Level 1: Individual Ecology We will measure 3 characteristics of individuals in 3 locations along the Upper Winter Creek trail. We will measure DBH (Diameter at Breast Height), tree height, and leaf size.  Each team will have to choose their own methods for each measurement and be sure to verify the precision, accuracy and bias. There is a freeware Image J program developed by the NIH described in a file attached to this module for leaf area measurement but you are welcome to try any app or other method you prefer. Level 2: Population Ecology We will document age structure using the DBH data and we will measure dispersion of the population.  Once again each team will choose a method for each. 2 methods for calculating dispersion are described in file attached to this module.  Level 3: Community Ecology We will measure species richness and species diversity using a species count and a calculation each of which, once again, will determined by each team. The final product will be a scientific poster with all of your data and and explanation of the synthesis of all 3 levels of ecology we sampled.  This will be communicated as a concept map with graphs of your data verifying the relationships among the components.  This is the first step in making a predictive systems model, like a climate model.   Small tree height: 3.5814 m     medium tree height:7.875m     tall tree height : 18.02m Small tree leaves length 3.81 cm mid tree leaves length: 5.08 width 2.54cm mid tree perimeter80 Width 2.54 cm tall tree leaves length 10.16cm width 6.36 cm Small Tree perimeter 50cm tall tree perimeter 290cm Small Shurb Community Butterfly 50 Black Bee 27 Yellow Bee 4 Lizard 5 Fly 25 Gnat 40 Beetle 4 snake 1 Medium Tree Community Birds 5 Catepillar 3 Gnats 20 Flys 15 Mouse 1 Snake 1 Mosquito 3 Spider 1 Tall Tree Community Woodpecker 2 Bluejay 3 Lizard 5 Beetle 3 Butterfly 34 Ladybug 300 Squirrel 4 Gecko 2 Waterbugs 27 Birds 7 As the prominent philosopher Jerry, Kaplan puts it “Viewpoint Artificial Intelligence Think Again” (Jerry, 2017). The purpose is that we need to use more hand-working and we do not need Artificial Intelligence replace our brain. Firstly, Social and cultural conventions are an often-neglected aspect of intelligent-machine development. (1) The DOMINANT PUBLIC narrative about artificial intelligence is that we are building increasingly intelligent ma- chines that will ultimately surpass human capabilities, steal our jobs, possibly even escape human control and kill us all. This misguided perception, not widely shared by AI researchers, runs a significant risk of delaying or derailing practical applications and influencing public policy in counterproductive ways. (1) Secondly, Machines don’t have minds, and there is precious little evidence to suggest they ever will. (2) Finally, So the robots are certainly coming, but not in the way most people think. So the robo ...

1. Level 1: Individual Ecology
We will measure 3 characteristics of individuals in 3 locations
along the Upper Winter Creek trail. We will measure DBH
(Diameter at Breast Height), tree height, and leaf size. Each
team will have to choose their own methods for each
measurement and be sure to verify the precision, accuracy and
bias. There is a freeware Image J program developed by the NIH
described in a file attached to this module for leaf area
measurement but you are welcome to try any app or other
method you prefer.
Level 2: Population Ecology
We will document age structure using the DBH data and we will
measure dispersion of the population. Once again each team
will choose a method for each. 2 methods for calculating
dispersion are described in file attached to this module.
Level 3: Community Ecology
We will measure species richness and species diversity using a
species count and a calculation each of which, once again, will
determined by each team.
The final product will be a scientific poster with all of your data
and and explanation of the synthesis of all 3 levels of ecology
we sampled. This will be communicated as a concept map with
graphs of your data verifying the relationships among the
components. This is the first step in making a predictive
systems model, like a climate model.
Small tree height: 3.5814 m medium tree height:7.875m
tall tree height : 18.02m
Small tree leaves length 3.81 cm mid tree leaves length: 5.08
width 2.54cm
mid tree perimeter80
Width 2.54 cm tall tree leaves length 10.16cm
width 6.36 cm

2. Small Tree perimeter 50cm tall tree perimeter 290cm
Small Shurb Community
Butterfly
50
Black Bee
27
Yellow Bee
4
Lizard
5
Fly
25
Gnat
40
Beetle
4
snake
1
Medium Tree Community
Birds
5
Catepillar
3
Gnats
20
Flys
15
Mouse
1
Snake
1
Mosquito
3

3. Spider
1
Tall Tree Community
Woodpecker
2
Bluejay
3
Lizard
5
Beetle
3
Butterfly
34
Ladybug
300
Squirrel
4
Gecko
2
Waterbugs
27
Birds
7
As the prominent philosopher Jerry, Kaplan puts it “Viewpoint
Artificial Intelligence Think Again” (Jerry, 2017). The purpose
is that we need to use more hand-working and we do not need
Artificial Intelligence replace our brain. Firstly, Social and
cultural conventions are an often-neglected aspect of

