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My name is Daria, I am a part of Exactpro's research team.
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In the previous video, we introduced you to A4Q’s new AI and Software Testing certification.
We talked about some of the major challenges surrounding AI
at this level of development of the technology.
We also compared this exam to the ISTQB’s Foundation Level curriculum.
So, check out our previous video if that's the information you're looking for.
Today, we will start reviewing the 1st of the 3 chapters of the Certification.
This chapter is entitled Key Aspects of AI.
We will make sure to put the links to the syllabus in the description below, so you can easily
find and download the document we are discussing. At the very beginning, it’s worth saying
that all sections of the syllabus are examinable, except for the Introduction.
So, the links and references to external resources that you see in the text are not examinable,
but are undoubtedly recommended for reading.
So, let’s dig deeper into Chapter 1.
This chapter gives a high-level representation of the following topics:
1.1 What are Human Intelligence and Artificial Intelligence?
1.2 AI History 1.3 Symbolic AI
1.4 Sub-Symbolic AI 1.5 Some ML algorithms in More Detail
1.6 Applications and Limits of AI
Today, we’ll concentrate on the first two sections.
So, what is Human Intelligence and what is Artificial Intelligence?
In 1983, Howard Gardner came up with the Theory of multiple intelligences
that has since been expanded.
In this theory, intelligence is defined as “an ability or a set of abilities that permits
an individual to solve problems or fashion products that are of consequence in a particular
cultural setting”.
In other words, intelligence is people’s natural inclination or
ability to engage with the world and affect it in their own unique way.
Currently, 9 types of intelligences have been agreed upon.
And while we look at the list, we will also
give you some examples of possible vocations that would allow a person to harness and nurture
their particular type of intelligence in a good way:
1. Musical-rhythmic (obvious applications would include being a musician, a Songwriter,
sound editor, a conductor, and so on) 2. Visual-spatial (its applications could
include being an Architect, a Graphic Designer, a Pilot, a Truck driver, or an Urban planner)
3. Verbal-linguistic (this can include taking any kind of post where you need to address
an audience, it includes politics, teaching, journalism, writing, broadcasting, it also
obviously includes linguistics and professional translation)
4. Logical-mathematical (People with this type of intelligence could go into Accounting,
Law, Computer Programming, Physics, Statistics, etc.)
5. Bodily-kinesthetic (this intelligence implies being good at sports, so one could be an athlete
or a coach, but they also become a Firefighter, Carpenter, a Mechanic, etc.)
6. Interpersonal (intelligence. it can be leveraged in Social Work, sales, counselling,
management positions, among other fields)
7. Intrapersonal intelligence (one could become a Psychologist, Career counselor, an entrepreneur,
and so on) 8. Naturalistic intelligence which enables
one to be an Animal Trainer, Conservationist, Marine Biologist, Veterinarian.
9. Existential intelligence. (this can be leveraged in being a Philosopher or a Meditation
instructor)
You might have noticed that within one type, the intelligences seem to overlap.
For example, an architect has to be as good at numbers and attention to detail, as they are at visual thinking.
Or being a veterinarian is as much about being able to relate to nature,
as it is about science. Thus, attributing a person to having this
or that type of intelligence is simply about stating which type is the most prominent against
the overall array of intelligences in any given person.
This reflects how much human intelligence actually encompasses and, thus, how difficult
it is to mimic and why traditional IQ assessments lack comprehensiveness.
So far, data scientists have concentrated on mimicking the computational and analytical
powers of the human brain quite successfully, but whenever specific research falls at
an intersection of several intelligences, it might meet roadblocks.
This is caused by the fact that, as a rule, one neural network can be tailored to performing
one narrow task, and not their combination.
One of the fundamental problems with intelligence
is that no single definition has yet been determined; the one given above is just one
of many existing versions. Needless to say, assessing something that
hasn't been not universally defined is a tough task.
In 1950, to assess artificial intelligence,
Alan Turing proposed a workaround approach to this problem.
The Turing Test is essentially conducted by a human put in front of an interface with
two chats, not knowing which chat has a chatbot and which has a human conducting the conversation.
And the fact that this expert can’t tell which is which would mean that the chatbot
has passed. To illustrate the Turing Test, we have talked
to Mitsuku, a famous chatbot and a five-time winner of the Loebner Prize.
Let’s see how human-like this interaction is:
Obviously, the amount of ways this conversation could have gone is infinite.
And at times it does feel quite organic, but this particular example illustrates that even
the most cutting edge ML tech is still far away from actually mimicking human intelligence
in its wider sense.
Moving on, the next part of the syllabus concentrates on AI History.
This part takes us back to when the term AI
appeared and contains a brief explanation of why at some moments AI “hibernated”
and when and due to which factors it regained explosive growth.
So, the 1940s-1950s - We owe the appearance of artificial intelligence to two scientists:
Marvin Lee Minsky and Frank Rosenblatt. In 1958, Frank Rosenblatt proposed
the perceptron model - the precursor of the modern neural networks.
At the same time, other data scientists laid the groundwork for automatic deduction techniques
based on mathematical logic. 1960 to early 1980s - as the work in both
directions continued throughout the next two decades, scientists started to realize
the limitations of both technologies: these included the need for more computing power, and difficulty
in formalizing general knowledge. 1980s to the early 2000s - saw the first success
and subsequent decline of knowledge-based systems and symbolic formalization of cognitive
reasoning. Unsuccessful industry applications led to
a decrease in industry investments from the 1990s onwards.
In the meantime, mathematical models based on probabilistic reasoning started being integrated
into sub-symbolic AI algorithms. This led to many new techniques
and research prototypes.
Since the beginning of the 2010s: the success of machine learning techniques, the availability
of large volumes of diverse data and computing power have accelerated the investments in
AI and increased the numbers of its applications (these include business and data analysis,
robotics, medicine, video games, self-driving cars, and so on).
And this is also the stage at which we are at now.
So, today we’ve looked into the history of AI, tried to define what AI is compared
to human intelligence, and talked about one way to assess AI, that being a Turing Test.
This concludes today’s video and the part 1of the first chapter review of
A4Q’s AI and Software Testing certification. In the next episode, we will concentrate on
the difference between symbolic and sub-symbolic AI.
We'll look at some ML algorithms in more detail and review the applications and limits of AI.
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