Tag Archives: AI

The picoXpert Story

picoXpert was one of the first (if not THE first) handheld artificial intelligence (AI) tools ever. It provided for the capture of human expert knowledge and later access to that knowledge by general users. It was a simplistic, yet portable implementation of an Expert System Shell. Here is the brief story of how it came to be.

When I was about 10, my grandfather (an accomplished machinist in his day) gave me his slide rule. It was a professional grade, handheld device that quickly performed basic calculations using several etched numeric scales with a central slider. I was immediately captivated by its near-magical power.

In high school, I received an early 4-function pocket calculator as a gift. Such devices were often called ‘electronic slide rules’. It was heavy, slow, and sucked battery power voraciously. I spent many long hours mesmerized by its operation. I scraped my pennies together to try to keep up with ever newer and more capable calculators, finally obtaining an early programmable model in 1977. Handheld machines that ‘think’ were now my obsession.

I read and watched many science fiction stories, and the ones that most fired my imagination were those that involved some sort of
portable computation device.

By 1980, I was building and programming personal computers. These were assembled on an open board, using either soldering or wire wrap to surround an 8-bit microprocessor with support components. I always sought those chips with orthogonality in memory and register architecture. They offered the most promise for the unfettered fields on which contemporary AI languages roamed. I liked the COSMAC 1802 for this reason. It had 5,000 transistors; modern processors have several billion. The biggest, baddest, orthogonal processor was the 16- or 32-bit Motorola 68000, but it was too new and expensive, so I used its little brother, the 6809, which was an 8-bit chip that looked similar to a 68000 to the programmer.

I spent much of the 1980s canoeing in Muskoka and Northern Ontario, with a Tandy Model 100 notebook, a primitive solar charger, and paperback editions of Asimov’s “Foundation” trilogy and sequels on-board (I read them five times).

By the mid 1990s, Jeff Hawkins had created the Palmtm handheld computer. The processor he chose was a tiny, cheap version of the 68000 called the ‘DragonBall’. I don’t know which I found more compelling – this little wonder or the fact that it was designed by a neuroscientist. I finally had in my hand a device with the speed, memory, and portability to fulfill my AI dreams.

The 1990s saw the death of Isaac Asimov (one of my greatest heroes), but also saw me finally gaining enough software skills to implement a few Palm designs. These were mainly created in Forth and Prolog.  The Mars Pathfinder lander in 1997 was based on the same 80C85 microprocessor used in the Tandy Model 100 that I had used years earlier. This fact warmed my heart.

In 2001, I formed Picodoc Corporation, and released picoXpert.

Here are: the original brochure, a primer on Expert Systems, and a few slides.

It met with initial enthusiasm by a few, such as this review:

Handheld Computing Mobility
Jan/Feb 2003  p. 51
picoXpert  Problem-solving in the palm of your hand
by David Haskin

However, the time for handheld AI had not yet come. After a couple of years of trying to penetrate the market, I moved on to other endeavours. These included more advanced AI such as Neural Networks and Agent-Based Models. In 2011, I wrote Future Psychohistory to explore Asimov’s greatest idea in the context of modern computation.

Picodoc Corporation still exists, although it has been dormant for many years. It’s encouraging to see the current explosion of interest in AI, especially the burgeoning Canadian AI scene. For those like me, who have been working away in near anonymity for decades, it’s a time of great excitement and hope. Today, I’m mainly into computational citizen science, and advanced technologies, such as blockchain, that might be applied to it.

Microsoft, Minecraft, and AI: Project Malmo

In a previous post, I complimented Microsoft on their purchase of Minecraft. I ruminated on the potential for STEM and experiential learning it opens up, particularly with the addition of HoloLens and augmented reality. Recently, Microsoft announced the public availability of their Project Malmo platform that uses Minecraft to provide an interactive environment for development and testing of intelligent agents. This further illuminates Microsoft’s long-term plans for Minecraft.

In contrast to highly specific, applied AI, Project Malmo harnesses the almost unlimited exploration and experimentation possibilities of Minecraft as a research tool for general artificial intelligence. Agents can learn, comprehend, communicate, and do tasks in the Minecraft world. A mod for Minecraft provides an API for programming, and uses XML and JSON to hold representations of the world in computer memory. Agents can explore their surroundings, see and touch Minecraft blocks, take actions, and receive feedback from the environment. This enables reinforcement learning. Instead of just applying deductive, symbolic reasoning, agents can benefit from inductive (experiential) learning.

The potential benefits are compelling.

minecraft block

 

Sandbox development allows strategies and algorithms developed in the Minecraft world to be later moved to a much larger simulation environment, possibly on distributed systems and/or supercomputers. Computational agents do not require sensory augmentation or biological interfaces, since they connect ‘directly’ to the simulated world. The ability to ‘overclock’ the simulation frees agents from the limits of our time scale (they can ‘live’ many days in an hour of real time).

For remote sensing applications, such as planetary probes & rovers, the benefits are huge. Autonomous machines must be developed and tested before they are deployed. The time delay incurred by vast distance precludes ground-based control. By the time people on Earth receive video from Mars and send the command, “don’t drive off that cliff”, it’s far too late. The ‘smarts’ to navigate and make decisions locally must exist in the robot. Hazardous locations, even here on Earth, also require considerable autonomous learning and decision-making.

Collaboration and comparison between people and teams is possible with a common testing ground. Minecraft can be played as an individual, but the real power is that a Minecraft world can be hosted on a server, enabling many people, wherever they are, to experience and simultaneously participate in that Minecraft world. Players, agents, landscapes & artifacts, or even a combination can all exist and interact. This is much more like the natural world.

Project Malmo encourages public participation and involvement. It’s an open architecture that invites experimentation by all. This is somewhat similar to the citizen science movement, which not only provides the benefits of ‘crowdsourcing’ to scientific research, but also enhances public understanding of scientific methods. In the modern world, a pervasive, basic understanding of artificial intelligence would be of tremendous benefit.

The ability to go far beyond the development of a single agent also opens up the possibility for social simulations of almost limitless scale. Agent-based models have long shown their usefulness in the study of social interaction, from ant colonies to human nations and cultures. One example is the simulation of ancient civilizations. This could lead to an entirely new approach to deciphering Linear A, for example. I considered a much more vast model in Future Psychohistory. In 2011, when I wrote that article, I had a standard neural network model in mind, but if I were to write it today, I’d definitely go with intelligent agents.

The concept of agency is central in many areas of life science. Relational databases are far too static for life science – Cassandra for Life Science. The proper representation of life is not tabular, but associative. Dynamic systems interact, adapt, and even learn. This is the basis of evolution. Machinery as tiny as enzymes and ribosomes, and possibly even medical nano-robots in the near future, all require much more dynamic models to study.

Project Malmo offers major benefits for teaching computer programming. Different paradigms such as procedural, functional, object oriented, declarative, and concatenative are all suitable and helpful in the construction of agents. And of course, there’s one of my favourite hobby horses – constructionism. This only adds to the already expansive use of Minecraft in education.

And for game play itself, Project Malmo can be of great usefulness. Dangerous and tedious tasks can be handed off to agents, amplifying a human player’s efforts. Tutoring and nurturing AI agents is a good use of human intelligence — and it’s a lot of fun.

Greek Pattern 1

AI research and Minecraft now have a powerful and resourceful champion – nice.
I look forward to seeing the fruits of Project Malmo.

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