You can't read all of your emails

The Radicati Group found in a 2011 survey that corporate employees received 105 emails per day.  You can't read 'em all.

Bot brain (value system)

This network was learned by a version of Asa H 2.0 and a small mobile robot using a scalar utility only: longevity.  The intermediate concepts (values) are given plausible names.

Can science be value free?

The goal of any intelligence will be to maximize rewards (R. Jones, Skeptic, vol. 12, #3, pg 14, 2006 and the work of P. Werbos and R. Sutton).  This is also true of an intelligence which is doing nothing but science.  Some value system defines and measures the "rewards."  Science can't be value free.  If it were you couldn't decide what theory to believe, what experiment to do next, or even what to think next.  Of course, some of what you value might well be things like logic, evidence, consistency/coherence, etc.

(see also my blogs of 25 Oct. 2011 and 1 Sept. 2012)

Computer lab software

I have more than a dozen computers in my AI lab (blog of 17 Dec 2012).  An adequate collection of software is just as important as hardware.  At a minimum I have found it's important to have packages for:

neural networks of various types
expert system shells
logic programming systems
genetic algorithm software
finite state machines
clustering software package
Bayesian network package
search software
decision tree software
statistical packages

various compilers/interpreters for languages like:

LISP
PROLOG
OPS-5
CLIPS
C++
BASIC
PYTHON
R
JAVA
etc.

Many of these can be standard commercial packages.  Certainly all of the languages can be. (I try to use commercial packages whenever and wherever possible. It can save a lot of time.)  But some neural networks, finite state machines, decision trees, clustering, search, and statistics code I had to develop myself.  Although less than half of my computers are running Windows more than half of this software is Windows software, probably because there is so much Windows software available out there.

Aided human brains

Lord Martin Rees, president of the Royal Society, has said that understanding how the universe works may not be possible "for unaided human brains."  I agree.  That's one reason we're building AIs.  Some of the first computing machinery was built to help do research in quantum mechanics.  Most simple math has been pushed out of our heads and off scratch paper and into pocket calculators.

Ontology change

In Asa H categories (concepts, classifiers, case vectors) change continually (R. Jones, Trans. Kansas Acad. Sci., vol. 108, No. 3/4, pg 169, 2005).  Some record patterns in space, some record patterns in time.
In typical knowledge-based AI systems (like, for example, CYC) ontologies are (relatively) fixed.
If process philosophy is correct such change is fundamental (Process Metaphysics, N. Rescher, SUNY press, 1996).
If the block universe view of relativity is correct a static ontology is possible.

work load

If you pile too much work on people they don't work harder.  They don't work smarter.  They look for a new job.

If you pile too much work on students they look for another course, or another major, or another school.

A new AI language

It's been a while since anyone has promoted a new major AI language (LISP, PROLOG, OPS-5, CLIPS, etc.).  Pedro Domingos is advocating Alchemy as such an AI language, a "language of thought" for AIs. (see Markov Logic, by Domingos and Lowd, Morgan and Claypool, 2009)  I have downloaded Alchemy (http://alchemy.cs.washington.edu) but have not run the package. The last few AI projects I have studied were all in C++.  I certainly wouldn't want to try to write Asa H in Alchemy, for instance.

Intelligence as a set of processes

Intelligence (like life) may be understood as a set of processes.  One then would try to build an AI by adding more and more intelligent processes to your program. Processes that we consider part of human intelligence would include things like: deduction, analogy, comparison, classification, organization, etc.