4. intelligent-machine development. (1) The DOMINANT PUBLIC
narrative about artificial intelligence is that we are building
increasingly intelligent ma- chines that will ultimately surpass
human capabilities, steal our jobs, possibly even escape human
control and kill us all. This misguided perception, not widely
shared by AI researchers, runs a significant risk of delaying or
derailing practical applications and influencing public policy in
counterproductive ways. (1) Secondly, Machines don’t have
minds, and there is precious little evidence to suggest they ever
will. (2) Finally, So the robots are certainly coming, but not in
the way most people think. So the robots are certainly coming,
but not quite in the way most people think. Concerns that they
are going to obsolete us, rise up, and take over, are misguided
at best. Worrying about super intelligent machines dis- tracts us
from the very real obstacles we will face as increasingly
capable machines become more intricately intertwined with our
lives and be- gin to share our physical and public spaces. (3)
David himself writes, “CAN INTELLIGENT MACHINES IN
THE WORKFORCE LEAD TO A NET GAIN IN THE NUMBER
OF JOBS” (David,2016) ? The purpose is that how to choose a
job in Artificial Intelligence times. We can meet kinds of
problem in the future. The first is that Innovation for jobs and
growth. Innovative firms are more competitive, able to capture
increased market share and more likely to increase employment
than their competitors. Over the period 2006-2011, 1.4 million
new jobs were created by firms aged less than three years old.
Employment in mature businesses, in contrast, fell 400,000(1).
The second is that Jobs of the future. A recent report sponsored
by the National Broadband Network (NBN) and the Regional
Australia Institute makes the case that by 2030 fully half of
Australians will need advanced IT skills, in addition to having
well- developed soft skills like communication, creativity and
critical thinking if they are to flourish in the labor market (2).
The third is that The report predicts three classes of work in the
world of 2030. Changing jobs – those that exist now but which
have evolved beyond their current form, sometimes radically,

5. through the integration of technology, and Fading jobs – those
replaced by intelligent machines. (3) Finally, Tomorrow’s Jobs.
When The Future Laboratory teamed up with Microsoft to bring
some clarity for career planners they produced Tomorrow’s
Jobs, a report that predicts some of the more important IT-
related jobs of the future. The Future Factory used a method
that all of us can use to good effect. First you look at the
patterns coming forward from the past ten or twenty years, and
then make predictions by projecting the same patterns into the
future. This is how to be proactive about seeing where the world
is heading. (5)
In William, Halal, Kolber Jonathan, and Davies Owen’s
views,“Forecasts of AI and Future Jobs in 2030: Muddling
Through Likely, with Two Alternative
Scenarios”(Walliam,2016). The purpose is that Artificial
Intelligence will be coming most people sill lose job because Ai
replace people’s work most behavior will auto machine. we do
not need labor power. After decades of failed promises,
artificial intelligence (AI) is now taking off. Yesterday’s
doubters have been silenced, and the only current debate is
about how deep and how fast intelligent machines will automate
jobs, and whether the same technological forces will generate
enough new work. Several forecasts suggest AI is likely to
eliminate almost half of present jobs by 2025, resulting in
massive unemployment (Rutkin, 2013). Ray Kurzweil, now at
Google, extrapolates the growth of computer power to estimate
that a US$1000 PC will match the human brain about 2020, and
powerful AI systems will soon follow (Frey, 2016). Ben
Goertzel, leader of the OpenCog project said “I am confident
that we will have human-level AI by 2025. Maybe sooner”
(Olson, 2013, p.1). Fears of mass unemployment by automation
have been a constant throughout industrialization, but they are
seldom realized. The evidence shows that automation reduces
costs and frees up labor, which allows further economic growth
and new jobs in areas of demand that were unexpected. Today’s
fears that AI will eliminate masses of jobs does not recognize

6. how this dynamic will play out in the new economy that is
emerging. They are somewhat reminiscent of the Y2K crisis that
never materialized (5).
By Steve writes, “New Tools Needed to Track
Technology’s Impact on Jobs, Panel Says” (Steve,2017). The
purpose is that America needs new tools for the timely
measurement and monitoring of technology, jobs and skills to
cope with the advance of artificial intelligence and automation.
Both the report and commentary were spurred by the advances
in A.I. in recent years, including document-reading software
and self-driving cars, which promise to make inroads into work
done by humans. That prospect has created angst for many
American workers about the difficulties of adapting to
technological change and the failure of institutions to help them
(2). New Tools Needed to Track Technology’s Impact on Jobs,
Panel Says - The New York Times Those moves could
eventually give a worker in a declining occupation useful
information about a more promising occupation, with some
similar skills but also requiring some new ones, Mr. Mitchell
said. Then the software tool might also pull information on job
placement rates for courses that teach those new skills (3).
In the Mitchell article said “the software tool might also pull
information on job placement rates for courses that teach those
new skills” (3). In the artificial intelligence has more area,
software tool can help our life. Let us life get convenience and
we save more time for doing some traditional events. In my I
say section, I want t o talk about software tool in the artificial
intelligence area.
Work cited
Jerry, Kaplan. Viewpoint Arti cial Intelligence: Think Again.
2017th ed., vol. 60, 2017, pp. 1-4, 1 vols.
Itamar, Arel, Rose Derek, and Karnowski Thomas. Deep
Machine Learning—A New Frontier in Artificial Intelligence
Research Research Frontier. The University of Tennessee, USA,
2010, pp. 1-6.
David, Tuffey. CAN INTELLIGENT MACHINES IN THE

7. WORKFORCE LEAD TO A NET GAIN IN THE NUMBER OF
JOBS? 2016th ed., Griffth University, pp. 1-7.
William, Halal, Kolber Jonathan, and Davies Owen. Forecasts
of AI and Future Jobs in 2030: Muddling Through Likely, with
Two Alternative Scenarios. 2016th ed., vol. 10, 2016, pp. 83-96.
STEVE, LOHR. New Tools Needed to Track Technology’s
Impact on Jobs, Panel Says. 2017th ed., New York Times,
NewYork, pp. 1-4.