self-awareness first (the hardest part), then being conscious of your environment, and being congnizant of the actual problems at hand.
Becoming more aware means become conscious of more: more facts, more objects, more opinions, more sensations, more perceptions, more feelings, more thoughts. We strive to see more pieces of any given puzzle. Being aware of more is the first step in beginning to see connections between ideas.
How do you cope ?
What techniques do you use?
How good is your peripheral vision?
How do you increase your awareness?
The quotes above are from Jim Canterucci (author of Personal Brilliance)
A good list of books on Innovation from Innovation Book Club.
Out of a list of 90 books related to innovation, the following were chosen by a group of avid readers and innovation practitioners as the reading list for 2006.
It was a difficult choice but these 12 were chosen for their ability to stimulate thought and offer practical tools and techniques to help make you a more productive advocate and leader of innovation.
In 1985, we wrote a small relational engine using Lattice C 2.0 on MS-DOS. It was a small team (about 4 of us) in Bangalore, India. We had no background on designing an SQL engine and the only source available to us was the "Introduction to Relational Database Systems" by C.J.Date. We took two decisions. One is to layer the database on top of an ISAM engine and another was to implement SQL as a set of relational algebraic operations (restrict, project, join). We later licensed the technology to The Santa Cruz Operations and sold it for a while as Integra SQL.
When I saw this article by James Tauber, on Relational Python, all those memories came flooding back to me.
The goal is not to try to implement a SQL database in pure Python.
Rather the goal is to extend Python's rich data structures like
dictionaries and sets with additional concepts from relational theory.
I think enriching Python with a set of relational operators is a great idea. There are several interesting possibilities with this approach. If these opeators are recursive and can work fast with in-memory data, they may help faster processing of XPath. This may result in more flexible architectures for buiding XSLT and XQuery and later SPARQL.
I have been thinking about relational Python like capability for a
project I am currently working on. This project requires you to build
an in-memory streaming database and provide continuous queries.
Currently we are playing with a prototype built on top of Telegraph CQ
and are looking at other alternative implementations. Using an approach
similar to Relational Python is definitely one possibility.
This is a topic of special interest to me. I have been fascinated by several aspects of using Internet as a Reseach Tool. Tracking information, ideas, trends is what we do as a company. So when I found this article about Internet as the Research Tool for Scientific Computing, I was curious.
From 2020 Computing: The Creativity Machine.
What will emerge from using the Internet as a research tool? The answer, Vernor Vinge argues, will be limited only by our imaginations.
We humans have built a creativity machine. It's the sum of three things: a few hundred million computers, a communication system connecting those computers, and some millions of human beings using those computers and
Vernor describes the increasing impact Internet is having on scientific research.
There are now physics, medical and proteomics projects enlisting the enthusiasm of people (and their computers) across the world. For linguists and sociologists, new questions can be investigated simply by observing what occurs on the publicly available Internet. Even experimental
sociology is possible: in their study of social influence on music preference, Salganik et al. recruited more than 14,000 subjects through a popular website, ran online trials on these subjects, and then obtained results directly from their experiment website.
Even MMORPGs (massively multiplayer online role playing games) and Virtual Worlds (like Second Life) have millions of users. It is a study in collaborative content creation. Massive collaboration may be the way we manage the world in the future. It is an exciting time to be here, now and be part of it all.
Finally Microformats are getting the visibility they deserve. I found an excellent presentation about Microformats.
Here are some thoughts about Microformats from Dave Orchard. Dave looks at the current state of Microformats and raises some interesting questions about tools, integration into web/blog creation tools and extensibility. He says:
Microformats look really interesting. They give the ability to embed
structured data in the "master" presentation format, HTML. The key for
adoption is what the killer-apps are going to be that justify the extra
cost. There are some challenges with current tooling that would surely
be solved over time. I worry about the architectural issues of
extensibility and versioning, but I wonder if that's a carry-over from
my work on XML.
Can't believe that Python has 0 bugs from this static analysis. Take a look at this table. Coverity along with Stanford is focused on identifying bugs in some of the popular open source projects.
One of the most remarkable and fascinating articles on Why and What If
Why do we need to have employees?
Why do we need people to sell the product, if it is so good?
Why do we measure performance by profit and growth?
What if we measured success by the happiness of our partners and our customers, and by the innovativeness, sustainability and resilience of the enterprise rather than its profitability?
What if we designed the enterprise so it didn’t need to constantly grow to be healthy, and so that it used only renewable energy and produced no waste, no pollution, and 100% recyclable products?
Bordering some times on philosophy, this article really makes you think.
This has been a recurring theme I heard about in the past couple of years. First from Clark Quinn who was working on a way to teach using games. Then from Werner Schaer, a friend of mine, who brought to my attention the research on Playing To Learn. I came across this article today on Dream Machines when I was reading a blog on Innovation Weblog.
As we play, we learn. And as we grow, our play gets more complicated. We add rules and goals. The result is something we call games.Now an entire generation has grown up with a different set of games than any before it – and it plays these games in different ways. Just watch a kid with a new videogame. The last thing they do is read the manual. Instead, they pick up the controller and start mashing buttons to see what happens. This isn’t a random process; it’s the essence of the scientific method. Through trial and error, players build a model of the underlying game based on empirical evidence collected through play. As the players refine this model, they begin to master the game world. It’s a rapid cycle of hypothesis, experiment, and analysis. And it’s a fundamentally different take on problem-solving than the linear, read-the-manual-first approach.
The positive aspects of gaming – creativity, community, self-esteem, problem-solving – are somehow less visible to nongamers.
But the Internet has morphed what we used to think of as a fancy calculator into a fancy telephone with email, chat groups, IM, and blogs. It turns out that we don’t use computers to enhance our math skills – we use them to expand our people skills.
Think about the impact of including games as a part of the curriculum. You do not have to coax the students to do homework any more.
More games now include features that let players invent some aspect of their virtual world, from characters to cars. And more games entice players to become creative partners in world building, letting them mod its overall look and feel. The online communities that form around these imaginative activities are some of the most vibrant on the Web. For these players, games are not just entertainment but a vehicle for self-expression.
This is changing the field of game design. About a couple of years ago, I heard two game designers speak at Accelerating Change Conference in Stanford.